To Target or Not to Target? The cost efficiency of indicator-based targeting

This paper assesses the cost efficiency of indicator-based targeting. Using household survey data from Malawi, we examine whether an indicator-based targeting of the poor is more target- and cost-efficient than the currently used mechanisms for targeting agricultural subsidy programs in the country. There is compelling evidence in favor of targeting Malawi’s poor based on the newly developed system. An indicator-based targeting system appears to be more target- and cost-efficient than the 2000/01 Starter Pack and the 2006/07 Agricultural Input Subsidy Program (AISP). While the Starter Pack and the AISP transferred about 50% of total transfer, under an indicator-based system, about 73% of transfers are delivered to the poor. Likewise, under an indicator-based system, the costs of leakage are cut down by more than 50% compared to Starter Pack and AISP. This work is prospectively relevant for Malawi as its policy makers reflect on improving the efficiency of the country’s pro-poor development programs. Likewise, the research can be applied in other countries with similar targeting problems.Malawi, poverty targeting, validation tests, cost efficiency, development policy, Agricultural and Food Policy, Community/Rural/Urban Development, Food Security and Poverty, Political Economy, Research Methods/ Statistical Methods, C01, C13, I32,

Targeting the poor and smallholder farmers: empirical evidence from Malawi

This paper develops low cost, reasonably accurate, and simple models for improving the targeting efficiency of development policies in Malawi. Using a stepwise logistic regression (weighted) along with other techniques applied in credit scoring, the research identifies a set of easily observable and verifiable indicators for correctly predicting whether a household is poor or not, based on the 2004-05 Malawi Integrated Household Survey data. The predictive power of the models is assessed using out-of-sample validation tests and receiver operating characteristic curves, whereas the model’s robustness is evaluated by bootstrap simulation methods. Finally, sensitivity analyses are performed using the international and extreme poverty lines. The models developed have proven their validity in an independent sample derived from the same population. Findings suggest that the rural model calibrated to the national poverty line correctly predicts the status of about 69% of poor households when applied to an independent subset of surveyed households, whereas the urban model correctly identifies 64% of poor households. Increasing the poverty line improves the model’s targeting performances, while reducing the poverty line does the opposite. In terms of robustness, the rural model yields a more robust result with a prediction margin ±10% points compared to the urban model. While the best indicator sets can potentially yield a sizable impact on poverty if used in combination with a direct transfer program, some non-poor households would also be targeted as the result of model’s leakage. One major feature of the models is that household score can be easily and quickly computed in the field. Overall, the models developed can be potential policy tools for Malawi.Malawi, poverty targeting, proxy means tests, out-of-sample tests, bootstrap, Food Security and Poverty, Research Methods/ Statistical Methods, I32, C15,

Targeting the poor and smallholder farmers : empirical evidence from Malawi

This paper develops low cost, reasonably accurate, and simple models for improving the targeting efficiency of development policies in Malawi. Using a stepwise logistic regression (weighted) along with other techniques applied in credit scoring, the research identifies a set of easily observable and verifiable indicators for correctly predicting whether a household is poor or not, based on the 2004-05 Malawi Integrated Household Survey data. The predictive power of the models is assessed using out-of-sample validation tests and receiver operating characteristic curves, whereas the model?s robustness is evaluated by bootstrap simulation methods. Finally, sensitivity analyses are performed using the international and extreme poverty lines. The models developed have proven their validity in an independent sample derived from the same population. Findings suggest that the rural model calibrated to the national poverty line correctly predicts the status of about 69% of poor households when applied to an independent subset of surveyed households, whereas the urban model correctly identifies 64% of poor households. Increasing the poverty line improves the model?s targeting performances, while reducing the poverty line does the opposite. In terms of robustness, the rural model yields a more robust result with a prediction margin ±10% points compared to the urban model. While the best indicator sets can potentially yield a sizable impact on poverty if used in combination with a direct transfer program, some non-poor households would also be targeted as the result of model?s leakage. One major feature of the models is that household score can be easily and quickly computed in the field. Overall, the models developed can be potential policy tools for Malawi

