REVENUE VOLATILITY: THE DETERMINANTS AND CONSEQUENCES

by

SUNJOO KWAK

A Dissertation submitted to the

Graduate School-Newark

Rutgers, The State University of New Jersey

in partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

School of Public Affairs and Administration

written under the direction of

Frank J. Thompson

and approved by

________________________________________

________________________________________

________________________________________

________________________________________

Newark, New Jersey

October, 2011

2011

Sunjoo Kwak

ALL RIGHTS RESERVED

ii

ABSTRACT OF THE DISSERTATION

Revenue Volatility: The Determinants and Consequences

by Sunjoo Kwak

Dissertation Director:

Frank J. Thompson

In response to the growing concerns over the recurring state fiscal crises, this

dissertation aims to shed light on the determinants and consequences of revenue volatility.

To this end, the dissertation specifically addresses two questions. First, it examines how

the composition of tax bases varies across states and what effects tax base composition

has on the cyclical volatility of tax revenues. With particular focus on two major revenue

sources relied upon by state governments, general sales tax and individual income tax,

this study develops a measure of revenue volatility and investigates the questions using

pooled OLS on state panel data over the sample period from 1992 to 2007. Overall, the

empirical analysis finds that there exists a wide variation in both sales and individual

income tax across states. Regression results indicate that tax base composition

significantly affects revenue volatility, with economic structure and demographic-

economic characteristics being controlled for. Specifically, tax exemptions for household

necessities (food and clothing) and producer goods are found to have statistically

significant effects on sales tax volatility. On the other hand, exemptions for Social

iii

Security benefits, public pensions, and long-term capital gains, along with deduction for

local tax property tax paid, are significantly related to income tax volatility.

Second, this dissertation examines how cyclical changes in tax revenues affect

state fiscal behavior in terms of the level of spending and taxation, using a panel data set

for state governments over the period of 1992 to 2007. Specifically, the study tests fixed

effects models that explain own-source expenditure and overall tax rate as a function of

revenue gap, the cyclical component of state tax revenue. Regression results reveal that

cyclical changes in tax revenues are positively related to changes in own-source

expenditures, whereas they are negatively related to changes in tax rates, suggesting the

relationship between revenue volatility and fiscal instability. Based on these findings, the

dissertation concludes by discussing the dynamics of state fiscal behavior over the

business cycle and suggesting spending-smoothing rules as a policy solution to structural

budget deficits and fiscal crises.

iv

TABLE OF CONTENTS

CHAPTER 1 GENERAL INTRODUCTION AND RESEARCH MOTIVATION…1

CHAPTER 2 TAX BASE COMPOSITION AND REVENUE VOLATILITY……7

2.1 Introduction……………………….……………………………………......….7

2.1.1 Previous Studies……….……………………………………........….9

2.2 Conceptual Framework………………………………………………………18

2.3 Data and Methods…………………………………………………………....30

2.3.1 Variables and Data Sources………………………………………..31

2.3.2 Models and Estimation Methods………………………………....72

2.4 Results and Discussion…………………………………………………...….74

CHAPTER 3 REVENUE VOLATILITY AND FISCAL INSTABILITY…………83

3.1 Introduction………………………….……………………………………….83

3.2 Literature Review……………………………….……………………………90

3.3 Conceptual Framework……………………………………………………109

3.3.1 The Rationale for the Revenue-Spending Hypothesis....................109

3.3.2 The Mechanisms of the Revenue-Spending Relationship…..........112

3.3.3 Other Relevant Factors…………….……………………..............119

3.4 Data and Methods…………………………………………………………..124

3.4.1 Variables and Data Sources………………………………………124

3.4.2 Models and Estimation Methods……………………………......130

3.5 Results and Discussion………………………………………………...….133

v

CHAPTER 4 POLICY IMPLICATIONS…………………………………………151

4.1 Implications for Revenue Stability………………………………………...151

4.2 Implications for Fiscal Stability……………………………………………158

CHAPTER 5 CONCLUSION………………………………………….…………......167

5.1 Summary of Findings and Contributions.......................................................167

5.2 Limitations…………………………………..……………………………170

5.3 Directions for Future Research………………………………………….…171

REFERENCES………………………………..………………………………………174

APPENDICES……………………………………………………….……….………186

CURRICULUM VITAE………………………..……………………….……………191

vi

LIST OF TABLES

Table 2.1 Sales Tax Treatment of Utility Services, Automotive Services, and Finance,

Insurance, and Real Estate.................................................................................................25

Table 2.2 Cyclical Volatility of General Sales Tax and Individual Income Tax by State

(1992–2007 Average)…………………………………………………………………....41

Table 2.3 Sales Tax Treatment of Food and Clothing and 1992−2007 Major Changes...46

Table 2.4 Sales Tax Treatment of Services……………………………………………47

Table 2.5 Sales Tax Treatment of Producer Goods and 1992−2007 Major Changes…...51

Table 2.6 Sales Tax Treatment of Utilities for Industrial Use…………………………..53

Table 2.7 Income Tax Treatment of Retirement Incomes and 1992−2007 Major

Changes..............................................................................................................................57

Table 2.8 Income Tax Treatment of Long-Term Capital Gains and 1992−2007 Major

Changes..............................................................................................................................63

Table 2.9 Deduction for Federal Income Tax Paid and 1992−2007 Major Changes……63

Table 2.10 Deduction for Local Property Tax Paid……………………………………64

Table 2.11 Personal Exemptions and 1992−2007 Major Changes…………….………..66

Table 2.12 Variable Descriptions and Data Sources………………………………….....69

Table 2.13A Descriptive Statistics for Sales Tax Model...................................................71

Table 2.13B Descriptive Statistics for Income Tax Model………………………………71

Table 2.14 Regression Results for Sales Tax Volatility……….……………………..74

Table 2.15 Regression Results for Income Tax Volatility………..…………………….75

Table 3.1 Variable Descriptions and Data Sources.........................................................129

Table 3.2 Summary Statistics..........................................................................................130

Table 3.3 Regression Results for Own-Source Expenditure...........................................137

Table 3.4 Regression Results for Overall Tax Rate.........................................................138

Table 4.1 Correlation between % Share of Fiscal Reserves and Revenue Volatility...163

vii

Table A.1 Long-Run Income Elasticity of General Sales Tax and Individual Income Tax

by State (1992−2007)…………………………………………………………………186

viii

LIST OF FIGURES

Figure 2.1 Plots from Two Hypothetical Regressions of Tax Revenue…………………35

Figure 2.2 Illustration of Orthogonal Deviation Calculation…………………………37

Figure 2.3A Box Plot of General Sales Tax Volatility…………………………………..43

Figure 2.3B Box Plot of Individual Income Tax Volatility……………………………44

Figure 3.1 Box Plot of Expenditure Gap by State……………………………………...135

Figure 3.2 Annual Percentage Changes in Overall Tax Rate by State…………………136

Figure 3.3 Regression of Aggregate Federal Grants on Year (1992–2007)……………144

Figure 4.1 Private Sector Participants in an Employment-Based Retirement Plan by Plan

Type, 1979–2008 (Among those who have a retirement plan)…………………………155

Figure 4.2 The Dynamics of State Fiscal Behavior over the Business Cycle…………159

1

CHAPTER 1

GENERAL INTRODUCTION AND MOTIVATION FOR RESEARCH

With increasing fiscal stress, long-term strategic fiscal planning and management

have grown in importance for state governments over the past decades. On the revenue

side, an ever-present anti-tax sentiment along with growing skepticism towards big

government, as shown by recent conservative political movements, have thwarted the

attempts from states to raise taxes. To make matters worse, state revenue bases have

steadily eroded (Lav, McNichol, and Zahradnik 2005): (1) the U.S. economy‘s shift from

goods to services have reduced sales tax revenues, because most states levy sales taxes

mainly on tangible goods not on services; (2) the rapid growth of e-commerce has

considerably eroded sales tax bases as states‘ ability to tax interstate sales has been

impaired; (3) with the baby boom generation beginning to retire, income tax revenues are

expected to decline significantly over the next decades as states provide income tax

preferences for the elderly, and also sales tax revenues are predicted to diminish as

elderly people spend less on taxable goods. On the expenditure side, state spending needs

have substantially increased due to growing health care and education costs since the

1980s (Lav, McNichol, and Zahradnik 2005)1 and the large influx of immigrants since

the 1970s (U.S. Census Bureau 1993).

Weakening revenue-raising capacities and increased spending needs have

combined to create structural budget deficits (Hovey 1998; Behn and Keating 2005),

which, in turn, have brought about fiscal crises whenever a recession hit. In particular, the

1 Lav, McNichol, and Zahradnik (2005) note that pressures to improve public education stem from three

fronts: public demands, court challenges, and student sub-populations with special needs (including special

education students, low-income students, and students with limited English proficiency).

2

state fiscal crises of the early 2000s that began with the 2001 national recession clearly

show how prevalent and severe fiscal problems were across states. According to the

National Bureau of Economic Research (NBER) business cycle dating protocol, the 2001

recession lasted for just 8 months, while the duration of the ones that took place between

1919 and 1945 and between 1945 and 2001 were 18 and 10 months, respectively. Also,

National Income and Product Accounts (NIPA) data indicate that GDP stayed even

during the 2001 recession (0.08% increase in real dollars), while it declined 2.64% and

1.36% during the early 1980s and 1990s.

Although the recession was never severe in terms of both length and magnitude

compared to prior ones, fiscal difficulties that states experienced during the recession and

subsequent years were far more severe than expected.2 Budget problems did not go away

and continued to distress states even as the economy improved. Using NIPA data, Knight,

Kusko, and Rubin (2003) analyze how the aggregate budget balance of state and local

governments (excluding social insurance funds) has changed relative to GDP since 1970.

From this analysis, they find that aggregate state/local deficit in 2002 as a percent of GDP

was the largest since 1970. In response to the fiscal crises, despite tax increases, states

enacted budget cuts even in major programs such as health and education to close budget

gaps for the years 2001 through 2003. The Center for Budget and Policy Priorities (CBPP)

reports that 34 states cut eligibility for public health insurance, causing 1.2 million to 1.6

million low-income people to lose health coverage, and at least 23 states cut eligibility

for child care subsidies or limited access to child care. The Center goes on to report that

34 states cut real per-pupil aid to school districts for K-12 education over the period

2 Sheffrin (2004: 205–206) and Behn and Keating (2005: 1) provide specific examples of state fiscal crises

and the resulting budgetary and political chaos.

3

2002–2004, and spending cuts in higher education led to double-digit increases in college

and university tuitions and reduced course offerings.

The fact that a brief and shallow economic recession left states reeling leads us to

the conclusion that state fiscal problems are not just cyclical and temporary but structural

and chronic in nature. From a broader perspective, some researchers have consistently

pointed to policymakers‘ myopic and opportunistic attitude to state finance and the

resulting poor fiscal planning and management as the underlying cause of the structural

fiscal problems. For example, Knight, Kusko, and Rubin (2003) provide useful insights to

the dynamics of structural deficits by examining contributing factors to the 2001 fiscal

crises using state/local aggregate data. Specifically, they decompose the sharp decline in

state/local budgets into three components: macroeconomy, capital gains realizations, and

policy factors. The results from the analysis reveal that most of the 2001/2002 budget

deficit stems from policy factors such as ―the relatively rapid increases in state and local

consumption spending between 1998 and 2001, and the return of double-digit growth in

Medicaid outlays after a quiescent period in the mid- to late 1990s, a series of tax

reductions between 1995 and 2001.‖ The authors then conclude, ―The bottom line of this

analysis is that neither the cyclical weakness in the economy, when measured relative to

its potential level, nor the direct effects of capital gains realizations, when measured

relative to their longer-run trend, account for very much of the deficit in 2002. The

implication is that the current deficit is structural for the most part and thus unlikely to be

eliminated in the absence of significant budgetary actions by these governments.‖ Taking

a step further, Edwards, Moore, and Kerpen (2003) and Schunk and Woodward (2005)

argue that blinded by large budget surpluses that the extraordinary economic boom of the

4

1990s brought, many states have made unsustainable spending increases and tax cuts

without serious consideration of their long-term fiscal impacts.

Although the lack of a long-term perspective had brewed up structural problems,

states, once again, responded to the early 2000s fiscal crises with short-term stopgap

measures (Bruce, Fox, and Tuttle 2006). States are currently undergoing another

recession that came along with the downfall of the financial market—which is said to be

one of the worst since the Great Depression. Whatever the cause, this unprecedented

economic crisis should be much harsher for states that have neglected to make efforts to

fix structural problems embedded in their fiscal systems, content with revenue growth

that economic expansion in the mid-2000s brought.

In light of the structural fiscal problems that have recurred across states over

multiple economic cycles, the general purpose of this dissertation is to examine how

fiscal problems arise over time and seek ways to restore fiscal sanity to states. In doing so,

this study brings a business cycle perspective to discussions of state fiscal policy.3 This

approach is considered critical in looking into fiscal issues, because a business cycle is

the most fundamental factor that explains the time dynamics of fiscal condition; therefore,

without a clear understanding of it, optimal fiscal policy is difficult. In the business cycle

framework, economies are assumed to swing back and forth between expansion and

contraction, thus giving rise to the issue of revenue volatility (or stability). As will be

discussed later, this study assumes that revenue availability induces spending, especially

3 While the term business cycle is commonly used, in recent years there has been a debate among

economists over the appropriateness of the term. Most notably, Milton Friedman reasons that in modern

economies, shifts between economic upturns and downturns result mostly from adjustments in monetary

policies primarily involving interest rate and credit. But in this article, the terms business cycle and

economic fluctuation are interchangeably used, because the primary purpose is to observe and explain

cyclical fluctuations in tax revenues, not to discuss the nature of those fluctuations.

5

in the public sector where its budgetary resources are likely to suffer from ―the tragedy of

the commons.‖ This assumption leads naturally to the hypothesis that states with a more

volatile revenue base will likely see larger fluctuations in spending and tax adjustments

(i.e. fiscal instability) over the business cycle as they make larger spending increases and

tax cuts during good times and, as a result, larger spending cuts and tax increases during

bad times. This relationship, in turn, highlights the necessity of an empirical investigation

into what factors determine the cyclical volatility of tax revenues.

An in-depth analysis of these causal links centering on revenue volatility is

particularly relevant and timely, considering the fact that state fiscal environments are

increasingly volatile and unpredictable with trade liberalization and advances in

transportation and communications technology, thus closely interweaving not only state

but national economies. In light of the importance and relevance of the subject matter to

state finance, the specific aim of this study is to empirically examine the determinants

and consequences of revenue volatility.

The present study is organized as follows. First, with a particular focus on general

sales and individual income tax, Chapters 2 examines how tax base composition affects

the cyclical volatility of tax revenues. Based on a discussion of the relative sensitivity of

industrial sectors and tax bases—taxable incomes and purchases—to the business cycle,

the second section develops a conceptual framework. The third section summarizes data

on how states tax specific types of incomes and purchases, and discusses empirical

methods. The last section presents and discusses regression results.

Chapter 3 examines how cyclical changes in tax revenues are related to spending

and tax adjustments. The second section reviews relevant literature with a focus on three

6

strands of research: what is so-called the ―tax-spend debate,‖ one on the effects of fiscal

institutions and rules, and one on the cyclicality of fiscal policy. The third section

provides a conceptual discussion of the commons nature of public budgetary resources

and the mechanisms through which revenue availability induces spending. The fourth

section develops a theoretical model, which explains fiscal policy as a function of

revenue gap (the cyclical component of tax revenue), federal grants (for ―flypaper

effects‖), debt, fiscal institutions/rules, partisan control, divided government, election

years (for ―political business cycles‖), and demographic-socioeconomic characteristics.

The rest of the chapter covers empirical analysis.

Based on empirical findings from these analyses, Chapter 4 discusses policy

implications. Specifically, the first section discusses the implications of tax exemptions

for tax base components under study for revenue volatility as well as other policy

considerations such as tax equity, economic neutrality and efficiency, and revenue

adequacy. The second section discusses the spending-smoothing approach as a solution to

revenue volatility and the resulting fiscal instability, and in doing so, compares it to the

―starve-the-beast‖ approach that argues for deficit reductions for tax cuts. Lastly, Chapter

5 concludes the dissertation, presenting a summary of the findings, contributions to the

literature, and directions for future research.

7

CHAPTER 2

TAX BASE COMPOSITION AND REVENUE VOLATILITY

2.1 Introduction

Revenue volatility, defined as the extent to which revenue fluctuates over the

course of the business cycle, is a serious concern particularly for state policymakers and

fiscal administrators operating within the context of balanced budget requirements. It

makes it hard to make accurate forecasts for future revenues and the establishment of

long-term fiscal plans for the stable operation of public programs and services. With the

global economy being liberalized and more tightly interwoven, the tax environment of

governments has been increasingly volatile and uncertain over the past thirty years, and

as a result, fiscal planning and management have become more challenging particularly

for state governments operating under the institutional constraints of balanced budget

requirements.

In the aftermath of the financial and economic crisis that began in late 2008, once

again, states with volatile revenue bases are experiencing severe budget problems. Recent

Census Bureau data on annual changes in state tax collections offers us a glimpse of the

prevalence and extent of revenue volatility. According to the data, despite tax increases,

states‘ total revenues fell, on average, by 8.9% (in real terms) from 2008 to 2009, with

only five states seeing slight increases. Sixteen states posted revenue declines of more

than 10%, and among them, Arizona and South Carolina are the most serious, reporting a

19.7% and 16.8% drop, respectively, in total tax collection. When the data are

8

disaggregated by type of tax, state revenue volatility is much more apparent. With more

than half of states recording double-digit percent declines, Arizona and South Carolina

each saw a 53.9% and 34.7% drop, respectively, in individual income tax.

The situation is more serious in the case of corporate income tax. Most states,

except only a few, reported double-digit percent drops in corporate income tax, and at the

top end of the list, Michigan, Oregon, and New Mexico's corporate income taxes

plummeted by more than half of what they collected in the previous year. As for general

sales tax, while the situation is a bit better compared to income taxes, the actual one

should be worse than it looks, considering the tax increases that have been enacted since

the recession began.

The important implication of revenue volatility for state finance is that in the

absence of adequate fiscal reserves, it is hard for states with volatile revenue bases to

avoid massive spending cuts and tax hikes in times of economic crisis when governments‘

countercyclical fiscal actions are needed more than ever. Another, maybe more important,

implication is that such procyclical austerity measures affect real economies, reducing

households and businesses' propensity to consume and consequently creating the vicious

circle of economic recession. A simple comparison of the data presented above with data

on state fiscal actions in the following year offers us some insight into the fiscal

consequences of revenue volatility. According to a fiscal survey of states conducted by

the Center for Budget and Policy Priorities (Johnson, Oliff, and Williams 2011), fifteen

states (e.g. Arizona, California, Florida, Georgia, Idaho, Maine, Maryland, Massachusetts,

Michigan, Ohio, Rhode Island, South Carolina, Utah, Virginia, and Washington) have

enacted budget cuts for all major state services (e.g. health care, services to the elderly

9

and disabled, K-12 education, and higher education) since 2008, and a closer inspection

of the data tells us that most of the states with across-the-board budget cuts are the ones

that faced sharp revenue falls in the previous year.4

In response to the increasing volatility of government revenues and the growing

concerns over its adverse effects on fiscal and policy stability, empirical research has

been done on revenue volatility. The next section reviews the existing literature to survey

what has been done and how previous work can be improved upon, and based on the

literature review, derives specific research questions for empirical analysis.

2.1.1 Previous Studies

Groves and Kahn (1952) is often cited as one of the earliest works on revenue

growth and volatility. Viewing revenue stability as a special case of adequacy, in their

seminal work, they stress that government tax systems should be stable to provide

approximately constant real revenues over a period of time. Based on the norm of

stability, they estimate how responsive (income-elastic) state and local tax revenues are

to income changes across time using a log-log regression. In the statistical analysis, they

find that state and local tax systems are more stable (less income-elastic) than the federal

tax system, while most state income taxes are less stable (more income-elastic).

Fox and Campbell (1984) renew interest in the issue by questioning the existing

conceptualization of revenue stability. They argue that revenue stability is a concept

concerned with short-run fluctuations in revenues over the business cycle; therefore,

4 According to the Census Bureau data, with Alaska excluded, Arizona, California, Florida, Georgia, Idaho,

Maine, Maryland, Massachusetts, Michigan, Ohio, Rhode Island, South Carolina, Utah, Virginia, and

Washington ranked 1, 4, 12, 11, 5, 24, 36, 10, 32, 22, 27, 2, 8, 6, and 19th respectively in revenue fall.

10

Groves and Kahn's estimates of revenue stability developed on the basis of long-run

measures are not appropriate for explaining the short-run dynamics of revenues. They

define a stable tax as one that is less sensitive to economic fluctuations. Fox and

Campbell (1984) analyze the income elasticities of ten categories of sales taxable in

Tennessee using an elasticity model regressing consumption expenditure on economic

conditions (position in the business cycle, interest rate, and inflation rate) as the

determinants of people's marginal propensity to consume (MPC). From the analysis, they

find that sales of durable goods are highly procyclical detracting from the tax‘s stability,

whereas those of nondurable goods and services are relatively countercyclical mitigating

the instability. Noting that it is not only politically difficult but economically undesirable

to reduce the instability simply by shifting the focus of sales taxation from durable to

nondurable goods, they conclude that the instability could be eased by expanding the

taxation of services.

Otsuka and Braun (1999) revisit Fox and Campbell‘s work using a random

coefficient model as an alternative to the fixed coefficient model. In this analysis, the

authors confirm the conclusion of Fox and Campbell (1984) that sales of durable goods

such as automobiles are generally variable over the business cycle, whereas service such

as utilities and lodging are countercyclical. Based on these findings, they conclude that

with information on revenue growth and variability on hand, the optimality of a tax

portfolio can be adjusted through the composition of tax bases.

Dye and McGuire (1991) extend the literature by investigating the trade-off

relationship between revenue growth and stability. Pointing out that the conclusions of

previous studies that sales taxes are less income responsive and more cyclically stable

11

than income taxes are too broad and general, they argue that responsiveness and stability

characteristics may depend on the specific structural characteristics of taxes—on what

components tax bases have. They estimate the trend rate of growth and cyclical

variability of several components of state general sales and individual income tax bases

(total personal consumption expenditures, representative broad/narrow base, food for

home consumption, motor vehicle fuels, household utilities, telephone services, personal

consumer services, personal business services, and recreation services) using national

aggregate time series data. From this analysis, the authors discover that for some tax

bases, growth rate and variability are negatively related. Based on these findings, they

argue that the commonly assumed trade-off relationship between these behavioral

properties is not always true, concluding that state tax systems can be better optimized in

terms of growth and stability through proper designing of the tax structures.

Sobel and Holcombe (1996) bring important methodological improvements to the

estimation of revenue volatility. They develop an estimation model for the short-run

income elasticity of tax bases using the log changes of the variables as opposed to the

logs as in the standard elasticity model, and apply the model to major state tax bases (e.g.

individual income, corporate income, retail sales, nonfood retail sales, and motor fuel

usage) approximated using national aggregate time series data. The results show that the

long- and short-run elasticity are 1.215 and 1.164 for individual income tax; 0.670 and

3.369 for corporate income tax; 0.660 and 1.229 for retail sales; 0.701 and 1.612 for non-

food retail sales; 0.996 and 0.729 for motor fuels usage. In this analysis, the authors find

that corporate income taxes are the most volatile over the business cycle, while motor

fuel taxes are the most stable. Another important finding from this study is that while

12

corporate tax bases and retail sales have nearly the same long-run growth potential, the

latter is much more stable, suggesting that substantial variety in revenue growth and

volatility exist across tax base components. As an extended effort, Holcombe and Sobel

(1997) estimate long- and short-run income elasticity for major state tax bases (individual

income, corporate income, retail sales, nonfood, retail sales, and motor fuel usage) using

combined and state-level data for the fifty states. In addition to the previous findings,

they discover that food exemption makes retail sales tax bases as variable as income tax

bases.

More recently, Bruce, Fox, and Tuttle (2006) bring a fresh perspective to the

subject matter by examining cross-state variations in the long-run income elasticities of

general sales and individual income tax bases. Their study is distinguished from previous

ones in that it uses actual tax bases or revenues, not proxy measures and attempts to

explain variations in the long-run growth rates as a function of structural features of the

state taxes, demographic characteristics, political circumstances, and economic structure.

In this analysis, they find that public and private pension exemptions have adverse effects

on the long-run income elasticity of individual income tax revenues. As for sales tax,

however, any main independent variables were not found to have expected effects.

Building on Sobel and Holcombe's estimation methods, Felix (2008) examines the

growth and stability characteristics of the tax revenue sources—general sales, personal

income, corporate income, selective sales, and severance tax—of seven states in the

Tenth Federal Reserve District—Colorado, Kansas, Missouri, Nebraska, New Mexico,

Oklahoma, and Wyoming for the sample period of 1967–2007. His empirical results are

generally consistent with those of previous studies; elasticity estimates exhibit that

13

individual income taxes have grown fastest, whereas corporate income taxes have grown

relatively slowly while fluctuating widely over the business cycle, and that sales taxes

have been the most stable revenue source.

In a study of North Carolina's tax system, Wagner (2005) examines the

composition of the state's revenue and the long- and short-run elasticity of various

revenue sources. Based on the estimates, he concludes that more reliance on the

individual income tax will enhance the state's revenue-raising capacity over the long run

but may add to the cyclical variability of the state's revenue, while less reliance on the

corporate income tax and more reliance on motor fuel taxes will enhance both the long-

and short-run stability. Pointing out that in response to economic downturns,

policymakers often adopt procyclical fiscal measures (i.e. spending cuts and tax increases)

in an attempt to meet the requirement of a balanced budget, Wagner discusses the role of

rainy-day funds and savings in mitigating the revenue impacts of economic downturns,

and argues that rainy-day funds should be governed by strict deposit and withdrawal rules.

Cornia and Nelson (2010) highlight the importance of considering economic

conditions and tax portfolios in determining the growth rate and volatility of state tax

revenues. Using 1989–2009 state data and simple graphical constructs, they conduct

various comparative analyses of the long-run growth rate and short-run volatility—in

percent changes—of state economies and tax revenues and state tax portfolios. In the

analyses, they find that wide variations in these respects exist among states, confirming

the stylized fact, as suggested by Groves and Kahn (1952), that there is a trade-off

between revenue growth and volatility. Their finding suggests that in the short run, states

14

cannot alter the underlying structure of the economy but can mitigate the impacts of the

business cycle on their fiscal conditions by making changes to their tax portfolios.

Building on modern portfolio theory (Markowitz, 1952), some studies estimate

the overall volatility of a revenue portfolio and examine whether revenue portfolio

diversification contributes to revenue stability. White (1983) defines revenue instability

as potential variability in tax revenue, and develops a measure of the concept based on

residual variance from a levels regression of tax revenue on time period. In addition, he

develops a measure of overall instability in the entire tax system using the variance of

each tax and the covariance between the taxes. Using data on Georgia's seven major taxes

(e.g. personal income, corporation, sales, alcoholic beverages, motor vehicle, tobacco,

and motor fuel) for the period of 1970–1981, he examines the instability of each tax and

the entire tax structure. In the analysis, he finds that personal income, corporation income,

and sales tax exhibit the highest growth rates among the seven major taxes, while

corporation income, alcoholic beverage, and individual income tax are the most unstable,

thus suggesting that taxes with higher growth rates are less stable. Using quadratic

programming, he also develops a set of feasible tax structures to minimize overall

revenue instability for any given growth rate.

Garrett (2006) examines state tax revenue variability using a volatility model

based on Markowitz‘s portfolio theory (1952), which evaluates how well a state‘s tax

portfolio is structured in terms of revenue variability through a comparison of the actual

tax structure with the structure where overall variance is minimized. In an application of

the model to state revenue data (on individual income taxes, corporate income taxes,

general sales taxes, and excise taxes) over the period of 1977 to 2000, he finds that in

15

Arkansas, Iowa, Louisiana and West Virginia, the actual tax revenue shares in some

states are very close to the variance minimizing shares. He also finds that in many states,

the actual shares of excise tax revenue and sales tax revenue, considered less sensitive to

the business cycle, are below the variance minimizing shares, and argues that states have

shifted towards more volatile revenue sources.

In a similar vein, Hou and Seligman (2007) recognize the recent trend of local

governments shifting away from property taxes towards sales taxes in designing their tax

portfolios, and raise the question, ―What impacts such a shift has on revenue growth and

volatility?‖ Specifically, they examine the effects of the adoption of LOST (Local Option

Sales Tax)—which allows a local government to substitute sales tax (up to 1%) for a

portion of a property tax—and SPLOST (Special-Purpose Local Option Sales Tax)—

which allows a local government to increase sales tax (up to 1%) for the purpose of

capital project financing—by Georgia local governments on the overall long- and short-

run elasticity of their own-source revenues. In an empirical analysis using a long panel

data set, they find that the adoption of LOST increases the short-run volatility of overall

revenues. Their findings suggest that sales tax tends to be a more volatile revenue source

than property tax.

Yan (2010) makes the case for revenue diversification. Specifically, she

investigates the impacts of revenue diversification and economic stability on revenue

stability using state panel data over the period of 1986–2004. Following White (1983),

she defines revenue instability as the short-run variability of the tax portfolio around its

expected growth rate and measures it by the portfolio standard deviation. Results suggest

16

that while revenue diversification enhances revenue stability, the effect depends on

economic stability.

In sum, the revenue volatility literature has focused predominantly on the

estimation of revenue volatility or stability, which has generated two approaches: one [e.g.

Sobel and Holcombe (1996)] focuses on the estimation of the cyclical volatilities (or

short-run income elasticities) of the individual components of tax bases (i.e. potentially

taxable incomes and sales) using national aggregate data,5 while the other [e.g. White

(1980)] is mainly concerned with the effect of tax portfolio structure on the overall

cyclical volatility of the revenue that the tax system generates. The former has

contributed especially to our understanding of the cyclical patterns of individual tax bases,

while the latter has been useful in assessing and designing the optimality of tax portfolio

structure in terms of growth and stability.

Although each approach has contributed in its own way to our understanding of

revenue volatility, little has been revealed as to whether tax policy and structure for

individual taxes indeed matter for their cyclical volatilities, more specifically, how

structural features of individual taxes vary across governments and how they affect the

cyclical volatilities of the tax revenues in particular economic environments. These

questions are particularly important for state governments, because they rely on various

revenue sources and each of the sources varies widely across states in base composition

5 For example, Dye and McGuire (1991) estimate the growth and variability of a state sales tax using

national aggregate time series data on home consumption, motor vehicle fuels, household utilities,

telephone services, personal consumer services, personal business services, and recreation services. For a

state income tax, out of the belief that the more important source of variation is tax rate structures rather

than the definition of the tax base, they uses national aggregate data on household money income by

income range.

17

and in the economic environment in which it operates. This implies that taxes, even if

they are the same kind, could exhibit varying degrees of cyclical volatility depending on

how their bases are composed and in what economic environment they operate. Hence, it

may be an oversimplification to generalize sales taxes as a volatile revenue source and

income taxes as stable, concluding that shifting from income tax to sales tax will

contribute to revenue stability.

Despite their importance and relevance, however, the questions have received

virtually no empirical investigation. This deficiency may be due in large part to the sheer

complexity of state tax systems and the resulting empirical challenges. As will be

discussed later, the empirical examination of the given questions requires panel data on

how states treat potentially taxable incomes and sales of interest in taxation, which, as

legal provisions, are hard to collect. Another empirical challenge is that revenue volatility

is hard to measure. For the accurate measurement of it, tax revenues should be adjusted

for tax rate changes, which add to difficulty in data collection. One way to remove the

effects of rate changes on revenue outcomes is to use tax bases,6 not revenues.

78 While

this method is conceptually simple and straightforward, the difficulty of collecting tax

rate data makes it hard to implement.

6 Actual tax bases are obtained using specific tax data for each state—by dividing actual revenues by tax

rates, and thus should be distinguished from the proxy measures of tax bases based on national aggregate

data that most previous studies have used. 7 Wagner (2005) provides an illustrative example regarding the usefulness of using tax bases as follows:

―The revenue generated from a general sales tax depends on (1) the sales tax rate and (2) the tax base.

Policy makers frequently change sales tax rates (especially during downturns), so examining how sales tax

revenue changes over time is not particularly insightful. A rate increase will ―bump‖ revenue beyond where

it would have been in the absence of the rate change. However, examining how a tax base changes with the

state‘s economic activity reveals how revenue from a given tax would fluctuate if the tax rate remained

constant.‖ 8 In this article, therefore, revenue volatility and tax base volatility are interchangeably used.

18

Dealing with these empirical challenges, this study answers the following

questions:

1. How does the composition of tax bases vary across states?

2. What effects does tax base composition have on the cyclical volatility of tax

revenues?

In doing so, this study focuses on two major revenue sources relied upon by most

state governments: general sales tax and individual income tax. Specifically, the study

empirically investigates the questions using state panel data over the sample period from

1992 to 2007.9 The cross-state heterogeneity of tax base composition, economic structure,

and demographic characteristics provides a natural laboratory for empirical analysis.

economic characteristics provides a natural laboratory for empirical analysis, and the

sample period is sufficiently long for the given questions, covering approximately two

business cycles—two troughs (in 1992 and 2001) and two peaks (in 2000 and 2007). The

rest of the chapter is organized as follows: the next section provides a conceptual

discussion of factors that affect cyclical fluctuations in tax revenues, and Section 2.3

discusses data and methods. Section 2.4 then presents and discusses analysis results.

2.2 Conceptual Framework

9 Using panel data has considerable merit for answering the given questions. It solves the "small N"

problem which is common in empirical studies taking states as the unit of analysis. A small sample size can

lead to large sampling variances and ultimately the violation of OLS assumptions. As a result, the problem

escalates as the number of explanatory variables increases. Adding a time-series dimension reduces the

problem of small sample size by multiplying the number of observations.

19

The cyclical volatility of taxes stems mainly from two sources. First, most

fundamentally tax revenues are directly affected by economic fluctuations. Generally,

aggregate output fluctuates around the long-term growth trend within the context of the

business cycle—shifting between periods of economic expansion and periods of

contraction. The fluctuation of ups and downs in output leads to ups and downs in

employment, income, and consumption, which, in turn, result in fluctuations in various

types of tax revenues.10

Given the overarching influences of business cycles on government revenues and

finances, it is important to discuss the different cyclical patterns of outputs by sector. The

degree of cyclical fluctuations in output should be affected by the sectoral composition of

the economy. The most common typology of output includes goods and services. Many

economists have observed that the output of services tends to be less sensitive to the

business cycle than that of goods and manufactures. They explain that this difference

comes from a difference in ―storability‖ between goods and services. Except perishable

food such as fruit, vegetables, and meat, most goods and manufactures can be stored for

long period of time, even though they differ in the extent. Storability has a significant

influence on cyclical fluctuations in output, because it affects the rate of purchase.

