Aniket Baksy

Between 1980 and 2000, growth in the skill premium and a decline in the relative price of capital led economists to conclude that capital-embodied technical change was driving up the relative demand for skilled labor. Given the continued steady decline in capital prices post 2000, these models predict a continual rise in the skill premium. However, post 2000, growth in the skill premium has slowed down. I argue that as the skill premium increased, firms adopted new technologies economizing on the use of skilled labor. I quantify this force using an equilibrium model with costly technology adoption. As capital prices fall, capital-skill complementarity initially drives up the skill premium. Firms respond by investing in new technologies which are less skilled-labor-intensive. The model successfully accounts for the slowing skill premium and the behavior of the labor share. Without technology adoption, the model predicts a skill premium in 2019 that is 5 percentage points higher and a labor share that is almost 12 percentage points higher. I provide microeconomic evidence for my mechanism by showing that accountants relatively more exposed to the adoption of accounting software saw slower wage growth.

We examine patterns of economic policy uncertainty (EPU) around national elections in 23 countries. Uncertainty shows a clear tendency to rise in the months leading up to elections. Average EPU values are 13% higher in the month of and the month prior to an election than in other months of the same national election cycle, conditional on country effects, time effects, and country-specific time trends. In a closer examination of U.S. data, EPU rises by 28% in the month of presidential elections that are close and polarized, as compared to elections that are neither. This pattern suggests that the 2020 US Presidential Election could see a large rise in economic policy uncertainty. It also suggests larger spikes in uncertainty around future elections in other countries that have experienced rising polarization in recent years.

We develop a methodology to consistently estimate employer-to-employer (EE) mobility toward higher paying jobs based on publicly available microdata from the Current Population Survey, and use it to document three facts on EE mobility toward higher paying jobs over the past half century. First, it fell in half between 1979 and 2023. Second, its decline reduced annual wage growth by over one percentage point. Third, it was particularly pronounced for women, those with less than a college degree, and recent cohorts. The decline does not appear to be the result of workers being better matched with their current jobs or the labor market being worse at matching workers and firms. Instead, we present new evidence consistent with the view that greater labor market concentration reduced workers' opportunities to switch employers.

The adoption of frontier technologies is slow and concentrated in large establishments, a puzzle given the large increases in the productivity of these technologies. I propose a simple explanation: that these technologies are complementary to the stock of technical talent hired by a firm. If workers with specialized technical skills earn high wages relative to the costs of equipment, this can generate strong increasing returns to scale in technology adoption. Using microdata on the adoption of specific digital technologies and on vacancy creation associated with the specific digital skills they require, I document that technical talent is expensive relative to IT capital, and that it is more concentrated in the largest firms than workers are. I will explore how this mechanism interacts with the ongoing decline in IT capital to produce the increase in concentration we have seen through the lens of a dynamic GE model featuring firm heterogeneity and worker careers. I will use the model to explore the implications of technology subsidies and ask whether these subsidies might be better spent elsewhere.

Why has worker mobility in the United States declined so much over the past decades? While previous work attributes this decline to reduced labor market dynamism, this paper reveals that one third of this decline is due to increased educational attainment among workers. Higher education affects labor mobility in two ways. First, having a larger share of young workers in school rather than in the labor market precludes these very workers, who are typically the most mobile, from switching jobs and occupations. Second, education provides workers an alternative to learning about their "type" making educated workers less reliant on experimenting with new jobs.

We argue that "Zombie lending”, where banks keep lending to insolvent and unproductive firms, attenuates the effects of recessions in the short-run at the expense of output in the long-run. We build a quantitative model in which heterogeneous firms finance themselves through retained earnings and bank debt. Banks face capital requirements, but have private information on whether a given loan is in default, allowing them to hide losses and bypass these requirements. In a recession, higher firm defaults lead to larger bank losses, raising the incentives to hide losses by keeping insolvent firms alive. In the short-run this allows banks to keep lending, which supports output. In the long-run however, this leads to misallocation due to the survival of relatively unfit firms and lower entry. We use the model to quantify the contribution of zombie lending during and after the 2008-09 crisis in Europe and to evaluate the implications of pro-cyclical capital requirements.

I argue that the successful incorporation of modern technologies into production processes requires large human capital investments. I argue that this can rationalize the divide between "digital superstars" who are enthusiastic early adopters of digital technologies and are reaping the productivity benefits, and small firms, who often make the case that new technologies are not useful in their businesses.

Although AI is increasingly applicable to business tasks, AI adoption remains low and concentrated in large firms, which increases inequality across firms and workers at those firms. We identify the key barriers to AI adoption as the high costs of AI customization to specific business needs of complementary data infrastructure needed to leverage AI. We propose two clusters of policies to lower AI adoption costs for small and medium enterprises (SME). First, we propose public support targeted at the creation and commercialization of flexible low/no-code AI platforms. Second, we propose creating public data repositories and a clearinghouse-like infrastructure to improve SME access to cutting edge pre-trained models and computational infrastructure. We also propose the creation of a medium-skilled data curator workforce to manage and reuse data and provide new opportunities for retrained workers.