Writing in 1959, Seymour Martin Lipset reported a strong and positive correlation between income per capita and democracy in a global cross section of nations (Lipset, 1959). Doing so, he not only laid the foundations of modernization theory in comparative politics but also defined a major portion of the contemporary agenda in political economy, with its focus on the relationship between political institutions and economic development.
Lipset’s finding invites a dynamic and causal interpretation. It was therefore startling when estimating Markov transition models, Przeworski et al. (2000) failed to find a significant relationship between the level of income per capita and the likelihood of transition to democracy. While Boix and Stokes (2003) and Epstein et al. (2006) have challenged Przeworski et al.’s finding, it has subsequently been replicated by (Acemoglu, Johnson, Robinson and Yared, 2008) (henceforth AJRY). In our contribution, we focus on this last article and challenge their result.
In doing so, we report the presence, rather than the absence, of a significant statistical relationship between income and democracy for a group of countries that have been neither consistent democracies or non-democracies over the past 50 years. And the relationship we find is negative. To be noted is that AJRY also reported, but failed to comment upon, negative coefficients in their estimates.2
We take as our point of departure the important critique of (Gundlach and Paldam, 2008) (GP hereafter), who argue that by including year and country fixed effects AJRY purged from their panels useful information, thereby predisposing them to fail in their search for a relationship between income and democracy. In mounting this critique, GP highlight an important methodological dilemma: Including country specific fixed effects purges informative variation from the data; but excluding them introduces omitted variable bias. We confront – and surmount – this dilemma. Employing an augmented version of the Pooled Mean Group (PMG) estimator of Pesaran et al. (1999) we account for both country and year effects while relaxing the assumption of cross-sectional parameter homogeneity. Even while controlling for (time-invariant) omitted variables, we thereby extract information from sources of variation that were previously ignored.
In addition, inspired by the literature on the “rentier state” (Mahdavy, 1970; Beblawi and Giacomi, 1987; Chaudry, 1994) and the “political resource curse” (Barro, 1996; Barro, 1999, Ross, 1999, Ross, 2001), we decompose overall per capita income into its resource and non-resource components and find that the source as well as the level of income matters: the larger the portion originating from natural resource rents, the lower the level of democracy.
I. Additional Background
Given its central place in the literature, Lipset’s law claims the central position in this paper. Our research resonates with two other literatures as well. One, which has already been mentioned, addresses the resource curse; the second focuses on the relationship between economic growth and political instability.
As noted by Ross (2001), in ″rentier states″, governments with large oil revenues reduce the level of political discontent by maintaining low taxes and high levels of public benefits. In addition, they spend more on internal security, forestalling the formation of political organizations inclined to demand political rights. Lastly, as, Ross states: ″if resource-led growth does not lead to higher education levels and greater occupational specialization”, as has commonly been the case,” it should also fail to bring about democracy″ (Ross, 2001, p.336/337).
High income might co-vary with low levels of democratization for a second reason: growth reversals might spark political protests which result in the overthrow of incumbent regimes (Burke and Leigh, 2010). In the case of democracies, one party might supplant another in power; but in the case of authoritarian governments, the authoritarian regime might give way to a democracy, leading to an emerging relationship between low national income and democratic governance.
Haggard and Kaufman (1997) provide qualitative evidence that growth declines weaken authoritarian regimes, reducing their ability to trade political benefits for political deference (see also Geddes, 1999). Burke and Leigh (2010) develop a game theoretic model that illustrates how economic contractions can reduce the opportunity costs of political protest, including demands for democratic reforms. And in their survey of the literature on growth, Alesina and Perrotti (1994) use data on the average annual per capita rate of growth of GDP with and without government changes, documenting that income growth is lower in years with government change, still lower in years with major change, and lowest in years with coups. They conclude that ″transitions from dictatorship to democracy, being associated with sociopolitical instability, should typically be periods of low growth″ (Alesina and Perotti 1994, p. 359). Londregan and Poole (1990) estimate a simultaneous equation model and similarly find that income per capita and economic growth are important determinants of coups: poverty and low growth increase the likelihood of coups. Over time, this channel too would yield a relationship between low income and democracy.
While our work targets Lipset’s law and thus the long-run relationship between income per capita and democracy, it relates to these literatures as well.
II. Data and Methods
Before testing Lipset’s law, we first introduce our data and our methods.