Stressing the difference between consumption and the rate of purchase, Fuchs

explains that ―In the case of consumer durable goods, true consumption (i.e. the use of

the goods or of their services) depends upon the stocks in the hands of consumers, not on

10

Economists observe that although output and consumption follow the same cyclical patterns, generally

they are rarely equal over a given period. Huffman (1994) finds that aggregate consumption tends to

fluctuate less than do aggregate output. It is not surprising, given that rational consumers tend to save and

invest in good times for future bad times. He explains that assuming that income changes may be

permanent in the long term but transitory and soon reversed in the short term, a rational consumer rarely

changes consumption by a change in income; he or she smoothes consumption over the business cycle

through saving or investment.

20

the rate of purchase of new goods. The latter, which is comparable to investment in

capital goods, may evidence wide cyclical swings in response to changes in availability

of credit, expectations, and other investment determinants, while the true consumption

rate remains relatively stable. … In the case of services, consumption and output must

coincide; inventories are nonexistent.‖ In short, while goods and services might be

similar in the rate of consumption, they greatly differ in the rate of purchase. This

difference has significant implications particularly for sales tax, because the tax is

realized when purchase, not consumption, takes place.

Another related characteristic of goods and manufactures is that most of them are

repairable. Durable goods do not quickly wear out, and are consumed not in one use but

gradually over time. This means that in the case of durables, product lives can be

prolonged, and as a result, new purchases can be delayed to some degree. This

phenomenon is more likely in recession, thereby deepening cyclical declines in aggregate

consumption and output. As the economy slows and economic uncertainty increases,

more consumers are likely to delay the new purchase of products until the lives of

existing ones come to an end. For example, in the face of recession, it is more likely that

consumers will put on hold their purchases of goods such as automobiles, furniture, and

appliances in anticipation of a further decline in the economy, deciding to persevere a bit

further with existing ones. The implication is that nondurable goods may be closer to

services in this regard.

Given the different cyclical behavior of goods and services output, it is likely that

states with larger goods-producing industries will see greater cyclical fluctuations in their

tax revenues than those with larger service-producing industries. According to the NIPA

21

definition, goods-producing industries are again divided into a number of sectors:

agriculture, mining, construction, and durable and nondurable manufacturing. It is a

widely accepted fact that input inventory (i.e. materials) investment tends to be more

volatile and procyclical than output inventory investment [see Iacoviello and Schiantarelli

(2007)]. Following this assumption, among goods-producing industries, particularly

agriculture and mining industries, will likely contribute to revenue volatility. Meanwhile,

nondurable goods, as noted above, are similar to services in terms of storability and

durability. Hence, the relative size of nondurable goods will likely contribute to stability

in aggregate output.

Cyclical fluctuations in tax revenues are also expected to be affected by tax policy

factors. States do not tax every sale and income; they levy taxes selectively on specific

types of sales and incomes, which constitute tax bases. States differ widely in tax base

composition as they allow varying levels of tax exemption for different types of sales and

incomes.

What is important to note here is that potentially taxable purchases and incomes

differ in sensitivity to changes in aggregate output—though they, for the most part,

behave in a procyclical manner. Some types of consumption and income fluctuate more

than aggregate output, whereas some others are relatively less sensitive to the business

cycle. Given the varying degrees of sensitivity of potential base components to the

business cycle, the cyclical volatility of taxes is likely to vary depending on what

components are included to or excluded from the tax bases; in other words, what types of

purchases and incomes are taxable.

22

For analytical purposes, therefore, it is useful to discuss the cyclical behavior of

each tax base‘s potential components listed above. To begin, in the case of sales tax, with

tangible goods dominating most states‘ sales tax bases, wide variations in base

composition are observed in the level of tax exemption for purchases of (1) goods used in

manufacturing (e.g. direct materials, machinery/equipment, and utilities)—so-called

producer goods, (2) (nonprepared) food,11

(3) clothing (including footwear), and (4)

services.

First, in light of the above discussion on a difference between goods and services,

exempting producer goods, the consumption of which tends to be sensitive to the

business cycle, from taxation is expected to dampen cyclical amplitudes in the sales tax

revenue. In other words, other factors being equal, states that offer a lower level of tax

exemption for producer goods are likely to see greater cyclical fluctuations in their sales

tax revenues. The favorable tax treatment of producer goods is offered exclusively to

manufacturing businesses. Hence, even if a state grants a high level of sales tax

exemption for a wide range of business purchases of manufacturing inputs, the policy

will not greatly affect the state's sales tax base if the state has a small manufacturing base

(e.g. Hawaii and Delaware). In other words, the effect of tax exemption for producer

goods on revenue outcomes will likely be greater in states with a larger manufacturing

sector.

For the task of predicting the effects of sales tax exemption, an understanding of

income elasticity of demand is necessary. Intuitive reasoning and empirical studies on

income elasticity of demand suggest that the more necessary a good is, the less sensitive

11

Prepared food or food marketed for immediate consumption generally does not qualify for sales tax

exemption.

23

the demand for the good is to economic changes, as people attempt to purchase it no

matter how tough the economy is. Given the notion of income elasticity of demand, it is

only logical to assume that exempting food that is the most basic necessity for everyone

from taxation will widen cyclical swings in the sales tax revenue; in other words, holding

other factors fixed, states that offer a higher level of tax exemption for food will likely

see greater cyclical fluctuations in their sales tax revenues.

Predicting the effect of tax exemption for clothing and footwear is a little bit

tricky. While clothing and footwear are generally classified as nondurable, they are much

more durable and also repairable compared to other typical nondurable goods such as

food and household goods (e.g. cosmetics, soap and light bulbs). Given the mixed

characteristics between clothing and footwear, it seems reasonable to hypothesize that

tax-exempting clothing will make the sales tax revenue more variable as opposed to food.

Meanwhile, predicting the impact of sales tax exemption for services is not

straightforward. As discussed, the consumption of services is considered less sensitive to

economic changes as compared to goods. It can therefore be reasonably assumed that

other things being held constant, the more services a state taxes, the more stable its sales

tax revenue will be throughout a certain business cycle. But another important

consideration is that services vary in income elasticity. This implies that the effect of

exemption for services on revenue volatility may differ depending on which services are

taxable. Some services such as investment counseling, swimming pool cleaning, and

private limo may be considered luxuries for average consumers, which will likely be

relatively sensitive to the business cycle compared to services that are accessible to more

24

average people such as repair services where the demand for it may rather increases

during a recession.

Given the variation in income elasticity that may exist across services, it may not

be appropriate to treat them as a homogeneous group and assume that tax-exempting

more services will increase revenue volatility. One simple way to take the possible

variation into account is to classify services in terms of income elasticity and estimate

their effects by category. But classifying services is such a task that is worth another

separate empirical study. Estimating the effect of each service without classifying (i.e.

including all services in the model), while possible theoretically, is not even practical,

given the large number of potentially taxable services.12

Alternatively, this study assumes

that states seek to be cost efficient in taxation, and in doing so, have a tendency to tax

services that are less income elastic (more stable). Expanding tax bases do not only bring

increased revenues to states; it takes costs as well. Administrative systems and a

professional workforce to operate them are required for proper and effective taxation.

Common sense and intuition suggest that it is more cost efficient for a state to expand its

sales tax base by incorporating services that are more universally consumed by a broader

range of people, and such services are likely to be more of a necessity (or less of a luxury)

that is less income elastic.

Mazerov (2009) discusses challenges facing states that attempt to expand their

sales tax base, one of which lends support to this assumption. He notes that ―State

revenue departments may not be equipped to integrate numerous new services and the

merchants selling them into their sales tax administration systems in a short period of

12

The Federation of Tax Administrators (FTA) periodically conducts a survey on state sales taxation of

services, and its 2007 update examines the taxable status of 168 services.

25

time. These factors likely explain why all the states that have expanded their taxation of

services in recent years did so incrementally, a few services at a time.‖ FTA survey

results on state sales taxation of services lend plausibility to states‘ tendency towards less

income elastic services.

Table 2.1 Sales Tax Treatment of Utility Services, Automotive Services, and Finance,

Insurance, and Real Estate

Services Total Number of States that Tax

Utility Services (for residential use)

Intrastate telephone & telegraph 41

Interstate telephone & telegraph 27

Cellular telephone services 44

Electricity 22

Water 12

Natural gas 22

Other fuel (including heating oil) 23

Automotive Services

Automotive washing and waxing 21

Automotive road service and towing services 19

Auto service. except repairs, incl. painting & lube 25

Parking lots & garages 21

Automotive rustproofing & undercoating 25

Finance, Insurance and Real Estate

Service charges of banking institutions 3

Insurance services 6

Investment counseling 6

Loan broker fees 3

Property sales agents (real estate or personal) 5

Real estate management fees (rental agents) 5

Real estate title abstract services 5

Tickertape reporting (financial reporting) 8

Investment counseling 6

Source: 2007 FTA Survey of State Sales Taxation of Services.

Table 2.1, even without statistical analysis, clearly shows that more states tax

services such as telephone services that are generally considered a necessity in modern

26

times and as automotive services that are also more of a necessity in the context of

American life, should be consumed by more people and thus less income elastic rather

than financial services. In light of states‘ tendency towards less income elastic services,

tax exemption for services is likely to have a nonlinear effect on revenue volatility. Put

differently, sales tax volatility will increase as the level of tax exemption for services

increases, but the effect will decrease once it goes over a certain point.

The same logic is applied to the explanation of income tax volatility. Most states

offer income tax preferences for the elderly, but the extent widely varies from state to

state. With most kinds of earned incomes (such as wages, salaries, and tips; interest and

dividends; capital gains) being taxable, states exhibit wide variations (1) in the level of

tax exemption for (1-a) pensions and retirement incomes, (1-b) long-term capital gains;

(2) in the level of tax deduction for (2-a) federal income tax paid and (2-b) local property

tax paid, and (3) in the level of personal exemption.

Pensions and retirement incomes are largely divided into three categories: Social

Security benefits, public, and private pensions. Pensions are generally defined as

financial arrangements in which participants receive payments upon retirement. Pensions

and retirement incomes are usually paid in regular installments and thus considered the

most stable source of income for retirees. Given the general nature of retirement incomes,

allowing taxpayers to exclude them from income tax bases is expected to exert an adverse

impact on income tax stability.

When an investor sells a capital asset such as stocks, bonds, and real estate, the

difference between the purchase price and the selling price arises, which is referred to as

27

a capital gain or a loss. Capital gains or losses realized on the sale of assets held more

than one year are considered ―long-term.‖ As with the federal government that favorably

treats long-term capital gains realizations by imposing a lower tax rate—the maximum

tax rate for net long-term capital gains income was reduced to 15% in 2003, an increasing

number of states have been providing preferential tax treatment for long-term capital

gains income. Capital gains realizations, whether long-term or short-term, have become

increasingly volatile over time. According to data released by the U.S. Treasury

Department, during the study period (1992 to 2007), net long-term capital gains of

individuals averaged 3.73% of GDP, ranging from 1.8% in 1992 to 6.12% in 2007. Given

the increasingly volatile nature of financial investment returns, it is assumed that the

exclusion of long-term capital gains realizations from taxation will decrease the cyclical

volatility of the income tax.

Given that investment gains are generally earned by high income people, the

effect of tax exemption for long-term capital gains income is likely to be greater in states

with a larger wealthy population. Investment can be seen as a type of activity of saving a

portion of disposable income or deferring consumption from high-earnings periods to

low-earnings periods. Investment opportunities should therefore be greater for higher

income earners. For example, the Minnesota House Research reports that in tax year

2007, about 24% of tax returns filed by Minnesota residents reported some capital gains

income and filers with incomes over $100,000 received over 86 percent of capital gain

income.

28

In addition to tax exemption for specific types of income, states also allow various

deductions, among which, this study focuses on deductions13

for federal income tax paid

and local property tax paid and personal exemption (including exemption for dependents).

Generally deductions and personal exemptions are likely to have adverse effects on

revenue stability by substantially reducing income tax bases. Taking them into account is

important, because they are allowed to relatively broad ranges of taxpayers when

compared to tax exemptions on pensions (only for retirees) and long-term capital gains

(only for investors).

Along with tax and economic structure, demographic and economic

characteristics may also potentially affect the cyclical volatility of both sales and

individual income tax. First, size might have an effect. Studies on the relationship among

region size, industrial diversification, and economic stability [see Kort (1981), Brewer

and Moomaw (1985), and Malizia and Shanzi Ke (1993)] suggest that the size of a

regional economy tends to be positively associated with industrial diversification and

economic stability. Thus, it is hypothesized that tax revenues will be relatively stable

over the business cycle in larger states.

Another potential factor that affects revenue volatility is population age

distribution. For income tax, two groups of population appear of particular relevance:

young (pre-college) and elderly population. States generally provide favorable tax

13

The vast majority of states allow taxpayers to choose between standard and itemized deduction. This

study focuses on the latter under the assumption that large portions of middle- and high-income taxpayers

choose itemization in their state income tax returns. Although state specific data are not available, IRS data

on federal tax returns warrant this approach. According to the IRS, in tax year 2007, 72%, 87%, and 94%

of tax returns filed by individuals with AGI over $75,000, $100,000, and $200,000, respectively, chose

itemized deduction.

29

treatment for taxpayers with dependent children, which reduces the tax bases and likely

increases income tax volatility. On the other hand, the major income source of the elderly

is retirement income such as Social Security benefits and pensions, which tends to be the

most stable, thereby increasing income tax stability.

As for sales tax, the proportion of the prime working-age and elderly population

may be relevant. Two well-known theories on consumption lend support to this idea.

Milton Friedman's permanent income theory (1956) argues that people are rational to

base their spending and saving decisions on permanent income as defined as the average

income that they assume they would be able to earn over their lifetime. According to the

theory, only changes in permanent income affects people's spending decisions. People

therefore do not respond sensitively to transitory income shocks, smoothing their

consumption over the business cycle. Modigliani and Brumberg's life-cycle theory of

consumption (1954; 1980) is closely related to the permanent income theory. According

to the life-cycle model, people make assumptions about their expected income over their

lifetime and base their spending decisions on the income expectations. The theory

suggests that especially working people do not reduce consumption too much in recession,

expecting that their disposable income will increase soon in the future. These theories

lead to the hypothesis that the relative size of the working-age population, who tends to

have a relatively high expectation of future permanent income, is likely to have a positive

effect on cyclical stability in sales tax collections. The other side of the theories also

suggests that people in old age have a low expectation of future income. The implication

is that the relative size of the elderly would likely exert a negative influence on sales tax

stability.

30

Lastly, income distribution may potentially exert influence on cyclical

fluctuations in both tax revenues. In particular, the proportion of the wealthy population

would likely have an impact on state finance, in light of the income group‘s relative

importance in the economy. Specifically, the income group is expected to contribute to

the cyclical volatility of both taxes, given that the income of high-ranking people tends to

be more sensitive to boom and bust cycles. A September 2009-Wall Street Journal article,

titled "Income Gap Shrinks in Slump at the Expense of the Wealthy," reports that the

income of the top 1 percent as a percent of the total U.S. personal income has fallen faster

than has that of any other income groups since the recession, and goes on to project that

the proportion of the very top income earners will drop from 23.5% in 2007 down to

between 15% and 19% in 2010. This may be in large part because, in the U.S.,

compensation systems for people in top management positions such as chief and senior

executives have shifted towards performance-based incentives. In such a system,

executive compensations are determined by overall organizational performance (for

example, stock prices and net profits). Thus, their incomes are likely to be more heavily

affected by economic cycles compared to that of middle- and low-ranking people.

Put together, the cyclical volatility of state general sales tax and income tax is

modeled as followed:

Revenue volatility = f (tax base composition, sector GSP

composition, demographic-economic characteristics)

2.3 Data and Methods

31

The main empirical goal of this study is to estimate the effect of tax base

composition on revenue volatility, with particular focus on two major state revenue

sources, general sales and individual income tax. Specifically, this Section develops two

separate models for sales tax and income tax on the basis of the conceptual discussion

above and presents estimation methods for estimating the models, and the following

section discusses results and implications.

2.3.1 Variables and Data Sources

The dependent variables in this study are the cyclical volatility of state general

sales tax and individual income tax, which is defined as the degree to which tax revenue

fluctuates around the long-term growth trend over the business cycle. Empirical studies

have attempted to develop a measure of the concept. White (1983) represents one of the

earliest attempts to measure the concept. Building on Harry Markowitz‘s portfolio theory,

he defines a stable tax structure as ―one that contains taxes that are not perfectly

correlated (i.e. one in which taxes do not move in exactly the same direction and

proportion).‖ He explains, ―If the revenue from one tax is down for some reason such as a

recession, then the decrease in the government's overall tax revenue is minimized because

other taxes have not experienced such a decrease in revenue.‖ In this view, estimating

revenue volatility involves calculating the variance of each tax—which represents the

degree of ―dispersion about the expected level of tax revenue‖—and the covariance

between the taxes. In mathematical terms, White‘s measure is expressed as follows:

32

where is the level of revenue from the th tax; is the level of revenue from the th

tax; is the correlation between the th and th tax; and are the standard deviation

of the th and th tax, respectively. And the formula for calculating the standard deviation

of each tax is:

where is the standard deviation of the th tax; is revenue from the th tax in period

; is expected revenue from the th tax in period ; is mean revenue of the th tax

for period 1 through m; is the number of time periods. This measurement method, as

originating from the portfolio selection approach, has been used in studies examining the

relationship between revenue portfolio diversification and volatility (see, for example,

Gentry and Ladd (1994) and Yan (2010)).

Another important approach is introduced by Sobel and Holcombe (1996).14

In

contrast to the portfolio approach, they employ parametric methods in estimating revenue

volatility. Arguing that the standard method using a regression of the log level of tax

14

An earlier study in this context is Williams, W., R. Anderson, D. Froehle, and K. Lamb, 1973, The

Stability, Growth, and Stabilizing Influence of State Taxes, National Tax Journal, 26, 267-274.

33

revenue on the log level of income only provides information about the long-run growth

rate of taxes, they develop a separate measure for short-run volatility. In the Augmented

Dickey-Fuller (ADF) test for the stationarity of the variables concerned, they find that the

variables are nonstationary in their regular or level form, thus suggesting that both

income and tax revenues systematically move upwardly together over time. Emphasizing

that revenue volatility is concerned with how much a tax base fluctuates around the long-

term trend over the business cycle, they argue that their change or first difference forms,

which are found to be stationary in the diagnostic tests, must be used for the estimation of

the short-run cyclical volatility of taxes. Therefore, the equation for the long-run growth

rate of a tax base is as follows:

where and denote the natural log of tax base and personal income of state

in year , and the regression coefficient represents the long-run growth rate of tax

base. Modifying the standard log model above, the equation for the short-run cyclical

volatility of a tax is:

where the regression coefficient represents the short-run cyclical volatility of tax base.

This log change model has been widely used by subsequent studies, which include Bruce,

Fox, and Tuttle (2006), Felix (2008), and Hou and Seligman (2007).

34

In addition to the log change model, Sobel and Holcombe (1996) use the error

correction model (Engle and Granger 1987) out of the recognition that the variables may

move up and down simply due to their tendency to move back towards the equilibrium.

To handle this so-called error correction bias, they propose a modified model by adding

an error correction term—the estimated error from the standard log model in the previous

time period—to the log change model. In a comparison of results from these two models,

Sobel and Holcombe find that there are only slight differences between estimates from

the log change model and ones from the error correction model.

In order to take full advantage of the panel structure of data used in this study in

determining the effect of tax base composition on revenue volatility, this study employs

the deviation-from-trend approach with some modifications as opposed to Sobel and

Holcombe‘s parametric method that estimates the average change in tax base for every

one-unit change in income over a given period. Specifically, revenue volatility is

measured by first regressing tax base on income based on the assumption that there is a

long-term relationship between the variables, and then calculating the absolute deviations

of annual tax bases from the fitted regression line (trend line) as a percent of the mean of

the sample tax bases. While this method is useful in that it allows for panel data analysis

and widely used in the field of public finance,15

it suffers from the problem of

measurement bias due to the nonstationarity of tax revenues. Sobel and Holcombe

correctly point out that the volatility measure based upon residual variances may be

incorrect because tax revenues tend to be not trend stationary but systematically trending,

usually upward. Ordinary least squares (OLS) estimates are obtained by minimizing the

15

For example, see Aisen and Veiga (2008) which examines the relationship between political instability

and inflation volatility using standard deviation.

35

sum of the squared vertical deviations of observations from the fitted regression line,

which are exaggerated as the slope is steeper (e.g. as the long-run rate of growth is

higher)—because the trend components of revenues are added on top of the cyclical ones.

This concern is easily illustrated through graphics.

Figure 2.1 Plots from Two Hypothetical Regressions of Tax Revenue

Figure 2.1A

Figure 2.1B

36

Figure 2.1 contrasts the plots from two hypothetical regressions of tax revenue on

income. As portrayed in Figure 1a and 1b, suppose that they have the same short-run

cyclical volatility but different long-run growth rates. Contrasting the regression plots

clearly shows that using conventional residuals (vertical deviations) leads one to a faulty

conclusion that Figure 2A has a greater cyclical volatility than Figure 2B—even though

they have the same. In generalized terms, this shows that the higher the rate of growth (i.e.

the steeper the slope is), the more incorrect the estimate becomes. Such an exaggeration

is particularly problematic within this study, given the wide cross-state variations in the

long-run growth rate of sales tax and income tax as shown in Appendix A16

[see also

long-run income elasticity estimates reported by Bruce, Fox, and Tuttle (2006)]. To

resolve this problem, this study measures revenue volatility using orthogonal (or

16

This study conducted a preliminary study to estimate the long-run elasticity of sales and income tax bases

with respect to income. Appendix A presents the estimation method used and the results. Results indicate

that the long-run growth rates of sales and income tax bases range from .16 to 1.85 and from .24 to 2.06,

respectively. Consistent with Bruce, Fox, and Tuttle‘s findings, results also suggest that income tax has a

higher growth potential than sales tax: the means of the long-run elasticities are .83 and 1.27, respectively.

This means that sales tax fails to grow in tandem with personal income in the long run, whereas income tax

grows more than personal income.

37

perpendicular) deviations as opposed to vertical deviations used in OLS regression.17

The procedure for calculating orthogonal deviations is as follows:

Figure 2.2 Illustration of Orthogonal Deviation Calculation

As an example, Figure 2.2 illustrates how the orthogonal deviation of tax revenue

for year from the long-run trend is calculated. As the first step, including three

hypothetical straight lines over Observation and its fitted regression line (

) creates two right triangles, T1 (filled with lines) and T2 (filled with dots). Suppose that

Line 1 goes through Observation , forming a right angle with the fitted line; Line 2 goes

through Observation , forming a right angle with X Axis; Line 3 forms a right angle

with Line 2, and the length (Side b) between the crossing point of Line 3 and the fitted

17

As a side note, the estimation method that uses orthogonal deviations is called the ―orthogonal distance

regression (ODR).‖ Boggs and Rogers (1990) note that ODR is used to solve the computational problem

concerned with ―finding the maximum likelihood estimators of parameters in measurement error models in

the case of normally distributed errors.‖ See Boggs, Byrd, and Schnabel (1987) for a more detailed

discussion of an algorithm for finding the solution of this problem. As a recent example using ODR in the

public finance literature, see Alm and James (2011).

38

line and the crossing point with Line 3 and Line 2 becomes one unit of the regressor,

income.

The sides of T1 and T2 have the same ratio, since they have the same interior

angles. The ratio of Side a to b equals that of Side d to e (the solution in this case);

therefore, the length of Side e is obtained by obtaining those of the remaining ones [Side

e = (b*d)/a]. Side d is the vertical residual for Observation , which is obtained by

subtracting the fitted value from the actual value for Observation [Side d =

]. Side c equals the slope coefficient , since Side b is 1. By the Pythagorean

theorem, therefore, . Expressed in mathematical terms, the

orthogonal deviation of Observation from the long-run trend is as follows:

The value obtained from this formula represents the absolute magnitude of the

deviation. Therefore, for valid cross-state comparisons, it needs to be adjusted for the

overall revenue size, which is approximated by the mean of the sample tax revenues.

Converting the adjusted into percentage form, the final formula for the size-

adjusted orthogonal deviation of tax revenue in time period is:

39

The estimation of revenue volatility requires two data sets: tax revenue and

income. Data on personal income are obtained from the U.S. Bureau of Economic

Analysis (BEA) website. As noted above, this study uses actual tax base as opposed to

tax revenue collection to remove the effects of tax rate changes on revenue outcomes.

This approach has an empirical merit, given the fact that states frequently change tax

rates. The problem that stems from revenue adjustments is especially troubling in the case

of income taxes that use inflation indexation in determining tax brackets. In order to

prevent inflation from causing indirect tax increases, what is called bracket creep, some

states apply annual indexation, and in this case, rate changes can be effectively said to

occur annually. Using tax base, however, allows one not to adjust for tax rate changes.

Tax revenue is obtained simply by multiplying tax base by tax rate. Hence, if there has

been no change in tax rate over a given period of time, mathematically the cyclical

volatility of tax revenue should be exactly the same as that of tax base from which the

revenue was collected.

Further, the measurement of tax base requires two data sets: tax revenue and rate.

Tax liability is determined by multiplying tax base (i.e. taxable incomes and purchases)

by tax rate; therefore, a state‘s tax base is obtained by dividing its tax revenue by tax rate.

Data on general sales tax and individual income tax revenues are drawn from the State

Government Finance series annually published by the U.S. Census Bureau, while each

state‘s annual sales tax rates are collected from the State Tax Reporter series published by

the Commerce Clearing House.

40

As for income tax, unlike sales tax for which flat tax rates are used, many states

use a progressive tax system with multiple tax brackets and rates, which makes it

extremely difficult to measure income tax bases. One conceivable way might be to use

top-bracket tax rates. But this method may yield incorrect estimates if changes in income

thresholds by bracket are not taken into account. Recognizing the potential problem with

using top-bracket tax rates, Bruce, Fox, and Tuttle (2006), which examine factors that

affect the long-run income elasticity of state income taxes using actual revenues but, not

bases, control for the effects of tax structures by including top-bracket income thresholds

and tax rates in their regressions.

This strategy, though reasonable, has the drawback that complicates the

interpretation of the analysis results, thereby making it difficult to derive meaningful

policy implications. To avoid this complexity, this study uses estimates of state average

marginal income tax rates published by the National Bureau of Economic Research

(NBER). Using a simulation program called TAXIM, the NBER calculates state income

tax liabilities from a sample of actual tax returns provided for public use by the Statistics

of Income Division of the Internal Revenue Service, and then derive the average marginal

income tax rate of each state by year and income type. Estimates take into account most

important features of state tax codes (including not only basic tax structures such as tax

rates and income thresholds by bracket, exclusions, deductions, exemptions, and credits,

but also maximum tax, minimum tax, alternative taxes, earned income credit, etc). Given

the comprehensiveness of the criteria, the estimates are well suited to the purpose of

adjusting tax revenues for changes in tax rates.

41

Using these formula and data, revenue volatility is calculated by state and tax.

Table 2.2 and Figure 2.3 present results and box plots, respectively.

Table 2.2 Cyclical Volatility of General Sales Tax and Individual Income Tax (1992–

2007 Average)

State General Sales Tax Individual Income Tax

Alabama 2.2571 3.4970

Alaska No ST No IIT

Arizona 2.1672 6.2014

Arkansas 1.8684 2.8338

California 1.3903 3.8986

Colorado 3.5431 4.8916

Connecticut 7.9598 3.9747

Delaware No ST 3.7310

Florida 2.7079 No IIT

Georgia 2.8197 2.4480

Hawaii 1.0402 4.1101

Idaho 1.5371 5.3365

Illinois 1.3802 3.3534

Indiana 2.1929 4.3725

Iowa 6.2776 5.0190

Kansas 3.1904 5.2838

Kentucky 1.4970 3.3227

Louisiana 3.4298 6.8230

Maine 2.4067 3.8517

Maryland 2.5864 2.1075

Massachusetts 3.7362 2.6426

Michigan 3.7522 6.5320

Minnesota 1.3302 3.4061

Mississippi 2.9523 4.4432

Missouri 3.3067 4.4272

Montana No ST 4.0113

Nebraska 3.5007 4.3833

Nevada 4.4378 Excluded

New Hampshire No ST No IIT

New Jersey 2.1755 3.7979

New Mexico 6.2798 4.7430

New York 1.8327 3.3910

North Carolina 3.5819 2.3265

North Dakota 3.2119 3.7530

Ohio 2.1886 2.5736

42

Oklahoma 2.5487 5.3811

Oregon No ST 3.4284

Pennsylvania 2.2939 2.2313

Rhode Island 1.9911 2.9956

South Carolina 2.2065 4.0264

South Dakota 2.0903 No IIT

Tennessee 2.0234 Excluded

Texas 2.0771 No IIT

Utah 2.9019 3.9343

Vermont 2.9579 4.7145

Virginia 3.9833 3.8062

Washington 1.8746 No IIT

West Virginia 2.3075 2.1022

Wisconsin 1.7642 4.1890

Wyoming 3.8190 No IIT

Mean 2.8306 4.0558

Std. Dev. 1.3643 1.4866

Min 1.0402 2.1022

Max 7.9598 10.5244

43

Figure 2.3A Box Plot for General Sales Tax Volatility

05

10

15

20

05

10

15

20

05

10

15

20

05

10

15

20

05

10

15

20

05

10

15

20

05

10

15

20

AL AZ AR CA CO CT FL

GA HI ID IL IN IA KS

KY LA ME MD MA MI MN

MS MO NE NV NJ NM NY

NC ND OH OK PA RI SC

SD TN TX UT VT VA WA

WV WI WY

Sale

s T

ax V

ola

tilit

y

Graphs by State

44

Figure 2.3B Box Plot for Individual Income Tax Volatility

05

10

15

20

25

05

10

15

20

25

05

10

15

20

25

05

10

15

20

25

05

10

15

20

25

05

10

15

20

25

AL AZ AR CA CO CT DE

GA HI ID IL IN IA KS

KY LA ME MD MA MI MN

MS MO MT NE NJ NM NY

NC ND OH OK OR PA RI

SC UT VT VA WV WI

Incom

e T

ax V

ola

tilit

y

Graphs by State

45

Mainly three groups of factors are used to explain revenue volatility: economic

structure, tax base composition, and demographic-economic characteristics. Starting from

the model of sales tax volatility, the effect of tax base composition is captured by

including the level of tax exemption by category of purchases discussed above in the

conceptual discussion: food, clothing, services, and producer goods. First, the level of

sales tax exemption for food and clothing (separately) is measured by 100 minus tax rate

for each category as a percent of general sales tax rate: 100 indicates full exemption, 0

indicates no exemption.18

Data on the sales taxation of food and clothing were collected

from the State Tax Reporter series (See Table 2.3 for a summary).

18

Revenue volatility, as described above, is expressed as a percentage. Thus, for more intuitive and

straightforward interpretation of analysis results, percent forms are used instead of 0-to-1 ratios. For

example, if a given state, where the general sales tax rate is 5%, applies a reduced sales tax rate of 3% for

food, then the level of tax exemption is 40 (100 - 3/5*100).

46

Table 2.3 Sales Tax Treatment of Food and Clothing and 1992−2007 Major Changes

State General Rate Food Clothing

As of Jan 2007 As of Jan 2007 Changes As of Jan 2007 Changes

Alabama 4%

Arizona 5.6%

Arkansas 6% Full

California 6.25% Full

Colorado 2.9% Full

Connecticut 6% Full

Florida 6% Full

Georgia

4% Full No exemption

prior to 1997

1997: 2%

1998: 1%

1999−2007:

Full

Hawaii 4%

Idaho 6%

Illinois 6.25% 1%

Indiana 6% Full

Iowa 5% Full

Kansas 5.3%

Kentucky

6% Full No exemption

prior to 2004

2004−2007:

Full

Louisiana 4% Full

Maine 5% Full

Maryland 6% Full

Massachusetts 5% Full Full (up to

$175)

Michigan 6% Full

Minnesota 6.5% Full Full

Mississippi 7%

Missouri

4.225% 1.225% No exemption

prior to 1998

1998−2007:

1.225%

Nebraska 5.5% Full

Nevada 6.5% Full

New Jersey 7% Full Full

New Mexico

5% Full No exemption

prior to 2005

2005−2007:

Full

New York

4% Full Full (up to

$110)

No exemption

prior to 2006

2006−2007:

47

Full (up to

$175)

North Carolina

4.25% Full No exemption

prior to 2002

2002−2007:

Full

North Dakota 5% Full

Ohio 5.5% Full

Oklahoma 4.5%

Pennsylvania 6% Full Full

Rhode Island 7% Full Full

South Carolina 6%

South Dakota 4%

Tennessee

7% 5.5% No exemption

prior to 2002

2002−2007:

5.5%

Texas 6.25% Full

Utah 4.65% 2.75% No exemption

prior to 2007

Vermont

6% Full Full (up to

$110)

No exemption

prior to 2000

2000−2007:

Full (up to

$110)

Virginia

4% 1.5% No exemption

prior to 2000

2000−2004:

3%

2005−2007:

1.5%

Washington 6.5% Full

West Virginia 6%

Wisconsin 5% Full

Wyoming

4% Full Temporary

exemption for

Jul 2006 – Jun

2008

Sources: State Tax Reporter series published by the Commerce Clearing House

Notes: Full denotes full exemption.

Table 2.4 Sales Tax Treatment of Services

State Total Number of Services Taxed Percent of Services Taxed

FY 1996 FY 2004 FY 2007 FY 1996 FY 2004 FY 2007

Alabama 32 37 37 80.5 78.0 78.0

Arizona 57 58 72 65.2 65.5 57.1

Arkansas 65 72 55 60.4 57.1 67.3

California 13 23 21 92.1 86.3 87.5

48

Colorado 14 14 14 91.5 91.7 91.7

Connecticut 87 80 79 47.0 52.4 53.0

Florida 64 64 63 61.0 61.9 62.5

Georgia 34 36 36 79.3 78.6 78.6

Hawaii 157 160 160 4.3 4.8 4.8

Idaho 29 30 94 82.3 82.1 44.0

Illinois 17 17 29 89.6 89.9 82.7

Indiana 22 23 17 86.6 86.3 89.9

Iowa 94 94 24 42.7 44.0 85.7

Kansas 76 71 74 53.7 57.7 56.0

Kentucky 26 29 28 84.1 82.7 83.3

Louisiana 58 55 55 64.6 67.3 67.3

Maine 27 24 25 83.5 85.7 85.1

Maryland 39 39 39 76.2 76.8 76.8

Massachusetts 20 19 18 87.8 88.7 89.3

Michigan 29 26 26 82.3 84.5 84.5

Minnesota 61 67 66 62.8 60.1 60.7

Mississippi 70 74 72 57.3 56.0 57.1

Missouri 28 28 26 82.9 83.3 84.5

Nebraska 49 76 77 70.1 54.8 54.2

Nevada 11 15 18 93.3 91.1 89.3

New Jersey 50 55 74 69.5 67.3 56.0

New Mexico 152 156 158 7.3 7.1 6.0

New York 74 56 57 54.9 66.7 66.1

North Carolina 28 30 30 82.9 82.1 82.1

North Dakota 25 27 26 84.8 83.9 84.5

Ohio 52 68 68 68.3 59.5 59.5

Oklahoma 32 32 32 80.5 81.0 81.0

Pennsylvania 61 55 55 62.8 67.3 67.3

Rhode Island 28 29 29 82.9 82.7 82.7

South Carolina 32 34 35 80.5 79.8 79.2

South Dakota 141 146 146 14.0 13.1 13.1

Tennessee 71 67 67 56.7 60.1 60.1

Texas 78 81 83 52.4 51.8 50.6

Utah 54 57 58 67.1 66.1 65.5

Vermont 23 29 32 86.0 82.7 81.0

Virginia 18 18 18 89.0 89.3 89.3

Washington 152 157 158 7.3 6.5 6.0

West Virginia 110 110 105 32.9 34.5 37.5

Wisconsin 69 74 76 57.9 56.0 54.8

Wyoming 63 62 58 61.6 63.1 65.5

Average 55.4 57.2 57.6 66.2 66.0 65.7

Sources: 1996/2004/2007 Federation of Tax Administrators (FTA) Survey on State Sales Taxation of

Services

49

The level of tax exemption for services is measured by the number of exempt

services as a percent of the total number of taxable services. Data on the taxation of

services were drawn from the Survey of State Services Taxation series published by the

Federation of Tax Administrators (FTA). The survey develops a list of feasibly taxable

services, and examines the taxable status of each service by state. One limitation of the

FTA survey data is that the survey has been updated periodically, not annually. The first

survey was conducted in 1990, and updated in 1996, 2004, and 2007. Among the series,

the 1996, 2004, and 2007 update are used, with each one covering the previous years.

