Inequality Regimes and Rawlsian Growth Rates: Some thoughts on the evolution of inequality 1960-2010, with special reference to Venezuela

(Mostly an excuse to play with the Standardized Worldwide Income Inequality Database, compiled by Frederick Solt, which I just discovered. This post also belongs, loosely speaking, to my long-running series on the quantitative history of political regimes. R code for everything in this blogpost available in this Git repository; you would need to download the dataset separately).

Inequality is difficult to measure. Socially relevant inequalities are manifold, and measurable inequalities in money income are not always especially important. (In the formerly communist states of Eastern Europe income was very evenly distributed; yet this did not mean that there were no important social inequalities). Even inequality in money income is not easy to measure properly. Most existing data is not very comparable accross countries or years, and it is often not even clear to what income concept the sorts of inequality measures people typically use to make a point in political discourse refer to: does it refer to after-tax, after-transfer income or to "market" income? Does it refer to individual or household income? What sorts of things are counted as "income"? How do we account for access to high-quality public services? At best, measures of income inequality are uncertain estimates of an unknown distribution of potential living standards, more or less valid for societies where "money income" is a useful proxy for the ability of people to enjoy various important goods, and of little value outside the context of a conception of a "just distribution" of these capabilities.

Despite the fact that estimates of what is essentially a statistical abstraction often play surprisingly big roles in current political debates (cf. the debate over The Spirit Level some years ago, or recent concern about a rise in New Zealand's gini index), I have recently become more skeptical about the importance of measured income inequality in politics: whatever importance the actual distribution of incomes has for political life in a society, it has to be mediated through complicated processes that refract local lived experience through the prism of context-dependent fairness norms that are only vaguely related, if at all, to the numbers used to measure its skewness.

Yet I'm curious: what sorts of income inequality have in fact increased, and where? How might these changes have mattered? Enter a new and shiny dataset: the Standardized Worldwide Income Inequality Database, which promises to ameliorate some of these measurement problems. The database uses the Luxembourg Income Study - very high quality income inequality data - to calibrate the much larger but less comparable United Nations University World Income Inequality Database. The result: lovely long time series estimates of both the market and the after tax, after transfer (net) gini index of inequality, including standard errors, for 153 countries. My first though on learning about this was: graphs! (Hopefully some non-obvious facts are also involved below).

Let's start by looking at a country that has often been cited as a great success in reducing inequality: Venezuela during the Chavez years. (Also, I was there recently visiting family after a long absence, and I have a personal interest in understanding the changes that have occurred during that time). One question I've been curious about concerns the evolution of inequality in Venezuela relative to other Latin American countries, especially since the coming to power of Chavez in 1999. How do changes in inequality in Venezuela compare to changes elsewhere?

In the following plot, we see changes in both the market ("equivalized (square root scale) household gross (pre-tax, pre-transfer) income", if you must know) and the net gini index of inequality (after tax, after transfer) in 19 Latin American countries from 1999 until 2010, ordered by the estimated rate of inequality reduction (countries that reduced inequality faster appear earlier; read the graph from right to left, top to bottom):

Inequality in Venezuela has indeed decreased relatively quickly since 1999 - the second fastest decrease after Ecuador, which has also had left-leaning governments (though a far more unstable political context, with five different presidents since 1998). Three things are worth noting about the context of these trends, however.

First and most important is that this reduction in inequality is not driven by direct redistribution: there is barely any difference between the "market" gini index (our measure of inequality before taxes and transfers) and the "net" gini index (our measure of inequality after taxes and transfers). To the extent that the reduction in inequality is the result of government action rather than something else, it must have come about through measures like investment in human capital and labor market policies (see Morgan and Kelly 2012, ungated here, for the proper peer-reviewed argument). This is true of all Latin American countries save for Puerto Rico (which is part of the USA in a sense) and (to a lesser extent) Brazil; indeed, redistribution in some countries (Peru) appears to have perversely increased inequality.

Second, most Latin American countries have experienced reductions in inequality during this period, though most remain highly unequal. But Venezuela was already among the most equal countries in Latin America; in 1999, only Uruguay and Costa Rica had lower measured inequality (and the difference in net gini was within the margin of measurement error, so it should probably be disregarded). This surprised me; I had expected higher levels of inequality in Venezuela when compared to other countries, given the level of class conflict on display during the Chavez era. More surprisingly perhaps, if we take a broader look we discover that inequality in Venezuela appears to have been remarkably stable over the past fifty years, fluctuating around a flat trend:

(Lines around dots represent 95% confidence intervals).

In fact, the low level of inequality in Venezuela as of 2010 only returned the country to the level of inequality it last experienced around ... 1992, the year of the February coup which made Chavez famous, and three years after the "neoliberal" paquete of 1989 (which was supposed to have triggered the Caracazo). Inequality did increase after 1992, and poverty had increased before then - the Venezuelan economy had been in decline for a while, as we can see below. Which all goes to show, I suppose, that political unrest and lived experiences of injustice are only very loosely connected, if at all, with measures of income inequality; whereas austerity and large income losses appear more immediately important to political outcomes (as Jay Ulfelder argues here):

(GDP per capita data from the Penn World Table v. 8.0).

Finally, it's probably worth noting that Venezuela's economic fortunes are deeply tied to oil prices, and that the rapid reduction in inequality in the last decade or so should also be placed in the context of the very large rise in the value of oil and gas during this period. Here is an estimate of the per capita value of oil and gas exports for Venezuela, from Michael Ross' oil and gas dataset:

In fact, Venezuela and Ecuador, the countries that have experienced the fastest inequality decreases, have been precisely the two countries that have benefitted the most from oil and gas price increases - money that flows directly to the state (especially since the Chavez government systematically asserted control over the state oil company) and can be used to provide employment and subsidize education, healthcare, housing, staples, and other goods, however inefficiently (e.g., the varios "Misiones" and other social programs created by the Chavez government). At least some of these programs must have played some role in the reduction of inequality, but given the amount of oil and gas money flowing directly to the Venezuelan state (representing most Venezuela's exports, which have become substantially less diversified over the last 15 years) and the typical patterns of clientelism and electoral politics in Venezuela it would have taken a bloody-minded kleptocrat not to reduce inequality by some amount. At any rate, inequality and poverty also diminished quite a bit during the 1970s oil boom, likely through similar channels - massive amounts of money flowing through the state, which increased its ability to employ people and subsidize public services. (I don't mean to sound grudging; though I have doubts about the effectivenes of some of these programs, some of the new housing built during the Chavez years looks decent, for example).

