Collaborative Research: Analyzing Development Finance Using PLAID Data

合作研究：使用 PLAID 数据分析发展金融

• 批准号: 0454433
• 负责人: Michael Tierney
• 金额: $ 15.37万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-01 至 2008-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0454433&HistoricalAwards=false
• 关键词: Collaborative; Research; Analyzing; Development; Finance

This project analyzes development finance at the project level to answer unresolved questions in the literatures on international relations, comparative political economy, foreign aid, and multilateral lending.The investigators apply a variety of principal-agent (PA) models on both the donor and recipient sides to explain the amount, type, and distribution of overseas assistance. They explore how changing preferences in the donor countries have altered the priorities of development agencies and multilateral institutions. These underlying changes in principal preferences are mediated by collective-action problems among the principals, information asymmetries vis-a-vis their bureaucratic agents, and delegation problems in recipient countries. The researchers test these hypotheses alongside conventional alternatives. The scholarly debates raise three overarching questions: (1) why and how do donors provide aid to particular countries? (2) What drives recipient countries in their pursuit and consumption of the assistance? And, (3) what are the effects of development finance - both intended and unintended?The completion of this project-level aid (PLAID) database will help answer these questions by allowing systematic tests of a large number of core hypotheses from the international relations and development-finance literatures. To date, such inclusive specifications and general tests have been lacking. Theory in this literature has developed in the context of plausibility probes and qualitative case studies in small-n samples. Quantitative work, conversely, has been largely descriptive and employs incomplete and biased data. Theoretically informed hypotheses are rare in extant quantitative work. Hence, accumulation of knowledge in this field has been limited. Hypotheses have not been systematically tested with data gathered at the appropriate level of analysis: at the level of development projects. Instead, scholars have aggregated incorrect and biased sums of aid and loans at the sectoral or country level. The reason: most quantitative studies are based on OECD data, for which the investigators have identified persistent and systematic errors.Broader Value: The PLAID dataset will provide detailed coding of key variables for over 500,000 development projects by bilateral and multilateral foreign aid agencies and concessional loans by international financial institutions. It will thus allow scholars (and policymakers), for the first time, to examine the universe of development finance data at the level of each project. With the data we explore the effects of international and domestic delegation on development finance.To evaluate the PA hypotheses and their alternatives, the researchers employ a variety of limited dependent variable regression techniques, including Tobit, Cragg, and Heckman estimation. With these tools they can test the effects of core independent variables-principals' preferences, collective-action problems among donors, information asymmetries with aid agencies, and recipient credibility and demands-simultaneously on both the receipt of a project and the project's amount. These quantitative tools are supplemented by ongoing qualitative research within both recipient and donor countries.

该项目在项目层面分析发展融资，以回答国际关系、比较政治经济学、对外援助和多边贷款等文献中尚未解决的问题。调查人员在捐赠方和受援方应用各种委托代理(PA)模型来解释海外援助的数量、类型和分配。他们探讨了捐助国不断变化的偏好如何改变了发展机构和多边机构的优先事项。委托人偏好的这些潜在变化是由委托人之间的集体行动问题、与其官僚机构代理人之间的信息不对称以及受援国的授权问题所调节的。研究人员将这些假设与传统的替代方案一起进行了测试。学术辩论提出了三个主要问题：(1)捐赠者为什么以及如何向特定国家提供援助？(2)是什么驱使受援国追求和消费援助？以及，(3)发展融资--包括有意的和无意的--的影响是什么？这个项目级援助数据库的完成将通过对国际关系和发展融资文献中的大量核心假设进行系统测试，帮助回答这些问题。到目前为止，还缺乏这样的包容性规范和一般测试。这篇文献中的理论是在似是而非的探索和小样本的定性案例研究的背景下发展起来的。相反，定量工作主要是描述性的，使用的是不完整和有偏见的数据。在现有的定量工作中，理论上知情的假设很少。因此，这一领域的知识积累是有限的。没有用在适当的分析层面上收集的数据系统地检验这些假设：在发展项目层面上。相反，学者们在部门或国家一级汇总了不正确和有偏见的援助和贷款金额。原因是：大多数定量研究是基于经合组织的数据，调查人员发现了这些数据的持续性和系统性错误。广泛的价值：格子数据集将为双边和多边外国援助机构的500,000多个发展项目和国际金融机构的优惠贷款提供关键变量的详细编码。因此，它将使学者(和政策制定者)第一次能够在每个项目的层面上审查发展融资数据的全貌。研究人员运用托比特、Cragg和Heckman估计等多种有限因变量回归技术对PA假设及其备选方案进行了评估。通过这些工具，他们可以同时测试核心独立变量对项目收款和项目金额的影响--委托人的偏好、捐助者之间的集体行动问题、与援助机构的信息不对称以及受援者的信誉和要求。受援国和捐助国正在进行的定性研究补充了这些量化工具。