Use of household food insecurity scales for assessing poverty in Bangladesh and Uganda

An important dimension of poverty is access to food. Household food security implies access to the food needed for a healthy and productive life. Lack of access to and/or impaired utilization of food contribute to household food insecurity. This study compares the usefulness of a standardized food insecurity scale for determining the food insecurity status of rural and urban households in Bangladesh and Uganda, and for predicting poverty status. The analysis uses data from the IRIS Composite Survey Household Questionnaire (2004), which consists of 1,587 households (approximately 800 households in each country). The coping mechanisms adopted in the presence of food shortages represent the building blocks for the development of the scale (7 items). In order to assess the suitability of the scale as an estimator of the households’ poverty status, the benchmark indicator “daily expenditures per capita” and its relation to the corresponding poverty line serves as the basis for evaluation for each country. The scale provides the means for classifying the households into 3 main groups: Non Food Insecure, Moderately Food Insecure, and Severely Food Insecure. The reliability of the scale is measured via the Cronbach’s Alpha statistic. In addition, the scale is used in regression analysis in order to predict per capita daily expenditures and the poverty incidence. The results show that food insecurity does not always reflect (income) poverty. However, the use of the scale as a predictor of poverty status produces rough estimates of poverty incidence that could be useful as background information. The differentiation of households according to their food security status may be valuable for focusing and developing improved food insecurity mitigation strategies.Food insecurity scale, poverty, Bangladesh, Uganda, Agricultural and Food Policy, Food Security and Poverty, I32, O11, Q18,

Use of household food insecurity scales for assessing poverty in Bangladesh and Uganda.

An important dimension of poverty is access to food. Household food security implies access to the food needed for a healthy and productive life. Lack of access to and/or impaired utilization of food contribute to household food insecurity. This study compares the utility of a standardized food security scale for determining the food insecurity status of rural and urban households in Bangladesh and Uganda and for predicting poverty status. The analysis uses data from the IRIS Composite Survey Household Questionnaire (2004), which consists of 1,587 households (approximately 800 households in each country). The coping mechanisms adopted in the presence of food shortage represent the building blocks for development of the scale (7 items). In order to assess the suitability of the scale as an estimator of the households poverty status, the benchmark indicator "daily expenditures per capita" and its relation to the corresponding poverty line serves as the basis for evaluation on each country. The scale provides the means for classifying the households into 3 main groups: Non Food Insecure, Moderately Food Insecure, and Severely Food Insecure. The reliability of the scale is measured via the Cronbach's Alpha statistic. In addition, the scale is used in regression analysis in order to predict per capita daily expenditures and the poverty incidence. The results show that food insecurity does not always reflect (income) poverty. However, the use of the scale as predictor of poverty status produces rough estimates of poverty incidence that could be useful as background information. The differentiation of households according to their food security status may be valuable for focusing and developing improved food insecurity mitigation strategies.Food Consumption/Nutrition/Food Safety, Food Security and Poverty,

How Best to Target the Poor? An operational targeting of the poor using indicator-based proxy means tests

This paper seeks to answer an operational development question: how best to target the poor? In their endeavor, policy makers, program managers, and development practitioners face the daily challenge of targeting policies, projects, and services at the poorer strata of the population. This is also the case for microfinance institutions that seek to estimate the poverty outreach among their clients. This paper addresses these challenges. Using household survey data from Uganda, we estimate four alternative models for improving the identification of the poor in the country. Furthermore, we analyze the model sensitivity to different poverty lines and test their validity using bootstrapped simulation methods. While there is bound to be some errors, no indicator being perfectly correlated with poverty, the models developed achieve fairly accurate out-of-sample predictions of absolute poverty. Furthermore, findings suggest that the estimation method is not relevant for developing a fairly accurate model for targeting the poor. The models developed are potentially useful tools for the development community in Uganda. This research can also be applied in other developing countries.Uganda, poverty assessment, targeting, proxy means tests, validations, bootstrap, Food Security and Poverty,

