Spring 2014 Senior Distinction Papers-Class of 2013 · smartphones and tablets. Such diffusion has...

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Omicron Delta Epsilon International Honor Society in Economics

Beta Chapter: St. Olaf College

Executive Board 2013-2014 President: Kelly Tomera and Nick Evens

Vice President: Erik Springer

ODE Journal Executive Editor: Rebecca Gobel

ODE Journal Associate Editor: William Lutterman

About Omicron Delta Epsilon

Omicron Delta Epsilon is one of the world’s largest academic honor societies.

The objectives of Omicron Delta Epsilon are recognition of scholastic

attainment and the honoring of outstanding achievements in economics, the

establishment of closer ties between students and faculty in economics within

colleges and universities, the publication of its official journal, The American

Economist, and the sponsoring of panels at professional meetings as well as the

Irving Fisher and Frank W. Taussig competitions.

Currently, Omicron Delta Epsilon has 578 chapters located in the United

States, Canada, Australia, the United Kingdom, Mexico, Puerto Rico, South

Africa, Egypt, France, and the United Arab Emirates. With such a broad

international base, chapter activities vary widely, ranging from invited

speakers, group discussions, dinners, and meetings, to special projects such

as review sessions and tutoring for students in economics. Omicron Delta

Epsilon plays a prominent role in the annual Honors Day celebrations at

many colleges and universities.

Senior Distinction Papers-Class of 2013 Spring 2014

2

Omicron Delta Epsilon International Honor Society in Economics

Beta Chapter: St. Olaf College

Executive Board 2013-2014 President: Kelly Tomera and Nick Evens

Vice President: Erik Springer

ODE Journal Executive Editor: Rebecca Gobel

ODE Journal Associate Editor: William Lutterman

About Omicron Delta Epsilon

Omicron Delta Epsilon is one of the world’s largest academic honor societies.

The objectives of Omicron Delta Epsilon are recognition of scholastic

attainment and the honoring of outstanding achievements in economics, the

establishment of closer ties between students and faculty in economics within

colleges and universities, the publication of its official journal, The American

Economist, and the sponsoring of panels at professional meetings as well as the

Irving Fisher and Frank W. Taussig competitions.

Currently, Omicron Delta Epsilon has 578 chapters located in the United

States, Canada, Australia, the United Kingdom, Mexico, Puerto Rico, South

Africa, Egypt, France, and the United Arab Emirates. With such a broad

international base, chapter activities vary widely, ranging from invited

speakers, group discussions, dinners, and meetings, to special projects such

as review sessions and tutoring for students in economics. Omicron Delta

Epsilon plays a prominent role in the annual Honors Day celebrations at

many colleges and universities.

St. Olaf College’s Beta Chapter of Omicron Delta Epsilon aims to build a bridge

between the economics faculty and students, actively providing input and

assistance as needed to improve departmental events; they also publish an in-

house economics journal, encouraging, reviewing, selecting, and publishing

original work from economics students at the college.

St. Olaf College’s Omicron Delta Epsilon Journal of Economic Research

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The St. Olaf College Economics Department’s

Omicron Delta Epsilon Journal of Economic Research

___________________________________________________________

Contents Spring 2014

___________________________________________________________

Shannon Cordes: Stuck in the Middle:

The Substitution Effect of Digital Technology on Middle Skilled

Jobs…….…….…….………………………………………..………....2

Kelly Tomera: Combating Obesity in America- Fat Tax Is Not the

Way To Go…………………..………………………………………..42

Ryan Johnsrud: US Stock Market as a Leading Indicator of the US

Econom…….……………………………………………………..…...54

Annabel Ansel: Obamacare: Healthcare Reform and the Ailing

Labor Market………………………….........................................................98

Spring 2014

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Stuck in the Middle:

The Substitution Effect of Digital Technology on Middle Skilled Jobs

Shannon Cordes

Abstract

Since the 1980s, middle-skilled occupations have experienced a

steady decline in the share of U.S. employment, a phenomenon often

attributed to advances in digital technology. Among the explanations

reported in the economic literature, the Autor Levy Murnane (ALM)

hypothesis suggests that routine processes are most vulnerable to digital

substitution – digital technology substitutes for routine occupations and

compliments non-routine occupations. Tests have involved a division of

occupations as routine vs. non-routine, which are subdivided further as

manual, cognitive or analytic.

Unlike existing literature that examines the effect of digital

technology on employment, this paper analyzes its effect on the

unemployed. Using data from the Current Population Survey and the

Dictionary of Occupational Titles, I find that routine cognitive workers are

more likely to be unemployed than non-routine cognitive workers, thus

reinforcing the ALM hypothesis. However, the effect of advancements in

digital technology on the unemployment gap between routine cognitive

and non-routine cognitive occupations depends on the type of technology.

Using VAR techniques, I find that the net effect of advances in hardware

technology on the unemployment gap is zero, while the net effect of

advances in software technology is positive.

I. Introduction

Since the financial crisis in 2008 from which the U.S. economy

spiraled into the deepest recession since The Great Depression, politicians

and citizens await ‘job creation’. Hoping that new jobs will be

forthcoming, many also expect former jobs to return once the economy

recovers. But what if those former jobs will not return? What if the labor


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market has structurally changed? Such that a job once occupied for over

ten years has been filled not by someone else, but something else – digital

technology.

The scope of digital technology is not limited to the

implementation of personal computers, but also encompasses the

application, diffusion and replication of software on computers,

smartphones and tablets. Such diffusion has created online marketplaces –

such as progromatic bidding – classrooms and applications that have

reduced skills at the hands of dozens of workers into one screen. This

paper aims to determine whether advancements in digital technology have

historically led to the displacement of workers employed in routinized

occupations.

It may be questioned whether advancing technology causes

permanent net job shifts. The corollary to job displacements by

technology is increased productivity and new job creation – per growth

models ranging from Solow’s to the Real Business Cycle model. Isn’t this

just another technology shock pushing aggregate demand outward?

In the last two decades, a vast array of literature has emerged citing

one of two forces to structural changes in the labor market: Skill-Biased

Technological Change (SBTC) and the Autor Levy Murnane (ALM)

hypotheses – the latter emerging from the inadequacy of the former to

“Stuck in the Middle” by Shannon Cordes

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explain the rising job polarization in Western labor markets. According to

the SBTC hypothesis, technology increases the demand for skilled labor

and reduces the demand for unskilled labor, resulting in a higher wage

compensation for skilled relative to unskilled labor. Acemoglu (1998)

and Kramarz (1998) establish the correlation between skill acquisition and

technological change. Machin and Van Reenen (1998) supplement the

SBTC hypothesis with empirical evidence that indicate faster skill

upgrading is associated with higher industry research and development.

Yet, if the SBTC hypothesis were true, then in the last decade, one

would expect positive growth rates of employment for middle & high-

skilled labor, as well as a decline in low-skilled employment. However,

this is not the case. As Autor, Katz and Kreuger (1998), Autor, Levy and

Murane (2003), Goos & Manning (2007), Nedelkoska (2012) have

stipulated, Western labor markets exhibit a polarizing trend that can only

be explained in part by the SBTC hypothesis. Indeed, employment growth

of high-skilled labor has increased, but so too has the growth of low-

skilled employment. The individuals losing out are not unskilled workers,

but ‘middling workers’ employed in routinized labor. Consequently, the

SBTC hypothesis cannot fully explain the twin peaks phenomena of the

labor market. Instead, the ALM hypothesis offers a nuanced view of the


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relationship between digital technology and the labor market, one that

surpasses the binary categorization of labor as skilled and unskilled.

II. The SBTC & ALM Hypotheses

Technological shocks can stimulate economic growth, yet the

impact of these shocks on the labor market requires closer scrutiny. In the

short run, the effect of a change in technology – such as the invention of

the railroad, computer or web browser – is palpable within the subsequent

boost in real GDP. However, the impact of a technological change on the

labor market reveals itself over a longer period of time. Often, the true

impact – as with computerization – does not emerge until decades after the

initial invention.

With increasing vigor, economists and citizens alike have raised

alarm about widening job polarization in the U.S. economy, which they

attribute to advancements in digital technology. The SBTC hypothesis

generalizes the impact of technology upon all skill types as equivalent.

Yet occupations within industries are characterized by varying types of

skills. So much so that the Dictionary of Occupational Titles (DOT)

assigns skill descriptions to twelve thousand unique jobs.

Contrary to the SBTC hypothesis, which categorizes occupations

as skilled and unskilled, the ALM hypothesis applies two tiers of skill


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decomposition. First, the tasks of labor are segmented into routine and

non-routine skills. Within these branches, tasks can be further broken

down into cognitive and manual skills. The ALM hypothesis predicts that

technology replaces routine cognitive and manual tasks, but complements

non-routine cognitive tasks.1 Once stated, this declaration seems obvious,

especially since the assembly line epitomizes the replacement of manual

labor by machines. However, we must reconsider our standard conception

of a routine task. Routine tasks have become synonymous with manual

tasks where the laborer repeats the same motion, whether that be

smoothing the surfaces of ceramic toilet bowls or individually wrapping

Galvadier chocolate truffles for packaging. Routine tasks, though, also

include routine cognitive skills.

Prior to the invention of the computer, tasks that required repetitive

information processing fell strictly within the mind’s domain. With the

invention of the computer, a machine whose primary function is to process

information, the mind’s domain has been encroached upon and in many

cases usurped by another domain – the network domain. Computers and

the bundles of software programs and communication capabilities

packaged with them have expanded the replacement capabilities of

technology to include human cognition. As Autor et. al (2003) articulate:

1 For non-routine manual tasks, the ALM hypothesis predicts that digital technology is

a weak, or limited, compliment.


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As symbolic processors–machines that store, retrieve, sort,

and act upon information–computers augment or supplant

human cognition in a vast set of information processing

tasks that were historically the mind’s exclusive

dominion…Computers have increasingly substituted for the

information processing, communications, and coordinating

functions of bookkeepers, cashiers, telephone operators and

other handlers of repetitive information processing tasks.

(5)

Computers have expanded the types of tasks replaced by technology to

include not only routine manual skills, but also routine cognitive skills.

Thus, computers function as substitutes for routine manual and cognitive

tasks and complements for non-routine cognitive tasks by increasing

productivity.

Over the past three decades, the share of employment has shifted

in favor of non-routine cognitive labor, as displayed in Figure 1.1.

Furthermore, while the employment share of routine cognitive labor has

fallen since 1970, the share of non-routine manual labor remains constant

over time. If the SBTC hypothesis was true, we would expect the share of

the lowest skilled occupations to decline as technology advances. Yet – as

the ALM hypothesis predicts – since computers function as a limited

complement of non-routine manual labor, its share of employment remains

stable as digital technology advances.


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Figure 1.1

As with all substitution effects, the primary component that drives

the replacement of one process for another is cost. In the decision to hire,

a firm must choose whether the marginal product of labor of hiring an

additional worker is equal to or exceeds the worker’s marginal cost. A

firm must also consider whether substitutes are available that offer a lower

marginal cost for the same or greater marginal product of the worker. As

substitutes, the price of computers and the quantity of routine cognitive

labor employed are positively related. Conversely as compliments, the

quantity of non-routine cognitive labor employed and the price of

computers are inversely related. Given this relationship between the price

0%

10%

20%

30%

40%

50%

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98

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Share of total

employment

Year

Employment Share by Skill Measure, 1968-2013

Nonroutine Cognitive Routine Cognitive Nonroutine Manual


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of computers and its substitutes and compliments, as the price of

computers declines, the quantity demanded of routine cognitive labor will

decrease while the quantity of non-routine cognitive labor will increase.

The corollary of this statement indicates that as computer prices decline,

the number of individuals employed in routine cognitive labor will either

become unemployed or switch occupations. In order for this effect to be

true, the price of computers must have declined since 1977. Indeed, as

Bresnahan (2000) cites, the quality-adjusted price of computers has

declined at a compound rate of twenty percent per year through the mid

1990s.2

These two functions of computers – as substitutes and

compliments –are the central claim of the ALM hypothesis. If the ALM

hypothesis models the interaction between the labor market and digital

technology, we would expect within the data that routine cognitive

workers represent an increasing share of the unemployed.

III. Hypotheses: Adding the Lens of Job Loss

The majority of economic literature concerning the displacement

of routinized workers by digital technology focuses on measuring the

share of employment differential that can be attributed to computers (Goos

2 Figure 6.1 in the Appendix displays the steady decline of the computer price index

since 2005.


