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Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India

  • Tarozzi, Alessandro

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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Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 25 (2007)
Issue (Month): (July)
Pages: 314-336

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Handle: RePEc:bes:jnlbes:v:25:y:2007:p:314-336
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  1. 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.
  2. 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.
  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. Erich Battistin & Raffaele Miniaci & Guglielmo Weber, 2003. "What do we learn from recall consumption data?," Temi di discussione (Economic working papers) 466, Bank of Italy, Economic Research and International Relations Area.
  5. 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.
  6. Erich Battistin, 2002. "Errors in Survey Reports of Consumption Expenditures," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C4-2, International Conferences on Panel Data.
  7. Martin Browning & Thomas F. Crossley & Guglielmo Weber, 2002. "Asking Consumption Questions in General Purpose Surveys," Social and Economic Dimensions of an Aging Population Research Papers 77, McMaster University.
  8. 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..
  9. 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..
  10. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
  11. 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.
  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. 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.
  14. 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.
  15. Bhattacharya, Debopam, 2005. "Asymptotic inference from multi-stage samples," Journal of Econometrics, Elsevier, vol. 126(1), pages 145-171, May.
  16. 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.
  17. Jeffrey M. Wooldridge, 1999. "Asymptotic Properties of Weighted M-Estimators for Variable Probability Samples," Econometrica, Econometric Society, vol. 67(6), pages 1385-1406, November.
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