© 2003 by default!slide 1 poverty mapping celia m. reyes introduction to poverty analysis nai,...

© 2003 By Default!Slide 1

Poverty Mapping Poverty Mapping

Celia M. ReyesCelia M. Reyes

Introduction to Poverty AnalysisIntroduction to Poverty AnalysisNAI, Beijing, ChinaNAI, Beijing, China

Nov. 1-8, 2005Nov. 1-8, 2005


Poverty MappingPoverty Mapping

Poverty analysis is often based on national level Poverty analysis is often based on national level indicators that are compared over time or across indicators that are compared over time or across countries.countries.

The broad trends that can be identified using The broad trends that can be identified using aggregate information are useful for evaluating and aggregate information are useful for evaluating and monitoring the overall performance of a country. monitoring the overall performance of a country.

For many policy and research applications, however, For many policy and research applications, however, the information that can be extracted from aggregate the information that can be extracted from aggregate indicators may not be sufficient, since they hide indicators may not be sufficient, since they hide significant local variation in living conditions within significant local variation in living conditions within countries.countries.



For example, poverty within a region can vary For example, poverty within a region can vary across districts. This makes small-area across districts. This makes small-area estimates of poverty very appealing. estimates of poverty very appealing.

However, often we are unable to compute However, often we are unable to compute poverty estimates for small areas like districts. poverty estimates for small areas like districts. Instead, we usually have poverty estimates for Instead, we usually have poverty estimates for regions or entire countries only.regions or entire countries only.



The main reason that poverty measures are The main reason that poverty measures are computed for large areas and not usually computed for large areas and not usually available for small areas is due to data available for small areas is due to data availability. availability.



There are two main types of welfare related information There are two main types of welfare related information sources available to policy-makers. sources available to policy-makers.

– Household surveys often include a detailed income Household surveys often include a detailed income and/or consumption expenditure module (such as and/or consumption expenditure module (such as the CSES 1999). However, due to relatively small the CSES 1999). However, due to relatively small sample size, the collected information is usually only sample size, the collected information is usually only representative for broad regions of the country. For representative for broad regions of the country. For example, with the CSES 1999 we can compute example, with the CSES 1999 we can compute poverty estimates for Phnom Penh, other urban poverty estimates for Phnom Penh, other urban areas and rural areas but not for every district.areas and rural areas but not for every district.



Census data (and sometimes large household Census data (and sometimes large household sample surveys) are available for all households sample surveys) are available for all households (or very large samples of households) and can (or very large samples of households) and can provide reliable estimates at highly provide reliable estimates at highly disaggregated levels such as small disaggregated levels such as small municipalities, towns, and villages. But municipalities, towns, and villages. But censuses do not contain the necessary income censuses do not contain the necessary income or consumption information to yield reliable or consumption information to yield reliable indicators of the level and distribution of welfare indicators of the level and distribution of welfare such as poverty rates or inequality measures.such as poverty rates or inequality measures.



More recently, some researchers are developing More recently, some researchers are developing statistical techniques to merge information from these statistical techniques to merge information from these two types of data sources (detailed household surveys two types of data sources (detailed household surveys like the CSES 1999, and census data) so that “poverty like the CSES 1999, and census data) so that “poverty maps” can be constructed. These detailed poverty maps” can be constructed. These detailed poverty maps will be appealing for several reasons.maps will be appealing for several reasons.

– Poverty maps capture the heterogeneity of poverty Poverty maps capture the heterogeneity of poverty

within a country, i.e., areas that are better off and within a country, i.e., areas that are better off and those that are worse off will be more clearly defined. those that are worse off will be more clearly defined. Sometime regions that have less aggregate poverty Sometime regions that have less aggregate poverty may have substantially pockets of poverty which is may have substantially pockets of poverty which is lost in the aggregate poverty statistics.lost in the aggregate poverty statistics.