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and Manning, 2007, Autor, Levy and Murnane, 2003, Autor, Katz and

Kreuger, 1998). Such work attempts to determine the degree to which

computers modify the tasks of a given occupation by examining those

currently employed. In part, this is a result of the nature of the data, since

most surveys only ask employed individuals whether they use a computer

at work and for what purpose. Yet, by only measuring the effects of

technology displacement on the employed, existing literature neglects the

most important people of interest – the unemployed.

What happens to the individuals whose routinized jobs are usurped

by digital technology? Do they find work elsewhere? Are they more

likely to become unemployed? Must they accept a lower wage if changing

occupations? Ljubica Nedelkoska attempts to answer this very question

for the case of another western economic power: Germany. Nedelkoska

(2012) attempts to track the adaptation process of German workers whose

occupations require routine tasks. An individual whose skills become

obsolete faces two choices: unemployment or occupational change.

Nedelkoska concludes that workers performing routine tasks incur a

higher probability of becoming unemployed and switching occupations.

Similar to Nedelkoska’s conclusions, I predict that workers performing

routine cognitive tasks face a higher probability of unemployment

compared to those performing non-routine cognitive tasks.


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A question that remains, however, pertains to the causality

assertion. Is technology to blame for the decline of occupations requiring

routine cognitive tasks? Nedelkoska concludes that if such directional

causality exists, then it is a weak causal relationship in production and

manufacturing, and for coding technologies other than computers, the

relationship can even be complementary, proving that not all digital

technologies interact uniformly with the labor market. Autor, Levy and

Murnane (2003) conclude opposite results. Instead, within the most

highly computerized industries, the trend exhibits an increase in labor

input for non-routine cognitive skills and a decrease in labor input for

routine cognitive skills. As with most relationships in economics, direct

causality remains elusive. Association, at best, can be attained. Thus the

second hypothesis to be tested is whether advancements in digital

technology are historically followed by an expansion of the

unemployment gap between routine cognitive and non-routine cognitive

occupations.

IV. Data Methodology

In order to measure the likelihood of unemployment for non-

routine cognitive occupations compared to routine cognitive occupations

and to test for causality between technological advancement and the

unemployment gap, we require measures of skill task for occupations for


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sectors from which the unemployed work. The National Academy of

Sciences and Committee of Occupational Classification and Analysis

aggregated Dictionary of Occupation Titles (DOT) characteristics for the

574 occupation categories of the 1970 U.S. Census. In the COC-DOT

aggregation, Census Occupation Codes (COC) are assigned a score for

General Education Development, Aptitudes and Temperaments measured

in the DOT.

Using the same methodology as Autor, Levy and Murnane (2003),

five characteristics indicate the degree to which an occupation is non-

routine or routine.

Non-routine Cognitive-Analytic: Mathematical General Education

Development (GEDMATH) captures an occupations quantitative

and analytical reasoning skills.

Non-routine Cognitive-Interactive: Directional, Control, Planning

(DCP) measures an occupation’s communication and management

skills.

Non-routine Manual: Eye-Hand-Foot Coordination (EYEHAND)

takes on high values for occupations requiring a high degree of

physical agility and spatial recognition.

Routine Cognitive: Set Limits, Tolerances or Standards (STP)

indicates a worker’s ability to adapt to work requiring set limits,

tolerances or standards.

Routine Manual: Finger Dexterity (FINGDEX) captures the level

of motor skills an occupation requires.


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Based on the score of the DOT skill measures in the COC-DOT

aggregation, I calculated a weighted DOT mean task in order to assign one

of the five skill measures to each of the 574 census occupation codes. In

other words, I defined each occupation as non-routine cognitive-analytic,

non-routine cognitive-interactive, non-routine manual, routine cognitive or

routine manual.

Although U.S. Census Data served to classify occupational codes

by skill measure, I drew from the Current Population Survey (CPS) to

analyze unemployment by skill measure. Since the U.S. Census and the

CPS code occupations differently, I created a crosswalk using the CPS

translation page to assign a skill measure to the CPS occupation codes

with base year of 1970.3 By using the 1970 base year occupation codes,

occupations are comparable over time. However, in using the base year,

we assume that the task requirement of occupations remains constant

overtime. Although this assumption could limit the regression results

since technological advancement leads to changes in an occupation’s

tasks, the benefit of the assumption outweighs its limitations. By

classifying each CPS occupation with a skill measure, characteristics of

3 The Current Population Survey provides the translation page. The page categorizes

CPS occupation codes by U.S. census occupation codes for all years. The U.S.

census occupation codes are more detailed than the CPS occupation code. Thus to

create the crosswalk, I matched the U.S. census codes to their CPS counterpart as

defined by the translation page.


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the occupation – instead of the individual – can be tracked overtime, in

particular, unemployment status.

Probit Model

To measure the likelihood of unemployment of an individual with

a routine cognitive or non-routine cognitive-analytic task, I created a

probit model using CPS data from 1972-2013.4 The probit model is

defined as follows:

1. ( | ) ( )

where is the likelihood of unemployment of individual i at year T,

is a vector of coefficients for a vector of characteristics that include

age, sex, race, income, education and industry and is a vector of

coefficients for a vector of dependent variables that include dummy

variables defining the skill measure of an occupation.5

Vector Auto-regression (VAR) Model

Testing for Granger Causality through Vector Auto-regression is

one method of determining whether a causal relationship exists between

4 The sample consists of individuals between the ages of 18-65 who are in the labor

force. For each year, T, the number of observations is between 40-90k individuals.

See Table 1.2 in the Appendix for the probit model output. 5 Income is measured using an individual’s wage. All measures of income are

inflation adjusted using the CPI less food and energy with 2007 as the base year.


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two forces. A casual relationship between digital technology and the

unemployment rate of non-routine cognitive and routine cognitive jobs has

yet to be established or refuted. If the probit model supports the

hypothesis that individuals employed in routine cognitive occupations

incur a greater likelihood of unemployment than those employed in non-

routine cognitive work, then the question remains as to why this disparity

exists. The VAR model tests whether this difference in unemployment

rates can be attributed to advancements in digital technology.

In order to create a VAR model, I converted the existing micro

data from the CPS into time-series data. From 1972-2013, I calculated the

annual unemployment rate for routine cognitive and non-routine cognitive

jobs within the aggregate economy. Since the variable of concern is the

disparity between the unemployment rate of routine and non-routine

cognitive occupations, the dependent variable is the difference between

the two unemployment rates. (Since I subtracted the unemployment rate

of non-routine cognitive jobs from the unemployment rate of routine

cognitive jobs, we would expect this difference to be positive).

For the primary explanatory variable, a measure of digital

technology must be chosen. Investment in digital technology will serve as

a proxy for technological advancement in the VAR model. The Bureau of

Labor Statistics provides aggregate and industry level measures of digital


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technology investment in the National Income and Product Accounts

(NIPA). Categories of investment include PC (personal computer),

printers, hard drives, user-owned software, licensed software etc. I

aggregated these sub-categories into two umbrella categories: hardware

and software investment.

Although investment in durable and non-durable computer goods

commenced during the same time period, the respective growth rates of

investment vary significantly. Figure 1.2 and 1.3 displays the level of

economic wide investment in hardware and software technology for the

U.S. from 1972-2011. Accompanied with the graphs is a timeline that

tracks the milestones of invention for the respective technologies.


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Figure 1.26

U.S. Hardware Investment, 1972-2011

6 The timeline of hardware and software technological advancements was compiled

from the Computer History Museum.


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Figure 1.3

U.S. Software Investment, 1972-2011

Investment in both hardware and software increase over time – as

expected – yet for hardware investment, the growth rate remains fairly

constant while software investment exhibits varying rates of exponential

growth. Within the contours of these growth rates, the history of the

digital technology revolution resides.

In 1989, Sir Timothy John Berners Lee invented the World Wide

Web (it was released in 1990), but this invention alone did not spark the

fastest rate of computer hardware investment from 1992-1996. Without a

format to navigate, read and post content, the World Wide Web was

inaccessible to widespread users. Once CERN uploaded the first website


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on August 6, 1991, the Internet became universally user-friendly, which

sparked the highest growth rate of hardware investment. Software

investment, however, did not respond in the same manner as hardware

investment to the creation of the first website. Not until 2002 – following

the DOT-COM bubble and the invention of the web-browser – did

software investment finally takeoff.

Since hardware and software investment responded with varying

growth rates to technological disturbances, they may also exhibit differing

effects on unemployment for routine cognitive and non-routine cognitive

occupations. Hence, the VAR model does not aggregate hardware and

software investment.

The VAR model is as follows:

2. ( )

( ) ( )

where ( ) is the difference in the unemployment rate at time T between

occupations with skill measures i and k, I is a vector consisting of the log of aggregate

investment in hardware and software, Y is the log of real GDP and is the log of the

CPI less food and energy.


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V. Results

Probit Model

The first hypothesis predicts that individuals employed in routine

cognitive work incur a higher likelihood of unemployment than their

counterparts in non-routine cognitive occupations. If this hypothesis

proves to be true and if we suspect that advancements in digital

technology contribute to the result, then we would expect the shift of

unemployment likelihood in favor of non-routine cognitive jobs to occur

after 1977, when the Apple computer made its debut.7 Indeed, the probit

model shows that routine cognitive workers become more likely to be

unemployed than non-routine cognitive workers after the invention of

computers.

The coefficient of the dummy variable for routine cognitive labor,

all else constant, becomes consistently significant in 1983, two years after

the first IBM personal computer arrived on the market and at the

beginning of the decade in which computer usage rapidly expanded across

all industries. Figure 1.4 presents the marginal effect of the coefficient for

routine cognitive occupations from 1968-2010.

7 Source: Autor (1998).


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Figure 1.4

From 1968-1983, the coefficient alternates between significance and non-

significance in the probit model, indicating that skill measure is not a

conclusive factor in predicting the probability an individual will be

unemployed from one year to the next. Post 1983, individuals employed

in routine cognitive occupations have a consistently higher probability of

unemployment by 1-2 percentage points. In other words, the

unemployment rate for routine cognitive occupations is 1-2 percentage

points higher than for non-routine cognitive occupations (both analytic

and interactive). This percent difference in the unemployment rate is

consequential – especially considering the unemployment rate fluctuates

within the bounds of 6 and 8 percent.

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

Year

Marginal effect of the routine cognitve coefficient

in the probit model, 1968-2011


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The only anomaly within the data occurs in the year 2000, when

the marginal effect of routine cognitive occupations is not statistically

significant. On March 10, 2000, the dot-com bubble crashed, sending the

job status of all Americans, regardless of skill type, into a tailspin of

uncertainty. CPS data is collected at the end of March. Thus in 2000,

CPS data was collected within weeks of the dot-com crash, which explains

why all jobs, regardless of skill measure, incurred the same probability of

unemployment.

The results of the probit model are consistent with the results of

Nedelkoska (2012), where German individuals experienced a higher

likelihood of unemployment given employment within a routine

occupation. Nedelkoska had access to a panel data set in Germany, which

allowed her to measure the probability of occupational changes of

individuals across time. As expected, individuals occupied in routine

work were more likely to switch occupations than those occupied in non-

routine work. When comparing the likelihood of unemployment vs. the

likelihood of occupational changes, individuals were significantly more

likely to switch occupations than to become unemployed. Here lies the

limitation of the CPS dataset: individuals cannot be tracked over time and

consequently, occupational changes are not captured in the probit model.

Although the probit model cannot capture the probability of occupational


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changes by skill measure, it must be recognized that advancements in

digital technology do not necessarily displace a worker, but instead force

an individual to change occupation.

The question remains, however, whether the 1-2 percentage point

difference in the likelihood of unemployment between routine cognitive

and non-routine cognitive occupations is a result of advancement in digital

technology. It cannot be assumed on the basis of skill measure alone that

digital technology causes workers of routine cognitive occupations to face

a higher unemployment rate than non-routine cognitive workers. The

VAR model attempts to supplement the results of the probit model by

determining whether a statistical link exists between the unemployment

gap and digital technology.

VAR Results

The VAR model measures whether advances in digital technology

are predictive of an expansion in the unemployment gap between routine

cognitive and non-routine cognitive occupations. Digital technology has

been categorized as hardware and software investment, since we expect

the effect on the unemployment gap to depend on the type of technological

advancement. In the VAR model, the unemployment gap is defined as the

difference in the unemployment rate between routine cognitive and non-


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routine cognitive-analytic occupations. Figure 1.5 provides a visual

representation of the unemployment gap.

