Michael Webb
Job Market Candidate

Stanford University
Department of Economics
579 Jane Stanford Way
Stanford, CA 94305
Cell: (617) 444-9137

Curriculum Vitae


Primary: Labor
Secondary: Innovation, Economics of Technology

Expected Graduation Date:

June 2020

Google Scholar Citations


Nicholas Bloom (Primary)

Matthew Gentzkow

Pete Klenow

Luigi Pistaferri

John Van Reenen

Job Market Paper

The Impact of Artificial Intelligence on the Labor Market [Data]

I develop a new method to predict the impacts of a technology on occupations. I use the overlap between the text of job task descriptions and the text of patents to construct a measure of the exposure of tasks to automation. I first apply the method to historical cases such as software and industrial robots. I establish that occupations I measure as highly exposed to previous automation technologies saw large declines in employment and wages over the relevant periods. I use the fitted parameters from the case studies to predict the impacts of artificial intelligence. I find that, in contrast to software and robots, AI is directed at high-skilled tasks. Under the assumption that the historical pattern of long-run substitution will continue, I estimate that AI will reduce 90:10 wage inequality, but will not affect the top 1%.


Are Ideas Getting Harder to Find? [VOX summary]

(with Nick Bloom, Chad Jones and John Van Reenen)

Forthcoming at American Economic Review

In many models, economic growth arises from people creating ideas, and the long-run growth rate is the product of two terms: the effective number of researchers and their research productivity. We present a wide range of evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore's Law. The number of researchers required today to achieve the famous doubling every two years of the density of computer chips is more than 18 times larger than the number required in the early 1970s. Across a broad range of case studies at various levels of (dis)aggregation, we find that ideas — and the exponential growth they imply — are getting harder to find. Exponential growth results from large increases in research effort that offset its declining productivity.

A Cross-Country Comparison of Dynamics in the Large Firm Wage Premium

(with Emanuele Colonnelli, Joacim Tåg, Stefanie Wolter)

AEA Papers and Proceedings, May 2018

We provide stylized facts on the existence and dynamics over time of the large firm wage premium for four countries. We examine matched employer-employee micro-data from Brazil, Germany, Sweden, and the UK, and find that the large firm premium exists in all these countries. However, we uncover substantial differences among them in the evolution of the wage premium over the past several decades. Moreover, we find no clear evidence of common cross-country industry trends. We conclude by discussing potential explanations for this heterogeneity, and proposing some questions for future work in the area.

Cost Effectiveness of a Government Supported Policy Strategy to Decrease Sodium Intake: Global Analysis Across 183 Nations

(with Saman Fahimi, Gitanjali M Singh, Shahab Khatibzadeh, Renata Micha, John Powles, Dariush Mozaffarian)

The BMJ, January 2017, 356: p. i6699

We quantified the cost effectiveness of a government policy combining targeted industry agreements and public education to reduce sodium intake in 183 countries worldwide. We studied a "soft regulation" national policy that combines targeted industry agreements, government monitoring, and public education to reduce population sodium intake, modeled on the recent successful UK program. Worldwide, a 10% reduction in sodium consumption over 10 years within each country was projected to avert approximately 5.8 million DALYs/year related to cardiovascular diseases, at a population weighted mean cost of I$1.13 per capita over the 10 year intervention. The intervention is projected to be highly cost effective worldwide, even without accounting for potential healthcare savings.

Research in progress

On-the-job Training and Human Capital: An Equilibrium Search Model of Wage Dynamics

(with Richard Blundell, Monica Costa Dias, and Costas Meghir)

We estimate a dynamic model of female labor supply and human capital accumulation using unique panel data from the UK. We allow returns to work experience to differ by level of education and also allow for differential experience effects of part-time and full-time employment. We use the model to examine the gender gap in earnings and to assess the impact of reforms to tax credits and to welfare benefits on human capital investments and labor supply. We also use direct measures of training to explore the potential role of training in offsetting human capital depreciation and enhancing life-cycle earnings.

Some Facts of High-Tech Patenting

(with Nick Short, Nick Bloom, and Josh Lerner)

NBER Working Paper, July 2018

Patenting in software, cloud computing, and artificial intelligence has grown rapidly in recent years. Such patents are acquired primarily by large US technology firms such as IBM, Microsoft, Google, and HP, as well as by Japanese multinationals such as Sony, Canon, and Fujitsu. Chinese patenting in the US is small but growing rapidly, and world-leading for drone technology. Patenting in machine learning has seen exponential growth since 2010, although patenting in neural networks saw a strong burst of activity in the 1990s that has only recently been surpassed. In all technological fields, the number of patents per inventor has declined near-monotonically, except for large increases in inventor productivity in software and semiconductors in the late 1990s. In most high-tech fields, Japan is the only country outside the US with significant US patenting activity; however, whereas Japan played an important role in the burst of neural network patenting in the 1990s, it has not been involved in the current acceleration. Comparing the periods 1970-89 and 2000-15, patenting in the current period has been primarily by entrant assignees, with the exception of neural networks.

How Would AI Regulation Change Firms' Behavior? Evidence from Thousands of Managers

(with Yong Suk Lee, Benjamin Larsen, and Mariano-Florentino Cuéllar)

SIEPR Working Paper, Nov 2019

We examine the impacts of different proposed AI regulations on managers' intentions to adopt AI technologies and on their AI-related business strategies. We conduct a randomized online survey experiment on more than a thousand managers in the U.S. We randomly present managers with different proposed AI regulations, and ask them to make decisions about AI adoption, budget allocation, hiring, and other issues. We have four main findings: (1) information about AI regulation generally reduces the rate of adoption of AI technologies. Nonetheless, industry- and agency-specific AI regulation has a smaller impact than general AI regulation. (2) Information about regulation induces firms to think. That is, firms spend more on developing AI strategy and hire more managers. This is at the cost of hiring other workers and training current employees. (3) The impact of information about AI regulation on innovation differs by industry and firm size. AI regulation increases intent to file patents in the healthcare and pharmaceutical sectors, but reduces it in the retail sector. Moreover, AI regulation information reduces AI adoption in small firms and is more likely to reduce their innovative activity. (4) Information about AI regulation increases firms' perceptions of the importance of safety and transparency issues related to AI.

How Does Automation Destroy Jobs? The 'Mother Machine' in British Manufacturing, 2000-2015

(with Daniel Chandler)

The machine tool is a key technology used in many manufacturing industries. Originally operated by hand, machine tools have seen substantial automation in recent decades. We first study the effects of the automation of machine tooling on firm survival and employment in the context of rising Chinese import competition between 2003 and 2015. Using an original private census of machine tool use in manufacturing plants, we show that the level of automation had a strong positive association with firm survival, and that greater initial automation was associated with increases in employment both within firms and commuting zones. In work still in progress, we combine the private census with administrative data to assess automation's effects on wages and productivity.