Identification of Sources of Variation in Poverty Outcomes

The international community has declared poverty reduction one of the fundamental objectives of development, and therefore a metric for assessing the effectiveness of development interventions. This creates the need for a sound understanding of the fundamental factors that account for observed variations in poverty outcomes either over time or across space. Consistent with the view that such an understanding entails deeper micro empirical work on growth and distributional change, this paper reviews existing decomposition methods that can be used to identify sources of variation in poverty. The maintained hypothesis is that the living standard of an individual is a pay-off from her participation in the life of society. In that sense, individual outcomes depend on endowments, behavior and the circumstances that determine the returns to those endowments in any social transaction. To identify the contribution of each of these factors to changes in poverty, the statistical and structural methods reviewed in this paper all rely on the notion of ceteris paribus variation. This entails the comparison of an observed outcome distribution to a counterfactual obtained by changing one factor at a time while holding all the other factors constant.

[1]  R. V. Mises On the Asymptotic Distribution of Differentiable Statistical Functions , 1947 .

[2]  A. Roy Some thoughts on the distribution of earnings , 1951 .

[3]  Anatol Rapoport,et al.  Two-person game theory , 1966 .

[4]  A. Blinder Wage Discrimination: Reduced Form and Structural Estimates , 1973 .

[5]  R. Oaxaca Male-Female Wage Differentials in Urban Labor Markets , 1973 .

[6]  H. Chenery,et al.  Redistribution with growth : policies to improve income distribution in developing countries in the context of economic growth : a joint study [commissioned] by the World Bank's Development Research Center and the Institute of Development Studies, University of Sussex , 1974 .

[7]  H. Chenery Redistribution with growth : policies to improve income distribution in developing countries in the context of economic growth : a joint study [commissioned] by the World Bank's Development Research Center and the Institute of Development Studies, University of Sussex , 1975 .

[8]  J. Heckman The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models , 1976 .

[9]  J. Alm The economics of taxation , 1978 .

[10]  J. Heckman Sample selection bias as a specification error , 1979 .

[11]  J. Muellbauer,et al.  Economics and consumer behavior , 1980 .

[12]  Lung-fei Lee Generalized Econometric Models with Selectivity , 1983 .

[13]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[14]  P. Schmidt,et al.  Limited-Dependent and Qualitative Variables in Econometrics. , 1984 .

[15]  W. Trockel The Parametric Approach , 1984 .

[16]  E. Thorbecke,et al.  A Class of Decomposable Poverty Measures , 1984 .

[17]  J. Heckman,et al.  Heterogeneity, Aggregation, and Market Wage Functions: An Empirical Model of Self-Selection in the Labor Market , 1985, Journal of Political Economy.

[18]  A. Atkinson ON THE MEASUREMENT OF POVERTY , 1987 .

[19]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[20]  P. Lambert The Distribution and Redistribution of Income , 1989 .

[21]  D. V. D. Walle,et al.  World development report 1990 : poverty , 1990 .

[22]  Bo E. Honoré,et al.  The Empirical Content of the Roy Model , 1990 .

[23]  M. Ravallion,et al.  Measuring changes in poverty : a methodological case study of Indonesia during an adjustment period , 1991 .

[24]  M. Ravallion,et al.  The sectoral structure of poverty during an adjustment period : evidence for Indonesia in the mid-1980s , 1991 .

[25]  Kevin M. Murphy,et al.  Accounting for the Slowdown in Black-White Wage Convergence , 1991 .

[26]  M. Ravallion,et al.  Growth and redistribution components of changes in poverty measures: A decomposition with applications to Brazil and India in the 1980s , 1992 .

[27]  A. Duncan Labour supply decisions and non-convex budget sets: the case of national insurance contributions in UK , 1993 .

[28]  Kevin M. Murphy,et al.  Wage Inequality and the Rise in Returns to Skill , 1993, Journal of Political Economy.

[29]  H. Peyton Young,et al.  Equity - in theory and practice , 1994 .

[30]  C. Schmertmann Selectivity bias correction methods in polychotomous sample selection models , 1994 .

[31]  James J. Heckman,et al.  Assessing the Case for Social Experiments , 1995 .

[32]  H. Coulombe,et al.  Modeling determinants of poverty in Mauritania , 1996 .