Figure 1.5

Although one can imagine that other unemployment gaps exist –

such as the gap between routine cognitive and non-routine cognitive-

interactive or routine manual and non-routine manual unemployment – to

avoid overcomplicating the empirical analysis, the unemployment gap will

only pertain to the difference in routine cognitive and non-routine

cognitive-analytic unemployment. Since Autor et. al (2003) concluded

that technological advancements had the greatest impact on the share of

employment of routine cognitive and non-routine cognitive-analytic

occupations, I will restrict my analysis to the unemployment gap between


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those two skill measures. Furthermore, the empirical results of the other

unemployment gaps did not generate significant findings.

Table 1.1 in the Appendix presents the results of the Granger

Causality test ordered by Cholesky Factorization for the estimated VAR

model.8 Before examining the model’s estimated effects of hardware and

software investment, we must first determine whether the model exhibits

expected relationships consistent with macroeconomic theory. In the

VAR model, GDP is exogenous and significantly effects inflation,

meaning that an unexpected rise in GDP is associated with rising inflation.

Figure 2.1 displays the impulse response of the CPI to a shock in GDP.

Figure 2.1

The impulse response generates a shock at time zero in order to measure

the response of one variable to an unanticipated rise in another variable,

8 The VAR model passes the unit root test and is stable. Furthermore, the lag

exclusion test recommends the use of two lags.


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thus simulating the dynamic between the two variables. In Figure 2.1, an

unexpected stimulus in GDP is followed by a rise in the CPI. Contrary to

expectations, hardware and software investment do not function as

complements. Instead, the model indicates that the primary driver of

hardware and software investment is computer prices. This result is

consistent with the conclusions of Autor et. al (2003). As demonstrated in

the impulse response in Figure 3.1 and 3.2, an unexpected rise in prices is

historically followed by a sudden decrease in software and hardware

investment over the short run (approximately two years).

Figure 3.1


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Figure 3.2

Once prices stabilize in the medium run, hardware and software

investment return to their previous levels. Since the VAR model exudes

the expected macroeconomic relationships supported by theory and

existing empirical analysis, the VAR model has the potential to capture the

dynamics between advancements in digital technology and the

unemployment gap.

As stated by the ALM hypothesis, computer technology serves as

substitutes for routine cognitive occupations and complements the work of

non-routine cognitive-analytic occupations. If this hypothesis is true, then

we would expect hardware and software investment to be associated with

an expansion of the unemployment gap. The results of the Granger

Causality test show that hardware and software investment, GDP and

inflation are all significant contributors to the unemployment gap between


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routine cognitive and non-routine cognitive-analytic occupations over

time.

The impulse response of the unemployment gap to a shock in

GDP indicates that the gap responds in alignment with the expansions and

contractions of the economy. During an economic expansion, the

unemployment gap contracts and the reverse is true during an economic

contraction. Moreover, the unemployment gap also exhibits the trade-off

between unemployment and inflation predicted by the Phillips Curve.

Figure 4.1 and 4.2 display the impulse response of the unemployment gap

to a shock in GDP and inflation, respectively.

Figure 4.1


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Figure 4.2

However, the unemployment gap cannot solely be attributed to

the cyclical nature of the economy. Technological advancement

contributes to the gap’s persistence, yet not all technologies exacerbate the

unemployment gap to the same degree. Contrary to expectations, an

unanticipated rise in hardware and software investment leads to a

contraction of the unemployment gap in the short-run (from year zero to

year two), as displayed in Figure 5.1 and 5.2.


30

Figure 5.1

Figure 5.2

However, in the medium run, the effect of hardware and software

investment on the unemployment gap differs. In response to a shock in

hardware investment, the unemployment gap marginally rises above zero

during the medium run (from year four to year seven). In contrast, a shock

in software investment leads to an expansion of the unemployment gap in

the medium run that exceeds the initial contraction in the short run. Thus


31

the net change in the unemployment gap following a shock in software

investment is positive, which means that advancements in software

technology are historically followed by increased unemployment for

routine cognitive workers and/or decreased unemployment for non-routine

cognitive-analytic workers.

In order to account for the differing effects of hardware and

software investment on the unemployment gap, two possible explanations

arise. First, unlike automated devices responsible for replacing routine

manual labor following the industrial revolution, the machine itself – the

personal computer – may not fully substitute workers for routine cognitive

tasks. The computer cannot produce output without software (hence they

are complementary goods). Perhaps the substitution value lies not in the

machine itself, but the application of the machine realized through

software.

The computer acts as a mechanism upon which software – the true

use-function of the computer – can run. Without the creation of software,

the widespread accessibility and usability of the machine would not have

been realized. For example, during the infancy of the World Wide Web, its

usability was diminished without a means of navigating its terrain. Until

Netscape created the first user-friendly web-browser, the webpages and


32

information contained upon those pages went unread, like lone signs along

an unpaved highway.

Although the complementary nature of hardware and software

lends itself to producing a good or service, digital technology’s greatest

contribution is its connective power – the ability to connect individuals

and to create a seamless interlay of all units of a firm. As Bresnahan

(2000) explains, advancement in digital technology alters the cost-

effective structure and organization of a firm. For example, Business

Workflow software fundamentally changed what was considered the most

cost-effective scale of a firm, which led to large organization changes.

According to Bresnahan, large organizational changes, which often

include the decentralization of decision-making, lateral communication

and a greater emphasis on the need for autonomous workers, have a larger

effect on the acquisition of higher skilled labor than the technological

change alone.

Such mass organizational re-structuring does not come without a

price – not only money, but also time. Restructuring a firm in order to

incorporate advances in digital technology requires time. Consequently, a

time-delay effect occurs that postpones the efficiency gains following the

implementation of the organizational changes. The time-delay effect is

precisely what occurs in the impulse response of the unemployment gap to


33

a shock in software investment. As Figure 5.2 displays, the expansion of

the unemployment gap does not occur until two years after the initial

shock. Thus, the substitution effect of advances in digital technology on

routine cognitive workers is not immediate. Only after firms achieve the

implementation of new digital technology do computers begin to replace

routine cognitive workers.

The substitution effect captured in the VAR model most likely

underrepresents the magnitude of the actual substitution effect occurring

in the U.S. labor market. Since the model only includes the number of

unemployed workers for each skill measure, those workers partially

replaced by digital technology are not included. ‘Partially replaced’ refers

to occupations in which computers do not replace the entire worker, but

only a subset of the worker’s skills. Bresnahan (2000) labels this partial

replacement effect as the ‘limited substitutability’ of digital technology.

Due to the nature of the dependent variable, the model does not capture

those routine cognitive workers who experienced a subset of their tasks

replaced by computers. If the VAR model could capture those routine

cognitive workers partially replaced by digital technology, we would

expect the impulse response of the unemployment gap to be significantly

larger.


34

VI. Conclusion

The replacement effect of technology upon labor is nothing new,

as this effect has occurred since the industrial revolution, during the

creation of the assembly line and now, through advancements in digital

technology. Previously, the Skill-Biased Technology Change hypothesis

provided a widely accepted explanation for the effect of advancing

technology upon the labor market: technology increases the demand for

skilled labor and decreases the demand for unskilled labor.

Yet, this explanation fails to explain the recent decline of middle

skilled labor in the last three decades – a decline that existing economic

literature has attributed to advancements in digital technology. The Autor

Levy Murnane (ALM) hypothesis provides a more nuanced view of the

effect of technology on the labor market by categorizing labor within

routine and non-routine occupations. Routine and non-routine

occupations can be further segmented by manual and cognitive

occupations. The ALM hypothesis states that digital technology behaves

as substitutes for routine cognitive occupations and as compliments for

non-routine occupations. Insofar as the ALM hypothesis accurately

explains the dynamic of the labor market and digital technology, the

substitution effect of digital technology impacts a large share of the labor


35

market, since routine cognitive occupations are concentrated within the

middle class.

I tested two hypotheses: 1) Individuals employed in routine

cognitive occupations incur a higher probability of unemployment than

individuals employed in non-routine cognitive occupations and 2) To what

extent the unemployment gap between routine cognitive and non-routine

cognitive occupations can be attributed to advancements in digital

technology. The probit model confirmed that since the 1980s, routine

cognitive workers are more likely to become unemployed than non-routine

cognitive workers. In order to determine whether the unemployment gap

between routine cognitive and non-routine cognitive occupations can be

statistically linked to advancements in digital technology, a Vector

Autoregression Model (VAR) measured to what extent expansions in the

unemployment gap can be explained by advancing digital technology.

Using hardware and software investment as a proxy for digital

technology, the results of the VAR model conclude that the effect of

digital technology upon the unemployment gap depends on the type of

technology. Advancements in hardware technology are statistically

significant, but the net effect on the unemployment gap over time is zero.

In contrast, an advancement in software technology leads to a contraction

of the unemployment gap in the short run, but an expansion of the gap in


36

the medium run that exceeds the initial contraction. Thus, the net effect of

software technology on the unemployment gap is positive, which means

that advancements in software technology are historically followed by an

increase in unemployment for routine cognitive occupations and/or a

decrease in unemployment for non-routine cognitive occupations.

The implications of these results indicate that as software

technology advances, a greater number of routine cognitive occupations

will either be fully displaced or partially displaced by digital technology.

Partial displacement refers to the replacement of a subset of skills required

within an occupation. Future research should be concerned with how to

transition workers with middling skills towards higher skilled occupations

that are complimented – not substituted – by digital technology. Such

transition efforts in the form of job training, education and skill-upgrading

programs will be of greatest importance not for new entrants of the labor

market (such as college graduates), but for existing laborers. As digital

technology –especially software – advances at an increasing rate, our

cultural expectation of a ‘lifetime’ career may be subject to evolution. In

the near future, the norm may no longer be to remain in one occupation

until retirement, but rather to reinvent our careers multiple times in order

to adapt to a labor market, economy and world buffeted by constant waves

of digital technological advancements.


37

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http://www.bea.gov/

40

Appendix

Table 1.1: VAR Granger Causality Test Results

The table presents the p-values for the Granger Causality Test.

Independent Variable

log(GDP) log(CPI) log(Hardware) log(Software)

URC - UNR-C/A

log(GDP) *** - - - -

log(CPI) 0.0256** *** - - -

log(Hardware) - 0.0015*** *** - -

log(Software) - 0.0091*** - *** -

URC - UNR-C/A 0.0643* 0.0536* 0.0434** 0.0252** ***

Adjusted R-Squared 0.74 Observations 34

URC - UNR-C/A refers to the difference in the unemployment rate between routine cognitive (RC) and non-routine cognitive-analytic occupations.

Figure 6.1

Source: Federal Reserve Bank


41

Appendix Table 1.2: Probit Model Output

Year Observations McFaddenR-squared

1968 42775 0.1137

1969 43535 0.1072

1970 42267 0.0959

1971 42831 0.1145

1972 41590 0.1214

1973 41453 0.1013

1974 41266 0.1065

1975 40798 0.1315

1976 43277 0.1238

1977 52652 0.1129

1978 52062 0.1207

1979 52991 0.1090

1980 63459 0.1093

1981 64275 0.1286

1982 57580 0.1267

1983 57608 0.1355

1984 64670 0.1301

1985 65713 0.1367

1986 64571 0.1214

1987 64573 0.1263

1988 64831 0.1256

1989 60767 0.1120

1990 66902 0.1095

1991 66737 0.1072

1992 66042 0.1177

1993 65414 0.1131

1994 63469 0.1205

1995 63356 0.1048

1996 55167 0.1049

1997 56402 0.1144

1998 56709 0.1100

1999 57021 0.0904

2000 53425 0.0988

2001 87056 0.0937

2002 85818 0.0818

2003 83782 0.0761

2004 81627 0.0866

2005 80067 0.0864

2006 79311 0.0799

2007 78511 0.0842

2008 78099 0.0906

2009 77982 0.0913

2010 76535 0.1025

2011 73380 0.1027


42

Combating Obesity in America- Fat Tax Is Not the Way To Go Kelly Tomera

Given the alarming increase in the rate of obesity in America, it is

no surprise that it has become one of the main concerns of the healthcare

industry. Since many expensive procedures result from obesity among

middle age or elderly Americans, the anticipated future Medicare costs are

of large concern (Daviglus 1). Furthermore, at this late stage in life,

policies that change habits to a healthier lifestyle are extremely hard to

implement, forcing many policy makers to take a stab at preventative

measures like taxes (Daviglus 2). The media has commonly titled these as

“fat taxes.” While they have not been implemented in the U.S. yet (NYC’s

soda tax has been stalled), many health advocates strongly endorse them

(Petrecca 2). The main argument for a national fat tax is that taxes are the

most efficient way to alter our behavior (Miao). They hurt us where we

feel it most—our wallet. While this may be true for most goods, due to

historically weak elasticities of demand among targeted fatty foods,

variations on what constitutes “bad” food, and nonexistent national

support for fatty food taxes, combating obesity using a fat tax will not be

successful in the United States.

