Knowledge that Transforms

Evidence across disciplines suggests that talk therapy is more curative than antidepressants for mild-to-moderate depression and anxiety. Yet, few patients use it. We develop a dynamic choice model to analyse patient demand for the treatment of depression and anxiety. The model incorporates myriad potential impediments to therapy use along with links between mental health improvements and earnings. The estimated model reveals that mental health improvements are valuable, directly through utility and indirectly through earnings. However, patient reluctance to use therapy is nearly impervious to reasonable counterfactual policies (e.g. lowering prices or removing other costs). Patient behaviour might reflect stigma, biases in beliefs about the effectiveness of therapy, or a distaste for discussing personal or painful issues with a stranger. More broadly, the benefits of therapy estimated in randomized trials tell only half the story. If patients do not use treatments outside of an experimental setting—and we fail to understand why or how to get them to—estimated treatment effects cannot be leveraged.

This study argues that the decline of coercive labour institutions over the course of industrialization was partly driven by complementarity between physical capital and effective labour in manufacturing. Given the difficulty of extracting labour effort in care-intensive industrial tasks through monitoring and punishment, capital-owning elites ultimately chose to emancipate workers to induce their supply of effective labour and, thus, boost the return to physical capital. This hypothesis is empirically examined in the context of serf emancipation in nineteenth-century Prussia. Exploiting variation in proto-industrialization across Prussian counties, the analysis finds that, consistent with the proposed hypothesis, the initial abundance of elite-owned physical capital is associated with a higher pace of serf emancipation and lower redemption payments to manorial lords.

We present a model of digital advertising with three key features: (1) advertisers can reach consumers on and off a platform, (2) additional data enhances the value of advertiser–consumer matches, and (3) the allocation of advertisements follows an auction-like mechanism. We contrast data-augmented auctions, which leverage the platform’s data advantage to improve match quality, with managed-campaign mechanisms that automate match formation and price-setting. The platform-optimal mechanism is a managed campaign that conditions the on-platform prices for sponsored products on the off-platform prices set by all advertisers. This mechanism yields the efficient on-platform allocation but inefficiently high off-platform product prices. It attains the vertical integration profit for the platform and the advertisers, and it increases off-platform product prices while decreasing consumer surplus, relative to data-augmented auctions.

In this paper, we study the effects of economic integration with democratic partners on democracy. We assemble a large country-level panel dataset from 1960 to 2015, and exploit improvements in air, relative to sea, transportation to derive a time-varying instrument for economic integration. We find that economic integration with democracies increases countries’ democracy scores, whereas the impact of economic integration with non-democracies is muted. Results are stronger when democratic partners have a longer history of democracy, grow faster, spend more on public goods, are culturally closer, and export higher quality goods. The effects we document are driven by imports, rather than exports, and by integration with democratic partners that account for a larger share of a country’s trade in institutionally intensive, cultural, and consumer goods, as well as in goods that involve more face-to-face interactions and entail higher levels of bilateral trust. These patterns are consistent with economic integration favouring the transmission of democracy by signalling the (actual or perceived) desirability of democratic institutions. Alternative mechanisms—including human capital accumulation and economic growth—cannot, alone, explain our findings.

Job applications have risen over time, yet job-finding rates remain unchanged. Meanwhile, separations have declined. We argue that increased applications raise the probability of a good match rather than the probability of job-finding. Using a search model with multiple applications and costly information, we show that when applications increase, firms invest in identifying good matches, reducing separations. Concurrently, increased congestion and selectivity over which offer to accept temper increases in job-finding rates. Our framework contains testable implications for changes in offers, acceptances, reservation wages, applicants per vacancy, and tenure, objects that enable it to generate the trends in unemployment flows.

We introduce two data-driven procedures for optimal estimation and inference in nonparametric models using instrumental variables. The first is a data-driven choice of sieve dimension for a popular class of sieve two-stage least-squares estimators. When implemented with this choice, estimators of both the structural function h0 and its derivatives (such as elasticities) converge at the fastest possible (i.e. minimax) rates in sup-norm. The second is for constructing uniform confidence bands (UCBs) for h0 and its derivatives. Our UCBs guarantee coverage over a generic class of data-generating processes and contract at the minimax rate, possibly up to a logarithmic factor. As such, our UCBs are asymptotically more efficient than UCBs based on the usual approach of undersmoothing. As an application, we estimate the elasticity of the intensive margin of firm exports in a monopolistic competition model of international trade. Simulations illustrate the good performance of our procedures in empirically calibrated designs. Our results provide evidence against common parameterizations of the distribution of unobserved firm heterogeneity.

For an incompatible patient–donor pair, kidney exchanges often forbid receipt-before-donation (the patient receives a kidney before the donor donates) and donation-before-receipt, causing a double-coincidence-of-wants problem. We study an algorithm, the Unpaired kidney exchange algorithm, which eliminates this problem. In a dynamic matching model, we show that the waiting time of patients under Unpaired is close to optimal and substantially shorter than under widely used algorithms. Using a rich administrative dataset from France, we show that Unpaired achieves a match rate of 63% and an average waiting time of 176 days for transplanted patients. The (infeasible) optimal algorithm is only slightly better (64% and 144 days); widely used algorithms deliver less than 40% match rate and at least 232 days waiting times. We discuss a range of solutions that can address the potential practical incentive challenges of Unpaired. In particular, we extend our analysis to an environment where a deceased donor waitlist can be integrated to improve the performance of algorithms. We show that our theoretical and empirical comparisons continue to hold. Finally, based on these analyses, we propose a practical version of the Unpaired algorithm.

We construct a novel panel dataset for interwar Soviet Union to study the causes of Ukrainian famine mortality (Holodomor) during 1932–3 and document several facts: (1) Ukraine produced enough food in 1932 to avoid famine in Ukraine; (2) 1933 mortality in the Soviet Union was increasing in the pre-famine ethnic Ukrainian population share and (3) was unrelated to food productivity across regions; (4) this pattern exists even outside of Ukraine; (5) migration restrictions exacerbated mortality; (6) actual and planned grain procurement were increasing and actual and planned grain retention (production minus procurement) were decreasing in the ethnic Ukrainian population share across regions. The results imply that anti-Ukrainian bias in Soviet policy contributed to high Ukrainian famine mortality, and that this bias systematically targeted ethnic Ukrainians across the Soviet Union.

Inflation expectations are key determinants of economic activity and are central to the current policy debate about whether inflation expectations will remain anchored in the face of recent pandemic-related increases in inflation. This article explores evidence of inattention by constructing two novel and direct measures of consumers’ inattention, and documents greater attention when inflation is high. This relationship can explain a substantial portion of the flattening of the Phillips curve and also suggests the possibility of upward attention-price spirals.

How do innovation and education policy affect individual career choices and aggregate productivity? This paper analyses the effect of R&D subsidies and higher education policy on productivity growth through the supply of innovative talent. We put scarce talent, higher education attainment, and career choice at the centre of a new endogenous growth framework with individual-level heterogeneity in talent, financial resources, and preferences. We link the model to micro-level data from Denmark on the backgrounds of who obtains a PhD and becomes an inventor and the outcomes of a set of policy interventions. We find that R&D subsidies can be strengthened when combined with higher education subsidies, which enable talented but poor youth to pursue a career in research. Education and innovation policies not only alleviate different frictions, but also impact innovation at different time horizons. Education policy is more effective in societies with higher income inequality.