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Discussion Paper
AI and the Labor Market: Will Firms Hire, Fire, or Retrain?
The rapid rise in Artificial Intelligence (AI) has the potential to dramatically change the labor market, and indeed possibly even the nature of work itself. However, how firms are adjusting their workforces to accommodate this emerging technology is not yet clear. Our August regional business surveys asked manufacturing and service firms special topical questions about their use of AI, and how it is changing their workforces. Most firms that report expected AI use in the next six months plan to retrain their workforces, with far fewer reporting adjustments to planned headcounts.
Journal Article
Opinion: Artificial Intelligence: Potentials and Prospects
We are at the dawn of a new technological revolution. The recent development of artificial intelligence (AI), especially the emergence of generative AI, has offered a plausible future in which machines will eventually free humans from a wide range of cognitive tasks, unleashing vast creativity and productivity gains.
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 ...
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 ...
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
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. ...
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
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 ...