Preface

At least since the 1950s, rising income differences between rich and poor nations began to wane as poorer nations started to catch up to richer ones, but inequality within many countries rose. Not only have the rich been getting richer since the 1980s, but this trend of rising inequality holds true for most countries with the exception of a number of countries in Latin America (the world’s most unequal continent). In short, the ultra-wealthy gained much prominence in both developed and developing countries. Typically, a population’s poorest half controls less than 10% of a nation’s wealth, and this gap is now getting bigger. According to Pew and other global attitudinal surveys, growing inequality within countries is becoming one of the biggest concerns facing the world economy. Whether this inequality is good or bad is open to debate. On the one hand, inequality stimulates risk taking and hence innovation and growth; on the other hand, inequality promotes jealousy, political protest, and even revolution. Thus enumerating and understanding inequality is important. Whereas Volume 43 dealt primarily with inequality in the United States, this volume concentrates on other countries. It leads with an overview paper presenting cross-country and time comparisons of inequality measures gleamed from five sources of data. In some countries, primarily Mexico and South America, inequality is decreasing, while in others, primarily Europe and China, inequality is increasing. Of the remaining papers, three concentrate on changes in the distribution over time, one examines inequality at a point in time relative to other countries, one looks at the effect of public policy on wage distribution, and finally one employs longitudinal data to get at earnings mobility of a given population.

To date, practitioners typically use five sets of data to analyze income distribution: the World Income Inequality Database (WIID), Standardized World Income Inequality Database (SWIID), the Luxembourg Income Study (LIS) Key Figures on Inequality, World Development Indicators (WDI) of the World Bank, and finally the Estimated Household Income Inequality (EHII) data set developed by the University of Texas Inequality Project. The latter EHII is a panel currently containing estimated Gini coefficients for 167 countries from 1963 to 2008. Each data set has positive and negative attributes. To get consistency, each data set is based on harmonizing information across countries. Clearly this process entails controversial judgement calls, many of which can be questioned. Thus it makes sense to compare each set of data. In the first paper, James K. Galbraith, Jaehee Choi, Béatrice Halbach, Aleksandra Malinowska, and Wenjie Zhang present Gini coefficients by country and year based on the EHII. They compare these income inequality measures with three of the other data collections. They generally find the EHII to be highly consistent with LIS, OECD, and EU-SILC, notwithstanding the difference in concepts measured, or differences in tax systems and welfare states. However, they argue EHII’s coverage and historical depth are far greater.

Looking at an overview of all countries simultaneously might miss the specific nuances differentiating one country’s trends from another. The remaining papers address this concern by examining aspects of income inequality in specific countries separately. First, take the case of Brazil. From 1995 to 2012, the wage distribution became less dispersed, but why? Otavio Bartalotti, Nora Lustig and Yang Wang utilize various decomposition techniques to show that the narrowing of the wage distribution coincided with the expansion of education. Rising education pulled up those that previously would be at the bottom, but at the same time educational premiums fell as more got educated. The latter, which the authors call a change in the wage structure, decreased prospects on the top, thus narrowing the overall distribution.

Peru also experienced a narrowing of its earnings distribution. From 2004 to 2013, poverty declined from 54% to 24% and the size of its middle class doubled to 39%. In the next paper, Adrian Robles and Marcos Robles decompose the changes in inequality and changes in welfare indicators based on counterfactual simulations. They find government transfers had only a very small effect, followed by demographic changes in the size of the working age population and changes in hours. However, as in the case of Brazil, the biggest impact was with education. Rising education levels increased earnings, but the rate of return declined given the greater proportion of the population getting more school. This decline in the rate of return lowered relative earnings on the top, thus narrowing the distribution.

Unlike Brazil and Peru, inequality in Spain increased in recent years (2008–2012) and is high relative to other European countries. In the next paper, Carlos Gradín uses the EU-SILC data to see why Spain’s relative inequality rose. By comparing Spain to Germany, France, Italy, Sweden, and the United Kingdom, he is able to isolate household and economy-wide characteristics associated with inequality. First, he finds both Spain’s higher unemployment rates, especially among the youth, and Spain’s higher incidence of self-employment caused by the recession, to be important. Second, he finds factors prevalent before the recession, particularly lower educational attainment, high immigration, and weak redistributive tax and family allowances, were also related to increased inequality. On the other hand, he finds structural factors such as relatively few high-skilled jobs, relatively few finance sector jobs, relatively generous transfer payments such as pensions, and transfer payments from extended family members, reduced inequality.

Explicit public policies can also affect a country’s earnings distribution. The next paper by Carl Lin and Myeong-Su Yun examines how raising the minimum wage affects income distribution in China. China is a country now experiencing extremely rapid growth, but also dramatic increases in income inequality. The deterioration of what was a realtively equal distribution led the Chinese government to consider and adopt policies aimed at increasing equality. The minimum wage is one such initiative. Beginning in January 2004, new minimum wage regulations required local governments to introduce a minimum wage increase at least once every two years. The legislation also increased coverage. Between 2004 and 2009, the average nominal minimum wage increased by 80%, but provinces, municipalities, and autonomous regions were able to legislate their own minimum wage, so there was lots of variation, enabling the authors to identify its effects. Using OLS and IV panel regressions with aggregated city level data, Lin and Yun examine the effect of minimum wage changes on the earnings gaps at the bottom and upper end of the earnings distribution. Their analysis shows that minimum wage increases significantly reduced earnings gaps, particularly at the bottom end of the distribution.

One can analyze upward mobility of a given set of individuals when using longitudinal instead of cross-sectional data. In the final paper, Rafael Novella, Laura Ripani, Agustina Suaya, Luis Tejerina, and Claudia Vazquez do that for Chile and Nicaragua, two countries that are similar in their levels of inequality, but different with regard to their opportunities to achieve success at work. To do so, their paper estimates autoregressive models using transitional matrices where the ranking of each individual depends on a set of past rankings conditional on personal characteristics. The authors find earnings mobility is lower in Nicaragua than in Chile after controlling for age, education, and work hours, but that previous rankings remain important in explaining an individual’s current position in the income distribution. However, in Chile, previous rankings are not significant in explaining one’s position in the distribution. Thus, a country’s basic underlying institutional governance structure can be important.

As with past volumes, we aim to focus on important issues and to maintain the highest levels of scholarship. We encourage readers who have prepared manuscripts that meet these stringent standards to submit them to Research in Labor Economics (RLE) via the IZA website (http://rle.iza.org) for possible inclusion in future volumes.