Let's take a broader look, however. How does the Venezuelan experience of inequality reduction compare to some countries outside of Latin America? Just because they've been in the news, let's look at the Venezuelan experience in coparison to Turkey and Egypt; and add the USA and New Zealand to see how two "developed" countries look as well.

That's right: Egypt and Turkey apparently reduced inequality faster than Venezuela in this period (though the error estimates of the gini index for both are also larger), and were less unequal than Venezuela by the end of the period! Also, despite the fact that the degree of "market" inequality was higher in the USA and NZ than in Venezuela, and did not decrease or even increased a little during this period (as measured by the net gini index), both countries remain less unequal than Venezuela (as measured by the net gini index), due to the effectiveness of their redistributive measures.

Now, this is perhaps surprising, but a bit of an aberration, and really, we are dealing with imperfectly estimated quantities (rates of decline) based on measurements with error. So there's really no point in arguing about whether inequality in Venezuela has in fact decreased faster than in Egypt or not; our methods of measuring inequality don't allow us to give a very precise answer to this question (error bars are large, etc.). In any case, it is clear that income inequality has declined pretty fast in Venezuela over the last 15 years, even allowing for some meaurement and estimation error, as we can see by calculating the trend rate of change in the net gini coefficient (the slope of a regression of log(gini_net) on year, to be technical, which yields the estimated trend annual percent rate change in the gini index) for all countries in the dataset:

(I took out countries that had too few datapoints, since the trends didn't look to me like they could be informative. The error estimates in the graph are nevertheless probably too small, since one would need to use the proper rules for error propagation to calculate them, which I have not done. Interestingly, the estimate of the rate of change in the net gini index for New Zealand and the USA since 1998 suggests basically that they have experienced no significant change in measured inequality from 1999 until 2010, contrary to popular belief; their important increases in inequality occurred earlier. More on this in a minute).

What strikes me about this graph is that countries that have achieved very fast reductions in inequality over this period appear to be quite disparate; though left governments are in evidence among these, many countries apparently achieved fast reductions in inequality with supposedly "neoliberal" policies (e.g., Egypt and Turkey) that are now in turmoil. Maybe this is evidence that the gini index does not capture socially relevant changes in inequality; but then it would also fail to capture changes in inequality in non-neoliberal Venezuela and Ecuador. (Of course, other confounding factors may be at work too).

It is also curious that many of the fastest reductions in inequality have occurred in states that do not engage in a lot of explicit redistribution. In fact, a simple correlation between the average redistributive capacity of a state (measured by the percentage difference between the "market" gini and the net gini coefficient) and the rate of decrease in measured "final" inequality over the period is slightly negative (so fastest reductions in inequality have occurred in states that are unable to affect market gini very much at all, or that even increase it through perverse redistribution):

It's probably not worth making too much of this correlation (measurement errors, the relatively short time period under consideration, and confounding factors are not taken into consideration), though it does suggest that many changes in inequality seem beyond the control of most governments. But even when we expand the period of observation all the way to 1960, the correlation does not entirely disappear, though it weakens greatly:

Ultimately, however, the more directly and explicitly redistributive the state has been, the more equal it also appears to be over the long run:

Or, to put it crudely, since 1960 at least market inequality has only been reliably reduced in states that take from the rich and give to the poor. And yet actually taking from the rich and giving to the poor seems to put nontrivial demands on state capacity and political life (witness the existence of robber states that take from the poor and give to the rich). The degree of change in the market distribution of income even appears to be a fair measure of that capacity; from the graph above, it's likely that a state that can consistently reduce gini index of market inequality by at least 30% is a pretty "strong" state (in the "infrastructural" sense of strong), whereas a state that cannot make a dent on the market distribution of income is more likely to be "weak" (with some communist exceptions like the USSR that did not engage in a great deal of explicit redistribution, since, to put it crudely the state owned everything and everyone more or less got paid the same).

And abilities to redistribute income appear to be remarkably "sticky." Few countries appear to become more able to affect the gini distribution over time:

(I've deleted cases with very few data points to make the graph look prettier. See the code for the details). What is striking about this graph is how stable the redistributive capacity of most states has remained over a period of more than six decades: many countries show basically zero change in their ability to change the income distribution. To be sure, some countries have increased their redistributive capacity -- France is a good example -- and others experience wild swings in redistributive capacity, probably related to big political conflicts -- note Bangladesh and Chile, the latter with a big bump around the time of Allende. But at best we can detect a long-term decline in redistributive capacity for the majority of cases (even if the decline is often slight); and often, after a decline, we see long periods of stability rather than change: countries settle into an "inequality regime," with some occasional big bumps which indicate new equilibria.

Note that in many cases the redistributive capacity of the state does not change even while inequality increases: thus, for example, while the net gini has increased over the past six decades in the USA and New Zealand, their capacity to affect the gini coefficient has remained approximately the same (New Zealand has been able to reduce the market gini by about 27%, though there's a slight downwards trend in this number; the USA by about 22%). The structure of their economies changed (by political action, in part), producing more inequality, but their redistributive capacity as states remained basically the same. To decrease inequality by redistribution in cases where the market gini increases substantially seems to require either a big political shock, or a long-run increase in state capacity.

There is another historical pattern that struck me as interesting: both levels of inequality and redistributive capacities seem to be highly correlated accross regions. Neighboring countries appear to have both similar levels of inequality and similar redistributive capacities. Linked economic and political histories seem to produce both the equilibrium level of inequality and the long-run redistributive capcity of the state.

Here, for example, we see the average redistributive capacity of states per region:

(Cases to the right of the solid line are states that on average made their income distribution more equal; the dashed line indicates the median redistributive capacity).

And here is a graph of net gini per region (all observations since 1960):

No great surprises here, perhaps: Sub-Saharan Africa and Latin America have been the world's most income-unequal regions over the last six decades, whereas Europe has been consistently equal - the home of both Northern social democracy and Eastern European communism, both of which have been able to keep the distribution of incomes relatively equal through explicit redistribution, though in somewhat different ways.

