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The Tails of Gravity: Using Expectiles to Quantify the Trade-Margins Effects of Economic Integration Agreements

Author

Listed:
  • Jeffrey H. Bergstrand

    (University of Notre Dame)

  • Matthew W. Clance

    (University of Pretoria)

  • Joao M.C. Santos Silva

    (University of Surrey)

Abstract

Economic integration agreements (EIAs) can have substantial welfare effects, but there is now considerable evidence suggesting that these effects can be very heterogeneous. However, most studies neglect this heterogeneity and simply estimate the coefficient of a single EIA dummy on the conditional mean of bilateral trade flows. We propose the novel use of Poisson-based expectile regressions to estimate the heterogeneous effects of EIAs across the entire conditional distribution. Like standard Poisson regression, this method does not need the dependent variable to be logged, accommodates observations at zero, and is easy to implement. Using the proposed estimator, we find systematic evidence that agreements have larger effects at the lower tail of the conditional distribution. Additionally, we use the method to investigate the causes of heterogeneity. Our results suggest that the success of trade liberalizations strongly depends on potential for expansions along the extensive margin. For instance, at the 10th conditional expectile, the estimated partial equilibrium effects of EIAs for country-pairs with large potential for expansion at the extensive margin is over six times larger than that of country-pairs with small potential.

Suggested Citation

  • Jeffrey H. Bergstrand & Matthew W. Clance & Joao M.C. Santos Silva, 2025. "The Tails of Gravity: Using Expectiles to Quantify the Trade-Margins Effects of Economic Integration Agreements," School of Economics Discussion Papers 0125, School of Economics, University of Surrey.
  • Handle: RePEc:sur:surrec:0125
    as

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    File URL: https://repec.som.surrey.ac.uk/2025/DP01-25.pdf
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    References listed on IDEAS

    as
    1. Breinlich, Holger & Corradi, Valentina & Rocha, Nadia & Ruta, Michele & Silva, J.M.C. Santos & Zylkin, Tom, 2021. "Machine learning in international trade research - evaluating the impact of trade agreements," LSE Research Online Documents on Economics 114379, London School of Economics and Political Science, LSE Library.
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    More about this item

    JEL classification:

    • F1 - International Economics - - Trade
    • F13 - International Economics - - Trade - - - Trade Policy; International Trade Organizations
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F15 - International Economics - - Trade - - - Economic Integration
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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