Three essays in behavioral economics

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Asad, Sher Afghan
Major Professor
Joydeep Bhattacharya
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Over the last few decades, behavioral economics, by introducing psychology in the decision making process, has changed the way we think about the policy problems of our age. Governments and non-governmental organizations all over the world are using insights from behavioral economics to solve wide ranging issues in the domains such as health, wealth, education, social security, environment etc.. In this dissertation research I use insights from behavioral economics to understand two unrelated but pressing problems of our age; labor-market discrimination and road-safety behavior.

In this dissertation, I first present a theory of worker side discrimination and highlight the importance of exploring worker side discrimination to improve our overall understanding of labor market discrimination. The main thesis of this work is that in many situations, workers' motivation to work for employer depends on employer's social group. More precisely, workers feel more motivated when they work for the same group employer and less so when working for an out-group employer. Workers' productivity differential provides incentive to the non-discriminatory employers to recruit workers from their own group and pay higher wages to own-group workers. The assortative matching of employers and workers leads to segregation of the labor force when there are enough same-group employers. However, under-representation of employers of one group leads to adverse labor market outcomes for the workers of that group in terms of wages. I show that this has implications for how we interpret the existence and source of discrimination in the labor markets. Specifically, I demonstrate that what is traditionally understood as discrimination by employers may, in fact, be a rational response to the worker's differential social preferences towards the employer's group identity. I also show that ignoring worker social preferences (and employer's beliefs about them) may lead to misleading conclusions about the sources of discrimination.

In another chapter of this dissertation, I (with my co-authors) explore the evidence for the above theorized channel of discrimination in an online labor market. Specifically, we examine whether workers in the online economy discriminate against their employers via their social-preferences / motivation. In this chapter, we focus on racial identity and ask, do workers discriminate (say, by under providing effort) for an out-race employer relative to an otherwise-identical, own-race one? We run a well-powered model-based experiment using subjects from Amazon's Mechanical Turk (M-Turk). Interestingly, we find that white workers do not discriminate against their out-group employers, in-fact they work harder for black employers as compared to white employers. The results are exciting because it reflects the lack of bias in workers preferences towards the minority group employers in the online economy. The results imply that as the economies transition to online jobs, the possibility of discriminatory behavior against minorities may diminish.

In the last chapter, I (along with my co-author) study another application of behavioral economics to understand road-safety behavior of the automotive drivers. Particularly, we look at the traffic-related messages such as “drive sober,” “x deaths on roads this year,” and "click it or ticket,”, displayed on major highways, on reported near-to-sign traffic accidents. To estimate the causal effect of these nudges, we build a new high-frequency panel dataset using the information on the time and location of messages, traffic incidents, overall traffic levels, and weather conditions using the data of the state of Vermont. We estimate several models that control for endogeneity of these messages, allow for spillover effects from neighboring messages, and look at the impact as the function of distance from the sign. We find that behavioral nudges, such as “drive sober” and “wear seat belt”, are at best ineffective in reducing the number of crashes while informational nudges, such as “slippery road” and “work zone”, actually lead to causal reduction in number of crashes. Our findings are robust to many different specifications and assumptions.

Sat Aug 01 00:00:00 UTC 2020