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.

这项建议研究广义上的“群体”之间的不平等。我们的特定调查线询问在何种程度上“信息外部性”可以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，但建议的重点是：（1）在一个自由贸易的世界跨国收入差异;（2）劳动力市场的统计歧视。在建议的第一部分，我们引入了不完全可观察的人力资本投资，否则标准的竞争性贸易模型。我们感兴趣的是，事前相同的国家之间是否会出现事后不平等。我们提出了一个模型，这是可能的，因为直接的价格效应和信息外部性之间的相互作用。低人力资本国家的公民比高人力资本国家的公民更糟糕，但这种情况是自我强化的，因为低人力资本国家的投资动机较低。激励措施是不好的，因为1）在投资者很少的情况下，“看起来不错”的人更有可能是一个人力资本较低的人，他得到了“幸运抽奖”，2）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低于国家不进行贸易的情况。在我们的研究中，我们将探讨这些影响如何相互作用，以及该模型是否提供了一个专业化的理由，以及探索该模型的一些次要含义。在这里，信息外部性属于主流理论，但在实证文献中不愿认真对待这一想法。我们认为这有两个原因。首先，统计歧视的模型被（公正地）批评为假定了信息问题的大多数合同解决方案。因此，我们建议调查如何更丰富的一套可接受的合同和/或学习的可能性影响，否则标准模型的统计歧视。我们的初步分析表明，在一个竞争性的市场学习，工人不能承诺留在一个公司，有一个有趣的搭便车的问题，在信息获取，可能会迫使公司使用“代理”，即使更好的信息可以获得。因此，事后学习不足以消除统计歧视。我们还将考虑事前合同（无需学习）。基于“不相关”特征的歧视仍然是可能的，并且在某种意义上，这种设置比标准模型更有吸引力，因为歧视现在可以在独特的平衡中出现。我们认为，持怀疑态度的第二个主要原因是，不清楚统计歧视的证据到底是什么。我们建议通过设计一个模型来处理这个问题，该模型“嵌套”了种族差异的两个主要解释，统计歧视和种族主义。