Democracy and Growth

1

2

3

4

5

6

Democracy stock


X

0.006***

0.002*

0.009***

0.007***




(0.001)

(0.001)

(0.002)

(0.002)

Democracy stock, t10














Democracy stock, t20














Democracy level

0.028

X






(0.021)






GDP pc (ln)

2.597***

X

2.961***


4.805***

4.655***


(0.452)


(0.488)


(0.519)

(0.668)

Inflation (ln)





0.450***

0.389***






(0.082)

(0.091)

Investment (pwt)





0.023

0.002






(0.023)

(0.024)

Instability (Banks)





0.099***

0.113***






(0.021)

(0.023)

Trade openness (pwt)





0.041***

0.047***






(0.007)

(0.008)

Life expectancy (wdi)





0.134***

0.194***






(0.037)

(0.058)

Oil shock (dummy)





1.338***

0.124






(0.318)

(17.053)

Growth pc






0.468***

(tradeweighted)






(0.117)

Population growth






13.472

(wdi)






(17.259)

Years independent






0.166







(0.280)

Regime durability






0.003

(Polity IV)






(0.010)

Social conflict






0.503

(Marshall)






(0.645)

Govt consumption






0.001

(pwt)






(0.025)

Illiteracy (ln)






0.640







(0.707)

Trend






0.136







(0.281)

Annual Dummies






YES

Constant

21.145***


23.885***

1.771***

28.654***

31.125


(3.387)


(3.670)

(0.085)

(3.576)

(0.000)

Observations

6264


6264

6430

3721

3296

Countries

180


180

187

149

129

Sample Period

195000


195000

195000

196199

196198

R squared (within)