Debt position of developing countries and new initiatives for debt reduction: a panel data fixed effects estimation of the impacts of the HIPC initiatives

In September 1996, the World Bank and the International Monetary Fund launched the Heavily Indebted Poor Countries Initiative (HIPC). This initiative was endorsed by 180 governments around the world as an effective and welcome approach to help poor, severely indebted countries reduce debt as a part of the overall poverty reduction strategy. Three years later, the initiative was enhanced to provide for faster, broader and deeper debt relief. Using a panel data fixed effect estimation, this study assesses the achievements of the first and second HIPC initiatives and explores further areas of intervention that might help the HIPCs graduate from debt rescheduling and achieve sustainable growth and poverty alleviation. Despite moderate achievements of the HIPC measures so far, this paper argues in favour of a HIPC III initiative. Much more relief is needed to link debt reduction to poverty alleviation if the expectations raised by the HIPC initiatives are to become reality

Operational poverty targeting by proxy means tests : models and policy simulations for Malawi

There is a long standing belief that accurate targeting of public policy can play a major role in alleviating poverty and fostering pro-poor economic growth. Many development programs fail to reach the poor in that a sizeable amount of program benefits leak to higher-income groups and a substantial proportion of poor are excluded. This is also the case in Malawi, one of the poorest countries in Sub-Saharan Africa. In response to widespread poverty and endemic food insecurity, the country decision makers enacted various programs, including free food, food-for-work, cash-for-work, subsidized agricultural inputs, etc. To target these programs at the poor and smallholder farmers in the country, policy makers rely mainly on community-based targeting systems in which local authorities, village development committees, and other community representatives identify program beneficiaries based on their assessment of the household living conditions. However, most of these programs have been characterized by poor targeting and significant leakage of benefits to the non-poor due to a number of factors, including various local perceptions, favoritism, abuse, lack of understanding of targeting criteria, political interests, etc. Almost all interventions are poorly targeted in the country. Therefore, this research explores potential methods and models that might improve the targeting efficiency of agricultural and development policies in the country. Using the Malawi Second Integrated Household (IHS2) survey data and a variety of estimation methods along with stepwise selection of variables, we propose empirical models for improving the poverty outreach of agricultural and development policies in rural and urban Malawi. Moreover, the research analyzes the out-of-sample performances of different estimation methods in identifying the poor and smallholder farmers. In addition, the model robustness was assessed by estimating the prediction intervals out-of-sample using bootstrapped simulation methods. Furthermore, we estimate the cost-effectiveness and impacts of targeting the poor and smallholder farmers. It is often argued that targeting is cost-ineffective and once all targeting costs have been considered, a finely targeted program may not be any more cost-efficient and may not have any more impact on poverty than a universal program. We assess whether this is the case using household-level data from Malawi. More importantly, we evaluate whether administering development programs using the newly developed models is more target- and cost-efficient than past agricultural subsidy programs namely the 2000/2001 Starter Pack and the 2006/2007 Agricultural Input Support Program (AISP). Estimation results suggest that under the newly designed system, mis-targeting is considerably reduced and the targeting efficiency of development policies improves compared to the currently used mechanisms in the country. Findings indicate that the estimation methods applied achieve the same level of targeting performance. The rural model achieves an average poverty accuracy of about 72% and a leakage of 27% when calibrated to the national poverty line of 44.29 Malawi Kwacha (MK). On the other hand, the urban model yields on average a poverty accuracy of about 62% and a leakage of 39% when calibrated to the same poverty line. The results are also confirmed by the Receiver Operating Characteristic (ROC) curves of the models which show that there is no sizeable difference in aggregate predictive accuracy between the estimation methods. The ROC curve is a powerful tool that can be used by policy makers and project managers to decide on the number of poor a program or development policy should reach and ponder on the number of non-poor that would also be wrongly targeted. Calibrating