But perhaps this is of little importance. On one (vaguely Rawlsian) view, what matters is not the income distribution per se, but the ways in which it affects the prospects of the worst off in society. How much does it matter whether or not inequality declines in any given society, especially for the poorest? This will depend on the growth rate of the economy; high growth with declining inequality will be better for the poor than low growth with increasing inequality, though the outcome of the comparison is ambiguous for high growth with increasing inequality or low growth with decreasing inequality.

Now, it occurs to me that with the average income for these countries as well as their level of inequality, we can make an informed guess (technically, a wild guess) about the average income of various deciles for the years in which data is available. To do this properly would be too painful for a blog post, but I assume that empirical income distributions more or less fit a lognormal distribution (even though they fit more exotic distributions better, like the Singh-Maddala distribution or the generalized Beta distribution). With a little help from R, I can then simulate the average income of each decile of every country in the SWIID dataset. (Take a look at the code for the gory details. Also: this is a VERY rough and ready simulation, extremely inefficient to run and cooked up in a day. Do not take these numbers too seriously). We can then provide some vaguely informed answers to a Rawlsian question: which countries have most increased the prospects of the poorest over the last six decades?

The question admits of two more precise formulations, which we'll take on in turn: what countries have had the highest growth rate of income for the lowest deciles of the population? And second, in which countries do the poor have the highest incomes? (The first corresponds to a sort of dynamic version of the difference principle, which I find more interesting). Let's start with looking at the Rawlsian growth rate (the rate of income growth for the lowest decile, which we'll assume represents the group whose position must be maximized in Rawls' theory); the higher the long-run Rawlsian growth rate, the more the country fulfills the dynamic version of the difference principle. Though in theory the long-run growth rate of the economy as a whole and the long-run growth rate of the income of the lowest decile should perhaps converge, in practice they diverge, even over long time periods - some groups do well over some time frame, others do badly. Now, what we would actually want to know from a strict Rawlsian perspecive is the highest long-run growth rate of income for a representative person in the lowest decile of a given country relative to the potential growth rate of the whole economy (in other words, what degree of inequality would produce the highest income growth for that representative person, given the particular structure of that economy), but this is a counterfactual quantity we cannot estimate, so we'll make do with simulating the incomes of the lowest decile for the actual combinations of growth and inequality in existing economies. (I repeat my warning: this is only a simulation!)

First, we estimate the long-run Rawlsian growth rate (for countries with data going back far enough - so we drop countries that don't have long enough time trends, say at least 30 years):

The "Asian Tigers" unsurprisingly top the list: over the last six decades, Taiwan, Singapore, and South Korea had the highest (simulated) Rawlsian growth rate (in countries with at least 30 years of both GDP and gini data). South Korea and Taiwan are below average in inequality, which makes sense, but Singapore is not. Over the long run, in other words, a high enough growth rate of income seems to compensate for higher than average inequality. But one surprise among the top countries is Egypt - where the poorest decile, if we believe this simulation (and you shouldn't), had a pretty good run over many decades, despite Egypt not being considered a big performer in terms of its average per capita growth. At the bottom, by contrast, we find that Venezuela has essentially experienced zero Rawlsian growth over six decades (in fact, its long-run trend in regular per-capita annual growth is also zero). Though below average in inequality, its income has suffered so many ups and downs (mostly following oil price changes) that the trend is flat; no wonder Venezuelans eventually got tired of all their politicians before Chavez.

Now, there obviously is a correlation between Rawlsian growth and regular growth, as well as between Rawlsian growth and average inequality, but it is not perfect, simply because the Rawlsian growth rate is a function of both the average growth rate and the gini index by construction, and these two things are not perfectly correlated; a high enough growth rate in the whole economy can overcome a large gini coefficient to produce high Rawlsian growth rates and vice-versa. But it's worth noting that extremely high levels of inequality do appear to be associated with plain low growth over the long run, bad for both the poor and everyone else except perhaps tiny kleptocratic elites:

We can now repeat the exercise for the last 15 years and see how Venezuela stacks up since then:

As we can see, the Chavez years (up to 2010; the data does not tell us what happened for the last three years) were quite good for the poor, according to this simulation: the combination of declining inequality and relatively high growth rates (due in great part to rising oil prices) made Venezuela a top ten Rawlsian performer - better even than China, which also had torrid growth rates but increasing inequality during this period. To be sure, this good "Rawlsian" growth rate is only relevant if we ignore the equal liberties principle, which from a strict Rawlsian perspective  is meant to have priority over the difference principle; and increasing disregard of classic liberal rights during this period counts against Venezuela. (I vaguely wondered whether perhaps a "Rawls index" could be constructed, using data like the UDS to measure compliance with the first principle, fair equality of opportunity using the gini index, and the difference principle using the rate of growth of the income of the lowest decile; but since the two principles are supposed to be lexicographically ordered, a combined Rawlsian index would be pointless, useful only if we relax that assumption. Nevertheless, if we relax that assumption, then we would have to face the question of how much the improvement in the condition of the least well off ought to count against the decline in the "equal liberties" of the first principle; and I don't know of any good principled answer).

At the same time, it is interesting to note the countries at the very top are not precisely all left-wing governments; Azerbaijan, Mongolia, and Ukraine appear there. This may be because the simulations are risibly wrong (an important possibility), or the data are wrong; or simply that policies of the kind the Chavez government tried out are not the only possible ones to bring about growth in the income of the poor (and now, with high inflation, sporadic shortages, a large black market premium for dollars, and other problems, they don't look especially sustainable either). Nevertheless, the high Rawlsian growth rate makes it easy to understand why many of the Venezuelan poor felt that Chavez improved their position, regardless of how much responsibility we ought to attribute to his government for that outcome, or how sustainable its policies may be with lower oil prices.

Regardless, a good growth rate for the poorest decile matters: if inequality had remained at its maximum level during the Chavez years instead of declining but the growth rate had stayed the same, I estimate that the a representative of the poorest decile would have earned about $2000 less over the entire period than they actually did. We can call this quantity the "Rawls gap": the amount of income the poorest decile would have gained (or lost) in a given period had inequality remained the same as at the beginning of the period. Of course, since the growth rate would have been different had inequality remained the same, this is merely a fiction; we can't really estimate this counterfactual.

