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Statistics and the Millennium Development Goals

  • David J. Fitch
  • Paul Wassenich
  • Paul Fields
  • Fritz Scheuren
  • Jana Asher

Keywords

Propensity Score Millennium Development Goal Poverty Reduction Impact Evaluation Medical Expenditure Panel Survey 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References and Selected Evaluation Bibliography

  1. Bourguignon F, Pereira da Silva LA (eds) (2003) The impact of economic policies on poverty and income distribution: evaluation techniques and tools. World Bank and Oxford University Press, Washington DCGoogle Scholar
  2. Bräutigam DA, Knack S (2004) Foreign aid, institutions, and governance in sub-Saharan Africa. Economic Development and Cultural Change 52:255–285CrossRefGoogle Scholar
  3. Campbell DT (1971) Methods for the experimenting society. Unpublished; available from dfitch@uvg.edu.gtGoogle Scholar
  4. Campbell, DT (1988a) Reforms as experiments. In: Overman ES (ed) Methodology and epistemology for social science: selected papers, University of Chicago Press, ChicagoGoogle Scholar
  5. Campbell, DT (1988b) The experimenting society. In: Overman ES (ed) Methodology and epistemology for social science: selected papers, University of Chicago Press, ChicagoGoogle Scholar
  6. CIDA (2004) CIDA evaluation guide. Canadian International Development Agency, Ottawa (www.acdi-cida.gc.ca/INET/IMAGES.NSF/vLUImages/Performancereview5/file/Evaluation%20Guide.pdf accessed May 6, 2007)Google Scholar
  7. Deichmann U (1997) African population database documentation – population projections and data quality, additional sources of error. National Center for Geographic Information and Analysis, University of California, Santa Barbara (www.glowa-volta.de/cd_v3.1/landuse/populat/africa.htm, accessed May 6, 2007)Google Scholar
  8. Duflo E, Kremer M (2004) Use of randomization in the evaluation of development effectiveness. In: Feinstein O, Ingram GK, Pitman GK (eds) Evaluating development effectiveness (World Bank Series on Evaluation and Development, Volume 7), Transaction Publishers, New Brunswick, pp 205–232 (http://econ-www.mit.edu/faculty/download_pdf.php?id=759, accessed May 6, 2007)Google Scholar
  9. Duflo E (2006) Field experiments in development economics. Prepared for the 2005 World Congress of the Econometric Society, London (http://cemmap.ifs.org.uk/papers/vol2_chap13.pdf, accessed May 6, 2007)Google Scholar
  10. Felkner J, Kohagen K, Mark K, Reynolds M, Telgarsky J, Wolter, K (2006) Design report: impact evaluation design and implementation services – Benin. Report prepared for Millennium Challenge CorporationGoogle Scholar
  11. Ferris R (2006) Heartbreak on the Serengeti. National Geographic 209:2, 4, 7–12, 14–20, 22–26, 28–29Google Scholar
  12. Halberstadt S, Wood S (2006) Global public health: the new frontier of statistics. Chance 19:35–39MathSciNetGoogle Scholar
  13. Haines A, Cassels A (2004) Can the Millennium Development Goals be attained? The British Medical Journal 329:394–397 (http://bmj.com/cgi/content/full/329/7462/394, accessed May 6, 2007)Google Scholar
  14. Heckman J, Ichimura H, Todd P (1998) Matching as an econometric evaluation estimator: evidence from evaluating a job training program. Review of Economic Studies 64:605–654MathSciNetGoogle Scholar