– Poverty maps can improve targeting of interventions. Poverty maps can improve targeting of interventions. In designing poverty alleviation projects and In designing poverty alleviation projects and allocating subsidies, resources will be used more allocating subsidies, resources will be used more effectively, if the most needy groups can be better effectively, if the most needy groups can be better targeted. This reduces the leakage of transfer targeted. This reduces the leakage of transfer payments to non-poor persons, and it reduces the payments to non-poor persons, and it reduces the risk that poor persons will be missed by a program. risk that poor persons will be missed by a program.

Many countries use geographic targeting schemes Many countries use geographic targeting schemes based on relatively unreliable welfare indicators such based on relatively unreliable welfare indicators such as the “basic needs indicators”, which are ad hoc as the “basic needs indicators”, which are ad hoc combinations of various indicators into one index.combinations of various indicators into one index.



– Poverty maps can help governments to Poverty maps can help governments to articulate their policy objectives. Basing articulate their policy objectives. Basing allocation decisions on observed geographic allocation decisions on observed geographic poverty data rather than subjective rankings poverty data rather than subjective rankings of regions increases the transparency of of regions increases the transparency of government decision making. Such data can government decision making. Such data can thus help limit the influence of special thus help limit the influence of special interests in allocation decisions. There is a interests in allocation decisions. There is a related role for well-defined poverty maps to related role for well-defined poverty maps to lend credibility to government and donor lend credibility to government and donor decision making.decision making.



This new method combines the respective This new method combines the respective strengths of survey and census data to estimate strengths of survey and census data to estimate welfare indicators for small administrative or welfare indicators for small administrative or statistical areas. statistical areas.

The approach uses household survey data to The approach uses household survey data to estimate a model of per capita consumption estimate a model of per capita consumption expenditure (or any other household or expenditure (or any other household or individual-level indicator of well-being) as a individual-level indicator of well-being) as a function of variables that are common to both function of variables that are common to both the household survey and the census.the household survey and the census.



The resulting parameter estimates from this The resulting parameter estimates from this first-stage regression are then used to predict first-stage regression are then used to predict per capita expenditures for each household in per capita expenditures for each household in the census. the census.

The estimated household level measures of The estimated household level measures of poverty and inequality are then aggregated for poverty and inequality are then aggregated for small areas, such as districts, villages, or even small areas, such as districts, villages, or even neighborhoods.neighborhoods.



Example:Example:

Poverty mapping done by World Food Poverty mapping done by World Food Programme in Cambodia to identify “poor” Programme in Cambodia to identify “poor” communescommunes


Multiple regression on Socioeconomic Multiple regression on Socioeconomic Survey (3000 households)Survey (3000 households)

Consumption Expenditure as explained by:Consumption Expenditure as explained by:

– household sizehousehold size– age of household headage of household head– education leveleducation level– proportion of elderlyproportion of elderly– widowed head of householdwidowed head of household– occupation of head of householdoccupation of head of household– electricity sourceelectricity source– water sourcewater source– urban householdurban household– distance to main roaddistance to main road


Coefficients plugged into Coefficients plugged into Census 98 (2.1 million households)Census 98 (2.1 million households)

Same indicators from census Same indicators from census

– household sizehousehold size– age of household headage of household head– education leveleducation level– proportion of elderlyproportion of elderly– widowed head of householdwidowed head of household– occupation of head of householdoccupation of head of household– electricity sourceelectricity source– water sourcewater source– urban householdurban household– distance to main roaddistance to main road


To derive predicted consumption To derive predicted consumption expenditure for each household.expenditure for each household.

The estimated household-level measures of The estimated household-level measures of poverty and inequality may then be aggregated for poverty and inequality may then be aggregated for small areas, such as districts, villages, or even small areas, such as districts, villages, or even neighborhoods.neighborhoods.


Poverty Mapping In EcuadorPoverty Mapping In Ecuador

Ecuador has about 400 cantons and Ecuador has about 400 cantons and over 1,000 parishes (over 1,000 parishes (parroquiasparroquias). The ). The purpose of the exercise was to get purpose of the exercise was to get poverty rates for each parish in the poverty rates for each parish in the country. In principle this would allow country. In principle this would allow a relatively finely-tuned targeting of a relatively finely-tuned targeting of the poorer parts of the country.the poorer parts of the country.

