We analyze a model of R&D alliance networks where firms are engaged in R&D collaborations that lower their production costs while competing on the product market. We provide a complete characterization of the Nash equilibrium and determine the optimal R&D subsidy program that maximizes total welfare. We then structurally estimate this model using a unique panel of R&D collaborations and annual company reports. We use our estimates to study the impact of targeted versus nondiscriminatory R&D subsidy policies and empirically rank firms according to the welfare-maximizing subsidies they should receive.

This paper introduces a structural model for the coevolution of networks and behavior. We characterize the equilibrium of the underlying game and adopt the Bayesian Double Metropolis-Hastings algorithm to estimate the model. We further extend the model to incorporate unobserved heterogeneity and show that ignoring this heterogeneity can lead to biased estimates in simulation experiments. We apply the model to study R&D investment and collaboration decisions in the chemical and pharmaceutical industry and find a positive knowledge spillover effect. Our model also provides a tractable framework for a long-run key player analysis.

We use matched employer-employee data to study the role of the firm in the transmission of income growth into consumption growth. We find that growth in income relative to the firm average (the within-firm component) translates significantly less into consumption than growth in firm average income (the between-firm component). These findings are explained by the lower persistence of the within-firm component of income, better self-insurance for workers more exposed to variations in income growth from the within-firm component, and peer effects in the workplace. Quantitatively, income persistence provides 43% of the explanatory power, self-insurance provides 35%, and peer effects provide 22%.

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). The standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair, and thus, is conservative. The analytical inference involves estimating multiple functional quantities that requires several tuning parameters. In this paper, we propose two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. In particular, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.

Using administrative tax records for U.K. businesses, we document both bunching in annual turnover below the VAT registration threshold and persistent voluntary registration by almost half of the firms below the threshold. We develop a conceptual framework that can simultaneously explain these two apparently conflicting facts. The framework also predicts that higher intermediate input shares, lower product-market competition, and a lower share of business to consumer sales lead to voluntary registration. The predictions are exactly the opposite for bunching. We test the theory using linked VAT and corporation tax records from 2004 to 2014, finding empirical support for these predictions.