Shatakshee Dhongde

Global Poverty Estimates: A Sensitivity Analysis

Current estimates of global poverty vary substantially across studies. We undertake a sensitivity analysis to highlight the importance of methodological choices by measuring global poverty using different data sources, parametric and nonparametric estimation methods, and multiple poverty lines. Our results indicate that estimates of global poverty vary significantly when they are based alternately on data from household surveys versus national accounts but are relatively consistent across estimation methods. The decline in poverty over the past decade is found to be robust across methodological choices.

A Non‐Parametric Measure Of Poverty Elasticity

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 worlds 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 Banks now‐defunct 1993‐PPP based poverty data) are systematically larger in magnitude than estimates based on the latest 2005‐PPP based data.

Global Poverty Estimates: Present and Future

We review the recent empirical literature on global poverty, focusing on key methodological aspects. These include the choice of welfare indicator, poverty line and purchasing power parity exchange rates, equivalence scales, data sources, and estimation methods. We also discuss the importance of the intra-household resource allocation process in determining within-household inequalities and potentially influencing poverty estimates. Based on a sensitivity analysis of global poverty estimates to different methodological approaches, we show that existing figures vary markedly with the choice of data source for mean income or consumption used to scale relative distributions; and with the statistical method used to estimate income distributions from tabulated data.

Testing Convergence in Income Distribution

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.

Binary Data, Hierarchy of Attributes, and Multidimensional Deprivation

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 grouped as basic attributes that are of fundamental importance for an individual’s quality of life and non-basic attributes which are at a much lower level of importance. Empirical illustrations of the proposed measures are provided based on the estimation of multidimensional deprivation among children in Ethiopia, India, Peru and Vietnam.

Assessing Multidimensional Deprivation Among the Elderly in the USA

The number of older adults in the USA is growing rapidly and by 2030 Americans aged 65 years or older will account for roughly 20% of the total population. We estimate multidimensional deprivation among the elderly in the US Deprivation is measured in four distinct dimensions of well-being: health condition, standard of living, education and economic security measured by housing costs. Three different indices are estimated to analyze the joint impact of deprivation in these dimensions on older adults’ overall welfare. The study uses the American Community Survey which is the largest nationally representative household survey in the USA. Results show that, around 38% of the elderly were deprived in at least one dimension, 12% in at least two dimensions. Deprivation prevalence was higher among Asians and Blacks and especially among the Hispanic. Older Hispanic adults were predominantly deprived in education and income. The study argues that measuring disparity in overlapping dimensions will be useful to design effective policies in the future.