  15. Heckman J, Ichimura H, Smith J, Todd P (1998) Characterizing selection bias using experimental Data. Econometrica 66:1017–1099MATHCrossRefMathSciNetGoogle Scholar
  16. Lall S, Spatafora N, Sommer M (2005) Building institutions. Chapter 3 in IMF World Economic Outlook, International Monetary Fund, Washington DCGoogle Scholar
  17. Lokshin M, Yemtsov R (2003) Evaluating the impact of infrastructure rehabilitation projects on household welfare in rural Georgia. World Bank Working Paper 3155. World Bank, Washington DCCrossRefGoogle Scholar
  18. Lee K, Walt G, Haines A (2004) The challenge to improve global health. Journal of the American Medical Association 291:2636–2638 (http://jama.ama-assn.org/cgi/content/full/291/21/2636, accessed May 6, 2007)Google Scholar
  19. Minkel JR (2005) Trials for the poor. Scientific American 293:18–20CrossRefGoogle Scholar
  20. Organization for Economic Cooperation and Development (2006) OECD Factbook 2006 Economic, Environmental and Social Statistics. OECD Publishing, Paris (http://new.sourceoecd.org/factbook, accessed May 6, 2007)Google Scholar
  21. Powell CS (2006) The discover interview: Jeffrey Sachs. Discover, November, pp 60–65Google Scholar
  22. Pritchett L (2002) It pays to be ignorant: a simple political economy of rigorous program evaluation. Journal of Policy Reform 5:251–269 (http://taylorandfrancis.metapress.com/openurl.asp?genre=article&issn=1384-1289&volume=5&issue=4&spage=251, accessed May 6, 2007)Google Scholar
  23. Rao V, Woolcock M (2004) Integrating qualitative and quantitative approaches in program evaluation. In: Bourguignon F, Pereira da Silva LA (eds) The impact of economic policies on poverty and income distribution: evaluation techniques and tools. World Bank and Oxford University Press, Washington DC (www.cultureandpublicaction.org/bijupdf/ch08.pdf, accessed May 6, 2007)Google Scholar
  24. Ravallion M (2001) The mystery of the vanishing benefits: an introduction to impact evaluation. World Bank Economic Review 15:115–140 (http://siteresources.worldbank.org/INTISPMA/Resources/383704-1130267506458/Mystery_Vanishing_Benefits.pdf, accessed May 6, 2007)Google Scholar
  25. Reinikka RS (1999) Using surveys for public sector reform. PREM Notes No. 23. World Bank, Washington DCGoogle Scholar
  26. Reinikka RS, Smith N (2004) Public expenditure tracking in education. International Institute for Education Planning, PeruGoogle Scholar
  27. Rosenbaum PR, Rubin DR (1985) Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39(1):33–38.CrossRefMathSciNetGoogle Scholar
  28. Sachs JD (2005) Can extreme poverty be eliminated? Scientific American 293:56–65Google Scholar
  29. Scheuren F (2005) Paradata and metadata. Proceedings of the 2005 Statistics Canada Methodology Conference (www.statcan.ca, accessed May 6, 2007)Google Scholar
  30. Scheuren F, Felkner J, Polen S (2006) Georgia summary design report. Prepared by the National Opinion Research Center for the Millennium Challenge CorporationGoogle Scholar
  31. Telgarsky J, Scheuren F (2006) Benin summary design report. Prepared by the National Opinion Research Center for the Millennium Challenge CorporationGoogle Scholar
  32. United Nations (2006) Millennium project (www.unmillenniumproject.org, accessed May 6, 2007)Google Scholar
  33. Wall B (2006) Facing global challenges while turning a profit. International Herald Tribune July 8–9:15–16Google Scholar