0.03


0.03

0.00

0.08

0.13

Prob > F

0.0000


0.0000

0.0673

0.0000

0.0000


Fixed effect regressions with AR(1) disturbance. Units of analysis: countryyear. Dependent variable: annual per capita growth rate. All predictors lagged one year. NeweyWest standard errors in parentheses. (twotailed tests) Variables and procedures defined in the text. Variables not indicated by source are constructed by the authors. *** p<.01 ** p<.05 *p<.10 
Notes for the next iteration: Exclude the following variables: regime durability (which will be dealt with in chapter 14), years independent (which is picked up by country fixed effects), trend (picked up by annual dummies). Substitute the new conflict variables for this conflict variable (drawn from Marshall).
The Democratic Growth Effect
We have shown thus far that the relationship between democratic stock and growth is robust in a variety of plausible specifications and operationalizations. (Further tests are relegated to the appendix.) We turn now to the question of its practical significance. Is the democratic growth effect significant in real (policy) terms?
Let us consider the results of model 5 in Table 3.1, in which we control for a range of other possible causal factors. We regard this model as offering a conservative estimate of causal effects since many of the variables introduced as controls in this model may be endogenous to democracy, and hence might be suppressing democracy’s true causal effect on growth. (Note that the coefficient for democracy is virtually unchanged from the reducedform equation in model 2, so it hardly matters which model one chooses to base this estimate on.)
Within the parameters of this model, a country with no existing stock of democratic capital (for example, Botswana in 1966) experiences the following democratic growth effect: for each full decade of highquality democracy (Polity2=10), democracy stock increases by approximately 100 points. To estimate the predicted effect of this change on growth, we simply multiply this change by the coefficient on democracy stock, 0.007. So from model 5 in Table 3.1, the predicted growth impact of a decade of high quality democracy is approximately 0.7 percent. Given the wellknown cumulative effects of small increases in the growth rate, these changes are significant. For instance, an increase in the annual growth rate from 2 percent to 2.7 percent reduces the time needed to achieve a doubling of incomes from thirtyfive to twentysix years; an increase to 3.4 percent further reduces the doubling period to 20.7 years.^{7}
Methodological Appendix
Specification problems pervade all crosscountry growth regressions.^{8} While the fixedeffect format handles the problem of invariant controls, it does nothing to control for factors that might vary over time. To control for convergence effects we include gdp/capita (natural logarithm) as part of the benchmark model. (Thus, we measure the effect of democracy on a country’s growth rate given its current level of economic wealth.)
Other controls are less obvious by virtue of their possibly endogenous relationship to democracy, their lack of robustness, or their theoretical status. At the same time, it is vital that we test as comprehensive a set of alternative controls as possible. These controls must encompass not only those identified by the prodigious literature on economic growth but also those factors that might affect the simultaneity problem discussed above.
However, for a variety of reasons, we do not maintain these controls in most of the tests shown above (Table 3.1) and below (Table 3.2). First, there is a substantial loss in degrees of freedom when the equation is expanded to include the full set of controls. Second, there is serious question about the theoretical justification (not to mention the empirical robustness) of all of these controls. Third, there is the danger of overspecifying causal relationships: note that democracy stock may affect any and all of these control variables. Indeed, the coefficient for democracy stock increases in the full models shown in Table 3.1, a result that seems dubious if one’s principal objective is to measure the independent effect of democracy stock. For all these reasons, it seems safer to work with a smaller benchmark equation, including only gdp per capita. This is the only control that can claim some degree of theoretical consensus, is empirically robust, and is—with respect to the causal question at hand—exogenous. (A recent reevaluation of crosscountry growth empirics concludes that the log of gdp per capita is the only variable that is robust across all models [Bleaney & Nishiyama 2002: 45].)
Results shown in Table 3.1 are based on annual data, fixed effects, and an AR1 correction for serial autocorrelation. We now attempt to show that our results are robust even when various elements of this methodology are altered: fixed effects versus random effects, annual versus fiveyear increments of data, the possible influence of OECD cases (tested this time in a randomeffects format), a laggeddependentvariable approach to modeling serial autocorrelation, the possible peculiarities of the democracy stock variable, and a wide variety of static (timeinvariant) control variables, as displayed in Table 3.2.
For each model with annual data we have followed our usual approach of measuring all independent variables in the year prior to the dependent variable. For each model with fiveyear increments we have maintained the same approach, this time measuring the independent variables in the first year of the period under study. The dependent variable in this case is a fiveyear average of growth performance during the subsequent period. So in both cases the dependent variable is forward lagged one timeperiod.
The standard method of correcting for autocorrelation is employed where annual data is used (AR1 error correction), but not when fiveyear increments are used. (By virtue of the fact that we are dealing with fiveyear increments, it should be less of a problem.) No correction for serial autocorrelation is usually necessary when a lagged dependent variable is included, as in models 8 and 9, and none is employed.
Model 1 is a fixedeffects model with one control (gdppc) and growth data aggregated across fiveyear periods. Model 2 is a randomeffects model with annual data and the same allpurpose control. Model 3 is a nonfixedeffects model with annual data that includes all largeN controls employed previously (see Table 2), plus some additional static controls, intended to model spatial heterogeneity. These include English legal origin (dummy), Muslims (as percentage of the population), ethnic fractionalization (the likelihood that two persons chosen randomly from a population will share the same ethnicity), East Asia (dummy), Middle East (dummy), Latin America (dummy), and latitude (logarithm of the absolute value of the distance of a country’s capital city from the equator).
Model 4 is a nonfixedeffects model with fiveyear data increments and the same set of controls. Model 5 is a fixedeffects model with fiveyear increments and all relevant (varying) controls. Models 6 and 7 replicate models 3 and 4, this time excluding OECD cases. Models 8 and 9 test the laggeddependent variable approach to tscs analysis, discussed previously. In model 10 we test the benchmark specification in an ArellanoBond format. This procedure combines firstdifferencing with a series of lags—equivalent to the total number of prior observations in the dataset—for each variable in the model.^{9} (Simple firstdifference models, without lagged instruments, show results similar to those in model 12.)
In each of these various tests we find that the democracy stock variable retains statistical significance, usually at the .01 level (twotailed tests).
Table 3.2:
Alternative Estimators and Models

