The gravity model is a workhorse tool applicable in a wide range of empirical fields. It is regularly used to estimate the impact of reciprocal trade agreements (RTAs) on trade flows between partners. The studies report very different estimates since there is a significant difference in datasets, sample sizes, and independent variables. This paper combines, explains, and summarizes a large number of results using a meta-analysis approach. We provide pooled estimates, obtained from fixed and random effects models of the RTAs’ effect size on bilateral trade: the hypothesis that there is no effect of RTAs on trade is robustly rejected at standard significance levels. The information collected on each estimate allows us to test the sensitivity of the results to alternative specifications and differences in the control variables considered, as well as the impact of the publication selection process.

Reciprocal trade agreements in gravity models: a meta-analysis

CIPOLLINA, Maria;SALVATICI, Luca
2010-01-01

Abstract

The gravity model is a workhorse tool applicable in a wide range of empirical fields. It is regularly used to estimate the impact of reciprocal trade agreements (RTAs) on trade flows between partners. The studies report very different estimates since there is a significant difference in datasets, sample sizes, and independent variables. This paper combines, explains, and summarizes a large number of results using a meta-analysis approach. We provide pooled estimates, obtained from fixed and random effects models of the RTAs’ effect size on bilateral trade: the hypothesis that there is no effect of RTAs on trade is robustly rejected at standard significance levels. The information collected on each estimate allows us to test the sensitivity of the results to alternative specifications and differences in the control variables considered, as well as the impact of the publication selection process.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/47573
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