Impact Evaluation: Overview

  1. Baker JL (2000) Evaluating the impact of development projects on poverty: a handbook for practitioners. World Bank, Washington DCCrossRefGoogle Scholar
  2. Chase RS (2002) Supporting communities in transition: the impact of the Armenia social investment fund. World Bank Economic Review, 16:219–240CrossRefGoogle Scholar
  3. Chen S, Ravallion M (2003) Hidden impact? Ex-post evaluation of an anti-poverty program. World bank poverty research working paper 3049. World Bank, Washington DCCrossRefGoogle Scholar
  4. Fields P (2006) Statisticians and the millennium development goals. Paper presented at the annual Joint Statistical Meetings, August 6–10Google Scholar
  5. Fitch DJ (2006) A role for experimental evaluation in efforts to achieve millennium development goals. Paper presented at the annual Joint Statistical Meetings, August 6–10Google Scholar
  6. Grosh M, Glewwe P (eds) (2000) Designing household survey questionnaires for developing countries: lessons from 15 years of the living standards measurement study, Volumes 1–3. The World Bank, Washington DCGoogle Scholar
  7. Gueron JM (2002) The politics of random assignment: implementing studies and impacting Policy. In: Mosteller F, Boruch R (eds) Evidence matters: randomized trials in education research, Brookings Institution Press, Washington DCGoogle Scholar
  8. Heckman J, Ichimura H, Todd P (1998) Matching as an econometric evaluation estimator: evidence from evaluating a job training program. Review of Economic Studies 64:605–654MathSciNetGoogle Scholar
  9. Heckman J, Ichimura H, Smith J, Todd P (1998) Characterizing selection bias using experimental data. Econometrica 66:1017–1099MATHCrossRefMathSciNetGoogle Scholar
  10. Howell EM, Yemane A (2006) An assessment of evaluation designs: case studies of 12 large federal evaluations. American Journal of Evaluation 27:219–236CrossRefGoogle Scholar
  11. Prennushi G, Rubio G, Subbarao K (2002) Monitoring and evaluation. In: Klugman J (ed) A sourcebook for poverty reduction strategies, World Bank, Washington DC, pp 105–130Google Scholar
  12. Ravallion M (2001) The mystery of the vanishing benefits: an introduction to impact evaluation. The World Bank Economic Review 15:115–140CrossRefGoogle Scholar
  13. Ravallion M (2005) Evaluating anti-poverty programs. The World Bank Economic Review 15: 115–140CrossRefGoogle Scholar
  14. Savedoff WD, Levine R, Birdsall N, Co-Chairs (2006) When will we ever learn? Improving lives through impact evaluation. Report of the evaluation gap working group, Center for Global Development, Washington DCGoogle Scholar
  15. Scheuren FJ (2006) Statistics and the millennium development goals: government statistics. Discussion at the annual joint statistical meetings, Seattle, August 6–10Google Scholar
  16. Skoufias E (2005) PROGRESA and its impacts on the welfare of rural households in Mexico. International Food Policy Research Institute research report 139. International Food Policy Research Institute, Washington DCGoogle Scholar
  17. Wassenich P (2006) The role of monitoring and evaluation in development programs. Paper presented at the annual joint statistical meetings, Seattle, August 6–10Google Scholar

Samtskhe-Javakheti Roads Rehabilitation Activity

  1. Bakht Z (2000) Poverty impact of rural roads and markets improvement & maintenance project of Bangladesh. Paper presented at the World Bank South Asia Poverty Monitoring and Evaluation Workshop, New Delhi, June 8–10Google Scholar
  2. Chomitz KM, Gray DA (1996) Roads, land use, and deforestation: a spatial model applied to Belize. The World Bank Economic Review 10:487–512Google Scholar
  3. Cook CC, Duncan T, Jitsuchon S, Sharma A, Guobao W (2005) Assessing the impact of transport and energy infrastructure on poverty reduction. Asian Development Bank, ManilaGoogle Scholar
  4. Escobal J, Ponce C (2002) The benefits of rural roads: enhancing income opportunities for the rural poor. GRADE working paper 20. Grupo de Analisis para el Desarrollo, LimaGoogle Scholar
  5. Grootaert C, with guidance from Calvo CM (2002) Socioeconomic impact assessment of rural roads: methodology and questionnaires. Roads and rural transport thematic group, World Bank, Washington DCGoogle Scholar
  6. Jacoby HG (2000) Access to markets and the benefits of rural roads. The Economic Journal 110 (July):713–737CrossRefGoogle Scholar
  7. Hine J, Cundill M (1994) Economic assessment of road projects: do our current procedures tell us what we want to know? Paper presented at the International Workshop on Impact Evaluation and Analysis of Transportation Projects in Developing Countries, Bombay, December 13–16Google Scholar
  8. Lokshin M, Yemtsov R (2003) Evaluating the impact of infrastructure rehabilitation projects on household welfare in rural Georgia. World Bank working paper 3155. World Bank, Washington DCCrossRefGoogle Scholar
  9. Miller HJ, Wu Y-H (2000) GIS software for measuring space-time accessibility in transportation planning and analysis. GeoInformatica 4:141–159MATHCrossRefGoogle Scholar
  10. Rosero-Bixby L (2004) Spatial access to health care in Costa Rica and its equity: A GIS-based study. Social Science & Medicine 58:1271–1284CrossRefGoogle Scholar
  11. van de Walle D (2002) Choosing rural road investments to help reduce poverty. World Development 30:575–589CrossRefGoogle Scholar
  12. van de Walle D, Cratty D (2002) Impact evaluation of a rural road rehabilitation project. World Bank, Washington DCGoogle Scholar