[33]  J. Dinardo,et al.  Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach , 1996 .

[34]  A. Deaton The Analysis of Household Surveys : A Microeconometric Approach to Development Policy , 1997 .

[35]  B. Essama-Nssah IMPACT OF GROWTH AND DISTRIBUTION ON POVERTY IN MADAGASCAR , 1997 .

[36]  Gaurav Datt,et al.  Computational tools for poverty measurement and analysis , 1998 .

[37]  R. Blundell,et al.  Labor Supply: A Review of Alternative Approaches , 1998 .

[38]  H. Nielsen Discrimination and detailed decomposition in a logit model , 1998 .

[39]  R. Stott,et al.  The World Bank , 2008, Annals of tropical medicine and parasitology.

[40]  Shaohua Chen,et al.  Measuring Pro-Poor Growth , 1999 .

[41]  Martin Fournier,et al.  Fast Development with a Stable Income Distribution : Taiwan, 1979-1994 , 2001 .

[42]  Denis Cogneau,et al.  Growth, distribution and poverty in Madagascar , 2000 .

[43]  World Bank and , 2001 .

[44]  M. Grimm A decomposition of inequality and poverty changes in the context of macroeconomic adjustment : a microsimulation study for Cote d'Ivoire , 2001 .

[45]  M. Ravallion Growth, Inequality, and Poverty: Looking Beyond Averages , 2001 .

[46]  J. Heckman,et al.  Removing the Veil of Ignorance in Assessing the Distributional Impacts of Social Policies , 2002, SSRN Electronic Journal.

[47]  J. Creedy,et al.  Behavioural Microsimulation with Labour Supply Responses , 2002 .

[48]  B. Salani'e,et al.  The Economics of Taxation , 2003 .

[49]  Myeong-Su Yun Decomposing Differences in the First Moment , 2003, SSRN Electronic Journal.

[50]  Myeong-Su Yun A Simple Solution to the Identification Problem in Detailed Wage Decompositions , 2003, SSRN Electronic Journal.

[51]  F. Bourguignon,et al.  Chapter 6 Ex-Ante Evaluation of Policy Reforms using Behavioral Models , 2003 .

[52]  Hervé Moulin,et al.  Fair division and collective welfare , 2003 .

[53]  Kenneth E. Train,et al.  Discrete Choice Methods with Simulation , 2016 .

[54]  Measuring Pro-Poor Growth , 2003 .

[55]  David Autor Lecture Note 5 : Self-Selection — The Roy Model , 2003 .

[56]  J. Duclos,et al.  What is Pro-Poor? , 2004 .

[57]  R. Oaxaca,et al.  Wage Decompositions with Selectivity-Corrected Wage Equations: A Methodological Note , 2004 .

[58]  Paul Milgrom,et al.  Putting Auction Theory to Work , 2004 .

[59]  J. Mata,et al.  Counterfactual decomposition of changes in wage distributions using quantile regression , 2005 .

[60]  R. Fairlie An Extension of the Blinder-Oaxaca Decomposition Technique to Logit and Probit Models , 2006, SSRN Electronic Journal.

[61]  M. Grimm Educational policies and poverty reduction in Côte d’Ivoire , 2005 .

[62]  Julia Kastner,et al.  Introduction to Robust Estimation and Hypothesis Testing , 2005 .

[63]  S. Kolenikov,et al.  A Decomposition Analysis of Regional Poverty in Russia , 2005 .

[64]  B. Melly Decomposition of differences in distribution using quantile regression , 2005 .

[65]  T. Bauer,et al.  Blinder–Oaxaca decomposition for Tobit models , 2005 .

[66]  Aart C. Kraay When is growth pro-poor? Evidence from a panel of countries , 2006 .

[67]  F. Bourguignon,et al.  Microsimulation as a tool for evaluating redistribution policies , 2006 .

[68]  Marginal effects and extending the Blinder–Oaxaca decomposition for nonlinear models , 2006 .

[69]  D. Sahn,et al.  The Demand for Primary Schooling in Madagascar: Price, Quality, and the Choice Between Public and Private Providers , 2006 .

[70]  James A. Robinson,et al.  World development report 2006 : equity and development , 2006 .

[71]  S. Bhaumik,et al.  A Note on Decomposing Differences in Poverty Incidence Using Regression Estimates: Algorithm and Example , 2006 .

