Searching for Effective Poverty Interventions

Conditional Cash Transfers in the Philippines

Norio Usui

• Publisher: Asian Development Bank
• Year: 2011
• Print ISBN: 9789290924104
• eBook ISBN: 9789290924111
• Edition: 1
• Topic: Social Sciences
• Subtopic: Poverty in Sociology
• Index: Social Sciences

Conditional cash transfer (CCT), which was initiated in 2007, is becoming a centerpiece of the social protection system in the Philippines. This note reviews economic rationales for transferring a cash grant to the poor contingent on their certain behavior, major challenges in designing a CCT program, targeting methodologies, and impact evaluation designs, to show how the Philippines' CCT program is designed to resolve major difficulties in its design, targeting, and evaluation. The CCT program with a rigorous impact evaluation offers an excellent opportunity for policy makers and development practitioners to learn what works and what does not work in searching for effective poverty interventions.

Impact Evaluation-How Can We Measure True Causal Impacts?

Dilemma of before-and-after comparison. A conventional approach for evaluating impacts of public programs, including social protection projects, is the so-called before-and-after comparison. In processing a project, we usually prepare time-bound and measurable performance indicators that allow the project to be monitored and evaluated subsequently.¹⁴ Figure 4 illustrates a hypothetical case. At the designing stage of the project, we set up an ex-ante target for each performance indicator with a specific time frame—how much and when. At a certain stage after project completion (T_A), the actual value of the indicator (X_A) is measured to know the impact of the project. Although we discuss possible causes for explaining the gap between the ex-ante target and the actual value, project impact is usually measured by comparing the actual indicator value over the baseline value (X_B). In this approach, the project impact is estimated as X_A–X_B.

Figure 4 Dilemma of Before-and-After Comparison—What We Need Is Counterfactual

Source: Author.

A key challenge of the before-and-after comparison lies in its complete lack of attribution or causality. Even if we find an improvement in the performance indicator, it is totally impossible to attribute the observed improvement, i.e., X_A–X_B, to the project. The improvement may be due partly to the project and partly to all other factors that are not related to the project. At most, what we can say is “our project might contribute, might be partially…might be.” Much worse, at the stage when the project is already completed (T_A), we have no way of knowing to what extent the project could contribute to the improvement. If we want to know the real impact of the project, we need to extract the contribution that can be distinctly attributable to the project.

What is a “counterfactual”? The impact attributable to the project can only be obtained by comparing the actual observed indicator (X_A) and a hypothetical value of the indicator that would have prevailed in the absence of the project (X_AC). This hypothetical value is generally called a counterfactual. The example in Figure 4 shows that, even without the project, the performance indicator could have improved because of other factors outside the project. However, the counterfactual value cannot be observed since the project was already in place. In the context of the Philippines’ CCT program, what we need is unobservable counterfactual outcomes, say school enrollment of recipient households when they hypothetically did not receive cash grants. However, the households cannot be observed simultaneously in two different states, i.e., with and without the CCT program. The central objective of impact evaluation is to construct this unobservable counterfactual value (X_AC) for measuring the real impact of the project, i.e., X_A–X_AC. There are several methodologies in impact evaluation.¹⁵

The CCT project of the Philippines adopts two different approaches: (i) a randomized control design (RCT) approach with phase-in design, and (ii) a regression discontinuity design (RD) approach. Three rounds of impact evaluation surveys are expected to be conducted, with the first round in 2011.¹⁶

Randomized control design (RCT) approach. In this approach, eligible households for the CCT are randomly assigned to a “treatment group” that will receive cash grants, and a “control group” that will not. By construction, the two groups are identical except for participation in the CCT program.¹⁷ It is thus possible to assume that both groups encounter the same external factors. Therefore, any difference in the outcomes between the two groups after project completion can be attributed solely to the CCT project. In the Philippines’ CCT program, the program phase-in is structured to divide eligible households into the treatment and control groups. The CCT covers all eligible households in a phased-in manner, which implies that the eligible households receive the cash transfers at different times. The timing of actual receipt of the cash grants is randomized: households that participate in the CCT program in the second phase can serve as the control group for others that join the program in the first phase (Figure 5).

Figure 5 Randomized Control Design Approach with Phase-In

CCT = conditional cash transfer.

Source: Author.

Sampling. The RCT approach is applied in some municipalities in each representative province. In each municipality, half of the barangays are randomly assigned to the CCT program immediately (treatment group), and the remaining half participate in the project after 1 year (control group).

Regression discontinuity design (RD) approach. This approach can be used when the program’s beneficiaries are determined by an explicit rule, such as the proxy means test (PMT) in the Philippines’ CCT program. Eligible households are determined based on their proxy scores against a cutoff point. This mea...

Table of contents

• Front Cover
• Title Page
• Copyright Page
• Contents
• List of Tables and Figures
• Abbreviations
• Summary
• Introduction
• Conditional Cash Transfer—Why Do We Need It? How Can We Design It?
• Targeting—How Can We Reach the Poor?
• Impact Evaluation—How Can We Measure True Causal Impacts?
• Conclusions
• Back Cover