CONCLUSION

The effect of democracy on redistribution and inequality is important for understanding how democracies function and use the available policy instruments. Nevertheless, our survey of the relevant literature shows that the social science literature on this topic is far from a consensus or a near-consensus on this topic.

We explained why the baseline expectation in the literature has been that democracy should increase redistribution and reduce inequality (for example, based on Meltzer and Richard’s, 1981 seminal paper), and why this expectation may not be borne out in the data because democracy may be captured or constrained; because democracy may cater to the wishes of the middle class; or because democracy may simultaneously open up new economic opportunities to the previously excluded, contributing to economic inequal­ity. This ambiguity may be one of the reasons why the large empirical literature on this topic comes to such inconclusive findings, though the use of datasets with different qualities and different methodologies and econometric practices, many of which are far from satisfactory, are also contributing factors. It may also be that because different researchers have looked at different sets of countries in different periods, the differing results are to some extent picking up situations where one or another of the mechanisms we have identified is more dominant.

The bulk of the chapter empirically investigated the (dynamic) relationship between democracy and various economic outcomes related to redistribution and inequality. Our results, which come from panel data models controlling for the dynamics and persistence in our outcome variables, indicate that democratization does indeed increase government taxation and revenue as fractions of GDP. This confirms the basic prediction of the stan­dard Meltzer-Richard model. In contrast, we have found no robust evidence that democracy reduces inequality, although our estimated coefficients are quite imprecise in this case.

These patterns suggest that the effect of democracy on redistribution and inequality may be more nuanced than often presumed and highly heterogeneous across societies. We tried to make some tentative progress on this issue by providing additional correla­tions pertaining to these heterogeneous effects and mechanisms on which they might be based. We found some results suggesting that democratization in the presence of pow­erful landed elites may increase inequality, and that structural transformation may induce an expansion of opportunities that counteract any additional redistribution, and either of these could explain the absence of an effect on inequality. This interpretation is con­firmed by our finding that democracy increases inequality in places that have a lower share of population in agriculture, and at times when the global technological and orga­nizational frontier is more inequality inducing. A natural next step for research is isolating exogenous variation in these heterogeneous effects across democracies and nondemocracies.

In addition, we also found some evidence consistent with a (modified) Director’s law, which suggests that democracy redistributes from the rich and the poor to the middle class, and therefore its effect on inequality may depend on the relative position of the middle class vis-a-vis the poor and the rich. Further research on whether and how democracies transfer from the poor to the middle class would be an important contribution.

(Overall, the evidence suggests that to the impact of democracy on inequality is lim­ited, and these limited effects work by altering pre-redistribution market outcomes, while the fiscal mechanisms stressed by the literature play at most a small role in explaining any effect of democracy on inequality, and may in fact be inequality-increasing.

ACKNOWLEDGMENTS

We are grateful to the editors for their detailed comments on an earlier draft and to participants in the Hand­book conference in Paris, particularly to our discussant Jose-Victor Rios-Rull.

APPENDIX A. COMPARISON TO RODRIK (1999)

This appendix replicates and extends the analysis in Rodrik (1999). At a first glance, the fact that we find no robust effect on net or gross income inequality seems at odds with Rodrik’s findings that democracies pay higher real wages in manufacturing. These oppo­site findings could be explained by a logic similar to the one outlined in Proposition 4. In particular, democracies may increase wages by allowing workers to reallocate to new sec­tors, but this may also increase inequality if there is sufficient heterogeneity in labor pro­ductivity and wages were previously compressed and reduced by labor market institutions. Besides this conceptual difference we also explore the differences between our empirical setting and Rodrik’s. We show that while the results are robust to our democracy measure, they are fragile in a number of other directions.

Rodrik’s data generating model is given by

with w_it manufacturing wages from the UNIDO dataset compiled by Martin Rama. However, this model cannot be estimated because wage data comes grouped on averages for the years t, t +1, t +2, t +3, t +4 for every 5 years from 1960 onward. Thus, only the average wages between 1960 and 1964, 1965 and 1969, and so on are observed. Thus, Rodrik estimates

with all variables averaged over 5 year periods (from t to t + 4), and the model is estimated in a panel covering 1960,1965,..., 1990.

In the top panel of Table 21.A1 we present different estimates of Equation (21.A1) using a normalized polity score between 0 and 1, a normalized Freedom House index between 0 and 1 and our democracy measure separately as proxies for democracy. We always control for the log of GDP per capita, the log of worker value added in

Table 21.A1 Replication of Rodrik's results on the log of manufacturing wages

Dependent variable is log of average wages between t and t + 4.

Note: OLS estimates include a full set of country and year fixed effects. All models control for the log of GDP per capita, log of worker value added and log of the price level, but these coefficients are not reported to save space. Robust standard errors, adjusted for clustering at the country level, are in parentheses. ***: significant at 1%; **: significant at 5%; *: sig­nificant at 10%.

manufacturing and the log of the price index (from the Penn World Tables) following Rodrik’s original setup. The estimates of β are multiplied by 100 to ease their interpre­tation. The left panel uses Rodrik’s original wage data and the right panel uses an updated version. In all models we present robust standard errors adjusting for clustering at the country level, which are reflected in slightly higher standard errors than the ones found by Rodrik.

Our estimates show that democracy, measured by any of the indices, is associated with higher wages using the original wage data, which replicates Rodrik’s findings. There are still some small differences caused by updates to Polity and Freedom House, but quali­tatively his conclusions hold. In particular, an increase in the polity score from 0 to 1 increases wages by 19.72% (s.e.

