Commodity Price Volatility and Inclusive Growth in Low-Income Countries

Chapter 10. Inequality and Unsustainable Growth: Two Sides of the Same Coin?

Rabah Arezki, Catherine Pattillo, Marc Quintyn, and Min Zhu
Published Date:
October 2012
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Andrew G. Berg and Jonathan D. Ostry 


In the long term, sustained growth is central to poverty reduction. The rapid growth seen in much of the world over the past few decades—notably, but not only, in China and India—has led to an unprecedented reduction in poverty. And in general, increases in per capita income tend to translate into proportionate increases in income of the poor. As Dollar and Kraay (2002) memorably put it, “Growth is Good for the Poor.” This is all the more reason, then, to place sustain-ability of growth at the center of any poverty reduction strategy.

The recent global crisis—and the impact this is having on economic activity, jobs, and the poor—is thus rightly spurring a renewed focus on the drivers of growth, including possible links between income inequality, crises, and growth sustainability. Piketty and Saez (2003) underscored the sharp rise in income inequality in the United States in the past two decades and its return to levels not seen since the late 1920s. A number of analysts have investigated how this may have contributed to the crisis. Rajan (2010) pointed to the political and economic pressures that led high-income individuals to save, low-income individuals to sustain consumption through borrowing, and financial institutions and regulators to encourage the process. Meanwhile, recent events in Egypt, Tunisia, and elsewhere in the Middle East underscore the importance of better understanding the complex relationship between growth, income distribution, and crises.

Some inequality is integral to the effective functioning of a market economy and the incentives needed for investment and growth (Chaudhuri and Ravallion, 2006), but too much inequality might be destructive to growth. Beyond the risk that inequality may amplify the potential for financial crisis, it may also bring political instability that can discourage investment. Inequality may make it harder for governments to make difficult but necessary choices in the face of shocks, or inequality may reflect lack of access of the poor to finance and thus fewer opportunities to invest in education and entrepreneurial activity.

Earlier analyses have recognized the complex linkages among income distribution, growth, and policies to counter inequality. In this chapter, we ask whether growth can in fact be sustained in the face of a highly uneven income distribution. Does less inequality help to increase the duration of growth? Are inequality and unsustainable growth two sides of the same coin or largely unrelated issues?

This chapter draws on earlier work (Berg, Ostry, and Zettelmeyer, 2012) that looked at growth in a way that emphasizes the turning points in countries’ growth trajectories and especially what determines when a long period of growth, that is, a “growth spell,” comes to an end. Here the focus is squarely on the relationship between income distribution and the length of growth spells and the relation of the empirical findings to the political and economic narratives of specific cases. We then review the earlier empirical literature on growth and distribution, relating it to the stylized facts of growth, and discuss the role of income distribution, and other determinants such as institutions, education and health, globalization, and macro policy, in growth duration. Finally, we propose some tentative policy implications.

The Hills and Valleys of Growth

By the late 1990s, many researchers had examined empirically the relationship between income distribution and growth. Following the broader growth literature, the typical approach was to relate a country’s income distribution at the beginning of a long sample (say, 1965–85) and the growth rate during that period, controlling for a few key variables such as initial per capita income. An empirical consensus had emerged that countries with more equal income distributions tended to grow faster (e.g., Alesina and Rodrik, 1994), though the evidence was admittedly not robust (Deininger and Squire, 1998; Barro, 2000).

Subsequently, attention turned to analysis of panel data to examine how changes in income distribution affected the growth rate in a subsequent medium-term (usually five-year) period. Forbes (2000) found that an increase in inequality tended to raise growth during the subsequent period. Banerjee and Duflo (2003) found an even more complex relationship between inequality and growth in which changes in inequality in either direction lowered growth in the subsequent five-year period. They interpreted this finding as supportive of the notion that redistribution hurts growth, at least over short- to medium-term horizons.