Substituting them for missing years is considered acceptable, given the fact that states

rarely changed their tax policies on the taxation of services over the study period. The

sales taxation of services by state is summarized in Table 2.4,19

which reveals that there

has been little change.

Producer goods are divided into three categories: direct materials,

machinery/equipment, and utilities for industrial use (electricity, natural gas, and fuel).

The level of tax exemption for each category is measured separately in the same way as

food and clothing, and then weighted according to the proportion that each category

makes up of the total manufacturing cost, and finally combined into a composite index.

Weight percentages for each category were obtained from the manufacturing industry

database jointly developed by the NBER and Census Bureau's Center for Economic

19

In the survey, services are classified into eight categories, which include utilities, personal services,

business services, admissions/amusements, professional services, fabrication, repair, and installation, and

other services.

50

Studies (CES).20

Data on the taxation of direct materials and machinery/equipment were

collected from the State Tax Reporter series (See Table 2.5 for a summary), while data on

utilities were drawn from the aforementioned FTA survey series21

(See Table 2.6 for a

summary).

20

The database covers manufacturing industries in two versions: a Standard Industrial Classification (SIC)

version for 1958–1996 and a North American Industry Classification System version for 1997–2005.

Among the two, I use the latter one that matches my study period. 21

As noted above, the survey is conducted not annually but periodically. Thus, this deficiency is handled

by using the 1996, 2004, and 2007 results for the years 1992–1996, 1997–2004, and 2005–2007,

respectively.

51

Table 2.5 Sales Tax Treatment of Producer Goods and 1992−2007 Major Changes

State General Sales

Tax Rate

Materials Machinery and Equipment

As of Jan 2007 As of Jan 2007 Changes As of Jan 2007 Changes

Alabama 4% Full 1.5%

Arizona 5.6% Full Full

Arkansas

6% Full Partial

exemption for

new and

expanded

industries

California

6.25% Full exemption

prior to 2004

Partial

exemption for

certain start-

ups prior to

2004

Colorado 2.9% Full Full

Connecticut 6% Full Full

Florida

6% Full Partial

exemption for

new and

expanded

industries

Georgia

4% Full Full No exemption

prior to 1995

1995−2007:

Full

Hawaii 4% 1.5%

Idaho 6% Full Full

Illinois 6.25% Full Full

Indiana 6% Full Full

Iowa 5% Full Full

Kansas 5.3% Full Full

Kentucky

6% Full Partial

exemption for

new and

expanded

industries

Louisiana 4% Full

Maine 5% Full Full

Maryland 6% Full Full

Massachusetts 5% Full Full

Michigan 6% Full Full

Minnesota 6.5% Full

Mississippi 7% Full 1.5%

Missouri 4.225% Full Full

Nebraska 5.5% Full

Nevada 6.5% Full

52

New Jersey 7% Full Full

New Mexico 5% Full

New York 4% Full Full

North Carolina 4.25% Full 1%

North Dakota

5% Full Partial

exemption for

new and

expanded

industries

Ohio 5.5% Full Full

Oklahoma 4.5% Full Full

Pennsylvania 6% Full Full

Rhode Island 7% Full Full

South Carolina 6% Full Full

South Dakota 4% Full

Tennessee 7% Full Full

Texas 6.25% Full Full

Utah

4.65% Full Partial

exemption for

new and

expanded

industries

Vermont 6% Full Full

Virginia 4% Full Full

Washington

6.5% Full Full No exemption

prior to 1996

1996−2007:

Full

West Virginia 6% Full Full

Wisconsin 5% Full Full

Wyoming 4% Full

Sources: State Tax Reporter series published by the Commerce Clearing House

Notes: Full denotes full exemption.

53

Table 2.6 Sales Tax Treatment of Utilities for Industrial Use

State Tax Rate

1996 2004 2007

Gene

ral

Electri

city

Natu

ral

gas

Fue

l

Gene

ral

Electri

city

Natu

ral

gas

Fue

l

Gene

ral

Electri

city

Natu

ral

gas

Fue

l

Alabama 4 4 4 4 4 4 4 4 4 4 4 4

Arizona 5 5 5 5 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6

Arkansas 4.5 4.5 4.5 4.5 6 6 6 6 6 6 6 6

Californi

a

6 0 0 7.2

5

6 0 0 7.2

5

6.25 0 0 7.2

5

Colorado 3 0 0 0 2.9 0 0 0 2.9 0 0 0

Connecti

cut

6 6 6 6 6 6 6 6 6 6 6 6

Florida 6 7 6 6 6 7 6 6 6 7 6 6

Georgia 4 4 4 4 4 4 4 4 4 4 4 4

Hawaii

4 5.885 5.88

5

5.8

85

4 5.885 5.88

5

5.8

85

4 5.885 5.88

5

5.8

85

Idaho 5 0 0 0 6 0 0 0 6 0 0 0

Illinois

6.25 5 5 6.2

5

6.25 5 5 6.2

5

6.25 5 5 6.3

Indiana 5 0 0 0 6 0 0 0 6 0 0 0

Iowa 5 5 5 5 5 5 5 5 5 5 5 5

Kansas 4.9 4.9 4.9 4.9 5.3 5.3 5.3 5.3 5.3 5.3 5.3 5.3

Kentucky 6 6 6 6 6 6 6 6 6 6 6 6

Louisiana 4 4 4 4 4 3.9 3.9 4 4 3.9 3.9 4

Maine 6 6 6 6 5 5 5 5 5 5 5 5

Maryland 5 5 5 5 5 5 5 5 5 5 5 5

Massachu

setts

5 5 5 5 5 5 5 5 5 5 5 5

Michigan 6 6 6 6 6 6 6 6 6 6 6 6

Minnesot

a

6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5

Mississip

pi

7 1.5 1.5 1.5 7 7 7 7 7 7 7 7

Missouri

4.22

5

4.225 4.22

5

4.2

25

4.22

5

4.225 4.22

5

4.2

25

4.22

5

4.225 4.22

5

4.2

25

Nebraska 5 5 5 5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5

Nevada 6.5 0 0 0 6.5 0 0 0 6.5 0 0 0

New

Jersey

6 0 0 0 6 6 6 0 7 7 7 0

New

Mexico

5 5 5 5 5 5 5 5 5 5 5 5

New

York

4 4 4 4 4.25 0 0 0 4 0 0 0

North

Carolina

4 3 3 1 4.5 3 0 4.5 4.25 3 0 4.5

North 5 0 2 0 5 0 2 0 5 0 2 0

54

Dakota

Ohio 5 0 0 5 6 0 0 6 5.5 0 0 5.5

Oklahom

a

4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5

Pennsylv

ania

6 6 6 6 6 6 6 6 6 6 6 6

Rhode

Island

7 7 7 7 7 7 7 7 7 7 7 7

South

Carolina

5 0 0 0 5 0 0 0 5 0 0 0

South

Dakota

4 4 4 4 4 4 4 4 4 4 4 4

Tennesse

e

6 6 6 6 7 1 1 1 7 1 1 1

Texas

6.25 6.25 6.25 6.2

5

6.25 6.25 6.25 6.2

5

6.25 6.25 6.25 6.2

5

Utah

4.87

5

0 0 0 4.75 0 0 0 4.75 0 0 0

Vermont 5 5 5 5 6 6 6 6 6 6 6 6

Virginia 3.5 0 0 4.5 3.5 0 0 5 4 0 0 5

Washingt

on

6.5 3.873 3.85

2

6.5 6.5 3.873 3.85

2

6.5 6.5 3.873 3.85

2

6.5

West

Virginia

6 4 4.29 5 6 4 4.29 5 6 4 4.3 5

Wisconsi

n

5 5 5 5 5 5 5 5 5 5 5 5

Wyoming 4 4 4 4 4 4 4 4 4 4 4 4

Sources: 1996/2004/2007 Federation of Tax Administrators (FTA) Survey on State Sales Taxation of

Services

55

To take sector GSP composition into account, the percent share of GSP by sector

(which are agriculture, mining, construction, and durable manufacturing, nondurable

manufacturing, and service sector) is included. Data on GSP by sector were drawn from

the BEA website. To observe the decreasing effect of services exemption, a quadratic

term in the variable is also included. For the interaction effect of the level of tax

exemption for producer goods and the percent share of manufacturing sector GSP

(durable and nondurable, separately), an interaction term between the two variables is

included as well.

To control for demographic and economic factors, six variables are included:

population; the proportion of the population aged 17 and under, the proportion of the

population aged 65 and over (for population age structure); per capita personal income;

the proportion of the population with federal adjusted gross income (FAGI) over

$200,000, and the proportion of the population below the federal poverty level (for

income distribution). Data for all the variables, except the proportion of the wealthy

population whose data source is the Internal Revenue Service (IRS) website, were

collected from the Statistical Abstract of the United States series published by the U.S.

Census Bureau and the BEA website.

For the model of income tax volatility, the effect of tax base composition is

captured by including mostly three groups of variables: (1) tax exemptions for Social

Security benefits, pensions, and long-term capital gains, (2) deductions for federal

income tax and local property tax paid, and (3) personal exemptions. First, tax exemption

for Social Security benefits is measured as a dummy variable, with 1 indicating full

56

exemption, 0 no or partial exemption.22

Pensions are again divided into private and public

pensions.23

Tax exemption for public and private pensions is also measured separately as

a dummy variable. Three groups are employed: no, partial and full exemption (1 if yes, 0

otherwise), with no exemption dropped as the base group. Data on the income tax

treatment of pensions were collected from the State Tax Reporter series (See Table 2.7

for a summary).

22

No and partial exemption for Social Security benefits are lumped into the same category, based on the

assumption that the effect of partial exemption would be minimal. With the vast majority of states allowing

taxpayers to fully exclude Social Security benefits from taxable incomes, states with partial exemption for

Social Security benefits provide favorable tax treatment only to low-income people (for example,

exclusively to taxpayers with FAGI less than $50,000 in Connecticut and with FAGI less than $25,000 in

Montana). 23

Public pension incomes include military, federal, state, and local government pension incomes, among

which, the study focuses on ones from states and locals. Generally there is little policy difference among

government levels and state/local government employees account for the majority of public employees.

According to 2008 Census data on public employment, the number of federal and state/local government

full-time employees is 2,518,101 (14%) and 14,857,827 (86%) respectively.

57

Table 2.7 Income Tax Treatment of Retirement Incomes and 1992−2007 Major

Changes

State Social

Security

benefit

exclusion

Private pension

exclusion

Public

pension

exclusion

As of

January

2007

Changes As of January

2007

Changes As of

January

2007

Changes

Alabama Full Defined benefit

plans only

Full

Arizona Full None $2,500

Arkansas Full $6,000 $6,000

California Full None None

Colorado None $24,000 (age

65−)

$20,000 (age

55−64)

Applicable to

qualified

pensions and

annuities, IRA

distributions,

Keogh plans,

and Social

Security

benefits

FY

1992−1999:

$20,000 (age

55−)

FY

2000−2007:

$24,000 (age

65−)

$20,000 (age

55−64)

Same as

private

Connecticut Full only

for

taxpayers

with AGI

less than

$50,000

None until

FY 1998

None None

Delaware Full $12,500 (age

60−)

$2,000 (−age

59)

FY

1992−1998:

$3,000 (age

60−)

$2,000

(−age 59)

FY 1999:

$5,000 (age

60−)

$2,000

(−age 59)

FY

2000−2007:

$12,500 (age

60−)

Same as

private

58

$2,000

(−age 59)

Georgia Full $30,000 FY

1992−1994:

$11,000

FY

1995−1998:

$12,000

FY 1999:

$13,000

FY 2000:

$13,500

FY 2001:

$14,000

FY 2002:

$14,500

FY

2003−2005:

$15,000

FY 2006:

$25,000

FY 2007:

$30,000

Same as

private

Hawaii Full Full for

noncontributory

plans; Only

distributions

attributable to

employer

contributions

for contributory

plans

Full

Idaho Full None $21,900 –

Social

Security

benefit

(age 65−)

only for

public

safety

officers

FY

1992−2007:

Amount

annually

adjusted for

inflation

Illinois Full Full Full for

qualified

retirement

plans

Indiana Full None None

Iowa 32% None until

FY 2006

$6,000 (age

55−)

FY 1992–

1994: None

FY

Same as

private

59

1995−1997:

$3,000

FY

1998−2000:

$5,000

FY

2000−2007:

$6,000

Kansas Full only

for

taxpayers

with AGI

less than

$50,000

None until

FY 2006

None Full

Kentucky Full $41,100 FY

1992−1994:

None

FY 1995:

$6,250

FY 1996:

$12,500

FY 1997:

$18,750

FY 1998:

$35,000

FY

1999−2005:

Amount

annually

adjusted for

inflation

with 1998 as

the base year

FY

2006−2007:

$41,100

$41,110

Full for

benefits

earned

before

1998

FY

1992−1997:

Full

FY

1998−2007:

Same as

private (Full

for benefits

earned

before 1998)

Louisiana Full $6,000 (age

65−)

Full

Maine Full $6,000 – Social

Security benefit

Applicable only

to 401(a), 403,

457(b) plans

FY

1992−1999:

None

FY

2000−2007:

$6,000 –

Social

Security

benefit

Applicable

only to

$6,000 –

Social

Security

benefit

FY

1992−1999:

None

FY

2000−2007:

$6,000 –

Social

Security

benefit

60

401(a), 403,

457(b) plans

Maryland Full $23,600 –

Social Security

benefit

Not applicable

to IRA

distributions

and Keogh

plans

FY

1992−2007:

Amount

annually

adjusted for

inflation

$23,600 –

Social

Security

benefit

FY

1992−2007:

Amount

annually

adjusted for

inflation

Massachusetts Full None Full

Michigan Full $42,240 (age

65−)

FY

1992−2007:

Amount

annually

adjusted for

inflation

Full

Minnesota None None None

Mississippi Full Full Full

Missouri 20% None until

FY 2006

None $6,000

Montana Full only

for

taxpayers

with AGI

less than

$25,000

$3,600 for

taxpayers with

AGI less than

$30,000

Same as

private

Nebraska None None None

New Jersey Full $15,000 (age

62−)

FY

1992−1999:

$7,500

FY 2000:

$9,735

FY 2001:

$11,250

FY 2002:

$13,125

FY

2003−2007:

$15,000

Same as

private

New Mexico None None None

New York Full $20,000 (age

59.5−)

Full

North Carolina Full $2,000 $4,000

North Dakota None None $5,000 –

Social

Security

benefit

only for

61

public

safety

officers

Ohio Full Retirement

income credit

$0−$500: None

$500−$1,500:

$25

$1,500−$3,000:

$50

$3,000−$5,000:

$80

$5,000−$8,000:

$130

$8,000−: $200

Same as

private

Oklahoma Full $10,000

Taxpayers with

AGI less than

$50,000

FY

1992−2003:

None

FY 2004:

$5,500

Taxpayers

with AGI

less than

$37,500

FY 2005:

$7,500

Taxpayers

with AGI

less than

$37,500

FY 2006:

$10,000

Taxpayers

with AGI

less than

$37,500

FY 2007:

$10,000

Taxpayers

with AGI

less than

$50,000

$10,000 FY

1992−2004:

$5,500

FY 2005:

$7,500

FY

2006−2007:

$10,000

Oregon Full 9%

Taxpayers with

household

income less than

$22,500

Same as

private

Pennsylvania Full Full Full

Rhode Island None None None

62

South Carolina Full $10,000 (age

65−)

$3,000 (−age

64)

Same as

private

Utah None $7,500 (age

65−)

$4,800 (−age

64)

Same as

private

Vermont None None None

Virginia Full $12,000 (age

65−)

$6,000 (age 62−

age 64)

Same as

private

West Virginia None $8,000 (age

65−)

$2,000

(Full for

public

safety

workers)

Wisconsin None None None

Sources: State Tax Reporter series published by the Commerce Clearing House

Notes: Exemption amounts are for single filers.

63

The level of tax exemption for long-term capital gains is measured as the percent

of long-term capital gains excluded from taxable income, and data for the variable were

from the State Tax Reporter series (See Table 2.8 for a summary).

Table 2.8 Income Tax Treatment of Long-Term Capital Gains and 1992−2007

Major Changes

State As of 2007 Changes

Arizona 30% of net capital gains excluded (from 1999) Fully taxable prior to 1999

Montana 30% of net capital gains excluded 15% in 2005 and 2006. Fully taxable

prior to 2005 (a)

New Mexico 50% of net capital gains excluded 10% in 2003; 20% in 2004; 30% in

2005; 40% in 2006; and 50% in 2007.

Fully taxable prior to 2003

North Dakota 30% of net capital gains excluded (from 2002) Fully taxable prior to 2002

South

Carolina

44% of net capital gains excluded (from 2001) Fully taxable prior to 2001

Vermont 40% of net capital gains excluded (from 2006) Fully taxable prior to 2006

Wisconsin 60% of net capital gains excluded

Sources: State Tax Reporter series published by the Commerce Clearing House

Notes:

(a) Montana allows taxpayers a tax credit against income tax in an amount equal to 2% (1% for the 2005

and 2006 tax years) of the taxpayers' net capital gains. The exclusion portion was calculated based on

Montana's top income tax rate of 6.9% for the taxable years involved.

Deductions for federal income tax and local property tax paid are measured

separately as a dummy variable, using three groups: no, partial and full deduction (1 if

yes, 0 otherwise), with no deduction dropped as the base group. Data for the variables

were from the State Tax Reporter series (See Table 2.9 and Table 2.10 for a summary).

Table 2.9 Deduction for Federal Income Tax Paid and 1992−2007 Major Changes

State As of 2007 Changes

Alabama Full deduction

Iowa Full deduction

Louisiana Full deduction

Missouri Partial deduction (up to $5,000)

Montana (a) Partial deduction (up to $5,000) (from 2005) Full deduction (until 2004)

North Dakota* Optional

64

Oklahoma (b) Optional

Oregon Partial deduction (up to $3,000)

Utah Partial deduction (up to 50%)

Sources: State Tax Reporter series published by the Commerce Clearing House

Notes:

Deduction amounts are for single filers.

(a) Taxpayers are allowed to choose between standard and itemized deduction. As of 2004, for taxpayers

with income under $129,000, full deductions are allowed, and then deduction amounts phase out as

incomes increase.

(b) North Dakota and Oklahoma provide two options: one is to opt for the deductions and be subject to

higher tax rates, and the other is to opt out of the deductions and subject to lower tax rates. These states are

coded as 0 (no deduction) on the assumption that revenue losses due to deductions are generally offset by

gains due to higher tax rates.

Table 2.10 Deduction for Local Property Tax Paid

State Amounts (As of 2007)

Alabama Full

Alaska No individual income tax

Arizona Full

Arkansas Full

California Full

Colorado Full

Connecticut Tax credit of up to $500

Delaware Full

Florida No individual income tax

Georgia Full

Hawaii Full

Idaho Full

Illinois 5% of property tax paid

Indiana Deduction of up to $2,500

Iowa Full

Kansas Full

Kentucky Full

Louisiana Deduction of up to $75K of assessed property value

Maine Full

Maryland Full

Massachusetts* Tax credit only to seniors, the blind, surviving spouses and minor

children, homeowners facing hardships, and certain disabled veterans

who meet financial, residency, and other eligibility requirements

Michigan Tax credit of up to $1,200. Taxpayers with household income over

$82,650 are not eligible for the credit.

Minnesota Full

Mississippi Tax credit of up to $300

Missouri Full

Montana Full

Nebraska Full

Nevada No individual income tax

New Hampshire Partial individual income tax only on income from interest and

65

dividends

New Jersey Deduction of up to $10K; $5K for taxpayers with gross income of

150K-250K; None for over 250K

New Mexico Full

New York Full

North Carolina Full

North Dakota Full

Ohio* Tax credit only to senior citizens and the disabled

Oklahoma Full

Oregon Full

Pennsylvania* Tax credit only to seniors and disabled residents

Rhode Island Full

South Carolina Full

South Dakota No individual income tax

Tennessee Partial individual income tax only on income from interest and

dividends

Texas No individual income tax

Utah Full

Vermont Full

Virginia Full

Washington No individual income tax

West Virginia Tax credit of up to $500

Wisconsin Tax credit of up to $300 for property tax paid of $2,500 or more

Wyoming No individual income tax

Sources: State Tax Reporter series published by the Commerce Clearing House

Notes:

Deduction amounts are for single filers.

While there have been some changes in partial exemption amounts, there has been virtual no change in the

status of full or partial exemption. For this reason, data only for 2007 are presented.

*States allowing the deductions only to a few minority groups such as seniors and disabled residents are

coded as 0 (no deduction), since their effects on the income taxes are assumed to be relatively minimal

compared to states doing so to all taxpayers.

Personal exemptions are measured by the sum of personal and dependent

exemption. Demographic data from the U.S Census Bureau 2000 report indicate that the

average household and family size are 2.59 and 3.14, respectively. Based on this statistic,

personal exemptions are calculated on the assumption that taxpayers, on average, claim

personal exemptions for themselves and three dependents. Data for the variables were

also collected from the State Tax Reporter series (See Table 2.11 for a summary).

66

Table 2.11 Personal Exemptions and 1992−2007 Major Changes

State Personal exemption amount Dependent exemption amount

As of 2007 Changes As of 2007 Changes

Alabama 1,500 500

Arizona 2,100 2,300 2,300 (2000–)

2,100 (–1999)

Arkansas 22 (Tax credit) 21 (2006–)

20 (–2005)

22 (Tax credit) 21 (2006–)

20 (–2005)

California 94 Indexed for

inflation

294 Indexed for

inflation

Colorado 799 (a) Tied to federal tax

system (b)

799 (a) Tied to federal tax

system (b)

Connecticut 12,750 (c) 12,625 (2004–

2006)

12,500 (2001–

2003)

12,250 (2000)

12,000 (–1999)

0

Delaware 110 (Tax credit) 110 (2000–)

100 (–1999)

110 (Tax credit) 110 (2000–)

100 (–1999)

Georgia 2,700 3,000 3,000 (2003–)

2,700 (1998–2002)

2,500 (1995–1997)

2,000 (–1994)

Hawaii 1,040 1,040

Idaho 3,400 Tied to federal tax

system (b)

3,400 Tied to federal tax

system (b)

Illinois 2,000 2,000 (2001–)

1,650 (2000)

1,300 (1999)

1,000 (–1998)

2,000 2,000 (2001–)

1,650 (2000)

1,300 (1999)

1,000 (–1998)

Indiana 1,000 1,000 1,000 (2000–)

2,500 (–1999)

Iowa 40 (Tax credit) 40 (1998–)

20 (–1997)

40 (Tax credit) 40 (1995–)

15 (–1994)

Kansas 2,250 2,250 (1998–)

2,000 (–1997)

2,250 2,250 (1998–)

2,000 (–1997)

Kentucky 20 (Tax credit) 20 (Tax credit)

Louisiana 4,500 1,000

Maine 2,850 2,850 (2000–)

2,750 (1999)

2,400 (1998)

2,100 (1997)

2,000 (–1996)

2,850 2,850 (2000–)

2,750 (1999)

2,400 (1998)

2,100 (1997)

2,000 (–1996)

Maryland 2,400 2,400 (2002–)

2,100 (2001)

1,850 (1999–2000)

1,750 (1998)

2,400 2,400 (2002–)

2,100 (2001)

1,850 (1999–2000)

1,750 (1998)

67

1,200 (–1997) 1,200 (–1997)

Massachusetts 4,125 4,125 (2007)

3,850 (2006)

3,575 (2005)

3,300 (2002–2004)

2,200 (–2001)

1,000

Michigan 3,300 3,300 (2006–2007)

3,200 (2005)

3,100 (2003–2004)

2,900 (2001–2002)

2,800 (2000)

2,595 (1999)

2,539 (1998)

2,500 (1997)

2,400 (1995–1996)

2,100 (1994)

2,000 (–1993)

3,300 3,300 (2006–2007)

3,200 (2005)

3,100 (2003–2004)

2,900 (2001–2002)

2,800 (2000)

2,595 (1999)

2,539 (1998)

2,500 (1997)

2,400 (1995–1996)

2,100 (–1994)

2,000 (–1993)

Minnesota 3,400 Tied to federal tax

system (b)

3,400 Tied to federal tax

system (b)

Mississippi 6,000 1,500

Missouri 2,100 2,100 (1999–)

1,200 (–1998)

1,200 1,200 (1999–)

400 (–1998)

Montana 1,980 Indexed for

inflation

1,980 Indexed for

inflation

Nebraska 111 (Tax credit) Indexed for

inflation

111 (Tax credit) Indexed for

inflation

New Jersey 1,000 1,500

New Mexico 3,400 Tied to federal tax

system (b)

3,400 Tied to federal tax

system (b)

New York 0 1,000

North Carolina 2,500 2,500

North Dakota 0 0

Ohio 1,450 Annually adjusted

by $50

1,450 Annually adjusted

by $50

Oklahoma 1,000 1,000

Oregon 165 (Tax credit) Indexed for

inflation

165 Indexed for

inflation

Pennsylvania 0 0

Rhode Island 0 0

South Carolina 3,400 Tied to federal tax

system (b)

3,400 Tied to federal tax

system (b)

Utah 2,550 Tied to federal tax

system (b): 75% of

federal personal

exemption

2,550 Tied to federal tax

system (b): 75% of

federal personal

exemption

Vermont 0 0

Virginia 900 900 (2005–2007)

800 (–2004)

900 900 (2005–2007)

800 (–2004)

68

West Virginia 2,000 2,000

Wisconsin 700 700 (2001–2007)

600 (2000)

0 (–1999)

700 700 (2001–2007)

600 (2000)

0 (–1999)

Sources: State Tax Reporter series published by the Commerce Clearing House

Notes:

Exemption amounts are for single filers and in constant dollars. They are converted to 2007 chained dollars

for use in regression analysis.

Tax credit amounts are converted to taxable income amounts by dividing tax credit amounts by top-bracket

tax rates.

(a) Colorado individual income tax system is tied to the federal income tax system: in the State, a person‘s

tax liability is determined by multiplying his or her federal taxable income by the income tax rate (for

example, 4.63% in 2007). This implies that personal exemptions are already reflected in the federal taxable

income which is the starting point for determining the State taxable income. Hence, Colorado personal

exemptions are calculated by multiplying federal personal exemptions by the federal average marginal

income tax rates, which are obtained from NBER TAXSIM data programs.

(b) Federal personal exemptions are $2,300 in 1992; $2,350 in 1993; $2,450 in 1994; $2,500 in 1995;

$2,550, in 1996; $2,650 in 1997; $2,700 in 1998; $2,750 in 1999; $2,800 in 2000; $2,900 in 2001; $3,000

in 2002; $3,050 in 2003; $3,100 in 2004; $3,200 in 2005; $3,300 in 2006; and $3,400 in 2007.

(c) Connecticut tax system does not differentiate between personal and dependent exemption.

69

To take demographic and economic factors into account, the same variables as in

the sale tax model are included, except that for age structure, the proportion of the prime

working-age (25–54 years old)24

population is included instead of the young population.

The data cover the period from 1992 to 2007, and the sample includes 45 states

for sales tax, with five states without sales tax—Alaska, Delaware, Montana, New

Hampshire, and Oregon—excluded, and 41 states for income tax, with five states without

income tax—Alaska, Florida, Nevada, South Dakota, Texas, Washington, and

Wyoming—and two states only taxing income from interest and dividends—New

Hampshire and Tennessee—excluded. All the monetary figures were transformed into

2007 chained dollars using the Census Bureau CPI deflator. The number of people with

income over $200,000 generally trends upward over time as the economy grows. To take

this account, as with monetary figures, the proportion of the wealthy population was also

adjusted for inflation. Table 2.12 presents variable descriptions and data sources, and

Table 2.13 presents summary statistics.

Table 2.12 Variable Descriptions and Data Sources

Variables Descriptions Data sources

stvol Cyclical volatility of general sales tax: Absolute

orthogonal deviation of sales tax base from the

trend line as % of the sample mean

State Government Finance series

State Tax Reporter series

BEA website

itvol Cyclical volatility of individual income tax:

Absolute orthogonal deviation of income tax base

from the trend line as % of the sample mean

State Government Finance series

State Tax Reporter series

NBER TAXSIM website

BEA website

svc Level of tax exemption for services:

Number of services tax exempt as % of total

number of "feasibly-taxable" services (Higher

value indicates higher level of exemption.)

FTA survey series (1996, 2004,

2007)

24

While there is no definite consensus on the range of prime working-age, literature (e.g. the Occupational

Outlook Handbook series published by the U.S. Bureau of Labor Statistics) generally refers to population

25 to 54 years old as prime working-age group.

70

food Level of tax exemption for food:

100 - (tax rate for food as % of general sales tax

rate)

State Tax Reporter series

clth Level of tax exemption for clothing:

100 - (tax rate for clothing as % of general sales

tax rate)

State Tax Reporter series

prod Level of tax exemption for producer goods:

Weighted sum of exemptions for categories of

producer goods

Exemption by category = 100 - (tax rate for each

category as % of general sales tax rate)

State Tax Reporter series

NBER manufacturing industry

database

ss Full exemption for Social Security benefits:

1 if full exemption; 0 if no or partial exemption

State Tax Reporter series

prv_pt Partial exemption for public pensions:

1 if yes; 0 otherwise

State Tax Reporter series

prv_fl Full exemption for public pensions

1 if yes; 0 otherwise

State Tax Reporter series

pbl_pt Partial exemption for private pensions:

1 if yes; 0 otherwise

State Tax Reporter series

pbl_fl Full exemption for private pensions:

1 if yes; 0 otherwise

State Tax Reporter series

ltcg Level of tax exemption for long-term capital

gains:

% of LTCG excluded

State Tax Reporter series

fit_pt Partial deduction for federal income tax paid:

1 if yes; 0 otherwise

State Tax Reporter series

fit_fl Full deduction for federal income tax paid:

1 if yes; 0 otherwise

State Tax Reporter series

lpt_pt Partial deduction for local property tax paid:

1 if yes; 0 otherwise

State Tax Reporter series

lpt_fl Full deduction for local property tax paid:

1 if yes; 0 otherwise

State Tax Reporter series

pers Dollar amount of personal exemption for self + 3

× (dollar amount of personal exemption for

dependent)

State Tax Reporter series

pop Population Statistical Abstract of the United

States series

under17 % of population aged 17 or under Statistical Abstract of the United

States series

over65 % of population aged 65 or over Statistical Abstract of the United

States series

pcpi Per capita personal income (in thousands) BEA website

weal % of population with FAGI over $200,000 IRS tax statistics

poor % of population below federal poverty level Statistical Abstract of the United

States series

agrGSP % of agriculture sector GSP BEA website

minGSP % of mining sector GSP BEA website

consGSP % of construction sector GSP BEA website

durGSP % of durable manufacturing sector GSP BEA website

71

ndurGSP % of nondurable manufacturing sector GSP BEA website

servGSP % of service sector GSP BEA website

Table 2.13A Descriptive Statistics for Sales Tax Model

Variable Mean Std. Dev. Min Max

stvol 2.839866 2.642922 0.00473 19.97958

food 67.55925 23.30801 4.487179 93.125

clth 63.48253 46.53473 0 100

svc 10.69444 29.42785 0 100

prod 83.81822 18.34428 1.720711 100

pop 6049965 6268548 466251 3.64E+07

workage 42.738 1.828713 37.28627 47.33019

over65 12.61292 1.966556 4.3 18.6

pcpi 33414.73 5597.793 21651.25 56510

weal 1.911954 0.822291 0.777973 5.421682

poor 12.885 3.52463 5.7 26.4

agrGSP 1.886038 1.980807 0.119016 11.92524

minGSP 2.128385 4.619672 0.009717 32.95344

consGSP 4.587076 1.007394 2.686019 10.35751

durGSP 8.740904 4.224646 0.485976 23.28784

ndurGSP 6.297988 3.400311 1.030329 18.97705

servGSP 63.71253 5.024774 45.57038 74.33044

Table 2.13B Descriptive Statistics for Income Tax Model

Variable Mean Std. Dev. Min Max

itvol 4.123569 3.643723 .003412 25.90669

ss .6341463 .4820363 0 1

prv_pt .3932927 .4888536 0 1

prv_fl .1219512 .3274792 0 1

pbl_pt .3978659 .489831 0 1

pbl_fl .2682927 .4434089 0 1

ltcg 3.060976 11.62752 0 60

fit_pt .097561 .2969465 0 1

fit_fl .0945122 .2927632 0 1

lpt_pt .2195122 .414232 0 1

lpt_fl .7073171 .4553413 0 1

pers 8448.938 4469.436 0 17733.6

pop 5511600 5890694 572751 3.64e+07

under17 25.36267 2.007615 21.02874 36.07281

over65 12.63453 2.037031 4.3 18.6

pcpi 33282.03 5726.689 21651.25 56510

weal 1.892837 .8452066 .777973 5.421682

poor 12.82409 3.597709 5.7 26.4

agrGSP 1.875769 1.846979 .1190157 11.92524

minGSP 1.551481 2.84 .0097172 16.46646

consGSP 4.474374 .7891637 2.686019 7.4177

durGSP 9.051881 4.332315 .4859761 23.28784

ndurGSP 6.568565 3.422808 1.030329 18.97705

72

servGSP 63.778 4.892006 49.76388 76.20969

2.3.2 Models and Estimation Methods

Based on the variables discussed above, the model for sales tax volatility is

specified as follows:

(1) = + + + + + +

+ 7 · + γ + + + ,

where the and subscript refer to the panel (state) and the time period (year),

respectively; denotes the cyclical volatility of general sales tax; , , ,

and are tax exemption for food, clothing, services, and producer goods, respectively;

and indicate the share of durable and nondurable manufacturing sector

GSP, respectively; V, W, and Z are a matrix of demographic-economic characteristics, the

share of GSP by sector, and year dummies, respectively; is the error term.

