This paper examines the problem of how to transfer money or other forms of assistance to poor households when one observes some characteristics of households, but not their incomes. This and related issues are often referred to as the targeting problem. The paper first sets out the problem formally as one of minimizing a poverty index given a fixed amount of money available for transfers. Assuming that household survey data are available which include accurate income and/or expenditure information, the solution for the problem is formulated as a non-linear mathematical programming exercise. Using household survey data from Cote d'Ivoire, the technique is applied to both urban and rural areas separately. The paper concludes with a general discussion and suggestions for future research.

Conditional Cash Transfer (CCT) programs aim to reduce poverty by making welfare programs conditional upon the receivers' actions. That is, the government only transfers the money to persons who meet certain criteria. These criteria may include enrolling children into public schools, getting regular check-ups at the doctor's office, receiving vaccinations, or the like. They have been hailed as a way of reducing inequality and helping households break out of a vicious cycle whereby poverty is transmitted from one generation to another. Do these and other claims make sense? Are they supported by the available empirical evidence? This volume seeks to answer these and other related questions. Specifically, it lays out a conceptual framework for thinking about the economic rationale for CCTs; it reviews the very rich evidence that has accumulated on CCTs; it discusses how the conceptual framework and the evidence on impacts should inform the design of CCT programs in practice; and it discusses how CCTs fit in the context of broader social policies. The authors show that there is considerable evidence that CCTs have improved the lives of poor people and argue that conditional cash transfers have been an effective way of redistributing income to the poor. They also recognize that even the best-designed and managed CCT cannot fulfill all of the needs of a comprehensive social protection system. They therefore need to be complemented with other interventions, such as workfare or employment programs, and social pensions.

Problem Definition: Cash transfer programs (CTPs) have spread in the last decade to help fight extreme poverty in different parts of the world. A key issue here is to ensure that the cash is distributed to the targeted beneficiaries in an appropriate manner to meet the goals of the programs. How do we design efficient and egalitarian allocation rules for these programs? Academic/Practical Relevance: Big data and Machine Learning have been used recently by several CTPs to target the right beneficiaries (those living in extreme poverty). We demonstrate how these targeting methods can be integrated into the cash allocation problem to synthesize the impact of targeting errors on the design of the allocation rules. In particular, when the targeting errors are ``well calibrated'', a simple predictive allocation rule is already optimal. Finally, although we only focus on the problem of poverty reduction (efficiency), the optimality conditions ensure that these allocation rules provide a common ex-ante service guarantee for each beneficiary in the allocation outcome (egalitarian). Methodology: We design allocation rules to minimize a key indicator in poverty reduction -- the squared gap of the shortfall between the income/consumption and the poverty line. The rules differ in how the targeting error distribution is being utilized. Robust and online convex optimization are applied for the analysis. We also modify our allocation rules to ensure that the cash is spreadmore evenly across the pool of beneficiaries, to reduce the (potential) negative effect on non-beneficiary households living close to the poverty line, but missing the benefits of the CTPs due to imperfect targeting.Results: Given a targeting method, we compare and contrast the performance of different allocation rules -- predictive, stochastic, and robust. We derive closed-form solutions to predictive and stochastic allocation models, and use robust allocation to mitigate the negative impact of imperfect targeting. Moreover, we show that the robust allocation decision can be efficiently computed using online convex optimization. Managerial Implications: Using real data from a CTP in Malawi, we demonstrate how a suitable choice of allocation rule can improve both the efficiency and egalitarian objectives of the CTP. The technique can be suitably modified to ensure that the wealth distribution after allocation is ``smoother", reducing the bunching effect that may be undesirable in some circumstances.

Drawing on a database of more than one hundred anti-poverty interventions in 47 countries, 'Targeting of Transfers in Developing Countries' provides a general review of experiences with methods used to target interventions in transition and developing countries. Written for policymakers and program managers in developing countries, in donor agencies, and in NGOs who have responsibility for designing interventions that reach the poor, it conveys what targeting options are available, what results can be expected as well as information that will assist in choosing among them and in their implementation. Key messages are: - While targeting 'works' - the median program transfers 25 percent more to the poor than would a universal allocation - targeting performance around the world is highly variable. - Means testing, geographic targeting, and self-selection based on a work requirement are the most robustly progressive methods. Proxy means testing, community-based selection of individuals and demographic targeting to children show good results on average, but with considerable variation. - Demographic targeting to the elderly, community bidding, and self-selection based on consumption show limited potential for good targeting. - There is no single preferred method for all types of programs or all country contexts. Successful targeting depends critically on how a method is implemented. The CD-ROM includes the database of interventions, an annotated bibliography (PDF) and Spanish and Russian translations of the book (PDFs).

There is a growing debate on the relative merits of universal and targeted social assistance transfers in achieving income redistribution objectives. While the benefits of targeting are clear, i.e., a larger poverty impact for a given transfer budget or lower fiscal cost for a given poverty impact, in practice targeting also comes with various costs, including incentive, administrative, social and political costs. The appropriate balance between targeted and universal transfers will therefore depend on how countries decide to trade-off these costs and benefits as well as on the potential for redistribution through taxes. This paper discusses the trade-offs that arise in different country contexts and the potential for strengthening fiscal redistribution in advanced and developing countries, including through expanding transfer coverage and progressive tax financing.