the models to a higher poverty line improves its targeting performances, while calibrating the models to a lower line does the opposite. For example, under the international poverty line of US$1.25 (i.e. MK59.18 in Purchasing Power Parity), the rural model covers about 82% of the poor and wrongly targets only 16% of the non-poor, whereas the urban model covers about 74% of the poor and wrongly identifies 26% of the non-poor. On the other hand, using an extreme poverty line of MK29.81 disappointingly reduces the model?s poverty accuracy and leakage: the rural model yields a poverty accuracy of 51% and a leakage of 39% while the urban model yields a poverty accuracy of about 48% and a leakage of 68%. Furthermore, a breakdown of targeting errors by poverty deciles indicates that the models perform well in terms of those who are mistargeted; covering most of the poorest deciles and excluding most of the richest ones. These results have obvious desirable welfare implications for the poor and smallholder farmers. It is all important to mention that the models selected cannot explain but predict poverty. A causal relationship should not be inferred from the results. There is compelling evidence in favor of targeting since considering all costs does not make targeting cost- and impact-ineffective. Findings suggest that the new system is considerably more accurate and more target-efficient than the currently used mechanisms for targeting agricultural inputs in the country. Likewise, simulation results indicate that targeting the poor and smallholder farmers is more cost- and impact-effective than universal coverage of the population. Better targeting not only reduces the Malawian Government?s direct costs for providing benefits, but also reduces the total costs of a targeted program. Though administrative costs increase with finer targeting, the results indicate that the overall benefits outweigh the costs of targeting. Likewise, finer targeting reduces the costs of leakage by a sizable margin and produces the highest impacts on poverty compared to universal regimes. However, the finest redistribution does not consistently yield the best transfer efficiency, nor does it consistently improve post-transfer poverty. Furthermore, the newly designed system appears to be more cost-efficient than the 2000/2001 Starter Pack and the 2006/2007 Agricultural Input Support Program (AISP). While the Starter Pack and the AISP transferred about 50% of total transfer, under the new system about 73% of transfer is delivered to the poor and smallholder farmers. Likewise, under the new proxy system the costs of leakage are cut down by 55% and 57% for the Starter Pack and AISP, respectively. Thus, under the new system it is possible to reduce leakage and undercoverage rates and improve the cost and transfer efficiency of development programs in the country. The proxy indicators selected reflect the local communities? understandings of poverty and include variables from different dimensions, such as demography, education, housing, and asset ownership. These indicators are objective and most can be easily verified. However, the collection of information on those indicators might entail an effective verification process. Likewise, the emphasis put on proxy means tests in this research does not imply that other potential targeting methods should be disregarded. Indeed, proxy means tests are not perfect at targeting; the system developed can be combined with other methods in a multi-stage targeting process. Furthermore, targeting can be a politically sensitive issue; the system developed does not take into account the reality that policy makers, program managers, or development practitioners may adjust eligibility criteria due to political, administrative, budgetary, or other reasons. The models developed can be used in a wide range of applications, such as identifying the poor and smallholder farmers, improving the existing targeting mechanisms of agricultural input subsidies, assessing household eligibility to welfare programs and safety net benefits, producing estimates of poverty rates and monitoring changes in poverty over time as the country and donors cannot afford the costs of frequent household expenditure surveys, estimating the impacts of development policies targeted to those living below the poverty line, and assessing the poverty outreach of microfinance institutions operating in the country. This broad range of applications makes the models potentially interesting policy tools for the country. However, the models developed are not sufficient. They must also be coupled with investments in education, rural infrastructure, economic growth related sectors, and strong political will to impact on the welfare of Malawian people. The research also provides a framework for developing and evaluating a simple and reasonably accurate system for reaching the poor and smallholder farmers in Malawi, but the methodology can be useful in other areas of applied research and replicated