One crucial aspect of poverty reduction is accurately targeting the beneficiaries of social protection programs (Coady, Grosh, and Hoddinott, 2004). For this reason, developing clear indicators and criteria to identify poor communities is a vital initial step.
The Kabupaten (District of) Bekasi Government has undertaken various efforts to reduce poverty in its region. As a result, the poverty rate in the area decreased from 5.21% in 2021 to 4.93% in 2023 (BPS, 2024). Extreme poverty has also decreased from 1.44% in 2021 to 0.48% in 2023.
To improve the impact of poverty alleviation programs, the Kabupaten Bekasi Government, through the Regional Development Planning Agency (Bappeda), has initiated the development of more comprehensive indicators and criteria for identifying poor communities. This document will serve as a key reference in the planning and implementation of poverty reduction programs in Kabupaten Bekasi. SMERU provides technical assistance to the Kabupaten Bekasi Government in developing these poverty indicators and criteria.
The purpose of developing these indicators and criteria is to identify household characteristics that reflect welfare in Kabupaten Bekasi. This information will help determine which households fall into the poverty category.
The outcomes of this study will serve as a reference for the Kabupaten Bekasi Government in designing poverty reduction programs and policies, including the preparation of the Regional Poverty Reduction Plan (RPKD).
The SMERU team employs the proxy-mean testing (PMT) method to identify indicators or criteria for poor communities. The PMT method uses observable household or household member characteristics to estimate their income or consumption when income information is unavailable or unreliable (Shivakumaran et al., 2018).
This modeling approach predicts household welfare levels by estimating household expenditure values using regression techniques. The initial stage of the regression process involves selecting specific household characteristic variables that are closely correlated with expenditure variables.
The characteristics of households in Kabupaten Bekasi depend on the significance of the regression results. The data used includes Susenas from March 2016 to 2022, with a combined sample size of 25,000 households in Kabupaten Bekasi.