Agribusiness Development Activity

  1. Alex G, Byerlee D (2000) Monitoring and evaluation for AKIS projects: framework and options. Agricultural knowledge and information systems (AKIS) good practice note. World Bank, Washington DCGoogle Scholar
  2. Birkhaeuser D, Evenson RE, Feder G (1991) The economic impact of agricultural extension: a review. Economic Development and Cultural Change 39:607–650CrossRefGoogle Scholar
  3. Conley TG, Udry CR (2005) Learning about a new technology: pineapple in Ghana. Paper presented at the productivity growth: causes and consequences conference of the Federal Reserve Bank of San Francisco, San Francisco, November 18–19Google Scholar
  4. Diaz JJ, Handa S (2006) An assessment of propensity score matching as a nonexperimental impact estimator: evidence from Mexico’s PROGRESA program. Journal of Human Resources 41:319–345Google Scholar
  5. Duflo E, Kremer M (2003) Use of randomization in the evaluation of development effectiveness. Paper prepared for the World Bank operations evaluation department conference on evaluation and development effectiveness, Washington DC, July 15–16Google Scholar
  6. Evaluation Partnership Limited (2005) Impact evaluation of four CFC funded projects in Uganda: final report. United Nations Common Fund for Commodities, AmsterdamGoogle Scholar
  7. Feder G, Slade R (1986) The impact of agricultural extension: the training and visit system in India. Research Observer 1:139–161CrossRefGoogle Scholar
  8. Friedlander D, Robins PK (1995) Evaluating program evaluations: new evidence on commonly used nonexperimental methods. The American Economic Review 85:923–937Google Scholar
  9. Gautam M, Anderson JR (1999) Reconsidering the evidence on the returns to T&V extension in Kenya. Operations evaluations department, World Bank, Washington DCCrossRefGoogle Scholar
  10. Gautam M (2000) Agricultural extension: the Kenya experience: an impact evaluation. Operations evaluation department, World Bank, Washington DCGoogle Scholar
  11. Kumar K (1995) Measuring the performance of agricultural and rural development programs. New directions for evaluation 67:81–91CrossRefGoogle Scholar
  12. Minten B (1999) Infrastructure, market access, and agricultural prices: evidence from Madagascar. International Food Policy Research Institute MSSD Discussion Paper No 26. International Food Policy Research Institute, Washington DCGoogle Scholar
  13. Owens T, Hoddinott J, Kinsey B (2003) The impact of agricultural extension on farm production in resettlement areas of Zimbabwe. Economic Development and Cultural Change 51:337–57CrossRefGoogle Scholar
  14. Sebstad J, Snodgrass D (2004) Assessing the impact of the Kenya BDS and the horticulture development center projects in the treefruit subsector of Kenya: baseline research design. USAID, Washington DCGoogle Scholar
  15. Sebstad J, Snodgrass D (2005) Assessing the impact of the Kenya BDS and the horticulture development center projects in the tree fruit value chain of Kenya: baseline research report. USAID, Washington DCGoogle Scholar
  16. Verna S, Burnett M (1999) Addressing the attribution question in extension. Paper presented at the annual conference of the American Evaluation Association. Orlando, November 3–6Google Scholar
  17. von Oppen M, Njehia BK, Ijaimi A (1997) The impact of market access on agricultural productivity: lessons from India, Kenya and the Sudan. Journal of International Development 9:117–132CrossRefGoogle Scholar

