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Erica Gould, Assistant Professor in the Department of Politics at the University of Virginia, will present "Unpacking the Distributional Effects of IMF Programs."
The recent empirical literature on the impact of Fund programs has yielded some disturbing results. Fund loan programs, which are supposed to help countries with payments imbalances stabilize and grow, have been shown to be negatively related to growth and contribute to greater income inequality (e.g., Vreeland 2003).
For all of their strengths, these studies vastly simplify the Fund’s effect on borrowers. The empirical work overwhelmingly assumes a constant program design, and constant program effects. They often use a simple dummy variable to capture the Fund’s influence, despite the fact that the terms of Fund programs are blamed for these negative results and acknowledged to vary. As a result, several questions are left unanswered. To what extent does Fund program design matter? Do programs with more intrusive conditions lead to progressive or regressive distributional outcomes? What are those conditions and how prevalent are they? To what extent have the distributional effects of Fund advice been considered or debated by Fund staff, management and Executive Directors? Have programs become better or worse in terms of their distributional effects since these considerations have become more prominent in recent years?
This study is a preliminary attempt to answer these questions by employing the Conditionality Dataset, an original dataset that codes 249 cases of IMF conditional loan arrangements (stand-by arrangements, EFF, SAF, and ESAF) from twenty countries between 1952 and 1995 according to their terms, and additional Fund archival sources. The presentation will include a discussion of the project's motivation and research design, as well as descriptive statistics and preliminary findings.