Proxy Means Tests for Targeting the Poorest Households -- Applications to Uganda

The motivation for this research stems from increasing interest showed for the issue of targeting. The paper explores the use of proxy means tests to identify the poorest households in Uganda. The set of indicators used in our model includes variables usually available in Living Standard Measurement Surveys (LSMS). Previous researches seeking to develop proxy means tests for poverty most often use Ordinary Least Squares (OLS) as regression method. In addition to the OLS, the paper explores the use of Linear Probability Model, Probit, and Quantile regressions for correctly predicting the household poverty status. A further innovation of this research compared to the existing literature is the use of out-of sample validation tests to assess the predictive power and hence the robustness of the identified set of regressors. Moreover, the confidence intervals are approximated out-of sample using the bootstrap algorithm and the percentile method. The main conclusion that emerges from this research is that measures of absolute poverty estimated with Quantile regression can yield fairly accurate in-sample predictions of absolute poverty in a nationally representative sample. On the other hand, the OLS and Probit perform better out-of sample. Besides it complexity, the Quantile regression is less robust. The Probit may be the best alternative for optimizing both accuracy and robustness of a poverty assessment tool. The best regressor sets and their derived weights can be used in a range of applications, including the identification of the poorest households in the country, the assessment of poverty outreach of Microfinance Institutions (MFIs), and the measurement of poverty and welfare impacts of agricultural development projects. To confirm or reject the conclusions in this paper, future research using datasets from other countries is needed.Uganda, poverty assessment, targeting, proxy means test, out-of-sample test, bootstrap, Consumer/Household Economics, Food Security and Poverty,

Developing Poverty Assessment Tools Based on Principal Component Analysis: Results from Bangladesh, Kazakhstan, Uganda, and Peru

Developing accurate, yet operational poverty assessment tools to target the poorest households remains a challenge for applied policy research. This paper aims to develop poverty assessment tools for four countries: Bangladesh, Peru, Uganda, and Kazakhstan. The research applies the Principal Component Analysis (PCA) to seek the best set of variables that predict the household poverty status using easily measurable socio-economic indicators. Out of sample validations tests are performed to assess the prediction power of a tool. Finally, the PCA results are compared with those obtained from regressions models. In-sample estimation results suggest that the Quantile regression technique is the first best method in all four countries, except Kazakhstan. The PCA method is the second best technique for two of the countries. In comparison with regression techniques, PCA models accurately predict a large percentage of households. With regard to out-of sample validations, there is no clear trend; neither the PCA method nor the Quantile regression consistently yields the most robust results. The results highlight the need to assess the out-of-sample performance and thereby the robustness of a poverty assessment tool in estimating the poverty status of a new sample. We conclude that measures of relative poverty estimated with PCA method can yield fairly accurate, but not so robust predictions of absolute poverty as compared to more complex regression models.poverty assessment, targeting, principal component analysis, Bangladesh, Peru, Kazakhstan, Uganda, Food Security and Poverty, H5, Q14, I3,

Macro Events and Micro Responses: Experiences from Bolivia and Guatemala

For Bolivia and Guatemala, the2007–08 food price crisis contributed to a slowdown in the economy and increased unemployment. For the poorer population the crisis meant an overstretching of the household finances and increased difficulties for ensuring household food security. Since 2010, food price increases have continued in both countries. Bolivian and Guatemalan households have coped and adapted to their current economic stress through a diverse set of mechanisms affecting not only family structures, dynamics and productivity, but also their future economic prospects. At an aggregate level, the outcomes are substantial. The reported and measured changes in dietary quality and intake have certainly had an impact on the population's nutritional status and general health. Longer?term effects at the national level will likely follow in the coming years. In both countries, the national governments need to strengthen their efforts for facilitating the access to quality employment, social protection, and to affordable and nutritious foods

Use of household food insecurity scales for assessing poverty in Bangladesh and Uganda

