Working Papers
Navigating the Exploitation-Exploration Tradeoff: An Empirical Study of Resource Allocation in Research Labs. 2025. Major Revision, Management Science.
[ Abstract | Latest Version + Appendices (September 2025)| December 2023 Version + Appendices | October 2022 Version | October 2022 Version Appendices ]
Balancing exploitation and exploration in resource allocation under uncertainty is a classic theoretical problem. Yet little research has empirically studied how organizations navigate the exploitation-exploration tradeoff in complex real-world situations. To address this gap, this paper introduces a novel setting of structural biology labs, featuring high-frequency, publicly available data on nearly one million discrete experimental trials allocated across 300,000+ research projects from 2000-2015. We model this setting as a stochastic bandit and develop a dynamic structural estimation approach to infer the allocation decision policies that best characterize lab behavior. We find the labs' decision models strongly resemble a simple Upper Confidence Bound (UCB) algorithm, which achieves superior in-sample fit (51–84% of the log-likelihood of the next-best model among the ones we tested with minimal additional parameters) and strong out-of-sample predictive accuracy (73–87% allocation probability for actually allocated trials versus 0.1–0.8% for unallocated ones). Through counterfactual simulations, we demonstrate how to leverage our policy inference results to incrementally evaluate and improve allocation decision making. For example, switching to a readily implementable alternative algorithm could have increased cumulative rewards by up to 28%, while earlier adoption of structured decision-making during these labs' initial pilot phases could have yielded further performance gains, though results vary significantly across labs due to organizational heterogeneity.
Managing the Unmitigated: Eighteen Years of Evidence on Why Risks Go Unfixed (with Raviv Murciano-Goroff and Shane Greenstein). 2025. Minor Revision, Organization Science.
[ Abstract | NBER Working Paper | HBS Working Knowledge | NBER Digest ]
Why do organizations differ in how much they invest in preventing adverse events? This study examines a setting in which organizations face the same cybersecurity vulnerabilities with freely available fixes, and yet make strikingly different choices. The study constructs a monthly panel of 150,836 medium and large U.S. organizations (2000–2018), linking the server software versions used to host their websites with detailed data on the software's vulnerabilities, the timing of updates, and organizational attributes. Descriptively, 57% of organizations run software with known, fixable severe vulnerabilities in a typical month, and only 34% install patches within six months of a fix’s release; updating behavior is highly persistent within organizations. Guided by a threshold-cost view of preventive action, panel and stratified hazard models relate patching levels and speed to proxies for perceived costs and benefits. The evidence indicates that cost-side factors—coordination demands, infrastructure scope and complexity, potential operational disruption, and sourcing of mitigation capacity—systematically predict whether and how quickly organizations mitigate vulnerabilities, whereas proxies for value at risk, regulatory stakes, and external risk salience do not. High-traffic and data-intensive organizations, if anything, accumulate more unfixed severe vulnerabilities, while organizations that outsource infrastructure and reduce technical debt patch more promptly. The findings reframe organizational risk mitigation as primarily an adjustment-cost and capability problem and highlight design levers for managers, vendors, and policymakers seeking to close the gap between “known and fixable” and “actually mitigated” risks.
Industrial Policy, Location Choice, and Firm Performance in High-Tech Manufacturing (with Audrey Tiew). 2025.
[ Abstract | August 8, 2025 version (NEW!) | August 1, 2025 version ]
High-tech manufacturing industries face constant technological change and sustained investment pressures as manufacturing technologies evolve. National policies from the U.S. and China—driven by concerns over security and industrial self-reliance—now heavily influence these investment pressures. This paper examines the effects of industrial policy on contract manufacturing investments in the global semiconductor industry. We assemble a novel dataset that combines quarterly facility-level capacity investments with global contract manufacturing orders from 2004 to 2015. Using these data, we estimate a structural model of contracting between semiconductor manufacturers and their clients, recovering key competitive parameters. We apply the model to a detailed case study of a major semiconductor manufacturer evaluating whether to locate a large fabrication facility in the U.S. or remain in its home region amid shifting industrial policies. In counterfactual simulations, we find that locating in the U.S. would require an additional investment of \$1.2 billion compared to the home region, which could roughly be offset by lump-sum subsidies comparable (percentage-wise) to those provided under the CHIPS and Science Act. However, the profit reduction caused by cost disadvantages and reduced competitiveness at the U.S. location has a greater impact. Across various policy scenarios motivated by real-world industrial policies—including a no-policy baseline, U.S. export controls alone, Chinese tax subsidies alone, or a combination of both—locating in the U.S. consistently results in an additional \$1.6–\$1.8 billion profit loss compared to staying in the home region. This loss is concentrated in later years as the facility's technology matures. Across all simulated scenarios, industrial policies reduce manufacturing profits by \$3.1–\$10.6 billion relative to the no-policy baseline, but U.S. import tariffs substantially change the pattern of these losses: under our conservative tariff rate calibration, relocating to the U.S. increases the firm's profit by \$5.9 billion relative to remaining in its home region, primarily by offsetting the cost disadvantages of a U.S. facility and enhancing the firm's competitiveness in capturing U.S. demand, particularly for mature technologies.
