Microfinance and Moneylenders: Long-run Effects of MFIs on Informal Credit Market in Bangladesh

This paper provides evidence on the effects of microfinance competition on moneylender interest rates and households' dependence on informal credit. The views among practitioners diverge sharply: proponents claim that the MFI competition reduces both the moneylender interest rate and households' reliance on informal credit, while critics argue the opposite. Taking advantage of recent econometric approaches to address selection on unobservables without imposing standard exclusion restrictions, we find that the MFI competition does not reduce moneylender interest rates, partially repudiating the proponents. There is no perceptible effect at low levels of the MFI coverage, but when the MFI coverage is high enough, the moneylender interest rate increases significantly. In contrast, a household's dependence on informal credit goes down after becoming an MFI member, which contradicts part of the critic's argument. The evidence is consistent with models where either the MFIs or the moneylenders engage in cream skimming, and fixed costs are important in informal lending.

Estimation and Inference for Linear Models with Two-Way Fixed Effects and Sparsely Matched Data

Models with multiway fixed effects are frequently used to address selection on unobservables. The data used for estimating these models often contain few observations per value of either indexing variable (sparsely matched data). I show that this sparsity has important implications for inference and propose an asymptotically valid inference method based on subsetting. Sparsity also has important implications for point estimation when covariates or instrumental variables are sequentially exogenous (e.g., dynamic models), and I propose a new estimator for these models. Finally, I illustrate these methods by providing estimates of the effect of class size reductions on student achievement.

Sample-Selection Biases and the Historical Growth Pattern of Children

ABSTRACTBodenhorn et al. (2017) have sparked considerable controversy by arguing that the fall in adult stature observed in military samples in the United States and Britain during industrialization was a figment of selection on unobservables in the samples. While subsequent papers have questioned the extent of the bias (Komlos and A’Hearn 2019; Zimran 2019), there is renewed concern about selection bias in historical anthropometric datasets. Therefore, this article extends Bodenhorn et al.’s discussion of selection bias on unobservables to sources of children’s growth, specifically focusing on biases that could distort the age pattern of growth. Understanding how the growth pattern of children has changed is important because these changes underpinned the secular increase in adult stature and are related to child stunting observed in developing countries today. However, there are significant sources of unobserved selection in historical datasets containing children’s and adolescents’ height and weight. This article highlights, among others, three common sources of bias: (1) positive selection of children into secondary school in the late nineteenth and early twentieth centuries; (2) distorted height by age profiles created by age thresholds for enlistment in the military; and (3) changing institutional ecology that determines to which institutions children are sent. Accounting for these biases adjusts the literature in two ways: evidence of a strong pubertal growth spurt in the nineteenth century is weaker than formerly acknowledged and some long-run analyses of changes in children’s growth are too biased to be informative, especially for Japan.

Job Flexibility and Occupational Selection in Ghana: An Application of Maximum Simulated Likelihood†

In many African labour markets, the vast majority of self-employed workers are female. It is often hypothesised that self-employment enables workers to balance income-generation with caring for children and other domestic tasks and, since responsibility for these activities is divided unequally in the household, this effect is stronger for women than men. However, testing whether ‘job flexibility’ matters is difficult because variables that proxy for domestic obligations—such as the number of dependents in the household—may be endogenous to occupational choice. In this paper, we build a new estimator using maximum simulated likelihood that allows us to use selection on observables as a guide to selection on unobservables within the multinomial choice problem individuals face when deciding their occupation. We apply this approach to detailed cross-sectional data from Ghana. Our results show that having extra dependents in the household pushes women towards own account self-employment substantially more than men, even under more conservative assumptions about the extent of endogeneity.

Online Learning, Offline Outcomes: Online Course Taking and High School Student Performance

This article uses fixed effects models to estimate differences in contemporaneous and downstream academic outcomes for students who take courses virtually and face-to-face—both for initial attempts and for credit recovery. We find that while contemporaneous outcomes are positive for virtual students in both settings, downstream outcomes vary by attempt type. For first-time course takers, virtual course taking is associated with decreases in the likelihood of taking and passing follow-on courses and in graduation readiness (based on a proxy measure). For credit recovery students, virtual course taking is associated with an increased likelihood of taking and passing follow-on courses and being in line for graduation. Supplemental analyses suggest that selection on unobservables would have to be substantial to render these results null.

Identification and Extrapolation of Causal Effects with Instrumental Variables

Instrumental variables (IV) are widely used in economics to address selection on unobservables. Standard IV methods produce estimates of causal effects that are specific to individuals whose behavior can be manipulated by the instrument at hand. In many cases, these individuals are not the same as those who would be induced to treatment by an intervention or policy of interest to the researcher. The average causal effect for the two groups can differ significantly if the effect of the treatment varies systematically with unobserved factors that are correlated with treatment choice. We review the implications of this type of unobserved heterogeneity for the interpretation of standard IV methods and for their relevance to policy evaluation. We argue that making inferences about policy-relevant parameters typically requires extrapolating from the individuals affected by the instrument to the individuals who would be induced to treatment by the policy under consideration. We discuss a variety of alternatives to standard IV methods that can be used to rigorously perform this extrapolation. We show that many of these approaches can be nested as special cases of a general framework that embraces the possibility of partial identification.

Racial and Ethnic Sentencing Differentials in the Federal Criminal Justice System

A large body of multidisciplinary research has documented how sentencing outcomes vary tremendously across racial and ethnic groups. The research challenge lies in establishing whether these sentencing differentials are driven by unobserved heterogeneity correlated to defendant race/ethnicity, or whether they reflect discrimination. We add to the debate by examining the robustness of racial/ethnic sentencing gaps, by gender, when allowing for selection on unobservables. We do so in the context of federal criminal cases, considering 250,000 cases, and using a dataset containing a rich set of covariates relating to defendant and legal characteristics of cases.