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Calculating Comparable Statistics from Incomparable Surveys, with an Application to Poverty in India

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  • Alessandro Tarozzi

Abstract

We develop an intuitive and easily implemented procedure to recover comparability over time of statistics computed using databases made incomparable by changes in survey design. Our methodology can be adopted whenever the statistic of interest satisfies a certain simple moment condition. The moment condition is satisfied by many interesting economic indicators, including a broad range of poverty and inequality measures. The procedure we propose requires the existence of a set of auxiliary variables whose reports are not affected by the different survey design, and whose relation with the main variable of interest is stable across the surveys. The adjusted estimates can be recovered by using a two-step method of moments framework. Root-n consistency follows easily under regularity conditions. Because most household surveys adopt a multi-stage design, we provide expressions for the asymptotic variance which are robust to the presence of clustering and stratification. We use our adjustment procedure to estimate poverty counts from the 55th Round of the Indian National Sample Survey, a large household survey carried out in 1999-2000. Due to important changes in the adopted questionnaire the unadjusted figures are likely to understate poverty relative to the previous rounds. We provide evidence supporting the plausibility of the identifying assumptions and we conclude that most of the very large reduction in poverty implied by the unadjusted figures is real

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Paper provided by Econometric Society in its series Econometric Society 2004 North American Winter Meetings with number 280.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nawm04:280

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Keywords: Poverty; India; Method of Moments; Household Surveys;

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  1. Angus Deaton & Alessandro Tarozzi, 2000. "Prices and poverty in India," Working Papers 213, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
  2. Lanjouw, Jean Olson & Lanjouw, Peter, 2001. "How to Compare Apples and Oranges: Poverty Measurement Based on Different Definitions of Consumption," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 47(1), pages 25-42, March.
  3. Gibson, John, 2002. " Why Does the Engel Method Work? Food Demand, Economies of Size and Household Survey Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 341-59, September.
  4. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
  5. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
  6. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
  7. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
  8. Bhattacharya, Debopam, 2005. "Asymptotic inference from multi-stage samples," Journal of Econometrics, Elsevier, vol. 126(1), pages 145-171, May.
  9. John Gibson & Jikun Huang & Scott Rozelle, 2003. "Improving Estimates of Inequality and Poverty from Urban China's Household Income and Expenditure Survey," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 49(1), pages 53-68, 03.
  10. Martin Browning & Thomas F. Crossley & Guglielmo Weber, 2003. "Asking consumption questions in general purpose surveys," Economic Journal, Royal Economic Society, vol. 113(491), pages F540-F567, November.
  11. Erich Battistin, 2003. "Errors in survey reports of consumption expenditures," IFS Working Papers W03/07, Institute for Fiscal Studies.
  12. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition and stratification," CeMMAP working papers CWP11/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  13. Jeffrey M. Wooldridge, 1999. "Asymptotic Properties of Weighted M-Estimators for Variable Probability Samples," Econometrica, Econometric Society, vol. 67(6), pages 1385-1406, November.
  14. Erich Battistin & Raffaele Miniaci & Guglielmo Weber, 2003. "What Do We Learn from Recall Consumption Data?," Journal of Human Resources, University of Wisconsin Press, vol. 38(2).
  15. Angus Deaton & Jean Dreze, 2002. "Poverty and Inequality in India: A Re-Examination," Working Papers 184, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
  16. Gibson, John & Huang, Jikun & Rozelle, Scott, 2001. "Why is income inequality so low in China compared to other countries?: The effect of household survey methods," Economics Letters, Elsevier, vol. 71(3), pages 329-333, June.
  17. Orazio Attanasio & Erich Battistin & Hidehiko Ichimura, 2004. "What Really Happened to Consumption Inequality in the US?," NBER Working Papers 10338, National Bureau of Economic Research, Inc.
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Cited by:
  1. Firpo, Sergio, 2010. "Identification and Estimation of Distributional Impacts of Interventions Using Changes in Inequality Measures," IZA Discussion Papers 4841, Institute for the Study of Labor (IZA).
  2. Eric V. Edmonds & Nina Pavcnik & Petia Topalova, 2010. "Trade Adjustment and Human Capital Investments: Evidence from Indian Tariff Reform," American Economic Journal: Applied Economics, American Economic Association, vol. 2(4), pages 42-75, October.
  3. Günther, Isabel & Grimm, Michael, 2007. "Growth and Poverty in Burkina Faso. A Reassessment of the Paradox," Economics Papers from University Paris Dauphine 123456789/4399, Paris Dauphine University.
  4. Victoria Fan, Anup Karan, and Anjay Mahal, 2012. "State Health Insurance and Out-of-Pocket Health Expenditures in Andhra Pradesh, India - Working Paper 298," Working Papers 298, Center for Global Development.
  5. Christiaensen, Luc & Lanjouw, Peter & Luoto, Jill & Stifel, David, 2011. "Small area estimation-based prediction methods to track poverty : validation and applications," Policy Research Working Paper Series 5683, The World Bank.
  6. Fulford, Scott L., 2013. "The effects of financial development in the short and long run: Theory and evidence from India," Journal of Development Economics, Elsevier, vol. 104(C), pages 56-72.
  7. World Bank, 2012. "Niger : Investing for Prosperity - A Poverty Assessment," World Bank Other Operational Studies 12312, The World Bank.
  8. Maciej Jakubowski & Artur Pokropek, 2011. "Measuring progress in reading achievement between primary and secondary school across countries," Working Papers 2011-20, Faculty of Economic Sciences, University of Warsaw.
  9. Firpo, Sergio Pinheiro, 2010. "Identification and estimation of interventions using changes in inequality measures," Textos para discussão 214, Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
  10. Victoria Fan & Anup Karan & Ajay Mahal, 2012. "State health insurance and out-of-pocket health expenditures in Andhra Pradesh, India," International Journal of Health Care Finance and Economics, Springer, vol. 12(3), pages 189-215, September.
  11. Melanie Morten, 2006. "Indian Poverty during the 1990s: Resolving Methodological Issues from the 55th NSS Round," ASARC Working Papers 2006-07, The Australian National University, Australia South Asia Research Centre.
  12. Houngbedji, Kenneth & Cogneau, Denis & Mesplé-Somps, Sandrine, 2013. "The fall of the elephant. Two decades of poverty increase in Côte d’Ivoire (1988 - 2008)," Economics Papers from University Paris Dauphine 123456789/12510, Paris Dauphine University.
  13. Angus Deaton & Valerie Kozel, 2005. "Data and Dogma: The Great Indian Poverty Debate," World Bank Research Observer, World Bank Group, vol. 20(2), pages 177-199.
  14. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2008. "Inverse Probability Tilting for Moment Condition Models with Missing Data," NBER Working Papers 13981, National Bureau of Economic Research, Inc.
  15. Astrid Mathiassen, 2009. "A model based approach for predicting annual poverty rates without expenditure data," Journal of Economic Inequality, Springer, vol. 7(2), pages 117-135, June.

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