[72]  Denis Cogneau,et al.  Growth, distribution and poverty in Madagascar : learning from a microsimulation model in a general equilibrium framework , 2007 .

[73]  Peter E. Kennedy,et al.  A computational trick for calculating the Blinder-Oaxaca decomposition and its standard error , 2007 .

[74]  Amedeo Spadaro Microsimulation as a tool for the evaluation of public policies , 2007 .

[75]  Amedeo Spadaro Microsimulation as Tool for the Evaluation of Public Policies: Methods and Applications , 2007 .

[76]  N. Fortin,et al.  Unconditional Quantile Regressions , 2007 .

[77]  B. Nguyen,et al.  A quantile regression decomposition of urban–rural inequality in Vietnam , 2007 .

[78]  Myeong-Su Yun Wage Differentials, Discrimination and Inequality: A Cautionary Note on the Juhn, Murphy and Pierce Decomposition Method , 2007 .

[79]  Petra E. Todd,et al.  Evaluating Social Programs with Endogenous Program Placement and Selection of the Treated , 2007 .

[80]  N. Fortin,et al.  Unconditional Quantile Regressions , 2007 .

[81]  F. Bourguignon,et al.  Selection Bias Corrections Based on the Multinomial Logit Model: Monte Carlo Comparisons , 2007 .

[82]  Ben Jann OAXACA: Stata module to compute the Blinder-Oaxaca decomposition , 2008 .

[83]  T. Bauer,et al.  An extension of the Blinder–Oaxaca decomposition to nonlinear models , 2006 .

[84]  Astrid Kunze Gender wage gap studies: consistency and decomposition , 2008 .

[85]  D. Crawford Introduction , 2008, CACM.

[86]  Denis Cogneau,et al.  Simulating Targeted Policies with Macro Impacts: Poverty Alleviation Policies in Madagascar , 2008 .

[87]  Joshua D. Angrist,et al.  Mostly Harmless Econometrics: An Empiricist's Companion , 2008 .

[88]  K. Train,et al.  Estimation on stated-preference experiments constructed from revealed-preference choices , 2008 .

[89]  T. Bauer,et al.  The Blinder–Oaxaca Decomposition for Nonlinear Regression Models , 2008 .

[90]  F. Bourguignon,et al.  The Impact of Macroeconomic Policies on Poverty and Income Distribution , 2008 .

[91]  F. Bourguignon,et al.  Beyond Oaxaca–Blinder: Accounting for differences in household income distributions , 2008 .

[92]  Ben Jann A Stata implementation of the Blinder-Oaxaca decomposition , 2008 .

[93]  B. Essama-Nssah,et al.  Measuring Pro-Poorness: A Unifying Approach with New Results , 2009 .

[94]  B. McCaig The Impact of Macroeconomic Policies on Poverty and Income Distribution: Macro-Micro Evaluation Techniques and Tools , 2009 .

[95]  Alan Sánchez,et al.  THE EVOLUTION OF URBAN INEQUALITY IN ETHIOPIA , 2009 .

[96]  Francisco H. G. Ferreira Distributions in Motion: Economic Growth, Inequality, and Poverty Dynamics , 2010 .

[97]  B. Essama-Nssah,et al.  A Counterfactual Analysis of the Poverty Impact of Economic Growth in Cameroon , 2010 .

[98]  N. Fortin,et al.  Decomposition Methods in Economics , 2010 .

[99]  Patrick M. Kline Blinder-Oaxaca as a Reweighting Estimator , 2010 .

[100]  Todd E. Elder,et al.  Unexplained Gaps and Oaxaca-Blinder Decompositions , 2009, SSRN Electronic Journal.

[101]  B. Essama-Nssah,et al.  Accounting for Heterogeneity in Growth Incidence in Cameroon , 2010 .

[102]  Patrick M. Kline Oaxaca-Blinder as a Reweighting Estimator , 2011 .

[103]  B. Essama-Nssah,et al.  Influence functions for distributional statistics , 2011 .

[104]  J. Bishop,et al.  Dominance testing for ‘pro-poor’ growth with an application to European growth , 2012 .

[105]  A. Shorrocks Decomposition procedures for distributional analysis: a unified framework based on the Shapley value , 2013 .