Tracking Poverty Over Time in the Absence of Comparable Consumption Data
Tracking Poverty Over Time in the Absence of Comparable Consumption Data David Stifel and Luc Christiaensen Following the endorsement by the international community of the Millennium Development Goals, there has been an increasing demand for practical methods for steadily tracking poverty. The minimum data requirements for this methodology are the availability of a household budget survey and a series of surveys with a comparable set of asset data also contained in the budget survey. JEL codes: C81, I32 The worldwide endorsement of the Millennium Development Goals and the shift to results-based lending in supporting developing countries have intensified the importance of being able to reliably gauge the evolution of poverty. While the method is straightforward, the predicted evolution of poverty holds only under a series of stringent assumptions such as distribution-neutral growth, a correct attribution of sectoral GDP growth to households (World Bank 2005), and a close correspondence between growth observed in the national accounts and income or consumption growth measured in household surveys (Ravallion 2003; Deaton and Kozel 2005). The empirical application uses the asset information from the 1993, 1998, and 2003 Kenyan Demographic and Health Surveys and the consumption measure from the 1997 Welfare Monitoring Survey (WMS). 3 Tracking Wt by tracking xt requires essentially three steps: developing an accurate empirical model of ct as a function of xt; estimating ct k as a function of xt k, where k is a positive or negative integer; and generating an estimate of expected Wt k from the estimated ct k. 3. Comparison of these indicators across the population in rural areas, other urban localities, and Nairobi between 1993 and 2003 based on the Demographic and Health Surveys (table 4) shows substantial improvements in primary and secondary enrollment rates and stunting prevalence in rural areas, even stronger improvements in these indicators in Nairobi, and a mixed picture in other urban areas, with primary enrollment rates increasing, secondary enrollment rates falling marginally, and stunting prevalence increasing Further inspection indicates that their relative prices (in terms of the overall consumer price index) declined substantially, possibly because of technological innovation, trade liberalization, or exchange rate misalignment (in particular, real exchange rate overvaluation). Going forward, comparing economic asset-based poverty measures with those derived from household budget surveys using actual consumption data emerges as an important research agenda for applied economists to shed further light on the empirical validity of the stationarity assumption. var(b To obtain estimates of the expected welfare indicator in stage 2, a vector of t beta coefficients (bs) is first drawn from a multivariate normal distribution with a mean btGLS and variance covariance V(btGLS) and applied to the target 0 t data xt k to predict household log expenditures (xcht kbs).
| Main Authors: | , |
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| Format: | Journal Article biblioteca |
| Published: |
World Bank
2007-05-30
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| Subjects: | household budget, household consumption, household surveys, income, indicators of poverty, inequality, Poverty Analysis, poverty mapping, Rural, Rural Development, |
| Online Access: | http://hdl.handle.net/10986/4460 |
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| Summary: | Tracking Poverty Over Time in the Absence of Comparable Consumption Data David Stifel and Luc Christiaensen Following the endorsement by the international community of the Millennium Development Goals, there has been an increasing demand for practical methods for steadily tracking poverty. The minimum data requirements for this methodology are the availability of a household budget survey and a series of surveys with a comparable set of asset data also contained in the budget survey. JEL codes: C81, I32 The worldwide endorsement of the Millennium Development Goals and the shift to results-based lending in supporting developing countries have intensified the importance of being able to reliably gauge the evolution of poverty. While the method is straightforward, the predicted evolution of poverty holds only under a series of stringent assumptions such as distribution-neutral growth, a correct attribution of sectoral GDP growth to households (World Bank 2005), and a close correspondence between growth observed in the national accounts and income or consumption growth measured in household surveys (Ravallion 2003; Deaton and Kozel 2005). The empirical application uses the asset information from the 1993, 1998, and 2003 Kenyan Demographic and Health Surveys and the consumption measure from the 1997 Welfare Monitoring Survey (WMS). 3 Tracking Wt by tracking xt requires essentially three steps: developing an accurate empirical model of ct as a function of xt; estimating ct k as a function of xt k, where k is a positive or negative integer; and generating an estimate of expected Wt k from the estimated ct k. 3. Comparison of these indicators across the population in rural areas, other urban localities, and Nairobi between 1993 and 2003 based on the Demographic and Health Surveys (table 4) shows substantial improvements in primary and secondary enrollment rates and stunting prevalence in rural areas, even stronger improvements in these indicators in Nairobi, and a mixed picture in other urban areas, with primary enrollment rates increasing, secondary enrollment rates falling marginally, and stunting prevalence increasing Further inspection indicates that their relative prices (in terms of the overall consumer price index) declined substantially, possibly because of technological innovation, trade liberalization, or exchange rate misalignment (in particular, real exchange rate overvaluation). Going forward, comparing economic asset-based poverty measures with those derived from household budget surveys using actual consumption data emerges as an important research agenda for applied economists to shed further light on the empirical validity of the stationarity assumption. var(b To obtain estimates of the expected welfare indicator in stage 2, a vector of t beta coefficients (bs) is first drawn from a multivariate normal distribution with a mean btGLS and variance covariance V(btGLS) and applied to the target 0 t data xt k to predict household log expenditures (xcht kbs). |
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