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Impact Evaluation Low-Income Housing Finance in Sri Lanka

Impact Evaluation Low-Income Housing Finance in Sri Lanka. Binh T. Nguyen Independent Evaluation Department Asian Development Bank 17 Oct 2010. Contents. Overview of ADB Assistance in Low-Income Housing Case Project: Urban Development and Low-Income Housing Project in Sri Lanka

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Impact Evaluation Low-Income Housing Finance in Sri Lanka

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  1. Impact EvaluationLow-Income Housing Finance in Sri Lanka Binh T. Nguyen Independent Evaluation Department Asian Development Bank 17 Oct 2010

  2. Contents • Overview of ADB Assistance in Low-Income Housing • Case Project: Urban Development and Low-Income Housing Project in Sri Lanka • The Impact Evaluation

  3. ADB Loans and Grants for LIUH

  4. Project Performance Ratings

  5. Case ProjectLoan 1632-SRI: Urban Development and Housing Project • Basic Data: • Approved 1998 Completed 2005 • Total cost $102.99M ADB loan $67.02M Govt/Banks $35.97M • Four Components: • Urban Development: roads, traffic improvement, water supply, drainage, etc. • Community Development: basic infra, tenure regulations • Housing Finance: housing loans to low-income households • Institutional Development: training on staff skills in municipal management, environment management, etc.

  6. Project Expenditure ($, Million)

  7. Housing Finance Component • Objectives: • Increase access of low-income households (LIHs) to market-based housing finance through the formal sector; • Facilitate improvements of housing conditions and quality of life; and • Promote formal banking sector interest in financing low-income housing market segment. • LIHs = households with monthly income below the 55th income percentile, i.e., below Rs12,500 (appr. $200) per month.

  8. Housing Finance Component (Cont.) • Basic Data: • Total Amount = $26.93M ADB loan = $19.93M PCIs = $7M • Total Borrowers = 28,378 • PCIs = 7 participating credit institutions • Housing Finance Development Corporation = 68.6% • 3 Regional Development Banks = 27.9% • 3 Commercial Banks (BOC, Hatton, National) = 3.5%

  9. Housing Finance Component (Cont.)Loan Disbursement by Province

  10. Housing Finance Component (Cont.)Loan Disbursement by Income Group

  11. Housing Finance Loan (Cont.)Loan Disbursement by Purpose

  12. Why This Project Was Chosen? • Findings of the evaluation will provide insights and lessons for urban development operations guidelines being prepared by the Urban COP • The project had clear assignment rule: Households with income below the 55th income percentile (this was confirmed during the Reconnaissance Mission in preparing for the evaluation) • The project appeared to be the best among ADB housing projects in terms of baseline data: loan applicants were required to submit a detailed household profile, and these are kept in PCIs

  13. The Impact Evaluation • Objectives: In addition to assessing the extent to which the housing finance component met its stated objectives by usingthe standard evaluation criteria, this impact evaluation will: • Empirically assess the welfare change of the beneficiaries that can be attributed to the housing finance component • Identify factors (social, economic, project design and implementation) influencing the project outcomes • Propose a sensible set of outcome indicators and benchmarks that can be used in future project design, monitoring and evaluation

  14. Evaluation Framework • Hypothesis: • Improved housing conditions will lead to increased labor productivity, income, and overall social well-being of the project beneficiaries. • Logic Model: • Inputs  Activities  Outputs  Outcomes  Impacts

  15. Impact Indicators • Follow IADB study (2008) and Field and Kremer (2006) • Household-level outcomes: • Housing quality index (HQI),[1] • Per capita household consumption expenditure (per year), • Household completeness (presence of spouse and formally married), • Occupation ratio (percent of working household members), • School attendance (of school age children), and • Nourishment ratio (percent of children under 6 who are not under-nourished). • [1] , where ai equals unity if the house has condition i, and zero • otherwise; and i runs through the seven dwelling quality indicators: potable water access, sewage connection, electricity connection, walls, floors, ceilings, and overcrowding problems (more than 2 persons living per room).

  16. Impact Indicators (Cont.) • Community-level outcomes: • Poverty rate (percent of households below the poverty line), • Housing shortage (percent of households without a house), • Loan default ratio, • Crime rates, and • Net migration (difference between migration in and out).

  17. Y Y ******************** ****************** *************** ********** ******************************************* ******************* ************ +++++++++++ ++++++++++++++++++ ++++++++++++++++++++ +++++++++++++++++++++++++++++++++++ +++++++ +++++++++++ ++++++++++++++++++ ++++++++++++++++++++ +++++++++++++++++++++++++++++++++++ +++++++ Participants Participants Non-participants Non-participants Cutoff Cutoff Score Score Estimation Methods • Regression Discontinuity Design: • Before the treatment After the treatment

  18. RDD (cont.) • The program impact is estimated by the mean difference in outcomes for persons above and below the cut-off point.

  19. Probability of Borrowing • Before treatment: • After treatment: .55 income .55 income

  20. Expected Outcome • Before Treatment: • After Treatment: .55 income .55 income

  21. Fuzzy Regression Discontinuity Design • Estimator: • where E[.] is the expectation operation; x* is the cutoff (i.e., the 55th income percentile); Yiand Xi are the outcome and forcing (treatment-determining) variable of household i, respectively; and is the assignment variable with 1[.] being the index function taking value 1 if the condition in the square brackets is correct, and zero otherwise. The denominator represents the jump in borrowing probability due to treatment assignment. • We will follow Imbens and Lemieux (2008)[1]to use the local linear regression estimation method, where both the difference in the outcome (numerator) and the difference in the borrowing probability (denominator) will be estimated by the fitted values of Y and W at both sides of the cutoff. • [1] Imbens, G. and T. Lemieux. 2008. Regression discontinuity designs: a guide to practice. Journal of Econometrics, vol. 142 (2): 615 – 635.

  22. Other Estimators: PSM and DD • The RDD estimator gives an unbiased estimate of the project impact. However, it only estimates the project effect near the cutoff. • If the project effect is constant, this poses no problem. • To give an estimate of the overall average treatment effect, we will also use the propensity score matching (PSM) method combined with a double/single difference estimator, pending data availability and quality.

  23. Data Requirement

  24. Household Sample

  25. Implementation Plan

  26. Thank you. • For more information: • http://www.adb.org/Evaluation/resources.asp

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