Norberto E. Milla1*
ABSTRACT
Philippines is among the many countries which has a perennial problem on poverty. The country has made various ways to reduce its poverty rate; one is through conditional cash transfer (CCT) scheme. This study measured the impact of CCT Program, particularly on food consumption among its household beneficiaries using Propensity Score Matching (PSM). Impact is measured in terms of the Average Treatment Effect on the Treated (ATT) using four matching algorithms: Nearest Neighbor Matching, Caliper (Radius) Matching, Kernel Matching, and Local Linear Regression matching. Binary logistic regression was used to identify covariates influencing program participation which include having children who are 6-12 years old, education of the household heads’ spouses, marital status and sex of the household head, housing tenure, and ownership of household assets. Balance test indicates nonsignificant difference between 4Ps and non-4Ps beneficiaries across these covariates. Of the four matching algorithms, the Caliper (radius) matching generated ATT estimate with the least standard error. On the average, using the Caliper matching method, the monthly food expenditure of the household beneficiaries have significantly increased by PHP501.39, Thus, the CCT program of the government has brought significant improvement on the household beneficiaries, not only on education, health, and nutrition but also on their monthly food expenditure. It is recommended that the implementation of the CCT program should be strengthened, sustained, and maintained properly and orderly to gradually alleviate the current poverty conditions in the identified poor barangays around the nation. Moreover, the implementing agencies should consistently monitor the proper and synchronized implementation of the program in order to wholly purge the intergenerational transmission of poverty which is a perennial experience of the households who belong to the poorest populace in the country.
Keywords: propensity score matching, nearest neighbor matching, caliper matching, kernel matching, local linear regression matching
Annals of Tropical Research 42(1):104-116(2020)
https://doi.org/10.32945/atr4218.2020
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