For significant poverty reduction, the key is to achieve rapid growth over long periods of time. For these purposes, the long-term growth regressions of Barro (2000) and similar studies would seem the most relevant. However, these analyses, and perhaps common perceptions, assume implicitly that development is like climbing a hill; the development is more-or-less steady increases in real income, punctuated by fairly small bumps (i.e., business cycle fluctuations), perhaps with the occasional takeoff as poor countries become integrated into the global economy. Figure 10.1a shows the level of real per capita income in a couple of advanced economies, with a pattern consistent with this idea of growth. If this were indeed the common pattern, the most interesting question—indeed the only important question about growth—would be how to explain why some countries grow faster than others do over long periods.

Figure 10.1aThe Hills of Growth

Source: Penn World Table, Version 6.2.

Note: Real GDP per capita is measured in logs, so a straight line implies a constant growth rate.

Figure 10.1b shows the level of real per capita income in a group of developing countries. In contrast to the visual impression from the advanced economy graphs, what strikes the eye here is the variety of experience. Looking at such pictures, Pritchett (2000) and others have been struck with the idea that an understanding of growth must involve looking more closely at the turning points, that is, not the ups and downs of growth over business cycle horizons, but rather why some countries are able to keep growing for long periods of time while others see growth downbreaks after just a few years, followed by stagnation or decay. To get a handle on this question, this chapter focuses on growth spells, defined as the time interval starting with a growth upbreak (the takeoff) and ending with a downbreak (or the end of the sample). The goal is to examine trends, not temporary events such as recoveries from recessions or the impact of sharp increases in the price of a principal export commodity. It follows that the object of inquiry (the growth spell) cannot be too short: In practical terms, we set its minimum length at eight years.1

Figure 10.1bThe Hills, Valleys, and Plateaus of Growth

Sources: Penn World Table, Version 6.2; Berg, Ostry, and Zettelmeyer (2012); and authors’ calculations.

Note: Vertical dashed lines represent statistically significant growth downbreaks; solid lines represent upbreaks. Real GDP per capita is measured in logs, so a straight line implies a constant growth rate.

The question of how to sustain a growth spell is particularly interesting for two reasons. First, looking at the broad cross-country evidence, igniting growth is much less challenging than sustaining growth (Hausmann, Pritchett, and Rodrik, 2005). That is, even the poorest of countries have managed to get growth going for several years from time to time. Where growth laggards differ from their more successful peers is in the degree to which they could sustain growth for long periods of time. Second, in recent years, a large number of countries have been enjoying the fruits of sustained growth spells—more indeed than at any time in the last 30 years or so. The higher incidence of growth spells is most dramatic in sub-Saharan Africa, where many countries had a takeoff in the mid-1990s. Thus, the following questions emerge: “Are these ongoing spells likely to persist?” and “How can they be kept going?”

Growth: Easy to Start, Hard to Keep Going

A first observation about growth breaks and growth spells is that both upbreaks and downbreaks are quite common, reflecting the notion that growth is not smooth. As Table 10.1 shows, upbreaks tend to be fairly spread out across regions and decades. A key message from the data is thus that the initiation of growth is not necessarily the hard part of achieving a long-term rise in per capita incomes. Latin America and Africa, for example, do not seem to suffer from an unusual dearth of growth spells. Rather, the real problem seems to stem from the inability to sustain growth over long periods. For example, almost all growth spells in industrial countries and emerging Asia last at least 10 years, but only about two-thirds of Latin American and African spells are this long (Table 10.2). Sustained growth over many years or decades seems to be what separates growth miracles from growth laggards.

Table 10.1Growth Breaks by Region and Decade

break size1
Total Upbreaks786.917132127
Industrial countries2115.05033
Emerging Asia195.64645
Latin America114.03242
Sub-Saharan Africa2210.341512
Other developing3157.41455
Total Downbreaks96−6.37483011
Industrial countries221−5.301515
Emerging Asia15−6.00753
Latin America13−4.60850
Sub-Saharan Africa26−8.031292
Other developing321−6.946101
Sources: Berg, Ostry, and Zettelmeyer (2008); and authors’ calculations.

