Job Market Paper
Persuasion with Partisanship: The Informational Content of Policymaking with Application to US Governors
November 2024
I study a model where a political executive's policy agenda generates information about her ability. Since policies favoring a single party are harder to pass, the partisanship of an agenda influences what success or failure of passage communicates about ability. The model delivers a U-shape relationship between an executive's ex-ante winning chances and her agenda's partisanship. Executives likely to lose pursue partisan policies to save their winning chances. Those likely to win embrace partisan policies whose failure can be blamed on the legislature. Those in the middle pursue bipartisan policies to secure reelection. I use these insights to analyze the partisanship of U.S. governors' policy proposals from 1990-2020, showing that while partisanship has risen on average, there is sizeable variation at the governor level. I interpret these variations as responses to electoral incentives by testing the model and showing a U-shaped relationship between partisanship and approval ratings.
Working Papers
Learning Traps
January 2025
Under policy uncertainty, policy choices generate a tradeoff between payoffs and information. Incumbent leaders can implement their preferred policy, but if the outcome is bad, constituents learn this and threaten office removal. I show that, in a repeated setting, the optimal policy of leaders constrained by this threat of information revelation is nonmonotone in beliefs about policy efficacy. At extremal beliefs, leaders experiment with the most extreme policy that keeps them in office. At middle beliefs, they implement spatially intermediate "learning trap" policies that halt learning about the optimal policy when it would be most useful. I apply the model to the post-1861 reforms ending serfdom in Imperial Russia, arguing that by combining elements of liberalism and serfdom to obfuscate inference about policy efficacy, these reforms represent a learning trap, in contrast to the pre-1861 status quo of serfdom and post-1906 experiments with liberal economic reform.
Transgressions in Organizations (with Kim Sarnoff)
April 2025
We study an organizational model where managers commit workplace abuse of unknown harm, or "transgressions." Employees can report transgressive managers for investigation and possible punishment, but managers face uncertainty over what actions employees consider transgressions. When employees' disutility from transgressions is low, we show that policies that disincentivize managers from committing transgressions —increasing the size of manager punishment, the ease of reporting, or the efficacy of investigation technology — may harm employees. These policies may motivate managers to commit harmful actions that employees do not want to report or induce managers to opt out of interacting with employees altogether. We provide a dynamic extension where reports generate information for the organization and employees, showing that the model converges to a steady state where employees are worse off than initially, harmful actions are never punished, and no organizational learning occurs.
A New Interpretation of Productivity Growth Dynamics in the Pre-Pandemic and Pandemic-Era U.S. Economy, 1950-2023 (with Robert J. Gordon)
October 2024, NBER Working Paper 30267, CEPR DP19569
This paper provides a unified framework that resolves recent puzzles in U.S. productivity growth that we show are interrelated. First, why was productivity growth in the 2010-19 decade the slowest of any decade in U.S. history? Second, why did the cyclicality of productivity growth change from procyclical in 1950-85 to acyclical in 1986-2006 and then back to procyclical in 2010-19? Third, why was productivity growth strongly countercyclical in the recessions of 2008-09 and 2020? The fundamental dynamic driving cyclical productivity fluctuations originates in the gradual adjustment of hours of work to demand-driven output fluctuations due to the costs of hiring and firing labor. Since productivity growth is a residual, equal to output growth less hours growth, productivity growth immediately jumps in response to an upward output movement because hours are slow to respond; then productivity growth falls back in subsequent quarters as hours complete their adjustment. We are able to explain the temporary 1986-2006 disappearance of procyclicality as the result of changes in the standard deviation and serial correlation of output changes.
We explain countercyclical productivity surges in 2008-09 and 2020 by showing that business firms in those two episodes overreacted with “excess layoffs,” cutting hours in response to the sharp output decline with a much higher elasticity than normal. By coupling these excess layoffs with a post-recession rehiring effect that gradually unwound the excess layoffs, our regression analysis explains why productivity growth on average was so slow in 2010-19. If this recession/rehiring effect had not occurred, productivity growth in the 2010-19 decade would have been 1.9 percent per year instead of 1.1 percent, suggesting that concern about U.S. “secular stagnation” has been overstated.
