Search Results
Discussion Paper
By Degree(s): Measuring Employer Demand for AI Skills by Educational Requirements
The rapid advancement of artificial intelligence (AI) has prompted widespread interest and discussion about its potential to transform the labor market. For workforce development practitioners, a key issue is how AI is changing the nature of work, mainly through changes in the skills workers need to be competitive for the jobs of today and of the future. In this Workforce Currents, we explore the growth of employer demand for AI skills in online job postings data between 2010 and 2024. Lightcast, a labor analytics firm, provides job postings data that includes several useful features of ...
Working Paper
Total Recall? Evaluating the Macroeconomic Knowledge of Large Language Models
We evaluate the ability of large language models (LLMs) to estimate historical macroeconomic variables and data release dates. We find that LLMs have precise knowledge of some recent statistics, but performance degrades as we go farther back in history. We highlight two particularly important kinds of recall errors: mixing together first print data with subsequent revisions (i.e., smoothing across vintages) and mixing data for past and future reference periods (i.e., smoothing within vintages). We also find that LLMs can often recall individual data release dates accurately, but aggregating ...
Working Paper
Artificial Intelligence Innovation by Financial Innovators: Evidence from US Patents
This paper examines the evolution of artificial intelligence (AI) patent rates (i.e., the number of AI patents/number of firms of the same type) and concentration metrics (i.e., the Herfindahl-Hirschman Index (HHI) and Gini coefficient) among financial market participants from 2000 to 2020. It documents the historical trajectories of AI innovation for regulated banking entities and less-regulated firms, revealing that nonfinancial companies exhibit the highest baseline AI patent rate, while banks show the highest growth in AI patent rate over time. Banks have the highest HHI, and nonfinancial ...
Working Paper
Research in Commotion: Measuring AI Research and Development through Conference Call Transcripts
This paper introduces a novel measure of firm-level Artificial Intelligence (AI) Research & Development—the AIR Index—derived from the semantic similarity between earnings conference call transcripts and leading AI research papers. The AIR Index varies widely across industries, with sustained strength in computer and electronic manufacturing, and accelerating growth in computing infrastructure and educational services seen after the introduction of ChatGPT in November 2022. I find that the AIR Index is associated with an immediate increase in Tobin’s Q and can help explain the ...
Working Paper
Artificial Intelligence in the Office and the Factory: Evidence from Administrative Software Registry Data
I use administrative data on artificial intelligence (AI) software created in Brazil to study its effects on the labor market. Owing to a unique copyright system, Brazilian firms have registered their software with the government since the 1980s, creating a detailed record of nearly all commercial AI applications developed in the country. Drawing on this registry, I show that AI is widely used not only in administrative tasks but also in production settings, where it primarily supports process optimization and quality control. Using an instrument based on variation in software development ...
Working Paper
Forecasting the Economic Effects of AI
We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. ...
Working Paper
Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values
Machine learning and artificial intelligence are often described as “black boxes.” Traditional linear regression is interpreted through its marginal relationships as captured by regression coefficients. We show that the same marginal relationship can be described rigorously for any machine learning model by calculating the slope of the partial dependence functions, which we call the partial marginal effect (PME). We prove that the PME of OLS is analytically equivalent to the OLS regression coefficient. Boot- strapping provides standard errors and confidence intervals around the point ...
Working Paper
Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values
Machine learning and artificial intelligence methods are often referred to as “black boxes” when compared with traditional regression-based approaches. However, both traditional and machine learning methods are concerned with modeling the joint distribution between endogenous (target) and exogenous (input) variables. Where linear models describe the fitted relationship between the target and input variables via the slope of that relationship (coefficient estimates), the same fitted relationship can be described rigorously for any machine learning model by first-differencing the partial ...
Working Paper
New Technologies and Jobs in Europe
We examine the link between labour market developments and new technologies such as artificial intelligence (AI) and software in 16 European countries over the period 2011-2019. Using data for occupations at the 3-digit level, we find that on average employment shares have increased in occupations more exposed to AI. This is particularly the case for occupations with a relatively higher proportion of younger and skilled workers. While there exists heterogeneity across countries, only very few countries show a decline in employment shares of occupations more exposed to AI-enabled automation. ...
Journal Article
Machine Learning a Ramsey Plan
We use a Python program to calculate a pair of infinite sequences of money creation and price level inflation rates that maximize a benevolent time 0 government’s quadratic objective function for a linear-quadratic version of Calvo (1978). The program computes an open-loop representation of the optimal plan and an associated monotonically declining, bounded from below sequence of continuation values whose limit is a worst continuation value that is associated with a “timeless perspective”. We run some least squares regressions on fake data to try to learn about the structure of the ...