1 Financial inclusion and Targeting Efficiency: How well can we identify the poor? A CMF study Principal Researcher: Abhijit Banerjee (MIT), Esther Duflo (MIT), Raghabendra Chattopadhyay(IIM, Calcutta) Partner Organization: Bandhan, Kolkata Funding Support: CGAP and Ford Foundation

2 How to include the poorest of the poor into microfinance? Background: Benefits of microfinance do not accrue to the poorest of the poor (Morduch 1999, Rabbani et al 2006). Microfinance Institutions (MFIs) are in general reluctant to lend to the poor. Reasons:  ultra-poor households tend to use loan for meeting consumption needs  Productive investment of loan is unlikely  Ultra-poor households are extremely vulnerable to shocks and hence more prone to default Poorer households maybe should be served by other interventions than credit

3 Bandhan Targeting Hard-core Poor (THP) intervention Objective of the program: Explore this idea, and prepare the poorest of the poor to successfully participate in regular microfinance programmes –To provide income generating assets: livestock, inventory etc. as grant to help ultra poor households secure a regular source of income –To impart weekly training and other assistance required for starting a small scale enterprise Programme is supported by CGAP and design was based on a BRAC intervention

4 Area of intervention: Murshidabad district, West Bengal Why Murshidabad district? This is one of the poorest districts of West Bengal. Targeted no. of beneficiaries: 300 To date, the identification process has occurred in 54 villages, with an average of 24 households identified as Ultra Poor in each village and 120 households have received assets so far. Overview of Bandhan’s Program

5 Identification of Ultra-poor households(Potential beneficiaries) Half of the identified beneficiaries are randomly chosen for asset distribution (for impact evaluation) Enterprise Selection Training Asset distribution Weekly Follow-up and monitoring Key Phases of the Program

6 Importance of targeting methodology Targeting is a crucial part of such programme Often, targeted programmes do not reach the intended beneficiaries –Criteria not appropriate –Criteria not properly implemented The criteria and the way there are applied are very important They need to identify not only the poorest in quantitative terms at on point of time, but the vulnerable people.

7 Different methodologies for targeting the poor Quantitative surveys –Standardized criteria –However, may miss important dimensions of poverty –Interviewing everybody in a village takes time –People doing the survey are not from the village itself Identification by community –The people who know the best about who are the poorest are the communities themselves –In the other hand, it is important to verify that communities are fair to all villagers and that no villagers are forgotten A combination of identification by communities and survey verification combines both methods.

8 Bandhan Ultra Poor Identification Process Identifying the poorest villages and hamlets of the district Conducting PRAs in the identified hamlets Social mapping Wealth ranking (from 1 to 6) First verification of the identified ultra-poor households: household survey according to a set of criteria Second verification: by the THP program coordinator Final selection

9 The Survey Verification criteria The household must have at least one active woman capable of undertaking some enterprise the household must not be associated with any MFI (in keeping with the aim of targeting those who lack credit access) or receive sufficient support through a government aid program primary source of income should be informal labor or begging land holdings below 20 decimals no ownership of productive assets other than land no able bodied male in the household and having school-aged children working rather than attending school

10 Objective of the study and methodology Evaluate how effectively Bandhan is targeting poorest of the poor through its methodology Also evaluate efficiency of government programmes targeting An economic census was carried out in five program villages. Each household was classified on a 1-5 scale along several characteristics (similar to the classification criteria adopted by government’s BPL census) 605 households across five villages satisfied the criteria of poor and ultra-poor households. Out of these 605 households a sample of 121 households was drawn at random. Our final dataset contains these 121 households and 92 households identified by Bandhan as ultra poor.

11 Targeting efficiency of government aid programmes Among our sample, which is drawn from the bottom of Indian economic spectrum, only 20% received a BPL card, and 10% an Antodaya card. We compare those who participated in 4 government programmes (BPL, Antodaya, Indiar Housing programme and NRGEA) to those who do not, on several measures of poverty Both groups are not different, overall Only those who have received work under a work employment scheme are poorer –Only hh who are poor enough to lacl other opportunities will take up these schemes This reveals the inefficiency of targeting by government programmes

12 Efficiency of PRAs: Key Findings - our sample in average Low consumption level –Mean per capita monthly average expenditure: Rs. 426 –Approximately 50% spends less than one dollar a day and nearly all the population spends less than two dollars a day. Land –Mean land holdings: 5.65 katthas (approximately acres) –21% of the sample landless. Access to credit –46% of hh have obtained loans, but only 8% from a formal source Low educational attainment –Av. completed years of education: 1.24 years –23% of hh have school aged children (5-14 years old) out of school. Highly vulnerable –only 66% report that everyone in the hh regularly eats two meals a day –Appr. 50% report had a medical shock in the last year –41% suffered an economic shock.

13 Key Findings: The different between Ultra poor and Non ultra poor in our sample Those assigned a higher rank during wealth ranking appear poorer than others in several important respects Households classified as ultra poor have less land –On average, they own 6.3 katthas (0.13 acres) less land. This difference represents 74% of mean land holdings among those not identified as Ultra Poor. –They are more likely to be landless They have less access to formal sources of credit They have fewer assets

14 Key Findings- The different between Ultra poor and Non ultra poor in our sample They are less educated They are more likely to have children out of school They are more likely to lack able bodied adult household members. For ex, households with a disabled female member are 37% more likely to have been classified as UP during the PRA Surprisingly at first, ultra poor appear to spend more per capita. –However this can be due to the fact that ultra poor households are smaller - because of economies of scale –Indeed when comparing two households with the same number of members, the ultra poor don’t spend more.

15 Key Findings: The efficiency of the survey verification In our sample, 110 hh were identified as very poor or exceptionally poor by the PRA Out of these, 85 were selected as ultra poor beneficiaries after the Bandhan verification survey –Among these 110 hh, we compare those who were identified with those who were not Those households do not seem different on some dimensions, but those classified as ultra poor are poorer in terms of land holdings and house size. So Bandhan did successfully narrow the population identified by the PRA to the poorest within the group, esp on indicators of poverty which are easily observed by household visits, such as land and house size

16 Conclusion and next steps The ranking from the PRA accurately identifies a poorer sub-population along various important dimensions of poverty, most notably with respect to land holdings, assets and credit access Additional steps taken by Bandhan narrows the identified population to those who are more disadvantaged in crucial respects, particularly land holdings In future, Centre for Microfinance will evaluate the impact of this intervention on various social and economic outcomes of interest through a randomized controlled trial. This intervention and study will bring important new insights on how to effectively expand financial services to those who need it.