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This paper makes the case for greater use of randomized experiments "at scale." We review various critiques of experimental program evaluation in developing countries, and discuss how experimenting at scale along three specific dimensions - the size of the sampling frame, the number of units treated, and the size of the unit of randomization - can help alleviate them. We find that program evaluation randomized controlled trials published in top journals over the last 15 years have typically been "small" in these senses, but also identify a number of examples - including from our own work - demonstrating that experimentation at much larger scales is both feasible and valuable.
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Anti-poverty programs in developing countries are often difficult to implement as intended; one common challenge is governments' limited capacity to deliver payments securely to targeted beneficiaries. We evaluate the impact of biometrically-authenticated payments infrastructure on public employment and pension programs in the Indian state of Andhra Pradesh, using a large-scale experiment that randomized the rollout of the new system over 158 sub-districts and 19 million people. We find that, while far from perfectly implemented, the new system delivered a faster, more predictable, and less corrupt payments process without adversely affecting program access. Distributions of key outcomes in treated areas first-order stochastically dominated those in control areas, and beneficiaries overwhelmingly favored the new payments system. The investment was cost-effective, as time savings to beneficiaries alone were equal to the cost of the intervention (in the case of the employment scheme). Overall the results suggest that investing in secure authentication and payments infrastructure can significantly add to "state capacity" to effectively implement social programs in developing countries.
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Public employment programs play a large role in many developing countries' anti-poverty strategies, but their net impact on the incomes of the poor will depend on both direct program earnings as well as indirect effects through changes induced in market wages and employment. We estimate this composite effect, exploiting a large-scale randomized experiment across 157 sub-districts and 19 million people that substantially improved the implementation of India's rural employment guarantee scheme. Despite no changes in government expenditure on the program itself, the earnings of low-income households rose 13%, driven overwhelmingly by market (90%) as opposed to program earnings (10%). Low-skilled wages increased 6% and days without paid work fell 7%, while migration and prices were unaffected. Effects on wages, employment, and income also spilled over into neighboring sub-districts, and estimates of program impact that adjust for these spillovers are substantially larger, typically double the unadjusted magnitudes. These results suggest that well-implemented public works programs can be highly effective at reducing poverty. They also highlight the importance of general equilibrium effects in program evaluation, and the feasibility of studying them using large-scale experiments.
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Should developing countries give everyone enough money to live on? Interest in this idea has grown enormously in recent years, reflecting both positive results from a number of existing cash transfer programs and also dissatisfaction with the perceived limitations of piecemeal, targeted approaches to reducing extreme poverty. We discuss what we know (and what we do not) about three questions: what recipients would likely do with the incremental income, whether this would unlock further economic growth, and the potential consequences of giving the money to everyone (as opposed to targeting it).
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Evidence from social psychology suggests that agents process information about their own ability in a biased manner. This evidence has motivated exciting research in behavioral economics, but has also garnered critics who point out that it is potentially consistent with standard Bayesian updating. We implement a direct experimental test. We study a large sample of 656 undergraduate students, tracking the evolution of their beliefs about their own relative performance on an IQ test as they receive noisy feedback from a known data-generating process. Our design lets us repeatedly measure the complete relevant belief distribution incentive-compatibly. We find that subjects (1) place approximately full weight on their priors, but (2) are asymmetric, over-weighting positive feedback relative to negative, and (3) conservative, updating too little in response to both positive and negative signals. These biases are substantially less pronounced in a placebo experiment where ego is not at stake. We also find that (4) a substantial portion of subjects are averse to receiving information about their ability, and that (5) less confident subjects are causally more likely to be averse. We unify these phenomena by showing that they all arise naturally in a simple model of optimally biased Bayesian information processing.
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Improving "last mile" public-service delivery is a recurring challenge in developing countries. Could the rapid adoption of mobile phones provide a simple, cost-effective means to do so? We evaluate the impact of a phone-based monitoring system on improving the delivery of a program that transferred nearly a billion dollars to farmers in the Indian state of Telangana, using an at-scale experiment randomized across 5.7 million farmers. A randomly selected sample of officials were told that a representative sample of beneficiaries in their jurisdiction would be called to measure the quality of program implementation. This simple announcement led to a 1.5% increase in the number of farmers receiving their benefits, with a 3.3% increase among farmers in the bottom quartile of landholdings. The program was highly cost-effective, with a cost of 3.6 cents for each additional dollar delivered.
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This paper makes the case for greater use of randomized experiments "at scale." We review various critiques of experimental program evaluation in developing countries, and discuss how experimenting at scale along three specific dimensions - the size of the sampling frame, the number of units treated, and the size of the unit of randomization - can help alleviate them. We find that program evaluation randomized controlled trials published in top journals over the last 15 years have typically been "small" in these senses, but also identify a number of examples - including from our own work - demonstrating that experimentation at much larger scales is both feasible and valuable.
Choose an application
Anti-poverty programs in developing countries are often difficult to implement; in particular, many governments lack the capacity to deliver payments securely to targeted beneficiaries. We evaluate the impact of biometrically-authenticated payments infrastructure ("Smartcards") on beneficiaries of employment (NREGS) and pension (SSP) programs in the Indian state of Andhra Pradesh, using a large-scale experiment that randomized the rollout of Smartcards over 158 sub- districts and 19 million people. We find that, while incompletely implemented, the new system delivered a faster, more predictable, and less corrupt NREGS payments process without adversely affecting program access. For each of these outcomes, treatment group distributions first-order stochastically dominated those of the control group. The investment was cost-effective, as time savings to NREGS beneficiaries alone were equal to the cost of the intervention, and there was also a significant reduction in the "leakage" of funds between the government and beneficiaries in both NREGS and SSP programs. Beneficiaries overwhelmingly preferred the new system for both programs. Overall, our results suggest that investing in secure payments infrastructure can significantly enhance "state capacity" to implement welfare programs in developing countries.
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