What did structural adjustment adjust?

Easterly, William

doi:10.1016/j.jdeveco.2003.11.005

Cited by 86 publications

(10 citation statements)

References 25 publications

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“…Since the mid-1980s, the IMF advocated structural conditions to tackle the purported root causes of economic weakness (Nowzad 1981;Khan and Knight 1983;Polak 1991). Structural conditions have attracted criticism because they impinge on the sovereignty of borrowers, while at the same time failing to demonstrate effectiveness in resolving economic crises (Easterly 2005). We add to this criticism that structural conditions undermine bu-reaucratic quality through their impact on the behavior of state bureaucrats and private businesses.…”

Section: Structural Conditions and Bureaucratic Qualitymentioning

confidence: 88%

“…In addition, we include the number of COUNTRIES UNDER PROGRAMS, since program participation is affected by the extent to which the IMF has resources available; this in turn depends on the current number of program countries (Vreeland 2003, p. 88). Moreover, past involvement of a country in an IMF program reliably predicts current participation (Easterly 2005). We thus include PAST PROGRAM, a dummy variable indicating whether a country had a program before.…”

Section: Methodsmentioning

confidence: 99%

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The World System and the Hollowing Out of State Capacity: How Structural Adjustment Programs Affect Bureaucratic Quality in Developing Countries

Reinsberg

Kentikelenis

Stubbs

et al. 2019

American Journal of Sociology

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Section: Structural Conditions and Bureaucratic Qualitymentioning

confidence: 88%

Section: Methodsmentioning

confidence: 99%

The World System and the Hollowing Out of State Capacity: How Structural Adjustment Programs Affect Bureaucratic Quality in Developing Countries

Reinsberg

Kentikelenis

Stubbs

et al. 2019

American Journal of Sociology

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“…Extending the 2SLS procedure, 3SLS estimation incorporates information from cross-correlations of error terms in a system of simultaneous equations for multiple endogenous variables to produce more efficient parameter estimates (Barro and Lee 2005). 6 Past research has relied on a range of political economy variables as instruments for IMF participation, which vary depending on the outcome of interest (Barro and Lee 2005;Butkiewicz and Yanikkaya 2005;Dreher 2006;Dreher and Gassebner 2012;Easterly 2005;Moser and Sturm 2011;Oberdabernig 2013;Steinwand and Stone 2008). Most studies rely on United Nations General Assembly (UNGA) voting similarity with the US (Dreher and Gassebner 2012;Steinwand and Stone 2008;Woo 2013); that is, all else equal, countries that vote similarly to the US are more likely to participate in IMF programs.…”

Section: Instrumental Variable Approachesmentioning

confidence: 99%

How to evaluate the effects of IMF conditionality

et al. 2018

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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.

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“…For example, avoiding significant low real exchange rate can be gleaned from the diverse experience with economic growth around the world (Dollar, 1992). Easterly (2005) overvaluations have an adverse effect on growth. shows positive relationship between exchange rate and the GDP growth rate, especially for developing countries.…”

Section: Exchange Rate and Gdpmentioning

confidence: 99%