This study examines the impact of access to confidential administrative data on the rate, direction, and policy relevance of economics research. To study this question, we exploit the progressive geographic expansion of the U.S. Census Bureau's Federal Statistical Research Data Centers (FSRDCs). FSRDCs boost data diffusion, help empirical researchers publish more articles in top outlets, and increase citation-weighted publications. Besides direct data usage, spillovers to non-adopters also drive this effect. Further, citations to exposed researchers in policy documents increase significantly. Our findings underscore the importance of data access for scientific progress and evidence-based policy formulation.

We explore how Large Language Models (LLMs) can serve as synthetic subjects to inform strategy research. We introduce a framework for designing and running simulated experiments with LLM-powered agents. We argue that this approach is useful for rapid, low-cost prototyping of human experiments and for generating novel hypotheses. We apply the framework to the exploration–exploitation dilemma and show that LLM-based experiments reproduce patterns observed among human participants. We then vary parameters and boundary conditions to illustrate how the same setup can support design iteration and surface hypotheses about when and why established results change. In the conclusion, we discuss the promise and limitations of artificial intelligence agents as “model organisms” for strategy.

The study of innovation depends heavily on high-quality patent data. Yet, datasets containing complete patent documents focus only on recent decades, while historical patent datasets with broader temporal coverage typically lack detailed information. Therefore, our ability to leverage advances in textual analyses to study long-run innovation dynamics remains limited. To this end, we introduce a large-scale dataset of the universe of technical specifications of British patents granted between 1617–1899. Our data consists of the full specification texts alongside linked information about inventors, including their disambiguated names, occupations, and addresses. We use our data to document changes over time in total inventive activity, the geography of innovation, inventor occupations, and patent novelty and impact. Finally, we discuss use cases and avenues for subsequent research.

Microdata from government agencies is believed to be valuable for economics research, and yet access to this data is highly restricted due to concerns about privacy and security. We provide an empirical assessment of the use and impact of restricted-access data that researchers can analyze at the U.S. Census Bureau's secure facilities. Our findings show that the use of Census Bureau's confidential data is growing and the publications employing it have a higher impact on the scientific and policy debate. However, adoption remains largely limited to established researchers from prestigious institutions. Our results and discussion inform the design of policies that balance privacy protection with accessibility to confidential microdata.

Big data is increasingly used to predict which opportunities are worth pursuing. By making technological search less dependent on prior expertise, data-driven predictions could level the playing field in innovation. Yet predictions based on codified information can be noisy when evaluating opportunities requires tacit and contextual knowledge. In such settings, domain knowledge helps firms screen out false positives, ultimately reinforcing the advantage of firms with deeper expertise. I test this argument in pharmaceutical innovation, where genome-wide association studies (GWAS) identify genes that may serve as drug targets. I find that GWAS stimulate corporate investment, but around one-third of this response targets false positives. Firms lacking gene-specific expertise rely more on GWAS and disproportionately pursue these misleading signals. As a result, the same data-driven opportunities widen performance gaps in drug discovery, with downstream gains concentrated among firms with deeper knowledge bases. Mechanism tests trace this effect to patient-oriented knowledge that helps firms judge whether statistical patterns are biologically meaningful. Together, the results show that domain knowledge remains crucial for competitive advantage, but its role shifts from finding opportunities to assessing those revealed by data.

Datasets spanning broad portions of a scientific landscape enable a data-driven approach to search, in which empirical patterns rather than prior theory guide which possibilities are investigated. Yet, if theory becomes less necessary for directing search, does this also weaken incentives to develop it? I examine this question in genetics research aimed at identifying new drug targets. I compare candidate gene studies, which select genes based on biological knowledge, with genome-wide association studies (GWAS), which systematically scan the genome for statistical associations with disease. I find that GWAS expand search breadth and increase the variability of scientific outcomes, but with a larger increase in breakthroughs. Importantly, associations uncovered by GWAS attract substantially more follow-on research aimed at explaining their causal basis. Mechanism tests show that these effects arise because GWAS are more likely to uncover empirical anomalies: unexpected associations of varying quality that nonetheless stimulate subsequent theorizing. Together, the findings suggest that reducing theory’s role in search can, paradoxically, create new reasons to develop it.

Does corporate authorship increase or decrease deceptive conduct in science? Commercial interests are often viewed as a threat to scientific integrity. Yet when firms build on their science in downstream innovation, fraudulent findings can derail costly development, creating private incentives to produce reliable evidence. We test this logic in Alzheimer’s research, using inappropriate image duplication as an observable marker of deceptive conduct. Combining AI-based detection with manual validation, we document a rising incidence of data manipulation. Such cases are nearly absent in corporate-authored papers and less frequent in academic-industry collaborations, but only when firms exercise meaningful control over the production of evidence. The reduction in problematic duplications is largest when firms are likely to build on the results and in settings where deceptive conduct is more prevalent. However, this difference disappears when research concerns drugs already commercialized by the authoring firm. These findings recast the role of commercial interests in science: they can undermine integrity when legitimating existing products, but strengthen it when firms need solid evidence to build on.

We study exploration under uncertainty and show how access to data on past attempts can paradoxically hinder breakthrough discovery. We develop a model of the ``streetlight effect'' demonstrating that when data highlights attractive but ultimately suboptimal projects, it can narrow exploration and suppress innovation. In a laboratory experiment, we find that revealing the value of an enticing project lowers payoffs and reduces breakthrough discoveries. This drop stems from increased free-riding behavior, which crowds out the generation of new data. We then apply our theory in the context of scientific research into the genetic origins of human diseases, focusing on the drivers of limited exploration. To identify the causal impact of past data, we use an instrumental variable that leverages exogenous genetic overlaps between humans and laboratory mice, which reduces research costs for specific genes and leads to prioritized data collection about them. We find that diseases with early evidence of promising genetic targets are 16 percentage points less likely to yield breakthroughs than those where early efforts failed. While competition attenuates the streetlight effect, it does not eliminate it. Our paper provides the first analysis of this phenomenon, outlining the conditions under which data leads agents to look under the lamppost rather than engage in socially beneficial exploration.