in other developing countries with similar targeting problems.Es ist eine generell akzeptierte Annahme, dass öffentliche Politikmaßnahmen eine wichtige Rolle bei der Armutsbekämpfung und bei der Entwicklung von Wirtschaftswachstum spielen können. Als Antwort auf die weitverbreitete Armut und endemische Ernährungsunsicherheit haben die Entscheidungsträger Malawis verschiedene Programme, insbesondere die Subventionierung landwirtschaftlicher Betriebsmittel, die ein wichtiges Element der Entwicklungspolitik des Landes darstellen, entwickelt. Um diese Programme gezielt auf die Armen und Kleinbauern des Landes auszurichten, bauen die Verantwortlichen meist auf gemeindebasierte Systeme bei denen lokale Behörden Programmbegünstigte auf Basis der Beurteilung der jeweiligen Lebensbedingungen der Haushalte identifizieren. Die meisten dieser Programme sind jedoch durch eine schlechte Zielgenauigkeit gekennzeichnet und hohe Anteile des Nutzens der Programme gehen aufgrund verschiedener Faktoren, darunter lokale Vorstellungen, Vetternwirtschaft, Missbrauch, Mangel an Verständnis für die Zielkriterien, politische Interessen etc, irrtümlicherweise an Nicht-Arme. Fast alle Maßnahmen im Land leiden unter einer unzureichenden Zielgenauigkeit. Daher untersucht diese Arbeit potenzielle Methoden und Modelle, die die Zielgenauigkeit von Agrar- und Entwicklungsmaßnahmen des Landes verbessern können. Darüber hinaus schätzen wir die Kosteneffektivität und Auswirkungen einer Fokussierung auf Arme und Kleinbauern. Es wird häufig argumentiert, dass zielgruppengenaue Programme nicht kosteneffektiv sind und dass, wenn sämtliche Kosten der Zielgruppenfindung berücksichtigt werden, ein gut abgestimmtes zielgruppenorientiertes Programm nicht kosteneffizienter wäre und keine größeren Effekte auf die Armutsreduzierung hätte als ein generelles Programm. Wir untersuchen diese These anhand von Haushaltsdaten aus Malawi. Darüber hinaus bewerten wir, ob die Administration und Durchführung von Entwicklungsprogrammen mit Hilfe der neu entwickelten Modelle zielgruppengenauer und kosteneffizienter ist als bisherige Programme zur Subventionierung von landwirtschaftlichen Betriebsmitteln, insbesondere das Starter Pack von 2000/2001 und das Agricultural Input Support Program (AISP) von 2006/2007. Unter Verwendung von Daten des Malawi Second Integrated Household Survey (IHS2) und einer Reihe von Schätzmethoden mit schrittweiser Auswahl von Variablen entwickeln wir empirische Modelle zur Verbesserung der Armutsminderung durch Agrar- und Entwicklungsprogramme im ländlichen und städtischen Malawi. Zusätzlich analysiert die Arbeit die über die Stichprobe hinausgehende Güte der verschiedenen Modelle bei der Identifizierung der Armen und Kleinbauern. Die Robustheit der Modelle wurde darüber hinaus mit Hilfe von Bootstrapping-Simulationen für die Vorhersageintervalle außerhalb der Stichprobe geschätzt. Die Schätzergebnisse legen nahe, dass mit dem neuentwickelten System eine fehlgerichtete Ausrichtung erheblich reduziert werden kann und dass die Zielgruppenausrichtung von Entwicklungsmaßnahmen im Vergleich zu bisher im Land genutzten Mechanismen verbessert werden kann. Die Ergebnisse legen nahe, dass die angewendeten Schätzmethoden alle die gleiche Zielgenauigkeit erreichen. Das ländliche Modell erreicht bei Kalibrierung auf die nationale Armutslinie eine Genauigkeit bei der Erreichung von Armen von 72% und ein Durchsickern an Nichtzielgruppen von 27%. Auf der anderen Seite erreicht das städtische Modell im Durchschnitt eine Zielgruppengenauigkeit von 62% und ein Durchsickern von 39% (ebenfalls bei Kalibrierung auf die nationale Armutslinie). Diese Ergebnisse werden ebenfalls durch die Receiver Operating Characteristic (ROC) Kurven der Modelle bestätigt, die keine beträchtlichen Unterschiede zwischen der aggregierten Vorhersagegenauigkeit der Schätzmodelle zeigen. Die ROC-Kurve ist ein mächtiges Werkzeug das von Programmverantwortlichen und Projektmanagern zur Entscheidungsfindung darüber genutzt werden kann, wieviele Arme ein Programm oder eine Entwicklungsmaßnahme erreichen soll und wieviele fälschlicherweise begünstigte Nicht-Arme gefördert werden. Die Kalibrierung der Modelle auf eine höhere Armutslinie verbessert ihre Zielgenauigkeit, während eine Kalibrierung auf eine niedrigere Linie zum Gegenteil führt. Zum Beispiel erreicht das ländliche Modell bei Verwendung der internationalen Armutslinie von 1,25 USD (d.h. MK 59,18 PPP) etwa 82% der Armen und fördert fälschlicherweise nur 16% der Nicht-Armen. Auf der anderen Seite verschlechtert die Verwendung einer extremen Armutslinie von MK 29,81 die Genauigkeit und das Durchsickern der Modelle: Das ländliche Modell erzielt eine Armutsgenauigkeit von 51% und ein Durchsickern von 39% während das städtische Modell eine Genauigkeit von 28% und ein Durchsickern von 68% erreicht. Darüber hinaus deutet ein Herunterbrechen der Fehlausrichtungen nach Armutsdezilen an, dass die Modelle in Bezug auf die fälschlicherweise Begünstigten gut funktionieren: Sie decken