1

2

3

4

5

6

7

8

9

10

AR correction

AR(0)

AR (1)

AR (1)

AR (0)

AR (0)

AR (1)

AR (1)

AR (0)

AR (0)

AR (1)

Frequency

5 years

1 year

1 year

5 years

5 years

1 year

5 years

1 year

1 year

1 year

Sample:

All

All

All

All

All

nonOECD

nonOECD

All

All

All

Country fixed effects:

Yes

No

No

No

Yes

No

No

Yes

Yes

No

Democracy stock

0.009***

0.002***

0.002**

0.002**

0.012***

0.003***

0.003**

0.006***

0.008***

0.034***

(1900)

(0.002)

(0.000)

(0.001)

(0.001)

(0.002)

(0.001)

(0.001)

(0.001)

(0.002)

(0.002)

Growth pc








0.272***

0.151***

0.107***

(lagged dep var)








(0.027)

(0.031)

(0.011)

GDP pc (ln)

4.581***

0.096

0.544**

0.632***

4.592***

0.556**

0.690***

3.053***

4.473***

20.960***

(WDI)

(0.864)

(0.077)

(0.212)

(0.230)

(0.662)

(0.241)

(0.266)

(0.423)

(0.591)

(0.421)

Inflation (ln)



0.250***

0.006

0.185*

0.230**

0.001


0.339***


(WDI)



(0.085)

(0.080)

(0.101)

(0.099)

(0.088)


(0.086)


Investment



0.004

0.013

0.008

0.001

0.006


0.021


(PWT)



(0.015)

(0.015)

(0.023)

(0.018)

(0.019)


(0.025)


Govt consumption



0.037***

0.030**

0.006

0.035**

0.029*


0.013


(PWT)



(0.013)

(0.014)

(0.024)

(0.015)

(0.016)


(0.024)


Trade openness



0.010***

0.010***

0.021**

0.009***

0.007*


0.042***


(PWT)



(0.003)

(0.003)

(0.009)

(0.004)

(0.004)


(0.007)


Population growth



24.731

46.596***

33.207*

20.794

44.483**


15.296


(WDI)



(15.804)

(15.445)

(18.896)

(17.533)

(18.894)


(16.500)


Instability



0.078***

0.001

0.007

0.101***

0.007


0.083***


(Banks)



(0.021)

(0.021)

(0.023)

(0.027)

(0.026)


(0.020)


Social conflict



0.045

0.217

0.177

0.042

0.047


0.520


(Marshall)



(0.435)

(0.464)

(0.607)

(0.499)

(0.541)


(0.607)


Years independent



0.000

0.000

0.103

0.001

0.001


0.071





(0.002)

(0.002)

(0.097)

(0.003)

(0.003)


(0.229)


Regime durability



0.013**

0.013**

0.013

0.016*

0.019**


0.004


(Polity IV)



(0.006)

(0.006)

(0.010)

(0.009)

(0.009)


(0.010)


Life expectancy



0.092***

0.093***

0.145***

0.074***

0.075**


0.167***


(WDI)



(0.024)

(0.025)

(0.051)

(0.029)

(0.030)


(0.053)


Illiteracy (ln)



0.312**

0.091

0.545

0.177

0.174


0.634


(WDI)



(0.146)

(0.150)

(0.531)

(0.221)

(0.218)


(0.604)


Trend



0.016

0.026

0.117

0.010

0.026


0.062





(0.016)

(0.019)

(0.102)

(0.021)

(0.025)


(0.230)


Oil shock



1.387***

1.171***

0.464

1.186**

0.995*


0.973***


(dummy)



(0.370)

(0.432)

(0.388)

(0.493)

(0.598)


(0.350)


English legal origin



0.161

0.131


0.143

0.025




(La Porta)



(0.230)

(0.265)


(0.317)

(0.359)




Muslims (% pop)



0.014***

0.009


0.014**

0.007







(0.005)

(0.006)


(0.006)

(0.007)




Ethnic fractionaliz.