Georgia Regional Development Fund

  1. Afrane S (2002) Impact assessment of microfinance interventions in Ghana and South Africa: a synthesis of major impacts and lessons. Journal of Microfinance 4:37–57Google Scholar
  2. Aivazian V, Mazumdar D, Santor E (2003) Financial constraints and investment: assessing the impact of a World Bank Loan program on small and medium-sized enterprises in Sri Lanka. Bank of Canada working paper 2003-37. Bank of Canada, OntarioGoogle Scholar
  3. Batra G, Mahmood S (2003) Direct support to private firms: evidence on effectiveness. World Bank policy research working paper 3170. World Bank, Washington DCGoogle Scholar
  4. Hulme D (1997) Impact assessment methodologies for microfinance: a review. Paper prepared for the AIMS project for the virtual meeting of the CGAP working group on impact assessment methodologies, April 17 – 19Google Scholar
  5. Mosley P (1997) The use of control groups in impact assessments for microfinance. International Labour Office working paper N 19. International Labour Office, GenevaGoogle Scholar
  6. Rogerson CM (2004) The impact of the South African government’s SMME programmes: a ten-year review (1994–2003). Development Southern Africa 21:765–784CrossRefGoogle Scholar
  7. Tan H, Acevedo GL (2004) Evaluating SME training programs: some lessons from Mexico’s CIMO program. World Bank, Washington DCGoogle Scholar