Download here. Press coverage in The Economist, NBER Digest.
Work in Progress
AI-Based Capital
This theoretical project assesses how the accumulation of artificial-intelligence-based capital shapes market structure. Consider an AI program that observes information about a consumer, whose efficacy — as a form of capital —is built up over time. What information will an AI choose to acquire? As AI products enter the market, how does endogeneity between product creation and learning about consumer preferences affect the rate at which an AI accumulates knowledge? Will an AI, in the long run, create a diverse array of heterogenous products or a small set of homogenous products? How will these different production outcomes affect demand for labor, worker welfare, and provide insight into political frictions that may arise from these developments? And, as a result, what public policies should be pursued to balance both consumer and labor force welfare — for example, constraining the amount of information AI can gather?
Inverted Digital Property Rights
Consumers often learn about the value of innovative products via other consumers' feedback. A larger quantity of an item sold hence generates more information about the value of an innovation. Consequently, traditional patent policy — where innovators are awarded with monopoly patent rights to recoup fixed costs — may not be optimal, as underprovision of goods generates underprovision of information. Instead, it may be optimal for regulators to invert property rights: allow competitors to imitate an innovator's product during a first trial period; endow an innovator with monopoly rights in a second period; and then permit competition again in a third period. I apply these insights to the algorithmic design of content production and incentivization on platforms like TikTok.
Model Based Inference (with Kim Sarnoff)
This is an experimental project that uses a "coefficient weight" method to elicit how participants' create mental models that organize data; and how those mental models vary and update with the structure of the data generating process.
Publications
Transatlantic Technologies: The Role of ICT in the Evolution of U.S. and European Productivity Growth (with Robert J. Gordon)
Spring 2020, International Productivity Monitor No. 38
We examine the role of the ICT revolution in driving productivity growth behavior for the United States and an aggregate of ten Western European nations (the EU-10) from 1977 to 2015. We find that the standard growth accounting approach is deficient when it separates sources of growth between ICT capital deepening and TFP growth, because much of the effect of the ICT revolution was channeled through spillovers to TFP growth rather than being limited to the capital deepening path- way. Using industry-level data from EU KLEMS, we find that most of the 1995-2005 U.S. productivity growth revival was driven by ICT-intensive industries producing market services and computer hardware. In contrast the EU-10 experienced a 1995- 2005 growth slowdown due to a paucity of ICT investment, a failure to capture the efficiency benefits of ICT, and performance shortfalls in specific industries in- cluding ICT production, finance-insurance, retail-wholesale, and agriculture. After 2005 both the United States and the EU-10 suffered a growth slowdown, indicating that the benefits of the ICT revolution were temporary rather than providing a new permanent era of faster productivity growth. Also circulated as NBER WP 27425.
Download here. Press coverage in NBER Digest, VoxEU.
The Industry Anatomy of the Transatlantic Productivity Growth Slowdown: Europe Chasing the American Frontier (with Robert J. Gordon)
Fall 2019, International Productivity Monitor No. 37
By merging KLEMS data covering 16 industry groups within the total economy and 11 manufacturing sub-industries, we compare and contrast productivity growth from 1950 to 2015 in the United States with an aggregate of the ten largest European nations (EU-10) from 1972 to 2015. We interpret the EU-10 performance as catching up to the United States in stages. Strikingly, the total economy "early-to-late" productivity growth slowdown from 1972-1995 to 2005-2015 in the EU-10 (-1.68 percentage points) was almost identical to the U.S. slowdown from 1950-1972 to 2005-2015 (-1.67 percentage points). There is a very high EU-U.S. correlation in the magnitude of the early-to-late slowdown in each industry, suggesting that the productivity growth slowdown from the early postwar years to the most recent decade was due to a retardation in technical change that affected the same industries by roughly the same magnitudes on both sides of the Atlantic. Also circulated as NBER WP 25703.
Download here. Press coverage in The Economist, VoxEU.