In addition, the model for income tax volatility is specified as follows:

(2) = + + + + + +

+ + + + + +

+ γ + + + ,

73

where denotes the cyclical volatility of individual income tax; , , ,

and , and is tax exemption for Social Security benefits; partial and full

exemption for private pensions; and partial and full exemption for public pensions,

respectively; is tax exemption for long-term capital gains; is the proportion of

the population with FAGI over $200,000; , , and indicate partial and full

deduction for federal income tax paid and full deduction for local property tax paid,

respectively; is personal exemption; V, W, and Z are a matrix of demographic-

economic characteristics, the share of GSP by sector, and year dummies, respectively;

is the error term.

The models are estimated using the pooled OLS estimator, and year dummies are

included to control for year-specific unobserved heterogeneity (Podesta, 2000). The data

indicate that the cross-time variations in the key variables for tax base composition are

not large, whereas the cross-state variations are wide. Given the data structure, the fixed

or random effects estimator, which is based on the within transformation, is not

appropriate; instead the pooled OLS estimator with year dummies is used. At this

juncture, it is important to assume that the various sectors of state economies are closely

interrelated under the roof of the U.S. national economy. As a result, state economies

move together and share the same business cycle characteristics. Under this assumption,

variations in cyclical fluctuations in tax revenues across states are largely attributed to

variations in economic and revenue structures.

For OLS estimators to be consistent, homoskedasticity and no serial correlation

assumption should hold. To diagnose the presence of heteroskedasticity and serial

correlation in the errors, two tests were performed: Breusch-Pagan/Cook-Weisberg test

74

for heteroskedasticity and Wooldridge test for serial correlation. Test results indicate that

both are present: all the null hypotheses of homoskedasticity and no serial correlation are

strongly rejected by each test (See Appendix B for detailed results). In correcting for

them, this study uses clustered robust standard errors which are robust to disturbances

being heteroskedastic and serially correlated.

2.4 Results and Discussion

Table 2.14 and 2.15 report the results of regression analyses for sales tax and

income tax volatility. Overall, the models perform well: most of the key variables for tax

base composition are statistically significant with the expected signs, and the regressions

explain 19% and 31%, respectively, of the variations in the dependent variables. Before

interpreting the regression results, it should be noted that most of the key variables

including the dependent variables were measured in percentage form. This means that the

coefficients on them represent percentage point changes in the dependents, not

percentage changes as in log functions.

Table 2.14 Regression Results for Sales Tax Volatility

Explanatory variables Coef. Clustered Robust

Std. Err.

Exemption for food 0.009117** 0.004503

Exemption for clothing -0.01559*** 0.005589

Exemption for services -0.00196 0.04146

(Exemption for services)^2 1.84E-05 0.000352

Exemption for producer goods -0.0356* 0.020508

Exemption for producer goods × % of durable

manufacturing sector GSP

0.000368 0.002043

Exemption for producer goods × % of

nondurable manufacturing sector GSP

0.006251* 0.003619

75

% of agriculture sector GSP 0.044363 0.083519

% of mining sector GSP 0.117755* 0.066276

% of construction sector GSP -0.05013 0.177785

% of durable manufacturing sector GSP 0.044331 0.207352

% of nondurable manufacturing sector GSP -0.42853 0.345164

% of services sector GSP 0.03807 0.084465

Population -1.40E-07*** 3.05E-08

% of population aged 25 to 54 -0.22149* 0.11963

% of population aged 65 or over 0.071406 0.06356

Per capita personal income 5.91E-05 0.00011

% of population with AGI over $200,000 1.297343* 0.664692

% of population below poverty level 0.095143* 0.054081

Constant 7.238988 10.05118

Number of observations 720

Number of states 45

R-squared 0.1876

Note: Year effects not reported.

***p<0.01; **p<0.05; *p<0.1

Table 2.15 Regression Results for Income Tax Volatility

Explanatory variables Coef. Clustered Robust

Std. Err.

Full exemption for Social Security benefits 0.684174* 0.35612

Partial exemption for private pensions -0.50986 0.433281

Full exemption for private pensions -0.24972 0.580233

Partial exemption for public pensions 0.626986 0.476724

Full exemption for public pensions 1.062584*** 0.357041

Exemption for long-term capital gains -0.08036* 0.041498

Exemption for long-term capital gains × % of

population with AGI over $200,000

0.063927** 0.024151

Partial deduction for federal income tax paid -0.1362 0.570368

Full deduction for federal income tax paid 0.523886 0.508445

Partial deduction for local property tax paid 1.239711** 0.485865

Full deduction for local property tax paid 2.080549*** 0.500427

Personal exemption 4.74E-05 4.04E-05

% of agriculture sector GSP 0.290092** 0.114027

% of mining sector GSP 0.463241*** 0.155492

% of construction sector GSP 0.139126 0.33256

% of durable manufacturing sector GSP 0.201583* 0.115118

% of nondurable manufacturing sector GSP 0.095391 0.093654

% of services sector GSP 0.087271 0.118443

Population 2.60E-08 2.70E-08

% of population aged 17 or under 0.118373 0.133713

% of population aged 65 or over -0.12376* 0.064133

76

Per capita personal income 0.00013 0.000134

% of population with AGI over $200,000 -0.21125 0.625919

% of population below poverty level -0.15787** 0.077824

Constant -13.0187 9.667144

Number of observations 656

Number of states 41

R-squared 0.3085

Note: Year effects not reported.

***p<0.01; **p<0.05; *p<0.1

Starting from the analysis for sales tax volatility, the results show that sales tax

exemptions for food and clothing have statistically significant effects on sales tax

volatility. The OLS coefficients on the variables indicate that exemption for food has a

positive effect, whereas exemption for clothing exerts a negative impact on the dependent

variable. Specifically, other things being equal, changes from no to full exemption for

food and clothing are predicted to lead to, on average, a .9 percentage point increase and

a 1.6 percentage point decrease in sales tax volatility, respectively [.009*100 = .9%;

0.016*100 = 1.6%]. These results suggest that the consumption of food is relatively

stable over the course of the business cycle, whereas that of clothing, as expected, is

sensitive to economic changes. Table 2.3 shows that only a small number of states are

offering sales tax exemption for clothing, whereas relatively many states are exempting

food from sales taxation. In light of the results, when it comes to household necessities,

state sales tax bases can be said to be, on average, prone to cyclical volatility.

On the other hand, the results indicate that exemption for services in both linear

and quadratic form is not statistically significant. This result implies that it is not entirely

correct to say that incorporating more services into a sales tax base would lead to

increased sales tax stability, generalizing services as a stable tax base. Although, in

theory, the consumption of services is considered relatively stable compared to that of

77

goods, it appears that there exists quite a difference in necessity and income elasticity

across services. Further, the statistical insignificance of services exemption suggests that

a more sophisticated measure of state sales taxation of services needs to be developed to

produce more detailed results. This study uses the number of services taxable to measure

the level of tax exemption for services, which does not capture possible differences in

income elasticity among services. Although this study included a quadratic function of

services exemption based on the assumption that state sales taxation tends towards less

income elastic services, it turns out to be not sophisticated enough to produce significant

results.

The regression reveals that exemption for producer goods on sales tax volatility

has a negative and statistically significant impact on sales tax volatility. The slope

coefficient on the variable indicates that a one percentage point increase in producer

goods exemption lowers sales tax volatility by .036 percentage points. For example, in

the case of Minnesota, one of states granting the lowest level (approximately 60%) of tax

exemption for producer goods, sales tax volatility is predicted to decrease by 1.44

percentage points if the state raises the current level to full exemption like most other

states.

The study also finds that there is an interaction effect between producer goods

exemption and nondurable manufacturing sector. The different signs of the coefficients

on producer goods exemption and the interaction term imply that the GSP share of the

nondurable manufacturing sector moderates the effect of producer goods exemption on

sales tax volatility. Taking the partial derivative of Equation (1) tells us that the partial

effect of producer goods exemption on sales tax volatility is now -0.036 +

78

0.006* [ = + ]. That is, as increases,

the effect of producer goods exemption on income tax volatility decreases. A further

analysis by group—states with the share of nondurable manufacturing sector GSP above

the state average (about 6.3%) and those below the average—confirms this conclusion:

the coefficient on producer goods exemption remains the same (negative) for the below-

the-average group, whereas the sign is flipped (positive) for the above-the-average group.

This indirectly shows that the consumption of nondurable goods, as discussed earlier,

tends to be less sensitive to economic fluctuations. On the other hand, the interaction term

between producer goods exemption and durable manufacturing sector GSP share is not

statistically significant. This result may be due in part to differences among durable

manufacturing industries in income elasticity of demand.

In contrast to the variables for tax base composition, all the variables for

economic structure, except for mining sector, are not statistically significant. Consistent

with prevailing notions that commodity prices are highly volatile, the results indicate that

the relative size of mining sector has an effect in increasing cyclical swings in sales tax

revenues. According to the coefficient estimate, a one percentage point increase in the

GSP share of mining sector is predicted to add to sales tax volatility by .118 percentage

points. Taken together, the results suggest that with tax structure taken into account,

economic structure is not a strong predictor of sales tax volatility, highlighting the

relative importance of policy factors in this respect.

As for the variables for demographic and economic characteristics, the results

confirm that population is statistically significant and dampens sales tax fluctuations. The

coefficient on the variable indicates that an increase in population by 100,000 leads to

79

about 0.14% point decrease in sales tax volatility. This result provides evidence for the

relationship among region size, industrial diversification, and economic stability. For

example, an average difference between New Jersey and Rhode Island in population over

the sample period is 7304864 [8346873 - 1042009], which, according to the estimated

coefficient, is predicted to make a difference of about 1.02 percentage points (higher in

Rhode Island) [7304864*0.00000014] in sales tax volatility.

The regression also reveals that age structure and income distribution significantly

affect sales tax volatility. The results confirm that the prime working-age population

exerts a negative and statistically significant influence on cyclical swings in sales tax

revenues. In other words, sales tax volatility is lower in states with a larger prime

working-age population, suggesting that this economically active population group does

not respond sensitively to recession-driven transitory income shocks. The coefficient on

the variable indicates that a one percentage point increase in the prime working-age

population causes sales tax volatility to decrease by .221 percentage points. This result

provides evidence in support of the life-cycle theory of consumption and the permanent

income theory, suggesting that in the interest of stable sales tax revenue, states also need

to keep an eye on the changing age structure of the population.

As for income distribution, the results display that the wealthy population adds to

cyclical fluctuations in sales tax revenues. The coefficient on the variable indicates that a

one percentage point increase in the proportion of the population with FAGI over $200K

results in a 1.30 percentage point increase in sales tax volatility. Summary statistics

indicate that the minimum and maximum of the variable are about 0.77 and 5.42,

respectively. According to the coefficient estimate, this difference, holding other factors

80

constant, is predicted to make a difference of about 6 percentage points [(5.42-0.77)*1.3

= 6.045] in sales tax volatility.

Turning to the analysis for income tax volatility, the results show that full

exemptions for Social Security benefits and public-sector pensions have positive and

statistically significant effects on income tax volatility. The coefficients on the variables

indicate that income tax volatility is .699 and 1.2 percentage points higher in states

allowing full exemption for Social Security benefits and public pensions than states

allowing no exemption. This implies that tax revenues from these income sources are

relatively stable over the business cycle, therefore excluding them from the list of taxable

incomes amplifies income tax volatility.

By contrast, both partial and full exemption for private-sector pensions and partial

exemption for public-sector pensions are shown to be not statistically significant. Such

different results between private and public pensions exemption in terms of statistical

significance may be due in part to the heterogeneous nature of private pensions.

Retirement plans are largely classified into two types according to how they are funded

and how the benefits are determined: defined benefit and defined contribution. Private

pensions are relatively mixed in this respect, compared to public pensions in which a

defined benefit plan is the dominant form of retirement plan. This issue will be later

discussed in more details.

As expected, exemption for long-term capital gains is found to lessen income tax

volatility. The coefficient indicates that for the same level of other factors, a ten

81

percentage point increase in exemption for LTCG lowers income tax volatility by 0.87

percentage points. The regression also reveals that there is an interaction effect between

LTCG exemption and the wealthy population. As predicted, the partial effect of LTCG

exemption on income tax volatility is moderated by the proportion of the wealthy

population. Specifically, taking the partial derivative of Equation (2) with respect to

gives: -0.087 + 0.066* [ = + ], suggesting that the negative

effect of LTCG exemption on income tax volatility is smaller in states with a larger

wealthy population. This result, as discussed earlier, is thought to be attributable to states‘

practices of not allowing this preferential tax treatment to high-income people.

Among the other tax structure variables, both partial and full deduction for local

property tax paid have relatively strong effects on income tax volatility, whereas both

partial and full deduction for federal income tax paid and personal exemption are not

statistically significant. The estimated coefficients on deductions for local property tax

paid suggest that states allowing partial and full deduction for local property tax paid are,

on average, 1.58 and 2 percentage points higher in income tax volatility than states

allowing no deductions.

As for the control variables, the results exhibit that agriculture and mining sector

are positively associated with income tax volatility. According to the coefficients on them,

a one percent increase in the GSP share of agriculture and mining sector increases income

tax volatility by .308 and .453, respectively. These results, once again, confirm the

inherent volatility of commodity-producing industries. Consistent with the theoretical

discussions, durable manufacturing sector is also found to be positively related to the

dependent variable. The coefficient indicates that a one percent increase in the GSP share

82

of durable manufacturing sector is predicted to increase income tax volatility by .215

percentage points. Lastly, among the demographic-economic variables, the low-income

and elderly population are shown to have statistically significant effects. A one

percentage point increase in the population groups decreases income tax volatility by .11

and .163 percentage points.

83

CHAPTER 3

REVENUE VOLATILITY AND FISCAL INSTABILITY

“Expenditures rise to meet income.”

C. Northcote Parkinson (1960)

3.1 Introduction

Fiscal stability characterizes a government‘s ability to maintain adequate levels of

funding for its programs and services especially during periods of recession. Stable fiscal

policies are the ones that are consistent and predictable over the course of the business

cycle, and ―budgeting processes thrive on stability (Caiden 1981).‖ In recent years,

achieving this fiscal principle has become challenging with increasingly volatile and

unpredictable economic environments. Gaps between the peaks and troughs of businesses

cycles are becoming wide, while the turnarounds and intervals becoming rapid irregular.

Governments and private businesses more frequently face economic fluctuations and

upheavals. The increasing volatility of fiscal environments has significant implications

for state governments, because they operate under legal constraints such as balanced

budget requirements (BBRs) and tax and expenditure limitations (TELs).

In response to increasingly volatile fiscal environments, scholars have

consistently argued for countercyclical fiscal policy. Hou (2006: 735) explicitly argues

that ―state governments can smooth the fluctuation of economic cycles by curbing

spending during booms in order to accumulate more reserves for expenditure during

84

cyclical downturns (See also Hou and Moynihan 2007).‖25

In a similar vein, Dothan and

Thompson (2006) contend that governments should smooth spending over the business

cycle by balancing their budgets in a present-value sense—in a way that matches the

present value of future outlays to that of future receipts plus net assets (or liabilities). In

the broader context, modern economic theory generally holds that governments should

keep the economy from overheating to head off inflation during expansionary periods and,

conversely, stimulate demands and encourage supplies to help the economy recover

during recessionary periods. Tax smoothing models (Barro 1979; Lucas and Stokey 1983)

suggest that tax rates be held constant over the business cycle so as not to distort

economic agents‘ decisions. Based on these theoretical discourses, fiscal instability is

defined as ―procyclicality in conducting fiscal policy‖ (i.e. spending increases and tax

cuts during booms and spending cuts and tax increases during recessions) in this study.26

Together, these arguments highlight the importance of smoothing spending over

the business cycle through countercyclical fiscal policy. This can be interpreted as

ensuring the ―affordability and sustainability‖ of government programs and services

(Shah 2005: xiii) and maintaining sustainable levels of spending and some form of

savings in times of plenty beyond merely balancing budgets. This approach makes sense,

because by nature, the economy swings back and forth between boom and bust.

Following a boom, governments invariably face a recession with revenue shortfalls and

expenditure overruns. Consequently, the ones that implemented unsustainable spending

25

He takes a step further, adding that the strong version of this policy involves ―raising taxes during boom

years and lowering them during recessions.‖ 26

Readers should note that here ―procyclicality‖ is a relative concept in this study. Empirical studies on the

cyclicality of fiscal policy at the international level have been consistent to find that developed countries

are by and large countercyclical in conducting fiscal policy, whereas developing countries procyclical. As

will be shown later, U.S. states as subnational units of a developed country are generally countercyclical.

Therefore, when a state is said to be procyclical, the term procyclical, precisely speaking, means ―relatively

procyclical‖ compared to other states, and concerns policy adjustments rather than fiscal outcomes.

85

increases and tax cuts during the preceding boom inevitably experience greater hardship

during the subsequent recession, as suggested by the popular adage, ―There‘s no such a

thing as a free lunch (Friedman 1975).‖

Despite the normative significance and practical virtue of fiscal stability and

spending-smoothing as a strategy for it, state governments have failed to put the principle

into practice. Edwards, Moore, and Kerpen (2003) report that state general fund spending

in real dollars grew 18.1% from 1990 to 2001, and more importantly, that its annual

growth rate was 2.9% greater than inflation and particularly rapid in the late 1990s when

the economy was at its peak, posting 7% in 1999, 6.6% in 2000, and 8% increase in 2001.

Similarly, Crain (2003) reports that annual state spending growth rate was approximately

1% higher on average than income growth during the 1990s when the nation experienced

the longest economic boom in history. Schunk and Woodward (2005) observe that

aggregate state general fund spending grew 18.4% faster than inflation plus population

growth between 1995 and 2001. The upturn years of the 1980s and the 2000s also show a

similar trend. Moore (1991) reports that state nominal expenditures increased 8.5% per

year between 1980 and 1989, outpacing inflation by 3.5%, while Stansel and Mitchell

(2008) discover that despite the 2001 recession and budget problems in subsequent years,

they rose 0.8% higher than income from 2000 to 2007.

Meanwhile, some studies point to tax cuts during expansionary periods. In a

report on state tax policy, Johnson (2002) reports that from 1994 to 2001, nearly every

state enacted tax cuts and most of them were substantial and permanent. According to his

report, aggregate net tax cuts in 43 states during that period were about 8.2 percent of

aggregate state tax revenues. In a summary of National Conference of State Legislatures

86

(NCSL) data, Knight, Kusko, and Rubin (2003: 432–435) observe that a series of tax cuts

enacted between 1995 and 2001 reduced state collections by about 36 billion dollars (8

percent) below baseline levels, arguing that they were partly responsible for the state

fiscal crises of the early 2000s.

Further, these studies suggest that rapid and sustained revenue growth and the

resulting large surplus revenue during booms tend to induce unsustainable spending

increases and tax cuts, creating structural budget deficits and ultimately causing fiscal

crises. In generalized terms, it can be said that revenue availability induces spending.

Moore (1991) observes that most of the states that experienced the most severe budget

deficits in the early 1990s recession were those that saw rapid revenue growth during the

preceding boom period. In case studies of states with severe budget deficits, Edwards,

Moore, and Kerpen (2003) observe that as the economy expanded, states incrementally

added new costly recurring spending commitments by expanding existing programs or

launching new programs in major policy areas such as education, health care, welfare,

and corrections without offsetting cuts in other areas. Based on this observation, they

argue that ―the structural problem that faces state budgets is that revenues rise too quickly

during economic booms and cause politicians to overspend.‖ They go on to argue that if

states had limited their spending growth to inflation while conducting tax policies such as

rebates in a flexible and timely fashion, they would have easily overcome recession-

driven revenue shortfalls. Similarly, Schunk and Woodward (2005) observe that during

the expansion phases of the late 1990s, nearly every state created ongoing programs

(annualizations) and permanent tax cuts using one-time (non-recurring) surplus funds,

assuming that revenues would continue to grow to provide funding for the new and

87

expanded programs. With regard to tax policy, Johnson (2002) argues that states were

able to enact large tax cuts in the 1990s maintaining spending growth at about the same

pace as in the previous decade, because of higher-than-expected tax revenues—due to

high capital gains realizations and corporate profits—and the resulting record surpluses.

In a bit broader context, Kaminski, Reinhart, and Vegh (2004) suggest that the roots of

most government fiscal crises often lie in governments themselves that go through bouts

of high spending and borrowing when the times are favorable and financial resources are

plentiful.

If it is true that cyclical increases in tax revenues induce spending growth (i.e.

spending increases and tax cuts), it would be reasonable to assume that state fiscal

conditions may differ depending on the extent of revenue fluctuations over the business

cycle. Government revenues and expenditures generally grow along their long-term trend

lines, while, at the same time, fluctuating over the short term through boom-bust cycles.

As the economy expands and contracts, revenues move in a procyclical manner.

Conversely, demands for many types of expenditures move in a countercyclical manner,

amplifying cyclical swings in budget balances. Demands for public services rise during

recessions and fall during booms (for example, more students are likely to choose public

schools, and more people to find municipal service facilities during recessions), and

claims for Medicaid and welfare benefits move up and down over the business cycle.

What should be noted here is that state tax revenues vary widely in cyclical volatility, as

found in Chapter 2. Given the wide cross-state variation in revenue volatility, it would be

logical to hypothesize that spending increases and tax cuts during booms will be larger in

states with a more volatile revenue base.

88

This line of reasoning is consistent with Parkinson‘s second law: ―Expenditure

rises to meet income,‖ which is one of the variants of Parkinson‘s famous first law,

―Work expands so as to fill the time available for its completion.‖ In his book The Law

and the Profits (1960), Parkinson illustrates how manifest his law is in our everyday life

using an example of households. He notes that the law applies not only to individuals but

also to organizations, arguing that bureaucracies, whether public or private, tend to spend

whatever is made available. According to him, their spending propensities usually distort

the budgetary process in such a way that they determine the levels of expenditures that

can be financed and then justify their budget requests. He argues that such a perverse

budgetary behavior is particularly likely in government organizations where there is

virtually no notion of profit, consequently, no incentive for saving and surpluses.

In a 1976 news article ―Parkinson Revisited,‖ Milton Friedman introduces as a

modern illustration of the law a comparative study of the fiscal systems of Vermont and

New Hampshire conducted by Campbell and Campbell (1976). The two states are in stark

contrast in that Vermont has both income tax and general sales tax, whereas New

Hampshire has neither. He summarizes that as Parkinson‘s law suggests, Vermont‘s

expenditures rose to meet its income in an inefficient manner: expenditures were much

higher (50%) in Vermont than in New Hampshire but it is doubtful that services in

Vermont were proportionately better than those in New Hampshire. Friedman goes on to

contend, ―Therefore, the only way to cut government waste and extravagance is to cut

government income (Friedman 1983).‖

This revenue-spending hypothesis naturally extends to recessionary periods. The

economy generally goes through a cycle of expansion and contraction, and unlike the

89

federal government, states operate under balanced budget requirements. Given the

inherent tendency of the economy to revert to its trend rate of growth (Steindl 2007: 189)

and the legal constraints under which states operate, spending cuts and tax increases are

inevitable for the states that have increased spending at unsustainable rates during booms,

without borrowing, off-budget spending (spending by means of off-budget enterprises

such as public corporations and quasi-governmental units), or ―creative accounting.‖

Based on this logic, it is also assumed that spending cuts and tax increases during

recessions will be larger in states with a more volatile revenue base.

Although there has been ample anecdotal evidence suggesting the association

between cyclical changes in tax revenues and policy adjustments (for the level of

spending and taxation) and further between revenue volatility and fiscal instability,

systematic empirical research is scant. This deficiency may be due in large part to the

difficulty of measuring the cyclical components of tax revenues (also called revenue

gap27

in this study) and quantifying tax and spending adjustments. This dissertation fills

this gap in the literature by empirically examining how cyclical changes in tax revenues

affect state fiscal behavior in terms of the level of spending and taxation, using a panel

data set for U.S. states over the period of 1992 to 2007. To this end, the present study

answers the following specific questions:

1. Are cyclical changes in tax revenues positively related to changes in

expenditures over the business cycle?

27

As will be discussed later, the way the concept is measured is, in essence, the same as output gap,

commonly used in the economics and business literature, and ―expenditure gap‖ often used in the public

finance literature (See Hou 2005) in that the measurement involves separating the trend and cyclical

component of tax revenue. In keeping with the existing terms, this study terms the concept ―revenue gap.‖

90

- Do cyclical increases in tax revenues during expansions lead to increases in

expenditures?

- Do cyclical decreases in tax revenues during recessions lead to decreases in

expenditures?

2. Are cyclical changes in tax revenues negatively related to changes in tax rates

over the business cycle?

- Do cyclical increases in tax revenues during expansions lead to decreases in tax

rates?

- Do cyclical decreases in tax revenues during recessions lead to increases in tax

rates?

The cross-state heterogeneity of revenue volatility, political-institutional

environments, and demographic-economic characteristics provides a natural laboratory

for the empirical analysis of the chosen question. The sample period—beginning after the

1990–1991 recession ending before the 2008 recession—is considered balanced, with

two peaks and troughs included. The rest of the chapter is organized as follows: the next

section reviews previous studies, and Section 3.3 provides a conceptual discussion as to

how revenue availability induces spending in the public sector. Section 3.4 discusses

other relevant factors that influence state fiscal behavior, and Section 3.5 presents data

and methods for empirical analysis. Lastly, Section 3.6 discusses analysis results.

3.2 Literature Review

91

To date, a vast body of literature has been established on the behavior of fiscal

policy, and along the way, various theoretical and methodological approaches have been

developed. In light of the research questions raised, this literature review focuses

primarily on empirical studies that explored factors that explain the level of spending and

taxation. Upon review of relevant literature, three strands of research stand out.

The first is literature known as the "tax-spend debate" or the "revenue-expenditure

nexus." This literature has emerged since 1980s in response to the growing concerns over

government expansion and budget deficits. The tax-spend literature has attempted to

determine the intertemporal relationship between tax and spending adjustments in

generation of budget deficits. Along the way, four main hypotheses have been developed.

First, the tax-spend hypothesis suggests a causal relation running from revenues to

expenditures (i.e. the hypothesis that changes in revenues would lead to changes in

expenditures). The tax-spend hypothesis has again diverged into two different views. The

conventional tax-spend hypothesis, advanced by Friedman (1978), holds that raising

taxes in an attempt to reduce budget deficits would only result in increases in

expenditures. Based on this view, the proponents argue that in order to rein in

government spending and bring down budget deficits, we should first curtail resources

available by cutting taxes.

Buchanan and Wagner (1977; 1978) set forth an alternative view, which

postulates that raising taxes would lead to decreases in expenditures due to fiscal illusion.

The central theme of the fiscal-illusion view is that how citizens are taxed (e.g. direct vs.

indirect taxation through borrowing and inflation; a single tax vs. a variety of smaller

92

taxes) can affect taxpayer perceptions of the price of public output and the size of the

public sector. According to this view, tax hikes increase the perceived prices of public

goods, which, in turn, make taxpayers hostile towards the attempts from governments to

increase spending. Conversely, tax cuts decrease the perceived price of government,

leading taxpayers to be favorable to spending increases.

Second, the spend-tax hypothesis postulates a causal relation running from

expenditures to revenues. This view, supported by Peacock and Wiseman (1979), holds

that temporary increases in expenditures for dealing with social disturbances or crises

would lead to permanent tax increases, as the social problems that have been treated as

peripheral policy issues become national policy agendas due to increased attention and

eventually lead to the creation of specific public programs that require annual

appropriations. This hypothesis is consistent with the ratchet theory of government

spending growth that ―temporary crises (such as severe economic crises and wars) cause

government spending to rise and to remain permanently higher than if the crises had not

occurred (Holcombe 1993).‖ This view is also consistent with the Ricardian equivalence

proposition that debt financing today would lead to increased tax liabilities in the future

(Barro 1979).

Third, the fiscal synchronization hypothesis suggests a bidirectional causal

relation between revenues and expenditures. This view assumes that governments make

decisions about revenues and expenditures simultaneously based on information obtained

through the comparison of the costs and benefits of government programs (Musgrave

1966; Meltzer and Richard 1981).

93

Lastly, the institutional separation hypothesis negates any type of causal relation

between revenues and expenditures. This view holds that decisions on revenues are

independent from decisions on expenditures due to the institutional separation of the

taxation and allocation process (Wildavsky 1988; Baghestani and McNown 1994; Hoover

and Sheffrin 1992).

Reflecting the variety of theoretical perspectives, empirical studies have yielded

mixed results. The tax-spend literature is vast, so this review focuses only on U.S.

evidence. First, at the federal level, evidence supporting the conventional (Friedman-type)

tax-spend hypothesis is found by Blackley (1986), Bohn (1991), Mounts and Sowell

(1997), Koren and Stiassny (1998), Garcia and Henin (1999), and Chang, Liu, and

Caudill (2002). Evidence supporting the fiscal illusion (Buchanan-Wagner-type) tax-

spend hypothesis is relatively scant. Niskanen (2005) and Romer and Romer (2007)

provide support for this view. Empirical studies in support of the spend-tax hypothesis

include von Furstenberg, Green, and Jeong (1985), Anderson, Wallace, and Warner

(1986), Joulfaian and Mookerjee (1991), Ross and Payne (1998), and Islam (2001). The

fiscal synchronization hypothesis is supported by Manage and Marlow (1986), Miller and

Russek (1989), Jones and Joulfaian (1991), Hasan and Sukar (1995), and Owoye (1995).

Evidence for the institutional separation hypothesis is found by Ram (1988a), Hoover and

Sheffrin (1992) and Baghestani and McNown (1994).

For the state level, there have been far fewer studies. von Furstenberg, Green, and

Jeong (1985) examine intertemporal relations between tax initiatives and expenditures at

the aggregate state-local level (and also at the federal level) in the United States over the

period of 1955–1982 (quarterly data), using vector autoregressive models [following

94

Sims (1980)] with potential GDP and grants. Their analysis finds evidence in support of

the spend-tax hypothesis. Using annual data covering the period of 1952 to 1982, Marlow

and Manage (1987; 1988) and Chowdhury (1988) conduct causality tests [following

Granger (1969)] with only revenue and expenditure variables for the aggregate state and

local level, separately. Their model for the aggregate state level provides support for the

tax-spend hypothesis, whereas the model for the aggregate local level supports the

institutional separation hypothesis (in Marlow and Manage) and the fiscal

synchronization hypothesis (in Chowdhury). In Granger-causality tests [following

Guilkey and Salemi (1982)] using annual and quarterly aggregate state-local data over the

period of 1929–1983 and 1947–1983, respectively, Ram (1988b) finds evidence in favor

of the spend-tax hypothesis. Joulfaian and Mookerjee (1990a) investigate the revenue-

expenditure nexus for Massachusetts over the period of 1955–1986 (annual data), using

VAR [following Sims (1980)]. Their model incorporating personal income and federal

grants along with revenues and expenditures lends support to the tax-spend (Friedman-

type) hypothesis. They also find that federal grants do not have a significant effect on

state expenditures. Another study by Joulfaian and Mookerjee (1990b) for the aggregate

state-local level employs the same estimation methods [following Sims (1980)] with

annual data over the period of 1960–1986, and finds evidence supporting the tax-spend

hypothesis. Payne (1998) undertakes extensive research for all U.S. states over the period

of 1942 to 1992, using Engle-Granger (1987) cointegration procedure and error

correction models with only revenue and expenditure variables. Results indicate that the

tax-spend, spend-tax, and fiscal synchronization hypothesis are supported for twenty-four,

95

eight, and eleven states, respectively. The remaining five states failed the diagnostic tests

for error correction modeling.28

Methodologically, the tax-spend debate has relied on the concept of Granger

causality. Following Granger (1969) and Sims (1972), early studies employed bivariate

vector autoregressive (VAR) models of revenue and expenditure. Recognizing the

possibility of omitted variable bias, the literature has shifted towards a multivariate

framework. The cointegration approach introduced by Engle and Granger (1987) and

Johansen (1988) brought another important development to causality analysis.

Recognizing the possibility of spurious regression between two time series processes

with a unit root, this approach involves determining cointegration and performing a

causality test using an error-correction model.

While the literature has grown significantly in theoretical elaboration and

methodological rigor, some empirical shortcomings need to be addressed. The first

relates to model specification. Placing a primary focus on the temporal relationship

between revenue and expenditure, the tax-spend debate has paid relatively little attention

to other potentially relevant factors such as fiscal institutions and political institutions

(Payne 2003). This is likely to lead to biased and inconsistent results especially at the

28

Specifically, the tax-spend hypothesis is supported for Arkansas, California, Connecticut, Florida,

Georgia, Idaho, Iowa, Kansas, Kentucky, Maryland, Michigan, Missouri, Nevada, New Hampshire, North

Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, South Carolina, Tennessee, Washington,

and West Virginia; the spend-tax hypothesis supported for Alabama, Delaware, Mississippi, New York,

Rhode Island, Utah, Wisconsin, and Wyoming; the fiscal synchronization hypothesis supported for Arizona,

Colorado, Illinois, Indiana, Massachusetts, Minnesota, Montana, Nebraska, Texas, Vermont, and Virginia.

In the case of Louisiana, Maine, and New Mexico, the coefficients were found to be not statistically

significant, while in the case of New Jersey and South Dakota, they were omitted from error-correction

modeling given that the respective time series were found to be not cointegrated [a linear combination of

the two series was not I(0)].

96

state level, because states operate under various fiscal control mechanisms and tend to be

more susceptible to local politics in the budgetary process.

The second shortcoming concerns the endogeneity of variables such as revenue

and expenditure. Fiscal policy, as hypothesized in this study, is likely to be affected by

cyclical factors in the economy; therefore, controlling for them is critical in explaining

either tax and spending adjustments. Recognizing this concern, some studies include

macroeconomic controls such as GDP, inflation rate, and interest rate (Payne 2003: 314)

to capture the effects of the business cycle. A study by Joulfaian and Mookerjee (1991)

represents one of the rare attempts to demonstrate how sensitive the results of Granger-

causality tests are to the inclusion of macroeconomic controls. Using time series data for

22 industrial countries, they compare the results of bivariate models of revenue and

expenditure with those of multivariate models incorporating output (GDP) gap and

inflation rate. In the baseline model, they find evidence in support of the spend-tax

hypothesis, but in the extended model, discover that decisions on taxation are

independent from the allocation of expenditures. While including macroeconomic

controls reduces the endogeneity problem to some degree, it is still a roundabout way.

The mixed and conflicting results observed in the literature may be a reflection of the

lack of consistency of this indirect method.

The second branch of literature on fiscal behavior and policy deals with the

problem of omitted variable bias by examining how fiscal institutions and rules affect

fiscal policy and whether they work as intended. Poterba (1994) examines how fiscal

rules (no deficit carry-over rules and tax and expenditure limitations) and political

97

circumstances affect the way states respond to deficit shocks. One of the interesting

features that sets his study apart from the previous studies is that it takes macroeconomic

effects into account. Recognizing the overarching impacts of the macroeconomy on

expenditure demands and revenue conditions, he develops a measure of fiscal stress by

calculating a difference between expenditure shock (actual expenditure – forecast

expenditure – spending adjustment) and revenue shock (actual expenditure – forecast

expenditure – tax adjustment). With these explanatory variables of interest, Poterba

analyzes panel data on 27states with an annual budget cycle over the period of the late

1980s economic recession using the fixed effects estimator. From this analysis, the author

finds that states (1) with greater deficit shocks, (2) with more stringent fiscal rules, and (3)

where one party controls both the governorship and the lower house in the legislature

tend to react to fiscal shocks more swiftly by making adjustments to spending and taxes.

