Post on 13-Sep-2014
Claremont Conference on Political Economy Indicators
Marina Arbetman RabinowitzKristin Johnson
Relative Political Capacity: New Models
& Data Updates
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Indicators of Political Development Indicators of Well being:
Electricity, calories per capita, education, health, etc. Deutsch (1966), Rokkan (1970), Gurr (1974), World Handbook of Social Indicators (WB), Human Development Index (UN), Millennium Development Goals.
Democracy Research: Participation, representation, electoral choice, institutions, bureaucracy (Campbell, Converse, Stokes and Miller, 1960; Verba, Nie and Petrocik, 1972; and Fiorina, 1981). Freedom House Index, Polity IV (democracy/ autocracy), Governance Indicators.
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Theoretical issues These approaches each fall into the trap of measuring the consequences or reflecting phenomena other than capabilities but have the advantage of measuring the phenomena directly.
Measures of well being reflect government and elite choice in resource allocation
Measures of governance favor democratic systems of government over other forms of governance.
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Relative Political CapacityMeasures the discretionary power of the government to
implement a desired policy.
The Relative Political Capacity of a government is the ratio of revenues a government extracts, compared to predicted levels a society could attain based on economic endowment.
RPC = Actual Government Revenue Predicted Government Revenue
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Relative Political Capacity
Predicted Performance
Boundary of High Performance
RA
TIO
OF
R
EV
EN
UE
T
O G
DP
Fragile States
Successful States
Boundary of Low Performance
Poor Developing Developed
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Examples: RPC across time
1960
1970
1980
1990
20002005
1960 19701980
19902000
2005
1970
1980
1990
2000
2005
0 2000 4000 6000 8000
GDP per Capita
Angola
1960 19701980
1990
2000
2005
High RPC
Low RPC
Bolivia
Thailand
Botswana
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RPC 2000
Argentina
Austria
Bangladesh
Barbados
Bolivia
Brazil
Burkina Faso
DenmarkEcuador
Fiji
Finland
Gabon
Greece
Haiti
Hungary
Indonesia
Iran
Israel
Jordan
Kenya
South Korea
Kuwait
Lesotho
Malaysia
Malta
Mauritius
Morocco
Myanmar
New Zealand
Papua New Guinea
Paraguay
Philippines
Poland
Portugal
Singapore
Sri Lanka
Sweden
Thailand
Tunisia
Uruguay
USA
Income0 40,000
po
sit
ive
0
ne
ga
tiv
eR
PC
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RPE General Model
Yit = α + β Xit + Vr
Yit = Adjusted tax revenue for country i at time t
X it = Vector of variables that determine potential tax collection
Vr= White noise disturbance
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Inter country comparisons:Models for Developing SocietiesModel 1: Tax/Gdp= α + β1 time - β2 min/gdp + β3 gdpCap + β4 exports/gdp
+ β5 Oil + ξ
Tax= (TaxRev-NonTax-SocSec)/Gdp
RPC1 = Real /Predicted Note: Control for expenditures
Model 2: Tax/Gdp= α + β1 time - β2 min/Gdp + β3 agr/Gdp + β4 exports/Gdp
+ β5 Oil + ξ
Tax= (TaxRev-NonTax-SocSec)/GdpRPC2 = Real /Predicted
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Inter country comparisons:Models for Developed SocietiesModel 3:Tax/Gdp= α + β1 time - β2 min/gdp + β3 gdpCap + β4
Exports/gdp + β5 health/gdp + ξ
Tax= (TaxRev-NonTax-SocSec)/GdpRPC3 = Real /Predicted
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Empirical Inventory National Level model
Unit of analysis: country
Number of countries in the sample: 129
Time span: 1960-2005
Notes: Sample includes Poland 86-on, Hungary 89-on, Romania 88 on. These countries use NMP in their National Accounts before those dates so the data is not comparable to GNP/GDP methodology). The sample includes China.
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Variables & Sourcescountry Country Name
year 1960 - 2004
totrev Total revenue divided by GDP*: Main Source GFS, IMF 1972-2005; World Bank 1960 – 1972, Where data was missing we used overlapping series and National Sources.
nontax Non tax revenue divided by GDP*. Main Source GFS, IMF 1972-2005; World Bank 1960 – 1972, Where data was missing we used overlapping series and National Sources.
socsec Social security revenues divided by GDP*. Main Source GFS, IMF 1972-2005; World Bank 1960 – 1972, Where data was missing we used overlapping series and National Sources.
agri Agriculture GDP divided by total GDP*. Main Source GFS, IMF 1972-2005; World Bank 1960 – 1972, Where data was missing we used overlapping series and National Sources.
exp Exports divided by total GDP*. Main Source GFS, IMF 1972-2005; World Bank 1960 – 1972, Where data was missing we used overlapping series and National Sources.
mining Mining production GDP divided by total GDP*. Main Source GFS, IMF 1972-2005; World Bank 1960 – 1972, Where data was missing we used overlapping series and National Sources.
Realgdp Real GDP per capita in constant dollars. Main Source: Penn World Tables, Summers & Heston, additional sources include the IMF GFS & World Bank, World Development Indicators. Where data was lacking we used national sources.
taxratio Totrev – nontax - socsec
Oil Oil Exports/total exports. Main Source: Direction of Trade Statistics.