Percentage-point change in real per capita GDP growth before and after the break.

Includes Hong Kong SAR, Japan, Republic of Korea, Singapore, and Taiwan Province of China.

Cyprus, Caribbean countries, Middle East, North Africa, and Turkey.

Note: A growth break is a statistically significant change in the per capita real GDP growth rate that persists for at least eight years.
Sources: Berg, Ostry, and Zettelmeyer (2008); and authors’ calculations.

Percentage-point change in real per capita GDP growth before and after the break.

Includes Hong Kong SAR, Japan, Republic of Korea, Singapore, and Taiwan Province of China.

Cyprus, Caribbean countries, Middle East, North Africa, and Turkey.

Note: A growth break is a statistically significant change in the per capita real GDP growth rate that persists for at least eight years.
Table 10.2Characteristics of Growth Spells
Frequency and durationAverage growth before, during and after 1
Number of countriesNumber of spellsMean duration (years)Percentage of spells lasting at leastAverage growth3 years
Region10 years16 yearsbeforeduringafterbeforeafter
Complete spells
Industrial countries237213.0100.
Emerging Asia22318.033.333.3−
Latin America18514.460.−1.3
Sub-Saharan Africa4338.30.00.0−2.79.9−4.0−10.6−6.5
Other developing320710.742.914.3–1.65.0–0.9–1.4–2.0
Total (including incomplete spells)
Industrial countries2371124.4100.−0.1na
Emerging Asia221624.287.556.2−0.35.8na0.4na
Latin America18715.771.442.90.44.4na0.1na
Sub-Saharan Africa431813.666.722.2−4.06.3na−7.7na
Other developing3201213.566.733.3−2.15.0na−2.8na
Source: Berg, Ostry, and Zettelmeyer (2008); and authors’ calculations.

Real per capita GDP growth, in percentage points.

Includes Hong Kong SAR, Japan, Republic of Korea, Singapore, and Taiwan Province of China.

Cyprus, Caribbean countries, Middle East, North Africa, and Turkey.

Note: A growth spell is a period between a growth upbreak and a growth downbreak, as long as per capita real growth is above 2 percent during the spell and falls to below 2 percent after the downbreak. Breaks are at least eight years apart. na: not available.
Source: Berg, Ostry, and Zettelmeyer (2008); and authors’ calculations.

Real per capita GDP growth, in percentage points.

Includes Hong Kong SAR, Japan, Republic of Korea, Singapore, and Taiwan Province of China.

Cyprus, Caribbean countries, Middle East, North Africa, and Turkey.

Note: A growth spell is a period between a growth upbreak and a growth downbreak, as long as per capita real growth is above 2 percent during the spell and falls to below 2 percent after the downbreak. Breaks are at least eight years apart. na: not available.

Beyond the duration issue, another salient feature of the data relates to the rate of growth both within and outside of growth spells. As Table 10.2 shows, all regions’ spells involve fairly fast growth, with those in Africa actually the most rapid. In contrast, there are big differences following the end of spells. Soft landings have tended to follow the end of growth spells in advanced economies and Asia, whereas African spells have tended to end in deep collapses.

Income Distribution and Growth Sustainability

To what extent is the duration of growth episodes related to differences in country characteristics and policies, including income distribution? It has long been recognized that the quality of economic and political institutions, an outward orientation, macroeconomic stability, and human capital accumulation are all important determinants of economic growth, and much work has gone into understanding the mechanisms and policy implications of these relationships. In this chapter, we argue that income distribution may also, and independently, belong in this “pantheon” of critical growth determinants.

Why Income Distribution?

To set the stage, Figure 10.2 presents a simple correlation between length of growth spells and the average income distribution during the spell for a sample of countries. The measure of inequality is the Gini coefficient, which varies from 0 (all households have the same income) to 100 (all income received by one household). The correlation coefficient between these two series is −0.56 and is statistically significant.

