Optimal Sequential Search

37 Pages Posted: 2 Mar 2020

See all articles by Michael Choi

Michael Choi

University of California, Irvine

Lones Smith

University of Wisconsin at Madison - Department of Economics

Date Written: January 31, 2020

Abstract

We introduce a simple new model of sequential search among finitely many options that fits many economic applications. Each payoff is the sum of a random “known factor” and a “hidden factor”, learned at cost. Weitzman (1979) solved the ex post Pandora’s box problem, given known factors. Ours is the ex ante model for estimation, unconditional on known factors, and so resolves major selection effects.

1. Search intensifies over time, as one increasingly exercises the current option, recalls a prior one, or quits. If one recalls, earlier options are recalled more often.

2. We solve a long open question in all search models: which stochastic changes lead one to search longer? Answer: more dispersed payoffs.

3. The stationary search model poorly approximates search with many options: If the known factor density lacks a thin tail (eg. exponential), the recall chance is boundedly positive with vastly many options.

4. Search lasts longer with more options. Hence, if low search frictions increase worker applicant pools of firms, vacancy duration increases.

Keywords: sequential and nonstationary search, duration, logconcavity, dispersion

JEL Classification: D81, D83

Suggested Citation

Choi, Michael and Smith, Lones, Optimal Sequential Search (January 31, 2020). Available at SSRN: https://ssrn.com/abstract=3530526 or http://dx.doi.org/10.2139/ssrn.3530526

Michael Choi (Contact Author)

University of California, Irvine ( email )

3151 Social Science Plaza
Irvine, CA 92697-5100
United States

Lones Smith

University of Wisconsin at Madison - Department of Economics ( email )

1180 Observatory Drive
Madison, WI 53706-1393
United States
608-263-3871 (Phone)
608-262-2033 (Fax)

HOME PAGE: http://www.lonessmith.com

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