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In this paper, we reassess the impact of inequality on growth. The majority of previous papers have employed (system) GMM estimation. However, recent simulation studies indicate that the problems of GMM when using non‐stationary data such as GDP have been grossly underestimated in applied research. Concerning predetermined regressors such as inequality, GMM is outperformed by a simple least‐squares dummy variable estimator. Additionally, new data have recently become available that not only double the sample size compared to most previous studies, but also address the substantial measurement issues that have plagued past research. Using these new data and an LSDV estimator, we provide an analysis that both accounts for the conditions where inequality is beneficial or detrimental to growth and distinguishes between market‐driven inequality and redistribution. We show that there are situations where market inequality affects growth positively while redistribution is simultaneously beneficial. [ABSTRACT FROM AUTHOR]

Recent headlines frequently refer to rising inequality and its implication on economic growth and social welfare. Addressing the latter is difficult and requires more than simply looking at GDP, as Kuznets long ago pointed out. In this paper we focus on the importance of the income measure underlying the inequality measure when examining the relationship between GDP growth and inequality. We create a mapping using Census Bureau household survey data and Bureau of Labor Statistics (BLS) consumer expenditure data to create distributional measures of the Bureau of Economic Analysis (BEA) personal income. We show that for the period 2000-2012, inequality using personal income is substantively lower than inequality measured using Census Bureau money income, and the trends in both inequality and median income are different. This demonstrates the importance of using a measure a national accounts based measure of income when examining the relationships between inequality and growth. [ABSTRACT FROM AUTHOR]

The EU-wide survey ' Statistics on Income and Living Conditions' ( EU- SILC) is extremely important for international social science research and policy advice. It is therefore crucial to ensure that the data are of the highest quality and international comparability. This paper is aimed at identifying unexpected developments in income levels, income mobility, and inequality in the EU- SILC data between 2005 and 2009. We examine the consistency of EU- SILC by comparing cross-sectional results with findings based on two-year longitudinal samples. Although the data represent similar populations, for several countries the results of this comparison differ widely. One important outcome is the high degree of variability over time in countries that obtain their income information from register data. This suggests methodological challenges in the clear designation of new subsample members, in the reweighting of the data, in imputation of missing values, and in other areas. [ABSTRACT FROM AUTHOR]

Many low- and middle-income countries are achieving good rates of economic growth, while high inequality remains a priority concern. Some countries meanwhile have low growth, high inequality, and pervasive poverty-often linked to their fragility. There is now active debate on how countries should set themselves goals for achieving both absolute poverty reduction and lower inequality. But policy action needs to be better served by analysis and data. [ABSTRACT FROM AUTHOR]

'Business climate indexes' characterize state economic policies, and are often used to try to influence economic policy debate. However, they are also useful in research as summaries of a large number of state policies that cannot be studied simultaneously. Prior research found that business climate indexes focused on productivity and quality of life do not predict economic growth, while indexes emphasizing taxes and costs of doing business indicate that low-tax, low-cost states have faster growth of employment, wages, and output. In this paper, we study the relationship between these two categories of business climate indexes and the promotion of equality or inequality. We do not find that the productivity/quality-of-life indexes predict more equitable outcomes, although some of the policies underlying them suggest they might. We do find, however, that the same tax-and-cost-related indexes that are associated with higher economic growth are also associated with increases in inequality. [ABSTRACT FROM AUTHOR]

This paper provides a normative framework for the assessment of the distributional incidence of growth. By removing the anonymity axiom, such framework is able to evaluate the individual income changes over time and the reshuffling of individuals along the income distribution that are determined by the pattern of income growth. We adopt a rank dependent social welfare function expressed in terms of initial rank and individual income change and we obtain partial and complete dominance conditions over different growth paths. These dominance conditions account for the different components determining the overall impact of growth, that is the size of growth and its vertical and horizontal incidence. We then provide an empirical application for Italy: this analysis shows the distributional impact of the recent economic crisis suffered by the Italian populaltion. [ABSTRACT FROM AUTHOR]

Income differences arise from many sources. While some kinds of inequality, caused by differential rewards to effort, might be associated with faster economic growth, other kinds, arising from unequal opportunities for investment, might be detrimental to economic progress. This study uses two new datasets, consisting of 117 income and expenditure household surveys and 134 Demographic and Health Surveys, to revisit the relationship between total inequality and economic growth. In particular, we ask whether inequality of opportunity, driven by circumstances at birth, has a negative effect on subsequent growth. Using the income and expenditure micro dataset, we find that while both total income inequality and inequality of opportunity are negatively associated with growth, the coefficient estimates are insignificant. The evidence is similarly equivocal using the Demographic and Health Surveys data. On balance, the data do not provide support for the hypothesis that inequality of opportunity is bad for growth. [ABSTRACT FROM AUTHOR]

A new dataset for charting the development of global inequality between 1820 and 2000 is presented, based on a large variety of sources and methods for estimating (gross household) income inequality. On this basis we estimate the evolution of global income inequality over the past two centuries. Two sets of benchmarks about between-country inequality (the Maddison 1990 benchmark and the recent 2005 ICP round) are taken into account. We find that between 1820 and 1950, increasing per capita income is combined with increasing global inequality. After 1950, global inequality as measured by the Gini coefficient or the Theil index remains more or less constant. It also appears that the global income distribution was uni-modal in the nineteenth century, became increasingly bi-modal between 1910 and 1970 with two world wars, a depression and de-globalization, and was suddenly transformed back into a uni-modal distribution between 1980 and 2000. [ABSTRACT FROM AUTHOR]

In spite of the great U-turn that saw income inequality rise in Western countries in the 1980s, happiness inequality has fallen in countries that have experienced income growth (but not in those that did not). Modern growth has reduced the share of both the 'very unhappy' and the 'perfectly happy.' Lower happiness inequality is found both between and within countries, and between and within individuals. Our cross-country regression results suggest that the extension of various public goods helps to explain this greater happiness homogeneity. This new stylized fact arguably comes as a bonus to the Easterlin paradox, offering a somewhat brighter perspective for developing countries. [ABSTRACT FROM AUTHOR]