Policy Brief Issue 6 | June 2019 Can Community-based Targeting Effectively Select Poorer Beneficiaries for a Large-scale EAST ASIA AND PACIFIC GENDER Program? Insights from the LASED Project INNOVATION LAB The East Asia and Pacific Gender Innovation Lab KEY FINDINGS (EAPGIL) carries out • Community-based targeting can effectively select impact evaluations poorer project beneficiaries when the community is and inferential fully integrated into the selection process. research to generate • Close targeting around poorer beneficiaries can be achieved across a evidence on what wide project area and an implementation process spanning years. works in closing gender gaps in • An iterative community-based selection process held over assets, economic several smaller meetings may improve selection outcomes. opportunities, and agency, and how closing these gaps CONTEXT can help achieve With 34% of the population at the national poverty line in 20081, the Royal Government of other development Cambodia established Social Land Concessions (SLCs) based on Sub-Decree 19 (No. 19 outcomes. Ultimately, ANK/BK/ March 19, 2003) to distribute state-owned land to the landless and land-poor. EAPGIL seeks to The Land Allocation for Social and Economic Development Project (LASED) was organized increase the welfare to pilot commune-based SLCs, allocating land and improving communal infrastructure. of women and men LASED is an ongoing project, organized into several phases. The first phase ran from 2008 in East Asia and the to 2013. Over 11,000 households applied for SLCs during this phase, and over 4,000 Pacific by promoting land plots were distributed across five provinces: Kampong Chhnang, Kampong Speu, the uptake of Kampong Thom, Tboung Khmum, and Kratie. LASED used community-based targeting effective policies and (CBT) to identify beneficiary selection criteria and the final recipient households. programs identified 1 W  orld Bank, World Development Indicators. (2012). Poverty headcount ratio at national poverty lines (% of population). based on evidence. [Data file]. Retrieved from https://data.worldbank.org/indicator/SI.POV.NAHC?locations=KH FIGURE 1: LAND RECIPIENT HOUSEHOLD SELECTION PROCESS 2-3 ROUNDS OF PUBLICLY REVIEWING AND REFINING Community THE APPLICATIONS AND RANKINGS involved at every step of process. 122-day minimum Transparent process. mandated for Applications posted application submission, publicly, open review, and for comment. STEP 1 STEP 2 STEP 3 STEP 4 STEP 5 selection HHs complete Applications Applications Priority recipients Communities review process applications for land. posted publicly, ranked according publicly posted their the information and Applications capture open to revision. to community applications avalable submit appeals Residents had demographic info. criteria. publicly for review. and corrections. time to review the documents, submit complaints and corrections. terative process: •I 2-3 review rounds  22 days reserved •1 STEP 9 STEP 8 STEP 7 STEP 6 for community HHs above cut-off score HHS submit Ranking cut-off Final pool of potential participation enter land plot lottery. appeals. score determined. recipients selected. HOW WAS LASED ORGANIZED? as eligible and ineligible households are nearly identical LASED piloted “commune-based SLCs”, which are before the program, differences seen later can be community-led—designed and administered from attributed to the program. Consequently, for LASED, if the lowest civil administrative units possible. Local households close to cut-off score were highly similar in poverty characteristics before land was allocated, later government determined specific selection criteria and differences in poverty could be attributed to the SLC managed the application and selection processes grants. for their areas. The land for distribution was also identified locally and allocation plans were reviewed We accordingly assessed the baseline poverty status of publicly at the village level to ensure communal households a few points above and below the SLC cut- consensus on the plan. To distribute the land, each off scores to determine whether RD analysis could be household received a score based on the selection used to evaluate LASED. criteria, and the community identified a pool of eligible households through an iterative process WHAT DID WE FIND? (Figure 1). The commune councils determined a cut-off score based on the number of available plots Exploration of the data showed that RD analysis would of land. Households above the cut-off score entered not be feasible. The households on either side of the cut-off scores were markedly different in poverty status. a lottery to receive specific land plots (agricultural However, though RD analysis could not be completed, and/or residential land). Those below the cut-off the data revealed a few positive findings regarding score entered a waitlist for future land allocations. program targeting. Ultimately, 4,441 SLC contracts were granted under the first phase of LASED. LASED targeting was strongly pro-poor Those above the cut-off score were significantly poorer WHAT DID WE DO? than those below the cut-off score, across a range of The LASED team invited EAPGIL to evaluate the characteristics. Land recipients had no, or significantly project’s effectiveness in improving the welfare smaller plots of land, fewer cattle and other livestock, and of poor households. We aimed to use regression were less likely to own a house. They were more likely to discontinuity (RD) analysis for the evaluation. This be widowed, to work as laborers, and to be registered2 method compares households immediately on either poor3. For example, while only slightly more than 10% side of the program eligibility cut-off score. As long of non-recipient households were registered poor, over half of recipient households were. Non-recipient Figure 2: Higher Percentage of Recipient households had nearly twice the amount of residential than Non-recipient Households with and agricultural land as recipient households. Notably, Poor and Vulnerable Characteristics significant differences between recipients and non- recipients remain for a range of band width around the cut-off score4. Registered Poor Richer households were progressively siphoned away as the project proceeded Own Other Livestock The iterative process of community review and correction of applications successfully pushed poorer Own Cattle households forward in the processes and removed richer households from consideration. All interested Own households could submit applications. During the initial House review of applications, richer households were culled from the pool (Step 2 in Figure 1). Poorer households Laborer progressed to the ranking stage. This can be seen by comparing characteristics between households that Widowed progressed to the ranking stage and households that were dropped without being ranked. Those that were ranked are poorer than those that were dropped. 