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Censored Quantile Regressions of Chronic and Transient Seasonal Poverty in Rwanda

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  • Christophe Muller

Abstract

It is crucial for social policy in Less Developed Countries to identify correlates of poverty at the household level. This has been done in the literature by estimating household poverty equations typically with Tobit and Probit models. However, when the errors in these equations are non-normal and heteroscedastic, which is usually expected, these models deliver biased estimates. Using quarterly data from Rwanda in 1983, we reject the normality and homoscedasticity assumptions for household chronic and transient latent poverty equations. We treat this problem by estimating censored quantile regressions. Our results of censored quantile regressions and of inconsistent Tobit regressions are substantially different. However, in the case of chronic poverty the signs of the apparently significant coefficients are generally in agreement, while for seasonal transient poverty different variables have significant effects for the two estimation methods. Our second contribution is to study, for the first time, correlates of poverty indicators based on quarterly consumptions. Our results show that in Rwanda different correlates are significant for chronic poverty and for transient seasonal poverty. The effects of the main inputs (land and labour) are more important for the chronic component of poverty than for the transient one. Household location and socio-demographic characteristics play important roles that are consistent with usual explanations of poverty in the literature. Copyright 2002, Oxford University Press.

Suggested Citation

  • Christophe Muller, 2002. "Censored Quantile Regressions of Chronic and Transient Seasonal Poverty in Rwanda," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 11(4), pages 503-541, December.
  • Handle: RePEc:oup:jafrec:v:11:y:2002:i:4:p:503-541
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    References listed on IDEAS

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    1. Lanjouw, Peter & Ravallion, Martin, 1995. "Poverty and Household Size," Economic Journal, Royal Economic Society, vol. 105(433), pages 1415-1434, November.
    2. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-766, May.
    3. John Mackinnon & Ritva Reinikka, 2002. "How Research Can Assist Policy: The Case of Economic Reforms in Uganda," World Bank Research Observer, World Bank Group, vol. 17(2), pages 267-292, September.
    4. Kakwani, Nanak, 1993. "Statistical Inference in the Measurement of Poverty," The Review of Economics and Statistics, MIT Press, vol. 75(4), pages 632-639, November.
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    Cited by:

    1. Salehi-Isfahani, Djavad & Majbouri, Mehdi, 2013. "Mobility and the dynamics of poverty in Iran: Evidence from the 1992–1995 panel survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(3), pages 257-267.
    2. Christophe Muller & Sami Bibi, 2006. "Focused Targeting against Poverty Evidence from Tunisia," IDEP Working Papers 0602, Institut d'economie publique (IDEP), Marseille, France, revised Apr 2006.
    3. Stefan Dercon & Joseph S. Shapiro, 2007. "Moving On, Staying Behind, Getting Lost: Lessons on poverty mobility from longitudinal data," Economics Series Working Papers GPRG-WPS-075, University of Oxford, Department of Economics.
    4. repec:dau:papers:123456789/4335 is not listed on IDEAS
    5. Christophe MULLER & Sami BIBI, 2008. "Focused Transfer Targeting against Poverty Evidence from Tunisia," THEMA Working Papers 2008-37, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    6. Ayal Kimhi, 2004. "Growth, Inequality and Labor Markets in LDCs: A Survey," CESifo Working Paper Series 1281, CESifo Group Munich.

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