Own experience bias in evaluating the efforts of others

Abstract We develop a model with which to explore how an individuals own experience in the labor market may influence her assessment of the efforts of other individuals. Specifically, we consider a two stage process in which individuals first learn, through experience, whether effort is rewarded and then subsequently have to estimate the effort of others. We derive a theoretical benchmark based on rational inference and then explore how own experience may lead to systematic bias from this benchmark. Our theoretical results suggest that those who are not rewarded for high effort will underestimate the effort of other individuals while those for whom effort is rewarded will (slightly) overestimate the effort of others. We empirically test and confirm this prediction in the lab.

[1]  R. Oaxaca,et al.  Wages, Employment, and Statistical Discrimination: Evidence from the Laboratory , 2014 .

[2]  Martin Strobel,et al.  Deconstruction and reconstruction of an anomaly , 2012, Games Econ. Behav..

[3]  Eva O. Arceo-Gómez,et al.  Race and Marriage in the Labor Market: A Discrimination Correspondence Study in a Developing Country , 2014 .

[4]  Jonathan Guryan,et al.  Taste�?Based or Statistical Discrimination: The Economics of Discrimination Returns to its Roots , 2013 .

[5]  Brian Mullen,et al.  The false consensus effect: A meta-analysis of 115 hypothesis tests , 1985 .

[6]  M. A. Girshick,et al.  Theory of games and statistical decisions , 1955 .

[7]  G Gigerenzer,et al.  Hindsight bias: a by-product of knowledge updating? , 2000, Journal of experimental psychology. Learning, memory, and cognition.

[8]  Ghazala Azmat,et al.  Gender and the Labor Market: What Have We Learned from Field and Lab Experiments? , 2014, SSRN Electronic Journal.

[9]  Andrea Moro,et al.  Theories of Statistical Discrimination and Affirmative Action: A Survey , 2010 .

[10]  L. Ross,et al.  The overconfidence effect in social prediction. , 1990, Journal of personality and social psychology.

[11]  P. Kuhn,et al.  Lab Labor: What Can Labor Economists Learn from the Lab? , 2010, SSRN Electronic Journal.

[12]  Tanya Rosenblat,et al.  Why Beauty Matters ∗ , 2005 .

[13]  George A. Akerlof,et al.  Animal spirits : how human psychology drives the economy, and why it matters for global capitalism : with a new preface by the authors , 2010 .

[14]  Marco E. Castillo,et al.  Discrimination in the lab: Does information trump appearance? , 2010, Games Econ. Behav..

[15]  L. Ross,et al.  The “false consensus effect”: An egocentric bias in social perception and attribution processes , 1977 .

[16]  Michael Ewens,et al.  Statistical Discrimination or Prejudice? A Large Sample Field Experiment , 2012, Review of Economics and Statistics.

[17]  Ernesto Reuben,et al.  How stereotypes impair women’s careers in science , 2014, Proceedings of the National Academy of Sciences.

[18]  Charles A. Holt,et al.  Discrimination: Experimental Evidence from Psychology and Economics , 2006 .

[19]  A. Shleifer,et al.  Beliefs About Gender , 2016, American Economic Review.

[20]  Jay J.J. Christensen-Szalanski,et al.  The hindsight bias: A meta-analysis , 1991 .

[21]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[22]  Bruce Western,et al.  Discrimination in a Low-Wage Labor Market , 2009, American sociological review.

[23]  A. Furnham,et al.  A literature review of the anchoring effect , 2011 .

[24]  Daniel Kahneman,et al.  Availability: A heuristic for judging frequency and probability , 1973 .

[25]  Joachim I. Krueger,et al.  The Truly False Consensus Effect: An Ineradicable and Egocentric Bias in Social Perception , 1994 .

[26]  Yves Breitmoser,et al.  Knowing me, imagining you: Projection and overbidding in auctions , 2015, Games Econ. Behav..

[27]  U. Böckenholt,et al.  A meta-analysis of extremeness aversion , 2016 .

[28]  Dan‐Olof Rooth,et al.  Evidence of Ethnic Discrimination in the Swedish Labor Market Using Experimental Data , 2006, SSRN Electronic Journal.

[29]  U. Fischbacher.  z-Tree: Zurich toolbox for ready-made economic experiments , 1999 .

[30]  M. Rabin.  Inference by Believers in the Law of Small Numbers , 2000 .