There are two more issues that are important to consider in weighing the importance of Rodrik’s evidence. The wage data are in the form of 5-year averages. First, this will tend to induce nontrivial serial correlation in the dependent variable, inducing error in the presence of lagged dependent variables on the right-hand side (which our estimates suggest are present). Second, by averaging the democracy index, Rodrik’s specification induces the correlation between wages at t and democracy at t + 1, t + 2, t +3 and t + 4, which of course does not reflect the effect of democracy on wages, to influence the esti­mate for β.

To address the second issue and get closer to the empirical strategy we used in this chapter, we can estimate the model

This model still averages the dependent variable, which cannot be undone given the wage data, but uses the baseline value of the democracy index and the controls for the years 1960,1965,..., 1990. The bottom panel in Table 21.A1 presents our results using the original wage data (left panel) and updated wage data (right panel). The esti­mates for β are significantly smaller and never significant. The comparison between the top panel—which uses Rodrik’s original specification—and our preferred specification in the bottom suggests that Rodrik’s results are, at least in part, driven by a correlation between wages at t and democracy at t + 1, t +2, t +3 and t + 4.

Finally, we present estimates of the model

which comes closest to the empirical specification we used throughout the paper. Table 21.A2 has the same structure as Table 21.2 in the paper and presents several esti­mates of the dynamic panel model in Equation (21.A1). In this case, the lagged dependent variable also controls for the nontrivial autocorrelation patterns induced by averaging the dependent variable. The results confirm that there is no effect of democracy at time t on average wages between t and t +4. Only the GMM estimates show large effects that are almost significant at conventional levels. But these estimates are unreliable because they are significantly above the fixed effect models with different imposed values of ρ (and these estimates should bracket them). Moreover, the estimated ρ is too small compared to the fixed effects estimates (it should typically be larger). We believe that this pattern may be caused by the averaging of the dependent variable, which invalidates the moment conditions of GMM estimation.

Table 21.A2 Effects of democratization on the log of manufacturing wages controlling for worker value added, prices and GDP per capita

Dependent variable is log of average wages between t and t + 4.

Note: OLS estimates (Columns 1—2) include a full set of country and year fixed effects. Arellano and Bond's GMM estimators of the dynamic panel model (Columns 3—4) remove country fixed effects by taking first differences of the data, or by taking forward orthogonal differences (Column 5) and then constructing moment conditions using predeterminedlags ofthe dependent variable anddemocracy. Columns 4 and 5 use up to the Afthlagofpredeterminedvariables to create moments, restrictingthe numberof moments used. Columns 6—10 impose different values for the autocorrelation coefficient in the tax revenue as a percentage of GDP series, and estimate the effect of democ­racy including a full set of country and year fixed effects. All models control for the log of GDP per capita, log of worker value added and log of the price level, but these coefficients are not reportedto save space. Robust standard errors, adjustedfor clustering at the country level, are in parentheses. ***: significant at 1%; **: significant at 5%; *: significant at 10%. We do not report long-run effects and their p-values in Column 10 because they are not defined for ρ ¼ 1.

Rodrik also estimates models using wage data compiled by the Bureau of Labor Sta­tistics for a smaller set of countries. The very small number of democratizations in this sample (only Portugal, South Korea, and Spain) makes these results less reliable. In any case, using Rodrik’s original specification, we find that our democracy measure is associated with a 37% increase in wages (standard error = 14.23), but when we estimate the specification in Equation (21.6), including the lagged dependent variable, the effect becomes smaller and no longer significant.

APPENDIX B. RESULTS USING OTHER MEASURES OF DEMOCRACY

In this section we study whether our results are driven by our new measure of democracy. Inparticularweuse Cheibub etal. (2010) Democracy-Dictatorship data (CGV) and Boix- Miller-Rosato’s Complete Dataset ofPolitical Regimes, 1800-2007 (BMR). Both datasets are different updates and revisions ofthe Przeworski et al. (2000) measure. We estimate our basic dynamic panel model using the log of tax revenue as a percentage of GDP, and the Gini coefficient for net and gross income as dependent variable. We only report fixed effects estimates and the Arellano and Bond GMM estimates for each of these variables.

The top panel in Table 21.A3 presents the results using Cheibub et al. (2010) democ­racy measure; while the bottom panel presents the results using Boix et al. (2012) democ­racy measure. We find a similar pattern and similar magnitudes, though our GMM estimates on the tax to GDP ratio are less precise and not significant. Again, there is

Table 21.A3 Effects Ofdemocratization on the log of tax revenue as a percentage of GDP per capita, and Gini coefficient of net and gross income

Continued

Table 21.A3 Effects of democratization on the log of tax revenue as a percentage of GDP per capita, and Gini coefficient of net and gross income—cont'd

Note: Odd columns present OLS estimates with a full set of country and year fixed effects. Even columns present Arellano and Bond's GMM estimators of the dynamic panel model which remove country fixed effects by taking first differences of the data and then constructing moment conditions using predetermined lags of the dependent variable and democracy. All models control for the lag of GDP per capita but these coefficients are not reported to save space. Robust standard errors, adjusted for clustering at the country level, are in parentheses. ***: significant at 1%; **: significant at 5%; *: significant at 10%.

an effect on tax revenue as a percentage of GDP, which holds in a more robust way when we focus on specifications in levels that are not reported here to save space. We also con­tinue to find no robust effect on inequality.

Overall, the results are broadly similar using other measures of democracy, though they are more precise and consistent with our preferred measure—as would be expected if our measure removes some of the measurement error present in other indices. This was one of the main goals for its construction.

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