Review of Economics of the Household

, Volume 17, Issue 3, pp 947–967 | Cite as

Microfinance programs and domestic violence in northern Cameroon; the case of the Familial Rural Income Improvement Program

  • Donatien Eze EzeEmail author


The aim of this paper is to examine the relationship between female participation to the familial rural income improvement program (PARFAR) and domestic violence in the rural northern Cameroon. To achieve this, two hypothesis based respectively on the theory of marital bargaining and the theory of men’s backslash are tested applying propensity score matching to survey data from a sample of households in the area, to consider the possibility of sampling bias. A battery of test and estimation methods is used to check the robustness of findings. The results support the backslash theory. PARFAR participation leads to an improvement in the contribution of women in decision-making within the targeted households. This effect is associated with a reduction in violence acceptability but an increase in violence prevalence. This double result which embedded household dynamics in an adversarial logic then raises the question of prior cultural adjustment program for targeted households. Among actions to undertake for such attitudinal change about gender considerations in Cameroon, besides those mobilizing local government, non-governmental organizations and community based organizations, an additional challenge for policymakers could be improving policies facilitating access to legal institutions for victims of domestic violence.


Microfinance Empowerment Domestic violence Cameroon 

JEL classification

D13 G21 J16 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.


  1. Aizer, A. (2010). The gender gap and domestic violence. American Economic Review, 100(4), 1847–1859.Google Scholar
  2. Ahmed, S. M. (2011). Intimate partner violence against women: Experiences from a woman development program in Matlab Bangladesh. Journal of Health, Population and Nutrition, 23(1), 95–101.Google Scholar
  3. Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61, 962–973.Google Scholar
  4. Bates, L. M., Schuler, S. R., Islam I., & Islam, M. K. (2004). Socioeconomic factors and processes associated with domestic violence in rural Bangladesh. International Family Planning Perspectives 190–199.Google Scholar
  5. Becker, G. (1981). A treatise on the family. Cambridge MA: Harvard Press.Google Scholar
  6. Beleche, T. (2017). Domestic violence laws and suicide in Mexico. Review of Economics of the Household, 1–20. Scholar
  7. Becker, S., & Caliendo, M. (2007). Sensitivity analysis for average treatments effects. Stata Journal, 7(1), 71–83.Google Scholar
  8. Bishai, D., & Grossbard, S. (2010). Far above rubies: The association between bride price and extramarital sexual relations in Uganda. Journal of Population Economics, 23(4), 1177–1188.Google Scholar
  9. Cochran, W. G., & Rubin, D. B. (1973). Controlling bias in observational studies: A review. Sankya: The Indian Journal of Statistics, 35(4), 417–446.Google Scholar
  10. Dehejia, R., & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. The Review of Economics and Statistics, 84(1), 151–161.Google Scholar
  11. Eze Eze, D., & Zedou, A. (2015). ‘Microfinance inegalités de genre et conflits intrafamiliaux dans la zone rurale de savane au Cameroun » Communication aux journées de d’économie sociale Bobigny.Google Scholar
  12. Eswaran, M., & Malhotra, N. (2011). Domestic violence and women’s autonomy in developing countries, theory and evidence. Canadian Journal of Economics, 44(4), 1222–1263.Google Scholar
  13. Farmer, A., & Tiefenthaler (1997). An economic analysis of domestic violence. Review of Social Economy, 55(3), 337–358.Google Scholar
  14. Ferrari G., & Iyengar, R. (2010). Discussion sessions coupled with finance may enhance the role of women in household decision making in Burundi. CEP discussion paper.Google Scholar
  15. Goetz, A. M., & Gupta, R. S. (1996). Who takes the credit? gender, power, and control over loan use in rural credit programs in Bangladesh. World Development, 24(1), 45–63.Google Scholar
  16. Guyer, J., & Peters, P. (1987). Conceptualising the household; Issues of theory and policy in Africa. Introduction. Development and Change, 9, 29–41.Google Scholar
  17. Hashemi, S., Schuler, S. R., & Riley, A. (1996). Rural credit programs and women’s empowerment in Bangladesh. World Development, 24(1), 45–64.Google Scholar
  18. Jalan, J., & Ravallion, M. (2003). Estimating the benefit incidence of an antipoverty program by propensity score matching. Journal of Business and Economic Statistics, 21(1), 19–30.Google Scholar
  19. Jewkes, R. (2002). Intimate partner violence: Causes and prevention. Lancet, 359, 1423–1429.Google Scholar
