![]() ![]() Thus collection of expenditure or income data using simple questionnaires tends to underestimate the actual expenditure or income. It is found that the expenditure data collected in the simple way are considerably lower than expenditure data collected using detailed questionnaires. This paper examines the quality of expenditure data collected in this simple way. This method can raise questions about the quality of the collected data on income. Then, the poverty rate of a commune is simply the ratio of the defined poor households in that commune. A household is identified as a poor one if their income per capita is below the poverty line. To estimate the poverty rate at the commune level, the Ministry of Labor, Invalids and Social Affairs firstly collects information on households’ income per capita, and compares their income with a defined poverty line. This research can also be applied in other developing countries. ![]() The models developed are potentially useful tools for the development community in Uganda. Furthermore, findings suggest that the estimation method is not relevant for developing a fairly accurate model for targeting the poor. 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, we analyze the model sensitivity to different poverty lines and test their validity using bootstrapped simulation methods. Using household survey data from Uganda, we estimate four alternative models for improving the identification of the poor in the country. This is also the case for microfinance institutions that seek to estimate the poverty outreach among their clients. 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. ![]() Re-weighting PMT indicators and increasing training and communication about qualification procedures could improve allocation of limited funds, though accurate targeting may continue to be challenging in contexts of low state capacity. Poor program awareness and uneven adherence to official eligibility-determination procedures among staff likely affected targeting. ![]() Re-weighting PMT indicators on electricity access, land ownership, and livestock ownership, and assigning weights to home ownership, households with elderly/disabled members, and household-head education levels could significantly improve targeting accuracy. HEF scores for PMT held little explanatory power for household income: 93% of individuals meeting the HEF eligibility criteria did not receive benefits post-hospitalization, while 23% of ineligible individuals received program support. We found large targeting errors (86% of households in the bottom consumption quartile would be excluded and 15% of households in the top consumption quartile deemed eligible). We modeled (linear regression) predictors of household consumption to improve PMT accuracy. Focus groups/interviews were conducted to understand administrative factors that influence targeting. We assessed receipt of benefits post-hospitalization against HEF eligibility rules and household income. We analyzed inclusion/exclusion errors by comparing household eligibility under the PMT used for HEF with household consumption (the gold standard proxy for income in LMICs). We assessed the PMT approach used in Myanmar's Hospital Equity Fund (HEF). Proxy means tests (PMT) for income are typically employed to identify eligible beneficiaries for subsidized services but often result in significant mistargeting of benefits. Many low- and middle-income countries (LMICs) provide subsidized access to health services for the poor. ![]()
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