We use the Penn World Tables’ (PWT 6.3) chain weighted real GDP per capita series and the Polity IV democracy index which distributes over a range spanning the interval between perfect autocracies (score of-10) and perfect democracies (score of 10). Based on a sample of 105 countries, Figure 1 shows that on average incomes and polity scores have risen over time. While incomes have grown relatively smoothly, in the late 1980s, the polity index jumped discontinuously from -0.4 in 1989 to 1.9 in 1992. As seen in Figure 2, which is based on the same sample, there are important regional differences in the movement toward democracy. While Asia and Latin America’s Polity scores started on an upward trend in the late 1970s, Latin America/Asia democratized prior/post to the fall of Communism. Africa and the Middle East both democratized after 1990; their polity scores then diverged, with those in Sub-Saharan Africa improving more rapidly.
Figure 1.Democracy and Income Global Averages, 1960-2008
Sources: Penn World Tables 6.3, Polity IV Project and authors’ calculations.
Figure 2:Regional Average Polity Scores, 1960–2008
Source: Polity IV Project and authors’ calculations.
Our sample includes 105 countries. Its size and composition is limited by the method we employ. Because the heterogeneous PMG estimator (discussed below) computes coefficients for each country separately, we can include only countries with long time series 3 and must exclude countries with no time variation in the dependent variable.4
When we disaggregate income per capita by sources of income (resource and non-resource), we use the United Nations Statistics Division National Accounts Database. The data are available for 104 out of the 105 countries in our sample, but run only from 1970–2007. The data classify GDP into several categories, one of which includes Mining and Quarrying. We use this category as a measure of resource wealth. Data description in terms of definition and sources are provided in the Appendix.
The PMG estimator allows intercepts, slope coefficients and error variances to vary across panel members. More specifically, it allows the short-run coefficients to vary across countries, while restricting long-run relationships to be homogeneous. In the context of this research, the estimator “assumes” that in the short run—or while adjusting to a common long-run equilibrium—each country’s political institutions respond differently to income shocks.
Because it allows for heterogeneous intercepts, the PMG estimator can incorporate country-specific fixed effects. But because it estimates the model for each country separately, it cannot allow the inclusion of year fixed effects. To correct for potential cross-section dependence in the estimated errors, we—as do Binder and Offermanns (2007)—therefore augment the model with the cross-sectional averages of the dependent variable and regressors.
We are particularly concerned with the possibility of error arising from two sources: unobserved sources of time specific heterogeneity and endogeneity bias.
Time Specific Heterogeneity: Given rapid increases in the global integration of financial and product markets, an economic shock could affect all units of a given cross section of a global sample. Heterogeneity would then introduce cross-section dependence in the errors of panel members, i.e. the errors would become contemporaneously correlated. In addition, if the same latent factor affects regressors and errors, the shock could be correlated with regressors, rendering conventional regressors inconsistent (Coakley et al., 2006).
Given the difficulty of modeling error arising from cross-section dependence in panel data, few have attempted to do so (Phillips and Moon, 1999). As the PMG estimator estimates the model separately for each cross-section, we cannot employ time dummies to account for common temporal effects. Given our focus on the importance of heterogeneity, we are reluctant to assume common country responses to global factors through simple cross-sectional demeaning which attempts to extract the factors prior to estimation. Instead, we wish to include proxies of these common factors as right-side variables and employ PMG estimation to capture country-specific differences in short-run adjustments to them. We therefore follow Binders and Offermanns (2007) by allowing short-run heterogeneity in responses to both observed and unobserved factors, and thus augment the PMG model by proxies for global factors: the cross-sectional averages of all model’s variables.
Endogeneity. In Table 1 we present Granger causality tests. The data suggest the presence of reverse causality for our sample, which, if unattended, would introduce endogeneity bias in our estimates of the coefficient on income.
|Democracy does not Granger cause income||4532||3||5.47||0.001|
|Income does not Granger cause democracy||4532||3||6.87||0.000|
We address endogeneity concerns in two ways. First, our preferred PMG estimator model is augmented with lags of the regressors and dependent variable to minimize the resultant bias and to ensure that the regression residuals are serially uncorrelated. Pesaran (1997) and Pesaran and Shin (1999) show that, for inference on the long-run parameters, sufficient augmentation of the order of the ARDL model can simultaneously correct for the problem of residual serial correlation and endogenous regressors. In choosing the optimal lag structure, we apply the Akaike Information Criterion (AIC) or the Schwartz Bayesian Criterion (SBC)). In doing so, we are constrained to a maximum of three lags by our time series dimension and number of our regressors.5 Note that any remaining bias works against our conclusion: since our estimates yield negative signs, the coefficients should be even more negative once this bias is taken into account.
