Collaborative Research: Human Capital and Income Inequality

合作研究：人力资本与收入不平等

• 批准号: 0110131
• 负责人: Andrea Moro
• 金额: $ 7.41万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-07-01 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0110131&HistoricalAwards=false
• 关键词: Collaborative; Research; Human; Capital; Income

This proposal studies inequalities between "groups", broadly defined. Our particular line of inquiry asks to what extent "informational externalities" can provide a credible theory for such inequalities. We briefly mention a few other potential applications, but the proposal is focused on: (1) Cross country income differentials in a world with free trade; (2) Statistical discrimination in the labor market.In the first part of the proposal we introduce imperfectly observable human capital investments in an otherwise standard competitive trade model. We are interested whether ex post inequalities can arise between ex ante identical countries. We propose a model where this is possible because of interactions between straightforward price effects and the informational externality. Citizens in a nation specializing as a low human capital country are worse off than citizens in the country specializing as a high human capital country, but the situation is self-enforcing because incentives to invest are lower in the low human capital country. Incentives are bad because 1) with few investors someone who "looks good" is more likely an individual with low human capital that got a "lucky draw", 2) the possibility to import goods intensive in human capital from the other country makes human capital less valuable compared to a situation where countries don't trade. In our research we will investigate how these effects interact and whether the model provides a rationale for specialization, as well as explore a number of secondary implications of the model.The second part considers statistical discrimination. Here, informational externalities belong to mainstream theory, but there is reluctance in the empirical literature to take the idea seriously. We believe there are two reasons for this. First, models of statistical discrimination have been (fairly) criticized for assuming away most any contractual solution to the information problem. We therefore propose to investigate how a richer set of admissable contracts and/or possibilities of learning affect an otherwise standard model of statistical discrimination. Our preliminary analysis suggests that, in a competitive market with learning where workers cannot commit to stay with a firm, there is an interesting free-riding problem in information acquisition that may force the firms to use "proxies" even if better information could be acquired. Hence, ex post learning is not sufficient to dismiss statistical discrimination. We will also consider ex ante contracts (without learning). Discrimination based on "irrelevant" characteristics is still possible and the setup is in a sense more appealing than the standard model, because discrimination can now arise in a unique equilibrium. The second major reason for the skepticism is, we think, that it is not clear what exactly would be evidence of statistical discrimination. We propose to deal with this by designing a model that "nests" the two major explanations for racial differences, statistical discrimination and racism.

该提案研究广义上的“群体”之间的不平等。我们特别的研究方向是，“信息外部性”能在多大程度上为这种不平等提供可信的理论。我们简要地提到了其他一些潜在的应用，但该提案的重点是：(1)自由贸易世界中的跨国收入差异；(2)劳动力市场的统计歧视。在本文的第一部分，我们在一个标准的竞争贸易模型中引入了不完全可观察的人力资本投资。我们感兴趣的是，以前相同的国家之间是否会产生事后不平等。我们提出了一个模型，由于直接价格效应和信息外部性之间的相互作用，这是可能的。一个专门从事低人力资本国家的公民比专门从事高人力资本国家的公民境况更差，但这种情况是自我执行的，因为低人力资本国家的投资激励更低。激励机制是不好的，因为1)在投资者很少的情况下，一个“看起来不错”的人更有可能是一个低人力资本的人，得到了“幸运的抽奖”；2)与各国不进行贸易的情况相比，从另一个国家进口人力资本密集型商品的可能性使人力资本的价值降低。在我们的研究中，我们将调查这些影响是如何相互作用的，以及该模型是否为专业化提供了基本原理，并探索该模型的一些次要含义。第二部分考虑统计歧视。在这里，信息外部性属于主流理论，但实证文献不愿认真对待这一观点。我们认为这有两个原因。首先，统计歧视模型（相当地）受到批评，因为它假设了大多数信息问题的契约解决方案。因此，我们建议研究一套更丰富的可接受契约和/或学习可能性如何影响统计歧视的标准模型。我们的初步分析表明，在一个具有学习能力的竞争市场中，员工不能承诺留在公司，在信息获取中存在一个有趣的搭便车问题，这可能迫使公司使用“代理”，即使可以获得更好的信息。因此，事后学习不足以消除统计上的歧视。我们也会考虑事前合同（不需要学习）。基于“不相关”特征的歧视仍然是可能的，从某种意义上说，这种设置比标准模型更有吸引力，因为歧视现在可以在一个独特的平衡中出现。我们认为，怀疑的第二个主要原因是，目前还不清楚究竟什么是统计歧视的证据。我们建议通过设计一个模型来解决这个问题，该模型将种族差异的两种主要解释——统计歧视和种族主义——“嵌套”起来。