Abhijit V. Banerjee

Many children from low-income backgrounds worldwide fail to master school mathematics¹; however, some children extensively use mental arithmetic outside school^2,3. Here we surveyed children in Kolkata and Delhi, India, who work in markets (n = 1,436), to investigate whether maths skills acquired in real-world settings transfer to the classroom and vice versa. Nearly all these children used complex arithmetic calculations effectively at work. They were also proficient in solving hypothetical market maths problems and verbal maths problems that were anchored to concrete contexts. However, they were unable to solve arithmetic problems of equal or lesser complexity when presented in the abstract format typically used in school. The children’s performance in market maths problems was not explained by memorization, access to help, reduced stress with more familiar formats or high incentives for correct performance. By contrast, children with no market-selling experience (n = 471), enrolled in nearby schools, showed the opposite pattern. These children performed more accurately on simple abstract problems, but only 1% could correctly answer an applied market maths problem that more than one third of working children solved (β = 0.35, s.e.m. = 0.03; 95% confidence interval = 0.30–0.40, P < 0.001). School children used highly inefficient written calculations, could not combine different operations and arrived at answers too slowly to be useful in real-life or in higher maths. These findings highlight the importance of educational curricula that bridge the gap between intuitive and formal maths.

Randomized controlled trials have found only modest effects of microfinance, but these studies focus on new clients. Existing estimates may thus understate ongoing gains for more experienced borrowers and the longer‐run potential of microfinance. We estimate impacts of microfinance on experienced borrowers, using an episode when a microfinance institution modestly increased existing clients’ fees in randomly selected villages (in exchange for a mandatory health insurance policy that turned out to be useless). This increase in fees led to a 22 percentage point decline in loan renewal in treatment villages (95% confidence interval: 16 to 27), compared to control villages where the policy was not introduced. Using this randomly generated variation in microfinance participation among experienced borrowers, we find impacts of microfinance that are strikingly similar to previous estimates for new clients: neither business outcomes nor household consumption were affected, on average. Also, consistent with prior studies, we find significant impacts on business outcomes among clients who had started their businesses before microfinance entered the village (0.06 standard deviation decline in an index of business outcomes from the loss of microfinance, 95% confidence interval: −0.002 to −0.12). However, despite these measured losses, these clients were just as willing to give up microfinance.

The promise of randomized controlled trials is that evidence gathered through the evaluation of a specific program helps us—possibly after several rounds of fine-tuning and multiple replications in different contexts—to inform policy. However, critics have pointed out that a potential constraint in this agenda is that results from small “proof-of-concept” studies run by nongovernment organizations may not apply to policies that can be implemented by governments on a large scale. After discussing the potential issues, this paper describes the journey from the original concept to the design and evaluation of scalable policy. We do so by evaluating a series of strategies that aim to integrate the nongovernment organization Pratham’s “Teaching at the Right Level” methodology into elementary schools in India. The methodology consists of reorganizing instruction based on children’s actual learning levels, rather than on a prescribed syllabus, and has previously been shown to be very effective when properly implemented. We present evidence from randomized controlled trials involving some designs that failed to produce impacts within the regular schooling system but still helped shape subsequent versions of the program. As a result of this process, two versions of the programs were developed that successfully raised children’s learning levels using scalable models in government schools. We use this example to draw general lessons about using randomized control trials to design scalable policies.

For field management; to Diva Dhar for supervision; and to Shaher Bhanu Vagh for the educational test design and analysis. Special thanks to the staff of Pratham for their openness and engagement, and to the William and Flora Hewlett Foundation, the International Initiative for Impact Evaluation, the Government of Haryana, and the Regional Centers for Learning on Evaluation and Results for their financial support and commitment. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Delivering health care to mystery patients: Many families in developing countries do not have access to medical doctors and instead receive health care from informal providers. Daset al.used “mystery” patients (trained actors) to test whether a 9-month training program improved the quality of care delivered by informal providers in West Bengal (see the Perspective by Powell-Jackson). The patients did not identify themselves to the providers and were not told which providers had participated in the training program. The results of this blinded assessment showed that medical doctors delivered better care than informal providers but that the training program closed much of the gap.Science, this issue p.80; see also p.34

This article discusses the two leading views of history and political institutions. For some scholars, institutions are mainly products of historical logic, whereas for others, accidents, leaders, and decisions have a significant impact. We argue that although there is clear evidence that history matters and has long-term effects, there are not enough data to help us distinguish between the two views. Faced with this uncertainty, what is a social scientist to do? We argue that given the possibility that policy decisions indeed make a difference, it makes sense to assume they do and to try to improve policy making.

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.

Introduction: How do the network positions of the first individuals in a society to receive information about a new product affect its eventual diffusion? To answer this question, we develop a model of information diffusion through a social network that discriminates between information passing (individuals must be aware of the product before they can adopt it, and they can learn from their friends) and endorsement (the decisions of informed individuals to adopt the product might be influenced by their friends’ decisions). We apply it to the diffusion of microfinance loans, in a setting where the set of potentially first-informed individuals is known. We then propose two new measures of how “central” individuals are in their social network with regard to spreading information; the centrality of the first-informed individuals in a village helps significantly in predicting eventual adoption. Methods: Six months before a microfinance institution entered 43 villages in India and began offering microfinance loans to villagers, we collected detailed network data by surveying households about a wide range of interactions. The microfinance institution began by inviting “leaders” (e.g., teachers, shopkeepers, savings group leaders) to an informational meeting and then asked them to spread information about the loans. Using the network data, the locations in the network of these first-informed villagers (or injection points), and data regarding the villagers’ subsequent participation, we estimate the parameters of our diffusion model using the method of simulated moments. The parameters of the model are validated by showing that the model correctly predicts the evolution of participation in each village over time. The model yields a new measure of the effectiveness of any given node as an injection point, which we call communication centrality. Finally, we develop an easily computed proxy for communication centrality, which we call diffusion centrality. Results: We find that a microfinance participant is seven times as likely to inform another household as a nonparticipant; nonetheless, information transmitted by nonparticipants is important and accounts for about one-third of the eventual informedness and participation in the village because nonparticipants are much more numerous. Once information passing is accounted for, an informed household’s decision to participate is not significantly dependent on how many of its neighbors have participated. Communication centrality, when applied to the set of first-informed individuals in a village, substantially outperforms other standard network measures of centrality in predicting microfinance participation in this context. Finally, the simpler proxy measure—diffusion centrality—is strongly correlated with communication centrality and inherits its predictive properties. Discussion: Our results suggest that a model of diffusion can distinguish information passing from endorsement effects, and that understanding the nature of transmission may be important in identifying the ideal places to inject information.