Second, we use alternative heterogeneous panel cointegration techniques that correct for endogeneity, namely the group mean Fully Modified OLS (FMOLS) of Pedroni (2000) and Dynamic OLS (DOLS) of Pedroni (2001).6 Both methods allow for regressors’ complete endogeneity, and treat all parameters, i.e. dynamics and cointegrating vectors, as heterogeneous across panel members. With group mean DOLS/FMOLS, the sample coefficients are simple averages of the member-specific DOLS/FMOLS estimators. More specifically, FMOLS runs a static OLS with fixed effects for each panel member individually and uses the estimated residuals to (non-parametrically) build member-specific adjustment terms, which are then used to correct for each member’s endogeneity. Differences in regressors are used as internal instruments. Instead, DOLS individually corrects for endogeneity parametrically by running OLS with fixed effects for each panel member including leads and lags of differenced regressors.7
To summarize formally, let dit represent democracy and yit represent income per capita for country i at time t, and
Crucially, the error term εit is identically and independently distributed across i and t even in the presence of common time effects. Country intercepts -- unobserved country heterogeneity – are captured by the term μi.
The second part of equation (1) includes the lagged changes of income and democracy; the coefficients represent the short-run adjustment terms and are assumed to vary across countries. We do not report the short-run coefficients below. The first part of equation (1) captures the common long-run relationship between income and democracy. The slope coefficients—β, η, and α—measure the long-run response of democracy to income, world income and world democracy. φ is the error correction coefficient and indicates the speed of adjustment If the system is dynamically stable and converges to a long-run equilibrium, then this coefficient will be negative and less than one in absolute value. We report these long-run coefficients below.
Starting with an initial estimate of the long-run parameters, the PMG estimator calculated estimates of error-correction and other short-run coefficients (including country-specific intercepts and error variances) as the averages of the estimated parameters for each cross-section.
It then employs these average estimates to update its estimates of the long-run parameters, repeating the process until convergence is achieved.
Note that we also report (in Table 3) the mean group (MG) estimator (Pesaran and Smith, 1995) which allows for complete (short-run and long-run) parameter heterogeneity across panel cross-sections. If the slope coefficients are heterogeneous, the MG estimator is consistent. Since our cross-sectional dimension is large, the MG estimator is less likely to be biased by outliers. The mean group estimator does not take into account that some economic conditions tend to be common across countries in the long run, however. The PMG estimator does, and so captures efficiency gains from assuming common long-run relationships while at the same time allowing for heterogeneous short-run dynamics. Using the difference between the two sets of estimates, we employ a Hausman-type test to assess the assumption of long-run homogeneity.
|Dependent variable: Demt||AJRY Sample||PMG Sample|
|Log income per capitat-1||0.114***|
|Dependent variable: Polity IV measure of democracy|
|Log income per Capita||-1.239***|
|Joint Hausman test||2.39|
|Error correction coefficient||-0.264***|
III. Estimation and Results
We begin by employing an extended version of our dataset to reproduce the results of AJRY and GP. This dataset includes, in addition to our overall sample, all the countries that were dropped due to the restrictions imposed by PMG (as discussed above). This results in a sample of 153 countries for the annual data panel, and 129 countries for the five- and ten-year data panels, over the 1960–2000 period. As did AJRY, we find (Table 2, columns 1-3) that the coefficient on the income variable is positive and significant, when estimated from pooled data using ordinary least squares, but does not significantly differ from zero when including time and country fixed effects.8 We also find that when we estimate their model employing our smaller PMG sample (Table 2, columns 4-6), their findings remain unchanged. Insofar as our results differ from those of earlier researchers, then, it is not because we are making use of different data. We next provide econometric evidence in support of that.
Table 3 presents the major (PMG) results derived from our model. Our results are reported in the first column of Table 3, while MG estimates appear in the second. The Hausman test in column 3 result testifies to the validity of the long-run homogeneity restrictions imposed by the PMG estimator.9 The coefficients generated by the pooled mean estimator suggest that income is negatively and significantly related to democracy. Given that the model is linear log, they suggest that a 10 percent increase in per capita income leads in the long run to a roughly 0.12 unit decrease on the polity scale. While AJRY report negative coefficients for the relationship between income and democracy, they refrain from commenting upon them. We instead conclude that not only is there no positive relationship between income and democracy; the relationship is negative.