Figure 10.2Duration of Growth Spells and Inequality

Sources: Penn World Table; and United Nations University (UNU)–World Institute for Development Economics (WIDER) World Income Inequality Database.

Note: This figure includes spells that end in sample (completed spells) only, because the length of incomplete spells is unknown. For this figure, minimum spell length is five years. The measure of inequality is the Gini coefficient, which varies from 0 (all households have the same income) to 100 (all income received by one household).

There is a pattern here: More inequality seems to be associated with less sustained growth. What are the possible channels through which income inequality affects growth sustainability?

  • Credit market imperfections: Poor people may not have the means to finance their education. A more equal distribution of income could thus increase investment in human capital and hence growth. In the data used here, there is a negative correlation between some indicators of human capital (notably secondary education achievement) and income distribution, even with per capita income controlled for. This echoes the arguments in Wilkinson and Pickett (2009) that more unequal countries suffer from relatively poor social indicators.
  • Political economy: In more economically unequal countries, political power may be distributed in a more egalitarian fashion than economic power. Efforts to use this political power to effect redistribution, say, through the tax system, may create disincentives to investment and result in lower or less durable growth (Alesina and Rodrik, 1994). Meanwhile, efforts by economic elites to resist this redistribution (e.g., through vote buying and other corrupt behavior) could be distortionary and wasteful and thus also detrimental to growth (Barro, 2000).
  • Political instability: Income inequality may increase the risk of political instability, and the resulting uncertainty could reduce incentives to invest and hence impair growth. Rodrik (1999) argued that inequality and political instability may hamper countries’ effectiveness in responding to external shocks. Similarly, Berg and Sachs (1988) found that unequal societies tended to experience relatively severe debt crises in the 1980s. The International Institute for Labour Studies (2010) highlighted links between unemployment and social unrest.

Against the background of these mechanisms, the question is whether the data lend support to the notion that societies with more equal income distributions have more durable growth.

Many Hazards to Growth

Growth is an inherently complex phenomenon, and many factors are likely to play a role in the duration of growth spells. In this section, the relationship between duration and inequality, and other key potential determinants, is examined more systematically. It goes almost without saying, given the nature of statistical relationships, that what follows should be interpreted as highlighting associations rather than causation, suggesting tentative stylized facts that seem to emerge from the data.

The approach here borrows from the medical literature that aims to gauge, for example, how long someone might be expected to live conditional on certain factors such as whether the person is a smoker and his or her weight, gender, and age (time “in the spell”). In our context, the probability that a growth spell will end depends on its current length and various hazards to growth. The analysis distinguishes between initial conditions at the onset of the spell and changes during the course of a growth spell. The latter are most interesting for the question of what policies might be able to extend the life of an ongoing spell.2

Unfortunately, there are not nearly enough data to test all the main growth theories, and hence candidate variables, at once. There are simply too few spells and too many candidates to disentangle everything. So the strategy is to look at possible determinants of duration one at a time and then try to synthesize the findings. The variable-by-variable analysis suggests that the following are correlated with longer growth spells:3