Nevertheless, just for fun, here is the Rawls gap for Latin America, per year:

This allows us to say that in Venezuela, the reduction of inequality that occurred during the Chavez period (assuming, per impossibile, that the growth rate would have stayed exactly the same had inequality remained at the 1999 level) gained a representative person in the poorest decile a total of about $1500-$2500 over 10 years, or about $200 per year, whereas the increase in inequality over the same period in Costa Rica cost a representative person in the poorest decile about $800-$1200 in income, or about $100 per year. This is nothing to sneeze at for the poorest decile (whose average yearly income is only about $3000 per year).

(It's kind of fun, though conceptually pointless and computationally expensive in my system given my crappy code, to calculate various Rawlsian gaps for arbitrary years and countries; for example, the "Rawls gap" for NZ is something like $2000 per year lost in income for the poorest decile if we assume the same growth trajectory but the level of inequality of the early 80s. Which of course we shouldn't - had inequality remained the same, the growth trajectory would have been different. As Adam Przeworski has said, everything is endogenous).

(We could also imagine even more exotic quantities, though I have no time to test them out here. Consider the Rawlsian compensatory growth rate, for example. This would be the growth rate that would compensate the poorest decile for an increase in inequality: if we want to say that some reform x would lead to higher income growth but higher inequality, then the compensatory Rawlsian growth rate is the growth rate where the income growth rate of the poorest decile at the higher level of inequality is identical to their income growth rate at a lower level of inequality but a lower overall growth rate for the economy; you would need a reform to produce at least the compensatory Rawlsian growth rate for it to be justified in terms of the difference principle. Which you may of course think is bogus).

Now, absolute incomes matter too; the difference principle in Rawls is not usually understood in terms of growth rates (though I think that should be the more natural interpretation). But the second version of the Rawlsian question above (where do the poor have the highest incomes?) has a much more obvious and boring answer: the Scandinavian countries, due to both generally high incomes and low levels of inequality due to high redistribution; and most of the countries at the top also score well in terms of the first principle (measured inexactly here by the UDS, which perhaps ought to be discounted a bit given recent developments in some countries). I include it here for completeness:

The roots of that ranking of countries are much older and deeper than this dataset allows us to see.

In theory, both the first ("equal liberties") and the second principle of Rawls' theory ("fair equality of opportunity" plus the "difference principle") ought to go together. In practice, however, Rawls himself thought that they did not always do so, though his reasons for thinking this were not always clear. Though I don't really have the tools to tackle the question of the relationship between liberal rights and the rest of the components of Rawls' theory properly (certainly not here), it looks as if we see a kind of inverted-U relationship in the data:

In other words, over the last half-century, the income of the poor has risen fastest under regimes that have not been on average highly democratic, but also has grown least in these regimes; non-democracy looks like a (potentially quite bad) gamble, though both democracy, long-run inequality, and the long-run growth rate of the income of the poor are probably determined by (or are a reflection of) some deeper social fact, like state capacity, which is not really susceptible to policy intervention. Whatever state capacity is (I have argued it is a kind of development of political technologies) it emerges out of political struggles that take a very long time to work themselves out with many tragic consequences along the way; and in any case the rate of improvement of state capacity is at times immeasurably low.

[Update 12/7/2013 - fixed a reference to a non-existing graph]

More on Inequality, Democracy, and Dictatorship: Is there a “Natural Rate of Inequality”?

(Continues the discussion in this post, with more graphs, more data, more theory, and more verbiage. Mostly exploratory, considering further research. Statistician General’s warning: all statistical analyses in this post should be taken with large heapings of salt, since they have not been produced by a trained and licensed statistician and do not provide appropriate guidance regarding the uncertainty of any estimates. If you don’t mind a spot of quantitative social science from someone who was not trained in these dark arts but who is overly excited about learning to produce pretty graphs, go on.).

In an earlier post, I discussed some recent models of the relationship between inequality, political regime types, and democratization (e.g., Acemoglu and Robinson or Boix). The basic ideas in these models are pretty simple, even simplistic. In democracies, governments are (ideally, at least) responsive to the interests of the majority of the population, and in particular to the interests of the “median” voter (the voter in the middle of the distribution of income among voters), whereas in dictatorships governments are more responsive to the interests of smaller – sometimes much smaller – groups. To the extent that dictators are responsive to the interests of constituencies where the median income is higher than the median income in society (the typical case), we should expect that dictatorships will tend to redistribute less (to lower income groups) and have higher levels of income inequality than democracies, other things being equal (and other things are not always equal!). Moreover, these models indicate, the higher the level of inequality, the higher the degree of social conflict over the level of redistribution and ultimately over the type of regime, since “one off” redistribution in the face of occasional protest or other contentious action is not sufficiently “credible.” Hence we should expect that in the long run, democracy should be unsustainable at very high levels of inequality, and the only stable regime outcomes should be forms of dictatorship: “leftist” dictatorships where the poor (or rather, people claiming to act in their name) expropriate the rich, and “rightist” dictatorships where richer elites restrain redistributive demands by non-elites through coercive means. Finally, we should observe more regime change at higher rather than lower levels of inequality, more stable regimes at lower rather than higher levels of inequality, and more transitions to stable democracy at middle levels of inequality.

Using data on inequality from the University of Texas Inequality Project (1960-1996) plus data from the World Bank (which I didn’t use in my previous post but extends the UTIP data to 2008 for some countries), and data on political regimes by Cheibub, Gandhi, and Vreeland (1956-2008), we can see that some of these theoretical expectations appear to be reasonably well validated.[1] Here is a plot of the distribution of inequality, as measured by the gini index, in democracies and dictatorships (reproducing the first plot in my earlier post, but with World Bank inequality data added):

The median gini for democracies is 38.6; the median gini for dictatorships is 45.6. (means 40.1 and 44, respectively, with N= 3,321 observations between 1963 to 2008; similar patterns appear if we look only at particular periods, like the post cold war era). If we could add the vast majority of non-democratic systems in human history, the pattern would be even more obvious; as Lindert, Milanovic, and Williamson have argued, ancient non-democratic societies (i.e., the vast majority of all ancient agricultural societies) were at the “inequality possibility frontier” – elites extracted the maximum surplus from society.

