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信号传递与雇主学习:基于工具变量的分析

Signaling and Employer Learning with Instruments
American Economic Review · 2022 · Gaurab Aryal、Manudeep Bhuller、Fabian Lange

中文摘要

本文在雇主学习模型中考察如何使用工具变量识别和估计教育的私人回报与社会回报。工具变量所能识别的内容取决于它对雇主而言是不可见的,还是透明的(即雇主能够观察到)。对雇主不可见的工具变量识别教育的私人回报,对雇主透明的工具变量识别教育的社会回报。我们利用挪威非中心市镇和中心市镇的义务教育法差异,分别构造对雇主不可见和透明的工具变量。我们估计的私人回报率为7.9%,其中70%源于生产率提高,其余30%源于信号传递。(JEL D82, H75, I26, I28, J24, J31)

Abstract

This paper considers the use of instruments to identify and estimate private and social returns to education within a model of employer learning. What an instrument identifies depends on whether it is hidden from, or transparent (i.e., observed ) to, the employers. A hidden instrument identifies private returns to education, and a transparent instrument identifies social returns to education. We use variation in compulsory schooling laws across noncentral and central municipalities in Norway to, respectively, construct hidden and transparent instruments. We estimate a private return of 7.9 percent, of which 70 percent is due to increased productivity and the remaining 30 percent is due to signaling. (JEL D82, H75, I26, I28, J24, J31)
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