  • Better political institutions: Many have argued that political institutions that constrain the executive and secure political accountability help to sustain growth. We also find that several measures of better political institutions are correlated with longer spells.
  • Increases in education, health, and physical infrastructure: One strong effect is of within-spell improvements in primary education. In addition, both the initial level and increases in child mortality reduce the expected duration of a spell, though with mixed significance and magnitude.
  • Financial development: Consistent with the arguments of many economists, measures such as increases in the ratio of bank deposits to GDP during the spell seem to have a protective effect.
  • Trade liberalization: There is a significant and large effect of trade liberalization, consistent with the notion that mechanisms such as increased market size, promotion of competition, and transmission of know-how may link trade openness and growth and make growth more durable.
  • International financial integration: Depending on the nature of the capital flow, FDI seems to help duration, whereas growth of external debt seems to hurt (consistent with the findings by Dell’Ariccia and others [2008]).
  • Competitiveness and export structure: Avoidance of exchange rate overvaluation, high shares of manufacturing exports in total exports, and various measures of the sophistication of export structures (Hausmann, Rodriguez, and Wagner, 2006; Hausmann, Hwang, and Rodrik, 2007) are all correlated with longer growth spells.
  • Macroeconomic volatility: Increasing rates of currency depreciation and inflation both reduce the expected length of spells.
  • External shocks: Reductions in the terms of trade and increases in U.S. interest rates, in particular, are associated with shorter spells.
  • Inequality: There is indeed a large and statistically significant association between income inequality and growth duration. Inequality is among the variables with the economically strongest effect on predicted spell duration. It is also among the most robust variables, in that it remains statistically significant across samples.

Overall, the results of the analysis have the flavor of some interpretations of the East Asian “miracle.” Growth is most enduring in countries that maintain outward orientation, have inward FDI but perhaps not much in the way of external debt or deficits, maintain macro stability, and have relatively equal income distribution. Given this, it is worth noting that overall results hold up even when Asia is excluded from the sample.

Putting the Hazards Together

So far, we have looked one by one at the possible factors influencing the duration of growth spells. It is possible that many of the identified determinants of spell duration are themselves correlated with each other. For example, perhaps inequality is only indirectly capturing the effects of poor institutions, poor health or education, or other factors that might be the true drivers of growth duration.

Many potential determinants of duration remain important in this joint analysis, though their statistical and economic significance varies substantially depending on the exact sample, whether or not other potentially important variables are also included, and so on. Several variables are significant in at least some samples and specifications.

Figure 10.3 presents the results from the preferred multivariate specification in Berg, Ostry and Zettelmeyer (2011). To give a feeling for the importance of each variable, the figure reports the increase in expected spell duration for a given increase in the variable in question, with other factors kept constant. To do so requires first calculating expected duration when all variables are at the median for the sample (the 50th percentile). The expected duration is then recalculated when the variable in question improves by 10 percentiles.4 The main results are as follows:

Figure 10.3Effect of Increase of Different Factors on Growth Spell Duration


Sources: Berg, Ostry, and Zettelmeyer (2012); and authors’ calculations.

Note: For each variable, the height of the figure shows the percentage increase in spell duration resulting from an increase in that variable from the 50th to the 60th percentile, with other variables at the 50th percentile. For trade, the figure shows the benefits of having an open instead of a closed regime, using the Wacziarg and Welch (2008) dichotomous variable. For autocracy, the figure shows the effects of a move from a rating of 1 (the 50th percentile) to 0 (the 73rd percentile). FDI: foreign direct investment.

  • Better political institutions (measured by “autocracy” according to the Polity IV database) are correlated with longer spells; a reduction in autocracy from a rating of 1 (which corresponds to the sample median) to 0 on the 10-point scale is associated with a 25 percent longer spell.
  • Liberalized trade (measured with the Wacziarg and Welch [2008] dichotomous variable that takes a value of 1 when trade has been liberalized and 0 otherwise) is associated with a 45 percent longer spell.
  • A smaller real exchange rate overvaluation (associated with more durable growth); a decrease in overvaluation by 10 percentage points of the real exchange rate—measured as a deviation from purchasing power parity, after per capita income is adjusted for—is associated with an 8 percent increase in expected spell length.
  • The effects of financial globalization again depend on the nature of the capital flow. Higher FDI inflows are associated with longer spells, with an increase from 8 to 12 percent of GDP in FDI liabilities associated with an expected spell duration that is 15 percent longer. Lower external debt is associated with longer spells; a decrease from 44 to 39 percent in the ratio of external debt to GDP suggests an increase in the duration of the growth spell of about 2 percent.

