Pan-African Research Journal Africa
Published October 21, 2026 | Version v1
Journal article Open

Rural Cooperative Credit Access and Interdisciplinary Sociology Extension Uptake in Kenyan Arid Counties: A Regression Discontinuity Analysis of Savings-Group Eligibility Thresholds

Authors/Creators

  • 1. University of Nairobi

Description

Because no observational data are available, the study characterises the design's power to detect effects of 3, 5 and 8 percentage points across 8, 16, 24 and 32 clusters under a declared variance structure with an intraclass correlation of 0.64, between-cluster standard deviation of 6.0 and residual standard deviation of 4.5. Power was estimated across 2000 replications per design cell.54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The regression discontinuity design is feasible for evaluating savings-group thresholds only when the number of clusters exceeds the range typically available to single-programme evaluations in arid counties, and it derives the implications of this constraint for the design of interdisciplinary sociology extension evaluations.

Notes

Published in African Rural Development Studies (Interdisciplinary -, vol 27, issue 1, pp 57-70. DOI: 10.5281/zenodo.23541719. A Pan-African Research Journals (PARJ Africa) publication, https://parj.africa

Files

article.pdf

Files (849.9 kB)

Name Size
md5:095a00ad2c1f889f058711e67577827e
362.2 kB Download
md5:d36486e10196503bc7cbd743dc3d74c5
487.7 kB Preview Download

Additional details

Related works

Is identical to
Journal article: https://parj.africa/ajis_ruraldev/article/view/36243 (URL)

References

  • Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press.
  • Athey, S., & Imbens, G. W. (2006). Identification and inference in nonlinear difference-in-differences models. Econometrica, 74(2), 431-497.
  • Bell, R. M., & McCaffrey, D. F. (2002). Bias reduction in standard errors for linear regression with multi-stage samples. Survey Methodology, 28(2), 169-181.
  • Bloom, H. S. (2005). Randomizing groups to evaluate place-based programs. In H. S. Bloom (Ed.), Learning more from social experiments (pp. 115-172). Russell Sage Foundation.
  • Burton, A., Altman, D. G., Royston, P., & Holder, R. L. (2006). The design of simulation studies in medical statistics. Statistics in Medicine, 25(24), 4279-4292.
  • Cameron, A. C., & Miller, D. L. (2015). A practitioner's guide to cluster-robust inference. Journal of Human Resources, 50(2), 317-372.
  • Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2019). A practical introduction to regression discontinuity designs: Foundations. Cambridge University Press.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Donner, A., & Klar, N. (2000). Design and analysis of cluster randomization trials in health research. Arnold.
  • Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
  • Hayes, R. J., & Bennett, S. (1999). Simple sample size calculation for cluster-randomized trials. International Journal of Epidemiology, 28(2), 319-326.
  • Hedges, L. V., & Rhoads, C. (2010). Statistical power analysis in education research. National Center for Education Research.
  • Hox, J. J. (2002). Multilevel analysis: Techniques and applications. Lawrence Erlbaum Associates.
  • Imbens, G. W., & Kalyanaraman, K. (2012). Optimal bandwidth choice for the regression discontinuity estimator. Review of Economic Studies, 79(3), 933-959.
  • Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.
  • Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5-86.
  • Karlan, D., & Morduch, J. (2010). Access to finance. In D. Rodrik & M. Rosenzweig (Eds.), Handbook of development economics (Vol. 5, pp. 4703-4784). Elsevier.
  • Killip, G. (2007). Savings groups and financial inclusion in Kenya. Journal of Microfinance, 9(1), 45-67.
  • Lee, D. S., & Card, D. (2008). Regression discontinuity inference with specification error. Journal of Econometrics, 142(2), 655-674.
  • Maas, C. J. M., & Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling. Methodology, 1(3), 86-92.
  • Murray, D. M. (1998). Design and analysis of group-randomized trials. Oxford University Press.
  • Pepe, M. S. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford University Press.
  • Raudenbush, S. W. (1997). Statistical analysis and optimal design for cluster randomized trials. Psychological Methods, 2(2), 173-185.
  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage.
  • Raudenbush, S. W., & Liu, X. (2000). Statistical power and optimal design for multisite randomized trials. Psychological Methods, 5(2), 199-213.
  • Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Sage.
  • Spybrook, J., Bloom, H., Congdon, R., Hill, C., Martinez, A., & Raudenbush, S. W. (2011). Optimal design plus empirical evidence: Documentation for the 'Optimal Design' software. William T. Grant Foundation.
  • Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51(6), 309-317.
  • Zhou, X.-H., Obuchowski, N. A., & McClish, D. K. (2011). Statistical methods in diagnostic medicine (2nd ed.). Wiley.