0 20 40 60 80 100 LASED selection continued to tighten around poorer Non-recipients Recipients households during the ranking phase. The poorer among ranked households predominately entered lucky draws to receive land, while the richer were again siphoned away. Figure 3: Recipient Households Own Significantly Smaller Plots of Pro-Poor targeting is seen across provinces Agricultural and Residential Land (m²) Across provinces, land recipients had poorer characteristics than those who did not receive land. For example, though the amount of difference between recipient and non-recipient households varied by Agricultural province, recipients were consistently less likely to own Land (m2) a house. Similarly, recipient households were more likely to be registered poor (IDPoor) — overall and by strata of poverty levels. The spread in pro-poor targeting across project areas indicates that LASED provided a systematic, rather than incidental, identification of Residential Land (m2) poorer households to receive social welfare benefits. WHAT CONTRIBUTED TO LASED’S SUCCESSFUL TARGETING? 0 1000 2000 3000 4000 5000 6000 7000 Community Based Targeting (CBT) programs often experience tension between communal acceptability Non-recipients Recipients  or most areas in Cambodia, the Ministry of Planning maintains a list, updated every three years, of the registered poverty status (“IDPoor”) of families. 2 F Marital status and occupation differences were calculated via chi-square testing. All other comparisons were calculated through t-tests of differences in group 3  means. Detailed analysis of feasibility of RD design available from authors upon request. 4  and targeting effectiveness5. LASED seemed to bridge this, with widespread community acceptance and participation, while also effectively identifying poorer households to receive program benefits. We reviewed the LASED’s design to explore reasons for its success and saw that several project design factors aligned with CBT strategies recommended by the literature. The following factors were taken into account: ACKNOWLEDGMENTS Community definition of criteria and ranking: Rather than receiving definitions of This brief is a product of eligible beneficiaries from external donors or other parties, local communities could collaboration between adjust selection criteria and determine their own target populations. Distribution of EAPGIL and the LASED project benefits therefore was more likely to resonate with the participants’ values, Team, led by Mudita contributing to acceptability. Chamroeun. It was prepared Concurrent community-based and proxy means targeting methodologies: Projects by Alana Teman and often implement an initial CBT component to identify a pool of potential beneficiaries, Elizaveta Perova, with inputs and then use a proxy means targeting (PMT) survey to validate the CBT results. from Andreas Groetschel The PMT component may come from a source external to the community. When and Kongkea Vong. discrepancies are found between CBT and PMT results, the PMT results are frequently used to “correct” the CBT results, negating the contribution of the CBT method and We gratefully acknowledge potentially causing tensions in the community. LASED integrated the CBT and PMT funding from the Umbrella components, and rooted both in community decision-making, unifying participatory Facility for Gender Equality methods with access to both the unique local knowledge base and an objective (UFGE) to carry out this selection rubric. work. EAPGIL is supported by UFGE in partnership with Transparent selection process with public posting of potential and ultimate the Australian Department beneficiaries: All stages of LASED’s selection process were open to the public’s review of Foreign Affairs and and criticism, reducing chances of elite capture, and increasing program acceptability and use of local knowledge. Part of project transparency included diversifying the Trade. UFGE has received decision-making body. Project materials were open to all, and not just project and generous contributions from community leadership. Included among these community members, women also Australia, Canada, Denmark, contributed to project monitoring and effective targeting. Others have identified Finland, Germany, Iceland, including women in project committees as a way to improve targeting effectiveness6. the Netherlands, Norway, Spain, Sweden, Switzerland, Iterative process extending over weeks: CBT is prone to exhausting participants. the United Kingdom, Restricting the CBT component to an hours-long meeting to review and rank potential and the United States. beneficiaries, as is often done, often induces participant fatigue7. Testing the order in which potential beneficiaries are ranked has shown a higher likelihood of beneficiary inclusion and exclusion errors among those ranked last. With the overall process spanning several weeks, and the community members having several days to review project materials during each iteration, LASED may have mitigated participant fatigue. Local decision-making by homogenous village and commune groups: Successful CBT programs are typically conducted among homogenous groups with shared FOR MORE INFORMATION values and local knowledge banks8. Although the overall project was large-scale, encompassing many households, decisions were orchestrated at the local level, Elizaveta Perova, EAP GIL promoting community consensus and acceptance. eperova@worldbank.org Aneesh Mannava, EAP GIL  latas, V., Banerjee, A., Hanna, R., Olken, B. A., & Tobias, J. (2012). Targeting the poor: evidence from a field 5 A amannava@worldbank.org experiment in Indonesia. American Economic Review, 102(4), 1206-40 and McCord, A. (2013). Community-based targeting in the Social Protection sector. ODI Working Paper 514. London: Overseas Development Institute. Premand, P., & Schnitzer, P. (2018). Efficiency, legitimacy and impacts of targeting methods: Evidence from an 6  http://www.worldbank.org/eapgil experiment in Niger. Policy Research Working Paper, 8412. Washington, DC: The World Bank. 7 Alatas, et al. (2012). McCord (2013) and Coady, D., Grosh, M., & Hoddinott, J. (2004). Targeting of transfers in developing countries: 8  Review of lessons and experience. Washington, DC: The World Bank.