, Volume 50, Issue 5, pp 1819–1843 | Cite as

Microcredit and Domestic Violence in Bangladesh: An Exploration of Selection Bias Influences



This article explores the relationship between women’s participation in microcredit groups and domestic violence in Bangladesh. Several recent studies have raised concern about microcredit programs by reporting higher levels of violence among women who are members. These results, however, may be attributable to selection bias because members might differ from nonmembers in ways that make them more susceptible to violence to begin with. Using a sample of currently married women from the 2007 Bangladesh Demographic Health Survey (BDHS) (N = 4,195), we use propensity score matching (PSM) as a way of exploring selection bias in this relationship. Results suggest that the previously seen strong positive association between membership and violence does not hold when an appropriate comparison group, generated using PSM, is used in the analyses. Additional analyses also suggest that levels of violence do not differ significantly between members and nonmembers and instead could depend on context-specific factors related to poverty. Members for whom a match is not found report considerably higher levels of violence relative to nonmembers in the unmatched group. The background characteristics of members and nonmembers who do not match suggest that they are more likely to be younger and from relatively well-to-do households.


Domestic violence Microcredit Propensity score matching Selection/selectivity Bangladesh 

Supplementary material

13524_2013_226_MOESM1_ESM.pdf (227 kb)
Online Resource 1(PDF 227 kb)


