Morten Nyborg Støstad
Lecturer at UC Berkeley, Spring '24
Post-doctoral Scholar at the FAIR Institute (NHH), Sept '23-25

Welcome to my personal website. I am currently a Lecturer at UC Berkeley (Spring '24) and a post-doctoral scholar at the FAIR Institute at the Norwegian School of Economics (NHH). I received my PhD in Economics from the Paris School of Economics in September 2023 and am a Fellow at the World Inequality Database.

My main research focus is inequality's societal effects, or the concept of inequality as an externality. I use theoretical and empirical methods to examine this and other subjects. 

Before Economics I studied Astrophysics, and published papers on the young stellar disk orbiting the super-massive black hole Sagittarius A* with a team of internationally renowned researchers. My astronomy research has been cited in Science, Nature Astronomy, the Astrophysical Journal, and more.

Fairness Beliefs Affect Perceived Economic Inequality
Morten Nyborg Støstad (Submitted)
Working paper here, Slides (UC Berkeley Public Finance Seminar)

Abstract: This paper establishes a causal link from fairness beliefs to perceived economic inequality. I conduct an experiment where participants are asked to estimate the level of income inequality in a hypothetical society. Respondents are informed of the true income distribution, which is simple and identical across treatments. Sources of incomes vary to indicate “fair” or “unfair” inequality, which affects incentivized top 10% income share estimates as much as the difference between Denmark and the United States. Other inequality metrics are similarly affected. The findings imply that ideological beliefs fundamentally alter how people perceive economic inequality.

Comparing Universes of Redistributive Arguments
Max Lobeck, Chloé de Meulenaer, Morten Nyborg Støstad
(Working paper on request)

Abstract: What are the properties of different types of redistributive arguments? We collect a large set of pro-redistribution arguments based on either fairness ideas or inequality's societal consequences and evaluate the average efficacy and emotional content of each type of argument. We collect arguments in a first survey, quality-check them in a second survey, and evaluate them in a third survey. This method allows us to evaluate an unbiased approximation of the "universe" of each type of argument. Our final data set has 32,300 argument evaluations across 160 redistributive arguments, 80 of each type. While both types of arguments are generally convincing, fairness arguments are relatively more convincing for low-income respondents. All respondents report significantly more anger in reaction to fairness arguments, which is largely driven by the average fairness argument having more emotional content.