Knowledge that Transforms

This paper develops a semi-structural model to jointly estimate “stars” — long-run levels of output (its growth rate), unemployment rate, real interest rate, productivity growth, price inflation, and wage inflation. It features links between survey expectations and stars, time-variation in macroeconomic relationships, and stochastic volatility. Survey data help discipline stars' estimates and have been crucial in estimating a high-dimensional model since the pandemic. The model has desirable real-time properties, competitive forecasting performance, and superior fit to the data compared to variants without the empirical features mentioned above. The by-products are estimates of various objects of great interest to the broader profession.

Do people around us influence our personality? We investigate this question through a field experiment where we randomly assign university students to study groups. We find personality spillovers along three dimensions: students become more conscientious when assigned to conscientious peers, more open-minded when assigned to open-minded peers, and more competitive when assigned to competitive peers. We find no effects for peers' extraversion, agreeableness, or neuroticism. Our findings are consistent with students' adopting peer traits that are predictive of academic achievement. Our paper provides novel evidence on spillovers in noncognitive skills and establishes that socialization with peers affects personality development.

We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees. From a methodological perspective, we provide a detailed discussion of different causal quantities to be predicted, which we call causal predictands, allowing for multiple treated units with treatment adoption at possibly different points in time. We illustrate our methodology with an empirical application studying the effects of economic liberalization on real GDP per capita for Sub-Saharan African countries. Companion software packages are provided in Python, R, and Stata.

This paper explores the causal effects of inflation literacy using a two-step randomized controlled trial. The first step shows that qualitative information about inflation and monetary policy (Literacy treatment) enhances knowledge about these issues and raises both the likelihood of providing inflation predictions and trust in the central bank. The second step tests the joint impact of the Literacy treatment and further quantitative treatments. While the level of posterior expectations is affected only by quantitative information, inflation forecast uncertainty and trust react to both qualitative and quantitative information. Moreover, different information types may interact when impacting forecast uncertainty and trust.

A growing literature has shown that people sometimes prefer to randomize between two options. We investigate how prevalent this behavior is in an experiment using a novel and simple method. Subjects face a list of questions in which one of the alternatives is fixed and the other varies, like a Multiple Price List, but in each row they can randomize between the options. We find that the majority of subjects chose to randomize in the majority of questions, and notably, they did so for ranges of values that were “very large.”

We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, and misclassified links. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer effects on students academic achievement using the widely used Add Health database, and show that network data errors have a large downward bias on estimated peer effects.

We show that early joiners—nonfounder employees in the first year of a startup—play a critical role in shaping firm performance. We use administrative employer-employee matched data on U.S. startups and utilize premature death as a natural experiment that exogenously separates talent from startups. We find that losing an early joiner has large negative effects on employment and revenues that persist for at least ten years. In contrast, losing a later joiner yields only a small and temporary decline in firm performance. Our results imply that organization capital, an important driver of startup success, is embodied in early joiners.

This study evaluates the effects of the 2018 UK Soft Drinks Industry Levy. We combine novel electronic point-of-sale data with longitudinal nutritional information and a variety of event-study specifications. All but a few global soft drink brands reduced sugar content and hence avoided the tiered levy. For brands that maintained their recipes, the levy was on average overshifted, resulting in substantial retail price increases and reductions in consumption, particularly for colas. The levy is responsible for a reduction in intake of about 6,600 calories per year per UK resident, 80% of which is attributable to reformulation by manufacturers.

This paper analyzes international externalities of a local shock to the global shipping network. The 2016 Panama Canal expansion removed a bottleneck in seaborne transportation. Using reduced-form and structural methods in combination with novel satellite data on ships, we find that trade increased significantly among country-pairs using the canal. We find that the global real income gains from the canal expansion were over three times greater than the income gains for Panama itself. A link removal analysis reveals that most shipping links are associated with positive and quantitatively important positive international externalities.

Although entry regulations are ubiquitous across countries, comprehensive evaluations on how such regulations affect firm dynamics and productivity are lacking. We examine a 2012-2014 pilot program in Guangdong (which later became a national policy) that was designed to reduce firm registration costs and encourage entrepreneurial activities. Using administrative data on firms' business registrations and annual reports, our analysis shows that the reform increased firm entry by 25% and firm exit by 8.7% in the manufacturing sector. The productivity of post-reform entrants was 1.1% higher than the productivity of pre-reform entrants, likely due to relaxed financial constraints and more intense competition.