A number of other variables that work one by one do not remain significant in the joint analysis. This may reflect the difficulty in identifying many different effects in a limited sample of spells, but it is also possible that they are, at least in part, not independent drivers of duration but rather manifestations of the underlying forces captured by some of the above variables.5

Inequality: A Significant Hazard to Growth Sustainability

The key result from the joint analysis is that income distribution survives as one of the most robust and important factors associated with growth duration. As Figure 10.3 demonstrates, a 10 percentile decrease in inequality—the sort of improvement that a number of countries have observed during their spells—increases the expected length of a growth spell by 50 percent. Remarkably, inequality retains a similar statistical and economic significance in the joint analysis despite the inclusion of many more possible determinants. This suggests that inequality seems to matter in itself and is not just proxying for other factors. Inequality also preserves its significance more systematically across different samples and definitions of growth spells than the other variables. Inequality is thus a more robust predictor of growth duration than many variables widely understood to be central to growth.

The estimates of the effects of inequality mainly rely on cross-country variation, because generally inequality is fairly stable through time for a given country. However, sometimes income distribution does change dramatically, as in the United States, China, and a number of developing countries over the past few decades. And the estimates suggest that such changes may have significant effects on expected growth duration. To take one example, reducing inequality in Latin America enough to close even half of the inequality gap between that region and emerging Asia would more than double the expected duration of a growth spell in Latin America.

Income distribution is only one measure of social heterogeneity. Several researchers have argued that ethnic or religious fractionalization plays a similar role to inequality in making a country more vulnerable to shocks or more unsta-ble.6 Anecdotally, there are clearly times when ethnic fractionalization seems to be associated with political and economic instability. And it seems plausible that ethnic and other sorts of fractionalization are correlated, and indeed interact, with income distribution in complex ways. We find some evidence to support the idea that higher ethnic fractionalization is associated with shorter growth spells, but the effect varies substantially across samples and is often not statistically significant. For growth spells, at least, the evidence seems firmer on the importance of income inequality.

Table 10.3The Ends of Six Spells
Growth in real per capita GDP(Share of total hazard)
Main contributing factors
CountrySpell datesDuring spellNext decadeHazard ratioInequalityLow FDI inflowIncreased external debtMore autocracyOver-valuation
Sources: Berg, Ostry, and Zettelmeyer (2008); and authors’ calculations.Note: The hazard ratio is the ratio of the predicted probability that the spell would end during the last five years of the spell (prior to its actual end) to the predicted probability of a spell ending for the average observation in the entire sample. Thus, a hazard ratio of 1 implies no unusual risk that the spell will end. The factors contributing to the hazard ratio are based on the Model 1 of Table 12 of Berg, Ostry, and Zettelmeyer (2008), with the contributions rescaled so that they sum to 1. Shown here are only the main factors for these particular observations. FDI: foreign direct investment.
Sources: Berg, Ostry, and Zettelmeyer (2008); and authors’ calculations.Note: The hazard ratio is the ratio of the predicted probability that the spell would end during the last five years of the spell (prior to its actual end) to the predicted probability of a spell ending for the average observation in the entire sample. Thus, a hazard ratio of 1 implies no unusual risk that the spell will end. The factors contributing to the hazard ratio are based on the Model 1 of Table 12 of Berg, Ostry, and Zettelmeyer (2008), with the contributions rescaled so that they sum to 1. Shown here are only the main factors for these particular observations. FDI: foreign direct investment.

A Closer Look at the Evidence

Do these statistical results find a voice in the political and economic narratives of the actual country growth episodes? Specifically, where the model predicts a high likelihood that the spell will end, does the narrative highlight the income distribution channels suggested by the empirics? The answer is indeed a tentative yes.

Consider the 20 complete spells in the main sample shown in Table 10.2. For all of them, the predicted risk that the spell will end during the last five years of the spell (prior to its actual end) is several multiples higher than for an average country in the sample. What was going on in these cases? Table 10.3 gives some data for the period corresponding to the end of spells in six cases with the highest predicted hazard.

