Predicting poverty trends by survey-to-survey imputation: The challenge of comparability

Poverty in low-income countries is usually measured using large and infrequent household consumption surveys. The challenge is to find methods to measure poverty rates more frequently. This study validates a survey-to-survey imputation method, based on a statistical model utilizing consumption surveys and light surveys to measure changes in poverty rates over time. A decade of poverty predictions and regular poverty estimates in Malawi provides a unique case study. The analysis suggests that this modelling approach works within the same context given that households’ demographic composition is included in the model. Predicting poverty using different surveys is challenging because of different aspects of comparability. A new way to account for seasonal coverage strengthens the model when imputing for surveys covering different seasons. It is important for national statistics offices and supporting agencies to prioritize maintaining consistency in the way data are collected in surveys to provide comparable trends over time.publishedVersio

A Statistical Model for Simple, Fast and Reliable Measurement of Poverty

The primus inter pares of the UN Millennium Development Goals is to reduce poverty. The only internationally accepted method of estimating poverty requires a measurement of total consumption based on a time and resource demanding household budget or integrated survey over 12 months. Rather than measuring poverty only, say every 5th year, a model is presented to predict poverty based upon a small set of household variables to be collected yearly between two 12 months household surveys. Information obtained from the light surveys may then be used to predict poverty rates. The key question is whether the inaccuracy in these predictions is acceptable. The standard errors presented are lower than the sampling errors to the poverty estimates based on the 12 months household surveys. Predictions based on this sample also indicate that the problem of misspecifications of models is not large. It is recommended to test these models at the country level and if the test results are comparable to those here, apply the approach presented.A revised version of DP 41

The predictive ability of poverty models : empirical evidence from Uganda

Abstract: This paper examines the performance of a particular method for predicting poverty. The method is a supplement to the approach of measuring poverty through a fully-fledged household expenditure survey. As most developing countries cannot justify the expenses of frequent household expenditure surveys, low cost methods are of interest, and such models have been developed and used. The basic idea is a model for predicting the proportion of poor households in a population based on estimates from a total consumption regression relation, using data from a household expenditure survey. As a result, the model links the proportion of poor households to the explanatory variables of the consumption relation. These explanatory variables are fast to collect and are easy to measure. Information on the explanatory variables may be collected through annual light surveys. Several applications have shown that this information, together with the poverty model, can produce poverty estimates with confidence intervals of a similar magnitude as the poverty estimates from the household expenditure surveys. There is, however, limited evidence for how well the methods perform in predicting poverty from other surveys. A series of seven household expenditure surveys conducted in Uganda in the period 1993-2006 are available, allowing us to test the predictive ability of the models. We have tested the poverty models by using data from one survey to predict the proportion of poor households in other surveys, and vice versa. All the models predict similar poverty trends, whereas the respective levels are predicted differently. Although in most cases the predictions are precise, sometimes they differ significantly from the poverty level estimated from the survey directly. A long time span between surveys may explain some of these cases, as do large and sudden changes in poverty. Keywords: Poverty prediction, Poverty model, Money metric poverty, Uganda, Household Survey JEL classification: C31, C42, C81, D12, D31, I3

A Statistical Model for Simple, Fast and Reliable Measurement of Poverty. A revised version of DP 415

Abstract: The primus inter pares of the UN-approved Millennium Development Goals is to reduce poverty. The only internationally accepted method of estimating poverty requires a measurement of total consumption based on a time-consuming and resource-demanding measure of household expenditure in an integrated survey over 12 months. Rather than measuring poverty, say, only every fifth year, a model is presented to predict poverty based on a small set of household variables to be collected annually between two 12-monthly household surveys. Information obtained from these "light" surveys might then be used to predict poverty rates. The key question is whether the inaccuracy in these predictions is acceptable. It is recommended that these models be tested at a country level and if the test results are similar to those found here, that this approach be adopted

A Statistical Model for Simple, Fast and Reliable Measurement of Poverty. A revised version of DP 415

Abstract: The primus inter pares of the UN-approved Millennium Development Goals is to reduce poverty. The only internationally accepted method of estimating poverty requires a measurement of total consumption based on a time-consuming and resource-demanding measure of household expenditure in an integrated survey over 12 months. Rather than measuring poverty, say, only every fifth year, a model is presented to predict poverty based on a small set of household variables to be collected annually between two 12-monthly household surveys. Information obtained from these "light" surveys might then be used to predict poverty rates. The key question is whether the inaccuracy in these predictions is acceptable. It is recommended that these models be tested at a country level and if the test results are similar to those found here, that this approach be adopted

