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Cities Transformed: Demographic Change and Its Implications in the Developing World (2003)

Chapter: Appendix E: Measuring Relative Poverty with DHS Data

« Previous: Appendix D: United Nations Estimates and Projections
Suggested Citation:"Appendix E: Measuring Relative Poverty with DHS Data." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 499
Suggested Citation:"Appendix E: Measuring Relative Poverty with DHS Data." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 500
Suggested Citation:"Appendix E: Measuring Relative Poverty with DHS Data." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 501
Suggested Citation:"Appendix E: Measuring Relative Poverty with DHS Data." National Research Council. 2003. Cities Transformed: Demographic Change and Its Implications in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/10693.
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Page 502

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E Measuring Relative Poverty with DHS Data This appendix briefly describes how the panel defined urban poverty for the pur- poses of its DHS analyses, leaving a more complete account to Hewett and Mont- gomery (2001~. Recall that in the DHS program, no information is collected on incomes or consumption expenditures, the two variables commonly used to mea- sure household standards of living. However, a few other items are collected that can serve as crude proxy measures of living standards. We can distinguish three categories of such items: the ownership of various consumer durables; descrip- tors of the quality of housing; and measures of access to services, such as water supply and electricity. Of the three, we limit attention to the consumer durables and housing measures. This is because we want to explore whether poverty is associated with a lack of access to public services, and with that goal in mind, poverty cannot be defined in terms of these same services. The items used in the panel's index are ownership of a refrigerator, television, radio, bicycle, motorcy- cle, or car; the number of sleeping rooms in the dwelling; and whether its floor is of a finished material. Montgomery, Gragnolati, Burke, and Paredes (2000) and Filmer and Pritchett (2001) discuss the performance of such proxies as measures of living standards. Using these durables and housing quality items, we proceeded to define poverty in relative terms. We carried out a principal components analysis a method not unlike factor analysis to extract from the DHS indicators a single score that could be interpreted as an index of the household's standard of living. (Hewett and Montgomery, 2001, compare results from confirmatory factor analysis with those from principal components analysis and find little empirical difference be- tween them.) For each DHS survey, we classified as "relatively poor" those urban households whose index scores fell into the lowest quartile of the urban scores for that survey. The same approach was used to characterize rural households. 499

500 CITIES TRANSFORMED This emphasis on the relative aspect of poverty was all but forced upon us by the nature of the data collected in the DHS surveys. The proxy measures form a heterogenous group and are not directly comparable to measures of income or consumption. These data do not provide the necessary raw materials for a defensible measure of absolute poverty. Furthermore, ownership of consumer durables differs so greatly between urban and rural settings that to form one index common to both settings might well be misleading. Having decided to estimate separate indices for urban and rural households, we were left with little alternative but to rank households within their sectors of residence. One advantage of such sector-specific relative poverty measures is that they retain their meaning when we make comparisons across countries at different levels of income per capita, whereas absolute poverty measures would be too closely associated with national income per capita to allow for meaningful comparisons. Unfortunately, the durables and housing measures are not always available and consistently defined in all DHS surveys. In Round 1 of the DHS program, questions on these measures were asked only of households that happened to have in residence a woman of reproductive age. Hence for the 27 DHS surveys in this round, the data gathered are not representative of the general population of households. The situation changed in Rounds 2 and 3 of the DHS program, when such information began to be collected for all households. Even in these rounds, however, the availability of any particular item in a survey is not guaranteed. The problem is compounded by differences in the coding schemes across surveys and by the lack of variation of some items in particular countries or sectors. The durables and housing measures that can be used to construct an index will there- fore vary from one country to another, and within countries from urban to rural areas. Although the panel's intention had been to explore rural differences in relative living standards as well as urban, we found that the DHS-based indices could not always identify a lowest quartile of rural households. This is especially so in very poor countries, where many households lack most or all of the consumer durables items. Further research on this front is needed, but to pursue the issue of rural diversity in any greater depth would have led the panel away from its main . . . priories. Tables E-1 and E-2 (mentioned in Chapter 5) shed light on the likelihood of (relative) poverty and access to services for households containing a woman who is a recent migrant, by comparison with other households.

