This paper presents a comprehensive analysis of multidimensional deprivation in the U.S. since the Great Recession, from 2008 to 2013. We estimate a Multidimensional Deprivation Index by compiling individual level data on several well-being dimensions from the American Community Survey. Our results indicate that the proportion of the population that is multidimensional deprived averages about 15 percent, which exceeds the prevalence of official income poverty. Lack of education, severe housing burden and lack of health insurance were some of the dimensions in which Americans were most deprived in. Though deprivation increased during the recession, it trended towards a decline between 2010 and 2013. Unlike the official and the supplemental poverty measure which did not show any decline, the deprivation index better reflects the economic recovery since the recession. Overall, the prevalence of deprivation was higher in the southern and the western states and among the Asian and the Hispanic population. Importantly, there was not much overlap between individuals who were income poor and those who were multidimensional deprived. In fact, almost 30 % of individuals with incomes slightly above the poverty threshold experienced multiple deprivations. Our analysis underscores the need to look beyond income based poverty statistics in order to fully realize the impact of the recession on individuals' well-being.

We estimate the growth elasticity of poverty (GEP) using recently developed non‐parametric panel methods and the most up‐to‐date and extensive poverty data from the World Bank, which exceeds 500 observations in size and represents more than 96 percent of the developing world's population. Unlike previous studies which rely on parametric models, we employ a non‐parametric approach which captures the non‐linearity in the relationship between growth, inequality, and poverty. We find that the growth elasticity of poverty is higher for countries with fairly equal income distributions, and declines in nations with greater income disparities. Moreover, when controlling for differences in estimation technique, we find that the reported values of the GEP in the literature (based on the World Bank's now‐defunct 1993‐PPP based poverty data) are systematically larger in magnitude than estimates based on the latest 2005‐PPP based data.

We study changes in social well-being and deprivation in the U.S. during the Great Recession and the subsequent recovery. We outline an analytical framework for measuring well-being and deprivation in a multidimensional fashion when data on achievement in each dimension is assumed to be ordinal and binary in nature. We use data from the American Community Survey between 2008 and 2015 and find that there was a decline in social well-being and a rise in social deprivation in the U.S. during the recession followed by a reversal of trends during the recovery. Despite low deprivation levels among the White population, this population experienced the largest increase in deprivation during the recession and the least decline in deprivation in the recovery period. These results underscore the fact that the impact of recession and the subsequent recovery varied significantly across population groups. JEL Codes: D36, I31, J10focus on the overall deprivation of the deprived individuals only, we introduce a benchmark level t (t > 0) such that an individual is considered deprived if and only if her overall achievement falls short of t. Our measure of social deprivation is then computed as the sum of overall deprivations of all individuals who are classified as deprived.Our measures are related to some existing measures introduced in the literature on measuring multi-dimensional well-being and deprivation (see, for example, Aaberge and Brandolini (2015), for an extensive survey on related studies). For example, if the transformation function that is used to transform an individual's overall achievement to well-being is linear, then our measure is equivalent to the counting measure (Atkinson, 2003) widely used in the literature. However, if the transformation function is not linear, then the family of our measures behaves very differently from counting measures, and can avoid many pitfalls suffered by various counting measures. By appropriately choosing a transformation function to transform an individual's overall achievement to her well-being (see our discussions in Section 3), we ensure that our measure satisfies certain attractive properties.We estimate the proposed indices using data from the American Community Survey (ACS), which is the largest household level surveys in the U.S. Our sample comprises more than 2 million individuals each year from ACS rounds: 2008 to 2015. We use the recommendations of the Commission on the Measurement of Economic Performance and Social Progress (Stiglitz et al., 2009) as a guide in choosing the different dimensions or dimensions in terms of which we assess an individual's well-being. We choose 9 variables from the ACS which broadly capture the well-being dimensions mentioned in the Commission's report. We estimate trends in overall well-being and deprivation over time and test their sensitivity to multiple thresholds and weights. We also estimate these indices for population groups based on age, gender, nativity, race and ethnicity and find some interesting differences among the different...

Empirical estimation of multidimensional deprivation measures has gained momentum in the last few years. Several existing measures assume that deprivation dimensions are cardinally measurable, when, in many instances, such data is not always available. In this paper, we propose a class of deprivation measures when the only information available is whether an individual is deprived in an attribute or not. The framework is then extended to a setting in which the multiple dimensions are

The generalized method of moments (GMM) estimator is often used to test for convergence in income distribution in a dynamic panel set-up. We argue that though consistent, the GMM estimator utilizes the sample observations inefficiently. We propose a simple ordinary least squares (OLS) estimator with more efficient use of sample information. Our Monte Carlo study shows that the GMM estimator can be very imprecise and severely biased in finite samples. In contrast, the OLS estimator overcomes these shortcomings. Copyright (c) Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2008.