Work in Progress
Demand Fluctuations and Supply Coordination in Semiconductor Manufacturing (with Audrey Tiew).
[ Abstract ]
We study how supply capacity coordination can reduce social inefficiency from demand uncertainty and market power in the context of the semiconductor manufacturing industry. Market power generates misalignment between firm profit-maximizing capacity investments and welfare-maximizing capacity investments. To quantify the extent of this inefficiency and explore how various forms of supply coordination can mitigate it, we estimate a static structural model of semiconductor demand and a dynamic model of supply-side investment in technology and capacity. The data we have assembled to perform this exercise are, to our knowledge, the most comprehensive data on the industry in academic research. We obtain: (i) detailed proprietary buyer-level product demand data, covering around 20% of world orders, from 2004 to 2015, and (ii) proprietary world-wide, plant-level technology and capacity investment in semiconductor manufacturing plants from 1995 to 2015. We compare in counterfactual scenarios the relative efficacy of various forms of supply coordination (e.g., social planner, monopoly manufacturer, coordination on technology and capacity investment but competition in product market) in reducing inefficiency.
Risk and Return in Scientific Research: Evidence from Structural Biology (with Pierre Azoulay and Soomi Kim).
How Industrial Policy Reshapes Firms’ Capacity Investment Portfolios: Evidence from Semiconductor Manufacturin (with Ali Ilhan Haliloglu and Stefanus Jasin).
Publications
Examining Selection Pressures in the Publication Process Through the Lens of Sniff Tests (with Christopher Snyder). 2026. Review of Economics and Statistics 108 (3): 613–627
[ Abstract | Publisher’s Version ]
The increasing demand for empirical rigor has led to the growing use of auxiliary tests (balance, pre-trends, over-identification, placebo, etc.) to help assess the credibility of a paper's main results. We dub these "sniff tests" because rejection is bad news for the author and standards for passing are informal. We use these sniff tests---a sample of nearly 30,000 hand collected from scores of economics journals---as a lens to examine selection pressures in the publication process. We derive bounds under plausible nonparametric assumptions on the latent proportion of significant sniff tests removed by the publication process (whether by p-hacking or relegation to the file drawer) and the proportion whose significance was due to true misspecification, not bad luck. For the subsample of balance tests in randomized controlled trials, we find that the publication process removed at least 30% of significant p-values. For the subsample of other tests, we find a that at least 40% of significant p-values indicated true misspecification. We use textual analysis to assess whether authors over-attribute significant sniff tests to bad luck.
Social Imitation Dynamics of Vaccination Driven by Vaccine Effectiveness and Beliefs (with Feng Fu and Xingru Chen). 2025. PLOS Computational Biology 21 (10), e1013586.
