Group lending has been widely adopted in the past thirty years by many microfinance institutions as a means to mitigate information asymmetries when delivering credit to the poor. This paper proposes an empirical method to address the potential omitted-variable problem resulting from unobserved group types when modeling the repayment behavior of group members. We estimate the model using a rich dataset from a group-lending program in India. The estimation results support our model specification and show the advantages of relying on a type-varying method when analyzing the probability of default of group members. In particular, our model helps to better understand the factors driving repayment behavior, which may differ across group types, and shows a higher predictive power than standard single-agent choice models.

The substantial differences in agricultural productivity between Asia and Africa can be largely explained by differences in use of modern inputs. The evidence suggests that better access to infrastructure (such as roads and irrigation) and agricultural services has given Asian farmers significantly better access to modern inputs, while Sub-Saharan African farmers without such an access are not able to fully exploit the benefits of modern agricultural inputs. This brief discusses the relationship between agricultural service provision and modern input use by farmers in Nigeria, with a focus on the differences among states and local government areas (LGA).

From 2006 to mid-2008 the international prices of agricultural commodities increased considerably, by a factor larger than two. This upward trend in agricultural prices captured the world's attention as a new food crisis was emerging. Several explanations for these movements in prices, ranging from demand-driven forces to supply shocks, have been provided by analysts, researchers, and development institutions. This paper is an attempt to empirically validate these explanations using time series econometrics and data at monthly frequency. We focus on the international price of corn, wheat, rice, and soybeans. First, we identify variables associated with the factors mentioned as causing the increase in these agricultural commodities prices. Second, we use time series analysis to try to quantitatively validate those explanations. The empirical work presented here includes first difference models and rolling Granger causality tests. Overall, our empirical analysis mainly provides evidence that financial activity in futures markets and proxies for speculation can help explain the observed change in food prices; any other explanation is not well supported by our time series analysis.