Colombia experienced a growth spell end in 1978. The spark was a crackdown on drug cartels, beginning a long civil conflict. According to Cárdenas (2007), the massive change in Colombia’s growth trajectory over two decades was related to “a fortuitous event that interacted with … high levels of inequality and poverty and the weak presence of the state.” The baseline multifactor model predicts that Colombia’s growth spell was indeed fragile—with a risk of ending 66 times higher than the average over all the spells in our sample. This higher risk can be decomposed into the various determinants included in the duration regression presented in Figure 10.3. Colombia’s high Gini (53 versus the sample average of 38) accounts for most of the higher risk.

Guatemala was in a state of civil war from 1960 until 1996. During these 36 years, thousands were executed, several coups took place, and civil liberties were denied. The war peaked in 1979, coincident with the end of the growth spell. In the words of Thorp, Caumartin, and Gray-Molina (2006), “By the late 1970s, Guatemala had entered a stage of polarization and radicalization of social organizations (trade unions, peasant organizations). In the face of increasing repression, many CUC [Comité de Unidad Campesina] or trade union members opted to join the guerrilla.” Carbonnier (2002) further noted that “peace or political conditionality induces the government to adopt lax economic policy in order to muster… political support to stay in power… [But] economic conditionality often means political turmoil and civil unrest… For instance, attempts to cut subsidy and raise transportation prices repeatedly spurred violent clashes in the streets of Guatemala City… The [result] has often been an increase in repression coupled with the reintroduction of a subsidy.” The prediction of the multivariate model is that the risk of Guatemala’s spell ending was indeed about 55 times higher than that of the average country between 1974 and 1979, with higher-than-average income inequality being one of the two factors (the other being FDI) driving the result.

The ends of spells in Cameroon, Nigeria, and Ecuador demonstrate how income distribution can interact with external shocks. Lewis (2007) noted for Nigeria that highly volatile politics, social incohesion, and external shocks drove bursts of economic volatility: “In Nigeria, ethnic and regional competition has hampered the formation of a stable growth coalition between the state and private producers. Political elites have turned instead to populist strategies and diffuse rent distribution among a fragmented and polarized business class. The populist option proved short-lived when oil revenues dwindled, while the residual alliances of rentiers were unstable, resulting in economic stagnation and disarray.” In Cameroon and Ecuador, oil wealth in the 1970s initially financed large increases in the public sector, particularly in the wage bill, which proved very difficult to cut when oil prices fell. “Although these measures [to cut government spending] were necessary to rescue the country from further economic crisis, they were very unpopular because they least affected the political elite and those in the upper echelon of government, whose privileges remained intact” (Mbaku and Takougang, 2003; see also Jácome, Larrea, and Vos, 1998; and Aerts and others, 2000). In all three countries, the model’s hazard ratio was very high (ranging from more than 100 times higher than normal for Cameroon to 29 times higher for Nigeria). In all three countries, high inequality and autocracy levels and low levels of FDI played important roles according to the regression.

The model attributes the (high) risk of Panama’s spell ending mainly to rising external debt, along with inequality. Indeed, Panama’s military dictatorship preserved power through the 1970s increasingly by borrowing externally to support transfers to government workers (Ropp, 1992). The global crisis of the early 1980s thus hit Panama hard. This pattern is consistent with the argument in Berg and Sachs (1988) that countries that suffered most from the debt crisis of the 1980s may have been those that used (unsustainable) foreign borrowing to bridge societal conflict.

The variety and complexity of the channels are evident in these examples. Crime, for example, seems key in Colombia, but not in other cases. The timing of crises seems to reflect an interaction of underlying vulnerabilities, including income distribution and shocks (such as a rise in the attractiveness of illegal drug production in Colombia and oil elsewhere). The narratives show the complexity of the debt/inequality/downbreak nexus, particularly with respect to timing. In Panama, debt grew prior to the crisis and thus shows up as a factor in the hazard regressions. In some of the oil exporters, debt grew mainly after the end of the spell, as an initial—and ultimately unsustainable—response to negative commodity shocks, thus helping to convert a shock into a sustained downturn. Clearly, ethnic fractionalization plays a role in some cases too. The regressions suggest that inequality is an underlying feature that makes it more likely that a number of these factors come together to bring a growth spell to an end.

