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Microcredit, Family Planning Programs, and Contraceptive Behavior: Evidence From a Field Experiment in Ethiopia

Abstract

The impact of community-based family planning programs and access to credit on contraceptive use, fertility, and family size preferences has not been established conclusively in the literature. We provide additional evidence on the possible effect of such programs by describing the results of a randomized field experiment whose main purpose was to increase the use of contraceptive methods in rural areas of Ethiopia. In the experiment, administrative areas were randomly allocated to one of three intervention groups or to a fourth control group. In the first intervention group, both credit and family planning services were provided and the credit officers also provided information on family planning. Only credit or family planning services, but not both, were provided in the other two intervention groups, while areas in the control group received neither type of service. Using pre- and post-intervention surveys, we find that neither type of program, combined or in isolation, led to an increase in contraceptive use that is significantly greater than that observed in the control group. We conjecture that the lack of impact has much to do with the mismatch between women’s preferred contraceptive method (injectibles) and the contraceptives provided by community-based agents (pills and condoms).

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Notes

  1. There is not much consensus, however, on the overall effectiveness of such programs in achieving these goals. While Pritchett (1994) is skeptical of family planning programs, others like Bongaarts (1994) have argued for an important albeit nondominant role. An intermediate position is taken by Freedman (1997), whose literature survey concluded that, while an impact on fertility preferences has rarely been documented convincingly, several family planning services have allowed families to meet existing demand for fertility control.

  2. Alternatively, Eq. 1 could be estimated directly using PA-fixed effects. An earlier draft of this paper used this approach and led to almost identical results.

  3. More specifically, we calculate the weights as the mean number of observations from a given PA in the baseline and in the follow-up. These numbers are not always identical, although they are always very similar (the correlation is approximately .97 in both regions). Note also that the dependent variable in Model 2 is a weighted PA-specific mean, where all observations are weighted using the village-specific sampling weights.

  4. Alternatively, Eq. 1 could be estimated with 2SLS and PA fixed effects. This estimation strategy, which we adopted in an earlier version of the paper, leads to almost identical results.

  5. We note, however, that in small samples, the inclusion of pre-intervention characteristics does not necessarily increase the precision of the estimates, even in situations in which the randomization was done carefully. On the one hand, the R 2 of the regression will, by construction, increase. However, in small samples, the orthogonality between assigned treatment and the residuals usually does not hold exactly, with the consequence that the inclusion of other covariates is not guaranteed to decrease the standard errors. We also note that, although the inclusion of additional covariates is often advocated by randomized controlled trials practitioners (see, e.g., Duflo et al. 2008), the practice has been criticized by others as an ex-post adjustment not justified by randomization (see Deaton 2010; Freedman 2008).

  6. The changes in socioeconomic status described in Table 4 were relatively similar across treatment groups with the exception of borrowing, which, not surprisingly, shows significantly larger increases in areas where microcredit was introduced. For almost all other variables, we cannot reject the null hypothesis that the changes were the same across the four (actual) treatment groups. The detailed results are available upon request from the authors.

  7. For a description of the test, which is identical to a Hausman test under conditional homoskedasticity, see Hayashi 2000:200–201 or Baum et al. 2007:16.

  8. A version of the test, robust to the presence of heteroskedasticity or clustering, can be performed by using the Stata command ivreg2. This test can be considered as a generalization of the Anderson canonical correlation rank statistic to the non-i.i.d. case. The null hypothesis is that the smallest canonical correlation is zero, in which case the equation is not identified. A rejection of the null hypothesis indicates instead that the excluded instruments are relevant. The results of the first-stage regressions are available upon request from the authors.

  9. In the presence of more than one endogenous variable, multivariate versions of the test have been developed that evaluate the first stage for all endogenous variables jointly. Critical values for such tests have been developed only for specific combinations of the number of instruments and endogenous variables (see Tables 1 and 2 in Stock and Yogo 2002). Unfortunately, such critical values do not exist for a case such as ours, where there are three endogenous variables and three excluded.

  10. In the model with pre-intervention controls estimated in columns 3 and 6, the intercept does not have a meaningful interpretation.

  11. The OLS estimates, which for brevity are not reported, are available upon request from the authors.

  12. The full results are available upon request.

  13. The full results, omitted for brevity, are available upon request. We also note that the results for Amhara should be interpreted with caution because the Kleibergen-Paap tests indicate that the null of underidentification is not rejected at standard levels.

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Acknowledgements

Jaikishan Desai was employed by Family Health International during the course of this study. We thank the David and Lucile Packard Foundation for financial support and encouragement for the study, the Packard Foundation in Ethiopia for assistance with coordinating all aspects of the study, Birhan Research and Development Consultancy and Miz Hasab Research Center for conducting the two household surveys, and the four subgrantee organizations―ACSI, ADA, OCSSCO, and ODA―who, despite several pressures, extended their cooperation in implementing their interventions according to the study design. Last but not least, we are grateful to Laura Chioda, Jed Friedman, Jonathan Robinson, seminar participants at the World Bank and NEUDC (Boston), the Editor, and especially two anonymous referees for valuable comments and suggestions. The authors remain solely responsible for all remaining errors and omissions as well as for all the views and interpretations expressed throughout the paper.

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Desai, J., Tarozzi, A. Microcredit, Family Planning Programs, and Contraceptive Behavior: Evidence From a Field Experiment in Ethiopia. Demography 48, 749 (2011). https://doi.org/10.1007/s13524-011-0029-0

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Keywords

  • Family planning
  • Microcredit
  • Randomized controlled trial
  • Community health workers
  • Ethiopia