  20. Johnson, S. (2004). Gender norms in financial markets: Evidence from kenya. World Development, 32(8), 1355–1374.Google Scholar
  21. Kabeer, N. (2001). Conflicts over credit: Re-evaluating the empowerment potential of loans to women in rural Bangladesh. World Development, 29, 63–84.Google Scholar
  22. Khandker, S. R. (2005). Microfinance and poverty: Evidence using panel data from bangladesh. World Bank Economic Review, 19(2), 263–286.Google Scholar
  23. Kim, G. G., Ferrari, T., Abramsky, C., Watts, J., Hargreaves, H., Mouson, G., Phetla, J., Porter, & Pronyk, P. (2009). Assessing the incremental effects combining economic and health interventions: Image study in South Africa. Bulletin of World Health Organization, 875(11), 824–832.Google Scholar
  24. Kishor, S. (2005). Domestic violence measurement in the demographic and health surveys: The history and the challenges. Geneva: Expert Paper prepared for The UN Division for the Advancement of Women.Google Scholar
  25. 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(2), 269–288.Google Scholar
  26. Luke, N., & Munki, K. (2011). Women as agents of change: Female income, social affiliation and household decision in south India. Journal of Development Economics, 94(1), 1–17.Google Scholar
  27. Lunceford, J. K., & Davidian (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study. Statistics in Medicine, 23(19), 2937–2960.Google Scholar
  28. Lundberg, S. J., & Pollak, R. (1993). Separate spheres, bargaining and the marriage market. Journal of Political Economy, 101, 988–1011.Google Scholar
  29. Mayoux, L. (1999). Questioning virtuous spirals: Microfinance and women’s empowerment in Africa. Journal of International Development, 11(7), 957–984.Google Scholar
  30. McElroy, M. B., & Horney, M. J. (1981). Nash bargaining household decisions; towards a generalization of theory of demand. International Economic Review, 22, 333–49.Google Scholar
  31. McElroy, M. B. (1990). The empirical content of nash bargained household behavior. Journal of Human resources, 25(4), 559–583.Google Scholar
  32. Nannicini, T. (2006). A simulation based sensitivity analysis for matching estimators». The Stata Journal.Google Scholar
  33. Osmani (2007). A breakthrough in women’s bargaining power: the impact of micro credit. Journal of International Development, 19, 695–716.Google Scholar
  34. Pitt, M. M., Khandker, S. R., McKernan, S., & 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.Google Scholar
  35. Rahman, A. (1999). Micro-credit initiatives for equitable and sustainable development: who pays? World Development, 27, 67–82. 35.Google Scholar
  36. Rosenbaum, P. R., & Rubin, D. B. (1983). The central of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.Google Scholar
  37. Ruffing, (2009). Cool head, warm heart: Governance and the mission of microfinance in the case of MC2 micro banks Cameroon ISP collection paper 730.Google 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 and Medicine, 43, 1729–42.Google Scholar
  39. Schuler, S. R., Hashemi, S. M., & Badal, S. H. (1998). Men’s violence against women in rural bangladesh: Undermined or exacerbated by microcredit programs? Development in Practice, 8, 148–156.Google Scholar
  40. Sen, A. (1990). Gender and cooperative conflicts in I. Tinker (ed.), Persistent inequalities: Women and world development. New York, Oxford University Press, pp. 123–149.Google Scholar
  41. Smith, J., & et Todd, P. (2005). Does matching overcome Lalonde’s critique on non experimental estimators? Journal of Econometrics, 125, 305–353.Google Scholar
  42. Steele, F., Amin, S., & Naved, R. T. (2001). Savings/credit group formation and change in contraception. Demography, 38(2), 267–282.Google Scholar
  43. Straus, M. A., & Douglass, E. M. (2004). A short form of the revised conflict tactics scales and typologies for severity and mutuality. Violence and Victims, 19, 507–520.Google Scholar
  44. Tan, Z. (2010). Bounded, efficient and doubly robust estimation with inverse weighting. Biometrika, 97, 661–682.Google Scholar
  45. Tauchen H. V., Witte, A. D., Long S. K. (1991). Violence in the family; a non-random affair. International Economic Review, 32(2), 491–511.Google Scholar
  46. Time, V. (2014). Women, law, and human rights in cameroon: Progress or status quo? Journal of Law and Conflict Resolution, 6(1), 1–6.Google Scholar
  47. Winship, C., & Morgan, S. (1999). The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659–706.Google Scholar
  48. Wooldrigde (2010). Econometric analysis of cross section and panel data. 2nd edition Cambridge MA: MIT Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.University of NgaoundéréNgaoundéréCameroon

Personalised recommendations