Knowledge that Transforms

While investment in most sectors declines in response to a contractionary monetary policy shock, investment in the manufacturing sector increases. Using manually digitized aggregate income and balance sheet data for the universe of US manufacturing firms, I show that this increase is driven by the types of firms which are least likely to be financially constrained. A two-sector New Keynesian model with financial frictions can match these facts; unconstrained firms take advantage of the decline in the user cost of capital caused by the monetary contraction, while constrained firms are forced to cut back. Removing firm financial constraints in the model dampens the response of manufacturing output to monetary shocks by about 25%.

We study judicial in-group bias in Indian criminal courts using newly collected data on over 5 million criminal case records from 2010–2018. After classifying gender and religious identity with a neural network, we exploit quasi-random assignment of cases to judges to determine whether judges favor defendants with similar identities to themselves. In the aggregate, we estimate tight zero effects of in-group bias based on shared gender or religion, including in settings where identity may be especially salient, such as when the victim and defendant have discordant identities. Proxying caste similarity with shared last names, we find a degree of in-group bias, but only among people with rare names; its aggregate impact remains small.

A flourishing empirical literature investigates the prevalence of p-hacking based on the distribution of p-values across studies. Interpreting results in this literature requires a careful understanding of the power of methods for detecting p-hacking. We theoretically study the implications of likely forms of p-hacking on the distribution of p-values to understand the power of tests for detecting it. Power can be low and depends crucially on the p-hacking strategy and the distribution of true effects. Combined tests for upper bounds and monotonicity and tests for continuity of the p-curve tend to have the highest power for detecting p-hacking.

This paper estimates the causal effect of scaling up access to psychotherapy. I study a 2008 reform of the Danish public health insurance, which introduced 60% coverage for psychotherapy by private practice psychologists for patients aged 18–37 diagnosed with depression or anxiety. Using administrative data from 1995–2019 and quasi-experimental methods, I show that psychotherapy coverage reduces psychiatric hospital contacts, physical health care use, and suicide attempts, but has no effect on labor market outcomes, including employment or disability pension. Still, savings from reduced use of other health care services exceed policy costs, making it both cost-reducing and welfare-improving.

Should healthcare providers strictly adhere to clinical guidelines, or is some discretion beneficial? Using bunching estimation and microdata from 619,907 patients in Chile, I examine discretion in the diagnosis of hypertension (blood pressure ≥ 140/90mmHg). I find bunching in blood pressure at 10% of clinics, where 6 to 62% of patients' test results expected just above the hypertension diagnostic threshold are instead recorded just below it, suggesting providers use their discretion to consider some tests false positives. Leveraging variation in bunching across clinics, I show that affected patients have lower cardiovascular risk, suggesting discretion improves patient classification, partially driven by heuristic thinking.

Using job posting wage data, we find a substantial decrease in the urban wage premium for occupations with high working-from-home (WFH) adoption following the COVID-19 pandemic, accompanied by an employment shift away from large cities. Based on a conceptual framework, the empirical findings suggest that WFH adoption lowered the productivity premium of large cities. A skill-level decomposition reveals that the urban wage premium decline was largely driven by reduced wage returns to interpersonal skills in large cities, suggesting that the reduced urban productivity premium was a result of weakened agglomeration economies due to decreased interpersonal interactions in large cities.

I study how domestic political considerations influence the foreign policy choices of autocratic regimes, by analyzing China’s foreign aid. First, using contractor-level data, I document how the regime uses foreign aid projects to help maintain domestic stability: aid projects are awarded to state-owned firms in Chinese prefectures hit by social unrest, increasing employment and future political stability. Second, I find that this strategy to manage domestic unrest affects the global allocation of Chinese aid, since state-owned firms pursue projects in countries where they have prior connections.

We consider the role of parental influence on the industry choice of entrepreneurs and the success of their ventures. Almost 75% of male entrepreneurs start a firm in an industry that is the same or closely related to their father’s industry of employment. Ventures started by same-industry entrepreneurs have superior mean outcomes and higher propensity to be positive outliers. The patterns cannot be explained by parents helping out or by inherited intrinsic abilities. We argue that entrepreneurs appear to obtain industry knowledge through interacting with parents during upbringing, or “dinner table human capital”.

This paper presents new causal evidence on the “power” of oral contraceptives in shaping women's lives, leveraging the 1970 liberalization of the Pill for minors in the Netherlands and demand- and supply-side religious preferences that affected Pill take-up. We analyze administrative data to demonstrate that, after Pill liberalization, minors from less conservative areas were more likely to delay fertility/marriage and to accumulate human capital in the long run. We then show how these large effects were eliminated for women facing a higher share of gatekeepers—general practitioners and pharmacists—who were opposed to providing the Pill on religious grounds.

We propose a novel framework for modeling time-varying persistence in economic time series, allowing for smoothly evolving heterogeneity in shock dynamics. We leverage localized regression techniques to flexibly identify changes in persistence over time, offering a data-driven alternative to traditional parametric models. We applied this methodology to U.S. inflation and stock market volatility data and found substantial persistence variations that align with key macroeconomic events and market conditions. The results reveal previously undetected pockets of predictability and provide significant increases in out-of-sample forecast accuracy. These findings have important implications for economic modeling, forecasting, and policy analysis.