But as I mentioned in my earlier post, it is obvious that the distribution of inequality in both democracies and dictatorships is very wide: lots of democracies have high gini values, and lots of dictatorships have low gini values. We do not see two clearly defined “peaks” in the distribution; rather, the distribution of inequality in both democracies and dictatorships appears to be “bimodal” – with distinct high inequality and low inequality peaks.

This is relatively easy to explain in the case of dictatorships: most of the dictatorships with low inequality appear to be communist countries, though there are fewer of these – exactly what we would expect from the theoretical models. (It is, after all, easier to organize a coup than a social revolution). Here’s a picture of the distribution of inequality in dictatorships only, split among communist and non-communist dictatorships:

The communist dictatorships are clustered narrowly at a low level of measured inequality (median gini 28.9; more on the “measured” bit in a minute), while the non-communist dictatorships have a somewhat broader distribution centered around a larger level of inequality (median gini 45.9).

The bimodal distribution of inequality in democracies is harder to explain; like dictatorships, democracies appear to have both a low inequality and a high inequality equilibrium. Why?

One factor that might seem to matter is simply the length of time a democratic regime has been in existence: redistribution capable of affecting the level of inequality in a society seems to take time. Here’s a plot of the distribution of inequality in democracies, split between those democracies that have endured for less than 10 years and those democracies that have endured for more than 10 years:

Younger democracies appear to have larger levels of inequality (median gini for established democracies: 36.2; median gini for new democracies: 44.4). In fact, while democracies appear to become more equal with time, dictatorships appear to become less equal:

 

And while it seems that democracies become more equal as they become richer, dictatorships appear to remain as unequal as before:

I won’t put too much stress on these graphs; the patterns in individual countries do not always or even often bear out the apparent overall pattern, and it is possible that this is just an artefact of the sparseness of the inequality datasets and the general badness of the data from dictatorships. Most “old” democracies in the dataset start at low levels measured inequality but appear to increase their level of inequality over time (e.g., the USA, France), whereas most “new” democracies start at high levels of inequality and appear to decrease these levels of inequality over time. Since there are more “new” democracies than “old” democracies here, it is possible that we are merely seeing is a kind of cohort effect, though one that is consistent with the basic theory: new democracies start at high levels of inequality, and many don’t last long (because of opposition to redistribution by elites), which skews the right hand panel so that it looks as if democracies become less unequal over time. Most dictatorships transition to democracy at a gini of around 45 (which is high for democracies), but that’s because that’s the median gini for dictatorships; similarly, most democracies transition to dictatorship at a gini of around 45 (perhaps because most new democracies are less stable, and they transition to dictatorship before engaging in significant redistribution?).

Moreover, it is obvious that democracies do become quite unequal sometimes. The USA is an obvious case. Here it is interesting to note that the USA is not a new democracy and is clearly quite rich, so (given the previous graphs) we would predict inequality to go down, but it seems to have been on an upward trend even looking at the long run (not just at the last decade):

(Similar patterns are visible in many rich democracies – France, for example). More on why this might be the case in a minute. But let’s think of different possibilities for why democracies might (or might not) decrease inequality. Consider what happened in Poland after the collapse of communism (similar trends are visible in Hungary, Bulgaria, and other communist countries that transitioned to democracy):

In this case, it seems that the transition to communism triggered the wholesale conversion of political access (the main inequality in these societies) into monetary assets, leading to a higher equilibrium level of measured income inequality. Measured income inequality was actually misleading about the distribution of power in communist countries; just because they were “equal” societies in income terms did not mean they were “equal” societies in the things that income can buy elsewhere, and when the basis of the regime changed, the “true” inequality in society reasserted itself in income terms, though it still remained relatively low in comparative terms. (An alternative story: perhaps with the to a market economy, people in Poland and other communist countries had the opportunity to trade off more income against increased inequality, and they took it. This is also plausible, but not my focus here; it is less plausible in places where wholesale conversion of communist apparatchiks to well-connected biznesmeni took place, as in Russia).

   

Sometimes democracy is, in a sense, too successful at an earlier time in redistributing income, prompting a reaction from elites. Consider Chile:

Inequality decreases fast until the 1973 coup, partly because of redistributive policies pushed by the left, at which point it increases again greatly. The interpretation is obvious: the elite could not stomach so much redistribution, and returns Chile to a higher level of inequality by coercive means. (The military Junta led by Pinochet made this point rather explicitly: their mission was to destroy communism and its leftist sympathizers in Chile. So they arrested and sometimes killed the leaders of leftist parties and coercively defanged or banned labor unions). After the transition to democracy in the late 1980s, inequality seems to stabilize at a higher level: the new democratic governments are constrained in the amount of redistribution they can undertake, both constitutionally and prudentially, and at any rate, the structure of Chilean society changes – labour unions have less power, elite assets are more mobile, etc. So elites are willing to transition to democracy, without fearing Allende-style redistribution. It’s like Chile’s long-term “natural” rate of inequality – the rate that is consistent with the maintenance of a democratic regime is somewhere around a gini of 45.

South Korea presents yet another possibility:

This pattern is also nicely consistent with the basic theory, though in a different way. Here we have a right-wing dictatorship facing a communist neighbour that presented a credible but far more redistributive model. (After the Korean War and until the mid 70s, most people thought the North was doing better than the South). In these circumstances, democracy was too threatening to elites: it was too easy to imagine a communist takeover by electoral means. It was still necessary to defuse the threat of social revolution through some redistributive measures (there were some fairly extensive land reforms, if I am remembering correctly), but not through institutionalized democracy, which was too risky. Promises of redistribution were made credible by the communist threat to the north, and in fact carried out to some extent. Eventually, however, inequality decreased sufficiently and the Northern model became sufficiently unattractive that democracy became much less costly to elites, leading to a transition in the late 1980s. Inequality again appears to stabilize after the transition.

