Knowledge sharing is economically important but also typically incomplete: we “filter” our communication. This paper analyzes the consequences of filtering. In the model, homogeneous agents share knowledge with their peers whenever the private benefits exceed communication costs. The welfare implications of this transmission mechanism hinge on whether units of knowledge complement, substitute for, or are independent of each other. Both substitutability and complementarity generate externalities; cheaper communication eliminates externalities in the former case but not necessarily in the latter. Complementary basic skills such as numeracy catalyze technology adoption, and adoption may be path dependent even when payoffs are certain and independent across agents.

Researchers have scrutinized foreign aid's effects on poverty and growth, but anecdotal evidence suggests that donors often use aid for other ends. We test whether donors use bilateral aid to influence elections in developing countries. We find that recipient country administrations closely aligned with a donor receive more aid during election years, while those less aligned receive less. Consistent with our interpretation, this effect holds only in competitive elections, is absent in US aid flows to non-government entities, and is driven by bilateral alignment rather than incumbent characteristics.

We examine the preferences of low-income households in Kenya over the structure of unconditional cash transfers. Most preferred lumpy transfers, and some preferred deferred receipt—in contrast to the typical structures of safety-net programs, but consistent with evidence on the financial challenges of poverty. Turning to consequences, receiving transfers later in the year raised income 1.5 years later—but willingness to defer receipt was sensitive to small changes in cash flow around the time of decision-making. Taken together, these results illustrate how adapting cash transfer design to the decision-making environment of those in poverty could improve financial choices and outcomes.

Multiple hypothesis testing (MHT) practices vary widely, without consensus on which are appropriate when. This article provides an economic foundation for these practices designed to capture leading examples, such as regulatory approval on the basis of clinical trials. MHT adjustments are appropriate in our framework to the extent that research costs are invariant to the number of hypotheses. Control of average size, as for example via a Bonferroni correction, emerges in the limit case where all costs are fixed; in the opposite limit, where costs vary in proportion to the hypothesis count, no correction is needed. We illustrate implications by calculating explicit critical values using data on actual costs in the drug approval process and in program evaluation research; these suggest that some MHT adjustment is warranted in these applications, but not as much as implied by standard practice.

Public employment programs may affect poverty both directly through the income they provide and indirectly through general equilibrium effects. We estimate both effects, exploiting a reform that improved the implementation of India's National Rural Employment Guarantee Scheme (NREGS) and whose rollout was randomized at a large (sub‐district) scale. The reform raised beneficiary households' earnings by 14%, and reduced poverty by 26%. Importantly, 86% of income gains came from non‐program earnings, driven by higher private‐sector (real) wages and employment. This pattern appears to reflect imperfectly competitive labor markets more than productivity gains: worker's reservation wages increased, land returns fell, and employment gains were higher in villages with more concentrated landholdings. Non‐agricultural enterprise counts and employment grew rapidly despite higher wages, consistent with a role for local demand in structural transformation. These results suggest that public employment programs can effectively reduce poverty in developing countries, and may also improve economic efficiency.

We study the impact of reforms that introduced more stringent, biometric ID requirements into India’s largest social protection program, using large-scale randomized and natural experiments. Corruption fell but with substantial costs to legitimate beneficiaries, 1.5–2 million of whom lost access to benefits at some point during the reforms. At the same time, adverse effects appear to have been driven primarily by decisions about the way the transition was managed, illustrating both the risks of rapid reforms, and how the impacts of promising new technologies can be highly sensitive to the protocols governing their use.

Antipoverty 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 pen sion (SSP) programs in the Indian state of Andhra Pradesh, using a large-scale experiment that randomized the rollout of Smartcards over 157 subdistricts 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.

A large literature has examined how best to target antipoverty programs to those most deprived in some sense (e.g., consumption). We examine the potential trade-off between this objective and targeting those most impacted by such programs. We work in the context of an NGO cash transfer program in Kenya, employing recent advances in machine learning methods and dynamic outcome data to learn proxy means tests that jointly target both objectives. Targeting solely on the basis of deprivation is not attractive in this setting under standard social welfare criteria unless the planner’s preferences are extremely redistributive.

How large economic stimuli generate individual and aggregate responses is a central question in economics, but has not been studied experimentally. We provided one‐time cash transfers of about USD 1000 to over 10,500 poor households across 653 randomized villages in rural Kenya. The implied fiscal shock was over 15 percent of local GDP. We find large impacts on consumption and assets for recipients. Importantly, we document large positive spillovers on non‐recipient households and firms, and minimal price inflation. We estimate a local transfer multiplier of 2.5. We interpret welfare implications through the lens of a simple household optimization framework.