Andrew Johnston


Andrew C. Johnston Economics Merced

Andrew Johnston

Assistant Professor of Economics, University of California at Merced

Ph.D. in Applied Economics from The Wharton School, University of Pennsylvania

Fields: Public Economics, Labor Economics, Applied Econometrics, Personnel Economics

Interests: Unemployment Insurance, Compensation Structure, Teacher Labor Markets, Pensions, Family Structure

Affiliations: JPAL-North America, IZA Institute for Labor Economics

Contact: acjohnston (at) ucmerced (dot) edu

Curriculum vitae



Unemployment-Insurance Taxes and Labor Demand: Quasi-Experimental Evidence from Administrative Data, Accepted at American Economic Journal: Economic Policy [journal link] [SSRN link] [IZA link (ungated)]

To finance unemployment insurance benefits, states raise payroll taxes on employers who engage in layoffs. Since tax rates increase in response to layoffs, taxes are highest for troubled firms after downturns, potentially hampering labour demand and employment during recoveries. Using full-population administrative records from Florida, I estimate the causal effect of these targeted tax increases on firm behavior leveraging a regression kink design in the tax schedule. UI tax hikes reduce firm hiring and employment substantially, with no effect on layoffs or worker earnings. Analysis of heterogeneity and timing suggests the role of cash constraints in explaining the magnitude of the estimates. The results imply unanticipated costs of the financing regime which, once accounted for, reduce the optimal benefit calculation by a quarter.

Is Compassion a Good Career Move?: Evidence on Nonprofit Earning Differentials from Employer Changes (with Carla Johnston), Journal of Human Resources, 2019 [journal link] [SSRN link]
 [IZA link (ungated)]

We explore the nonprofit earnings penalty. To separate the influence of demand and supply, we leverage workers who change employers in administrative tax data. The average nonprofit worker earns 5.5 percent less than the average for-profit worker. Supply-side factors (worker selection) contribute 80 percent of the nonprofit differential. The remaining 20 percent is from demand (a nonprofit penalty). Within-worker nonprofit variation generates several insights about the influence of nonprofits on the labor market. Nonprofits compress the wage distribution and reduce inequality among earners. Nonprofit penalties are much more pronounced in classic charities than in “commercial” nonprofits, which sometimes exhibit nonprofit premia. 

Potential Unemployment Insurance Duration and Labor Supply: Evidence from a Benefit Cut (with Alexandre Mas), Journal of Political Economy, 2018 [journal link] [local link] [SSRN link]

We examine how a 16-week cut in potential unemployment insurance (UI) duration in Missouri affected search behavior of UI recipients and the aggregate labor market. Using a regression discontinuity design (RDD), we estimate a marginal effect of maximum duration on UI and nonemployment spells of approximately 0.45 and 0.25 respectively. We use the RDD estimates to simulate the unemployment rate assuming no market-level externalities. The simulated response, which implies almost a one percentage point decline in the unemployment rate, closely approximates the estimated change in the unemployment rate following the benefit cut. This finding suggests that, even in a period of high unemployment, the labor market absorbed this influx of workers without crowding-out other jobseekers. 

The Effect of Unemployment Benefits on the Duration of Unemployment Insurance Receipt: New Evidence from a Regression Kink Design in Missouri, 2003-2013 (with David Card, Pauline Leung, Alexandre Mas, and Zhuan Pei)American Economic Review: Papers & Proceedings, 2015. [journal link] [manuscript link]

We provide new evidence on the elasticity of unemployment insurance weekly benefit amount on unemployment insurance spells based on administrative data from the state of Missouri covering 2003-2013.  Identification comes from a regression kink design that exploits the quasi-experimental variation around the kink in the UI benefit schedule.  We find that unemployment durations are more responsive to benefit levels during the recession and its aftermath, with an elasticity of about 0.9 as compared to 0.35 pre-recession.

Working Papers:

Teacher Preferences, Working Conditions, and Compensation Structure, Submitted
[SSRN link] [EWP link] [IZA link (ungated)]

Improving schools depends on attracting high-caliber teachers and increasing retention, both made possible by appealing to teacher preferences. I deploy a discrete-choice experiment in a setting where teachers have reason to reveal their preferences. There are three main findings: (1) I calculate willingness-to-pay for a series of workplace attributes including salary structure, retirement benefits, performance pay, class size, and time-to-tenure. (2) Highly rated teachers have stronger preferences for schools offering performance pay, which may be used to differentially attract and retain them. (3) Under various criteria, schools seem to underpay in salary and performance pay while overpaying in retirement benefits.

The Finance of Unemployment Compensation and its Consequence for the Labor Market, Submitted (with Audrey Guo), [SSRN link] [IZA link (ungated)]

For every payment, there is an equal and opposite tax. A vast literature considers the impact of unemployment benefits on labor supply, but scarce little has been done to understand the consequences of the unique tax that finances benefits in the United States. We present new evidence on the incidence of the tax along the income distribution, across industries, by geography, and over the business cycle. This tax presents several fascinating questions for economists: Do the penalties produced by experience rating reduce layoffs amid recessions? What impact do tax increases have on hiring and recovery? Does rating discourage firms from hiring risky workers? We mark these questions and help analysts engage them by explaining the unique institutions at play.

Coming Soon:

Family Structure and its Consequence: Evidence from within-Family Exposure to Marriage, Death, and Divorce (with Margaret Jones and Nolan Pope)

The Influence of Pensions on Labor Supply (with Jonah Rockoff)

Teacher Preferences and Student Outcomes (with Michael Bates, Michael Dinerstein, and Isaac Sorkin)

Experience Rating in Recession and Recovery 

Skill Erosion in Unemployment (with Jonathan Cohen and Attila Lindner)

On Teacher Shortages (with Laura Giuliano)

Parent Absence and Human Capital Formation: Evidence from Quasi-Random Occupational Absences (with Richard Patterson, David Lyle, and Kevin Rinz)

Other Publications:

A Privacy-Oriented Deferred Multi-Match Recommender System for Stable Employment (with Amar Saini and Florin Rusu), Recommender Systems 2019 [manuscript link]

Coordination failure reduces match quality among employers and candidates in the job market, resulting in unfilled positions and unstable employment. Centralized matching connects mutually interested employers and job-seekers, but it require users to commit ex-ante to the outcome of the match while disclosing personal information. In this paper, we present PrivateJobMatch a privacy-oriented deferred multi-match recommender system which generates stable pairings while requiring users to provide only a partial ranking of their preferences and imposes no commitment on users. We explore a series of adaptations of the game-theoretic Gale-Shapley deferred acceptance algorithm (DAA) which combine the flexibility of decentralized markets with the intelligence of centralized matching. We identify the shortcomings of our original algorithm when applied to a job market and propose novel solutions that rely on machine learning techniques. Experimental results on real and synthetic data confirm the benefits of the proposed algorithms across several quality measures. Over the past year, we implemented a PrivateJobMatch prototype and deployed it in an active job market. Using the gathered real-user preference data, we find that the match recommendations are superior to a typical decentralized job market while requiring only a partial ranking of the user preferences.

Johnston Wharton Graduation Economics


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