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: 0454384
• 负责人: Daniel Nielson
• 金额: $ 9.92万
• 依托单位: Brigham Young University
• 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=0454384&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.

本研究从项目层面对开发金融进行分析，回答国际关系、比较政治经济学、对外援助、多边借贷等文献中尚未解决的问题。研究者在援助国和受援国双方运用各种委托-代理模型，解释对外援助的数量、类型和分布。它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。 校长偏好的这些根本变化是由校长之间的集体行动问题、官僚代理人的信息不对称以及受援国的代表问题所介导的。 研究人员将这些假设与传统的替代品一起进行测试。学术辩论提出了三个首要问题：（1）捐助者为什么以及如何向特定国家提供援助？ (2)是什么推动受援国寻求和消费援助？ （3）发展融资的效果是什么-有意的和无意的？这一项目一级援助数据库的完成将有助于回答这些问题，因为它可以对国际关系和发展筹资文献中的大量核心假设进行系统的检验。 到目前为止，这种包容性的规格和一般测试一直缺乏。 在这篇文献中的理论发展的背景下，在小样本的可扩展性探针和定性的案例研究。 相反，定量工作主要是描述性的，并使用不完整和有偏见的数据。理论上知情的假设是罕见的现存的定量工作。 因此，这一领域的知识积累有限。 这些假设尚未用在适当的分析层次（即发展项目层次）收集的数据进行系统的检验。 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总额。 原因：大多数定量研究都是基于经合组织的数据，研究人员已经发现了这些数据的持续性和系统性错误。更广泛的价值：PLAID数据集将为双边和多边外国援助机构的50多万个发展项目以及国际金融机构的优惠贷款提供详细的关键变量编码。 因此，它将使学者（和决策者）第一次能够在每个项目一级审查整个发展筹资数据。 我们利用数据探讨了国际和国内授权对发展融资的影响。为了评估PA假设及其替代方案，研究人员采用了多种有限因变量回归技术，包括Tobit、Cragg和Heckman估计。 有了这些工具，他们可以测试核心独立变量校长的偏好，捐助者之间的集体行动问题，与援助机构的信息不对称，以及受援国的信誉和需求，同时对项目的接收和项目的金额的影响。 这些定量工具得到受援国和捐助国正在进行的定性研究的补充。