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Trade, gravity and aggregation

Author

Listed:
  • Breinlich, Holger
  • Novy, Dennis
  • Santos Silva, J. M. C.
Abstract
Gravity regressions are a common tool in the empirical international trade literature and serve an important function for many policy purposes. We study to what extent micro-level parameters can be recovered from gravity regressions estimated with aggregate data. We show that estimation of gravity equations in their original multiplicative form via Poisson pseudo maximum likelihood (PPML) is more robust to aggregation than estimation of log-linearized gravity equations via ordinary least squares (OLS). In the leading case where regressors do not vary at the micro level, PPML estimates obtained with aggregate data have a clear interpretation as trade-weighted averages of micro-level parameters that is not shared by OLS estimates. However, when regressors vary at the micro level, using disaggregated data is essential because in this case not even PPML can recover parameters of interest. We illustrate our results with an application to Baier and Bergstrand's (2007) influential study of the effects of trade agreements on trade flows. We examine how their findings change when estimation is performed at different levels of aggregation, and explore the consequences of aggregation for predicting the effects of trade agreements.

Suggested Citation

  • Breinlich, Holger & Novy, Dennis & Santos Silva, J. M. C., 2021. "Trade, gravity and aggregation," LSE Research Online Documents on Economics 113858, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:113858
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    File URL: http://eprints.lse.ac.uk/113858/
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    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
    2. Redding, Stephen & Amiti, Mary & Weinstein, David, 2019. "The impact of the 2018 trade war on U.S. prices and welfare," LSE Research Online Documents on Economics 102619, London School of Economics and Political Science, LSE Library.
    3. Bas, Maria & Mayer, Thierry & Thoenig, Mathias, 2017. "From micro to macro: Demand, supply, and heterogeneity in the trade elasticity," Journal of International Economics, Elsevier, vol. 108(C), pages 1-19.
    4. J. M. C. Santos Silva & Silvana Tenreyro, 2015. "Trading Partners and Trading Volumes: Implementing the Helpman–Melitz–Rubinstein Model Empirically," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 93-105, February.
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    Cited by:

    1. Pablo Durán-Santomil & Luís Otero-González, 2022. "Capital Allocation Methods under Solvency II: A Comparative Analysis," Mathematics, MDPI, vol. 10(3), pages 1-14, January.

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    More about this item

    Keywords

    free trade agreements; gravity equation; OLS; PPML; trade costs;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F15 - International Economics - - Trade - - - Economic Integration
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation

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