Network-Based Hiring: Local Benefits; Global Costs

Arun G. Chandrasekhar, Melanie Morten, A. Peter
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引用次数: 13

Abstract

Entrepreneurs, particularly in the developing world, often hire from their networks: friends, family, and resulting referrals. Network hiring has two benefits, documented extensively in the empirical literature: entrepreneurs know more about the ability of their network (and indeed they are often positively selected), and network members may be less likely to engage in moral hazard. We study theoretically how network hiring affects the size and composition (i.e., whether to hire friends or strangers) of the firm. Our primary result is that network hiring, while locally beneficial, can be globally inefficient. Because of the existence of a network, entrepreneurs set inefficiently low wages, firms are weakly too small, rely too much on networks for hiring, and resulting welfare losses increase in the quality of the network. Further, if entrepreneurs are uncertain about the true quality of the external labor market, the economy may become stuck in an information poverty trap where forward-looking entrepreneurs or even entrepreneurs in a market with social learning never learn the correct distribution of stranger ability, exacerbating welfare losses. We show that the poverty trap can worsen when network referrals are of higher quality.
基于网络的招聘:本地利益全球成本
企业家,特别是发展中国家的企业家,经常从他们的关系网中招聘:朋友、家人,以及由此产生的推荐。网络招聘有两个好处,在实证文献中有广泛的记载:企业家更了解他们的网络的能力(事实上,他们经常被积极选择),网络成员可能不太可能参与道德风险。我们从理论上研究网络招聘如何影响公司的规模和构成(即,是雇佣朋友还是陌生人)。我们的主要结论是,网络招聘虽然对当地有利,但对全球来说可能效率低下。由于网络的存在,企业家设定的低工资效率低下,企业规模过小,过度依赖网络招聘,由此导致的福利损失增加了网络的质量。此外,如果企业家不确定外部劳动力市场的真实质量,经济可能会陷入信息贫困陷阱,在这种陷阱中,具有前瞻性的企业家甚至具有社会学习的市场中的企业家永远不会了解陌生人能力的正确分配,从而加剧福利损失。我们表明,当网络转诊的质量较高时,贫困陷阱可能会恶化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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