Knowledge that Transforms

We track the movement of high-potential startups using cross-state business registrations and estimate the utility of cities to moving startups using a revealed preference approach. 6.6% of these startups move across state borders during their first five years. Startup hubs like Silicon Valley and Boston tend to lose startups to other cities. Our findings show that startups prefer traditional hubs when they move soon after being founded, but later prefer cities with lower taxes. This pattern is not due to vertical sorting or industrial specialization.

This article evaluates the impact of the Chinese minimum-wage policy on consumption of low-wage households for the period 2002–2009. Using a representative panel of urban households, we find that the consumption response to minimum-wage income hikes increases in the share of minimum-wage income in total household income. In particular, poorer households fully consume their additional income, while meaningful negative employment effects are absent. The large marginal propensity to consume is driven by households with at least one child, while poor, childless households save two-thirds of a minimum-wage hike. The expenditure increase is concentrated in health care and education with potentially long-lasting benefits to household welfare.

Seminal theories of the firm posit that firm ownership is allocated to minimize contractual inefficiencies. Yet, it remains unclear how much the optimal ownership choice affects firm performance in practice. This paper provides a first quantification of the gains from optimal ownership within multinational firms by exploiting a major liberalization of China’s policy restrictions on foreign ownership. The liberalization allowed previously restricted firms to become fully foreign-owned. We find that these reoptimized ownership choices raise firm output by 40% and productivity by 7.5% on average. An extended property-rights theory of the multinational firm rationalizes these effects and their heterogeneity.

Between 1883 and 1919, Andrew Carnegie funded the construction of more than 1,500 public libraries across the United States, reducing the costs of accessing knowledge for millions. We study the effect of these libraries on innovation. Patenting in recipient places increased on average by 10%–12% in the 20 years following library construction relative to a novel control group of cities that applied for but did not build libraries. We show that access to scientific knowledge and increased collaboration opportunities are possible mechanisms.

We document a new way that discrimination operates: through sequential spillover effects. Employers in an incentivized resume rating experiment evaluate a sequence of hypothetical candidates with randomly assigned characteristics. Candidates are rated worse when following white men than when following women or minorities. Exploring the mechanisms, we find that spillover effects are inversely related to direct bias. When reviewing high-quality resumes or recruiting in STEM (science, technology, engineering, and math) industries, employers directly favor white men and display no spillover effect. For low-quality resumes or non-STEM industries, we find no direct bias but a strong spillover effect. Results suggest that discrimination arises in subtle ways.

We develop a general theory of omitted variable bias for a wide range of common causal parameters, including average treatment effects, average causal derivatives, and policy effects from covariate shifts. We show how plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the bias, facilitating sensitivity analysis in otherwise complex models. Finally, we provide statistical inference methods that can leverage modern machine learning algorithms for estimation. These results allow empirical researchers to perform sensitivity analyses in a flexible class of machine-learned causal models using very simple tools. Empirical examples demonstrate the utility of our approach.

This paper provides a method to study quantile effects in discrete choice with social interactions. The method is based on a behavioral social interactions model from quantile preference in decision making and demonstrates peer effects on different quantiles of discrete outcomes. The peer effects parameters are estimated by a nested pseudoscore (NPS) approach, which is developed to tackle the computational burden pertaining to the social interactions model. Consistency and asymptotic normality are established for the proposed NPS estimator. We illustrate the finite sample performance of the model and the estimator by Monte Carlo experiments and an application of peer effects among students on exercise decisions, using the National Longitudinal Study of Adolescent Health dataset.

In 2001 a large and sustained supply shock halted a heroin epidemic in Australia. We use drug offenses to identify individual opioid users and examine how the shock affected their mortality risks and criminal activity over the next eight years. Initially, gains from fewer overdoses are offset by drug substitution and more crime, including homicides. Most adverse effects dissipate over time, whereas persistent mortality reductions save the lives of around one in 48 individuals in our sample. Our results demonstrate that reducing the supply of illicit opioids can lead to meaningful longer-term improvements, even when the short-term effects are ambiguous.

Several studies have linked rising insolvency rates to increasing inequality and argued that this might be explained by individuals’ desire to “keep up with the Joneses.” Using unique administrative register data on individual insolvencies in Sweden, I test whether the probability to become insolvent is related to one’s income distance relative to peers. Identification relies on area fixed effects, an extensive set of background characteristics, and varying the definition of the relevant reference group. I find that higher inequality increases the individual’s probability to become insolvent and that this effect is primarily driven by men.

Regulators often rely on regulated entities to self-monitor compliance, creating strategic incentives for endogenous monitoring. This paper builds a framework to detect whether local governments skip air pollution monitoring when they expect air quality to deteriorate. The core of our method tests whether the timing of monitor shutdowns coincides with the counties’ air quality alerts—public advisories based on local governments’ own pollution forecasts. Applying the method to a monitor in Jersey City, New Jersey, suspected of a deliberate shutdown during the 2013 “Bridgegate” traffic jam, we find a 33% reduction of this monitor's sampling rate on pollution-alert days. Building on large-scale inference tools, we then apply the method to test more than 1,300 monitors across the United States, finding fourteen metropolitan areas with clusters of monitors showing similar strategic behavior. We assess geometric imputation and remote-sensing technologies as potential solutions to deter future strategic monitoring.