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Data relies on national government accounts
Variances in tax reporting and accounting are noted by IMF or WB, in most cases.
Currency changes and differences in reporting timing in high inflation situations need to be normalized.
National sources may be necessary to supplement
Data Collection Issues
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Health expenditures: before 1990s mostly government reported public expenditures, now private are part of the mix in both developed and developing but not always reported
Social Security: 1) some countries have privatized part of SS. 2) The argument to exclude social security has been that those transfers have been allocated previous to the collection and therefore there is little room for any further manipulation by the government. This is not the case, those funds are not untouchable, and the deficit can be manipulated by Central Banks or Congress
Agriculture: Still our best empirical fit for developing countries (not for developed) but 1960s is the anchor so we need to control for time.
Taxation: Indirect vs. direct taxation
Measurement & Conceptualization
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Yit = α + β Xit + Vr Yit = Adjusted tax revenue for country i at time t
X it = Vector of variables that determine potential tax collection
Vr= White noise disturbance
o Conceptually, the same model as national level.o Constraints to choosing the variables are country
specific
Sub National Models
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Relative Provincial Political Capacity (RPPC)Model 1:Transf/gdp = α + β1 time + β2 min/gdp + β3 agr/gdp + β4
rev/gdp + ξ
Model 2: Transf/gdp= α + β1 time + β2 gdpCap + β3 rev/gdp + β4
min/gdp + ξ
Model 3:Rev/gdp= α + β1 time + β2 transf/gdp + β3 agr/gdp + β4
min/gdp + + β5 Subsidies+ ξ
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Empirical Inventory Sub National Level Model
Unit of analysis: province/state/region
Time span: Ideally 1990 – 2005, dependant on individual country
Bolivia, Brazil, China, EU, India, Indonesia, Iran, Mexico, Nigeria, Thailand, Sudan, USA,
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Data Collection In many countries the transfers are decided according to a
Congressional Allocation Funds formula. If the allocations are decided on annual negotiations (1), the variable captures the concept of RPPC. If the formula is inflexible (2), the flexibility comes from the allocation of central government expenditures to the provinces.
It is also important to know that the General Appropriation Act does not show if or how much of the money is released, so instead of following to entry number (Transfers, Subsidies. Models 1& 3) in some cases is better to look at the exit number (Expenditures. Model 2)
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province state province name State name
Year 1980-2005
Code 2 digit codes except for split provinces (3rd digit added for identification)
Split 1 or 0 for provinces that have not split
Population number of people reported in census
Totrev * Includes 1.Surplus + 2. Local Gov Original Receipts + 3. Balanced Budget + 4. Local Gov. Loans
Localrev Taxes collected by the local government from the local population
Transfers transfers, from General Allocation Fund
Transf_gdp Transfers divided by total GDP*
Expend_gdp Expenditures divided by total GDP*
Grdp constant YEAR gross regional domestic product in constant YEAR prices in local currency
Grdp market gross regional domestic product at market prices in local currency
agriculture Agriculture GDP divided by total GDP*
mining Mining production GDP divided by total GDP*
Oil Production Dummy or % oil produced/Total country production
RPC1 controlling for agriculture (specify all variables used in the model)
RPC2 controlling for gdp per capita (specify all variables used in the model)
Variables: RPPC
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Sources Examples: RPPCExpend_gdp Main Source: Statistical Center of Iran, available at:
http://www.sci.org.ir/english/default.htm
agriculture Main Source: Statistical Center of Iran, available at: http://www.sci.org.ir/english/default.htm
mining Main Source: Statistical Center of Iran, available at: http://www.sci.org.ir/english/default.htm
Rev_gdp Main Source: Economic & Financial Review, The Central Bank of Nigeria 1979 – 1987; ISSN: 008-9281; Central Bank of Nigeria, Annual Report & Statement of Accounts. 1980 – 2005. ISSN: 0069-1577
agriculture Main Source: Economic & Financial Review, The Central Bank of Nigeria 1979 – 1987; ISSN: 008-9281; Central Bank of Nigeria, Annual Report & Statement of Accounts. 1980 – 2005. ISSN: 0069-1577. When data was lacking, overlapping series were obtained from FAO Statistics available from the UN.
mining Main Source: Economic & Financial Review, The Central Bank of Nigeria 1979 – 1987; ISSN: 008-9281; Central Bank of Nigeria, Annual Report & Statement of Accounts. 1980 – 2005. ISSN: 0069-1577
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Applications International Conflict
Organski & Kugler 1980; Lemke 1996; Tammen et al 2002. Internal Conflict
Kugler et al 1997; Benson & Kugler 1998; Johnson 2007. Demographic Transitions
Arbetman, Kugler & Organski 1980, 1994, 1999; Feng Kugler & Zak 2000, 2007.
Economic Applications Exchange Rates: Arbetman 1990; Growth: Leblang 1997;
Inflation: Alcazar 1997; Private Investment: Feng & Chen 1997, Feng 2004; Policy Implementation: Snider 1997; Informal Markets: Arbetman 1990, 1994; Arbetman & Ghosh 1997.
Sub National Rouyer 1997; Swaminanthan 2005; 2007; Johnson 2007.
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RPC at different levels of development
Poor Developing Developed
_
+
Predicted Performance
Very Low
Very High
Average
High
Low
Relative Political Capacity