In contrast to the coefficients on per capita income, those on global changes in output and democratization over the sample period positively affected the level of democracy: both are significant and large. When the global democracy score increases by one unit, the democracy score improves by an average of 0.8 units; and on average, a 10 percent increase in world income improves the democracy score by 0.3 units.
The error correction coefficient is significant; it suggests about 26 percent of error correction in the single-period response of democracy to a departure from its long-run equilibrium value as predicted by the level of per capita income. These results are robust to the optimal lag selection criterion (AIC vs. SBC), to the number of lags, to the cross-sectional demeaning of the data, and to whether Sub-Saharan Africa is included or not in the overall sample.
Recall that GP found that when country fixed effects alone were included in the model, the coefficient on lagged income per capita was significant and positive. AJRY also reported negative and often significant coefficients for income in models which include both country and annual dummies.10,11
As discussed earlier, we also run the FMOLS and DOLS estimators on our sample. Results, shown in Table 4, point in the same direction: coefficients on income per capita are yet again found to be negative and statistically significant.12
|Dependent variable: Democracy||DOLS||FMOLS|
|Log of income per capita||-2.293***|
|Country and year FE||YES||YES|
When analyzing the difference between our findings and those of AJRY, it is useful to turn to Table 5, which reports the results we secure when we employ the pooled error correction OLS model to regress democracy on its lags and on the level of income per capita (also with lags, and country and year fixed effects) while using the PMG sample. As can be seen, we then get negative and significant long-run coefficients on income per capita in our PMG sample, and the magnitudes are similar to our long-run PMG coefficients (discussed below). However, estimating the pooled error correction model while using the AJRY (bigger) sample 13 yields long-run coefficients on income per capita that are insignificant, regardless of the number of lags.14
|Dependent variable: Democracy Longrun coefficient||Lags=2||Lags=3||Lags=4||Lags=5|
|Log of income per capita||-1.325*||-1.236||-1.595*||-1.683*|
|Error correction coefficient||-0.141***||-0.145***||-0.142***||-0.144***|
|Country and year FE||YES||YES||YES||YES|
The difference between our results and those of AJRY thus arise from 1) our estimation methods, which exploit both the dynamic and heterogeneous properties of the data and 2) our samples, which exclude both consistent autocracies and consistent democracies, as noted above. The differences in the samples prove consequential: by restricting our sample to country-years that experienced changes in both their incomes and polity scores, i.e. countries that witnessed movements either away or towards more democracy, we are able to detect the relationship between income and democracy, one that turned out to be significantly negative.15 Both the sample choice and the methodology thus led us to our results.
It is also worth noting that in relation to Figure 2, where global income and democracy appear to be negatively correlated up to 1985, we check whether our results on the negative relationship between income and democracy are driven by the pre-1985 period. Estimating both the PMG and the pooled OLS ECM for the sub-period 1985-2007, results (not reported here) maintain the same negative relationship, with the only difference that global output in the PMG model is now significantly negatively related to democracy.
IV. Digging Deeper
In this section, we explore the possible impact of additional sources of variation: variation in the composition of the national income and regional relationships between income and democracy.
The sectoral composition of output: Returning to the literature on the “rentier state” (Mahdavy, 1970; Beblawi and Giacomi, 1987; Chaudry, 1994) and the “resource curse” (Barro, 1996, Barro, 1999; Ross 1999, Ross, 2001), we augment our baseline regression with the World Bank measure of natural resource rents as a percent of GDP. Doing so reduces our overall sample to 98 countries over the period 1970-2007. As seen in Table 6, we too find a negative and significant coefficient for the relationship between resource rents and democracy.
|Dependent variable: Polity IV measure of democracy Long-run coefficients||PMG||MG||Hausman Test|
|Log income per capita||-0.651***|
|Natural resource rents to GDP||-0.021***|
|Joint Hausman test||4.2|
|Error correction coefficient||-0.355***|
Table 7 decomposes national income into two components: that deriving from natural resources and that deriving from other sources. Our sample now consists of the 102 countries over the period 1970-2007. To highlight the results of interest, we refrain from reporting the coefficients on the cross-sectional averages. Column 1 of Table 7 reproduces the specification employed in Table 3, but estimated from the current sample. The coefficients of interest remain roughly the same as that in Table 3. Columns 2, 3, and 4 report the PMG coefficient on resource and non-resource GDP per capita, first separately and then combined. The results confirm that it is only the resource proportion of income per capita that is negatively and significantly related to democracy.
