Client attendance is vital for the success of HIV vertical transmission prevention programs, yet 23.4% of clients missed follow-up appointments after enrolling in a community health worker-led program (n=24,807, Aug–Dec 2022). Predicting which clients are most likely to miss appointments could enable targeted interventions to improve retention. While machine learning appears well-suited for this prediction task, its effectiveness compared to community health worker judgment remains unexplored. We evaluated three machine learning approaches—logistic regression, balanced random forest, and gradient-boosted trees—trained on client enrollment records (n=51,297 training; n=18,577 test) and compared their performance to predictions by community health workers (n=61), who possess direct client interactions and contextual insights. Machine learning models achieved modest predictive performance, with the balanced random forest showing the best performance (ROC AUC=0.689). Community health worker predictions showed no evidence of strong predictive performance in the matched sample, although the analysis cannot rule out modest predictive ability. Qualitative insights identified complex and dynamic barriers including stigma, transportation difficulties, and competing personal commitments, often unpredictable at enrollment. These findings highlight limitations in predicting client attendance from enrollment-period information alone. Instead, resources might be better allocated to addressing systemic barriers identified by community health workers.

In peer mechanisms, the competitors for a prize also determine who wins. Each competitor may be asked to rank, grade, or nominate peers for the prize. Since the prize can be valuable, such as financial aid, course grades, or an award at a conference, competitors may be tempted to manipulate the mechanism. We survey approaches to prevent or discourage the manipulation of peer mechanisms. We conclude our survey by identifying several important research challenges.

We analyze the design of a mechanism to extract a ranking of individuals according to a unidimensional characteristic, such as ability or need. Individuals, connected on a social network, only have local information about the ranking. We show that a planner can construct an ex post incentive compatible and efficient mechanism if and only if every pair of friends has a friend in common. We characterize the windmill network as the sparsest social network for which the planner can always construct a complete ranking.

Over sixty percent of employees at a large South African company contribute the minimum rate of 7.5 percent to a retirement fund, far below the rate of 15 percent recommended by financial advisers. I use a field experiment to investigate whether providing employees with a retirement calculator, which shows projections of retirement income, leads to increases in contributions. The impact is negligible. The lack of response to the calculator suggests many employees may wish to save less than the minimum. I use a model of asymmetric information to explain why the employer sets a binding minimum.

We use harmonized survey data from the Luxembourg Income Study to assess the redistributive impact of taxes and transfers across 22 OECD countries over the 1999-2016 period. After imputing missing tax data (employer social-security contributions), we measure the reduction in income inequality from four key levers of tax and transfer systems: the average tax rate, tax progressivity, the average transfer rate, and transfer targeting. Our methodological improvements produce the following results. First, tax redistribution dominates transfer redistribution (excluding pensions) in most countries. Second, targeting explains very little of the cross-country variation in inequality reduction. In contrast, both tax progressivity and the average tax rate have large impacts on redistribution. Last, there seem to be political tradeoffs: high average tax rates are not found together with highly progressive tax systems.

While some research fields have a long history of collaborating with domain experts outside academia, many quantitative researchers do not have natural avenues to meet experts in areas where the research is later deployed. We explain how conversations -- interviews without a specific research objective -- can bridge research and practice. Using collaborative autoethnography, we reflect on our experience of conducting conversations with practitioners from a range of different backgrounds, including refugee rights, conservation, addiction counseling, and municipal data science. Despite these varied backgrounds, common lessons emerged, including the importance of valuing the knowledge of experts, recognizing that academic research and practice have differing objectives and timelines, understanding the limits of quantification, and avoiding data extractivism. We consider the impact of these conversations on our work, the potential roles we can serve as researchers, and the challenges we anticipate as we move forward in these collaborations.

A planner aims to target individuals who exceed a threshold in a characteristic, such as wealth or ability. The individuals can rank their friends according to the characteristic. We study a strategy-proof mechanism for the planner to use the rankings for targeting. We discuss how the mechanism works in practice when the rankings may contain errors.

Rising cost-of-living pressures continue to strain household budgets across Australia. To respond, policymakers require timely and reliable measures of financial hardship. This research note introduces a new high-frequency indicator of financial stress derived from failed direct debit payments in bank transaction data.