[ Abstract | Publisher’s Version ]
Declines in vaccination coverage for vaccine-preventable diseases, such as measles and chickenpox, have enabled their surprising comebacks and pose significant public health challenges in the wake of growing vaccine hesitancy. Vaccine opt-outs and refusals are often fueled by beliefs concerning perceptions of vaccine effectiveness and exaggerated risks. Here, we quantify the impact of competing beliefs – vaccine-averse versus vaccine-neutral – on social imitation dynamics of vaccination, alongside the epidemiological dynamics of disease transmission. These beliefs may be pre-existing and fixed, or coevolving attitudes. This interplay among beliefs, behaviors, and disease dynamics demonstrates that individuals are not perfectly rational; rather, they base their vaccine uptake decisions on beliefs, personal experiences, and social influences. We find that the presence of a small proportion of fixed vaccine-averse beliefs can significantly exacerbate the vaccination dilemma, making the tipping point in the hysteresis loop more sensitive to changes in individuals’ perceived costs of vaccination and vaccine effectiveness. However, in scenarios where competing beliefs spread concurrently with vaccination behavior, their double-edged impact can lead to self-correction and alignment between vaccine beliefs and behaviors. The results show that coevolution of vaccine beliefs and behaviors makes populations more sensitive to abrupt changes in perceptions of vaccine cost and effectiveness compared to scenarios without beliefs. Our work provides valuable insights into harnessing the social contagion of even vaccine-neutral attitudes to overcome vaccine hesitancy.
Hidden Software and Veiled Value Creation: Illustrations from Server Software Usage (with Raviv Murciano-Goroff and Shane Greenstein). 2021. Research Policy 50 (9): 104333.
[ Abstract | Publisher’s Version ]
How do you measure the value of a commodity that transacts at a price of zero from an economic standpoint? This study examines the potential for and extent of omission and misattribution in standard approaches to economic accounting with regards to open source software, an unpriced commodity in the digital economy. The study is the first to follow usage and upgrading of unpriced software over a long period of time. It finds evidence that software updates mislead analyses of sources of firm productivity and identifies several mechanisms that create issues for mismeasurement. To illustrate these mechanisms, this study closely examines one asset that plays a critical role in the digital economic activity, web server software. We analyze the largest dataset ever compiled on web server use in the United States and link it to disaggregated information on over 200,000 medium to large organizations in the United States between 2001 and 2018. In our sample, we find that the omission of economic value created by web server software is substantial and that this omission indicates there is over $4.5 billion dollars of mismeasurement of server software across organizations in the United States. This mismeasurement varies by organization age, geography, industry and size. We also find that dynamic behavior, such as improvements of server technology and entry of new products, further exacerbates economic mismeasurement.
The Impact of the General Data Protection Regulation on Internet Interconnection (with Bradley Huffaker, kc claffy, and Shane Greenstein). 2021. Telecommunications Policy 45 (2): 102083.
[ Abstract | Publisher’s Version | VoxEU Column ]
The Internet comprises thousands of independently operated networks, interconnected using bilaterally negotiated data exchange agreements. The European Union (EU)'s General Data Protection Regulation (GDPR) imposes strict restrictions on handling of personal data of European Economic Area (EEA) residents. A close examination of the text of the law suggests significant cost to application firms. Available empirical evidence confirms reduction in data usage in the EEA relative to other markets. We investigate whether this decline in derived demand for data exchange impacts EEA networks' decisions to interconnect relative to those of non-EEA OECD networks. Our data consists of a large sample of interconnection agreements between networks globally in 2015–2019. All evidence estimates zero effects: the number of observed agreements, the inferred agreement types, and the number of observed IP-address-level interconnection points per agreement. We also find economically small effects of the GDPR on the entry and the observed number of customers of networks. We conclude there is no visible short run effects of the GDPR on these measures at the internet layer.
Do Low‐Price Guarantees Guarantee Low Prices? Evidence from Competition between Amazon and Big‐Box Stores. 2017. Journal of Industrial Economics 65 (4): 719-738.
[ Abstract | Publisher’s Version ]
It has long been understood in theory that price-match guarantees can be anticompetitive, but to date, scant empirical evidence is available outside of some narrow markets. This paper broadens the scope of empirical analysis, studying a wide range of products sold on a national online market. Using an algorithm that extracts data from charts, I obtain a novel source of data from online price trackers. I examine prices of goods sold on Amazon before and after two big-box stores (Target and Best Buy) announced a guarantee to match Amazon's prices. Employing both difference-in-difference and regression-discontinuity approaches, I robustly estimate a positive causal effect of six percentage points. The effect was heterogeneous, with larger price increases for initially lower-priced items. My results support anticompetitive theories which predict price increases for Amazon, a firm that did not adopt the guarantee, and are consistent with plausible mechanisms for the heterogeneous impact.