MEASURING RELATIVE POVERTY WITH DHS DATA 501 TABLE E-1 Urban Migrant-Nonmigrant Differences in Poverty and Access to Services, All Recent Migrants, by Region Access to Piped or DHS Surveys Relatively In-home Flush in Region Poor Water Toilet Electricity Migrant Proportion Less Nonmigrant Proportion North Africa 0.015 0.037 0.011 - 0.016 Sub-Saharan Africa 0.009 0.029 0.042 0.025 Southeast Asia 0.035 0.036 0.056 0.042 South, Central, West Asia 0.014 0.035 0.042 0.062 Latin America 0.054 - 0.018 0.001 - 0.015 TOTAL 0.022 0.021 0.031 0.023 Number of Surveys and Significance North Africa Surveys Significant Significant migrant disadvantage Sub-Saharan Africa Surveys Significant Significant migrant disadvantage Southeast Asia Surveys Significant Significant migrant disadvantage 1 0 0 South, Central, West Asia Surveys Significant Significant migrant disadvantage 2 0 Latin America Surveys Significant Significant migrant disadvantage TOTALS Surveys Significant Significant migrant disadvantage 6 3 2 o 39 8 5 36 13 4 4 o o o 33 16 3 2 11 9 10 5 4 4 18 10 10 78 25 19 o o 35 6 3 1 o 6 4 o 14 16 3 66 64 21 25 3 9 3 3 54 14 4 NOTE: Estimates and tests from probit models, adjusted for city size and woman's age.

502 CITIES TRANSFORMED TABLE E-2 Urban Migrant-Nonmigrant Differences in Poverty and Access to Services, by Type of Origin Area and Region Access to DHS Surveys in Region North Africa Surveys Disadvantage, city origin Disadvantage, town origin Disadvantage, rural origin Sub-Saharan Africa Surveys Disadvantage, city origin Disadvantage, town origin Disadvantage, rural origin Southeast Asia Surveys Disadvantage, city origin Disadvantage, town origin Disadvantage, rural origin South, Central, West Asia Surveys Disadvantage, city origin Disadvantage, town origin Disadvantage, rural origin Latin America Surveys Disadvantage, city origin Disadvantage, town origin Disadvantage, rural origin TOTALS Surveys Disadvantage, city origin Disadvantage, town origin Disadvantage, rural origin Relatively Poor Piped or In-home Water Flush Toilet Electricity 6 o o 5 3 o o o o o 33 32 29 31 2 o 9 4 4 O O 2 0 0 2 0 0 7 5 7 o o o 1 15 12 13 5 65 56 5 2 10 0 33 14 o o o o o 11 11 o o 54 45 o 20 15 3 o o o 3 o o o 7 o 4 o 2 NOTE: Estimates and tests from probit models, adjusted for city size and woman's age.

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Virtually all of the growth in the world’s population for the foreseeable future will take place in the cities and towns of the developing world. Over the next twenty years, most developing countries will for the first time become more urban than rural. The benefits from urbanization cannot be overlooked, but the speed and sheer scale of this transformation present many challenges. A new cast of policy makers is emerging to take up the many responsibilities of urban governance—as many national governments decentralize and devolve their functions, programs in poverty, health, education, and public services are increasingly being deposited in the hands of untested municipal and regional governments. Demographers have been surprisingly slow to devote attention to the implications of the urban transformation.

Drawing from a wide variety of data sources, many of them previously inaccessible, Cities Transformed explores the implications of various urban contexts for marriage, fertility, health, schooling, and children’s lives. It should be of interest to all involved in city-level research, policy, planning, and investment decisions.

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