Replication Data for: How to evaluate the effects of IMF conditionality: An extension of quantitative approaches and an empirical application to public education spending

Following calls for a more disaggregated approach to studying the consequences of IMF programs, scholars have developed new datasets of IMF-mandated policy reforms, or ‘conditionality.’ Initial studies have explored how conditions have, inter alia, affected tax revenues, public sector wages, and health systems. Notwithstanding the important contributions of these studies, a methodological quandary arises as to how to quantitatively examine the effects of conditionality, as distinct from other aspects of IMF operations (e.g., credit, technical support, or aid and investment catalysis). In this article, we review and advance these methodological debates by developing an identification strategy for addressing the multiple endogenous components of IMF programs. We begin by surveying the main strategies for studying the effects of IMF programs: matching methods, instrumental variable approaches, system GMM estimation, and variants of Heckman estimators. We then adapt these methods for studying the effects of conditionality per se. Specifically, we utilize a compound instrumental variable design over a system of three equations to address sources of endogeneity related to, first, the IMF participation decision and, second, the conditions included within the program. In Monte Carlo simulations, we demonstrate that our approach is unbiased and performs better than alternatives on standard diagnostics across a range of scenarios. Finally, we apply these methods to investigate how IMF programs impact government education spending as a share of GDP on a sample of 132 developing countries for the period 1990 to 2014, finding exposure to an additional condition results in a 0.05 percentage point decline.

随着学界呼吁采用更细分的研究路径来探究国际货币基金组织（IMF）项目的影响，学者们已构建出一套关于IMF要求的政策改革——即「条件性（conditionality）」的全新数据集。早期相关研究已探讨了这些条件如何影响税收收入、公共部门薪酬以及医疗体系等诸多方面。尽管这些研究作出了重要贡献，但仍存在一个方法论困境：如何量化分析条件性的影响，并将其与IMF运作的其他方面（如信贷、技术支持，或援助与投资催化效应）区分开来。本文通过构建一套识别策略以应对IMF项目中多重内生性构成问题，梳理并推进了这场方法论讨论。首先，本文梳理了探究IMF项目影响的主流研究策略：匹配法、工具变量法、系统广义矩估计（system GMM）以及赫克曼估计量的各类变体。随后，本文对这些方法进行适配，以专门研究条件性本身的影响。具体而言，本文采用基于三方程系统的复合工具变量设计，以解决两类内生性来源问题：一是IMF项目参与决策，二是项目中所纳入的条件性条款。在蒙特卡洛（Monte Carlo）模拟实验中，本文证明了所提方法具备无偏性，且在多种场景下的标准诊断检验中表现优于其他替代方法。最后，本文将该方法应用于1990至2014年间132个发展中国家的样本，探究IMF项目对政府教育支出占GDP比重的影响，结果发现每多一项条件性条款，政府教育支出占比将下降0.05个百分点。