A fourth pattern is found in Thailand:

(The highlighted points are years of successful coups; there were more unsuccessful ones, and the 2006 coup is not shown – no gini data for that year). We might interpret this as follows. Democracy is introduced in the late seventies (though resisted, as shown by coups in 1976) and is immediately associated with a decline in inequality, presumably through redistributive policies, but remains plagued with coups (more unsuccessful ones not shown), mostly supported by the elite. Yet the elites do not have enough power to sustain a military regime indefinitely; military coups merely postpone the resumption of redistributive politics. (I don’t know of Thailand inequality data for 2006 and after, but it would be interested to see what it looks like).   

The individual patterns are not always so clear. In fact, in most cases where there is data, no pattern is readily discernible: the aggregate pattern is clear, but the level of inequality in individual countries sometimes appears to fluctuate without any apparent connection to regime type. In some countries, transitions to democracy occur as inequality is going down (the South Korean pattern), in others, as it is going up; and in others the trend appears flat, at least given the available data. In some cases, periods of dictatorship are associated with increases in inequality, in others with decreases in inequality, and similarly for periods of democracy.

I suppose someone with knowledge of individual country histories could make sense of any given country pattern, and someone with better statistical skills could design an appropriate measure of whether inequality goes up or down, on average, during democratic or dictatorial periods. My best guess is that you would need to do a fixed effects model – controlling both for factors that affect the level of inequality at a global scale, and for country-specific factors that affect the level of inequality in a specific country. For example, inequality appears to have increased in many countries in the world in the last decade, presumably due to changes in the global economy – but more in some countries than others, presumably due to particularities of each country, including the influence of the political regime. So we would need to distinguish those global effects that are not subject to political control from the effects of political regime on inequality. Fiddling around a bit with something along these lines (using some advice provided by Eric Crampton, though all errors are mine), I cannot find any specific pattern when controlling for obvious things; if anything, democracy seems to increase inequality over time relative to dictatorship when using a full time series model (though it may be that I am not very good at interpreting the coefficients I’m getting, or that I’m mispecifying the thing somehow; for example, maybe one needs specific kinds of lags, etc. If you have expertise and would like to collaborate on figuring this out, please let me know.).

But what does come out of that fiddling as an important factor determining the level of inequality over time is the number of previous transitions to authoritarianism, which we might interpret as a proxy for the power of the elite to prevent redistribution. In various simple regressions, an additional transition to authoritarianism seems to increase the level of inequality by a unit in the gini index, and democracies with higher numbers of transitions to authoritarianism in the past seem to exhibit higher long-term levels of inequality. Consider this scatterplot:

When looking at the average gini of democracies that have existed for more than ten years, we see that democracies with fewer transitions to authoritarianism in the 58 year span (1962-2008) of the dataset seem to be scattered all over the place, though the upper range is less populated. But inequality clearly increases with previous transitions to authoritarianism, and the range of “permissible” inequality seems to narrow – with more transitions to authoritarianism, the narrow the range of inequality and the higher the mean. (The pattern is visible when looking not just at the mean gini of democracies existing for more than 10 years, but also at the mean gini of all democracies).  

How can we understand this? Here’s one possibility, and (finally) the justification for the title of this post. With fewer experiences of dictatorship in the recent past, democracies can enact a wide range of redistributive policies, depending on beliefs about luck and hard work, “ideological investment” by various interested parties, reasonable arguments, the organizational ability of various actors like labor unions and business associations, the visibility of wealth and the economic structure of society, etc. Such policies have a wide range of consequences, and so inequality may fluctuate quite drastically over time in established democracies, though it will in general experience some downward pressure relative to dictatorships. Perhaps there are “arms races” in which organizational and ideological innovations by representatives of the “left” (labor unions and particular forms of contentious politics like strikes, Marxism, etc.) are in time neutralized by organizational innovations by representatives of the “right” (think-tanks and particular business organizations, legal ways of constraining the power of unions, “neoliberal” ideas, etc.) and vice-versa, so that in the long run, inequality follows a trend determined by structural factors in the economy: the long-run “natural rate of inequality” for the society, kind of like what economists sometimes refer to as the “natural rate of unemployment.” Something like this has perhaps happened in the USA (at least if we credit people like Hacker and Pierson), where “right-wing” actors developed organizational and ideological innovations to counter the organizational and ideological tools of the “left;” but in the long run, what we should see, short of a change in regime, is a reversion to the mean trend, itself determined by economic factors that are not easily susceptible of political control.

 In some societies, however, some of these redistributive policies prove intolerable to elites, who then stage coups. When the society returns to democratic rule, representatives of non-elites know something about the ability of elites to credibly commit to extreme measures in the face of redistributive policies, so the range of distributive outcomes that appears acceptable to all narrows. (Something like this appears to have happened in Chile, for example, where successive “left” governments have “learned” from the past not to engage in certain kinds of redistributive policies). The more successful coups there are in a society, the narrower this range. Over time, you see the development of a bimodal distribution of inequality in democracies: societies where people have learned about the “red lines” that ought not to be crossed have a higher long-run level of inequality given the structure of their economy.

As noted earlier, take all of this analysis with a grain of salt; this is more exploratory than anything else, and I am certainly not a properly trained statistician. I am just curious, and considering further research on these topics. (Collaboration possibilities are also welcome).

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[1] Data on inequality is patchy and often of poor quality. The UTIP dataset is basically the best there is for cross country comparisons over time (going back as far as 1963 for some countries); the World Bank adds some more data points, especially after 1996 (when the UTIP data ends). I mentioned in a previous post why I like the Cheibub, Gandhi, and Vreeland dataset on political regimes so much - in particular, it operationalizes a clear and theoretically justified distinction between democracies and non-democracies - but more perhaps later.

Inequality, injustice, and democratization

(Warning: an epically long post that meanders through the literature on inequality and democratization and comes to conclusions that probably sound unsurprising. Written partly as an attempt to construct a workable set of lecture notes for my course this term).

Was economic inequality important in triggering the anti-regime protests in Tunisia and Egypt? A number of news articles I’ve read mention, often in passing, that rising inequality was one of the causes of the unrest in both countries. This inequality was manifested in the large fortunes accumulated by both the Ben Ali and the Mubarak clans and other influential insiders (including senior government figures in both countries) and in the lack of opportunity for relatively well educated people, who struggled to get jobs even with university educations. And many people believe inequality had been rising there (for reasons that are common to a lot of other countries – some “liberalizing” reforms that basically produced forms of crony capitalism that enriched well-connected insiders at the expense of most people).