The predictive ability of poverty models : empirical evidence from Uganda

Abstract: This paper examines the performance of a particular method for predicting poverty. The method is a supplement to the approach of measuring poverty through a fully-fledged household expenditure survey. As most developing countries cannot justify the expenses of frequent household expenditure surveys, low cost methods are of interest, and such models have been developed and used. The basic idea is a model for predicting the proportion of poor households in a population based on estimates from a total consumption regression relation, using data from a household expenditure survey. As a result, the model links the proportion of poor households to the explanatory variables of the consumption relation. These explanatory variables are fast to collect and are easy to measure. Information on the explanatory variables may be collected through annual light surveys. Several applications have shown that this information, together with the poverty model, can produce poverty estimates with confidence intervals of a similar magnitude as the poverty estimates from the household expenditure surveys. There is, however, limited evidence for how well the methods perform in predicting poverty from other surveys. A series of seven household expenditure surveys conducted in Uganda in the period 1993-2006 are available, allowing us to test the predictive ability of the models. We have tested the poverty models by using data from one survey to predict the proportion of poor households in other surveys, and vice versa. All the models predict similar poverty trends, whereas the respective levels are predicted differently. Although in most cases the predictions are precise, sometimes they differ significantly from the poverty level estimated from the survey directly. A long time span between surveys may explain some of these cases, as do large and sudden changes in poverty. Keywords: Poverty prediction, Poverty model, Money metric poverty, Uganda, Household Survey JEL classification: C31, C42, C81, D12, D31, I32This work was supported by the Norwegian Agency for Development Cooperation (NORAD)

A Statistical Model for Simple, Fast and Reliable Measurement of Poverty. A revised version of DP 415

The primus inter pares of the UN Millennium Development Goals is to reduce poverty. The only internationally accepted method of estimating poverty requires a measurement of total consumption based on a time and resource demanding household budget or integrated survey over 12 months. Rather than measuring poverty only, say every 5th year, a model is presented to predict poverty based upon a small set of household variables to be collected yearly between two 12 months household surveys. Information obtained from the light surveys may then be used to predict poverty rates. The key question is whether the inaccuracy in these predictions is acceptable. The standard errors presented are lower than the sampling errors to the poverty estimates based on the 12 months household surveys. Predictions based on this sample also indicate that the problem of misspecifications of models is not large. It is recommended to test these models at the country level and if the test results are comparable to those here, apply the approach presented.Stochastic model; Poverty measurement; Money metric poverty; Survey methods

The predictive ability of poverty models. Empirical Evidence from Uganda

This paper examines the performance of a particular method for predicting poverty. The method is a supplement to the approach of measuring poverty through a fully-fledged household expenditure survey. As most developing countries cannot justify the expenses of frequent household expenditure surveys, low cost methods are of interest, and such models have been developed and used. The basic idea is a model for predicting the proportion of poor households in a population based on estimates from a total consumption regression relation, using data from a household expenditure survey. As a result, the model links the proportion of poor households to the explanatory variables of the consumption relation. These explanatory variables are fast to collect and are easy to measure. Information on the explanatory variables may be collected through annual light surveys. Several applications have shown that this information, together with the poverty model, can produce poverty estimates with confidence intervals of a similar magnitude as the poverty estimates from the household expenditure surveys. There is, however, limited evidence for how well the methods perform in predicting poverty from other surveys. A series of seven household expenditure surveys conducted in Uganda in the period 1993-2006 are available, allowing us to test the predictive ability of the models. We have tested the poverty models by using data from one survey to predict the proportion of poor households in other surveys, and vice versa. All the models predict similar poverty trends, whereas the respective levels are predicted differently. Although in most cases the predictions are precise, sometimes they differ significantly from the poverty level estimated from the survey directly. A long time span between surveys may explain some of these cases, as do large and sudden changes in poverty.Poverty prediction; Poverty model; Money metric poverty; Uganda; Household Survey

A practical approach for modelbased poverty prediction

The objective of this report is to provide practical guidance for producing poverty estimates based on ”light” household surveys. Mathiassen (2005) outlines the theoretical model. A household budget survey is used to estimate a statistical consumption model where a small set of variables are linked to consumption and poverty. These indicators are then collected through light surveys in years where no household budget survey is made available. By combining the light survey indicators and the parameters from the consumption model, poverty rates and their standard errors can be predicted. The report takes the reader through each step of the procedure, from preparing and utilizing the survey datasets, selecting good indicators and predicting the poverty rates, to evaluating the predictions. The SPSS syntax generated by the INE workshops is available at: www.ssb.no/en/int