Alt and Lowry (1994) investigate how political and institutional factors affect

state spending and taxing levels. They model (1) revenues as a function of party control

(unified Republic/Democratic control of government, split branch, and split legislature)

and fiscal rule (no deficit carry-over balanced budget provision) along with lagged

revenue, aggregate personal income, federal government contribution, and lagged surplus,

and (2) expenditures as a function of revenue, unemployment rate, and lagged surplus.

They test the models using state panel data from 1968 to 1987 and employing three-stage

OLS procedures and fixed effects estimators. From the analysis, Alt and Lowry find that

states where unified party control exists and deficit carry-over is prohibited tend to react

more quickly to deficit shocks in downturn years compared to states with divided

government, especially, split legislature.

98

Bohn and Inman (1996) take on the question of whether balanced budget

requirements of varying stringency across states have positive effects in limiting deficits.

In modeling state deficits, they employ a host of balanced budget provisions as the key

explanatory variables of interest. They test the model using panel data from 47 states

(excluding Alaska, Hawaii, and Wyoming) for the sample period of 1970–1991. The

analysis finds that stringent end-of-year balance requirements have significant effects on

general fund balances through spending cuts rather than tax increases.

Endersby and Towle (1997) examine the effects of legal and political controls on

state per capita expenditures and per capita debts. Specifically, their model includes

gubernatorial veto (line-item veto and item reduction veto) and recision (a governor's

power to withhold budgetary appropriations without legislative approval) power,

legislative budget responsibility, debt limitation, deficit carry-over, override (percentage

vote necessary to override a governor's veto), and a host of electoral and political factors.

They analyze data for all fifty states for three years, 1988, 1990, and 1992 using the

pooled cross-sectional OLS and Generalized Least Squares (GLS) estimator. From the

analysis, Endersby and Towle find that political and electoral factors play more

significant roles in limiting spending and minimizing debt, whereas legal restrictions,

except deficit carry-overt provisions that have positive and statistically significant effects

on spending and debt levels, exert limited influences.

Smith and Hou (2008) investigate the effects of budgetary institutions and rules

on state spending behavior using a long-panel analysis (1950 to 2004). They pointing out

that very little of the variance (or lack thereof) attributable to BBRs" have been found due

to "the absence of analyses of panel data over multiple economic cycles and across state

99

budgetary reforms. Unlike the previous studies that have relied mostly on measures

developed by the United States Advisory Commission on Intergovernmental Relations

(ACIR), the National Association of State Budget Officers (NASBO), and the National

Conference of State Legislatures (NCSL), they use a more detailed and comprehensive

set of balanced budget rules which was developed by the same researchers (2006). Along

with the fixed effects estimator, their study employs a Maximum Likelihood Estimation

(MLE) estimator in an attempt to take into account a state-specific intercept composed of

both a fixed and a random component. In the analysis, they find that technical provisions

such as no deficit carry-over that take a financial management approach are more

effective in limiting spending than balanced budget provisions, such as ―Governor must

submit a balanced budget‖ and ―Legislature must pass a balanced budget,‖ that focus on a

political control mechanism.

Hou and Duncombe (2008) examine whether fiscal institutions affect state saving

behavior. In their model, the dependent variable is total saving [general fund balance

(GFB) plus budget stabilization fund (BSF)] as a percent of general fund expenditure, and

the independent variables include two fiscal rules, budget stabilization funds (BSF) and

balanced budget requirements (BBR). In the analysis of a panel of all 50 states covering

three business cycles from 1972 to 2003 using the fixed effect estimator, they find that

more stringent BSF, such as 4–7% BSF balance cap; 7–12%; no limit on BSF balance,

and BBR provisions, such as a limit on debt for deficit reduction and no deficit carry-over,

tend to have positive effects on state savings.

Mullins and Joyce (1996) look into the fiscal impacts of TELs in the context of

state-local relations. They deal with the questions of whether TELs have had effects in

100

altering revenue systems away from broad-based taxes such as property, sales, and

income taxes toward narrow-based revenue sources such as user fees and whether TELs

have played a role in shifting responsibilities for expenditures from local to state

governments. They employ four dependent variables, which include (1) changes in the

relative size of the state-local public sector, (2) the shifts in reliance among various

revenue sources, (3) the changes in the state "share" of total state and local revenue, and

(4) changes in the state share of expenditure responsibility. They test their model by

analyzing state panel for the sample period of 1970 to 1990 with the fixed effects

estimator. From the analysis, they discover that TELs do not have substantial impacts on

aggregate state-local spending (measured as the ratio of total state-local general

expenditure to gross state product), but they lead to increased centralization in service

delivery and the shift of locals toward narrow-based revenue sources decreasing local

responsiveness.

Bails and Tieslau (2000) add to the literature by examining the effects of fiscal

institutions on state and local spending. Fiscal institutions examined in their study include

TELs, line item veto power, and BBRs, as a budgetary constraint mechanism; term limits

for state legislatures, bill introduction limits, and the length of the budget cycle as an

administrative constraint mechanism; initiative procedures and state referenda as a direct

democracy mechanism. Their analysis uses a panel data set of 49 states (excluding

Alaska) obtained at five year intervals from 1969 through 1994. The ten fiscal discipline

mechanisms are measured as a binary dummy variable (present or not), and the

dependent variable is real per capita state expenditure. They use a random-effects (RE)

estimator for the reason that five key variables are time invariant in each state over the

101

sample period. Their analysis finds that the presence of TELs, citizen initiative

procedures, and term limits is effective in limiting government spending.

Knight and Levinson (1999) examine the effects of RDFs on state saving behavior.

They use three measures of state RDFs, which include the presence of RDF, RDF balance,

and RDF legal provisions obtained from The National Council of State Legislatures

(NCSL) (deposit by formula, deposit of year-end surplus, 5-9% limit, over 9% limit, no

limit, withdrawal by formula, and withdrawal by shortfall), with total balances as the

dependent variable. They analyze state panel data set for the sample period of 1984–

1997—during which 27 states adopted RDFs—employing the cross-sectional OLS and

fixed effects estimator. The results reveal that states with RDFs have higher total

balances than states without RDFs and also have higher balances after adoption than

before adoption.

In three closely related studies, Hou (2005 and 2006) analyzes state panel data for

the years 1979 to 1999 to examine the effects of fiscal reserves (BSF and GFS,

collectively and individually) on state own-source expenditures during downturn years.

He uses gaps between expected and actual expenditures (expressed as a percent of

expected expenditures) as the dependent variable, which is obtained using the residual-

from-trend approach. He also employs the Heckman sample selection model as a method

to extract periods of economic downturn from upturn. Data on fiscal reserves are from

two sources: the Fiscal Survey of the States and the Comprehensive Annual Financial

Report. From the analysis, he finds that fiscal reserves (particularly BSF) have positive

effects on state own-source expenditures during downturn years, and based on the

102

findings, concludes that fiscal reserves help ease the negative impacts of recessions on

state spending and maintain fiscal stability over the business cycle.

Hou and Moynihan (2007) explores the effects of BSF and GFS defined as

"countercyclical fiscal capacity" on state reactions to revenue shortfalls using a 1985–

2003 state panel data set. Three dependent variables are used: budget cuts, revenue

actions, and net revenue changes. The results indicate that fiscal reserves as a total are

effective in reducing the levels of all the dependent variables, in other words, in

countering revenue shocks and increasing fiscal stability during recession periods.

While this line of research provides a broad understanding of how and when fiscal

institutions work, as in the tax-spend literature, improvements in model specification are

needed. Although fiscal institutions and rules are certainly a crucial factor in explaining

state fiscal behavior, it is undeniable that the most fundamental factor is how much

revenue is available and how economic and fiscal conditions are. Therefore, failure to

take the cyclical position of state finance into account may lead to omitted variable bias

and ultimately incorrect statistical inferences. A few studies have made conscious efforts

to account for the effects of cyclical factors. Alt and Lowry (1994) include a lagged

surplus in modeling state expenditures. Smith and Hou (2008) take account of revenue-

raising mechanisms by incorporating binary variables for the presence of three major

state tax instruments (general sales, individual income, and corporate income tax).

Perhaps the most impressive attempt is Poterba (1994), which develops a measure of

fiscal shock (revenue and expenditure, separately) and uses it as the key explanatory

variable in explaining state fiscal reactions during downturn years. While the inclusion of

103

such variables certainly reduces the problem of omitted variable bias to some degree, as

pointed out in the review of the tax-spend literature, they are still a roundabout way. In

contrast to these previous studies, this study deals with this concern in a more direct and

straightforward way by developing a measure of revenue gap, the cyclical component of

tax revenue.

Finally, the third stream of literature relates to the cyclicality of fiscal policy.

Based on the idea that in order to maintain economic stability and minimize distortionary

costs associated with government interventions, fiscal policy should be countercyclical,

as suggested by Keynesian economics (i.e. decreases in spending and increases in tax

rates during expansions and the opposite policy adjustments during recessions), or neutral

over the business cycle, as proposed by Barro (1979),29

this empirical literature has

attempted to determine and explain the cyclical patterns of fiscal policies. Along the way,

general consensus has been established that fiscal policy tends to be relatively procyclical

in developing countries, compared to industrialized countries such as G7 countries where

it tends to be either countercyclical or acyclical.

As an early attempt at this research program, Gavin and Perotti (1997) compare

the cyclical volatilities—measured as average standard deviations—of fiscal outcomes

such as budget balances, revenues, and expenditures in 13 major Latin American

economies with those in industrialized countries over the period of 1968 to 1995. In this

29

With Keynesian prescriptions, a correlation between government spending and output and a correlation

between tax rates and output are expected to be negative and positive, respectively. With Barro‘s

prescriptions, theoretically, no correlations are expected. Even if a government follows Barro‘s prescription

by holding constant tax rates and discretionary spending as a share of GDP over the cycle, a pattern in

fiscal outcomes is likely to be countercyclical because of automatic stabilizers and progressivity in tax rates

(Alesina, Campante, and Tabellini 2008).

104

analysis, they find that fiscal policy is more procyclical in Latin America than in

industrial economies, arguing that this phenomenon arises because of binding borrowing

constraints.

Using data for 104 countries over the period of 1960–2003, Kaminski, Reinhart,

and Vegh (2004) examine the cyclical properties of fiscal policy in developing countries.

Recognizing the endogeneity of fiscal outcomes such as fiscal balance and tax revenues,

they define the cyclicality of fiscal policy in terms of policy instruments such as tax rates.

The cyclical position of the economy is measured using three approaches: the

nonparametric approach, which involves dividing the sample into good times where

annual real GDP growth is above the median and bad times where growth falls below the

median; the ubiquitous Hodrick-Prescott (HP) filter; and the bandpass filter developed in

Baxter and King (1999), which involve decomposing each time series into its stochastic

trend and cyclical component. By comparing the cyclical behavior of the fiscal policy

indicators in good and bad times (using correlations), they discover that fiscal policy is

generally either countercyclical or acyclical in OECD countries, whereas it is

predominantly procyclical in developing countries.

Talvi and Vegh (2005) point to the dysfunctional political systems that pervade

developing countries. Specifically, using a sample of 56 countries (20 industrial countries

and 36 developing countries) over the period of 1970–1994, they tackle the questions of

how countries conduct fiscal policies over the business cycle and whether tax base

variability has an influence on procyclical fiscal policy. For the first question, the cyclical

components of fiscal variables (government consumption, revenues, and inflation tax

rates) are first isolated from the trend line using the Hodrick-Prescott filter, and the

105

cyclical volatility and the procyclicality of the fiscal variables are measured by the

standard deviation and the correlation coefficient, respectively. In the analyses, they find

evidence confirming the stylized fact that fiscal policy in developing countries is more

procyclical, compared to industrial countries. The authors then develop an optimal fiscal

policy model in which running budget surpluses becomes costly as they create pressures

to increase public spending. According to this model, due to political distortions,

developing countries that face large fluctuations in the tax bases find it optimal to run a

procyclical fiscal policy, even though it is suboptimal for the society as a whole. They

assess their model through a cross-country regression, which finds that output volatility is

associated with procyclicality in fiscal policy.

In a similar vein, Alesina, Campante, and Tabellini (2008) provide a political

economy explanation for the procyclicality of fiscal policy, with a special focus on

corruption. In a political agency framework, they argue that when faced with corrupt

governments that can appropriate public money for political rents in the form of favors

paid to special interests, voters, who are poorly informed about fiscal policies, will

rationally demand higher government spending or lower taxes during booms. The authors

test this model using data on 87 countries going from 1960 to 1999. Specifically, they

estimate a cross-country regression in which the dependent variable is the

countercyclicality of fiscal policy, while the independent variables are control of

corruption, real per capita income, the state of democracy, the relative size of government,

and credit constraints. The countercyclicality of fiscal policy is measured by first

regressing the central government‘s overall budget surplus as a percentage of GDP on

output gap, terms of trade, and a lagged budget surplus and then obtaining the coefficient

106

on output gap. In the test, they find that there is a strongly significant relationship

between control of corruption and the procyclicality of fiscal policy.

Battaglini and Coate (2008) develop a dynamic political economy model for the

behavior of fiscal policy over the business cycle in which elected representatives attempt

to target public spending to their own home districts (i.e. pork-barrel spending).

Barseghyan, Battaglini, and Coate (2010) assess the quantitative predictions of the model

by conducting a set of numerical experiments calibrated to the U.S. economy using data

from 1979 to 2009. As predicted by their theoretical model, the results show that

government spending increases during booms and decreases during recessions, whereas

tax rates fall during booms and increase during recessions.

The literature on the cyclicality of fiscal policy is of most relevance to this study

in that it most explicitly addresses the fiscal implications of the business cycle. However,

with most studies focusing on the comparison of developing and industrial countries,

very few have paid attention to differences that may exist across subnational government

units. Although, as an industrialized country, the United States has been found to be

countercyclical in conducting fiscal policy, it is reasonable to assume that there may be a

difference in the countercyclicality across states, given the wide cross-state variation in

tax base volatility, as found in Chapter 2. Furthermore, it may be more necessary to look

into the issue at subnational levels, because subnational governments tend to be more

vulnerable to ―capture‖ by special interests compared to the central government. In this

regard, Sturzenegger and Werneck (2006) argue that political distortions are more likely

in ―subnational government, usually suspected of being subject to a higher degree of

cronyism and corruption than the national government.‖

107

This study is in contrast to previous research in several important ways. First, it

introduces a new variable, revenue gap (the cyclical component of tax revenue) in

explaining state fiscal behavior. As discussed in Introduction, how much revenue is

available and how fiscal condition is, which will most likely be affected by the business

cycle, may be the most fundamental factors that affect fiscal policy. Therefore, failure to

take the cyclical positions of state economies and finances into account may lead to

omitted variable bias and ultimately incorrect statistical inferences. As reviewed above, a

few studies attempt to take this factor into account. For example, Kaminski, Reinhart, and

Vegh (2004) and Talvi and Vegh (2005) do so by using output gap, which, however, may

produce biased and inconsistent results, because the cyclical component of output could

differ substantially from that of tax revenue, as suggested by the results of Chapter 2. In

light of these empirical shortcomings in previous research, this study takes the cyclical

positions of state finances into account in a more direct and straightforward way by

measuring revenue gaps using the actual tax bases of four major state revenue sources,

general sales tax, individual income tax, corporate income tax, and selective sales tax on

motor fuel.

Second, to take full advantage of the information that revenue gap contains about

the cyclical positions of state economies and finances, this study divides the sample into

two subgroups, upturn and downturn years, and performs separate analyses for them in

addition to an analysis using the entire sample. This attempt stands out in comparison to

some studies (Poterba 1994; Hou 2005; 2006; and Hou and Moynihan 2007) that focus

only on downturn years. As will be discussed in more details in the next section, this

108

study sees that the root cause of recent state fiscal crises lies in unsustainable spending

growth during upturn periods (Knight, Kusko and Rubin 2003); therefore, it may be more

useful and relevant in understanding the dynamics of fiscal problems to look into states‘

fiscal behavior during both boom and bust phases of the business cycle rather than

recessions only. Furthermore, dividing the sample using revenue gap has a

methodological merit as well. Hou (2005; 2006) separates downturn years from upturn

years using the Heckman sample selection model (Heckman 1979) based on the

dependent variable, expenditure gap, which, as a type of endogenous sample selection,

may suffers from sample bias. By contrast, this study uses sample selection based on the

independent variable, which, as a type of endogenous sample selection, does not cause

bias or inconsistency in OLS (Wooldridge 2002).

Third, this study employs tax rates alongside expenditures as the dependent

variable in explaining the level of spending. While tax policy, as a half part of fiscal

policy, requires a close examination, there have been relatively few attempts at the

taxation side due to difficulty in data collection. Regarding the use of fiscal balances as

an alternative, Kaminski, Reinhart, and Vegh (2004) correctly point out that fiscal policy

should be defined ―in terms of policy instruments as opposed to outcomes,‖ suggesting

that tax policy should be defined in terms of tax rates as opposed to fiscal balances which

are simply differences between revenues and expenditures. However, they use inflation

tax rates as a proxy for the reason that there is no systematic data on tax rates.30

By

contrast, this study collects annual tax rate data for three major state taxes, sales,

individual income, and corporate income tax, and obtains overall tax rate for each state.

30

See also Talvi and Vegh (2005) as an example.

109

3.3 Conceptual Framework

As discussed in Introduction, this study views procyclical fiscal policy during

recessionary periods (i.e. spending cuts and tax increases) as the consequence of

unsustainable spending increases and tax cuts during the preceding expansionary periods.

Thus, the following conceptual discussion focuses primarily on the spending propensities

of government institutions during booms.31

Specifically, this section discusses the

fundamental nature of public budgetary resources that brings about demands and

pressures for spending, the mechanisms through which revenue availability induces

spending, and other relevant factors that may potentially affect the level of government

spending.

3.3.1 The Rationale for the Revenue-Spending Hypothesis

Most fundamentally, the effect of revenue gap on fiscal policy arises from the

―commons‖ nature of government budgetary resources. Public production is justified

over private production for certain goods and services on the basis of market failures. In

particular, nonexcludability, a situation where buyers cannot prevent nonbuyers from

using what they pay for, inhibits the exchange of resources through a free market, and

this phenomenon is particularly evident in services such as national defense and

environmental protection. In practice, public production involves collecting taxes from

individual entities, lumping them together, and allocating them in the form of budgets. In

31

tax cuts are viewed as another form of spending in this study

110

doing so, the logical linkage between who pays and who receives is blurred, and

government revenues essentially become a common pool of resources.

In the absence of a market-exchange mechanism, the process of allocating

financial resources essentially becomes value-based and thus political. Describing ―a

budget as a representation of monetary terms of government activity,‖ Wildavsky (1988),

―If politics is regarded in part as conflict over whose preferences will prevail in the

determination of public policy, then a budget records the outcome of this struggle.‖ What

is important here is that such a political struggle takes place over limited resources. The

implication is that one‘s gain leads to someone else‘s loss, therefore the policy and

budgetary process becomes contentious among competing demands. As a result, people

as potential users of the budgetary commons and their political agents have a tendency to

obtain as larger portions of the common pool as possible by overstating the needs for and

the importance of policy programs that would benefit themselves, while, at the same time,

trying to evade contributions to public funds. In other words, the ―up for grabs‖ nature of

government budgetary resources motivates potential users to seek the maximum

extraction of resources out of the common pool for themselves as an individual rather

than the optimal utilization of limited resources for the society as a whole.

Brubaker (1997) describes this phenomenon as ―the tragedy of the public

budgetary commons,‖ noting, ―Exploiters act they do because each one knows that if he

does not exploit the resource, someone else will.‖ As described in Garrett Hardin‘s

influential article, The Tragedy of the Commons, the commons refers to resources that are

communally owned for use by a community as a whole. The basic idea of the tragedy of

the commons is that if the commons is open to all individuals of a community—who

111

presumably only pursue their own self-interests without consideration of the long-term

interests of their community, acting independently—without any proper intervention and

regulation by either themselves or the third party, then the commons will be ruined and

depleted in the end. In a parable about the grazing of animals on a common pasture,

Hardin notes, ―Therein is the tragedy. Each man is locked into a system that compels him

to increase his herd without limit in a world that is limited. Ruin is the destination toward

which all men rush, each pursuing his own interest in a society that believes in the

freedom of the commons (Hardin 1968).‖

Given the commons nature of government resources, demands and pressures for

spending are likely to increase particularly in times of plenty when budget surpluses are

generated, ultimately causing government spending to grow more than proportionally

relative to an increase in income. In this regard, Posner and Gordon (2001) explicitly

address the politics of budget surpluses. They argue that the idea of running a budget

surplus is less likely to gain popular support in times of financial abundance, because

benefits achieved by maintaining surpluses are generally perceived as vague and remote

as they are diffused across the society as a whole and over time. On the other hand,

benefits achieved by increasing spending are tangible and immediate in nature, as they

are concentrated on specific population groups. With such perceptions prevailing, they

argue, large budget surpluses make it increasingly difficult for politicians to turn away

from the policy demands of their constituencies that have been restrained over periods of

recession.

Political economy models provide more systematic explanations for such political

distortions. In addition to Talvi and Végh (2005), Alesina, Campante and Tabellini

112

(2008), and Battaglini and Coate (2008) reviewed above, Lane and Tornell (1998) and

Tornell and Lane (1998; 1999) put forth a dynamic common pool model in which

competition for a common pool of funds among multiple interest groups interacts with

revenue windfalls to lead government spending to increase more than proportionally

relative to an increase in income. This political distortion in conducting fiscal policy has

been famously dubbed the ―voracity effect.‖ In a similar vein, Ilzetzki (2010) suggests

that a political friction, where alternating governments disagree on the desired

distribution of public expenditures, leads to suboptimal fiscal policy.

3.3.2 The Mechanisms of the Revenue-Spending Relationship

While the idea of the public budgetary commons is intuitively persuasive, it

provides little information on specific institutional mechanisms through which revenue

availability induces spending. Public choice theory remedies this deficiency by offering

rational political explanations for the fiscal and budgetary behavior of policymakers and

program administrators operating under institutional constraints and the influence of

special interest groups. The basic assumption underlying the theory is that actors in

public decision-making settings are motivated by their own self-interests and their

behaviors are driven by the goal of utility maximization, just as people in free markets are

so. As such, voters and interest groups seek to gain policy favors; politicians to be elected

or reelected to office; and bureaucrats to advance their own careers. Under this

assumption, the theory has sought to explain the inefficiency of public decision-making

and the expansion of the public sector, and the efforts have generated various models

113

including budget-maximizing bureaucracy, vote-maximizing politicians, and interest

group influence. Public choice models suggest that in a political system of representative

democracy, government institutions, the executive and legislative branch, generally have

strong spending propensities and often interact to lead to the oversupply of public goods

beyond the levels that are socially desirable—economically optimal and financially sound.

First, on the legislative side, spending propensities arise as politicians converge

on median voter preferences. The ultimate goal of politicians is to maximize votes to stay

in office, and to this end, they need to represent the interests of voters by supplying

public goods and services in line with their preferences. According to the median voter

theorem, vote maximization can best be met by focusing on median voters‘ preferences.

Public choice theory suggests that expenditures are likely to increase beyond optimal

levels as politicians attempt to accommodate median voter preferences and please self-

interested majorities on different issues.

This type of bias towards inefficiency is particularly likely in social welfare

policy areas involving minority groups. Many important social issues and problems are

concerned with minority groups who generally lack the ability to mobilize political

support from the society at large and gain majority voting support in the legislative

process governed by majority rule. In the public choice view, the significance of

minority-related issues and problems makes politicians to act on them, and in negotiation

and bargaining processes, political compromises are made to transform them into ones

that can obtain majority support. This eventually leads to original plans expanding into

large-scale policy programs for all. Dunleavy and O‘Leary (1987: 109) present a useful

114

example as to how politicians‘ attempt to accommodate median voter preferences pushes

up government budgets artificially. The authors note:

For example, suppose it becomes clear that the very poorest groups in society

cannot acquire decent accommodation in the housing market without some

element of state subsidy. Although this economic need can be met by a small and

carefully targeted program of transfer payments, in political terms this solution is

likely to be a non-starter, because it attracts support only from the small minority

who would benefit but imposes costs on everyone else in the society. To increase

support for the issue, politicians must try to construct a vote-winning package of

generalized housing subsidies, perhaps linking housing subsidies for the poor

with tax exemptions for younger middle-class homeowners in a structure which

is ‗fair‘ (i.e. gives some benefits) to enough groups to ensure success. Effectively

this electoral logic means that many of the most efficient and carefully targeted

social policies get rules out of consideration, pushing the government into a

position where it can remedy many genuine social evils only by adding

economically and socially unnecessary private goods provisions into its programs

in order to secure median voter support.

Inefficiency due to politicians‘ tendency towards median voter preferences is

closely related to what is called universalism in distributive politics. Not all winning

coalitions are dominant in negotiation and bargaining processes. Many barely win

majority support, and high-profile and controversial issues often face stiff oppositions

and long debates even after going through legislative approvals. According to the theory,

rational politicians tend to prefer to avoid ―hard-ball‖ coalition politics, and their

preferences often produces ―universalistic‖ coalitions, transforming original policy

schemes designed for small and targeted areas or population groups into large-scale

social policies that will serve the interests of all members of the legislature and society.

This strategy is assumed to be a rational choice for politicians in terms of vote

maximization, because it prevents vote losses that would be caused by excluding

particular groups of constituents in distributing policy benefits.

115

Policy activism is another source of bias towards inefficiency in public service

provision. In modern politics, it is essential for politicians to build an image as a policy

activist who is attentive and responsive to public concerns. To earn such a political image,

they need to constantly ―get things started‖ by bringing social problems and issues to the

media and public attention and developing them into major policy agendas through

awareness-raising activities. In doing so, politicians tend to prefer initiating new policy

programs and projects rather than improving existing ones. Generally, it is very hard to

evaluate the policy outcome of a certain government program, hence questioning the

effectiveness of a policy program is controversial at best. Rather it is much more

effective for politicians in building a policy-activist image to find out new issues, set new

policy agendas, and initiate new programs and projects rather than follow through on

existing programs.

In the public choice view, politicians‘ spending propensities also arise from

electoral interactions between politicians and interest groups. Politicians wish to bring

benefits to their constituents and home districts in return for political support in elections,

while interest groups continuously strive for policy gains and government favors. The

convergence of the two political groups‘ interests is particularly apparent in pork barrel

projects and distributive policies. Strong incentives for pork barrel projects are created

despite their inherent inefficiency, because by nature the benefits are concentrated in

specific local areas or groups of people, whereas the costs are spread among all tax

payers.

Logrolling (or vote-trading) has long been cited as a legislative strategy

commonly used by politicians and interest groups to win majority support for such

116

localized projects. Logrolling involves two or more policy groups with different interests

working together in the legislative process to achieve both groups‘ policy objectives by

trading votes with each other (i.e. voting for each other‘s policy options). In legislative

processes, issues are considered under majority rule not individually but in combination.

It is therefore critical for legislators to come up with packages of policy measures and

form winning coalitions. Public choice theorists argue that majority coalitions usually

dominate policymaking processes using their voting power and push through inefficient

pork barrel projects, passing the production costs on to the general public.

As described, legislators‘ spending propensities basically stem from their desires

to represent the interests of their constituencies. But the role as a representative is not the

only one for them to perform; they are also expected to keep their governments

financially sound by cutting budgets proposed by executives. Although both roles are

equally important, they are likely to be inclined towards the narrowly defined role as a

representative of their home districts in real-world politics. While legislators are well

aware of their role as a budget cutter for the interest of the general public, it is politically

rational for them to prevent budget cuts from affecting their constituencies who hold

control over their political futures.

Of course, not all legislative members put the role of a spender first over that of a

budget cutter. Jacobsen (2006) explains legislators‘ different spending preferences

according to positions and ranks within parties and committees. Political parties, as in

most other formal organizations, operate in the context of formal structure. Individual

politicians‘ roles and responsibilities are defined along the hierarchical dimension, and

their behavioral patterns are shaped depending on their positions and ranks. According to

117

the author, legislators at leadership positions such as party leaders and executives and

committee (or subcommittee) chairs or on budget or finance committees, such as the

Appropriations Committee and the Finance Committee at the federal level, have

responsibility for balancing different demands within budget constraints. Furthermore,

they generally have broader constituencies and, consequently, broader perspectives on

government policies and finances acting more responsibly for the long-term interests of

the general public. Meanwhile, members on substantive standing committees have

limited roles and responsibilities in specific areas. As a result, they tend to ―fight for

single causes and act in a way that is more parochial,‖ paying less attention to the overall

fiscal health of the government.

Although certain legislative members at leadership positions may be more

conservative in spending decisions and attempt to tone down expenditure demands from

constituency-oriented politicians, the weak leadership of American political parties (Lee

and Joyce 2008) is likely to make it difficult for them to control their party members.

Recognizing legislators‘ inclination towards a parochial protector of their constituents‘

interests and weak party leadership, Lee and Joyce (2008) note that ―While in years past

legislatures sometimes had a reputation for being budget cutters, more recently their role

has been to represent constituents who would be harmed if proposed budget cuts were

implemented.‖ Given such a dysfunctional political system in representative democracy,

it is predictable that a legislature‘s ability and will to keep diverse sources of spending

propensities in check will weaken particularly in times of plenty.

118

Second, spending propensities come from the executive side as well. In

Niskanen‘s budget-maximizing model, rational bureaucrats are expected to seek to

maximize the budgets of their bureaus or programs and projects over which they have

responsibilities in an attempt to advance their own careers and improve compensations

and benefits ranging from additional staff, capital, and office perquisites to public

reputation, power, and patronage. In this view, although legislators have the formal

authority to monitor their administrative agents, it is likely that the monitoring power is

not exercised properly due to information asymmetry inherent in the relationship between

bureaus and their political overseers, as suggested by the principal-agent model. That is,

bureaucrats have an advantage in bargaining processes with their political supervisors

because the latter lacks information on the actual costs of producing services. As a result,

they have a superior bargaining power in budget decision making over their political

supervisors.

Niskanen (1991) later modifies his original theory, pointing to particular types of

budgets as the object of budget maximization. He argues that in the bilateral monopoly

relation between bureaus and their political sponsors (i.e. where bureaus are a monopoly

supplier of public services, while legislators are a monopoly buyer of those services on

behalf of citizens as the ultimate consumer of the services), bureaucrats try to maximize

their bureaus‘ discretionary budgets rather than budgets as a whole. The author goes on

to argue that budgets of this particular type are shared by both bureaucrats and their

political superiors to serve their own interests.

Another modification is that political officials are not necessarily passive in the

review of bureaus‘ budget proposals. They do make efforts to obtain more information on

119

administrative activities and production costs and also employ bargaining techniques. But

he argues that incentives for legislators to make conscientious efforts for bureau

monitoring generally do not exceed incentives to serve ―their political interests of

committee members, subject to the approval of the committee proposal by the whole

legislature,‖ because the benefits achieved by careful monitoring and detailed reviews are

a public good that is shared by the legislature as a whole and citizens at a large. Based on

these ideas, Niskanen concludes that public production becomes inefficient, as bureaus

gain too large budgets and produce too small outputs in terms of the demands expressed

by the political sponsors.

3.3.3 Other Relevant Factors

Revenue gap certainly is not the only factor that affects state fiscal behavior in

terms of expenditures and tax rates. Upon review of relevant literature, seven groups of

factors appear of particular relevance.

First, the so-called ―flypaper effect‖ may be an important factor in explaining

state fiscal behavior. Theories of intergovernmental grants advanced by Bradford and

Oates (1971a; 1971b) and expanded on by Gramlich and Galpher (1973) and Gramlich

(1977) argue that grants to lower-level governments would result in increases in recipient

government expenditures equivalent to increases in income, since the recipients have the

same propensity to spend out of their own-source revenues or grants (Aragon 2009: 1).

Inconsistent with these theoretical predictions, however, empirical studies have found

evidence that grants lead to greater increases in government expenditures than increases

120

in income. This phenomenon has been widely called the flypaper effect that ―money

sticks where it lands.‖ The notion suggests that grant-receiving governments use the

additional revenues to expand public programs rather than substitute them for their own-

source revenues through tax cuts. Volden (1999) argues that this stimulative effect on

spending is particularly true for matching grants, because programs under this funding

scheme are perceived less expensive by the receiving governments as portions of the

costs of the programs are paid by the granting governments. Martell and Smith (2004)

suggest that the flypaper effect tends to last even after grants are no longer available.

Empirical results indicate that the relationship between federal grants and state

expenditures is asymmetric; states tend to decrease expenditures when a grant is

withdrawn less than they had increased expenditures when a grant was awarded. That is,

when a decrease in grants occurs, that money is ―replaced‖ by other revenue—what is

called ―fiscal replacement‖ occurs.

Second, borrowing may affect expenditures and tax rates. Obviously, debt

financing provides states that are required to balance their budgets with financial aids

particularly during recessions when the legal requirements become challenging to meet.

Observing that states and localities substantially increased both long- and short-term

debts in 2002 and early 2003, Knight, Kusko, and Rubin (2003: 435–437) note that debt

measures are used to finance capital projects to promote economic recovery in the middle

and long term and reduce budget shortfalls in the short term, scaling down states‘

spending cuts and tax increases and increasing general fund balances to which balanced

budget requirements apply mainly. One of the short-term measures used to close budget

gaps is to generate one-time revenues, one of which is revenues obtained from the so-

121

called ―tobacco settlement.‖ According to a report issued by the U.S. Government

Accountability Office (GAO), over the period from 2001 to 2003, fourteen states have

sold bonds backed by payments from tobacco companies (in other words, securitized

future tobacco revenue streams), and the tobacco revenues generated during this period

totaled more than 10 billion dollars. Further, Behn and Keating (2004) point out that the

financial practice of securitization involves not only tobacco funds but also future

revenue streams in general such as revenues from an increase in sales tax. In light of the

above discussion, it is hypothesized that expenditures will be higher, while tax rates will

be lower in states with larger debts.

Third, fiscal institutions and rules are expected to affect state fiscal behavior. In

their seminal work The Calculus of Consent, Buchanan and Tullock (1962) argue that

institutions and rules defining the way collective choices are made have important effects

on policy outcomes. This view has been widely applied to state budgetary processes to

create various fiscal control mechanisms, among which, this study focuses on balanced

budget requirements (BBRs), tax and expenditure limitations (TELs), gubernatorial line

item veto, and budget stabilization funds (or rainy-day funds). As discussed in the

literature review, empirical studies suggest that these fiscal institutions, when properly

implemented, have positive effects in restraining government spending and minimizing

deficits and debts across business cycles.