This seems plausible enough, even if the evidence is scattered and anecdotal; reliable and recent statistical estimates of inequality in Egypt and Tunisia do not appear to exist. What does exist provides inconsistent information. For example, the CIA World Factbook reports a Gini of 0.34 for Egypt in 2001 (which is below average), but without giving sources, and a gini of 0.4 for Tunisia in 2005 (which middling), yet a more complete dataset of inequality measures developed by the University of Texas Inequality Project  (which is pretty complete as these things go, i.e., not that complete) suggests inequality was higher in both Egypt in 2001 (gini of .47 in 1999, and an average of .42 for the period 1963-1999; .47 is above average) and Tunisia in 2000 (gini of .48 in 1998, and an average of .47 for the period 1963-1999). (If you ask Wolfram|Alpha, you seem to get yet another set of numbers, without sources, though they are probably based on some complicated computation involving the CIA factbook.)

At any rate, most of the recent theoretical work on democratization supports a role for inequality in regime change, though not without qualifications, and it certainly would  not support a "high inequality leads to democracy" thesis. Boix and Acemoglu and Robinson, among others, have argued that the level of inequality is a pretty important factor in whether or not a country moves from dictatorship to democracy. Their arguments are slightly different, but the logic is similar. The basic idea is that democracy is more responsive to the wishes of the “median individual” than dictatorship. As inequality increases, the median voter in a democracy will be poorer and will benefit more from redistribution. Under some reasonable assumptions (e.g., voters vote their interests), democracy will thus tend to be, all other things equal, more redistributive than a dictatorship controlled by the economic elite. (The key word is “tend”. Democracies vary greatly in the degree to which they are actually redistributive, as we will see below, for all sorts of reasons you can probably figure out for yourself: ethnic and religious diversity, different constraints on taxation, etc .).

The poor should thus normally prefer democracy to dictatorship; demand for democracy should come from “below” not from “above,” and should be higher the higher the level of inequality. In this framework, moreover, the poor prefer democracy not just because of the redistribution it offers “today” but also because it acts as a commitment device: sure, the rich may offer some economic concessions in the face of unrest, but without an institutional voice the poor cannot be certain that the rich will continue to offer such concessions in the future. Conversely, the rich should normally prefer a dictatorship in which they control the state to democracy, and this preference will be stronger the higher the level of inequality (and thus the expected redistributive effects of democracy). This is obviously a highly simplified story about why people prefer one regime over another – it hardly accounts, for example, for the humiliations of arbitrary power that appear to be the immediate and evident causes of the anger against dictators that we see in actual episodes of mass protest, or for the complexity of class alliances in actual transitions to democracy. But it is not a terrible starting point for thinking about the incentives of key actors in processes of potential democratization.

For one thing, dictatorships do seem to be, on average, more unequal than democracies. Though measures of inequality are not always of very great quality (and should always be taken with a grain of salt), correlations between democracy and equality seem to persist across a range of measures. Using the DD measure of democracy and dictatorship by Cheibub, Gandhi, and Vreeland and the UTIP data we can see that democracies  in the period 1963-1999 had a mean Gini coefficient of  0.4, whereas dictatorships had a mean Gini coefficient of 0.45. Not an enormous difference, perhaps, but at least expected from the theory. Measures of “capital shares” – which are more appropriate in this context, since they basically measure what part of the national income goes to the “capitalist class” – tell the same story. Using a somewhat truncated version of the dataset on capital shares compiled by Rodriguez and Ortega, we find that in the 1963-2001 period the share of the national income going to capital was on average 60% in dictatorships, and 56% in democracies. (I should actually give you confidence intervals for that sort of thing, but I am not quite sure how to produce them).

The actual distribution of inequality across regime types is fairly wide, however: some democracies are more unequal than most dictatorships, and some dictatorships are more equal than most democracies. Consider the following density plot (essentially a smoothed histogram), using the UTIP data and the DD measure of democracy and dictatorship:

(Democracies are represented by the purple line, dictatorships by the blue line). The graph basically tells you that though democracies are clustered towards the low inequality end of the spectrum (the median Gini for democracies is 0.39) and dictatorships towards the high inequality end of the spectrum (median Gini  0.46), there is still a fairly wide spread, with many democracies with high inequality and some dictatorships with low inequality. We can dig deeper, however. If we consider the dictatorships that have lower inequality than most of the democracies (lower than the median level of inequality for democracies), most of them are communist or former communist countries: Albania 1988-1990, Azerbaijan 1991, Bulgaria 1963-1989, Bosnia and Herzegovina 1991, China 1977-1986, Cuba 1977-1989, Hungary 1963-1989, Poland 1970-1988, Romania 1963-1969, Russia in 1993. A few other countries round out the list: Afghanistan (which could be counted as a formerly communist country, and anyway figures for Afghanistan are likely to be nonsense), Singapore 1978-1999, the Seychelles 1976-1986, Syria 1983-1992, Algeria 1976-1994 (which also had a “leftist” revolution), Egypt 1974-1978 (the Sadat years, I believe, though the foundation for this would have been laid during Nasser’s rule), Iran 1981-1989 (another country that experienced a revolution), Malaysia 1993-1999, Portugal 1975, Senegal 1974-1986, South Korea 1979-1987, and Uruguay  1976-1979. The rest of the dictatorships for which there is inequality data in this sample have higher inequality than most democracies. (If you run the same exercise for the democracies that have higher inequality than most dictatorships, you find basically younger democracies – an average age of 13 years, compared to 37 in the entire sample- or democracies that have been repeatedly undermined by coups, though India and Venezuela are clear outliers, Venezuela probably for reasons discussed in this post.).