Recent history has demonstrated the weak elasticities of fatty

foods. Other nations have implemented fat taxes under different names.


43

Most notably, Denmark was the first European nation in 2011 to enact a

fat tax (Kliff 1). They did not tax certain brands or specific food groups.

Rather, they across the board taxed anything with a saturated fat content

greater than 2.3% (Kliff 1). Products affected ranged from fancy cheeses

to bacon. The tax was not very effective for two reasons. First, Danes

merely drove across the boarder a few times a year to load up on fatty

foods in neighboring countries. Thus, their purchasing power was not

affected; instead, the local stores in Denmark felt the burden of the tax,

and the economy suffered (Kliff 2). Although most Americans would not

have this luxury, unless they lived near Canadian or Mexican borders, the

fact that the tax still generated revenue suggests that people continued on

their unhealthy diets. Secondly, the tax had varying results when it came

to fatty meats. The tax was imposed logically per carcass. However, this

resulted in higher prices for fatty burgers as well as lean steaks (“A Fat

Chance”). Although the tax was abandoned a year later, we can speculate

that since both meat groups were affected, consumers could not afford to

buy leaner high-end meats that were now even more expensive. Instead

they purchased even more of the poor quality cheaper cuts, having an

adverse affect on their health.

After a year of discontent from the people, Denmark’s government

caved and repealed the tax. It is interesting that the tax was so short lived

“Combating Obesity in America” by Kelly Tomera

44

since Europeans are used to much higher tax rates than the United States.

The European Average tax-to-GDP ratio is 38.8%, and Denmark’s is at

47.7% (European Commission). Compare this to the mere 25.2% tax-to-

GDP ratio in the U.S (European Commission). Still, the tax seemed just to

be another nuisance with no long-term effects. For a nation with a

population of 5.5 million people, the tax raised $200 million in revenues,

which shows that people still bought fatty foods (“Denmark” & “Taxation

Trends In The European Union”). To put this in perspective, Denmark’s

tax revenue from products and imports generated $3.8 billion Euros that

same year (“Taxation Trends In The European Union”). Thus, in less than

a full year, the fat tax comprised roughly 4% of this revenue category for

Denmark. Undeniably it would have comprised an even larger sector if

the majority of people had not spent their money in neighboring countries.

This is saddening, as the purpose of the tax was not to fund the Danish

government, but to instill healthier incentives in the population.

Denmark’s failed fat tax, demonstrates the strong purchasing power of

consumers when it comes to enjoyable food.

Furthermore, Denmark is not the only country with poor results

after creating a fat tax. France implemented taxes on sweetened beverages

in 2011 as well, and on some foods such as Nutella in later months

(Watson 2). Researchers Olivier Allais, Patrice Bertail, and Veronique


45

Nichele found that this fat tax in France had little effect on purchases

made by French households (4). For purchasing power to be affected,

recent studies have shown that taxes must be very large. Some articles

suggest the tax must be 20% or more to impact behavior on unhealthy

foods (Campbell 1). This large amount is hard to implement on goods,

since severe changes in industries like food are difficult for society to

accept. These drastic findings paired with the historical failures of

Denmark and France, show that for many countries fat taxes are simply

not effective.

While elasticities of demand seem to be low for food consumption,

what makes a fat tax even more difficult is the varying opinion on what

constitutes unhealthy nutrition. There really are no bad foods. Yes, some

nutrients should be eaten in moderation, but even fats can good for our

diets. Humans need various foods in order to have a balanced diet, and

nutrition needs to be taken from a more holistic approach. In the 1980s the

fat free movement started, yet, in the same decade, more Americans

became overweight (Nestle, Goldberg, Willet, and Taubes 2). Food had

more processed carbohydrates, and people replaced fat with these. Thus,

“fat free” on a label does not necessarily imply good nutrition. Imposing a

fat tax could potentially shift production of foods in America even further.


46

Companies may begin replacing fat with ingredients that are possibly even

worse, like high caloric sugars.

Defining “bad” foods will never be easy, as it varies by person.

This is one reason why fat taxes are so disputable. Not only were there

unexpected mishaps with Denmark’s fat tax, but also it may have gone too

far. Some saturated fats can be good for you. Saturated fats are made up

of short chains, so they do not stack up in your blood easily and can be

beneficial for your metabolism. There is a difference however between

plant based saturated fats, and animal based saturated fats, which can raise

cholesterol (Kounaves 2). Plant based saturated fats are now heralded by

some as beneficial. In a study published by the American Medical

Association, researchers found that randomized trial groups that ate more

plant fats had lower lipoprotein levels (LDL) than those eating minimal

saturated fats. LDL is associated with cardiovascular disease, and low

levels are desired (Foreman). Due to studies such as this, coconut oil, for

instance, has been gaining recent hype among health fanatics. This is just

one example of the varying opinions on what constitutes “bad” food.

Even leading health organizations cannot agree on food standards.

The U.S. Institute of Medicine conducted a review stating that “25% of

energy from caloric sweeteners is acceptable”, while the World Health

Organization concluded that only 10% of energy from caloric sweeteners


47

is acceptable for nations with high income levels such as the United States

(Popkin 5). These notable organizations draw from much of the same data

and leading scientific studies, yet arrive at different conclusions. This

exemplifies the varying opinion regarding the latest recommendations for

nutrition that leads to much confusion among consumers. This confusion

about what qualifies as bad nutrition, poses a challenge when justifying fat

taxes.

While a lack of consensus on nutrition makes preventing obesity

difficult, the lack of national support in the United States on the

seriousness of obesity makes tackling the subject even harder. National

support for fat taxes is virtually nonexistent due to the political

atmosphere surrounding the issue. The government grants many resources

towards fatty food sectors, and with much bias. For instance, 60% of all

agricultural subsidies go towards meat and dairy production—foods

notoriously high in fat content—while less than 1% of farming subsidies

are granted to fruit and vegetable growers (Physicians Committee For

Responsible Medicine 1). A national fat tax would affect several

industries already stimulated by the government, resulting in intense

lobbying from these large corporations.

Not only is lobbying at the federal level dominating the discussion,

but also legislation at the state level is opposed due to their constituents.


48

When New York City mayor Bloomberg attempted to pass a large soda

ban in his state, Mississippi (a state with the highest obesity rate in the

U.S) signed a bill prohibiting any future laws from limiting what

Mississippians eat or drink (Ford, Sutton, and Yan 2). The New England

Journal of Medicine captures this libertarian spirit perfectly stating, “The

American emphasis on the value of individual responsibility creates a

reluctance to intervene in what are seen as personal behavioral choices”

(Schroeder). Furthermore, when it comes to taxes, states have the most

influence. For example, taxing cigarettes as well as banning smoking in

public places in the United States, were sparked at the state level, not the

federal (Klein & Dietz 391). The already growing fear from states such as

Mississippi as well as others manifests the lack of support for national fat

taxes in the U.S.

Fat taxes are also concerning to states and people because they are

a regressive tax and the economic tax incidence falls on workers, whether

they are obese or not. In the United States as well as many countries, those

in lower income brackets purchase more fast food (Popkin 4). Fatty food

tastes better and more importantly is significantly less expensive.

Imposing a fat tax would hurt this group’s disposable income

considerably, and therefore can never gain full support without large

subsidies granted on healthier fruits and vegetables by the government.


49

In addition as a regressive tax, fat taxes can hurt those who do not

even purchase fatty foods. The 328,000 employees at Nestle, 142,000

employees at the Coca-Cola plant in Georgia, 297,000 employees at

PepsiCo, 34,000 employees at Dole Food Company, 35,000 employees at

General Mills, and the other numerous workers at food corporations in

America would all suffer (9). Taxing fatty foods creates a decreased

demand for these products and hurts the economic prosperity of the

workers at these plants.

Along with disrupting these markets, taxing fatty foods faces

another problem: there is no negative externality associated with obesity.

Other than maybe the occasional inconvenience of being squeezed into

your airplane seat because an obese person is too large to fit comfortably

in their own, there really are no dire impacts of obesity on others.

Although some suggest that obese people place a burden on others with

their large health care costs, it is difficult to pinpoint a disease on obesity

when other factors may be at play. As a counter example, energizing the

nation against preventative measures for smoking was easy because

breathing in second hand smoke harms others. Economists Jonathan Klein

and William Dietz, emphasize in their paper “Childhood Obesity: The

New Tobacco,” the fact that successful health movements such as tobacco

9 These statistics can easily be found at the respective companies websites


50

control measures were successful because they had the “mobilization of

grass-roots” to establish a threat within the nation (Klein & Dietz 389).

Although there are some movements in the United States to decrease

obesity, the majority are captured by the private sector. Take

WeightWatchers and Curves for example, both of which are lucrative

private businesses. Our government has not supplied these services, since

the public has not demanded them—at least not on a large-scale level. In

fact, the government is funding organizations that help ease acceptance of

obesity. The National Association to Advance Fat Acceptance (NAAFA)

was established in 1969; implemented into society early on as a founding

step towards social acceptance of obese people. As well, attitudes in

America have changed from fawning over thin supermodels, to demanding

more plus sized models (Olson 1). Dove’s campaign on “Real Beauty”

over the past few years has emphasized the curves and fat of everyday

woman and portrayed them as both lovely and alluring. Today, popular

groups on Pinterest and Tumblr are popping up under the name “Fat

Power”, encouraging women to embrace their love handles with catchy

and comical phrases. Groups and organizations such as these demonstrate

that there is not a strong social disapproval towards obesity in America.

In order to initiate disapproval, Americans need solid science to

back up policy. The research on what foods cause obesity, as mentioned


51

earlier, is simply not there. Imposing a fat tax will never bear the urgency

it requires, without the scientific research first. The prevention of

smoking was not successful until a plethora of scientific studies were

released (Klein, Jonathan, & Dietz 1). Causality between foods and

obesity have not been solidified, evoking an irresolute attitude within the

nation. Currently, America simply does not have the national support

when it comes to obesity measures, making fat taxes inherently faulty

measures to adopt at this time.

The World Health Organization ranked the U.S. as the 8th

most

obese nation, and we are one of the few countries at the top without

cultural fattening ceremonies (“World's Top 10 Most Obese Countries”).

America is starting to realize that obesity is a problem, yet it is not

perceived as an immediate threat. Historically weak elasticities among

targeted fatty foods, variations on what constitutes “bad” food, and

nonexistent national support for fat food taxes, demonstrate that

combating obesity using a fat tax will not be successful in the United

States. These reasons evince the need for reforms other than taxes when it

comes to preventing and reducing obesity rates in America.


52

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54

US Stock Market as a Leading Indicator of the US Economy

Ryan Johnsrud

Abstract

The most recent stock market crash began slightly prior to the

recession of late 2007. Did the stock market crash predict the heavy

decline in economic activity? This paper deals with this question more

broadly, asking if the stock market is a leading indicator of the economy.

There are two main arguments in favor of the stock market as a leading

indicator of economic activity. The first is that stock prices already factor

in expectations about the future and so give hints about what is to come.

The second is the wealth effect theory, which states that when stock prices

rice, investors are wealthier and spend more, increasing consumption and

thus GDP. Using carefully selected macroeconomic data to present an

overall picture of US economic activity and the S&P 500 index to

represent the broader US stock market, this study also attempts to show

what state the economy would have been in between 2008 and 2011 had a)

the stock market decreased less violently, or b) the stock market not

crashed at all. A vector autoregressive (VAR) system is estimated with

this data, and subsequent conditional forecasting is performed using

different scenarios of changes in the S&P 500. This paper finds that the

stock market is indeed a leading indicator of the US economy, and the

Great Recession would not have been so great had the stock market not

crashed so intensely.