0.761

0.547


0.546

0.661




(Alesina)



(0.499)

(0.562)


(0.705)

(0.785)




East Asia



2.624***

2.919***


2.862***

3.111***




(dummy)



(0.409)

(0.477)


(0.600)

(0.688)




Middle East



1.305***

1.181**


1.653***

1.395**




(dummy)



(0.488)

(0.565)


(0.565)

(0.659)




Latin America



0.409

0.565


0.330

0.783




(dummy)



(0.333)

(0.362)


(0.493)

(0.518)




Latitude (ln)



0.248

0.143


0.211

0.073







(0.168)

(0.195)


(0.191)

(0.234)




Constant

35.797***

1.073*

2.854

3.522

21.313***

3.697

6.038**

24.082***

27.400**

0.298***


(6.457)

(0.583)

(1.937)

(2.157)

(8.061)

(2.654)

(2.921)

(3.171)

(13.951)

(0.010)

Observations

1082

6264

3231

601

615

2335

435

6136

3302

5954

Countries

179

180

128

119

121

101

92

180

130

178

Sample Period

196095

195000

196198

196595

196595

196198

196595

195100

196198

195000

R squared (within)

0.18

0.01

0.11

0.28

0.24

0.09

0.25

0.11

0.11


Prob > F

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000


Sargan test (prob)










0.0000

All regressions are OLS (NeweyWest standard errors in parentheses) except for model 12 which is estimated using the ArellanoBond estimator. Corrections for autocorrelation in the residual and inclusion of country fixed effects are included as noted. Dependent variable: growth rates, either annually or in fiveyear increments (mean). All predictors lagged one year. Variables and procedures defined in the text. Where no source is listed, the variable is constructed by the authors. *** p<.01 ** p<.05 *p<.10 (twotailed tests)

Variable Definitions
English legal origin: dummy variable (La Porta et al. 1999).
Ethnic fractionalization: the likelihood that two persons chosen randomly from a population will share the same ethnicity (Alesina et al. 2003).
GDP per capita: from the World Development Indicators (WDI 2003), with a small number of missing cases from the 1950s imputed from the Penn World Tables (pwt 6.1) dataset (Summers & Heston 1991).
Government consumption: government share of real gdp per capita (pwt 6.1).
Growth per capita, tradeweighted: Each country is assigned the mean value of the growth rate of all other countries in the world in that year, weighted by their bilateral trade with the country in question).
Illiteracy: percent of population who cannot read and write a sentence in their native tongue (World Bank 2003), natural logarithm.
Inflation: annual percent change in consumer prices (natural logarithm; World Bank 2003).
Instability: includes assassinations, general strikes, guerilla warfare, government crises, purges, riots, revolutions, and antigovernment demonstrations, all from the Banks dataset. Each is added together to form a composite index (construction of index by the authors).
Investment: the share of real gdp comprised by investment (pwt 6.1).
Latitude: logarithm of the absolute value of the distance of a country’s capital city from the equator (calculated by authors).
Life expectancy: life expectancy at birth (World Bank 2003)
Muslims: percentage of total population
Oil shock: dummy variable: 1950–1973=0, 1974–2000=1).
Population growth: (World Bank 2003).
Regime durability: the number of years since the last threepoint change in the composite Polity2 score (Polity IV).
Regional dummies: East Asia, Middle East, Latin America.
Social conflict: includes civil violence, civil war, ethnic violence and ethnic war.
Trade openness: imports and exports as a share of gdp (pwt 6.1)
Years independent: the number of years a country has been sovereign (coded by the authors).
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