  1. Amin, S., Basu, A., & Stephenson, R. (2002). Spatial variation in contraceptive use in Bangladesh: Looking beyond the borders. Demography, 39, 251–267.CrossRefGoogle Scholar
  2. Amin, S., Diamond, I., & Steele, F. (1997). Contraception and religious practice in Bangladesh. In G. W. Jones, J. C. Caldwell, R. M. Douglas, & R. M. D’Souza (Eds.), The continuing demographic transition (pp. 268–289). Oxford, UK: Oxford University Press.Google Scholar
  3. Banerjee, A., & Duflo, E. (2009). The experimental approach to development economics. Annual Review of Economics, 1, 151–178.CrossRefGoogle Scholar
  4. Banerjee, A., Duflo, E., Glennerster, R., & Kinnan, C. (2009). The miracle of microfinance? Evidence from a randomized evaluation (Working paper). Cambridge, MA: MIT Department of Economics and Abdul Latif Jameel Poverty Action Lab.Google Scholar
  5. Campbell, J. C. (2002). Health consequences of intimate partner violence. Lancet, 359, 1331–1336.CrossRefGoogle Scholar
  6. Dehejia, R., & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. The Review of Economics and Statistics, 84, 151–161.CrossRefGoogle Scholar
  7. Diop-Sidibe, N. (2001). Domestic violence against women in Egypt: Risk factors and health out-comes of wife beating (Unpublished doctoral dissertation). School of Hygiene and Public Health, Johns Hopkins University, Baltimore, MD.Google Scholar
  8. Duvendack, M., Palmer-Jones, R., Copestake, J. G., Hooper, L., Loke, Y., Rao, N. (2011). What is the evidence of the impact of microfinance on the well-being of poor people? (EPPI-Centre Report No. 1912). London, UK: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London.Google Scholar
  9. Ellsberg, M. C., Pefia, R., Herrera, A., Liljestrand, J., & Winkvist, A. (1999). Wife abuse among women of childbearing age in Nicaragua. American Journal of Public Health, 89, 241–244.CrossRefGoogle Scholar
  10. Filmer, D., & Pritchett, L. (2001). Estimating wealth effects without expenditure data—Or tears: An application to educational enrollments in states of India. Demography, 38, 115–132.Google Scholar
  11. Gazmararian, J. A., Adams, M. M., & Pamuk, E. R. (1996). Associations between measures of socioeconomic status and maternal health behavior. American Journal of Preventive Medicine, 12, 108–115.Google Scholar
  12. Goetz, A. M., & Sen Gupta, R. (1996). Who takes the credit? Gender, power and control over loan use in rural credit programmes in Bangladesh. World Development, 24, 45–63.CrossRefGoogle Scholar
  13. Hashemi, S. M., Schuler, S. R., & Riley, A. P. (1996). Rural credit programs and women’s empowerment in Bangladesh. World Development, 24, 635–653.CrossRefGoogle Scholar
  14. Heise, L. L. (1998). Violence against women: An integrated, ecological framework. Violence Against Women, 4, 262–290.CrossRefGoogle Scholar
  15. Jalan, J., & Ravallion, M. (2003). Estimating the benefit incidence of an antipoverty program by propensity score matching. Journal of Business and Economic Statistics, 21, 19–30.CrossRefGoogle Scholar
  16. Jewkes, R. (2002). Intimate partner violence: Causes and prevention. Lancet, 359, 1423–1429.CrossRefGoogle Scholar
  17. Kabeer, N. (2001). Conflicts over credit: Re-evaluating the empowerment potential of loans to women in rural Bangladesh. World Development, 29, 63–84.CrossRefGoogle Scholar
  18. Karlan, D., & Zinman, J. (2010). Expanding credit access: Using randomized supply decisions to estimate the impacts. Review of Financial Studies, 23, 433–464.CrossRefGoogle Scholar
  19. Karlan, D., & Zinman, J. (2011). Microcredit in theory and practice: Using randomized credit scoring for impact evaluation. Science, 322, 1278–1284.CrossRefGoogle Scholar
  20. Khandker, S. R. (2005). Microfinance and poverty: Evidence using panel data from Bangladesh. World Bank Economic Review, 19, 263–286.CrossRefGoogle Scholar
  21. Kishor, S. (2005). Domestic violence measurement in the demographic and health surveys: The history and the challenges (Expert paper prepared for the UN Division for the Advancement of Women). Geneva, Switzerland: United Nations.Google Scholar
  22. Kishor, S., & Johnson, K. (2006). Reproductive health and domestic violence: Are the poorest women uniquely disadvantaged? Demography, 43, 293–307.CrossRefGoogle Scholar
  23. Koenig, M. A., Ahmed, S., Hossain, M. B., & Mozumder, A. (2003). Women’s status and domestic violence in rural Bangladesh: Individual- and community-level effects. Demography, 40, 269–288.CrossRefGoogle Scholar
  24. Leuven, E., & Sianesi, B. (2003). PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing (Version 1.2.3). Retrieved from
  25. Montgomery, R., Bhattacharya, D., & Hulme, D. (1996). Credit for the poor in Bangladesh. In D. Hulme & P. Mosley (Eds.), Finance against poverty (pp. 123–227). London, UK: Routledge.Google Scholar
  26. Morduch, J. (1998). Does microfinance really help the poor? New evidence from flagship programs in Bangladesh (RPDS Working Paper No. 198). Princeton, NJ: Research Program in Development Studies, Woodrow Wilson School of Public and International Affairs, Princeton University.Google Scholar
  27. National Institute of Population Research and Training (NIPORT), Mitra and Associates, and Macro International. (2009). Bangladesh Demographic and Health Survey 2007. Dhaka, Bangladesh, and Calverton, MD: National Institute of Population Research and Training, Mitra and Associates, and Macro International.Google Scholar
  28. Naved, R., & Persson, L. (2005). Factors associated with spousal physical violence against women in Bangladesh. Studies in Family Planning, 36, 289–300.CrossRefGoogle Scholar
  29. Pitt, M., & Khandker, S. (1998). The impact of group-based credit on poor households in Bangladesh: Does the gender of participants matter? Journal of Political Economy, 106, 958–996.CrossRefGoogle Scholar
  30. Pitt, M. M., Khandker, S. R., McKernan, S. M., & Latif, M. A. (1999). Credit programs for the poor and reproductive behavior in low-income countries: Are the reported causal relationships the result of heterogeneity bias? Demography, 36, 1–21.CrossRefGoogle Scholar
  31. Pronyk, P., Hargreaves, J., Kim, J., Morison, L., Phetla, G., Watts, C., & Porter, J. (2009). Effect of a structural intervention for the prevention of intimate-partner violence and HIV in rural South Africa: A cluster randomized trial. The Lancet, 368, 1973–1983.CrossRefGoogle Scholar
  32. Rahman, A. (1986). Impact of Grameen Bank on the situation of poor rural women (BIDS Working Paper No. 1). Dhaka: Bangladesh Institute for Development Studies.Google Scholar
  33. Rahman, A. (1999). Micro-credit initiatives for equitable and sustainable development: Who pays? World Development, 27, 67–82.CrossRefGoogle Scholar
  34. Robsenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.CrossRefGoogle Scholar
  35. Roodman, D., & Morduch, J. (2009). The impact of microcredit on the poor in Bangladesh: Revisiting the evidence (CGD Working Paper No. 174). Washington, DC: Center for Global Development.Google Scholar
  36. Schuler, S. R., & Hashemi, S. M. (1994). Credit programs, women’s empowerment, and contraceptive use in rural Bangladesh. Studies in Family Planning, 25, 65–76.CrossRefGoogle Scholar
  37. Schuler, S. R., Hashemi, S. M., & Badal, S. H. (1998). Men’s violence against women in rural Bangladesh: Undermined or exacerbated by microcredit programmes? Development in Practice, 8, 148–157.CrossRefGoogle Scholar
  38. Schuler, S. R., Hashemi, S. M., Riley, A. P., & Akhter, S. (1996). Credit programs, patriarchy and men’s violence against women in rural Bangladesh. Social Science & Medicine, 43, 1729–1742.CrossRefGoogle Scholar
  39. Steele, F., Amin, S., & Naved, R. T. (2001). Savings/credit group formation and change in contraception. Demography, 38, 267–282.CrossRefGoogle Scholar
  40. Straus, M. A. (1990). Manual for the conflict tactics scales. Durham: Family Research Laboratory, University of New Hampshire.Google Scholar
  41. World Bank. (2009). Gender-based violence, health and the role of the health sector (Online brief). Washington, DC: World Bank.Google Scholar

Copyright information

© Population Association of America 2013

Authors and Affiliations

  1. 1.Population Council, Program on Reproductive HealthGulshanBangladesh
  2. 2.Population Council, Program on Poverty, Gender and YouthNew YorkUSA

Personalised recommendations