Ecuador has about 400 cantons and Ecuador has about 400 cantons and over 1,000 parishes (over 1,000 parishes (parroquiasparroquias). The ). The purpose of the exercise was to get purpose of the exercise was to get poverty rates for each parish in the poverty rates for each parish in the country. In principle this would allow country. In principle this would allow a relatively finely-tuned targeting of a relatively finely-tuned targeting of the poorer parts of the country.the poorer parts of the country.

[1] Jesko Hentschel, Jean Olson Lanjouw, Peter Lanjouw and Javier Poggi, “Combining Census and Survey Data to Trade the Spatial Dimensions of Poverty: A Case Study of Ecuador,” The World Bank Economic Review, 14(1): 147-166.


Box: Poverty Mapping In EcuadorBox: Poverty Mapping In Ecuador

The 1994 The 1994 Encuesta sobre las Condiciones de Encuesta sobre las Condiciones de VidaVida (a living standards measurement survey) (a living standards measurement survey) obtained 4,391 usable responses from obtained 4,391 usable responses from households, which was clearly inadequate for households, which was clearly inadequate for measuring poverty at the level of each parish measuring poverty at the level of each parish or even canton. However, the 1990 census or even canton. However, the 1990 census counted about 2 million households, and counted about 2 million households, and collected information on a range of collected information on a range of demographic variables such as household demographic variables such as household size, age, education, occupation, housing size, age, education, occupation, housing quality, language and locationquality, language and location

The 1994 The 1994 Encuesta sobre las Condiciones de Encuesta sobre las Condiciones de VidaVida (a living standards measurement survey) (a living standards measurement survey) obtained 4,391 usable responses from obtained 4,391 usable responses from households, which was clearly inadequate for households, which was clearly inadequate for measuring poverty at the level of each parish measuring poverty at the level of each parish or even canton. However, the 1990 census or even canton. However, the 1990 census counted about 2 million households, and counted about 2 million households, and collected information on a range of collected information on a range of demographic variables such as household demographic variables such as household size, age, education, occupation, housing size, age, education, occupation, housing quality, language and locationquality, language and location



The research team used the data from the The research team used the data from the EncuestaEncuesta to estimate regressions of the form to estimate regressions of the formLn yLn yii = X’ = X’ii + + iifor each region of the country. The dependent for each region of the country. The dependent variable was income per capita, and the variable was income per capita, and the independent variables were comparable to independent variables were comparable to ones that were also available from the census. ones that were also available from the census. With RWith R22 values of about 0.5, the fits were values of about 0.5, the fits were adequate. Then data from the census for each adequate. Then data from the census for each household were used in the equation in order household were used in the equation in order to predict income for each household, and to predict income for each household, and poverty rates computed for each parish.poverty rates computed for each parish.

The research team used the data from the The research team used the data from the EncuestaEncuesta to estimate regressions of the form to estimate regressions of the formLn yLn yii = X’ = X’ii + + iifor each region of the country. The dependent for each region of the country. The dependent variable was income per capita, and the variable was income per capita, and the independent variables were comparable to independent variables were comparable to ones that were also available from the census. ones that were also available from the census. With RWith R22 values of about 0.5, the fits were values of about 0.5, the fits were adequate. Then data from the census for each adequate. Then data from the census for each household were used in the equation in order household were used in the equation in order to predict income for each household, and to predict income for each household, and poverty rates computed for each parish.poverty rates computed for each parish.



The main finding what that while the poverty The main finding what that while the poverty rates for each of the broad regions are robust, rates for each of the broad regions are robust, the same is not true of the poverty rates by the same is not true of the poverty rates by parish, where the standard errors of the parish, where the standard errors of the estimates are relatively high. In an important estimates are relatively high. In an important test of the robustness of poverty mapping, the test of the robustness of poverty mapping, the authors re-estimated the income equation using authors re-estimated the income equation using part of the part of the EncuestaEncuesta sample, predicted income sample, predicted income for all households that were not included in the for all households that were not included in the estimation (the validation sample), and then estimation (the validation sample), and then compared the predicted income with actual compared the predicted income with actual income. These out-of-sample predictions proved income. These out-of-sample predictions proved to be quite close to the actual values.to be quite close to the actual values.

