This paper presents nonparametric evidence on the effects of the expansion of the Old Age Pension program in South Africa on child health. Did this increase in household resources improve child health and nutrition? Does the gender of the recipient of the pension affect its impact? The answers to these questions have very important policy implications. There is evidence that inadequate nutrition during childhood (and even in utero) affects long-term physical development, as well as the development of cognitive skills. This in turn affects productivity later in life (see Partha Dasgupta, 1993; John Strauss and Duncan Thomas, 1998; T. Paul Schultz, 1999). In the United States, the evidence suggests that monetary transfers to the poor have very little impact on child welfare (Janet Currie, 1995; Susan Mayer, 1997). However, the effects of parental income and monetary transfers on child outcomes are likely to be of greater magnitude among poor households in developing countries. The South African Pension program provides an unusual opportunity to evaluate the possible effects of such a monetary transfer. This paper exploits the rapid increase in the coverage and benefits of the Old Age Pension program in South Africa which took place in the early 1990’s (Anne Case and Angus Deaton, 1998). At the end of the apartheid era, the government committed to achieving parity of benefits and eligibility requirements between whites and Africans. This was achieved mostly by increasing the benefits received by the Africans. In 1993, 80 percent of African women above age 60 and 77 percent of African men above 65 received the pension. The maximum benefit of 370 rands per month (aproximately $3 per day) was equal to half of the minimum wage, and about twice the median income per capita in rural areas. Due to living arrangements inherited from the apartheid era, close to onethird of African children under the age of 5 currently live with a pension recipient. Children who live with a pension recipient tend to come from relatively disadvantaged backgrounds. As a consequence, they tend to be smaller than other children their age. To estimate the effect of receiving a pension on the anthropometric status of children, this paper exploits the fact that height reflects accumulated investments in child nutrition. The larger the proportion of her life during which a child is well-nourished, the taller she will be, given her age. Due to the expansion of the program in the early 1990’s, individuals of qualified age became more likely to receive a pension, and the benefits became substantially larger. Thus, children born after the expansion of the program are more likely to have spent a larger fraction of their lives wellnourished, if they live with a pension recipient, to the extent that the pension resulted in improved nutrition. In this paper, I present nonparametric evidence of the program’s effect on nutrition based on this observation.

This data set uses political reservations for women in India to study the impact of women's leadership on policy decisions. Using a dataset we collected on 265 village councils in West Bengal and Rajasthan, we compare the type of public goods provided in reserved and unreserved village's councils. Data sets based upon information provided by GP Pradhans, local villagers, and the 1991 Indian Census.

This paper examines evidence of the role that reputation plays in determining contractual outcomes. We conduct an empirical analysis of the Indian customized software industry, using a data set we collected containing detailed information on 230 projects carried out by 125 software firms. We propose a model ofthe industry where reputation determines contractual outcomes. The evidence supports the view that reputation matters. Ex ante contracts, as well as the outcome after ex post renegotiation, vary with firms' characteristics plausibly associated with reputation. This holds after controlling for project, client, and firm characteristics.

Massive Online Open Courses (MOOCs) present the potential to deliver high quality education to a large number of students. But they suffer from low completion rates. This paper identifies disorganization as a factor behind failure to complete a MOOC. Students who enroll one day late are 17 percentage points less likely to earn a certificate than students who enroll exactly on time. This reflects selection, but it does seem to be related to demographic characteristics, motivation to complete the course, or ability. This suggests that building in even more structure in the MOOC could be a factor in improving performance.

Microfinance institutions have started to bundle their basic loans with other financial services, such as health insurance. Using a randomized control trial in Karnataka, India, we evaluate the impact on loan renewal from mandating the purchase of actuarially-fair health insurance covering hospitalization and maternity expenses. Bundling loans with insurance led to a 16 percentage points (23 percent) increase in drop-out from microfinance, as many clients preferred to give up microfinance than pay higher interest rates and receive insurance. In a Pyrrhic victory, the total absence of demand for health insurance led to there being no adverse selection in insurance enrollment.

We use a randomized experiment and a structural model to test whether monitoring and financial incentives can reduce teacher absence and increase learning in India. In treatment schools, teachers' attendance was monitored daily using cameras, and their salaries were made a nonlinear function of attendance. Teacher absenteeism in the treatment group fell by 21 percentage points relative to the control group, and the children's test scores increased by 0.17 standard deviations. We estimate a structural dynamic labor supply model and find that teachers respond strongly to financial incentives. Our model is used to compute cost-minimizing compensation policies.

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups , and average characteristics of most and least impacted units . The approach is valid in high‐dimensional settings, where the effects are proxied (but not necessarily consistently estimated) by predictive and causal machine learning methods. We post‐process these proxies into estimates of the key features. Our approach is generic; it can be used in conjunction with penalized methods, neural networks, random forests, boosted trees, and ensemble methods, both predictive and causal. Estimation and inference are based on repeated data splitting to avoid overfitting and achieve validity. We use quantile aggregation of the results across many potential splits, in particular taking medians of p ‐values and medians and other quantiles of confidence intervals. We show that quantile aggregation lowers estimation risks over a single split procedure, and establish its principal inferential properties. Finally, our analysis reveals ways to build provably better machine learning proxies through causal learning: we can use the objective functions that we develop to construct the best linear predictors of the effects, to obtain better machine learning proxies in the initial step. We illustrate the use of both inferential tools and causal learners with a randomized field experiment that evaluates a combination of nudges to stimulate demand for immunization in India.

Between 1863 and 1890, phylloxera destroyed 40% of French vineyards. Using the regional variation in the timing of this shock, we identify and examine the effects on adult height, health, and life expectancy of children born in the years and regions affected by the phylloxera. The shock decreased long-run height, but it did not affect other dimensions of health, including life expectancy. We find that those born in affected regions were about 1.8 millimeters shorter than others at age 20, a significant effect since average heights grew by only 2 centimeters in the entire nineteenth century.