1. Introduction

The Great Recession forced macroeconomic and stock market

information to the front pages of newspapers and websites everywhere, to

the forefront of the 2008 presidential debate, and into everyday

conversations across the country. The National Bureau of Economic

Research (NBER) announced that the recession officially began in

December 2007 and ended in June 2009. Another economic data source,


55

Fedreal Reserve Economic Data (FRED) reports that the Standard and

Poor’s (S&P) 500 Index declined 57% from its peak in October 2007 and

bottomed out in March of 2009.10

Families saw college and retirement

funds dwindle in value, corporations saw their market capitalizations

decrease substantially, unemployment levels climbed rapidly. In sum, US

economic growth stalled.

Because the stock market (represented by the S&P 500 index)

began to quickly decline from its October ’07 peak, should economists,

politicians, corporations, and average Americans have known the

recession was coming? In other words, if the stock market crashes, is

heavy economic contraction imminent? This paper attempts to answer the

question: is the stock market a leading indicator of US economic activity?

The paper also examines Granger causality of different macroeconomic

and financial measures, and out-of-sample forecasts of US economic

activity by a simple model constructed using coefficients from the

estimated VAR. Conditional forecasting is used to provide a view into

macroeconomic health if 1) the stock market not crashed in late 2007 and

2) the stock market crash not been as severe.

Much has been written about the stock market as a leading or

lagging indicator of US economic expansion/contraction. Many of the

10 See Appendix A for an explanation as to why S&P 500 is used to represent the whole

stock market.

“US Stock Market as a Leading Indicator” by Ryan Johnsrud

56

arguments as to why stock price movements are a leading indicator center

around two key points:

1) valuation of stock prices based on future dividend

expectations, and

2) the “wealth effect.”

The Dividend discount model (Gordon 1959) values a stock price by

estimating the value of all future dividends (cash returned to shareholder)

and calculating their present value. This simple way of valuing a share

price factors in future dividend expectations. Increasing profitability

indicates increasing cash balances in corporate America, and is often

followed by increased dividend payouts. Since stock prices reflect

expectations about future profitability, changes in stock prices are thought

to reflect changes in future profitability, and profitability is directly linked

to economic activity. However, dividends are never certain until the

shareholder receives the money, so expectations surrounding future

dividends are subject to human error. Some US corporations have upwards

of 30 equity research analysts at financial firms who are paid (very well in

some cases) to cover the company’s every move and recommend its stock

with a “buy,” “hold,” or “sell” rating. So dividend expectations can be

reliable, but not perfect, and thus stock market movements have the

potential to mislead the direction of the US economy.


57

There are also professionals around the world who are paid (also

very well) to analyze the broad US stock market and make predictions

about what will happen in the near- and long-term future. Financial firms

then sell this advice to fund managers, portfolio managers, and the general

public alike. Despite all of this coverage on the stock market, investors are

the ones who drive the market. Average, everyday investors buy and sell

stocks as do managers of large endowments and mutual funds. This is

where the human error concept comes in. The psychology of investing is

very complex; fear and greed are the two key emotions that motivate many

trades (both smart and foolish).11

This human error is why expectations of

future share price of overall stock market movements, while generally

correct, can be misleading. Comincioli cites the stock market crash of

1987 as evidence. The drop in stock prices falsely predicted the fate of

economic growth, as the economy continued to grow until the early 1990s.

Similarly, this author cites a study by Robert J. Barro in 1989 which

revealed that stock prices predicted three recessions (1963, 1967, 1978)

that did not occur. The stock market is not a perfect leading indicator.

Pearce (1985) cites the “wealth effect” as another argument as to

why the stock market could potentially lead the direction of the economy.

Theory suggests that as stock prices increase, the value of investors’

11 For more information on fear and greed in the financial markets, see Bernstein (2009).


58

portfolios increases, thus investors become wealthier and spend more.

This causes a decrease in consumption (C), and (because consumer

spending is a hefty portion of total GDP) in economic activity. On the

other hand, when stock prices decrease, investors become less wealthy,

therefore they spend less, and the economy contracts. Poterba and

Samwick (1995) investigated this theory, and found clear evidence that

stock price changes foreshadow growth in consumer spending, in

particular spending on consumer durables (goods that don’t wear out

quickly: cars, furniture, household appliances, cellphones).

This paper uses US economic and stock market index data to

investigate the stock market as a leading indicator of the broad US

economy. A vector autoregressive (VAR) model is used to determine

Granger causality. Simple models are estimated, and then conditional

forecasts are made to compare the baseline scenario to what would have

happened had the stock market not crashed in late 2007. Section two

discusses prior literature on leading indicators (including the S&P 500

index and the NYSE Composite index), section three outlines econometric

methods employed, section four describes data used, section five outlines

results and interpretations, and section six concludes.


59

2. Literature Review

There has been substantial econometric research performed in

order to gauge the appropriateness and accuracy of several economic and

financial measures as leading indicators of the US economy as a whole.

This type of research, and the models that emerge from it can be extremely

helpful in forecasting the economic performance of the country. Some

authors include discussions about multiple leading indicators combined

into one indicator with very good predictive accuracy, while others merely

consider one economic indicator, whether it’s stock prices, money supply,

weekly manufacturing hours, interest rate spreads, etc. For the purposes of

this paper, the focus is on past literature that concerns stock prices as an

economic indicator alone or combined with other economic or financial

measures.

The papers reviewed in this section use different econometric

models and concepts to investigate stock prices as a leading indicator of

future economic activity. Estrella and Mishkin (1998) utilize a probit

model, where the dependent variable can take on only two possible values.

In this probit model, the two possible values are

0 – not in recession

1 – in recession.


60

The authors also develop a forecasted probability of a future recession; a

probability that is of great interest to politicians, policymakers,

corporations, small-business owners, college students, and families across

the country. On the other hand, Stock and Watson (1989) utilize a VAR

(Vector Autoregressive) system and Granger causality to test multiple

financial and economic measures (including stock prices) as leading

indicators. Comincioli (1996) also uses the causality test originally

proposed by C.J. Granger in 1969 to see if changes in stock prices

“Granger cause” changes in economic activity. Comincioli focuses solely

on the stock market as a leading indicator, ignoring the appropriateness of

other factors as leading indicators.

Nearly every piece of literature relating to the movement of stock

prices as a leading indicator for the economy comments on the reasons

why stock prices are a possible leading indicator. Most papers comment

on the traditional valuation of stock prices (the expected discounted values

of future dividend payments) and the wealth effect, but Stock and Watson

offer another reason as to why the stock market could be a leading

indicator of economic activity. They discuss the role of stock prices as a

determinant of the cost of capital. This theory suggests that changes in a

corporation’s share price leads to changes in its capital structure, which in

turn can either increase of decrease the cost of capital. If a company


61

decreases its equity financing relative to its debt financing, the overall cost

of capital decreases, because equity is more expensive than debt. If capital

is less expensive, business spending increases, and the economy most

likely expands. And the leading indicator in this case would be a change in

stock prices.12

Similar results are achieved in the three papers discussed above.

Comincioli employs a Granger causality test to determine the leading

indicator effect. First, the author establishes a relationship between GDP

and the S&P 500 by regressing the % change in GDP over the percent

change in the S&P 500 lagged 6 quarters. 3 lags are statistically

significant, and positively related to GDP (the economy). Next, the author

estimates an unrestricted and a restricted OLS model and uses an F-test to

determine if the lagged S&P 500 terms belong in the regression when the

lagged GDP terms are in the equation with the % change in GDP as the

dependent variable. The conclusion is that the coefficients on the lagged

S&P 500 terms are not zero, and that stock prices do Granger cause the

economy. After interchanging the S&P 500 terms with the GDP terms and

running the same F-test, the F-statistic is not large enough to reject the null

hypothesis that the %GDP coefficients are 0. This result suggests the

economy does not Granger cause the stock market.

12 Benninga (2006).


62

Stock and Watson conclude that the growth of the S&P 500

Granger causes changes in the “state of the economy.”13

However, this

result is obtained from a simple regression of the state of the economy

variable on 12 lags of the growth in the S&P 500 index. Using a VAR

including other elements of the LEI (index of Leading Economic

Indicators), growth in the S&P 500 has no marginal predictive content for

the state of the economy. So the stock market is in fact a leading indicator

of the state of the economy, however examining other factors can capture

the expectations inherent in the stock market.

Estrella and Mishkin focus on variables predicting a recession

rather than predicting quantitative measures of future economic expansion

or contraction. Using their probit model, the authors come to similar

conclusions to those of the previous two papers, examining out-of-sample

results. As an example, take Q2 1971. The model is estimated using data

from the beginning of the sample up until the second quarter of 1970, and

then a forecast of recession or not is made for the second quarter of 1971.

The authors conclude that stock prices are indeed useful predictors,

particularly one to three quarters ahead. Estrella and Mishkin explain that

they are not proposing the stock market as a replacement to more complex

13 A measure of the CEI – the Coincident Economic Indicator index: industrial

production, personal income, manufacturing and trade sales, and employee-hours in non-

agricultural establishments.


63

macroeconomic forecasting models, but that the stock market can serve as

a simple check.

3. Data

United States economic data is tracked very closely by various

government and non-government organizations. Investors, corporate

boardrooms, small business owners, college students, and more anxiously

await the release of weekly, monthly, quarterly, and yearly data, as it

offers a glimpse of the current health of the economy. Indices and

measurements of housing prices, unemployment insurance claims, GDP,

etc. are frequently reported. This data is readily available (some data

requires a small fee) from online databases. Federal Reserve Economic

Data (FRED), maintained by the Federal Reserve Bank of St. Louis is a

database that contains data obtained from agencies like the U.S. Census

and the Bureau of Labor Statistics (BLS). The US Bureau of Economic

Activity is another government agency that provides timely and relevant

economic data to its users, who then report it, use it in forecasting models,

and compare how their forecast compares to the actual number for the

reported period.

Stock market data is also very widely and readily available. A visit

to Yahoo! Finance offers archived stock prices and index values many


64

years into the past. The S&P 500 index is one of the most quoted indices

in the United States and in the world, because is serves as a national index

for the US stock market. S&P 500 index data was retrieved from the same

place as all of the macroeconomic data.

Data used in the VAR systems for this paper is all conveniently in

one place and was fairly easy to compile. The St. Olaf College Economics

department purchases data from Haver Analytics in the form of an EViews

database. The U.S. Economic Statistics (Haver) database contains

economic and financial data provided by government, public, and private

organizations. This database contains over 40,000 time series for the US

economy, everything from housing and construction to international trade

and business cycle indicators, dating back to 1947 in some cases.

Economic data used in the estimation of the VAR systems and

conditional forecasting is quarterly. Included in this data are GDP (Gross

Domestic Product), unemployment rate, CPI (Consumer Price Index),

trade-weighted value of the USD (United States dollar) versus major

currencies, exports of goods and services, M2 (money supply), and finally

the S&P 500 index. This combination of economic measures was chosen

to present an appropriate overall picture of the economy. Some papers

may use GDP alone to represent economic activity, but in this analysis

more data was deemed necessary for proper estimation.


65

GDP was included in the estimation because it is an overall

measure of economic activity. Recessions and depressions are

characterized by certain movements of GDP over time, and therefore it is

a vital measure of US economic health. Many aspects of the economy are

reflected by GDP in some way, as the formula includes Consumer

Expenditures, Investment, Government Expenditures, and Net Exports.

Unemployment rate was used to represent the health of the job

market. This is a big topic for college students as they enter the workforce.

They see first-hand how difficult it was to find a job during and

immediately following the recession of late ‘07 – early ‘09. The Consumer

Price Index was included to get a glimpse into price levels in the US and

see what all this means for inflation.

The trade-weighted value of the USD versus major currencies is a

good barometer of how much the national currency in this country is

worth when compared to those of other major players in the world

economy. In calculating this value, more weight is assigned to currencies

of nations with whom the US engages in more export/import relations.

Major currencies in this comparison belong to the European area, Canada,

China, Japan, Mexico, United Kingdom, and Australia. Along with the

value of the US dollar compared to other major currencies, exports were


66

included in the VAR to help represent external views of and relations with

the United States economically.

