Xiaofeng Gong

We establish a reliable counterfactual for evaluating racial disparities in traffic stops using telemetric data to estimate the racial composition of motorists. We measure motorist racial composition using telemetric mobility data and a novel two-stage correction methodology anchored by an independent measure, racial composition of motorists involved in accidents. Applying this to Massachusetts State Police stops, we find non-White motorists are stopped at rates exceeding their roadway presence by 6.7 percentage points. We demonstrate that uncalibrated telemetric data significantly understates minority presence (15.2 vs. 28 percent), and the uncalibrated telemetric data yields disparities that are over twice the size of our preferred estimates. We use this validated measure to assess the accuracy of several commonly-used disparity tests. We find that the widely used “Community Standard” test dramatically overestimates disparities, even when restricting to local roads or non-commuting hours. In contrast, the less-often used “Crash Benchmark” accurately captures disparities even when aggregated to higher temporal levels covering up to 94 percent of stops. Finally, the “Veil of Darkness” test yields smaller estimates than our telemetric measure, consistent with its focus on disparate treatment rather than the broader legal standard of motorist composition.

Why do comparable economic sanctions yield divergent compliance trajectories? Our dynamic public-choice model shows how sanctions can entrench noncompliance: cross-border restrictions create concentrated rents for domestic groups controlling residual access, and domestic allocation rules determine how much of these rents an organized group captures and invests in political influence. Low capture sustains compliance. Intermediate capture creates history dependence, so a temporary shock can lock in noncompliance. High capture can eliminate the compliance steady state. Sanction-duration and trade data document declining resolution rates for long-lived sanctions and trade losses that outlast legal termination, and the calibrated model partly replicates both.

Innovative entrepreneurs create new businesses based on inventions of new or improved products and processes, generating employment opportunities and stimulating economic growth. This chapter examines the ethnic composition of inventors named on patents awarded to young U.S. firms, with particular attention to Chinese and Indian inventors, two groups that are highly represented in U.S. patenting overall. Combining data on firm founding years with patent- and inventor-level information, we analyze patents filed between 2000 and 2018 and examine the representation of Chinese and Indian inventors among patents assigned to young firms. We find robust evidence that they are less represented on patents assigned to young firms, especially firms five years old or younger, relative to their participation in patents assigned to older firms. This is particularly pronounced among teams composed entirely of younger inventors. We discuss how U.S. immigration policy, particularly visa constraints facing immigrants at early career stages, may help explain these patterns.

When does open-source software (OSS) close a proprietary lead, and when can it not? I study a dynamic quality-ladder model in which user-developers decide whether to supply an OSS improvement after a proprietary firm chooses whether to invest. Contributors are rewarded as users. Behind a proprietary lead, the firm’s profit-maximizing price limits what high-valuation users gain from improving OSS, so the lead weakly lowers OSS progress. In the other direction, an OSS match that arrives before pricing removes a new lead’s current return and can make the firm wait rather than invest. The two effects coexist only at intermediate market size, and a lead that stops OSS progress removes the second. Equilibrium paths take three forms: same-period matching, catch-up after a temporary lead, or a persistent proprietary lead that OSS can narrow but not close. When OSS closes a one-step lead, the firm never switches from waiting to investing. Cheaper contribution that leaves the firm’s costs unchanged, as from AI-assisted development, weakly raises OSS progress and, if it lowers the smallest contribution cost, can reopen catch-up; in numerical examples a small reduction lowers welfare by turning proprietary investment into waiting. The paper gives an exact test for when stronger OSS incentives reduce discounted paid investment and an exact local condition for when OSS contribution at equal quality raises welfare.

This paper studies foreign entry in media markets when culturally distant content must first create an experienced audience before formal monetization is profitable. Media goods combine high fixed costs for localization, distribution, rights administration, marketing, and enforcement with low marginal costs of digital circulation and heterogeneous consumer preferences. I model a foreign media entrant, a domestic incumbent, and an unauthorized distribution fringe in a two-period game with heterogeneous adoption costs and endogenous enforcement. Unauthorized circulation both constrains later official prices by creating a piracy ceiling and can serve as a pre-entry adoption and informal-localization technology. I interpret this mechanism as a dynamic preference externality: early unauthorized circulation expands the entrant’s effective future audience by reducing cultural discount and adoption friction. I characterize a Strategic Ignorance equilibrium in which the entrant delays official entry and enforcement, allowing unauthorized distribution to build an adopted base before formal monetization. The sliding window survives as a stationary one-period-delay rule in an infinite-horizon version of the game with fixed per-period profits, and the mechanism can self-terminate at the genre level as adoption costs fall. Relative to a universal public-enforcement benchmark, private enforcement discretion can expand the range of fixed costs for which foreign media entry is viable. The results show how enforcement policy affects entry, product availability, audience formation, and surplus distribution.

Using a global sanctions database, this paper analyzes whether sanctions can achieve their intended objectives. It further constructs a game model to analyze the underlying mechanisms and validates them with cross-country time-series data. The main conclusions are: First, the probability of sanctions achieving their goals is very low. Second, most sanctions instruments struggle to facilitate goal attainment. Third, heterogeneity analysis identifies factors influencing whether sanctions achieve preset goals. Fourth, mechanism analysis shows that sanctions impact both target and initiating countries, but the long-term effects on both sides are relatively limited.

This article summarizes and combs the relevant theories and practical effects of economic and financial sanctions between countries, especially analyzes the economic mathematical modeling and prediction that are easy to be ignored in economic sanctions, and points out the shortcomings of previous research mainly based on game theory, especially the problems that exist when facing the “black-box of sanctions”. The article summarizes the main methods of sanctions research, especially statistical and econometric research methods, mathematical models, data simulation based on dynamic stochastic general equilibrium model (DSGE) and prediction based on computable general equilibrium model (CGE). The article also summarizes the latest public choice model, explains the effective mechanism of sanctions, and discusses the future research direction and possible breakthrough points, including the use of models to predict the gains and losses brought to the country by a series of economic and financial sanctions such as trade wars, or more objectively calculate them by means of figure networking and programming.