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 document a convergence of the residual wage distribution toward the distribution of residual wages among hires from unemployment since the mid-1980s in the U.S. Viewed through the lens of a textbook job ladder model, this pattern indicates a substantial decline in net mobility toward higher-paying jobs. An estimated quantitative version of the model finds that changes in job-to-job mobility has lowered cumulative real wage growth by 3.3 percentage points between 1982-1991 and 2012-2021. Of this decline, a 0.6 percentage points reduction stems from increased job-to-job mobility to lower-paying (but potentially higher-value) jobs, while less mobility toward higher-paying jobs has reduced real wage growth by 2.5 percentage points. The latter is in turn the result of lower aggregate matching efficiency, which has reduced real wage growth by 1.3 percentage points; reduced search efficiency or intensity by employed workers that has lowered real wage growth by 0.9 percentage points; and rising employer concentration, which has contributed to 0.6 percentage points less real 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.

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.