M2 was included in the model to gauge the monetary policy

activity by the Federal Reserve. This is especially interesting in the

conditional forecasting model, examining how M2 reacts based on the

estimated VAR when the S&P 500 is manipulated. If the stock market had

not crashed in 2007, the money supply would have been much different

because there would most likely have been no stimulus package or

quantitative easing (buying financial assets from banks to increase the

money supply – M2) necessary. Finally, the S&P 500 index is used in this

econometric analysis to represent the broad United States stock market.

This index and its representation of the US market are explained in further

detail in Appendix A.

All of these variables, except for one, are measured in USD. The

unemployment rate (lr) is already a percentage. For ease of interpretation

and comparison, each variable except lr has been logged. For a small

change (d) in a variable (x), ln(x+d) – ln(x) = d/x (approximately). By

logging the variables, VAR and conditional forecasting are actually

estimated using (approximate) % change in each of the data sets described.

Variable y (used in VAR estimation) is actually log(gdp), variable m is


67

actually log(m2) and so forth. Variables in the model are defined as

follows:

y = log(GDP)

p = log(CPI)

x = log(exports)

fx = log(trade-weighted value of USD)

m = log(m2)

sp = log(S&P 500)

lr = unemployment rate

Conditional forecasting with the estimated VAR provides an

interesting view of many of these variables and how their trajectory would

have been altered had the stock market crash not occurred beginning in

late ’07. The next section describes how this data was used in estimating a

VAR and performing conditional forecasting.

4. Econometric Methods

This section describes estimation and models used en route to

results and interpretation. First, a VAR system was estimated using all

seven variables described above. A VAR system contains a set of

variables, and expresses each of these variables as a linear function of


68

itself and all of the other variables in the set. Here is a simple example of a

VAR:14

So it’s similar to an OLS, except that Z, A, and epsilon are vectors of

endogenous variables, coefficients, and residuals, respectively. P is the lag

length. The lag length in the VAR for this paper was selected using the

rule of thumb to capture at least a full cycle of data, (# periods per year + 1

extra period) 4 quarters in a year + 1 = lag length 5. The system was

estimated over a span of 32 years (1975q1-2007q4). The results of the

Granger causality tests are included in Appendix B (all output discussed is

included here). From this, the causality ordering is as follows, starting

with the most exogenous variable and continuing through to the most

endogenous:

sp→x→y→lr→p→m→fx

SP X Y LR P M FX

SP --

X * -- *

Y ** -- **

LR ** * * --

P * --

M * ** * -- *

FX * *** *** --

14 Wooldridge (2012).


69

After the re-ordering, Lag Exclusion Tests were performed, and the 5th

lag

cannot be excluded, as the p-value on fx is 0.031516 and we reject the null

hypothesis that Lag 5 can be excluded. Also this VAR is stable, as the

greatest autoregressive root is 0.996732. As shown in the results, no

variable statistically significantly Granger causes changes in the S&P 500

index. SP is does not lag the US economy in this time period based on

these variables and this economic data. Because it is clearly exogenous,

another VAR is estimated with the 4 lags of the variable sp as exogenous

variables. The same ordering is used with the remaining exogenous

variables in this new VAR. This new VAR has a joint p-value for the 5th

lag exclusion test of .061419, so the lag length remains at 5. As far as

stability, the new VAR with sp as an exogenous variable also satisfied the

stability condition as its largest AR root is 0.982445.

Next, conditional forecasting was done with a simple model made

from the new VAR with lags of sp as exogenous variables. This model

uses all the coefficients from the new VAR to estimate a value of each

variable as the dependent variable. Forecasts are made from 2008q1 to

2011q2 in order to compare with the actual data. The first case is the

baseline test, where the model simply forecasts as it is. Then the

conditional forecasts are made. Scenario 1 is where .1 is added to the


70

variable sp in each quarter from 2008q4 to 2011q2. This means that the

stock market would still decrease as it did in early 2008, but the decrease

would be 10% less than what it actually was. Thus, in scenario 1, the stock

market crashed, but not as bad as what actually transpired between ’07 and

’09. Scenario 2, on the other hand, forecasts what would have happened if

the S&P 500 continued to grow at the average growth rate from 2002q1 to

2007q4. Here sp is regressed on a constant and a trend variable in the

given period to get the average growth rate. (This regression is shown in

Appendix 2) Then forecast sp using this simple OLS model from 2008q1

onwards. This way, the stock market never crashed, it continued on its 5-

year historic path of growth. Note the graph below of the baseline

scenario, scenario 1, and scenario 2 of sp for visual clarity.


71

5. Results and Interpretation

From the original VAR, after performing the Granger causality

tests and discovering that not one of the logged macroeconomic variables

Granger causes changes in the S&P 500 index, it is rather safe to say that

the S&P 500 does not lag the US economy. Further, it can be said that the

S&P 500 index may be a leading indicator of the US economy, as it

adding in lags of the logged S&P 500 creates a statistically significant

6.6

6.8

7.0

7.2

7.4

7.6

7.8

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

SP (Scenario 1)

SP (Scenario 2)

SP


72

effect on three fairly exogenous variables themselves (y – GDP, lr – the

unemployment rate, and x – exports,). From the original VAR it is

apparent that changes in the S&P 500 are completely exogenous from

changes in the six macroeconomic variables in that VAR system. Next, the

effects of changing the conditions of the S&P500 index on GDP, the

unemployment rate, and exports are discussed.

Effect of different S&P 500 conditions on y

Note that in scenario 1, where the stock market decreased but it was a 10%

less awful than the crash that actually occurred, y (the log of GDP) only

9.44

9.46

9.48

9.50

9.52

9.54

9.56

9.58

9.60

I II III IV I II III IV I II III IV I II III IV I II III IV I II III IV

2007 2008 2009 2010 2011 2012

Y Y (Baseline)

Y (Scenario 1) Y (Scenario 2)


73

declines between 2008q4 and 2009q2, so there still may have been a

recession, but it would not have been as substantial or as lengthy as the

Great Recession. In scenario 2, if the stock market had continued growing

at the actual average growth of 2002-2007, GDP would have continued its

upward trajectory as well and there would have been no recession. This

provides more evidence for the stock market (S&P 500) as a leading

indicator for the economy.

Effects of different S&P 500 conditions on lr

Unemployment spiked during the recession, shown by the blue line which

represents the actual unemployment rate. In conditional scenario 1,

4

5

6

7

8

9

10

11


2007 2008 2009 2010 2011 2012

LR LR (Baseline)

LR (Scenario 1) LR (Scenario 2)


74

similar to Gross Domestic Product, unemployment would still spike, but

not as much as the recessionary unemployment spike. In scenario 2, the

unemployment rate stays fairly steady from its 2007 level, and actually

slowly decreases. This represents a healthy job market if the stock market

had not crashed in late 2007.

Effect of different S&P 500 conditions on x

By now the reader can probably anticipate how scenario 1 and 2 will

affect exports. In scenario 1 exports stay above recessionary levels, and in

scenario 2 exports continue on their long-term growth trajectory. All these

7.25

7.30

7.35

7.40

7.45

7.50

7.55

7.60


2007 2008 2009 2010 2011 2012

X X (Baseline)

X (Scenario 1) X (Scenario 2)


75

effects together form a fairly strong argument in favor of the US stock

market as a leading indicator of the US economy.

6. Conclusion

These interpretations are not to argue that the stock market alone

caused the recession, and that the stock crash had no other causes. Those

topics are a whole other subject of debate, and much has been written and

analyzed with respect to causes of the Great Recession. What this paper

argues is that the stock market is exogenous with respect to

macroeconomic variables, and that had the stock market not crashed in

late 2007, the economy would have followed the stock market’s lead and

continued on its long-term growth trajectory. According to this VAR

model and its conditional forecasting, the recession would have been

shorter and less severe had the stock market decreased 10% less than it

did. Additionally, and fairly intuitively, the recession would not have

happened had the stock market continued on its average growth rate

trajectory between 2002 and 2007. This study concludes that the stock

market (represented by the S&P 500 Index) is a leading indicator for US

economic activity.

This is but a simple analysis; there are many topics for further

investigation in this field. Some of these include matters such as:


76

Is the S&P 500 Index the best representative of the broader

US stock market, or should a different index be used?

How many times has the stock market given a false signal

about the direction of the economy?

What is the stock market’s success rate at predicting US

recessions?

How does the stock market compare to other measures that

are referred to as “leading indicators” of the economy?

And many more. The results in this paper may be helpful to economists

and policymakers as they attempt to build more sophisticated models. This

model is not meant to replace any forecasting models, because there are

other leading indicators as well which forecast as well or better than the

stock market. This simple model can be used within another model, or

serve as a simple check to the more complicated models.


77

Appendix A: S&P 500 Index

The Standard & Poor’s 500 Index is a stock market index that

tracks the 500 most widely held stocks on the New York Stock Exchange

(NYSE). It is generally considered to be representative of the total United

States stock market, as it reflects the risk-return characteristics of the

largest companies in America. Standard & Poor’s indicates that the

committee responsible for maintaining (addition/removal decisions, etc.)

the S&P 500 index attempts to ensure that the index remains a leading

indicator of US stocks.15

Informally, the S&P 500 is a good representative

of the health of corporate America. Although it does not directly reflect

the performance of small- and medium-sized corporations or any private

businesses, a vast majority of these entities sell goods and services through

larger companies and thus are indirectly reflected in the S&P 500 which

tracks the performance of shares of the largest US companies. The largest

holdings in the index include Apple Inc., Exxon Mobil Corp., General

Electric Co., Google Inc., Pfizer Inc., etc.

15 S&P 500 Factsheet (http://us.spindices.com/documents/factsheets/fs-sp-500-

ltr.pdf?force...true%E2%80%8E)


78

Appendix B: Model Output and Charts

1. Granger Causality for original VAR with all variables as

endogenous

VAR Granger Causality/Block Exogeneity Wald

Tests

Date: 12/18/13 Time: 14:00

Sample: 1975Q1 2007Q4

Included observations: 127

Dependent variable: SP

Excluded Chi-sq df Prob.

P 9.195607 5 0.1015

M 1.627866 5 0.8979

LR 2.862956 5 0.7211

Y 0.740531 5 0.9807

X 7.882102 5 0.1629

FX 4.250607 5 0.5139

All 38.35387 30 0.1409

Dependent variable: P


SP 5.235294 5 0.3878

M 3.564758 5 0.6136

LR 10.44379 5 0.0636

Y 2.930824 5 0.7107

X 3.000720 5 0.6999

FX 4.942534 5 0.4229

All 48.66480 30 0.0170

Dependent variable: M


79


SP 4.578384 5 0. 4695

P 9.796535 5 0.0812

LR 13.40563 5 0.0199

Y 10.90745 5 0.0532

X 2.191492 5 0.8221

FX 10.57508 5 0.0605

All 76.62607 30 0.0000

Dependent variable: LR


SP 12.01836 5 0.0345

P 5.178249 5 0.3945

M 5.524131 5 0.3553

Y 9.679729 5 0.0848

X 10.03173 5 0.0743

FX 8.316988 5 0.1396

All 56.93654 30 0.0021

Dependent variable: Y


SP 11.08655 5 0.0497

P 4.669482 5 0.4575

M 1.021198 5 0.9608

LR 14.86652 5 0.0109

X 6.397807 5 0.2694

FX 1.966302 5 0.8538

All 53.99197 30 0.0046

Dependent variable: X



80

SP 9.877321 5 0.0788

P 1.510621 5 0.9118

M 6.643644 5 0.2485

LR 4.349350 5 0.5003

Y 3.551835 5 0.6156

FX 12.34010 5 0.0304

All 68.64448 30 0.0001

Dependent variable: FX


SP 9.099344 5 0.1052

P 21.26463 5 0.0007

M 18.20451 5 0.0027

LR 9.727446 5 0.0833

Y 8.663596 5 0.1233

X 4.451813 5 0.4864

All 44.17647 30 0.0460


81

2. Lag Exclusion Tests for original VAR

VVAR Lag Exclusion Wald Tests

DDate: 12/18/13 Time: 14:32

SSample: 1975Q1 2007Q4

IIIncluded observations: 127

CChi-squared test statistics for lag exclusion:

Nnumbers in [ ] are p-values

SP X Y LR P M FX Joint

Lag 1 137.2269 117.6186 125.4653 162.8454 218.9442 228.3195 139.5034 1027.956

[ 0.000000] [ 0.000000] [ 0.000000] [ 0.000000] [ 0.000000] [ 0.000000] [ 0.000000] [ 0.000000]

