Is Group Lending A Good Enforcement Scheme for Achieving High Repayment Rates?: Evidence from Field Experiments in Vietnam

Microfinance institutions employ various kinds of incentive schemes but estimating the effect of each scheme is not easy due to endogeneity bias. We conducted field experiments in Vietnam to capture the role of joint liability, monitoring, cross-reporting, social sanctions, communication and group formation in borrowers’ repayment behavior. We find that joint liability contracts cause serious free-riding problems, inducing strategic default and lowering repayment rates. When group members observe each others’ investment returns, participants are more likely to choose strategic default. Even after introducing a cross-reporting system and/or penalties among borrowers, the default rates and the ratios of participants who chose strategic default under joint liability are still higher than those under individual lending. We also find that joint liability lending often failed to induce mutual insurance among borrowers. Those who had been helped or who had repaid a little in the previous round were more likely to default strategically and repay a little again in the current round and those who paid large amounts were always the same individuals.

The Risk of Using the Q Heterogeneity Estimator for Software Engineering Experiments

All meta-analyses should include a heterogeneity analysis. Even so, it is not easy to decide whether a set of studies are homogeneous or heterogeneous because of the low statistical power of the statistics used (usually the Q test). Objective: Determine a set of rules enabling SE researchers to find out, based on the characteristics of the experiments to be aggregated, whether or not it is feasible to accurately detect heterogeneity. Method: Evaluate the statistical power of heterogeneity detection methods using a Monte Carlo simulation process. Results: The Q test is not powerful when the meta-analysis contains up to a total of about 200 experimental subjects and the effect size difference is less than 1. Conclusions: The Q test cannot be used as a decision-making criterion for meta-analysis in small sample settings like SE. Random effects models should be used instead of fixed effects models. Caution should be exercised when applying Q test-mediated decomposition into subgroups.