Another commonly cited fiscal institution is biennial budgeting. The literature

suggests contrasting views on its effect on fiscal process and outcomes. One view argues

that biennial budget cycle leads to more deliberative and meaningful budgeting by

bringing a longer-term perspective to the budgetary process and by allowing more time

122

for legislative oversight (Fisher 1997). The other view holds that a two-year cycle could

contribute to spending growth by weakening legislative control of spending in the

appropriation process (Greenstein and Horney 2000). Supporting this view, Kearns (1994)

finds empirical evidence that biennial budgeting has a positive effect on state spending.

The fourth factor to be considered is partisan control. The most common way of

modeling party ideologies is the left-right political spectrum. In the United States, the

Republican Party, conventionally considered a right wing and characterized as fiscal

conservatism, is expected to be more hostile towards the expansion of the public sector

than the Democratic Party positioned as a left wing and characterized as social liberalism.

In light of these prevailing notions about American political parties and their policy

stances, it is hypothesized that gubernatorial and legislative control by the Republican

Party will exert a negative influence on government spending.

Fifth, divided government may also affect state fiscal behavior. Divided

government refers to a situation in which one party control the executive branch and

another party controls one or both chambers of the legislature. While the variable is

assumed to affect fiscal behavior and policy, whether the effects are positive or negative

is not clear. Conventional wisdom holds that divided government tends to restrain

spending by creating political gridlock between the executive and the legislative branch

which serves as a check on the abuse of power and limits spending. In keeping with the

conventional wisdom, Niskanen (2003) makes the case for divided government as a

political mechanism for fiscal restraint. Presenting as evidence an annual percentage

increase in real federal spending by administration (from Eisenhower to Clinton), he

123

finds that the rate of growth of real (inflation-adjusted) federal spending is usually lower

with divided government.

By contrast, Gould (2009) finds evidence that runs counter to the gridlock

hypothesis. In an empirical study of state expenditures, he finds that divided government

has a positive effect on per-capita expenditures. Emphasizing that not only the presence

of divided government but also its degree is an important consideration, he explains that

when the gridlock caused by political division is not hostile but friendly, the government

loses the ability to make "tough choices" between partisan spending priorities and ends

up accommodating the expenditure demands of both parties. His reasoning is, indeed,

consistent with policy universalism described above based on the public choice

explanation of legislative behavior. In light of the contrasting views, this study leaves the

prediction of the effect of divided government on spending open for empirical analysis.

Sixth, electoral cycles might affect state fiscal decisions. Theoretical reasoning

and empirical evidence (Nordhaus 1975; Tufte 1978) suggest that political business

cycles exist, in other words, incumbent politicians are likely to increase expenditures in

periods of election races in an attempt to attract votes. Although there have been many

studies that find evidence against the political cycle hypothesis since Richards (1986)32

and Keech (1989),33

there is no reason not to assume the general existence of political

business cycles, in light of the political benefits that such strategic political behavior will

bring to incumbents seeking reelection at least in the short run.

32

Richards (1986) suggests that political business cycles may have existed for some time, but they have

gradually disappeared since the mid 1970s. 33

Keech and Pak (1989) find no strong evidence that electoral cycles contribute to the growth of

government spending, and reason that politicians have given up electoral cycles because they have found

them not as helpful for their electoral prospects.

124

Lastly, demographic and socioeconomic characteristics such as population age

distribution and income distribution might potentially influence policy and budget

outcomes. A public budget is a reflection of institutional judgments of the community‘s

values, preferences, and priorities (Wildavsky 1988). Hence, fiscal policy decisions may

depend on what characteristics the population has. Based on prevailing notions that age

and economic minorities tend to lean towards liberal fiscal policy, expenditures and tax

rates are hypothesized to be higher in states with more underage youths, seniors in

retirement, and people in poverty.

Put together, state fiscal behavior in terms of the level of spending and taxation is

modeled as follows:

State expenditures / tax rates = f (revenue availability, federal grants, borrowing,

fiscal institutions/rules, partisan control, divided government, electoral cycles,

demographic-socioeconomic characteristics)

3.4 Data and Methods

For the empirical analysis of the proposed research questions, this Section

develops econometric models based on the above conceptual discussion; presents

estimation methods; and discusses results.

3.4.1 Variables and Data Sources

125

Two dependent variables are employed as an indicator of state fiscal policy: total

own-source expenditure and overall tax rate. First, own-source expenditure (OSE) is

obtained by deducting revenue from the federal government from state general

expenditure. Following previous studies (White 1983; Dye and McGuire 1991; Braun and

Otsuka 1998; Hou 2005; Hou 2006), OSE is measured as expenditure gap, that is, the

deviation of OSE from the trend line. The way the measure is calculated is essentially the

same as revenue volatility34

in Chapter 2. It involves separating the trend and cyclical

component of OSE by detrending the original time series. Specifically, expenditure gap is

obtained by (1) regressing OSE on income,35

(2) obtaining the orthogonal deviation of

OSE from the trend line—unlike the conventional method that uses the residual (actual

expenditure - trend expenditure), and (3) dividing the deviation by the sample mean,

which is expressed as a percentage. Using expenditure gap has two merits. One is that it

better captures the effects of legislated adjustments to spending by detrending time series

on expenditures, and the other is that it allows for the more intuitive and meaningful

interpretation of regression results as the measure has a percentage point interpretation

along with the independent variable, revenue gap, as will be specified next. Data on own-

source expenditure and personal income were obtained from the State Government

Finances series and the BEA website, respectively. The second dependent variable,

overall tax rate, is measured by the weighted sum of the tax rates of three major state

revenue sources, general sales tax, individual income tax, and corporate income tax. In

34

The only difference is that the latter uses absolute values. 35

Wagner‘s Law suggests that economic development leads to an increase in government spending in the

long run as it increases government activities (i.e. increases in social and welfare functions and long-term

investment projects). Under the assumption that the level of government spending is associated with the

degree of industrialization and economic development—commonly approximated by income, own-source

expenditure is detrended using a levels regression of expenditure on income.

126

the case of income taxes with progressive tax rate structure, top-bracket tax rates are used.

Annual tax rates for each tax were collected from the State Tax Reporter series.

The independent variable of primary interest is revenue availability over the

business cycle, which is measured as revenue gap, the cyclical component of tax revenue.

State tax systems consist of various revenue sources, among which, this study focuses on

four major ones: general sales, individual income, corporate income, and motor fuel tax.

Revenue from these revenue sources, on average, accounts for about 80 percent of total

state revenue over the sample period. Specifically, therefore, revenue gap is measured by

the weighted sum of the cyclical components of the four major tax revenues. The cyclical

component of each tax revenue is measured by the deviation of a tax base from the trend

line as a percent of the sample mean, in the same way as revenue volatility in Chapter

2—using tax bases, which are unaffected by tax rate changes, instead of actual tax

revenues.

As noted in Chapter 2, tax base is obtained by dividing tax revenue by tax rate.

General sales and individual income tax bases obtained in Chapter 2 are used again for

this analysis. For corporate income tax bases, top bracket tax rates are used, and for

motor fuel tax bases, annual motor fuel consumption is used as a proxy. Data on tax

revenues by type were obtained from the State Government Finances series, while data

on tax rates were collected from the State Tax Reporter series. Data on annual motor fuel

consumption were drawn from the Highway Statistics series published by the Federal

Highway Administration (FHA). Given that the budgetary process requires political

consensus among policymakers or support from members in the society, it may take

fiscal years for budget surpluses or deficits to lead to specific fiscal actions such as

127

spending increases or cuts. It is also plausible that the cyclical component of tax revenue

may have effects on spending in a quadratic manner. Thus, three lags and a quadratic of

the variable are added.

To capture the flypaper effect, per capita federal grant is included. As with

revenue gap, it may take time for additional resources from the federal government to

lead to increases in expenditures; therefore, a lag of per capita federal grant is also

included. Data for the variable were from the State Government Finance series. For

borrowing, total debt as a share of GSP is included with a lag, and data for the variable

were also from the State Government Finance series.

For fiscal institutions, six variables are employed: no deficit carry-over; revenue

limitation; expenditure limitation; line item veto; budget stabilization fund; and biennial

budget cycle. Each of them is measured as a dummy variable: 1 if in place, and 0

otherwise. Balanced budget requirement systems comprise a group of legal provisions,

among which, this study focuses on a no deficit carry-over provision that has been found

to be most relevant in restraining the rate of growth in state spending.36

Data on fiscal

institutions were collected from the Budget Processes in the States series published by

the National Association of State Budget Officers (NASBO).

To capture the effect of partisan control, four dummy variables are used:

Republican majority in the Senate (with Democratic majority in the Senate as the base

36

Hou and Smith (2006) define BBRs as ―a system of interrelated rules of a political and/or technical

nature governing the executive preparation, legislative review, and implementation phases of the budget

cycle,‖ and put forth an analytical framework that categorizes BBR systems as ―from a procedural rule

through two technical rules.‖ Based on this framework, Smith and Hou (2008) examine the effects of BBR

systems on state spending behavior. In the analysis, they conclude that ―balanced budget requirements

governing the political process of public budgeting are ineffective in restraining state spending. In the

interest of forcing a state government to restrain expenditures, legislators must look to technical provisions

that govern the financial management systems under which the budget is constructed and executed (Smith

and Hou 2008: 23).‖

128

group), Republican majority in the House (with Democratic majority in the House as the

base group), Republican Governor, and Independent Governor (with Democratic

Governor as the base group). Divided government is also measured as a dummy variable,

with 1 indicating yes and 0 otherwise. To control for the effects of electoral cycles,

gubernatorial election years are included as a dummy variable along with a lag and a lead:

1 indicates yes, and 0 indicates otherwise. Data for partisan control, divided government,

and election cycles were obtained from the Book of the States series published by the

Council of State Governments (CSG).

Lastly, the effects of demographic and socioeconomic characteristics are captured

by age structure and income distribution, specifically, the proportion of the population

aged over 65 and the population under 17, and the proportion of the population below the

federal poverty level and the population with federal adjusted gross income (FAGI) over

$200,000. In keeping with the norms of econometric analysis in the field of public

budgeting and finance, total population and per capita personal income are also included.

All population-related data were drawn from the Statistical Abstract of the United States

series published by the U.S. Census Bureau, except data on the proportion of the

population with FAGI over $200,000, which were obtained by modifying data on federal

income tax returns from the Internal Revenue Service (IRS) tax statistics. Finally, data

for personal income per capita were from the website of the BEA.

The data covers 49 states (excluding Alaska)37

and 16 fiscal years from 1992 to

2007. As a note on data format, all monetary figures are converted into 2007 chained

dollars. The proportion of the wealthy population is also adjusted for inflation, since the

37

Alaska was excluded because of its unique economic and fiscal structure (due to its heavy reliance on the

oil industry) that may adversely affect mean-based statistical analysis.

129

original IRS data do not take inflation into account in categorizing tax returns by income

level. Variable definitions and data sources are summarized in Table 3.1, and descriptive

statistics are presented in Table 3.2.

Table 3.1 Variable Descriptions and Data Sources

Variables Descriptions Data sources

ose Own-source expenditure: Orthogonal

deviations from the trend line as % of the

sample mean

State Government Finance series

taxrat Overall tax rate: Weighted sum of tax

rates for general sales, individual income,

and corporate income tax

State Government Finance series

revgap Cyclical component of tax revenue:

Weighted sum of the orthogonal

deviations of tax bases (general sales,

individual income, corporate income, and

motor fuel sales tax) from the trend lines

as % of the sample mean

State Government Finance series

State Tax Reporter series

NBER TAXSIM website

BEA website

Federal Highway Administration website

grant Per capita revenue from the federal

government

State Government Finance series

debt Total debt as % of GSP State Government Finance series

nodef No deficit carry-over provision: 1 if in

place; 0 otherwise

Budget Processes in the States series

revlim Revenue limitation: 1 if in place; 0

otherwise

Budget Processes in the States series

explim Expenditure limitation: 1 if in place; 0

otherwise

Budget Processes in the States series

liv Line item veto: 1 if in place; 0 otherwise Budget Processes in the States series

bsf Budget stabilization fund: 1 if in place; 0

otherwise

Budget Processes in the States series

bien Biennial budget cycle: 1 if in place; 0

otherwise

Budget Processes in the States series

repsen Republican majority in Senate: 1 if yes; 0

otherwise

The Book of the States series

rephou Republican majority in House: 1 if yes; 0

otherwise

The Book of the States series

repgovn Republican governor: 1 if yes; 0

otherwise

The Book of the States series

indgovn Independent governor: 1 if yes; 0

otherwise

The Book of the States series

divgov Divided government: 1 if yes; 0 otherwise The Book of the States series

eltyr Gubernatorial election year: 1 if yes; 0

otherwise

The Book of the States series

pop Population Statistical Abstract of the United States

130

series

under17 % of population aged 17 or under Statistical Abstract of the United States

series

over65 % of population aged 65 or over Statistical Abstract of the United States

series

pcpi Per capita personal income (in thousands) BEA website

weal % of population with FAGI over

$200,000

IRS tax statistics

poor % of population below federal poverty

level

Statistical Abstract of the United States

series

Table 3.2 Summary Statistics

Variables Mean Std. Dev. Min Max

ose -0.0069 5.287894 -20.008 19.75871

taxrat 6.176218 1.302307 3 10.36349

revgap 0.320603 4.159046 -12.9676 23.04729

grant 1240.116 412.3397 571.3274 4060.431

debt 6.88963 3.876823 0.866069 22.77977

nodef 0.718112 0.450206 0 1

revlim 0.105867 0.307864 0 1

explim 0.446429 0.497439 0 1

liv 0.839586 0.367227 0 1

bsf 0.9225 0.26755 0 1

bien 0.420918 0.494022 0 1

repsen 0.487245 0.500156 0 1

rephou 0.44898 0.497708 0 1

repgovn 0.542092 0.498543 0 1

indgovn 0.019133 0.137079 0 1

divgov 0.563776 0.496233 0 1

eltyr 0.265306 0.441778 0 1

pop 5684498 6138458 466251 3.64E+07

under17 25.39138 1.986387 21.02874 36.07281

over65 12.64841 1.913482 4.3 18.6

pcpi 33458.15 5531.674 21651.25 56510

weal 1.906012 0.805704 0.777973 5.421682

poor 12.74222 3.513371 5.3 26.4

3.4.2 Models and Estimation Methods

Specifically, this study estimates the following regression models for state fiscal

policy:

131

(1) / = + + + +

+ +

+ +

+ + + + +

+ + + + + +

+ + + + +

+ + + γ + + ,

where the and subscript refer to panel (state) and time period (year), respectively;

and denote total own-source expenditure and overall tax rate; is the

cyclical component of tax revenue, measured by the weighted sum of the deviations of

major tax bases from the trend line as a percent of the sample mean; is per capita

revenue from the federal government; is total debt as a share of GSP; ,

, , , , and denote no deficit carry-over provision, revenue

limitation, expenditure limitation, line item veto, budget stabilization fund, and biennial

budgeting, respectively; , , , and refer to Republican

majority in the Senate, Republican majority in the House, Republican Governor, and

Independent Governor, respectively; represents divided government;

denotes election years; V and Z are a matrix of socioeconomic characteristics and year

dummies,38

respectively; is the error term.

A common concern with panel data models is that there may be unobserved

effects correlating with explanatory variables. It can never be assumed for sure that there

exists no association between certain unobserved factors (e.g. cultural factors) and state

38

A full set of year dummies—all years but the first—are included to control for state trends in own-source

expenditure and overall tax rate.

132

spending. To remove time-constant unobserved factors, this study employs a fixed effects

model.39

Modifying Equation (1), the fixed effects models are as follows:

(2) / = + + + +

+ +

+ +

+ + + + +

+ + + + + +

+ + + + +

+ + + γ + + + ,

where is the state fixed effect and is the idiosyncratic error (or time-varying error);

other terms are defined the same as in Equation (1); time-constant effects are cancelled

out in the fixed effects model. For the fixed effects estimation, the study assumes that the

time-varying explanatory variables and the individual fixed effect are not correlated with

the error term in any time periods for any states in the data. This assumption is sufficient

for the consistency of the fixed effects estimators where N is relatively large while T is

small (Wooldridge 2002).

Regressions using the entire sample provide an overall but not detailed picture of

fiscal behavior over the course of the business cycle. Another useful way of gaining a

39

Two methods are available for estimating unobserved effects panel data models: the fixed effects and the

random effect model. To determine which one is appropriate for the given data, the Hausman test was

conducted. Test results indicate that it cannot be assumed that the unobserved factors are uncorrelated with

the explanatory variables, suggesting that fixed effects is appropriate in this case:

Test: Ho: difference in coefficients not systematic

chi2(38) = (b-B)'[(V_b-V_B)^(-1)](b-B)

= 191.93

Prob>chi2 = 0.0000

133

sense of it is to look at it by cyclical position. For this purpose, this study divides the

study sample into two subgroups, upturn and downturn years, based on revenue gap

(upturn if revenue gap is above zero, and downturn if below zero), and run separate

regressions for each subgroup.

As in Chapter 2, diagnostic tests were performed for the presence of

heteroskedasticity and serial correlation in the errors using modified Wald test (for

heteroskedasticity) and Wooldridge test (for serial correlation). The tests find that the

errors here are heteroskedastic and serially correlated. In correcting for the problems, this

study uses clustered robust standard errors which are reportedly robust to

heteroskedasticity and serial correlation.

3.5 Results and Discussion

Before discussing regression results, it is useful to see how widely the dependent

variables, own-source expenditure and overall tax rate, vary across states. Summary

statistics in Table 2 indicate that the mean and standard deviation of OSE are -0.0069 and

5.287894, respectively, with the minimum and maximum being -20.008 and 19.75871.

This suggests that with positive OSE gaps during upturn years almost fully offset by

negative OSE gaps during downturn years, as indicated by the approximately zero mean,

there is a substantial variation in OSE across states. Figure 3.1 provides a visual

representation of this variation. Expenditures in New Jersey, Ohio, and Georgia are

shown to fluctuate most widely over the business, whereas ones in Virginia, Utah, and

Hawaii are the most stable.

134

Meanwhile, Figure 3.2 shows that with most annual changes in overall tax rate

being zero or small, some states enacted large tax cuts. An interesting observation is that

large changes (more than 10 percent) in overall tax rate are all positive in sign (i.e. tax

increases) and were made in economically tough times. Graphics by state reveal that

Indiana, New Jersey, and Tennessee enacted large tax hikes following the 2001 recession,

while Wyoming, Michigan, Vermont, and Rhode Island in the aftermath of the 1990–

1991 recession. On the other hand, tax cuts are relatively small in size, except for two

states, Kansas and Hawaii which enacted a nearly 10 percent tax cut in 1998 and 1999,

respectively, when the economy was at its peak. Overall, these results suggest that when

it comes to large tax cuts, state tax policy was procyclical over the sample period.

135

Figure 3.1 Box Plot of Expenditure Gap by State

-20

-10

010

20

-20

-10

010

20

-20

-10

010

20

-20

-10

010

20

-20

-10

010

20

-20

-10

010

20

-20

-10

010

20

AL AK AZ AR CA CO CT DE

FL GA HI ID IL IN IA KS

KY LA ME MD MA MI MN MS

MO MT NE NV NH NJ NM NY

NC ND OH OK OR PA RI SC

SD TN TX UT VT VA WA WV

WI WY

Expe

nd

iture

Gap

136

Figure 3.2 Annual Percentage Changes in Overall Tax Rate by State

-10

010

20

30

-10

010

20

30

-10

010

20

30

-10

010

20

30

-10

010

20

30

-10

010

20

30

-10

010

20

30

1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005 1990 1995 2000 2005

1990 1995 2000 2005 1990 1995 2000 2005

AL AK AZ AR CA CO CT DE

FL GA HI ID IL IN IA KS

KY LA ME MD MA MI MN MS

MO MT NE NV NH NJ NM NY

NC ND OH OK OR PA RI SC

SD TN TX UT VT VA WA WV

WI WYAnn

ua

l C

han

ge

s in O

vera

ll T

ax R

ate

(%

)

YearGraphs by State

137

Table 3.3 Regression Results for Own-Source Expenditure

Explanatory

variables

Entire sample Upturn years Downturn

years

Coef. Clustered

Robust Std.

Err.

Coef. Clustered

Robust Std.

Err.

Coef. Clustered

Robust Std.

Err.

revgap 0.140728* 0.074844 -0.08245 0.360614 -0.12894 0.565706

revgap_1 0.206678*** 0.059937 0.276177** 0.105922 0.109673 0.133672

revgap_2 0.152506** 0.067194 0.116481 0.104047 0.240948* 0.131712

revgap_3 0.207201** 0.082126 0.246823** 0.09577 0.266712* 0.135387

revgap2 0.00073 0.00788 0.019651 0.033347 -0.03116 0.044702

revgap2_1 -0.01817*** 0.005975 -0.02162* 0.012405 -0.01764 0.017733

revgap2_2 -0.01364 0.009715 -0.01029 0.020864 -0.02038** 0.00956

revgap2_3 -0.00638 0.011458 -0.00344 0.015417 -0.00371 0.018617

grant 0.007986*** 0.002286 0.005407 0.003601 0.012288*** 0.003772

grant_1 0.002357* 0.001338 0.000907 0.002492 0.00518* 0.002786

debt 0.585109** 0.27289 0.48046 0.384622 0.476312 0.667204

debt_1 -0.21148 0.320162 -0.5577 0.373779 0.328163 0.506066

nodef 3.433418** 1.695718 5.449216* 3.019447 -0.25955 2.568337

revlim 3.582779*** 1.191588 2.450598* 1.382282 5.736686** 2.823636

spdlim 0.905916 0.961293 0.316687 2.252248 1.634223 2.438122

liv -1.05108 1.780661 -0.18865 1.635543 -5.80484 3.603278

bsf -1.45635 1.349294 -2.01099 2.226945 -3.15117 1.997457

bien -2.86484*** 1.017006 -3.03458** 1.340435 -3.34809 4.965941

repsen 0.052127 0.673867 1.030087 1.149071 -1.40425 1.406549

rephou -0.76934 0.908064 0.67241 0.907488 -1.07495 1.402015

repgovn -0.76386 0.596234 -1.04083 1.16738 -0.26981 0.741225

indgovn 3.302643 2.424698 3.161515 2.944127 2.839553 4.024915

divgov -1.06311** 0.468083 -1.10612 0.804678 -0.70992 0.901887

eltyr 0.224332 0.497814 0.156877 0.79641 1.091207 1.354092

eltyr_lg 0.15972 0.408047 -0.19284 0.727649 1.241845 1.446606

eltyr_ld -0.25762 0.291272 -0.37224 0.606004 0.612076 0.991889

pop 9.75E-09 6.84E-07 5.11E-07 8.86E-07 -2.09E-06 1.30E-06

under17 -0.21708 0.529245 -0.41037 0.66557 -0.74724 0.867867

over65 0.688738 0.444442 1.086779** 0.502784 -0.69684 1.133569

pcpi -0.00156*** 0.000428 -0.00168*** 0.000591 -0.0024*** 0.000661

weal 2.266504 2.126179 0.091506 2.642696 9.835721*** 3.154147

poor 0.2215 0.149888 0.183162 0.141164 0.427334 0.515932

_cons 28.58141 21.34315 44.49318 30.18426 78.44587 36.84793

Number of

observations

580 338 242

Number of

states

49 49 49

R-squared

(within)

.6001 .6008 .5671

Note: Year effects not reported.

***p<0.01; **p<0.05; *p<0.1

138

Table 3.4 Regression Results for Overall Tax Rate

Explanatory

variables

Entire sample Upturn years Downturn

years

Coef. Clustered

Robust

Std. Err.

Coef. Clustered

Robust

Std. Err.

Coef. Clustered

Robust

Std. Err.

revgap -0.00257 0.004038 -0.0119 0.015192 -0.00064 0.016268

revgap_1 0.002245 0.002723 0.00449 0.004653 0.000974 0.005067

revgap_2 -0.00083 0.004411 0.001735 0.003813 0.003687 0.006497

revgap_3 0.005716 0.004154 0.005487 0.004777 0.011785 0.0087

revgap2 -0.00085*** 0.000308 -0.00053 0.001317 -0.00018 0.001419

revgap2_1 -0.00105*** 0.000332 -0.00078 0.000545 -0.00168*** 0.000618

revgap2_2 -0.00159*** 0.000431 -0.00194*** 0.000502 -0.00118* 0.000662

revgap2_3 -0.00167*** 0.00057 -0.00324*** 0.000812 -0.00095 0.000802

grant 4.86E-05 8.61E-05 7.71E-05 9.73E-05 9.26E-07 0.000137

grant_1 5.92E-06 8.98E-05 -0.00017 0.000159 4.47E-06 8.37E-05

debt 0.030581 0.024808 0.084093*** 0.01695 0.012189 0.026385

debt_1 -0.02988 0.039204 -0.02987 0.030798 -0.02096 0.045858

nodef 0.483379*** 0.105265 0.33781** 0.13188 0.602802*** 0.081071

revlim 0.119832 0.15526 0.102934 0.089928 -0.09264 0.265432

spdlim 0.222629 0.214246 0.183884 0.217971 0.502359 0.337519

liv -0.19096*** 0.064705 -0.19492** 0.092133 -0.49427*** 0.090433

bsf -0.09407 0.221345 -0.27245 0.299961 -0.07655 0.222464

bien -0.03788 0.046136 -0.11069 0.092042 -0.02263 0.145025

repsen -0.08446* 0.047438 -0.07399 0.04895 -0.05635 0.062844

rephou -0.03931 0.04343 -0.01067 0.058165 -0.00486 0.072761

repgovn 0.013755 0.047806 0.01872 0.049658 -0.05466 0.045343

indgovn 0.204424 0.14384 0.202546 0.166271 0.036646 0.094493

divgov -0.02215 0.034659 0.018838 0.029708 -0.01088 0.035949

eltyr -0.02756* 0.015328 -0.01921 0.022867 -0.00411 0.030017

eltyr_lg -0.00759 0.018063 -0.03637 0.022639 0.038317 0.034519

eltyr_ld -0.01811 0.017569 -0.06146** 0.024413 0.057727 0.038261

pop 1.98E-08 4.76E-08 3.01E-08 4.65E-08 -3.25E-08 6.70E-08

under17 0.055569 0.037423 0.013088 0.060696 0.031884 0.050961

over65 0.026444 0.03401 0.013871 0.026605 0.016575 0.041748

pcpi -2.65E-06 2.39E-05 -2.4E-05 2.92E-05 1.81E-05 4.35E-05

weal -0.02133 0.135119 0.083799 0.153344 -0.051 0.217659

poor 0.007655 0.006392 0.001508 0.006453 0.020933 0.024996

_cons 4.23275 1.933999 6.484937 3.016582 4.782678 2.439343

Number of

observations

580 338 242

Number of

states

49 49 49

R-squared

(within)

.2194 .4138 .2911

Note: Year effects not reported.

***p<0.01; **p<0.05; *p<0.1

139

Table 3.3 and 3.4 report the results of regression analyses for own-source

expenditure (OSE) and overall tax rate by sample—the entire sample and two subgroups

(upturn and downturn years). Overall, both models perform well. The key variable,

revenue gap, in linear and/or quadratic form, is statistically significant in the expected

direction across the models, which explain the 60.01% and 21.94% in a regression using

the entire sample period, 60.08% and 41.38% in a regression using the upturn-year

sample, and 56.71% and 29.11% in a regression using the downturn-year sample, of the

(within) variation in own-source expenditure and overall tax rate, respectively.

Starting from the analysis for OSE, the results show that, as expected, there is a

positive relationship between revenue gap and own-source expenditure. The results of the

regression using the entire sample, in Column 1 of Table 3.3, report that all the revenue

gap variables, , , _2, and , are statistically significant

(at the 10%, 1%, 5%, and 5% level, respectively) with positive signs. This means that

revenue gap affects OSE both contemporaneously and with lags. Specifically, the slope

coefficients indicate that, holding all other independent variables fixed, a one percentage

point increase in current, one-, two-, and three-year lagged revenue gap increases own-

source expenditure, on average, by .141, .207, .153, and .207 percentage points. The

combined effect of revenue gap is substantial in magnitude. The sum of the coefficients

is .708, which means that another percentage point on revenue gap is associated with .708

of a percentage point on OSE, or much more than half a percentage point over years.

Together, these results suggest that spending policy is more procyclical (or less

140

countercyclical) in states with a larger revenue gap (in other words, more unstable in

states with a more volatile revenue base).

The results also show that the quadratic term in one-year lagged is

statistically significant. This implies that OSE depends on one-year lagged , but it

does so in a quadratic fashion. The sign of the coefficient on the first lag is positive,

whereas that of the coefficient on its quadratic is negative. This means that one-year

lagged has a diminishing effect on OSE. For example, in one-year lagged

going from 0 to 1, OSE is predicted to increase by .189 percentage points [about

]; in one-year lagged going from 10 to 11,

OSE is predicted to decrease by .218 percentage points [about

112 102 ]. The effect of one-year lagged

becomes zero at 11.5; before this point, the variable has a positive effect on OSE; after

this point, its effect turns negative.

Moving on to Column 2 and 3 of Table 3.3, the regressions using subgroups,

upturn and downturn years, produce less statistically significant but still theoretically

consistent results. The results in Column 2 indicate that one- and three-year lagged

are statistically significant and positively associated with OSE. The coefficients

indicate that during upturn years, other things being equal, a one percentage point

increase in one- and three-year lagged increases OSE on average by .276

and .247 percentage points, respectively, with a lag of one and three years. Put together,

has the combined effect of increasing OSE by .523 percentage points with lags.

As in the regression using the entire sample, one-year lagged during booms has a

decreasing effect on OSE. Increases in revenue gap from 0 to 1 and from 10 to 11 are

141

predicted to result in a .254 percentage point increase and a .186 percentage point

decrease in OSE.

Meanwhile, the results in Column 3 of Table 3.3 report that during downturn

years, two- and three-year lagged are statistically significant and positively

linked to OSE. A one percentage point decrease in two- and three-year lagged

decreases OSE by .241, and .267 percentage points, respectively. The results also report

that the quadratic term in two-year lagged is statistically significant and

negatively related to OSE. This implies that the marginal effect of two-year lagged

on OSE decreases as the variable increases. All other factors being held fixed,

two-year lagged decreasing from 0 to -1 and from -10 to -11 during downturn

years leads to OSE going down by about .221 and up by .179 percentage points,

respectively.

In sum, all the revenue gap variables in linear form that are statistically significant

have positive effects on own-source expenditure, even though some of them have

diminishing effects. These results clearly show that revenue gap affects state spending

policy in a procyclical fashion over phases of the business cycle. Cyclical increases in tax

revenues induce increases in spending, while cyclical decreases in tax revenues lead to

decreases in spending. In other words, states with a more volatile revenue base tend to

increase spending during expansions and decrease it during contractions at faster rates.

Further, the results can be also interpreted as indicating that volatile states are likely to

adopt unsustainable spending increases during booms overheating the economy and

disruptive spending cuts during recessions deepening economic downturn.

142

The results also suggest that revenue gap affects OSE even contemporaneously,

suggesting that cyclical changes in tax revenues lead to mid-year budget adjustments.

Although this result is from the regression using the entire sample, not particularly

downturn years, the covariance between the two variables is thought to reflect the

covariance between cyclical decreases in revenue gap and mid-year budget cuts rather

than the one between cyclical increases in revenue gap and mid-year budget increases,

considering the urgency of balancing year-end budgets during downturn years and

business cycle asymmetry (Sichel 1993).

As mentioned in the Methods section, this study divides the sample into upturn

and downturn years based on the sign of revenue gap (i.e. years that revenue gap is

positive and years that revenue gap is negative). In the regressions using subgroups, this

method causes the loss of data on variations in the variables between years when the sign

of revenue gap is flipped. The effect of revenue gap on OSE should be much larger when

revenue gap changes from positive to negative rather than from negative to positive,

considering that balanced budget requirements are concerned with keeping their accounts

not in deficit rather than maintaining certain levels of balances.

Another explanation is related to asymmetry in business cycles. Empirical studies

on the cyclical behavior of macroeconomic variables suggest that contractions tend to be

more rapid and steeper than expansions (in other words, the economy tends to gradually

expand over a long period of time, but rapidly contract over a relatively short period).

This implies that changes in revenue gap and OSE between years when the economy

turns downward tend to be much larger than between years when the economy turns

upward. Therefore, the contemporaneous effect of revenue gap on OSE can be interpreted

143

as an indication that in response to negative revenue shocks, states tend to make mid-year

budget cuts in an attempt to balance their budgets.

As for the control variables, federal grants are found to be statistically significant

in the regressions using the entire sample and downturn years, as indicated in Column 1

and 3 of Table 3.3. Consistent with the theoretical prediction, the results show that

federal grants have effects in increasing own-source expenditure. The coefficients on

indicate that over the entire sample period and during contractions, other things

being equal, a one dollar increase in current per capita federal grant increases OSE

by .008 and .012 percentage points, respectively. The regressions also find that federal

grants affect OSE not only contemporaneously but with a lag. The coefficients on lagged

indicate that a one dollar increase in lagged per capita federal grant results in

a .002 and .005 percentage point increase in OSE, respectively. Together, these results

serve as evidence in support of the flypaper effect hypothesis. States tend to use federal

grants to increase expenditures rather than substitute the additional revenues from outside

for own-source revenues.

By contrast, federal grants during upturn years, both current and lagged, are not

statistically insignificant. That is, the positive relationship between federal grants and

OSE only holds during contractions. What is important here is that federal grants tend to

move in a countercyclical fashion. The federal government usually increases funding to

states and locals during recessions as it attempts to stimulate the economy, while

decreasing (or at best stagnating) during growth periods. The regression diagnostic plot in

Figure 3.3 from a simple regression of aggregate federal grants to states on year clearly

illustrates the countercyclical movement of federal grants. The implication is that the

144

effects of federal grants on OSE during expansions and contractions are not equal.

Although an increase in federal grants during downturns increases OSE, a decrease

during upturns do not significantly lower it. This means that federal grants only increase

OSE over the business cycle.

Figure 3.3 Regression of Aggregate Federal Grants on Year (1992–2007)

Meanwhile, the results display that borrowing is statistically significant and

positively associated with OSE, but only in the regression using the entire sample. As

with the results for current revenue gap in the regression using the entire sample, this

result appears to be attributable to the covariance between debt and OSE when the

economy shifts from above to below the trend line, given the necessity of debt financing

during downturn years. The coefficient on current indicates that a one percentage

point increase in debt as a share of GSP increases OSE by .585 percentage points. This

result is not surprising, because borrowing, though it should be paid off sometime in the

40

00

00

040

10

00

040

20

00

040

30

00

0

Co

mpo

ne

nt p

lus r

esid

ual

1990 1995 2000 2005 2010var2

145

future, provides financial relief at least in the short term to states required to balance their

budgets.

As for the fiscal institution controls, no deficit carry-over and revenue limitation

rules, among the fiscal control mechanisms, produce statistically significant but

counterintuitive results across the regressions. The coefficients on these legal provisions

indicate that on average, OSE is 3.43 and 3.58 percentage points higher, respectively, in

states with no deficit carry-over and revenue limitation rules in place than states without

them. While these results do not justify claims that these rules contribute to spending

growth and thus should be removed, they certainly suggest that the rules do not work as

intended.