die meisten der ärmsten Dezile ab, während die meisten der reichsten Dezile nicht berücksichtigt werden. Diese Ergebnisse haben naheliegende wünschenswerte Wohlfahrtseffekte für Arme und Kleinbauern. Es ist wichtig zu erwähnen, dass die ausgewählten Modelle Armut nicht erklären sondern lediglich voraussagen können. Ein kausaler Zusammenhang kann auf Grundlage der Ergebnisse nicht hergestellt werden. Es bestehen zwingende Anhaltspunkte zu Gunsten von Zielgruppenorientierung da auch die Berücksichtigung sämtlicher Kosten die Zielgruppenorientierung nicht kosten- und ergebnisineffizient werden lässt. Die Ergebnisse legen nahe, dass das neue System erheblich genauer und zieleffizienter ist als der bisher verwendete Mechanismus zur zielgruppengenauen Programmgestaltung für landwirtschaftliche Betriebsmittel. Ebenso deuten die Simulationsergebnisse an, dass die Fokussierung auf Arme und Kleinbauern kosten- und ergebniseffektiver ist als eine globale Erfassung der gesamten Bevölkerung. Bessere Zielgruppenausrichtung verringert nicht nur die direkten Kosten der Regierung Malawis für unterstützende Maßnahmen sondern reduziert auch die Gesamtkosten eines Programms. Obwohl die administrativen Kosten mit genauerer Zielgruppenausrichtung ansteigen, zeigen die Ergebnisse, dass die Vorteile insgesamt die Kosten überwiegen. Ebenso verringert eine genauere Ausrichtung die Kosten für das Durchsickern in großem Maßstab und sorgt für die größten Auswirkungen auf die Armut verglichen mit generellen Verfahren. Mit steigender Genauigkeit der Ausrichtung erhöht sich jedoch weder in jedem Fall die Verteilungseffizienz, noch verringert sich in jedem Fall die Folgearmut. Weiterhin scheint das neu entwickelte System kosteneffizienter zu sein als das Starter Pack von 2000/2001 und das Agricultural Input Support Program (AISP) von 2006/2007. Während das Starter Pack und das AISP etwa 50% sämtlicher Mittel an Arme und Kleinbauern verteilen, erreichen unter dem neuen System etwa 73% der Mittel Arme und Kleinbauern. Ebenso werden unter dem neuen System die Kosten des Durchsickerns um 55% gegenüber dem Starter Pack und um 57% gegenüber dem AISP gesenkt. Unter dem neuen System ist es daher möglich, Durchsickern und Fehlallokation zu verringern und die Kosten- und Verteilungseffizienz von Entwicklungsprogrammen des Landes zu verbessern. Die ausgewählten Indikatoren spiegeln das Armutsverständnis lokaler Gemeinden wider und beinhalten demografische Variablen ebenso wie Bildung, Lebensverhältnisse und Eigentum. Diese Indikatoren sind objektiv und die meisten können leicht verifiziert werden. Die Sammlung von Informationen bezüglich dieser Indikatoren könnte jedoch effektiv einen Überprüfungsprozess darstellen. Es sollte erwähnt werden, dass der Schwerpunkt in dieser Arbeit zwar auf Proxy Means Tests gelegt wurde, was aber nicht impliziert, dass andere mögliche Methoden zur Zielgruppenfokussierung abgelehnt werden sollten. Proxy Means Tests sind tatsächlich nicht einwandfrei bei Armutsidentifizierung und das entwickelte System kann in einem Mehrstufenprozess mit anderen Methoden kombiniert werden. Zielgruppenfokussierung kann darüber hinaus eine politisch sensible Angelegenheit sein; das entwickelte System berücksichtigt nicht die Tatsache, dass Programmverantwortliche und Projektmanager oder Entwicklungshelfer die Kriterien zur Anspruchsberechtigung aufgrund von politischen, verwaltungs- und haushaltsbezogenen oder anderen Gründen anpassen. Die entwickelten Modelle können in einem weiten Spektrum von Fällen verwendet werden, z.B. bei der Identifizierung von Armen und Kleinbauern, bei der Verbesserung bestehender Vergabemechanismen für subventionierte landwirtschaftliche Betriebsmittel, bei der Beurteilung der Anspruchsberechtigung von Haushalten, zur Schätzung von Armutshöhe und beim Monitoring von Armutsveränderungen im Zeitverlauf. Da sich das Land und Geldgeber die Kosten häufiger Untersuchungen zu den Lebenshaltungskosten der Haushalte oft nicht leisten können, sind die Modelle auch hilfreich bei der kostengünstigen Schätzung der Auswirkungen von Entwicklungsprogrammen die auf Bedürftige unterhalb der Armutslinie abzielen und bei der Beurteilung der Armutsbekämpfung von im Land tätigen Mikrofinanzinstitutionen. Diese große Bandbreite von Anwendungen lässt die Modelle zu potenziell interessanten Politikinstrumenten für das Land werden. Die entwickelten Modelle sind jedoch nicht ausreichend. Sie müssen einhergehen mit Investitionen in Bildung, ländliche Infrastruktur, Wirtschaftswachstum in verwandten Wirtschaftssektoren und mit einem starken politischen Willen, die Wohlfahrt der Bevölkerung Malawis zu steigern. Diese Arbeit stellt ein Grundgerüst für die Entwicklung und Bewertung eines einfachen und recht genauen Systems zur Identifizierung von Armen und Kleinbauern in Malawi bereit, doch die Methodik kann auch in anderen Bereichen angewandter Forschung nützlich sein und kann in anderen Entwicklungsländern mit ähnlichen Problemen bei der Zielgruppenfokussierung repliziert werden