None of this proves anything (correlation is not causation and all that, besides the fact that the data on inequality is poor and I have not controlled for anything, though other people who know more about this than I do have done so), but it’s interesting (for some values of interesting, I suppose). There is also, among other things, the fact that most coups in Latin America in the 20^th century were supported by economic elites fearful of redistribution and confiscation, and that historically objections to the extension of the suffrage focused on the threat to property more than anything else. This is what we would expect from the highly simplified models of Boix or Acemoglu and Robinson: when inequality is high, the wealthy elite have large incentives to invest substantial resources in controlling the state to prevent redistribution, up to staging a coup (in a democracy) or otherwise supporting high levels of repression (in dictatorship), at least so long as they do not have an “exit” option (perhaps because their assets are mobile or otherwise easily hidden from taxation). But when inequality is high, the poor non-elite also have the most to gain from redistribution, so high levels of inequality should be associated with high levels of class conflict and ultimately with dictatorship (either of the “right” or of the “left,” depending on the balance of forces: so we see revolutionary dictatorships with relatively low levels of measured inequality). Very high inequality seems to lead to irreconcilable class conflict, which a small wealthy elite is more likely to “win” (since its collective action problems are smaller; hence the rarity of great social revolutions). Hence it is no surprise that high levels of inequality appear to be statistically associated with the breakdown of democracy (see, for example, the recent work of Christian Houle [gated]).  One may think that this logic probably works against democracy in Egypt; if the military is as highly embedded in the economy as recent stories note, then it would seem to be less likely that they will agree to relinquish enough power to a genuinely democratic government, especially when much of that wealth funds the lifestyles of senior officers. (The same does not appear to be true in Tunisia, where the military did not appear to have been part and parcel of the “winning coalition” in recent years).

But if the theory is very good at telling us when democracy breaks down or when it is unlikely to emerge (namely, when inequalities are large and visible and wealthy elites cannot easily take their assets elsewhere, as in agrarian economies, so that they have very strong incentives to prevent the poor from gaining control or substantial influence over the state) it seems to be less good at telling us when it is likely to emerge. There is no clear statistical relationship between the level of inequality and transitions to democracy: democracy has emerged in countries with low, high, and medium levels of inequality [Houle again], even though the high inequality democracies have tended to have higher rates of breakdown afterwards. (I do not know if the data is good enough to bear a look at the within-country pattern: do countries that experience an increase in inequality have higher or lower probabilities of transitioning to democracy? Anybody wants to help me look at that? How would you do that?). Part of the problem is that it isn’t clear how lower levels of inequality affect both the “demand” for democracy (Acemoglu and Robinson suggest demand would be lower, but then Boix suggests that the wealthy would have less incentive to oppose it, which seems to produce an indeterminate result) and the ability of non-elite groups to coordinate action (my guess is that low levels of status inequality would make it easier to coordinate collective action and to generate collective protest identities, but I don’t really know). There may also be income effects: high or low inequality may produce different political outcomes at different levels of development (just-so story: it may be that as income increases throughout society, and its marginal contributions to happiness and physical security decrease, direct redistribution becomes less and less important, and other things like physical repression may be more important).  

Moreover, many historical episodes of democratization seem to have been driven by emerging economic elites who wished to avoid predation by other elites controlling the state, and who therefore enlisted the lower classes in their struggles against these predatory state elites, something that does not fit neatly with the “redistributive” models of Boix or Acemoglu and Robinson (as discussed, for example, by Ansell and Samuels here [gated]). Consider the fact that in Egypt the main protests seem to have been “led” (to the extent that they were led at all) by young professionals who would not necessarily be well connected to the state but instead probably suffered quite a bit from its predation. The point is more general: in actual episodes of democratization, the demands for an end to arbitrary treatment by the state – for “dignity”, an end to corruption or police harassment, for free expression, etc. – seem pretty prevalent, even though we can also find demands for redistribution driven by more economic interests.

My guess is that the effects of inequality on democratization are mediated by beliefs about justice or fairness. Here’s a sketch of a theory. There is some cultural equilibrium, produced by long-lasting regimes, between beliefs about what constitutes a “fair” distribution and the actual level of redistribution in society, determining the long-run level of inequality in a society. This equilibrium can be partly genuine (there are differences in what different societies accept as fair processes of distribution, leading to different levels of inequality; see this article by Alesina and Angeletos for more [gated]), but in non-democratic societies the equilibrium is also enforced by coercion and opacity (the people do not know how rich the wealthy really are). But sharp departures from this equilibrium lower the mobilization threshold of people dissatisfied with the status quo – they make more people angry, and hence more likely to mobilize (anger is a much better spur to take risks than plain vanilla self-interest). These departures might be produced by increases in the “visibility” of elite wealth (“ostentation”) or by increases in the incidence of wealth due to unfair processes of distribution (“corruption”). Hence the importance of incidents like the Shah’s grandiose party on the anniversary of the Persian monarchy for triggering mobilization, or even things like the publicity surrounding the comparatively luxurious living conditions of the leaders of the GDR early during the 1989 fall of the communist regime there; in Eastern Europe, where people had been socialized for decades into an ethos of “equality,” even the relatively small privileges of the nomenklatura compared to the rest of the population were seen as galling. (Honecker and Krenz lived luxuriously by egalitarian GDR standards, but not that luxuriously). In general then, we should expect that it is not the level of inequality that matters for mobilization, but sharp changes in inequality, relative to the “fairness” baseline. Yet the level of inequality may still be related to mobilization, since it may be that at high levels of inequality, even small departures from the fairness baseline will be easily perceived as forms of injustice. So that high inequality societies should be, on this view, more prone both to mobilization leading to democratization (as we see in the data assembled by Houle, referenced above, where high levels of inequality seem to be associated with more transitions to democracy: see table 2) and to the breakdown of democracy (because of its threat to the interests of the wealthy).

To finish this epically long post, if you are still reading, consider this interesting statistic from Egypt: according to the World Values Survey of 2005 (if I’m reading the answers to question V120 correctly – link may not take you to the question directly), an astonishing 52% of Egyptians thought that “In the long run, hard work usually brings a better life” – much higher numbers than in the USA or New Zealand. And yet this belief was bound to be tested by the way the Egyptian economy worked (where only the well connected ultimately prospered, and where large numbers of college graduates apparently failed to find jobs). I do not know why the Egyptians seemed to be so optimistic in 2005, but I can imagine that in the face of the realities of the Egyptian economy, a lot of (especially young and previously unmobilized by existing parties) people became very angry, especially young people. Egypt was a very equal society by some measures, but given its “fairness” baseline, it was probably failing spectacularly in the last few years.