On the other hand, as a fiscal institution, biennial budgeting is statistically

significant (at the 1% and 5% significance level) with the expected sign in the regressions

using the entire sample and upturn years. The coefficients indicate that a biennial budget

cycle exerts a negative influence on OSE. Holding other factors constant, OSE is

predicted to be, on average, 2.86 (in the entire-sample regression) and 3.03 (in the upturn-

year regression) percentage points lower in states budgeting on a biennial basis than

states budgeting on an annual basis. In stark contrast to the findings of Kearns (1994) that

biennial budgeting leads to spending growth, these results provide empirical evidence

supporting the proponents‘ claim that a biennial budget cycle contributes to restraining

state spending (particularly during expansions, as suggested by the result that the

negative relationship only holds for upturn years) by leading budget makers to be prudent

in conducting fiscal policy and prepare for hard times ahead with a long-term view of

their fiscal conditions.

146

Among the political controls, only divided government is found to be statistically

significant at the 5% level. Its coefficient indicates that OSE is 1.03 percentage point

lower on average in states where the executive or legislative branch is divided in terms of

partisan control than a state unified. This result suggests that political division and

deadlock within a government as a whole or a legislature lead to lower spending, which

can be interpreted as evidence supporting Niskanen‘s view (2003). On the other hand, the

statistically insignificant coefficients on the variables relating to partisan control suggest

that an important political factor in terms of spending restraint is whether there is political

check and balance in the budgetary process rather than which party, among Democratic

and Republican, controls the government. Meanwhile, election year and its lag and lead

are all shown to be statistically insignificant, and these results invalidating the political

business cycle hypothesis at least on the expenditure side.

For the demographic and socioeconomic controls, per capita personal income is

found to be statistically significant (at the 1% level) and negatively associated with OSE

across all the regressions. Holding other factors constant, a one hundred dollar increase in

per capita personal income is predicted to lead to a .002 percentage point decrease in

OSE (in all the regressions). These results confirm that overall, state spending is

countercyclical in absolute terms. More importantly, the results that even with such a

strong macroeconomic predictor controlled for, revenue gap exerts consistently positive

and statistically significant effects on OSE clearly show that revenue gap is a valid and

robust predictor of state spending policy.

Lastly, the proportions of the elderly and wealthy population are found to be

statistically significant in the regressions for upturn and downturn years, respectively.

147

The coefficient on the elderly population indicates that a one percentage point increase in

the elderly population causes OSE to increase by 1.09 percentage points during upturn

periods. This makes intuitive sense, because the population group has relatively high

demands for public services. Meanwhile, the coefficient on the wealthy population

indicates that a one percentage point decrease in the elderly population results in a large

(9.84% percentage point) decrease during downturn periods. This result is also intuitively

sensible, because even a little decrease in the wealthy population means large decreases

in tax revenues.

Turning to the results for overall tax rate in Table 3.4, the first result worth

pointing out is the less statistically significant coefficients on the revenue gap variables.

While the statistically significant ones have the expected (negative) signs suggesting that

states, on average, tend to conduct tax policy in a procyclical manner (i.e. tax cuts during

booms and tax increases during recessions), no current revenue gap is statistically

significant. This shows that the effects of revenue gap on tax rates are relatively small

and limited compared to those on spending.

Column 1 of Table 3.4 reports the results of the regression using the entire sample.

All the quadratic terms are statistically significant with the negative signs. This suggests

that the negative effects of revenue gap on tax rates increase as the variable escalates.

Specifically, in current, one-, two-, and three-year lagged going from 0 to 1,

overall tax rate is predicted to decrease by about .001, .001, .002, and .002 percentage

points, respectively, and in going from 10 to 11, overall tax rate is to be down

by .021, .021, .042, and .042 percentage, respectively. An interesting result worth

148

pointing out is that as in the spending policy model, revenue gap contemporaneously

affects tax rates. Following the same logic as discussed earlier for the contemporaneous

effect of revenue gap on OSE, this result is judged to be attributable in large part to the

covariance between revenue gap and overall tax rate when the economy falls into

recession. The result can therefore be interpreted as indicating that a rapid decrease in

revenue gap tends to lead to mid-year tax increases. Together with the results for OSE,

these results clearly show that revenue gap contemporaneously affects the level of both

spending and taxation in a procyclical fashion, when budget balance figures turn from

blue to red, that is, when countercyclical fiscal policy is more needed than ever.

The regressions using subgroups provide a more detailed picture of state fiscal

behavior on the taxation side. The results in Column 2 of Table 3.4 report that the

quadratic terms in two- and three-year lagged are statistically significant and

negatively related to overall tax rate. Their coefficients indicate that a first one percentage

point increase in revenue gap during upturn years leads to a .002 and .003 percentage

point decrease in overall tax rate, respectively, with a lag of two and three years.

Similarly, in the regression using downturn years, two quadratic terms in one- and two-

year lagged revenue gap are statistically significant. The non-zero coefficients mean that

a first one percentage point decrease in revenue gap during downturn years increases

overall tax rate by .002 and .001 percentage points, respectively, with a lag of one and

two years.

Taken together, the results for the relationship between revenue gap and overall

tax rate lead to the conclusion that the tax smoothing hypothesis does not hold at least at

the state level. That is, states do not hold their tax rates constant over the business cycle.

149

While revenue gap has effects on tax rates in a quadratic fashion, the results certainly do

not support the tax policy model that is widely considered optimal among economists.

Moving on to the institutional controls, no deficit carry-over rules are found to be

statistically significant consistently across the regressions. The slope coefficients indicate

that having this fiscal rule in place increases overall tax rate by .483, .338, and .603

percentage points, respectively, over the business cycle and during upturn and downturn

periods. While these results certainly lead to the conclusion that no deficit carry-over

provisions contribute to balancing budgets by leading states to raise tax rates, the fact that

they only have a positive effect on overall tax rate over phases of the business cycle

suggests that they are used as a tool for merely balancing budgets rather than fiscal

restraint or stabilization. This interpretation makes more sense, when considered together

with the earlier results that no deficit carry-over rules do not play any significant role in

curbing spending throughout the business cycle.

Another fiscal control mechanism that is statistically significant is line-item veto.

The non-zero coefficients on this variable (in all the regressions) indicate that overall tax

rate is .191, .195, and .494 percentage points lower, on average, in states granting the

gubernatorial authority to line-item veto appropriations bills. These results may not make

logical sense, because a line-item veto is, in essence, a fiscal control mechanism

concerned with appropriations, not taxes (Gouras 2011). One possible explanation is that

the adoption of a line-item veto law increases governors‘ influences across various

aspects of the fiscal process, thereby leading the fiscal policy into a direction that they

prefer, probably tax cuts. Governors‘ constituency is generally state residents as a whole,

and as a result, they are likely to attempt to accommodate the median voter‘s preference

150

through tax cuts, benefits from which are spread across the entire state population, rather

than spending increases, benefits from which are, in many cases, concentrated on

particular population groups. While legislators still have the last say in making fiscal

decisions, they would likely find it politically disadvantageous to object to governors‘

proposal for tax cuts, considering general public sentiment against government expansion.

Among the variables for partisan control, only Republican control of the Senate is

statistically significant (in the entire-sample regression). As expected, the variable has a

negative effect on overall tax rate. Overall tax rate is .084 percentage points lower in

states where the Republican Party controls the Senate than states where the Democratic

Party does so.

Another statistically significant political control is gubernatorial election year (in

the regressions using the entire and upturn-year sample). The results indicate that election

years exert negative effects on overall tax rate contemporaneously and with a lead. The

coefficient on current election year suggests that overall tax rate is .028 percentage points

lower, on average, in an election year. The results suggest that election years lead to

larger tax cuts when the economy is in an expansion phase. Overall tax rate is .061

percentage points lower, on average, with an election one year ahead. These results can

be interpreted as evidence showing that political business cycles exist, in other words,

with elections ahead, politicians have a tendency to offer generous tax policies in an

attempt to attract votes.

151

CHAPTER 4

POLICY IMPLICATIONS

Based on the results of the empirical analyses in Chapter 2 (on the determinants of

revenue volatility) and Chapter 3 (on its consequences), this chapter discusses policy

implications, which are organized into two sections. The first discusses what policy

choices are available for states to reduce sales and individual income tax volatility, given

other considerations such as equity and efficiency, and what implications current or

future changes in tax environments have. The second section discusses what approach

needs to be taken to maintain fiscal stability during expansions and thus avoid fiscal crisis

during recessions.

4.1 Implications for Revenue Stability

The results of the empirical analyses in Chapter 2 on the relationship between tax

base composition and revenue volatility reveal that sales and individual income tax

volatility are significantly affected by how their tax bases are composed. These results

suggest that this behavioral property should be incorporated as an important dimension,

along with other commonly cited principles such as equity, efficiency, and revenue

adequacy, in designing tax structure. With this general implication, several specific

policy implications are drawn from the analysis results of Chapter 2.

First, the analysis results suggest that while food and clothing fall into the same

category as household necessities, different tax treatments need to be applied. The

152

analysis finds that the consumption of food is stable over the business cycle, whereas that

of clothing is volatile. First, in the case of clothing, preferential tax treatment seems less

controversial at least in terms of equity and revenue volatility, given the current state

practices of limiting exemptions to purchases under certain prices (usually under $200).

The issue of food exemption, however, involves conflicting interests and policy goals.

Some may support this policy on the grounds that it makes sales tax less regressive and

more equitable, but some others may oppose it based on its negative effect on revenue

stability. Such a conflict could be resolved to some degree by selectively granting tax

exempt status to particular groups of purchases. Bahl and Hawkins (1997) provide useful

insights in this regard. In a study of Georgia‘s sales tax system, recognizing that

preferential tax treatment is applied not only to purchases of bread and milk but also

lobster and filet mignon, in which they calculate the specific dollar amounts of tax reliefs

that households by income group receive from food exemption. They find that the tax

benefits that average poor and wealthy households receive are respectively $68 and $166.

Their study suggests that states may be able to move closer to the efficiency frontier in

terms of tax equity and revenue volatility by focusing their food exemption programs on

purchases disproportionately made by lower-income people.

Second, the results suggest that expanding services taxation does not necessarily

reduce sales tax revenue volatility. There has been an assertion that broadening sales tax

bases with more services may make the sales tax revenues fluctuate less over the business

cycle. The reasoning is that, as noted earlier, purchases of big-ticket consumer goods

such as automobiles and household appliances, which account for a large portion of state

sales tax revenues, tend to be highly procyclical and volatile, whereas those of services

153

stable. The statistical insignificance of services exemption and its quadratic, however,

implies that there exists a considerable variation in cyclical volatility among services,

with states exhibiting no noticeable tendency in taxing services, thus suggesting that

relating more services to less sales tax volatility is not perfectly valid.

Third, the results confirm that exempting business purchases from sales taxation

is beneficial in terms of revenue volatility, reinforcing the case against levying a sales tax

on business-to-business transactions. Scholars and experts have argued for this tax policy

on the grounds that it eliminates ―tax pyramiding‖ and reduces allocative inefficiency

while boosting economic development. Tax pyramiding occurs as businesses pass the

additional costs caused by sales taxes on production inputs into the selling prices of their

goods. This business practice effectively leads to households, which are final consumers

of the products, paying additional taxes for the inputs already taxed (Chamberlain and

Fleenor, 2006). Taxing business purchases may also lead to inefficiency in resource

allocation by inducing ―vertical integration.‖ Vertically integrated companies in a supply

chain are not subject to a sales tax on production inputs. Consequently, taxing business

purchases artificially creates a market environment that induces businesses to opt for self-

supply or in-house production rather than outsourcing in an attempt to gain competitive

advantage in competition with single-process independents in the same industries (Perry,

1988). Another argument against sales tax on business purchases is that taxation on

production inputs has adverse effects on economic development. While some argue that

the negative effects of taxing production inputs have been somewhat exaggerated, broad

taxation certainly lowers the price competitiveness of businesses (Mazerov, 2009). Along

154

with these reasons, the result of this study that taxing producer goods increases sales tax

volatility provides additional support for producer goods exemption.

Fourth, this study finds that only particular types of retirement incomes contribute

to the stability of income tax bases. The results indicate that the exclusion of public

pensions, the majority of which are DB plans, detracts from revenue stability, whereas

the exclusion of private pensions, which are mixed in terms of the way contributions and

benefits are determined, does not make a significant difference in income tax volatility. A

further discussion of a changing trend in pension plans appears useful. As the term

suggests, a DB plan is a type of plan in which, upon retirement, a certain amount of

benefit is guaranteed through a lump sum or in monthly payment according to a set

formula usually based on the employee‘s salary and length of service. On the other hand,

in a DC pension plan, the most well-known example of which is 401(k), an employee

makes a certain amount of contributions—usually with employer matching

contributions—to his or her retirement account during employment. Directed by the

participant, the contributions are invested in financial securities such as stocks, bonds,

money market instruments, and mutual funds. In exchange for being given more

discretion over where their contributions are invested, the employee assumes more

responsibility for funding. That is, while the contribution is defined, the benefit is not

guaranteed but depends on the account balance which is the sum of the contributions and

returns on the investments.

A noticeable change in this respect is that unlike the public sector where a DB

pension plan has been the dominant form of pension plan, the private sector has

155

significantly shifted from DB to DC pension plan over the past decades.40

In the case of

the public sector, numbers have not changed much. According to data from the Bureau of

Labor Statistics‘ Employee Benefits Survey, 84% of state and local government workers

have access to defined benefit pension plans in 2010, and among those with access to DB

pension plans, nearly all workers (94%) chose to participate in the plans. Foster (1997)

reports that in 1993–1994, 91% of state and local government workers participated in the

DB pension plans. Given the historical comparison, there has been no significant change

in the public sector‘s orientation towards DB pension plans.

Figure 4.1 Private Sector Participants in an Employment-Based Retirement Plan by

Plan Type, 1979–2008 (Among those who have a retirement plan)

Source: Employee Benefit Research Institute estimates. Available at

http://www.ebri.org/publications/benfaq/index.cfm?fa=retfaq14

40

This study represents policy experiments for testing the consequences of complicated tax policies in a

given economic and demographic environment. As found, how tax bases are composed is an important

factor in determining the cyclical volatility of two major state revenue sources. However, constantly

changing tax environments may potentially outdate the current results. Thus, in order for the empirical

findings here to be of more relevance in the future, there needs to be careful consideration of changes in

state tax environments.

0%

10%

20%

30%

40%

50%

60%

70%

80%

19

79

19

81

19

83

19

85

19

87

19

89

19

91

19

93

19

95

19

97

19

99

20

01

20

03

20

05

20

07

Defined Benefit Only

Defined Contribution Only

Both DB & DC

156

Meanwhile, according to the BLS survey data, only 20% of private-sector

workers have access to the DB pension plans in 2010. The Employee Benefit Research

Institute (EBRI) provides historical data on the share of pension plans by type in the

private industry, which clearly show a trend in the private industry towards DC pension

plans. As shown in Figure 4, the proportion of private-sector workers with only a 401(k)-

type defined contribution plan among those who had a retirement plan rose from 38% in

1990 (17% in 1980) to 67%, whereas that of private-sector workers with only a DB plan

fell from 29% (60% in 1980) to 7%. In brief, public and private pensions are contrasted

in terms of retirement plan type: public pensions are dominated by stable DB plans,

whereas private pensions are characterized by volatility-prone DC plans.

Such a shift in the private sector has important implications for income tax

volatility. According to the general distribution rules set by the Internal Revenue Service

(IRS), minimum required distributions (MRD) from a qualified 401(k) plan are

determined by dividing the fair market value (FMV) of the retirement plan at year end by

the applicable distribution period, which normally is the life expectancy of the account

owner after retirement. An important point here is that, as suggested earlier by the results

on capital gains exemption, investment returns on which benefits from DC plans are

based are closely linked to economic fluctuations. The implication is that with the first

generation of workers who have widely adopted 401(k) plans since the 1980s beginning

to retire (Browning 2011), private pension income may likely become less stable in the

future, thereby making the effect of tax exemption for private pension income on income

tax volatility less positive. This is particularly likely, due to the growing volatility of the

157

U.S. and global financial market, which was witnessed by the 2008 financial meltdown

and the subsequent economic crisis.

The fifth policy discussion emerges from the findings on long-term capital gains

exemption. How capital gains income should be treated in individual income tax has been

one of the major policy debates at both the federal and state level. Supporters for

preferential tax treatment for capital gains argue that it encourages private investment and

potentially promotes business activities. On the other end, opponents emphasize potential

revenue losses that this policy will bring to already fiscally strained governments. With

increasingly volatile financial markets, what impacts capital gains exemption has on

income tax volatility certainly brings another important dimension to this policy

discussion. This policy is likely to raise concerns about tax equity, because its benefits,

without question, go disproportionately to high-income people with more savings and

financial resources. But the policy is expected to gain more political support if states

continue to maintain the current practices of limiting this special tax treatment only to

taxpayers in low- and middle-income brackets.

The last policy discussion relates to the structure of the U.S. manufacturing sector.

According to Census data, U.S. manufacturing industries have steadily declined over the

past decades. The GDP for the durable and nondurable manufacturing sector GDP has

respectively fallen about 6.2% (12.8 to 6.6%) and 3.3% (8.5 to 5.2%) from 1980 to 2010.

Although the trend has been so, it certainly will not go unchanged. While economists

generally suggest that with relatively labor-intensive industries moving their production

bases to low-wage countries such as China and India, the nondurable manufacturing

sector will not improve anytime soon (Ezell and Atkinson 2011), some economists are

158

carefully predicting a turnaround in the durable manufacturing sector such as the capital-

and technology-intensive industries such as the auto industry (Krugman 2011). A shift of

the economy from nondurable to durable manufacturing may potentially increase the

cyclical volatility of state tax bases in general, as suggested by the conceptual discussion

earlier. In particular, this has implications for sales tax. Producer goods will become more

durable and consequently their positive effect on sales tax volatility will then be greater

than now.

4.2 Implications for Fiscal Stability

The major finding of the empirical analysis in Chapter 3 is that cyclical changes

in tax revenues are positively related to expenditures and negatively related to tax rates.

This implies that fiscal policy is more likely to be unsustainable (during booms) and

disruptive (during recessions) in states with a more volatile revenue base. Based on these

findings, Figure 4.2 provides a simplified illustration of how structural budget deficits

and fiscal crises arise over phases of the business cycle. Dotted lines represent what

would have been original expenditure and revenue if there had not been spending and tax

adjustments, while solid lines actual expenditure and revenue. Lines that go through

expenditure and revenue lines represent long-run growth trend. According the results,

expenditure is adjusted upward, while revenue is adjusted downward during the

expansion phase. While structural imbalance occurs as much as the sum of the gap

between actual and original expenditure and the one between actual and original revenue,

it is obscured by cyclical budget surplus that is as much as the sum of the gap between

159

original and trend expenditure and the one between original and trend revenue. The

effects of the spending and tax adjustments, though, continue through the contraction

phase. Actual expenditure is higher than original, while actual revenue is lower than

original. In other words, budget deficit is amplified as structural deficit is added to

cyclical deficit. While cyclical deficit during the contraction is generally offset by

cyclical budget surplus during the preceding expansion, structural deficit remains, forcing

the state, which is required to balance its budget, into spending cuts and tax increases (or

borrowing).

Figure 4.2 The Dynamics of State Fiscal Behavior over the Business Cycle

160

These findings naturally lead to two strategies for fiscal stability: reducing the

cyclical volatility of tax revenue by adjusting the tax structure or restricting the ability to

spend surplus revenue by means of spending rules combined with budget stabilization

funds or tax rebates. The former was extensively discussed in the previous section;

therefore this section focuses on the latter solution. While both strategies are certainly

plausible, the former is relatively difficult to implement, because revenue stability is not

the only principle for state tax systems to pursue. There are other principles such as

equity (again, vertical and horizontal equity), economic neutrality/efficiency, revenue

adequacy, and administrative simplicity. With each of them being of equal importance,

one principle often conflicts with the others. The difficulty of designing optimal tax

structure brings the ex-post solution using spending rules to the fore.

The basic idea of this approach is to smooth spending over the course of the

business cycle by offsetting revenue shortfalls in times of scarcity with revenue windfalls

in times of plenty. In practice, this approach can be embodied by establishing rules which

prescribe desired rates of annual spending growth and saving. The spending-smoothing

approach is justified in part by the difficulty of accurately forecasting future economic

conditions and receipts and outlays. Dothan and Thompson (2006) express skepticism

about the formulation of fiscal policy based upon revenue forecasting. They note that ―the

standard approach to revenue forecasting is inherently flawed. States generally use

econometric models to forecast revenues. These models are often quite complex, with

scores of exogenous and endogenous variables and constants. … As forecasts, they are

not significantly better than naive extrapolations and are sometimes worse.‖

161

The idea of smoothing spending is certainly not new. Broadly, research on TELs

and budget stabilization funds can be grouped under this theme. Schunk and Woodward

(2005) represent one of the well developed articulations of the necessity of spending

stabilization rules as a fundamental solution to recurring state fiscal crises. Using

aggregate state spending and revenue data for the period of 1992–2002, they analyze

―what would have happened in the 1990s if states had a spending limit combined with a

rainy-day balance rule.‖ From this simulation, they find that states could have maintained

stable growth in spending throughout the recession of the early 2000s and avoided the

severe fiscal crises if they had only invested part of surplus revenues in a rainy-day fund.

Based on this finding, they conclude:

The key to any successful spending rule is an accompanying rule governing the

use of surplus revenue. Under the stability principle advanced here, surplus

revenue is one-time money that cannot be counted on in the future. Instead,

surplus revenues should be used for adding to rainy-day funds, for one-time

capital expenditures, or for temporary tax relief.

Once consensus is built on this approach, the next question naturally is how much

of surplus revenue should be saved for rainy days? This study finds that there is a wide

variation in revenue volatility across states, suggesting that a one-size-fits-all solution

cannot exist as in most other areas of public policy; that is, a certain level of rainy day

fund balance, say five percent of general fund expenditure, may be adequate for some

states, but not for others. Describing the widely accepted target of five percent as

―oversimplified,‖ Joyce (2001) compares the actual size of rainy day funds in each state

to the volatility of fiscal environments (share of revenue from corporate tax, economic

environment, reliance on federal aid and gambling revenues, and Medicaid expenditures)

162

as a proxy for the optimal size. In this analysis, he finds that there is little relationship

between actual and optimal balances, and based on this finding, concludes that states fail

to take into account the unique characteristics of fiscal environments in terms of cyclical

volatility in determining the adequacy of rainy days funds. Dothan and Thompson (2006)

expand on the literature by introducing the present-value balance approach. Correctly

pointing out that ―an arbitrary, one-size-fits-all solution (such as the five-percent rule)

ignores variations in revenue codes and consequently differences in growth trends and

revenue volatility,‖ they propose an optimal spending rule based upon a present-value

balance, which is identified by ―modeling revenue growth as a Wiener process (Brownian

motion) with drift, a continuous-time, continuous-state Markov process.‖

While ―a rather roundabout way,‖ as Dothan and Thompson call, assessing the

adequacy of rainy day funds using the standard deviation of actual revenues is certainly

worthwhile at least as a preliminary study to provide rough estimates prior to more

sophisticated analyses using mathematical modeling. Thus, this study replicates Joyce‘s

analysis using its own volatility measure and data. Following Hou (2006), the level of

fiscal reserves is measured by the percentage share of the sum of general fund balance

(GFB) and budget stabilization funds (BSF) of OSE. Revenue volatility is measured by

the mean of the absolute values of revenue gaps over the sample period.

Table 4.1 presents the result of a correlation analysis between fiscal reserves and

revenue volatility. The correlation coefficient is nearly zero (-.0843) and not even

positive. This implies that there is virtually no correlation between the variables,

confirming Joyce‘s finding that states fail to reflect the volatility of fiscal environments

in determining the size of fiscal reserves.

163

Table 4.1 Correlation between % Share of Fiscal Reserves and Revenue Volatility

State % of Fiscal Reserves Revenue Volatility

Alabama 2.409073 2.852564

Arizona 6.033182 3.159395

Arkansas 0.217075 2.424032

California 6.167943 2.982762

Colorado 7.068583 4.199058

Connecticut 4.574547 3.8783

Delaware 16.44397 3.854388

Florida 5.875159 2.660813

Georgia 9.049477 2.996147

Hawaii 4.693555 1.473511

Idaho 4.132171 3.624568

Illinois 2.778668 2.920007

Indiana 8.02592 3.943351

Iowa 6.053224 4.402094

Kansas 8.319192 4.49367

Kentucky 3.327303 126.3827

Louisiana 2.554394 3.827134

Maine 2.839488 3.276741

Maryland 6.512641 2.124943

Massachusetts 9.014068 2.851664

Michigan 3.282884 4.678667

Minnesota 17.53485 2.64163

Mississippi 4.483664 2.482862

Missouri 4.099988 4.037358

Montana 4.802539 4.563871

Nebraska 9.677273 1.693929

Nevada 6.821588 3.702078

New Hampshire 1.520214 3.887869

New Jersey 6.378742 2.083997

New Mexico 6.923473 5.544979

New York 2.488961 3.159573

North Carolina 4.204438 2.781249

North Dakota 5.002338 4.851521

Ohio 3.425808 1.44793

Oklahoma 5.383904 4.644167

Oregon 4.74402 3.285877

Pennsylvania 2.34455 1.829702

Rhode Island 3.00542 1.648559

South Carolina 4.780872 2.905602

South Dakota 3.401749 1.863492

164

Tennessee 3.154332 2.517853

Texas 7.722879 1.879281

Utah 2.843235 4.359538

Vermont 0.598717 3.408754

Virginia 4.473652 3.635443

Washington 4.388638 1.710954

West Virginia 5.146307 3.817349

Wisconsin 2.582509 2.952727

Wyoming 9.629311 3.345789

Corr. Coef. -.0843

This cursory analysis partly explains the insignificant effects of most of fiscal

control mechanisms, particularly tax and expenditure limitation mechanisms and budget

stabilization funds which are considered of most relevance in terms of achieving fiscal

sustainability during booms, on spending and tax policy. Taken together, these results

suggest that while having such mechanisms in place will certainly provide an institutional

basis for fiscal control and bring renewed interest in fiscal sustainability to policymakers

and the public, they would likely be ineffective unless they specifically lead to the states

building adequate amounts of budget reserves according to the cyclical volatility of fiscal

environments in general and tax revenues in particular. In this regard, Schunk and

Woodward (2005) emphasize that:

Notably, TELs have not been designed to fund rainy-day accounts.

Overwhelmingly, TELs are oriented toward fiscal restraint, not stability. …

Indeed, of varying TELs in place, the chief failure is that they do not build rainy-

day and other stabilization funds.

From this point of view, the spending-smoothing approach needs to be

distinguished from what is so-called ―starve-the-beast‖ approach. The basic idea of this

approach is that a government has a tendency to attempt to eat away whatever is available.

165

Therefore, the most effective way to restrain government spending and reduce a deficit is

to cut resources (i.e. taxes) available to the government (Friedman 2003). In response to

the criticism that tax cuts will add to budget deficits hindering economic growth, starve-

the-beast proponents argue that although increased deficits may adversely affect the

economy in the short term, they will ultimately have positive effects in the long term as

they lead to increased public concerns about fiscal health and eventually elected officials

acting on the deficits by cutting unnecessary spending (Templeman 2006).

These two approaches, though they may look similar, in essence differ in that the

―starve-the-beast‖ approach is concerned with fiscal restraint and discipline, whereas the

spending-smoothing approach with fiscal stability and predictability over the business

cycle; the former focuses on reducing deficits and ultimately making governments small,

whereas the latter on preventing unsustainable spending increases and tax cuts during

upturn years and disruptive spending cuts and tax increases during downturn years; the

former is aimed at bringing down the trend line itself, whereas the latter at smoothing out

the actual line—in this view, the trend line is adjusted only when structural imbalances

occur; and the former, as suggested from the term ―starve,‖ is reactive and destructive in

nature, whereas the latter proactive and constructive. From this point of view, the

spending-smoothing approach should not be confused with either conservative fiscal

policies based on neoclassical economics or liberal ones based on Keynesian economics.

That one must live within his or her means is an undisputable principle beyond the left-

right paradigm, which applies to any economic entity, whether individual or corporate,

living in an environment of limited resources. And the most rational way for governments

166

to achieve this canon in an uncertain and unpredictable fiscal environment would be to

smooth spending over the business cycle by maintaining sustainable fiscal policy.

At this juncture, it is important to note that many states are currently facing

structural budget deficits and chronic fiscal stress (Behn and Keating 2005) and must

deal with the existing fiscal problems anyhow through either spending cuts or tax

increases (or both) apart from enforcing spending stabilization rules. While either way

certainly will contribute to reducing structural imbalances, the results of this study

suggest that tackling the spending side would be desirable over tapping into the revenue

side if budget deficits are moderate. The first reason is that structural deficits are likely to

have stemmed from the spending side rather than the revenue side. As discussed earlier,

the American political system based on electoral districts creates incentives for

politicians, whether an incumbent or a challenger, to attempt to maximize votes through

specific spending programs whose benefits are concentrated in particular constituencies

rather than tax cuts, if not targeted at particular taxpayers or income brackets, whose

benefits are spread out across all members. A more important reason is that tax increases,

whether through increasing tax rates or broadening tax bases, will amplify cyclical

increases in tax revenues, particularly in states with progressive income tax, during the

following expansion period, inducing expenditure demands again in the absence of strict

spending rules.

167

CHAPTER 5

CONCLUSION

5.1 Summary of Findings and Contributions

Motivated by recurring state fiscal crises, this dissertation examined how the

composition of general sales and individual income tax bases varies across states, how

widely each tax revenue fluctuates over the business cycle, how tax base composition

affects the cyclical volatility of the tax revenues, and what effects revenue volatility has

on state fiscal policy, using pooled OLS and fixed effects regressions on panel data over

the period from 1992 to 2007. The empirical findings of this study are summarized as

follows:

There is a substantial variation in the cyclical volatility of sales tax and

income tax across the states, much of which is explained by a variation in the

composition of the tax bases.

Tax exemptions for household necessities (food and clothing) and producer

goods exert statistically significant effects on sales tax volatility, while

exemption for services has no significant impacts.

The prime working-age population has a strong effect on cyclical fluctuations

in sales tax revenue, providing evidence in support of the life-cycle hypothesis

of consumption and the permanent consumption hypothesis.

168

The preferential tax treatment of Social Security benefits and pubic pensions

increases income tax volatility, but private pensions have no effect.

The exclusion of long-term capital gains from income taxation reduces

income tax volatility, and the negative effect of this exclusion is weaker in

states with a larger wealthy population as the special tax treatment is generally

offered only to low- and middle-income taxpayers.

The effect of economic structure, measured by sector GSP composition, is not

as strong especially in the sales tax volatility model, highlighting the relative

importance of tax structure in terms of revenue volatility.

Revenue gap has a positive effect on OSE and a negative effect on overall tax

rate. In other words, cyclical upturns in tax revenues during expansion periods

induce spending increases and tax cuts, while cyclical downturns in tax

revenues during contraction periods cause spending cuts and tax increases.

Taken together, these results suggest that revenue volatility is significantly

related to fiscal instability over the business cycle.

With revenue gap having strong effects on the levels of spending and tax rates,

biennial budgeting and divided government tend to play a significant role in

restraining spending growth during booms, whereas most of the fiscal control

mechanisms are mostly ineffective.

Tax cuts (but not spending increases) tend to be enacted ahead of

gubernatorial elections, suggesting that political business cycles do exist.

169

Overall, this study contributes to the literature on state fiscal policy and behavior

in four important ways.

First, this study develops a measure of revenue volatility and gap using

orthogonal deviation as opposed to the standard technique using vertical

deviations, based on the concern that there exists a wide cross-state variation

that exists in long-run revenue growth and this could potentially cause using

vertical deviation to produce biased and invalid estimates.

Second, this study is among the few to estimate revenue volatility using actual

tax bases by state as opposed to national aggregates. The use of actual tax

bases is meaningful in that it makes possible a cross-sectional analysis of what

factors determine revenue volatility by tax type and what consequences

overall revenue volatility has on fiscal and policy stability.

Third, this study introduces a new variable, revenue gap which represents the

cyclical position of state finance, in explaining state fiscal behavior. With

other relevant factors controlled for, this variable is found to have strong

effects on own-sources expenditures and tax rates.

Lastly, to take full advantage of the information that revenue gap contains,

this study divides the sample into two subgroups, upturn and downturn years,

and estimates the models separately for each sample in addition to the entire

sample. These additional regressions provide a more detailed picture of state

fiscal behavior by revealing how it changes over phases of the business cycle.

170

5.2 Limitations

This study has several limitations. First, some of the measures used in the

regressions in Chapter 2 potentially limit the validity of the results. In the regression for

sales tax volatility, the level of tax exemption for services was measured by the number

of services exempt as a percentage of feasibly taxable services. While this measure is

certainly useful and relevant, its failure to take into account the difference in income

elasticity of demand that exists across services undermines the validity of the measure.

To resolve this potential problem, this study included the quadratic in the original model

and additionally employed an auxiliary model in which the variable was measured by

category. But it should be acknowledged that these methods are still the second best. The

same concern applies to the measurement of economic structure. Although the GDP

shares of economic sectors are widely used in the public finance literature as measures to

capture the effects of economic structure, more sophisticated measures are warranted to

better control for the structural characteristics of state economies, considering that there

may be differences in sensitivity to the business cycle even among industries in the same

category.

Second, fiscal control mechanisms, particularly tax and expenditure limitations, in

the regressions for state fiscal policy need to be better measured. This study included the

presence of a tax and spending limitation provision to capture the effects of TEL

mechanisms. Whether or not tax and spending limits are in place, though commonly used

in the fiscal institution literature, are too rough to capture the differing impacts of TEL

mechanisms, considering that they vary in stringency across states. Limiting spending

171

and revenue growth to population growth plus inflation is considered the most stringent,

while restricting to certain percents of personal income more flexible. Besides limit

formulas, there are other important dimensions that characterize the stringency of tax and

spending limits such as how it is codified (i.e. is it statutory or constitutional?); what type

of approval is required to override limits (i.e. is it voter or legislative approval?); and how

surplus revenue is treated (i.e. should it be it returned to taxpayers or reserved in rainy

day funds?).

Third, the panel data set used in this study covers a relatively short time period

(16 years and roughly two business cycles), which limits the validity of the results on the

effects of fiscal institutions on state fiscal policy. As part of budgetary reform, fiscal

institutions and rules have been adopted and amended over decades, and this implies that

only a small number of groups in the panel have a variation in the related variables and,

further, that the effects of fiscal institutions may not have been accurately estimated.

5.3 Directions for Future Research

This study suggests several directions for future research. First, future research

needs to look into the determinants of corporate income tax volatility. While this study

focused only on sales tax and individual income tax, as one of the major state revenue

sources, corporate income tax certainly is worth a separate examination, considering

increasingly complex and controversial issues surrounding its structure [e.g. an

apportionment formula (sales-only or three-factor, property, payroll, and sales, formula),

172

deductions for dividend received and foreign royalty, and the reporting method of income

tax returns (combined or separate)].