|Dependent variable: Polity IV measure of democracy|
|Long-run PMG coefficients||1||2||3||4|
|Log Income per capita||-1.228***|
|Log of resource GDP per capita||-0.306***|
|Log of non-resource GDP per capita||0.710**|
|Error correction coefficient||-0.305***|
In this paper we have scrutinized Lipset’s law, which states that there is a positive and significant relationship between income and democracy and that higher incomes lead to democratization (Lipset, 1959). Lipset’s law is commonly regarded as the foundation of modernization theory in comparative politics and the debate over it shapes the contemporary agenda in development economics, with its focus on the relationship between political institutions and economic development.
We revisit this debate and use dynamic panel data methods to examine the direction of causation as well as the short and long run relationship between democracy and income. Our tests fail to establish the direction of causality: income ‘Granger’ causes democracy and vice versa. With this in mind, we turned to modelling democracy as a function of income. We use the augmented PMG estimator (Pesaran et al., 1999) which enables us to examine the short/long run relationship while allowing for country and year effects as well as for parameter heterogeneity across panel members. Accounting for the dynamics, we then find a significant and negative relationship between income and democracy for the group of countries that have been neither consistent democracies nor consistent non-democracies over the sample period. Doing so, we draw information from two sources of variation overlooked by previous scholars. One is cross country variation in the short term responses to income shocks; another is variation in the structure and composition of income. In countries that receive little or no income from resources the relationship between democracy and income is positive and significant. In resource rich countries, the reverse is true: higher incomes bear a negative relationship with democracy.
|Central African Rep||1960–2007||Mozambique||1975–2007|
|Congo, Dem. Rep.||1960–2007||Pakistan||1955–2007|
|Egypt, Arab Rep.||1955–2007||Romania||1955–2007|
|Guinea||1958–2007||Syrian Arab Rep||1961–2007|
|Honduras||1955–2007||Trinidad & Tobago||1962–2007|
|Iran, Islamic Rep.||1955–2007||Venezuela, RB||1955–2007|
|Tables 3, 4|
|Log of Real Income per Capita||5076||105||8.0||1.0||5.0||11.5|
|Log of Real Income per Capita||3723||98||8.1||1.0||5.0||11.5|
|Rents to GDP||3724||98||7.7||13.5||0.0||116.5|
|Log of Real Income per Capita||3875||102||8.1||1.0||5.0||11.5|
|Log of Resource Income per Capita||3869||102||5.2||1.8||-2.4||11.1|
|Log of non-Resource Income per Capita||3875||102||8.0||1.0||5.0||10.5|
|Share of Resource Income||3876||102||10.0||13.9||0.0||77.5|
|Central African Rep||6.1||Mozambique||2.4|
|Congo, Dem. Rep.||12.7||Oman||47.6|
|Egypt, Arab Rep.||8.9||Portugal||2.8|
|India||4.3||Trinidad & Tobago||22.2|
|Iran, Islamic Rep.||20.2||Turkey||2.9|
|Income per capita||Data measured as log real GDP per capita (chain weighted method, income measured in constant USD) from Penn World Tables 6.3.||http://pwt.econ.upenn.edu/|
|Democracy||Polity IV index ranging from-10 for perfect autocracies to +10 for perfect democracies.||http://www.systemicpeace.org/polity/polity4.htm|
|Natural resource rents||Expressed in percent of GDP. Rents are measured as the market value of extracted material minus the average extraction cost. Natural resources include bauxite, coal, copper, forest, gold, iron, lead, lignite, natural gas, nickel, oil, phosphates, silver, tin and zinc.||World Bank data:http://go.worldbank.org/OV4R25M150|
|Resource and non-resource income shares||Resource income share is defined as the share of Mining and Quarrying in GDP. Non-resource income constitutes the rest.||United Nations Statistics Division National Accounts Database.|Figure A:Country-Specific FMOLS Income Per Capita Coefficients, N=105
Source: Authors’ calculations.
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