Associate Professor and Director for Graduate Teaching and Training
- School of Economics
- Development Studies Program
Shatakshee Dhongde is an Associate Professor in the School of Economics. She obtained her Ph.D. from the University of California, Riverside. She is a research affiliate with the Institute of Research on Poverty at the University of Wisconsin, Madison. Her research has focused on studying the impact of globalization on economic growth and income inequality and measurement of poverty and multidimensional deprivation. She was awarded the Nancy and Richard Ruggles Prize by the International Association for Research in Income and Wealth in 2012. The award is given for the best research paper by a young scholar under the age of 35. Her work has been published in leading economics journals. In addition to her research, she enjoys teaching and is the recipient of multiple teaching awards at Georgia Tech.
- Ph.D., University of California, Riverside, United States
- M.A., Gokhale Institute of Politics and Economics, India
- B.A., University of Pune, India
- Fellow, Society for Economic Measurement (2019)
- The Ivan Allen Jr. Legacy Award at Georgia Tech (2019)
- Provost Teaching and Learning Fellow at Georgia Tech (2017-2018)
- The Nancy and Richard Ruggles Memorial Prize in Economics, IARIW(2012)
- Chancellor's Distinguished Fellowship Award (1999)
- Applied Econometrics
- Development Economics
- Economic Measurement
- Inequality and Poverty
- Asia (South)
- United States
- United States - Georgia
- Inequality and Social Justice
- International Development
- Weapons and Security
- Human Capital
- ECON-3110: Adv Microeconomic Analys
- ECON-3161: Econometric Analysis
- ECON-4411: Economic Development
- ECON-6105: Macroeconomics
- ECON-6360: Development Economics
- ECON-7012: Microeconomic Theory I
- Well-being, Poverty, and the Great Recession in the U.S.: A Study in a Multidimensional Framework
In: Review of Income and Wealth [Peer Reviewed]
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.
- Are Countries becoming Equally Unequal?
Literature on convergence in inequality is sparse and has almost entirely focused on the notion of testing beta convergence in the Gini indices. In this paper, for the first time, we test for sigma convergence in decile income shares across countries. We compile panel data on decile income shares for more than 60 countries over the last 25 years. Regardless of the level of development, within country inequality increased; income shares of the poorest deciles declined and those of the top decile increased significantly. Importantly, the decile income shares exhibited a statistically significant decline in dispersion between 1985 and 2011, providing strong evidence of sigma convergence in inequality. Convergence was more prominent among developing countries and less so among developed countries. The findings are robust to an array of sensitivity tests. Our analysis suggests that cross-country income distributions became more unequal but noticeably similar over time.
- 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.
- Convergence in income distributions: Evidence from a Panel of Countries
There is growing evidence that countries' income distributions have changed significantly since globalization accelerated in the early 1990s. Using a large panel of Gini indices covering 81 countries between 1990 and 2010, we find strong evidence that inequality declined in nations that were initially highly unequal, while inequality increased in nations with initially low inequality. Developed countries' relative income distributions converged at a more rapid pace. These findings are robust to the method of estimation, level of economic development, time horizon, data source or measure of inequality. Our results suggest that income distributions in countries are becoming increasingly unequal yet more similar to each other.
- Measuring Segregation of the Poor: Evidence from India
There is extensive literature on measures of poverty, yet the question of how the poor are distributed regionally has received less attention. This paper fills the gap by providing a conceptual framework to measure inequality in the distribution of the poor. A poverty segregation curve is used to compare a region's share of the poor population with its share in the overall population. A unique contribution of the paper is formulating a generalized version of the poverty segregation curve. The generalized segregation curve also takes average poverty rates into account while ranking distributions. The segregation curves are used to analyze changes in the distribution of the poor in India since the economic reforms in the early 1990s. In the decades following the reforms, India witnessed high growth rates and declining poverty rates. Despite the reduction in poverty, our analysis is the first to reveal that there was a significant rise in segregation of the poor over time.
- Multi-Dimensional Deprivation in the U.S.
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 data on multiple 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. Overall, the prevalence of deprivation was higher in the southern and the western states and among the Asian and the Hispanic population. Importantly, 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.
- On Distributional Change, Pro-Poor Growth and Convergence
This paper proposes a unified approach to the measurement of distributional change. The framework is used to define indices of inequality in proportional growth rates, convergence, and pro-poorness of growth and associated equivalent growth rates. A distinction is made between non-anonymous and anonymous measures. The analysis is extended by using the notion of generalized Gini index. This unified approach is then implemented to study the link between income and other non-income characteristics, such as education and health. Empirical illustrations based on Indian data on individual educational achievements and on state wide infant survival levels highlight the usefulness of the proposed measures.
- 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 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 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.
- 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.
- Measuring the Impact of Growth and Income Distribution on Poverty in India
Since the economic reforms of the early 1990s, the Indian economy witnessed a rapid rise in the mean income level, and, simultaneously, changes in the distribution of income. This paper tries to capture how these changes affected poverty levels across major states in India. Total change in poverty is decomposed into the change due to a rise in the mean income level and the change due to changes in the distribution of income. It is observed that, in India, rapid growth led to a significant decline in poverty though changes in the distribution of income adversely affected the poor.