Second, it would be interesting to look into what categories of state spending and

what taxes are particularly affected by cyclical changes in tax revenues. Analyses using

total spending and overall tax rate obscure variations that may exist across spending

categories and taxes. Further analyses by spending category and tax will possibly reveal

what categories of spending are the main driver of spending growth during expansionary

periods and subject to larger cuts during recessions; and what taxes are preferred for rate

cuts during upturn years and are more frequently used to reduce budget gaps during

downturn years.

Another intriguing question to ponder would be what factors lead some states to

choose spending increases over tax cuts during booms (tax increases over spending cuts

in the face of a deficit during recessions) and some others to do the opposite. Immediate

intuition suggests that with surplus revenue in hand, states with more conservative voters

would likely prefer tax cuts to spending increases, and in the face of a budget deficit,

spending cuts to tax increases. Empirically, such a political factor could be accounted for

by including a broad range of voter preferences.

Lastly, future research needs to be done on factors that influence policy choices

regarding tax base composition. In Chapter 2, the study develops the causal system that

links tax base composition—which is considered exogenous—to revenue volatility. A

natural question that follows is, ―What factors determine state policy decisions in

designing their tax bases?‖ More specific questions are, ―What factors make some states

to tax a wide range of services and some others to exempt capital gains to a great extent?‖

173

While one may be intuitive to presume that political factors such as voter preferences will

have influences on policy choices concerning tax structure, these questions certainly

require separate empirical investigations.

174

REFERENCES

Aisen, Ari, and Francisco José Veiga. 2008. Political Instability and Inflation Volatility.

Public Choice 135(3-4): 207–223.

Alesina, Alberto, Filipe R Campante, and Guido Tabellini. 2008. Why Is Fiscal Policy

Often Procyclical? Journal of the European Economic Association 6(5): 1006–

1036.

Alt, James E, and Robert C Lowry. 1994. Divided Government, Fiscal Institutions, and

Budget Deficits: Evidence from the States. The American Political Science

Review 88(4): 811–828.

Alm, James, and Janet Rogers. 2011. Do State Fiscal Policies Affect State Economic

Growth? Public Finance Review 39(4): 483–526.

Anderson,William, Myles S Wallace, and John T Warner. 1986. Government Spending

and Taxation: What Causes What? Southern Economic Journal 52(3): 24–31.

Aragon, Fernando M. 2009. The Flypaper Effect Revisited. LSE STICERD Research

Paper No. EOPP004. Available at SSRN: http://ssrn.com/abstract=1546898.

Baghestani, Hamid, and Robert McNown. 1994. Do Revenues or Expenditures Respond

to Budgetary Disequilibria? Southern Economic Journal 61(2): 311–322.

Bahl, Roy W, and Richard Hawkins. 1997. The Sales Tax in Georgia: Issues and Options.

Fiscal Research Program, School of Policy Studies, Georgia State University

Bails, Dale, and Margie A Tieslau. 2000. The Impact of Fiscal Constitutions on State and

Local Expenditures. Cato Journal 20(2): 255–277.

Barro, Robert J. 1979. On the Determination of the Public Debt. Journal of Political

Economy 87(5): 940–971.

Barro, Robert J. 1990. On the Predictability of Tax-Rate Changes. In Macroeconomic

Policy, ed. Robert J. Barro, 268–297. Cambridge, MA: Harvard University Press.

Baxter, Marianne, and Robert G King. 1999. Measuring Business Cycles: Approximate

Band-Pass Filters for Economic Time Series. Review of Economics and Statistics

81(4): 575–593.

Behn, Robert D, and Elizabeth K Keating. 2004. Facing the Fiscal Crises in State

Governments: National problem; National responsibilities. Faculty Research

Working Papers Series. John F. Kennedy School of Government.

Behn, Robert D, and Elizabeth K Keating. 2005. The Fiscal Crisis of the States:

Recession, Structural Spending Gap, or Political Disconnect? Taubman Center,

Kennedy School of Government, Harvard University.

175

Blackley, Paul R. 1986. Causality between Revenues and Expenditures and the Size of

the Federal Budget. Public Finance Quarterly 14(2): 139–156.

Boggs, Paul T, Richard H Byrd, and Robert B Schnabel. 1987. A Stable and Efficient

Algorithm for Nonlinear Orthogonal Distance Regression. Society for Industrial

and Applied Mathematics (SIAM) Journal of Scientific and Statistical Computing

8(6): 1052–1078.

Boggs, Paul T, and Janet E Rogers. 1990. Orthogonal Distance Regression. In Statistical

Analysis of Measurement Error Models and Applications: Proceedings of the

AMS-IMS-SIAM Joint Summer Research Conference (held June 10-16, 1989), eds.

Philip J Brown and Wayne A Fuller, 183–194. Providence, RI: American

Mathematical Society.

Bohn, Henning. 1991. Budget Balance through Revenue or Spending Adjustments?

Journal of Monetary Economics 27(3): 333–359.

Bohn, Henning, and Robert P Inman. 1996. Balanced-Budget Rules and Public Deficits:

Evidence from the U.S. States. Carnegie-Rochester Conference Series on Public

Policy 45: 13–76.

Bradford, David F, and Wallace E Oates. 1971a. The Analysis of Revenue Sharing in a

New Approach to Collective Fiscal Decisions. Quarterly Journal of Economics

85(3): 416–439.

Bradford, David F, and Wallace E Oates. 1971b. Towards a Predictive Theory of

Intergovernmental Grants. American Economic Review 61(2): 440–448.

Braun, Bradley M, and Yasuji Otsuka. 1998. The Effects of Economic Conditions and

Tax Structures on State Revenue Flows. International Advances in Economic

Research 4(3): 259–269.

Brewer, H L, and Ronald L Moomaw. 1984. A Note on Population Size, Industrial

Diversification and Regional Economic Instability. Urban Studies 22(4): 349–354.

Browning, E S. 2011. Retiring Boomers Find 401(k) Plans Fall Short. Wall Street

Journal. Available at

http://online.wsj.com/article/SB1000142405274870395960457615279274870735

6.html?mod=WSJ_hp_MIDDLENexttoWhatsNewsTop.

Brubaker, Earl R. 1997. The Tragedy of the Public Budgetary Commons. The

Independent Review 1(3): 353–370.

Bruce, Donald, William F Fox, and Markland Tuttle. 2006. Tax Base Elasticities: A

Multistate Analysis of Long Run and Short Run Dynamics. Southern Economic

Journal 73(2): 315–341.

Buchanan, James M, and Gordon Tullock. 1962. The Calculus of Consent. Ann Arbor,

MI: University of Michigan Press.

176

Buchanan, James M, and Richard E Wagner. 1977. Democracy in deficit: The Political

Legacy of Lord Keynes. New York, NY: Academic Press.

Buchanan, James M, and Richard E Wagner. 1978. Dialogues Concerning Fiscal

Religion. Journal of Monetary Economics 4(3): 627–636.

Caiden, Naomi. 1981. Public Budgeting amidst Uncertainty and Instability. Public

Budgeting and Finance 1(1): 6–19.

Campbell, Colin Dearborn; Campbell, Rosemary G. 1976. A Comparative Study of the

Fiscal Systems of New Hampshire and Vermont, 1940–1974: A Report to the

Wheelabrator Foundation, Inc. Hampton, NH: The Foundation.

Catao, Luis A, and Bennett W Sutton. 2002. Sovereign Defaults: The Role of Volatility.

IMF Working Papers 02/149. International Monetary Fund.

Center for Budget and Policy Priorities. 2005. A Brief Overview of State Fiscal

Conditions and the Effects of Federal Policies on State Budgets. Available at

http://www.cbpp.org/cms/index.cfm?fa=view&id=1329.

Center for Budget and Policy Priorities. 2010. An Update on State Budget Cuts.

Available at http://www.cbpp.org/cms/index.cfm?fa=view&id=1214 &

http://www.cbpp.org/cms/?fa=view&id=711.

Chamberlain, Andrew, and Patrick Fleenor. 2007. Tax Pyramiding: The Economic

Consequences of Gross Receipts Taxes. Washington, DC: Tax Foundation.

Chang, Tsangyao, Wen Rong Liu, and Steven B Caudill. 2002. Tax-and-Spend, Spend-

and-Tax, or Fiscal Synchronization: New Evidence for Ten Countries. Applied

Economics 34(12): 1553–1561.

Chowdhury, Abdur R. 1988. Expenditures and Receipts in State and Local Government

Finances: Comment. Public Choice, 59(3): 277–285.

Cornia, Gary C, and Ray D Nelson. 2010. State Tax Revenue Growth and Volatility.

Regional Economic Development 6(1): 23–58.

Crain, W Mark. 2003. Volatile States: Institutions, Policy, and the Performance of

American State Economies. Ann Arbor, MI: University of Michigan Press.

Dothan, Michael U, and Fred Thompson. 2006. Optimal Budget Rules: Making

Government Spending Sustainable Through Present-Value Balance. Available at

http://ssrn.com/abstract=939815.

Dunleavy, Patrick, and Brendan O'Leary. 1987. Theories of the State. Basingstoke, UK:

Macmillan Education.

Dye, Richard F, and Therese J McGuire. 1991. Growth and Variability of State

Individual Income and General Sales Taxes. National Tax Journal 44(1): 55–66.

177

Edwards, Chris, Stephen Moore, and Phil Kerpen. 2003. States Face Fiscal Crunch after

1990s Spending Surge. Cato Briefing Paper No. 80. Washington, DC: Cato

Institute.

Eichengreena, Barry, and Tamim Bayoumib. 1994. The Political Economy of Fiscal

Restrictions: Implications for Europe from the United States. European Economic

Review 38(3-4): 783–791.

Endersby, James W, and Michael J Towle. 1997. Effects of Constitutional and Political

Controls on State Expenditures. Publius 27(1): 83–98.

Engle, Robert F, and Clive W J Granger. 1987. Co-integration and Error Correction:

Representation, Estimation, and Testing. Econometrica 55(2): 251–276.

Ezell, Stephen J, and Robert D Atkinson. 2011. The Case for a National Manufacturing

Strategy. The Information Technology and Innovation Foundation.

Felix, R Alison. 2008. The Growth and Volatility of State Tax Revenue Sources in the

Tenth District. Economic Review 93(3): 63–88.

Fiorito, Riccardo. 1997. Stylized Facts of Government Finance in the G7. IMF Working

Paper 97/42.

Fiorito, Riccardo, and Tryphon Kollintzas. 1994. Stylized Facts of Business Cycles in the

G7 from a Real Business Cycles Perspective. European Economic Review 38(2):

235–269.

Fisher, Louis. 1997. Biennial Budgeting in the Federal Government. Public Budgeting

and Finance 17(3): 87–97.

Foster, Ann C. 1997. Public and Private Sector Defined Benefit Pensions: A Comparison.

Compensation and Working Conditions.

Fox, William E, and Charles Campbell. 1984. Stability of the State Sales Tax Income

Elasticity. National Tax Journal 37(2): 201–212.

Friedman, Milton. 1975. There's No Such Thing as a Free Lunch. La Salle, IL: Open

Court Publishing Co.

Friedman, Milton. 1978. The Limitations of Tax Limitation. Policy Review 5(Summer):

7–14.

Friedman, Milton. 1983. Bright Promises Dismal Performance: An Economist's Protest.

San Diego, CA: Harcourt Brace Jovanovich.

Friedman, Milton. 2003. What Every American Wants. Wall Street Journal (January 15):

A10.

Gamage, David S. 2010. Preventing State Budget Crises: Managing the Fiscal Volatility

Problem. California Law Review 98(3): 101–163.

178

Garcia, Sophie, and Pierre-Yves Henin. 1999. Balancing Budget through Tax Increases

or Expenditure Cuts: Is It Neutral? Economic Modeling 16(4): 591–612.

Garrett, Thomas A. 2006. Evaluating State Tax Revenue Variability: A Portfolio

Approach. FRB of St. Louis Working Paper No. 2006-008A. Available at SSRN:

http://ssrn.com/abstract=881778.

Gavin, Michael, and Roberto Perotti, 1997a. Fiscal Policy in Latin America. NBER

Macroeconomics Annual 12: 11–61.

Gavin, Michael, and Ricardo Perotti. 1997b. Fiscal Policy and Saving in Good Times and

Bad Times. In Promoting Savings in Latin America, eds. Ricardo Hausmann and

Helmut Reisen. Inter-American Development Bank/OECD.

Gavin, Michael, Ricardo Hausmann, Roberto Perotti, and Ernesto Talvi. 1996. Managing

Fiscal Policy in Latin America. Working Paper. Inter-American Development

Bank.

Gentry, William M, and Helen F Ladd. 1994. State Tax Structure and Multiple Policy

Objectives. National Tax Journal 47(4): 747–772.

Gildenhuys, J.S.H. 1997. Public Financial Management. Pretoria, South Africa: J.L. van

Schaik Publishers.

Gould, Brandon. 2009. Divided Governments and Tough Choices: An Empirical Study of

the Effect of Divided Government on State Expenditures. Presidential Fellows

Program Draft Paper. Center for the Study of the Presidency. Available at

http://64.177.72.250/pubs/Fellows_2009/drafts_Domestic_Challenges.pdf.

Gouras, Matt. 2011. GOP Questioning Mont. Gov's Line-Item Veto on Tax.

Businessweek. Available at

http://www.businessweek.com/ap/financialnews/D9N2019O0.htm.

Gramlich, Edward M. 1977. Intergovernmental Grants: A Review of the Empirical

Literature. In The Political Economy of Fiscal Federalism, ed. Wallace E Oates,

219–240. Lexington, MA: Lexington Books.

Gramlich, Edward M, and Harvey Galper. 1973. State and Local Fiscal Behavior and

Federal Grant Policy. Brookings Papers on Economic Activity 1: 15–65.

Granger, Clive W J. 1969. Investigating Causal Relationships by Econometric Models

and Cross Spectral Methods. Econometrica 37(3): 424–438.

Greenstein, Robert, and James Horney. 2000. Biennial Budgeting: Do the Drawbacks

Outweigh the Advantages? Center on Budget and Policy Priorities. Available at

http://www.cbpp.org/cms/?fa=view&id=1243.

Groves, Harold M, and C Harry Kahn. 1952. The Stability of State and Local Tax Yields.

American Economic Review 42(1): 87–102.

179

Guilkey, David K, and Michael K Salemi. 1982. Small Sample Properties of Three Tests

of Granger-Causal Ordering in a Bivariate Stochastic System. Review of

Economics and Statistics 64(4): 668–680.

Hardin, Garrett. 1968. The Tragedy of the Commons. Science 162: 1243–1247.

Hasan, Seid Y, and Abdul Hamid Sukar. 1995. Short and Long-run Dynamics of

Expenditures and Revenues and the Government Budget Constraint: Further

Evidence. Journal of Economics 22(2): 35–42.

Holcombe, Randall G. 1993. Are There Ratchets in the Growth of Federal Government

Spending? Public Finance Review. 21(1) 33–47.

Holcombe, Randall G, and Russell S Sobel. 1997. Growth and Variability in State Tax

Revenue: An Anatomy of State Fiscal Crises. Greenwood Press: Westport, CT.

Holcombe, Randall G, and Jeffrey A Mills. 1994. Is Revenue-Neutral Tax Reform

Revenue Neutral? Public Finance Quarterly 22(1): 65–85.

Hoover, Kevin D, and Steven M Sheffrin. 1992. Causation, Spending, and Taxes: Sand in

the Sandbox or Tax Collector for the Welfare State? American Economic Review

82(1): 225–48.

Hou, Yilin. 2005. Fiscal Reserves and State Own-Source Expenditure in Downturn Years.

Public Finance Review 33(1): 117–144.

Hou, Yilin, 2006. Budgeting for Fiscal Stability over the Business Cycle. Public

Administration Review 66(5): 730–741.

Hou, Yilin, and Jason S Seligman. 2007. Lost Stability? Consumption Taxes and the

Cyclical Volatility of State and Local Revenues. Available at SSRN:

http://ssrn.com/abstract=1029697.

Hou, Yilin, and Daniel L Smith, 2006. Informal Norms as a Bridge between Formal

Rules and Outcomes of Government Financial Operations: Evidence from State

Balanced Budget Requirements. Journal of Public Administration Research and

Theory 20(3): 655–678.

Hou, Yilin, and William D Duncombe. 2008. State Saving Behavior: Effects of Two

Fiscal and Budgetary Institutions. Public Budgeting and Finance 28(3): 48–67.

Hou, Yilin, and Donald P Moynihan. 2008. The Case for Countercyclical Fiscal Capacity.

Journal of Public Administration Research and Theory 18(1): 139–159.

Hovey, Hal. 1998. The Outlook for State and Local Finances: The Dangers of Structural

Deficits to the Future of American Education. Washington, DC: National

Education Association.

180

Huang, Chao-His, and Kenneth S Lin. 1993. Deficits, Government Expenditures, and Tax

Smoothing in the United States: 1929–1988. Journal of Monetary Economics

31(3): 317–339.

Huffman, Gregory W. 1994. A Primer on the Nature of Business Cycles. FRB of Dallas

Economic Review (First Quarter): 27–41.

Iacoviello, Matteo M, Fabio Schiantarelli, and Scott D Schuh. 2010. Input and Output

Inventories in General Equilibrium. FRB International Finance Discussion Paper

No. 1004. Available at SSRN: http://ssrn.com/abstract=1708202.

Ilzetzki, Ethan. 2010. Rent-Seeking Distortions and Fiscal Procyclicality. Journal of

Development Economics 96(1): 30-46.

Ilzetzki, Ethan, and Carlos A Vegh. 2008. Procyclical Fiscal Policy in Developing

Countries: Truth or Fiction? NBER Working Paper No. 14191.

Islam, Muhammad Q. 2001. Structural Break, Unit Root, and the Causality between

Government Expenditures and Revenues. Applied Economics Letters 8(8): 565–

567.

Jacobsen, Dag Ingvar. 2006. Public Sector Growth: Comparing Politicians‘ and

Administrators‘ Spending Preferences. Public Administration 84(1): 185–204.

Johansen, Soren. 1988. Statistical Analysis of Cointegration Vectors. Journal of

Economic Dynamics and Control 12(2-3): 231–254.

Jones, Jonathan D, and David Joulfaian. 1991. Federal Government Expenditures and

Revenues in the Early Years of the American Republic: Evidence from 1792–

1860. Journal of Macroeconomics 13(1): 133–155.

Johnson, Nicholas. 2002. The State Tax Cuts of the 1990s, the Current Revenue Crisis,

and Implications for Services. Center on Budget and Policy Priorities. Available

at http://www.cbpp.org/cms/index.cfm?fa=view&id=1379.

Joulfaian, David, and Rajen Mookerjee. 1990a. The Government Revenue-Expenditure

Nexus: Evidence from a State. Public Finance Quarterly 18(1): 92–103.

Joulfaian, David, and Rajen Mookerjee. 1990b. The Intertemporal Relationship between

State and Local Government Revenues and Expenditures: Evidence from OECD

Countries. Public Finance 45(1): 109–117.

Joulfaian, David, and Rajen Mookerjee. 1991. Dynamics of Government Revenues and

Expenditures in Industrial Economies. Applied Economics 23(12): 1839–1844.

Joyce, Philip G. 2001. What's So Magical about Five Percent? A Nationwide Look at

Factors That Influence the Optimal Size of State Rainy Day Funds. Public

Budgeting and Finance 21(2): 62–87

181

Kaminski, Graciela, Carmen Reinhart, and Carlos Vegh. 2004. When It Rains It Pours:

Procyclical Capital Flows and Macroeconomic Policies. In NBER

Macroeconomic Annual 2004, eds. Mark Gertler and Kenneth Rogoff. Cambridge,

MA: MIT Press.

Kearns, Paula S. 1994. State Budget Periodicity: An Analysis of the Determinants and the

Effect on State Spending. Journal of Policy Analysis and Management 13(2):

331–362.

Keech, William R, and Kyoungsan Pak. 1989. Electoral Cycles and Budgetary Growth in

Veterans' Benefit Programs. American Journal of Political Science 33(4): 901–

911.

Knight, Brian G, Andrea L Kusko , and Laura Rubin. 2003. Problems and Prospects for

State and Local Governments. State Tax Notes 29(6): 427–439.

Knight, Brian, and Arik Levinson. 1999. Rainy Day Funds and State Government

Savings. National Tax Journal 52(3): 459–472.

Koren, Stefan, and Alfred Stiassny. 1995. Tax and Spend, or Spend and Tax? An

International Study. Journal of Policy Modeling 20(2): 163–191.

Kort, John R. 1981. Regional Economic Instability and Industrial Diversification in the

U.S. Land Economics, 57(4): 596–608.

Krugman, Paul. Making Things in America. New York Times. Available at

http://www.nytimes.com/2011/05/20/opinion/20krugman.html.

Lane, Philip, and Aaron Tornell. 1998. Why aren‘t Savings Rates in Latin America

Procyclical? Journal of Development Economics 57(1): 185–199.

Lav, Iris J, Elizabeth McNichol, and Robert Zahradnik. 2005. Faulty Foundation: State

Structural Budget Problems and How to Fix Them. Center on Budget and Policy

Priorities. Available at www.cbpp.org/5-17-05sfp.pdf.

Lee, Robert D, and Philip G Joyce. 2007. Public Budgeting Systems (8th ed). Sudbury,

MA: Jones and Bartlett Publishers.

Lucas, Robert E, Jr., and Nancy L Stokey. 1983. Optimal Fiscal and Monetary Policy in

an Economy without Capital. Journal of Monetary Economics 12(1): 55–93.

Malizia, Emil E, and Shanzi Ke. 1993. The Influence of Economic Diversity on

Employment and Stability. Journal of Regional Science 3(2): 221–235.

Manage, Neela, and Michael L Marlow. 1986. The Causal Relation between Federal

Expenditures and Receipts. Southern Economic Journal 52(3): 617–629.

Markowitz, Harry. 1952. Portfolio Selection. Journal of Finance 7(1): 77–91.

Marlow, Michael L, and Neela Manage. 1987. Expenditures and Receipts: Testing for

Causality in State and Local Government Finances. Public Choice 5(3): 243–255.

182

Marlow, Michael L, and Neela Manage. 1988. Expenditures and Receipts in State and

Local Government Finances: Reply. Public Choice 59(3): 287–290.

Martell, Christine R, and Bridget M Smith. 2004. Grant Levels and Debt Issuance: Is

There a Relationship? Is There Symmetry? Public Budgeting and Finance 24(3):

65–81.

Mazerov, Michael. 2009. Expanding Sales Taxation of Services: Options and Issues.

Center on Budget and Policy Priorities. Available at http://www.cbpp.org/files/8-

10-09sfp.pdf.

Meltzer, Allan H, and Scott F Richard. 1981. A Rational Theory of Size of Government.

Journal of Political Economy 89(5): 914–927.

Mikesell, John L. 2001. Sales Tax Incentives for Economic Development: Why Shouldn't

Production Incentives be General? National Tax Journal 54(3): 557–567.

Miller, Stephen M, and Frank S Russek. 1989. Co-Integration and Error-Correction

Models: The Temporal Causality between Government Taxes and Spending.

Southern Economic Journal 57(1): 221–229.

Mitchell, Wesley C. 1941. What Happens During Business Cycles: A Progress Report.

National Bureau of Economic Research.

Modigliani, Franco, and Richard H Brumberg. 1954. Utility Analysis and the

Consumption Function: An Interpretation of Cross-Section Data. In Post-

Keynesian Economics, ed. Kenneth K. Kurihara, 388–436. New Brunswick, NJ:

Rutgers University Press.

Modiglinai, Franco, and Richard H Brumberg. 1980. Utility analysis and aggregate

consumption functions: an attempt at integration. In The Collected Papers of

Franco Modigliani: Volume 2, The Life Cycle Hypothesis of Saving, ed. Andrew

Abel, 128–197. Cambridge, MA: MIT Press.

Moore, Stephen. 1991. State Spending Splurge: The Real Story behind the Fiscal Crisis

in State Government. Cato Policy Analysis 152. Cato Institute.

Mounts, W Stewart, and Clifford Sowell. 1997. Taxing, Spending, and the Budget

Process: The Role of Budget Regimes in the Intertemporal Budget Constraint.

Swiss Journal of Economics and Statistics 133(3): 421–439.

Mullins, Daniel R, and Philip G Joyce. 1996. Tax and Expenditure Limitations and State

and Local Fiscal Structure: An Empirical Assessment. Public Budgeting and

Finance 16(1): 75–101.

Musgrave, Richard. 1966. Principles of Budget Determination. In Public Finance:

Selected Readings, eds. Helen A Cameron and William L Henderson. New York,

NY: Random House.

183

Niskanen, William A. 1991. A Reflection on Bureaucracy and Representative

Bureaucracy. In The Budget-Maximizing Bureaucrat: Appraisals and Evidence,

eds. Andre Blais and Stephane Dion. Pittsburgh, PA: University of Pittsburgh

Press.

Niskanen, William A. 2003. A Case for Divided Government. Cato Policy Report

(March/April): 2.

Niskanen, William A. 2005. ―Starving the Beast‖ Will Not Work. In Cato Handbook on

Policy (6th ed): 113–116. Cato Institute.

Nordhaus, William. 1975. The Political Business Cycle. Review of Economic Studies

42(2): 169–190.

Tufte, Edward R. 1978. Political Control of the Economy. Princeton, NJ: Princeton

University Press.

Otsuka, Yasuji, and Bradley M Braun. 1999. The Random Coefficient Approach for

Estimating Tax Revenue Stability and Growth. Public Finance Review 27(6):

665–676.

Owoye, Oluwole. 1995. The Causal Relationship between Taxes and Expenditures in the

G7 Countries: Cointegration and Error Correction Models. Applied Economics

Letters 2(1): 19–22.

Parkinson, C Northcote. 1960. The Law and the Profits. New York, NY: Ballantine

Books.

Payne, James E. 1995. The Tax-Spend Debate: Time Series Evidence from State Budgets.

Public Choice 95(3-4): 307–320.

Payne, James E. 2003. A Survey of the International Empirical Evidence on the Tax-

Spend Debate. Public Finance Review 31(3): 302–324.

Perry, Martin K. 1988. Vertical Integration: Determinants and Effects. In Handbook of

Industrial Organization, eds. Richard Schmalensee and Robert D Willig: Ch.4.

Amsterdam, NY: North-Holland.

Peacock, Alan T, and Jack Wiseman. Approaches to the Analysis of Government

Expenditure Growth. Public Finance Review 7(1): 3–23.

Posner, Paul L, and Bryon S Gordon. 2001. Can Democratic Governments Save?

Experiences of Countries with Budget Surpluses. Public Budgeting and Finance

21(2): 1–28.

Poterba, James M. 1994. State Responses to Fiscal Crises: The Effects of Budgetary

Institutions and Politics. Journal of Political Economy 102(4): 799–821.

Ram, Rati. 1988a. A Multicountry Perspective on Causality between Government

Revenue and Government Expenditure. Public Finance 43(2): 261–269.

184

Ram, Rati. 1988b. Additional Evidence on Causality between Government Revenue and

Government Expenditure. Southern Economic Journal 54(3): 763–769.

Richards, Daniel J. 1986. Unanticipated Money and the Political Business Cycle. Journal

of Money, Credit, and Banking 18(4): 447–457.

Romer, Christina D, and David H Romer. 2007. Do Tax Cuts Starve the Beast? The

Effect of Tax Changes on Government Spending. NBER Working Paper No.

13548.

Ross, Kevin L, and James E Payne. 1998. A Reexamination of Budgetary Disequilibria.

Public Finance Review 26(1): 67–79.

Savage, Jaivies D. 1992. California's Structural Deficit Crisis. Public Budgeting &

Finance 12(2): 82–97.

Schunk, Donald, and Douglas Woodward. 2005. Spending Stabilization Rules: A

Solution to Recurring State Budget Crises? Public Budgeting and Finance 25(4):

105–124.

Shah, Anwar. 2005. Fiscal Management. Washington, D.C.: World Bank.

Sheffrin, Steven M. 2004. State Budget Deficit Dynamics and the California Debacle.

Journal of Economic Perspectives 18(2): 205–226.

Sichel, Daniel E. 1993. Business Cycle Asymmetry: A Deeper Look. Economic Inquiry

31(2): 224–236.

Sims, Christopher A. 1972. Money, Income, and Causality. American Economic Review

62(4): 540–552.

Sims, Christopher A. 1980. Macroeconomics and Reality. Econometrica, 48(1): 1–48.

Smith, Daniel, and Yilin Hou. 2008. State Spending Behavior under Heterogeneous

Balanced Budget Requirement Systems: A Long-Panel Study. Unpublished paper

available on the Social Science Research Network.

Sobel, Russell S, and Randall G Holcombe. 1996. Measuring Measuring the Growth and

Variability of Tax Bases over the Business Cycle. National Tax Journal 49(4):

535–552.

Stansel, Dean, and David T Mitchell. 2008. State Fiscal Crises: Are Rapid Spending

Increases to Blame? Cato Journal 28(3): 435–448.

Steindl, Frank G. 2007. What Ended the Great Depression? It Was Not World War II.

The Independent Review 12(2): 179–198.

Strazicich, Mark C. 1997. Does Tax Smoothing Differ by the Level of Government?

Time Series Evidence from Canada and the United States. Journal of

Macroeconomics 19(2): 305– 326.

185

Sturzenegger, Federico, and Rogério L F Werneck. 2006. Fiscal Federalism and

Procyclical Spending: The Cases of Argentina and Brazil. Económica 52(1-2):

151–194.

Tempelman, Jerry H. 2006. Does ‗Starve the Beast‘ Work? Cato Journal 26(3): 559–571.

Tornell, Aaron, and Philip Lane. 1998. Are Windfalls a Curse? Journal of International

Economics 44(1): 83–112.

Tornell, Aaron, and Philip Lane. 1999. The Voracity Effect. American Economic Review

89(1): 22– 46.

U.S. Census Bureau. 1993. We the American… Foreign Born. U.S. Department of

Commerce Economics and Statistics Administration. Available at

http://www.census.gov/apsd/wepeople/we-7.pdf.

Volden, Craig. 1999. Asymmetric Effects of Intergovernmental Grants: Analysis and

Implications for U.S. Welfare Policy. Publius 29(3): 51–73.

von Furstenberg, George M, R Jeffery Green, and Jin-Ho Jeong. 1985. Have Taxes Led

Government Expenditures? The United States as a Test Case. Journal of Public

Policy 5(3): 321–348.

Wagner, Gary A. 2006. Financing Government: Revenue Variability and the Role of

Rainy Day Funds. Popular Government 71(3): 24–30.

White, Fred C. 1983. Trade-Off in Growth and Stability in State Taxes. National Tax

Journal 36(1): 103–114.

Wildavsky, Aaron. 1988. The New Politics of the Budgetary Process. Glenview, IL: Scott,

Foresman.

White, Fred C. 1983. Trade-off in Growth and Stability in State Taxes. National Tax

Journal 36(1): 103–114.

Wooldridge, M. Jeffrey. 2002. Econometric Analysis and Cross Section and Panel Data.

Cambridge, MA: MIT Press.

Yan, Wenli. The Impact of Revenue Diversification and Economic Base on State

Revenue Stability. Journal of Public Budgeting, Accounting & Financial

Management. (Forthcoming)

186

Appendix A The Long-Run Income Elasticity of State Income Tax and Sales Tax

Revenues

Technically, elasticity is the ratio of the percent change in one variable to the

percent change in another variable. Following the standard estimation method of constant

elasticity, the following model is used to estimate the long-run elasticity of tax revenue

with respect to income:

where and denote the natural log of tax base and personal income of state

in year , and the regression coefficient represents the long-run income elasticity of

the tax revenue. Results are as follows:

Table A.1 Long-Run Income Elasticity of General Sales Tax and Individual Income

Tax by State (1992−2007)

State Sales Tax Income Tax

Alabama 0.8049 1.2059

Alaska No ST No PIT

Arizona 0.6640 1.0507

Arkansas 0.7880 1.1909

California 0.6947 1.8087

Colorado 0.8750 1.1957

Connecticut 0.2577 2.0579

Delaware No ST 1.3813

Florida 0.9193 No PIT

Georgia 0.7128 1.2476

Hawaii 1.4442 1.7838

Idaho 0.7558 1.0436

Illinois 0.8074 1.1424

Indiana 0.9179 0.2403

187

Iowa 0.1609 0.6652

Kansas 0.5756 1.7413

Kentucky 0.9828 1.0368

Louisiana 1.8479 1.2752

Maine 1.3640 1.4567

Maryland 0.7817 1.1274

Massachusetts 0.9473 1.3459

Michigan 0.3732 1.6766

Minnesota 0.5565 1.0089

Mississippi 0.8740 1.2627

Missouri 0.3331 1.2667

Montana No ST 1.2181

Nebraska 1.1694 1.0851

Nevada 0.9163 No PIT

New Hampshire No ST Excluded

New Jersey 0.8747 1.0068

New Mexico 0.2555 1.5367

New York 0.8245 1.5739

North Carolina 0.6669 1.1775

North Dakota 0.7387 1.1955

Ohio 1.3709 1.7949

Oklahoma 0.5950 1.7278

Oregon No ST 1.1884

Pennsylvania 0.9954 1.0314

Rhode Island 1.6021 1.3656

South Carolina 0.9384 0.9141

South Dakota 1.1725 No PIT

Tennessee 0.9119 Excluded

Texas 0.6914 No PIT

Utah 0.8251 1.2134

Vermont 0.3755 1.2793

Virginia 0.5900 1.3314

Washington 0.6496 No PIT

West Virginia 0.3351 0.9679

Wisconsin 0.9898 1.2564

Wyoming 1.2269 No PIT

Mean .8257 1.2702

Std. Dev. .3547 .3310

Min .1609 .2403

Max 1.8479 2.0579

188

Appendix B Results of Diagnostic Tests for Revenue Volatility Models

The results of diagnostic tests for the sales tax volatility model are as follows:

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

Ho: Constant variance

Variables: fitted values of stvol

chi2(1) = 243.29

Prob > chi2 = 0.0000

Wooldridge test for autocorrelation in panel data

H0: no first-order autocorrelation

F( 1, 44) = 27.612

Prob > F = 0.0000

The results of diagnostic tests for the income tax volatility model are as follows:

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

Ho: Constant variance

Variables: fitted values of itvol

chi2(1) = 146.32

Prob > chi2 = 0.0000

189

Wooldridge test for autocorrelation in panel data

H0: no first-order autocorrelation

F( 1, 40) = 14.667

Prob > F = 0.0004

190

Appendix C Results of Diagnostic Tests for Fiscal Policy Model

The results of diagnostic tests for the fiscal policy model are as follows:

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model

H0: sigma(i)^2 = sigma^2 for all i

chi2 (49) = 881.10

Prob>chi2 = 0.0000

Wooldridge test for autocorrelation in panel data

H0: no first-order autocorrelation

F( 1, 47) = 45.836

Prob > F = 0.0000

191

CURRICULUM VITAE

SUNJOO KWAK

1976 Born February 27 in Incheon, Republic of Korea

2002 Bachelor of Public Administration, Hankuk University of Foreign Studies, Seoul,

Republic of Korea

2005 Master of Public Administration, Hankuk University of Foreign Studies, Seoul,

Republic of Korea

2011 Ph. D. in Public Administration, Rutgers University, Newark, New Jersey