Some Tentative Policy Implications

One reasonably firm conclusion is that it would be a big mistake to separate analyses of growth and income distribution. To borrow a marine analogy when talking about economic growth and its beneficiaries: A rising tide may lift all boats, but our analysis indicates that helping to raise the smallest boats may help to keep the tide rising for all craft, big and small.

The immediate role for policy, however, is less clear. More inequality may shorten the duration of growth, but poorly designed efforts to reduce inequality could be counterproductive. If these efforts distort incentives and undermine growth, they can do more harm than good for the poor. For example, the initial reforms that ignited growth in China involved giving stronger incentives to farmers. Overall, this increased the income of the poor and reduced overall inequality as it gave a tremendous spur to growth. However, it probably led to some increased inequality among farmers, and efforts to resist this component of inequality would likely have been counterproductive (Chaudhuri and Ravallion, 2006).

Still, there may be some “win-win” policies, such as better-targeted subsidies, better access to education for the poor that improves equality of economic opportunity, and active labor market measures that promote employment. Market-oriented reforms in Brazil were complemented with progressive social policies aimed directly at poverty reduction (Ravallion, 2009). The multivariate estimates presented above would suggest that the resulting decline in Brazil’s Gini would, other things being equal, increase the expected length of a growth spell by some 40 percent.

When there are short-term trade-offs between the effects of policies on growth and income distribution, the evidence we have does not in itself say what to do. But our analysis should tilt the balance toward the long-term benefits—including for growth—of reducing inequality. Over longer horizons, reduced inequality and sustained growth may be two sides of the same coin.

The analysis calls to mind the developing country debt crises of the 1980s and the resulting “lost decade” of slow growth and painful adjustment. That experience brought home the fact that sustainable economic reform is possible only when its benefits are widely shared. In the face of the current global economic turmoil and the need for difficult economic adjustment and reform in many countries, it would be better if these lessons were remembered rather than relearned.


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Berg, Ostry, and Zettelmeyer (2012) also look at five-year minimum lengths to gauge the sensitivity of the results. To also take account of economic significance of the spells, a growth spell (1) begins with a statistical upbreak followed by a period of at least 2 percent average real per capita growth and (2) ends either with a statistical downbreak followed by a period of less than 2 percent average growth or with the end of the sample.


Many of the spells have not ended and their eventual length is unknown. However, the statistical techniques used in this section take these incomplete spells into account. If some factor is common to long incomplete spells but absent in short complete spells, a protective effect on duration can be identified.


Even these “bivariate” estimations include initial income, in addition to the variable of interest, to avoid misattributing to another variable the effects of underdevelopment itself, with which that variable might be correlated. It turns out that low initial income is independently a significant predictor of longer spells. The estimations can also shed some light on whether the length of the spell itself is a risk factor, which it appears to be (the hazard is increasing in the time spent in the spell), even after the other potential determinants are included.


To take the Gini as an example, the median in the sample is 40. A 10 percentile improvement takes the Gini to 37, which represents more equality than 60 percent of the Gini observations in the sample.


Lack of significance of manufactured exports may reflect the notion that these operate mainly by creating stronger institutions and reform constituencies, as suggested by Johnson, Ostry, and Subramanian (2007). Macro stability variables are also not particularly robust, possibly reflecting the idea that inflation reflects deep distributional conflicts (Taylor, 1991).


Easterly and Levine (1997), for example, attribute differences in a number of important public policy and economic indicators such as low schooling, political instability, and macroeconomic mismanagement to high ethnic fractionalization. However, they do not also control for income distribution.

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