Learning to constrain: Political competition and randomized controlled trials in development
Michael Dorsch () and
Paul Maarek ()
No 2017-24, THEMA Working Papers from THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise
This paper provides a political economic analysis of impact evaluation experiments con- ducted in international development. We argue that in more politically competitive environments, where incumbents face a higher probability of losing power, govern- ments have stronger incentive to run Randomized Controlled Trial (RCT) experiments to constrain successors' margin of policy discretion. Moreover, the effect of compe- tition on the probability to host RCTs is stronger in more polarized societies since the incumbent's cost of losing power is higher. We first propose a formal model and then empirically examine its theoretical predictions using a unique data set on RCTs that we have compiled. Over a panel of Indian states and a cross-national panel, we nd that certain RCTs are more likely to occur in electorally competitive jurisdictions, and that the effect is amplified by political polarization. We demonstrate that politics matter for when, where, and with which partners RCTs in development happen.
Keywords: Program evaluation; RCT; External validity; Political accountability; Political competition; Development policy. (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dev and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ema:worpap:2017-24
Access Statistics for this paper
More papers in THEMA Working Papers from THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise Contact information at EDIRC.
Bibliographic data for series maintained by Stefania Marcassa ().