Bobak Pakzad-Hurson
Job Market Candidate

Stanford University
Department of Economics
579 Serra Mall
Stanford, CA 94305
(814) 880-3099
bph1@stanford.edu

Curriculum Vitae

Fields:
Market Design, Internet Economics, Microeconomic Theory

Expected Graduation Date:
June, 2017

References:
Al Roth (Primary):
alroth@stanford.edu

Fuhito Kojima (Primary):
fkojima@stanford.edu

Matt Jackson:
jacksonm@stanford.edu

Nick Bloom:
nbloom@stanford.edu

Working Papers

Equilibrium Effects of Pay Transparency (with Zoe Cullen)
The public conversation about increasing pay transparency largely ignores equilibrium effects, namely how it leads firms to change hiring and wage-setting policies and workers to adjust bargaining strategies. In this paper, we study these effects with a methodologically diverse approach. Our analysis combines longitudinal study of thousands of workers and employers facing different levels of pay transparency on TaskRabbit, an online labor market, with a parsimonious equilibrium model of dynamic wage setting and negotiation. We find, theoretically and empirically, that increasing pay transparency can increase employment, decrease inequality in earnings, and shift surplus away from workers and toward their employer. Intermediate levels of pay transparency, achieved through a permissive environment to discuss relative pay, can exacerbate the gender pay gap by virtue of network effects. There may be a direct need for government intervention in order to maintain a desirable level of transparency. Any scheme in which employers vary transparency based on private characteristics is unsustainable, as the signal sent to prospective workers is sufficiently strong to cause unraveling toward full transparency. We observe this unraveling on TaskRabbit. We also conduct a field experiment on internet workers to investigate an alternative model in which wage compression is driven by social aversion to observed wage inequality. Our findings are consistent with our bargaining model but not with this alternative.

Strategic Disaggregation in Matching Markets (with Stephen Nei)--Non-technical summary
Matching theory has not adequately studied what happens after agents match. Post-match information arrival and actions can affect the value of a partner, which leads to previously unexplained strategic behavior during and leading up to the matching process. We introduce a game in which universities can force students to commit to majors before matriculating or to allow students to pick their majors during their studies. Students are initially uncertain about their preferred majors but can resolve this uncertainty before matching by paying a cost. Fine-tuning the direct matching mechanism does not lead to drastically different outcomes, while pre- and post-match considerations are of first-order importance. The interaction between competition for better students, moral hazard and adverse selection leads to two different equilibria mirroring the American and English admissions systems.

With monetary transfers, our model provides new insight into whether student athletes should be paid. Price competition removes the surplus to enrolling top students, making it impossible to sustain the American admissions equilibrium without an exogenous transfer cap. We show that properly designed transfer caps can achieve the first-best welfare outcome, and can lead to Pareto improvements over the status quo.

Crowdsourcing and Optimal Market Design
Suppose an optimal allocation in a market depends on characteristics of goods which are imperfectly observed by agents. How well can a mechanism aggregate information in a way that induces agents to be truthful? I show that it is possible to achieve nearly the same allocation as in a full-information market by first aggregating the information of all agents and then running an optimal full-information mechanism. To ensure proper incentives, agents are punished when their reports do not match up with the “wisdom of the crowd.” The punishment scheme is independent of the desired allocation, and can be enacted with or without monetary transfers. Even when the number of objects being assessed is much larger than the number of assessors, the proposed mechanism asymptotically correctly identifies every object's quality, while imposing a worst-case total punishment that converges exponentially to zero. Therefore, I am able to generate nearly optimal allocations in two-sided matching markets with interdependent preferences, a setting for which impossibility results exist. I give necessary and sufficient conditions for recovering desirable properties when information acquisition is endogenous and costly.

Stable and Efficient Resource Allocation With Contracts
Consider a model of indivisible-object allocation with contracts, such as college admissions in which contracts specify majors. Does including contracts in a market allow designers to guarantee a stable and (student) efficient matching? I find that it is difficult to ensure both properties, as adding contracts can often put stability and efficiency at odds. Theorem 1 shows that a necessary condition to secure these properties is student-lexicographic priorities—colleges must rank all contracts from “second-tier” students consecutively. I develop a new framework to analyze a broad class of mechanisms with contracts. The main result characterizes the restriction which guarantees efficiency, stability and group strategy-proofness for this class of mechanisms. I use this framework to apply the main result to two famous mechanisms, deferred acceptance and top trading cycles. I conclude by extending the main result to many-to-many matching and substitutable college priorities.

Work in Progress

Hiding Large Orders: From Tarantino to Finance (with Mohammad Akbarpour and Shengwu Li)
How does a buyer with high demand and high value purchase goods in a market when sellers are not sure of her presence? If she buys too much at once she tips her hand to sellers, who respond by raising the price. If she delays, she misses out on consuming. We model a dynamic game between a long-lived Big Buyer, and myopic small buyers and sellers. Sellers and small buyers live for only one instant and attempt to maximize profit. Small buyers arrive stochastically, and so the Big Buyer optimally restricts demand at each time to hide in the crowd. We characterize the optimal pricing strategy of the sellers and the optimal buying strategy of the Big Buyer and small buyers. In equilibrium, the Big Buyer demands more when sellers either have a very low belief or a very high belief of her presence. Sellers almost surely learn of the Big Buyer’s existence in the market over time. We study optimal information revelation in this market.