An important dimension of poverty is access to food. Household food security implies access to the food needed for a healthy and productive life. Lack of access to and/or impaired utilization of food contribute to household food insecurity. This study compares the usefulness of a standardized food insecurity scale for determining the food insecurity status of rural and urban households in Bangladesh and Uganda, and for predicting poverty status. The analysis uses data from the IRIS Composite Survey Household Questionnaire (2004), which consists of 1,587 households (approximately 800 households in each country). The coping mechanisms adopted in the presence of food shortages represent the building blocks for the development of the scale (7 items). In order to assess the suitability of the scale as an estimator of the households’ poverty status, the benchmark indicator “daily expenditures per capita” and its relation to the corresponding poverty line serves as the basis for evaluation for each country. The scale provides the means for classifying the households into 3 main groups: Non Food Insecure, Moderately Food Insecure, and Severely Food Insecure. The reliability of the scale is measured via the Cronbach’s Alpha statistic. In addition, the scale is used in regression analysis in order to predict per capita daily expenditures and the poverty incidence. The results show that food insecurity does not always reflect (income) poverty. However, the use of the scale as a predictor of poverty status produces rough estimates of poverty incidence that could be useful as background information. The differentiation of households according to their food security status may be valuable for focusing and developing improved food insecurity mitigation strategies

Use of household food insecurity scales for assessing poverty in Bangladesh and Uganda.

An important dimension of poverty is access to food. Household food security implies access to the food needed for a healthy and productive life. Lack of access to and/or impaired utilization of food contribute to household food insecurity. This study compares the utility of a standardized food security scale for determining the food insecurity status of rural and urban households in Bangladesh and Uganda and for predicting poverty status. The analysis uses data from the IRIS Composite Survey Household Questionnaire (2004), which consists of 1,587 households (approximately 800 households in each country). The coping mechanisms adopted in the presence of food shortage represent the building blocks for development of the scale (7 items). In order to assess the suitability of the scale as an estimator of the households poverty status, the benchmark indicator "daily expenditures per capita" and its relation to the corresponding poverty line serves as the basis for evaluation on each country. The scale provides the means for classifying the households into 3 main groups: Non Food Insecure, Moderately Food Insecure, and Severely Food Insecure. The reliability of the scale is measured via the Cronbach's Alpha statistic. In addition, the scale is used in regression analysis in order to predict per capita daily expenditures and the poverty incidence. The results show that food insecurity does not always reflect (income) poverty. However, the use of the scale as predictor of poverty status produces rough estimates of poverty incidence that could be useful as background information. The differentiation of households according to their food security status may be valuable for focusing and developing improved food insecurity mitigation strategies

Insights from the Guatemalan food system: an application of exploratory spatial data analysis techniques for food security analysis

The achievement of food security for all remains one of the main development objectives worldwide. Most commonly, non-spatial models are developed for either explaining the underlying determinants of food insecurity and undernourishment or for predicting their changes, so as for identifying vulnerable groups for targeting support. Such approach ignores geographic determinants and the spatial dependency of food security and nutrition outcomes. This paper seeks to address this issue. We use nationally representative data from Guatemala, which faces high and rising rates of undernourishment and child stunting in spite of the efforts engaged on their reduction. Through exploratory spatial data analysis and overlay techniques, some elements embedded in the food system are explored and integrated with the aim of providing complementary information for the analysis of food security. The preliminary results show that these elements are spatially related and that they display geographic trends and spatial dependency. The consideration of these patterns in research and modelling applications can improve the understanding of the related information and its use for the development of food security enhancing strategies. We conclude with recommendations on methodology so as to include spatially explicit analysis in causal or predictive models of food security

How Best to Target the Poor? An operational targeting of the poor using indicator-based proxy means tests

This paper seeks to answer an operational development question: how best to target the poor? In their endeavor, policy makers, program managers, and development practitioners face the daily challenge of targeting policies, projects, and services at the poorer strata of the population. This is also the case for microfinance institutions that seek to estimate the poverty outreach among their clients. This paper addresses these challenges. Using household survey data from Uganda, we estimate four alternative models for improving the identification of the poor in the country. Furthermore, we analyze the model sensitivity to different poverty lines and test their validity using bootstrapped simulation methods. While there is bound to be some errors, no indicator being perfectly correlated with poverty, the models developed achieve fairly accurate out-of-sample predictions of absolute poverty. Furthermore, findings suggest that the estimation method is not relevant for developing a fairly accurate model for targeting the poor. The models developed are potentially useful tools for the development community in Uganda. This research can also be applied in other developing countries