The main finding what that while the poverty The main finding what that while the poverty rates for each of the broad regions are robust, rates for each of the broad regions are robust, the same is not true of the poverty rates by the same is not true of the poverty rates by parish, where the standard errors of the parish, where the standard errors of the estimates are relatively high. In an important estimates are relatively high. In an important test of the robustness of poverty mapping, the test of the robustness of poverty mapping, the authors re-estimated the income equation using authors re-estimated the income equation using part of the part of the EncuestaEncuesta sample, predicted income sample, predicted income for all households that were not included in the for all households that were not included in the estimation (the validation sample), and then estimation (the validation sample), and then compared the predicted income with actual compared the predicted income with actual income. These out-of-sample predictions proved income. These out-of-sample predictions proved to be quite close to the actual values.to be quite close to the actual values.



In another test, Hentschel at al. simulated In another test, Hentschel at al. simulated the effect of providing subsidies to the the effect of providing subsidies to the poorest parishes, and then asked what poorest parishes, and then asked what proportion of these benefits went to each proportion of these benefits went to each income quintile. The results are reproduced income quintile. The results are reproduced here, and show that 77% of the subsidies here, and show that 77% of the subsidies would have gone to households in the lowest would have gone to households in the lowest two quintiles – a respectably high level of two quintiles – a respectably high level of successful targeting.successful targeting.

In another test, Hentschel at al. simulated In another test, Hentschel at al. simulated the effect of providing subsidies to the the effect of providing subsidies to the poorest parishes, and then asked what poorest parishes, and then asked what proportion of these benefits went to each proportion of these benefits went to each income quintile. The results are reproduced income quintile. The results are reproduced here, and show that 77% of the subsidies here, and show that 77% of the subsidies would have gone to households in the lowest would have gone to households in the lowest two quintiles – a respectably high level of two quintiles – a respectably high level of successful targeting.successful targeting.



Lowest Low-mid Middle Mid-upr Upper% of beneficiary households

51 27 13.1 8 0.9

Distribution of beneficiary households with geographic targeting at the regional level

Income quintile

Source: Hentschel et al.



Their conclusion? “The most useful Their conclusion? “The most useful practical application of this methodology is practical application of this methodology is probably in making comparisons with regional probably in making comparisons with regional patterns of other indicators of well-being, patterns of other indicators of well-being, opportunity, and access” (Hentschel et al., opportunity, and access” (Hentschel et al., p.162). Thus, for instance, one could map p.162). Thus, for instance, one could map health indicators against estimated income at health indicators against estimated income at the regional level, and find a close and useful the regional level, and find a close and useful link. link.

Their conclusion? “The most useful Their conclusion? “The most useful practical application of this methodology is practical application of this methodology is probably in making comparisons with regional probably in making comparisons with regional patterns of other indicators of well-being, patterns of other indicators of well-being, opportunity, and access” (Hentschel et al., opportunity, and access” (Hentschel et al., p.162). Thus, for instance, one could map p.162). Thus, for instance, one could map health indicators against estimated income at health indicators against estimated income at the regional level, and find a close and useful the regional level, and find a close and useful link. link.


Use of Geographical Information System Use of Geographical Information System (GIS) (GIS)

To highlight spatial differences in indicatorsTo highlight spatial differences in indicators


Community Based Monitoring Systems Community Based Monitoring Systems (CBMS)(CBMS)

Alternative to methodology described Alternative to methodology described previously.previously.

Features of CBMS:Features of CBMS:– Data collected by the communitiesData collected by the communities– Covers a set of socio-economic indicatorsCovers a set of socio-economic indicators– Data used for planning and program Data used for planning and program

implementationimplementation

© 2003 by default!slide 1 poverty mapping celia m. reyes introduction to poverty analysis nai,...

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