NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH
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Julien Labonne

University of Oxford
United Kingdom

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org
Institutional Affiliation: University of Oxford

NBER Working Papers and Publications

November 2019Political Dynasties, Term Limits and Female Political Empowerment: Evidence from the Philippines
with Sahar Parsa, Pablo Querubín: w26431
We investigate the effect of term limits on female political representation. Using data from Philippine municipalities where strict term limits have been in place since 1987, we show that term limits led to a large increase in the number of women running and winning in mayoral elections. However, we show that this increase is entirely driven by female relatives of the term-limited incumbents. We further show that the differential gender impact of this policy is driven by political dynasties' adaptive strategies to stay in power.
June 2018Making Policies Matter: Voter Responses to Campaign Promises
with Cesi Cruz, Philip Keefer, Francesco Trebbi: w24785
Can campaign promises change voter behavior, even where clientelism and vote buying are pervasive? We elicit multidimensional campaign promises from political candidates in consecutive mayoral elections in the Philippines. Voters who are randomly informed about these promises rationally update their beliefs about candidates, along both policy and valence dimensions. Those who receive information about current promises are more likely to vote for candidates with policy promises closest to their own preferences. Those informed about current and past campaign promises reward incumbents who fulfilled their past promises; they perceive them to be more honest and competent. However, voters with clientelist ties to candidates respond weakly to campaign promises. A structural model allows us to di...
January 2016Using Split Samples to Improve Inference about Causal Effects
with Marcel Fafchamps: w21842
We discuss a method aimed at reducing the risk that spurious results are published. Researchers send their datasets to an independent third party who randomly generates training and testing samples. Researchers perform their analysis on the former and once the paper is accepted for publication the method is applied to the latter and it is those results that are published. Simulations indicate that, under empirically relevant settings, the proposed method significantly reduces type I error and delivers adequate power. The method – that can be combined with pre-analysis plans – reduces the risk that relevant hypotheses are left untested.
 
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