Lag 2 8.436880 9.534107 11.87405 7.399297 26.98292 35.99694 17.73660 111.8699

[ 0.295650] [ 0.216548] [ 0.104778] [ 0.388521] [ 0.000336] [ 7.26e-06] [ 0.013217] [ 8.05e-07]

Lag 3 12.02972 8.569125 5.054153 2.808760 23.48394 20.55103 8.986765 79.48111

[ 0.099586] [ 0.285088] [ 0.653355] [ 0.902112] [ 0.001403] [ 0.004495] [ 0.253608] [ 0.003812]

Lag 4 6.605649 9.195626 2.885306 2.289260 12.99945 11.15129 11.63935 56.33660

[ 0.471060] [ 0.238914] [ 0.895406] [ 0.942113] [ 0.072122] [ 0.132154] [ 0.113060] [ 0.219567]

Lag 5 6.001909 10.69869 4.389959 4.308822 1.923263 8.289551 15.37199 60.25364

[ 0.539526] [ 0.152313] [ 0.733924] [ 0.743599] [ 0.963965] [ 0.307755] [ 0.031516] [ 0.130074]

df 7 7 7 7 7 7 7 49


82

3. AR Roots for original VAR

Roots of Characteristic Polynomial

Endogenous variables: SP X Y LR P M FX

Exogenous variables: C

Lag specification: 1 5

Date: 12/18/13 Time: 14:31

Root Modulus

0.996732 0.996732

0.968921 - 0.096761i 0.973740

0.968921 + 0.096761i 0.973740

0.949862 - 0.040872i 0.950741

0.949862 + 0.040872i 0.950741

0.930411 - 0.186064i 0.948833

0.930411 + 0.186064i 0.948833

0.806539 - 0.354352i 0.880949

0.806539 + 0.354352i 0.880949

0.466003 + 0.618485i 0.774392

0.466003 - 0.618485i 0.774392

0.555095 - 0.508580i 0.752851

0.555095 + 0.508580i 0.752851

-0.077458 + 0.745182i 0.749197

-0.077458 - 0.745182i 0.749197

-0.184835 + 0.710848i 0.734485

-0.184835 - 0.710848i 0.734485

0.333427 - 0.634092i 0.716412

0.333427 + 0.634092i 0.716412

-0.699853 0.699853

-0.356626 + 0.591189i 0.690425

-0.356626 - 0.591189i 0.690425

0.012601 - 0.683634i 0.683750

0.012601 + 0.683634i 0.683750

-0.564365 - 0.379819i 0.680272

-0.564365 + 0.379819i 0.680272

-0.443059 - 0.494033i 0.663604

-0.443059 + 0.494033i 0.663604

0.640587 + 0.063885i 0.643765

0.640587 - 0.063885i 0.643765

-0.559658 - 0.312044i 0.640772

-0.559658 + 0.312044i 0.640772

0.286815 + 0.432876i 0.519273

0.286815 - 0.432876i 0.519273

0.141799 0.141799

No root lies outside the unit circle.

VAR satisfies the stability condition.


83

4. sp forecast for scenario 2

Dependent Variable: SP

Method: Least Squares

Date: 12/17/13 Time: 20:11

Sample: 2002Q1 2007Q4

Included observations: 24

Variable Coefficient Std. Error t-Statistic Prob.

C 4.554757 0.254770 17.87790 0.0000

@TREND 0.020974 0.002128 9.854230 0.0000

R-squared 0.815290 Mean dependent var 7.061120

Adjusted R-squared 0.806894 S.D. dependent var 0.164250

S.E. of regression 0.072178 Akaike info criterion -2.339719

Sum squared resid 0.114611 Schwarz criterion -2.241548

Log likelihood 30.07663 Hannan-Quinn criter. -2.313674

F-statistic 97.10586 Durbin-Watson stat 0.605764

Prob(F-statistic) 0.000000


84

5. Remaining conditional scenario graphs

m

8.80

8.85

8.90

8.95

9.00

9.05

9.10

9.15


2007 2008 2009 2010 2011 2012

M M (Baseline)

M (Scenario 1) M (Scenario 2)


85

fx

4.32

4.36

4.40

4.44

4.48

4.52


2007 2008 2009 2010 2011 2012

FX (Scenario 2) FX (Scenario 1)

FX (Baseline) FX


86

p

5.78

5.80

5.82

5.84

5.86

5.88

5.90

5.92

5.94

5.96


2007 2008 2009 2010 2011 2012

P P (Baseline)

P (Scenario 2) P (Scenario 1)


87

References

Benninga, Simon. Principles of Finance with Excel. New York: Oxford

University Press, 2006.

Bernstein, William J. The Investor's Manifesto: Preparing for Prosperity,

Armageddon, and Everything in Between. 2009.

Comincioli '95, Brad "The Stock Market as a Leading Indicator: An

Application of Granger Causality," The Park Place Economist: Vol. 4

Estrella, Arturo, and Frederic S. Mishkin. "Predicting US recessions:

Financial Variables as Leading Indicators." Review of Economics

and Statistics 80, no. 1 (1998): 45-61.

Gordon, Myron J. "Dividends, Earnings, and Stock Prices." The Review of

Economics and Statistics 41, no. 2 (1959): 99-105.

Pearce, Douglas K., "Stock Prices and the Economy," Federal Reserve

Bank of Kansas City Economic Review, November 1983, pp. 7 22.

Poterba, James M., Andrew A. Samwick, Andrei Shleifer, and Robert J.

Shiller. "Stock Ownership Patterns, Stock Market Fluctuations,

and Consumption." Brookings papers on economic activity 1995,

no. 2 (1995): 295-372.

Stock, James H., and Mark W. Watson. "New Indexes of Coincident and

Leading Economic Indicators." In NBER Macroeconomics Annual

1989, Volume 4, pp. 351-409. MIT Press, 1989.

Wooldridge, Jeffrey M. Introductory Econometrics: a Modern Approach.

Cengage Learning, 2012.


88

Obamacare: Healthcare Reform and the Ailing Labor Market

Annabel Ansel

President Obama’s Affordable Care Act (ACA), has not only fallen

short of the lofty goals of affordable, universal health insurance coverage,

but has left ruinous impacts on the labor market. Hoping to revamp what

he and his supporters viewed as the inadequate and inefficient healthcare

system of the United States, President Obama signed the Patient

Protection and Affordable Care Act into law on March 23, 2010 (The

Kaiser Family Foundation). Contrary to supporters’ expectations, the

outcomes of this already controversial legislation, commonly known as

Obamacare, has caused, and will continue to cause, problems for

individuals and the economy as a whole. While the health of U.S. citizens

was supposed to be improved by the ACA, their economic wellbeing is

more likely at risk when the legislation’s implications for labor are

considered. Strikingly, initial reports show minimal progress in enhancing

healthcare accessibility and affordability, underscoring the validity of

predictions that “Obamacare’s distortions to the labor market will

outweigh any growth from lowering health costs (“Health Reform and

Employment”). Due to the negative impact on labor demand, the ensuing

reduction in employee wages, and ultimate decrease in the supply of labor,

Obamacare should be promptly defunded and rescinded. This conclusion


89

is supported not only by concrete economic theory and empirical findings,

but through ethical reasoning as well.

To fully understand its detrimental effects on the labor market, a

basic explanation of the policies embodied in the Affordable Care Act is

critical. Generally, the Act aims to expand healthcare coverage, minimize

healthcare costs for individuals, and ease the process for purchasing

insurance plans. The individual mandate requires all citizens who can

afford coverage, with few exceptions, to purchase it or face a fine.

Similarly, the employer mandate forces all businesses with fifty or more

full-time employees (those working thirty or more hours a week) to offer

health insurance to these employees, or face a large penalty. Other

significant points include forbidding companies from denying coverage or

charging higher premiums based on preexisting conditions, and the

allowance of children up to age twenty-six to be covered by their parents’

plan. The ACA outlines various minimum benefit requirements on

healthcare coverage, and implements a number of spending cuts and tax

increases to fund its programs (The Kaiser Family Foundation). All of

these programs and mandates have already unfavorably impacted the labor

market, leading to the conclusion that although Obamacare was intended

to help people in many ways it has and in the future will, hurt them.

“Obamacare” by Annabel Ansel

90

The Affordable Care Act’s various policies and taxes have

increased immediate and expected future costs for businesses, producing a

diminishing demand for full-time employees, and labor in general. New

costs confronting businesses first come in the form of taxes. In order to

fund the multitude of programs, subsidies, and benefits provided by the

ACA, businesses and individuals will pay an additional 0.9% tax on

taxable income and an additional 3.8% on larger capital gains (Obamacare

Facts). As with any operation, future expenses are a vital factor in firm

production decisions. Thus, the large expense of the employer mandate,

requiring companies to offer preapproved health insurance plans to their

full-time employees or pay 60% of worker premiums, will inevitably

influence business strategy. If companies fail to comply, they will face a

fine of $2,000-$3,000 (Blase). The higher anticipated cost of production

will discourage expansion and reduce full-time labor demand (The

Manhattan Institute). Further, according to the National Health

Expenditures Survey, insurance premiums will rise by 7.9%, which is 4.1

% higher than without Obamacare in place (Tanner). Therefore, even

those employers already providing healthcare to employees will face

added, burdensome costs due to the reform. New costs of production

cannot easily be absorbed by businesses, especially those that are small or

struggling to stay afloat with current costs in the unstable economy. Even


91

large corporations, such as Delta Air Lines, expect an upsurge in expenses

that will affect hiring and compensation decisions (Thomas). It is

predicted by the Congressional Budget office (CBO) that the ACA will

cost businesses $52 billion in tax penalties from 2014-1019 (Blase). The

long list of new expenses imposed by Obamacare will ultimately alter

output and revenue because labor is a variable cost. While some firms

will respond by cutting benefits, many are cutting employment. With the

employer mandate, and general taxes, businesses are discouraged to hire

due to the new costs that each new worker represents, regardless of labor

productivity.

Interestingly, some economists argue that although wage cuts will

occur, unemployment will be a more common result as businesses search

for possible ways to offset costs. Because of substantial worker resistance

to wage reductions, as well as prohibitory minimum wage laws on firms,

the decline in wages that is possible, will not be enough to recoup the

significant amount of lost revenue due to the ACA (Tanner). Notably, a

recent National Association of Business Economics (NABE) survey

showed that in the Service Sector, 1 in 5 businesses believe health care

reform cost pressures have hurt employment in their firms in the last 3

months (Lange). There is clear disincentive for growth and thus loss of

future employment opportunities due to Obamacare and its policies.


92

This negative impact of the ACA on labor demand is illustrated

through a basic supply and demand graph (Figure 1). When the employer

mandate and other ACA policies are implemented, the additional costs

imposed on employers is viewed as part of the price in employing an

additional unit of labor. As labor costs rise, labor demand will fall, shifting

the curve from DL0 to DL

1, and causing the amount of labor hired at

equilibrium to be reduced from L0 to L1. This undesired decline in labor

demand, generated by the ACA is also observed on an isoquant and

isocost diagram (Figure 2). The slope of the isocost is the ratio of the price

of labor (PL), to the price of capital (PK). The price of labor not only

includes the monetary wage (W) for the unit of labor employed, but

implicitly, any benefits, such as healthcare, as well. With an increase in

the relative cost of labor, demonstrated by the steeper sloped isocost (I2),

the amount of labor employed by the firm, at the least-cost combination of

inputs, decreases from L1 to L2. Obamacare raises the unit price of labor

by way of taxes, penalties, and mandates on the firm. The adjustments

within Figures 1 and 2 depict the unfortunate effects this healthcare

legislation has on employment. Finally, consistent with the theory, the

Congressional Budget Office predicted that the amount of labor being

used by the economy will decrease by about half a percent, which is

approximately 7,000 additional Americans unemployed (Blase).


93

As discussed, the healthcare system reform inevitably leads to a

fall in the total demand for labor. Additionally though, because the

employer mandate only requires coverage for full-time employees, part-

time employment is not only falling to a lesser extent, but perhaps even

increasing, relative to full-time labor. Because shifting to a primarily part-

time workforce allows the similar, if not the same level of production for

many firms, and, “Companies don’t want to pay for health care

unnecessarily if they can avoid it”, they will do just that (Jargon, Cronin,

and Needleman). For the entire U.S. workforce, in 2013, the addition of

part-time employees seasonally adjusted averaged 93,000 a month, while

the addition of full-time employees averaged 22,000. The reverse was true

in 2012, as more full-time than part-time jobs were added. (Jargon,

Cronin, and Needleman). Also, 15% of service sector businesses plan to

shift to more part-time workers as result of healthcare reform costs,

according to an NABE survey (Lange). This shift in businesses hiring

predominantly part-time workers to avoid paying for insurance plans is

harmful to both the labor market and the individual employee. The

individual is an ‘involuntary part-timer’, underemployed at a suboptimal

indifference curve (I2), with more leisure (L2) than preferred (Figure 3).