Some Recent Propensity Scoring Method Literature14

  1. Anstrom KJ, Tsiatis AA (2001) Utilizing propensity scores to estimate causal treatment effects with censored time-lagged data. Biometrics 57:1207–1218CrossRefMathSciNetGoogle Scholar
  2. Baker SG, Lindeman KS (2001) Rethinking historical controls. Biostatistics (Oxford) 2:383–396MATHCrossRefGoogle Scholar
  3. Bakken DG, Terhanian G, Bremer J (2001) Projecting Internet survey results to the general population: a propensity score adjustment method. Paper presented at the ART forum, Amelia IslandGoogle Scholar
  4. Cochran WG (1968) The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24:205–213CrossRefMathSciNetGoogle Scholar
  5. Díaz-Tena N, Potter F (2003) Nonresponse adjustments for a survey of children with disabilities using information of a responsible adult. ASA proceedings of the joint statistical meetings 1217–1222Google Scholar
  6. D’Agostino Jr RB, Rubin DB (2000) Estimating and using propensity scores with partially missing data. Journal of the American Statistical Association 95:749–759CrossRefGoogle Scholar
  7. D’Agostino Jr RB, Craven T, Slone S, Lang W, Morgan T (2000) Estimating and using propensity scores with nonignorable missing covariates. ASA proceedings of the epidemiology section 85–90Google Scholar
  8. Duncan KB, Stasny EA (2001) Using propensity scores to control coverage bias in telephone surveys. Survey Methodology 27:121–130Google Scholar
  9. Elliott MR, Davis WW (2005) Obtaining cancer risk factor prevalence estimates in small areas: Combining data from two surveys. Journal of the Royal Statistical Society, Series C: Applied Statistics 54:595–609MATHCrossRefMathSciNetGoogle Scholar
  10. Ellis BH, Bannister WM, Cox JK, Fowler BM, Shannon ED, Drachman D, Adams RW, Giordano LA (2003) Utilization of the propensity score method: an exploratory comparison of proxy-completed to self-completed responses in the Medicare Health Outcomes Survey. Health and Quality of Life Outcomes 1:47Google Scholar
  11. Hong G, Raudenbush SW (2003) Causal inference for multi-level observational data with application to kindergarten retention study. ASA proceedings of the joint statistical meetings 1849–1856Google Scholar
  12. Joffe MM, Rosenbaum PR (1999) Propensity scores. American Journal of Epidemiology 150: 327–333Google Scholar
  13. Kondratovich M (2002) Matched receiver operating characteristic (ROC) analysis and propensity scores. ASA proceedings of the joint statistical meetings 1904–1908Google Scholar
  14. Larsen MD (1999) An analysis of survey data on smoking using propensity scores. Sankhyā, Series B 61:91–105Google Scholar
  15. Marcus SM, Gibbons RD (2001) Estimating the efficacy of receiving treatment in randomized clinical trials with noncompliance. Health Services & Outcomes Research Methodology, 2: 247–258CrossRefGoogle Scholar
  16. Patterson BH, Dayton CM, Graubard BI (2002) Latent class analysis of complex sample survey data: application to dietary data. Journal of the American Statistical Association 97:721–729MATHCrossRefMathSciNetGoogle Scholar
  17. Ridgeway G (2006) Assessing the effect of race bias in post-traffic stop outcomes using propensity scores. Journal of Quantitative Criminology 22:1–29CrossRefGoogle Scholar
  18. Rosenbaum PR (1995) Observational studies. Springer, New YorkMATHGoogle Scholar
  19. Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55MATHCrossRefMathSciNetGoogle Scholar
  20. Rosenbaum PR, Rubin DB (1984) Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 79:516–524CrossRefGoogle Scholar
  21. Rubin DB (1985) The use of propensity scores in applied Bayesian inference. In: Bernardo JM, DeGroot MH, Lindley DV, Smith AFM (eds) Bayesian statistics 2, Elsevier/North-Holland, New York and Amsterdam, 463–472Google Scholar
  22. Rubin DB (1997) Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine 127:757–763Google Scholar
  23. Rubin DB (2001) Using propensity scores to help design observational studies: application to the tobacco litigation. Health Services & Outcomes Research Methodology 2:169–188CrossRefGoogle Scholar
  24. Rubin DB, Thomas N (1996) Matching using estimated propensity scores: relating theory to practice. Biometrics 52:249–264MATHCrossRefGoogle Scholar
  25. Schonlau M, van Soest A, Kapteyn A, Couper MP, Winter J (2004) Adjusting for selection bias in web surveys using propensity scores: The case of the health and retirement study. ASA proceedings of the joint statistical meetings 4326–4333Google Scholar
  26. Stürmer T, Schneeweiss S, Brookhart MA, Rothman KJ, Avorn J, Glynn RJ (2005) Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal anti-inflammatory drugs and short-term mortality in the elderly. American Journal of Epidemiology 161:891–898CrossRefGoogle Scholar
  27. Taylor H (2000) Does Internet research work? Comparing online survey results with telephone survey. International Journal of Market Research 42:1, 51–83Google Scholar
  28. Wassell J (2002) Causal analysis of back belts to prevent low back pain. ASA proceedings of the joint statistical meetings 3645–3650Google Scholar
  29. Wong EL (1999) Propensity scores in epidemiology, selectivity models in econometrics: bridging the gap. ASA proceedings of the business and economic statistics section, 191–196Google Scholar
  30. Wun L-M, Ezzati-Rice TM, Machlin SR, Baskin R (2003) Investigation of alternative nonresponse adjustment methods in the Medical Expenditure Panel Survey. ASA proceedings of the joint statistical meetings, 4612–4618Google Scholar
  31. Wun L-M, Ezzati-Rice TM, Baskin R, Greenblatt J, Zodet M, Potter F, Diaz-Tena N, Touzani M (2004) Using propensity scores to adjust weights to compensate for dwelling unit level nonresponse in the Medical Expenditure Panel Survey. ASA Proceedings of the Joint Statistical Meetings, 4625–4631Google Scholar
  32. Zaccaro DJ, Wolfson M, Preisser JS (2000) Use of propensity scores in a non-randomized community trial: evaluating the enforcing underage drinking laws program. ASA proceedings of the epidemiology section, 74–79Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • David J. Fitch
  • Paul Wassenich
  • Paul Fields
  • Fritz Scheuren
  • Jana Asher

There are no affiliations available

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