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,

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,

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,

How best to target agricultural subsidies? The case for an indicator-based targeting system in Malawi

Over the past few years Malawi made remarkable progress toward increasing its national maize production and achieving food security owing to its long-running policy of subsidizing fertilizer. The implementation of these subsidy programs is continuously being improved upon as the country learns from past experiences.1 Recent evaluations of the current Farm Input Subsidy Program by Dorward and Chirwa (2011, 2012) suggest that various components of the program have been redesigned over time. These include timeliness of fertilizer delivery, fertilizer coupon receipts, regional distribution, area targeting, allocation and distribution processes, and coupon use and re-demption. However, the issue of beneficiary identification and targeting remains a challenge for the program. Draw-ing on recent research by Houssou and Zeller (2010, 2011, 2012), this note proposes an alternative approach to effec-tively target the poor within the group of potential program beneficiaries. In our analysis we are mindful of the fact that targeting criteria for the current program are vague at best. One interpretation of these criteria is that the pro-gram intends to target the “productive poor”, and since poverty is widespread in Malawi, some would argue that the focus of the targeting mechanism should be on identifying farmers who would make best use of subsidized fertilizer (e.g., as measured by their marginal productivity per unit of fertilizer applied). Many evaluations, however, have criti-cized the program for not being pro-poor enough; hence aNon-PRIFPRI1; MaSSPDSG