Countless workers struggling to make ends meet will be forced to

moonlight, taking another part-time job. Also, these part-time workers are


94

usually not covered by their employer for health insurance, and thus pay

out of pocket, reducing their income further. In general, even though part-

time labor demand has increased to some extent, the result is contrary to

the intent of the Act: fewer people are provided coverage through

employers because they are part timers, and the cost of health care

coverage for those who once were full timers goes up, not down, making

health care less affordable for those people under the Act. In addition, the

large accumulation of costs now facing businesses has caused the overall

demand for labor to fall. These are fundamental signs that the Affordable

Care Act is doing more harm than good.

The second key point necessitating the dissolution of the ACA is

its effective drop in the market equilibrium wage for labor. A surge of

non-wage compensation, like the mandated employee healthcare, will

necessarily call for a cut in wages in order for the firm to remain on the

same isoprofit line, and continue business as is. As seen in Figure 4, this

downward movement along the isoprofit line will leave the worker at a

suboptimal combination of wages (W1) and fringe benefits (F1), on a lower

indifference curve (I2). One study, by Jonathan Gruber, in which

companies were required to provide health insurance with specific

childbirth benefits to their staff, “found strong evidence that employers

reduced wages to pay for the benefits”(Tanner, 9). These empirical


95

findings heavily support the theory outlined above. Because employers

pay their workers through a variety of forms besides wages, when one

mode of compensation increases, the others must decrease.

Obamacare’s costly implications for employers, which cause the

contraction in labor demand, is ultimately the reason for the falling wage.

While employment will be reduced in avoidance of the employer mandate,

Mercer, a financial consulting agency says that many firms, for one reason

or another will end up covering their employees. The cost increases will

force businesses to pay their employees less though, and one study found

that “every extra dollar spent on insurance comes out of wages” (“Health

Reform and Employment”). As explained by the basic Law of Supply and

Demand, when labor costs rise, labor demand DL0 will shift left to DL

1.

Equilibrium market wage paid to workers will consequently decrease from

W0 to W1 (Figure 1). Costs will especially surge for employers not

currently offering healthcare, but all employers will face costs regardless,

whether it be through taxes, penalties, compliance with the mandates or

simply higher premiums. Overall, due to the increase in the marginal tax

rate from Obamacare and parallel programs, Casey B. Mulligan estimates

a 17% fall in “the reward for working”, and a dampening of consumer

spending (Mulligan, p. 22). This fall in labor income is not only bad for

individual employees, but also for the consumer-driven U.S. economy in


96

general. Consumer spending is critically important to growth of the

economy and derived labor demand. Other compensation, besides pure

monetary wages alone, are being cut as well. For example, UPS is a

company for which Obamacare will increase costs by 4% for 2014, in

addition to health care inflation, resulting in another 7.25% increase in

costs. Along with similarly large companies, UPS, in response to the cost

increase, has been forced to reduce employee benefits such as coverage for

spouses (Thomas). The ACA will lead to lower wages and reduced

benefits across industries, as employers pass the reform’s costs onto their

employees.

As a third and final argument, multiple factors resulting from

Obamacare have caused a contraction in the aggregate supply of labor, as

seen through the actions of various groups of workers. Because of the

Affordable Care Act’s expansion of mandatory benefits included in the

new health insurance plans, premiums have increased for a majority of

individuals, even when the extra benefits included, such as maternity care

and the provision for dependents, are not needed. Due to the individual

mandate, those who do not qualify for Medicaid or government-subsidized

plans must pay out of pocket for their healthcare, thus reducing their

income significantly. This has led many to intentionally work less in order

to decrease their annual earnings, and qualify for government assistance


97

(de Rugy). This substantial subsidy results in an income effect for the

individual, as their earnings are essentially larger, and thus they are

incentivized to work less. A typical income/leisure diagram shows a labor

force participant’s choice to purchase more leisure (L2), shifting from I1 to

I2, because of the government transfer’s disincentive to work (Figure 5).

Many people are expected to leave the labor force, and older people will

be more likely to retire earlier (“Health Reform and Employment”). The

Congressional Budget Office agrees, stating, “The expansion of Medicaid

and the availability of subsidies through the exchanges will effective

increases beneficiaries’ financial resources. Those additional resources

will encourage some people to work fewer hours or to withdraw from the

labor market.” Moreover, the CBO found that the legislation would reduce

the total amount of labor by half a percent (Blase).

In addition to those choosing to work less, many individuals who

are already in the low-income position to qualify for government

subsidized healthcare coverage will automatically leave the workforce.

Craig Garthwaite, Tal Gross, and Matthew J. Notowidigdo calculated, “a

decline in employment of between 530,000 and 940,000”, due to their new

subsidy eligibility (Garthwaite, Gross, and Notowidigdo, p. 33). A factor

in this exit from the market can be explained, at least partially, by a

phenomenon known as ‘employment lock’. This is when individuals stay


98

in their jobs mainly because of the healthcare provided to them by their

employer. With the heavily subsidized public health insurance newly

available to many, workers feel less inclined to remain working

(Garthwaite, Gross, and Notowidigdo). The income/leisure graph in

Figure 6 shows this as well.

Although Yaa Akosa Antwi, Asako S. Moriya, and Kosali Simon’s

analysis of the Survey of Income and Program Participation (SIPP) reveals

the encouraging fact that more adults ages 19 to 25 are insured as

compared to before the healthcare reform, the ACA’s policy for

dependents will not be completely advantageous (Antwi, Moriya and

Kosali). Obama’s healthcare legislation, by allowing children to stay

under their parents plan until the age of twenty-six, will ultimately

decrease the supply of labor. Before Obamacare, many young people were

incentivized to find work that offered health insurance as part of the fringe

benefits package, as it would be too expensive otherwise. With the new

legislation in place, there is less need and incentive to find a job offering

these benefits, or a job at all. Further, even once these recent graduates are

employed, the individuals will be at a suboptimal position on the Fringe

Benefits diagram. The smaller slope of the indifference curve relative to

the isoprofit curve (I1) reveals their relatively greater valuation of wages,

due to the need to pay off large education debts (Figure 7). The dependent


99

provision in the ACA simultaneously reduces the supply of and the utility

of the worker. Casey B. Mulligan even argues that, “it is unlikely that

labor market activity will return even near to its pre- recession levels as

long as the ACA’s work disincentives remain in place” (Mulligan, p. 25).

A reduction in Labor supply is unquestionably dangerous for the well-

being of the fragile U.S. economy.

While the facts and figures all advocate for the termination of

Obamacare, when discussing the well-being of a nation, the ethics of a

situation must also be examined. Through an understanding of

Libertarianism, Utilitarianism, and the teachings of Martin Luther, it is

even more apparent that Affordable Care Act is unacceptable.

Fundamentally, Libertarians emphasize limited government, voluntary

association and political freedom. Robert Nozick, an influential American

philosopher, stressed the importance of a minimal state, with entitlement

only owing to one’s own production, or voluntary transfers. The

Affordable Care Act enables the U.S. government to be a more extensive

state, as it redistributes income and services, hence violating principles of

justice in acquisition and transfer. Specifically, the employer mandate is

an “intrusion into voluntary arrangements made between employer and

employee”(Blase). Similarly, Nozick opposes any “time slice” or “end-

state” patterned distributions, as they focus solely on the final distribution


100

of goods to an individual, rather than consider the justice in procedure of

distribution (Nozick, p. 228). Obamacare is unethical as it is patterned and

end-state in nature, guaranteeing universal healthcare, regardless of the

negative impacts on the labor economy. Also, from the perspective of

Nozick, the ACA’s tax increases are unjust, as they are equivalent to

forced labor.

Another prominent Libertarian, Ayn Rand, embraces similar ideas

as Nozick, believing fully in the superiority of Laissez-faire capitalism.

Rand argues against collectivism, altruism, and any form of redistribution,

urging that individuals act with “rational self-interest” for their own

happiness (Rand, p. 8). Obamacare should be defunded as it embodies a

collectivist mindset, giving the responsibility of providing health

insurance for dependent individuals to the government and businesses.

Also, like Nozick, Rand believes that a transfer concerning more than one

person must involve “voluntary consent of every participant,” which the

taxes, employer and individual mandates, and even additional benefits in

new healthcare plans, certainly do not entail (Rand, p. 93). Normative

understanding of Libertarian ideals undoubtedly shows that the labor costs

on business, and unemployment caused by, the Affordable Care Act is

unacceptable.


101

The Utilitarian view on the Affordable Care Act is simplistic, yet

clear. Although Obamacare does make some individuals better off, the

reform does not result in the greatest good for the greatest number, or the

greatest good overall. There is disutility to employees, as they are not

allowed to work as much as they would like due to the firm’s profit

maximizing decisions. Even people who may be receiving enhanced

benefits with the new “minimum essential coverage” requirements, may

not be as satisfied with their coverage, because these new benefits could

“involve tradeoffs in terms of consumer preferences, moral choices, and

cost” (Tanner, p. 7). Finally, firms face increasing costs, hurting expansion

and hiring, ultimately reducing profits of the firm.

A final aspect in the normative analysis of Obamacare’s impact on

labor is the ethics of Martin Luther. Although a strong proponent of

salvation by faith alone, Luther insisted on the importance of using

authority for justice and the good of others. He advised against unjust

economic practices including cheating, immoral forms of pricing and

asymmetric information, all of which emphasize protection of the

vulnerable (Luther). Obamacare, although intended to help everyone,

leads employers to ignore certain duties of their secular or temporal

offices. They fail to live by Christian virtues, acting in a self-interested

manner due to cost hikes. Firms fail to follow the principle of loving one’s


102

neighbor as they fire people, reduce hours, reduce wages, and drop

benefits. The Affordable Care Act also leads to the exploitation of the

vulnerable, which Luther explicitly forbade. Employees, exposed to and

dependent upon their employers, face higher taxes, lower wages, and

unemployment, while employers, vulnerable to the policies of the

government, are exploited by mandates, higher taxes, and increasing

healthcare premiums. Finally, those at most risk for losing their jobs in the

process of cost cutting, are minimum wage workers-the most vulnerable

people in the labor market (The Manhattan Institute). As demonstrated by

the philosophy of Libertarianism, Utilitarianism, and Martin Luther,

Obama’s lofty healthcare reform is not only unsuccessful, but unethical,

and should be brought to an end.

The Affordable Care Act is costly to businesses, individuals and

the government. It is inefficient in providing affordable and universal

healthcare coverage. The labor market, although only a single facet of a

national economy, inevitably dictates other market outcomes. It is

unmistakable however, that through the drop in labor demand and wages,

as well as the fall in labor supply, Obamacare has, and will continue to,

adversely impact labor. The various injuries to the labor market discussed

in this paper would be of concern in a prosperous, stable economy, but

with current economic conditions, and the slow, jobless recovery the U.S.


103

faces, the nation’s labor market should be of high concern. The health of

the people and the health of the labor market should go hand-in-hand. It is

imperative that the Affordable Care Act and its provisions be ended

promptly.


104

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106

Omicron Delta Epsilon

St. Olaf College: Beta Chapter

Class of 2014 Annabel Ansel

Shannon Cordes

Nick Evens

Rebecca Gobel

Duy Ha

Ryan Johnsrud

Mark Lee

Jane Meyer

Apoorva Pasricha

Michael Tillman

Kelly Tomera

Gina Tonn

Gabriel Trejos Durán

Rachel Turbeville

Class of 2015 Sara Anderson

Alex Everhart

Erik Gartland

Audrey Kidwell

William Lutterman

Camille Morley

Bjorn Thompson

Erik Springer

Sarah Stevens

Leah Voigt

107

Omicron Delta Epsilon Journal of Economic Research

St. Olaf College: Beta Chapter

Executive Editor Rebecca Gobel

Associate Editor William Lutterman

Spring 2014 Papers Shannon Cordes

Kelly Tomera

Ryan Johnsrud

Annabel Ansel

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