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Regulatory efforts to protect against algorithmic bias have taken on increased urgency with rapid advances in large language models (LLMs), which are machine learning models that can achieve performance rivaling human experts on a wide array of tasks. A key theme of these initiatives is algorithmic "auditing," but current regulations -- as well as the scientific literature -- provide little guidance on how to conduct these assessments. Here we propose and investigate one approach for auditing algorithms: correspondence experiments, a widely applied tool for detecting bias in human judgements. In the employment context, correspondence experiments aim to measure the extent to which race and gender impact decisions by experimentally manipulating elements of submitted application materials that suggest an applicant's demographic traits, such as their listed name. We apply this method to audit candidate assessments produced by several state-of-the-art LLMs, using a novel corpus of applications to K-12 teaching positions in a large public school district. We find evidence of moderate race and gender disparities, a pattern largely robust to varying the types of application material input to the models, as well as the framing of the task to the LLMs. We conclude by discussing some important limitations of correspondence experiments for auditing algorithms.

We gather detailed data on organizational practices and information technology (IT) use at 253 firms to examine the hypothesis that external focus—the ability of a firm to detect and therefore respond to changes in its external operating environment—increases returns to IT, especially when combined with decentralized decision making. First, using survey-based measures, we find that external focus is correlated with both organizational decentralization, and IT investment. Second, we find that a cluster of practices including external focus, decentralization, and IT is associated with improved product innovation capabilities. Third, we develop and test a three-way complementarities model that indicates that the combination of external focus, decentralization, and IT is associated with significantly higher productivity in our sample. We also introduce a new set of instrumental variables representing barriers to IT-related organizational change and find that our results are robust when we account for the potential endogeneity of organizational investments. Our results may help explain why firms that operate in information-rich environments such as high-technology clusters or areas with high worker mobility have experienced especially high returns to IT investment and suggest a set of practices that some managers may be able to use to increase their returns from IT investments., Massachusetts Institute of Technology. Center for Digital Business, National Science Foundation (U.S.) (Grant IRI-9733877)

This dissertation includes two essays that explore in depth how computers are altering the economics of human resources management. The first essay is an empirical study relating offshoring to the displacement of US workers. I use micro-data from several sources to evaluate how firms' decisions to offshore information technology (IT) work affect hiring and displacement outcomes for their US-based IT workers. Offshoring raises the risk of displacement for the average IT worker by about 1-2% each year. Moreover, workers who produce services that do not require face-to-face contact, such as computer software programmers, are at significantly higher risk than those who provide services that require face-to-face interaction, such as sales or project management. I also find that displaced workers are less likely to be hired into offshoring firms, resulting in a net reallocation of these workers from firms that are offshoring to firms that are not. I also cross-validate many of these findings using external survey data. In the second essay, I explore the role of IT worker mobility in generating spillovers of knowledge related to workplace computerization. I use new longitudinal and nationally representative inter-firm mobility data to model employee flows among firms and find evidence that the diffusion of information technology related know-how through IT worker mobility played an important role in driving productivity growth over the last two decades. The estimates indicate that doubling the external stock of IT-related knowledge to which a firm has access is equivalent to raising internal IT investment by 30%. I discuss implications for managers, policy makers, and regional planners.

We analyze a new dataset on workers’ career paths to examine whether private equity (PE) investments can have positive spillover effects on workers. We study leveraged buyouts in the context of recent information technology (IT) diffusion, and find evidence supporting the argument that many employees of companies acquired by PE investors gain transferable, IT-complementary human capital. Our estimates indicate that these workers experience increases in both long-run employability and wages relative to what they would have realized in the absence of PE investment. The findings underscore PE’s role in mitigating the effects of workforce skill obsolescence resulting from technological change.