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The role of socio-cultural factors in static trade panel models

Author

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  • Fischer, Manfred M.
  • LeSage, James P.

Abstract

The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.

Suggested Citation

  • Fischer, Manfred M. & LeSage, James P., 2018. "The role of socio-cultural factors in static trade panel models," Working Papers in Regional Science 2018/04, WU Vienna University of Economics and Business.
    • Handle: RePEc:wiw:wus046:6361
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    File URL: https://epub.wu.ac.at/6361/
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    References listed on IDEAS

    as
    1. Manfred M. Fischer & Jinfeng Wang, 2011. "Spatial Data Analysis," SpringerBriefs in Regional Science, Springer, number 978-3-642-21720-3, July.
    2. James P. LeSage & Manfred M. Fischer, 2016. "Spatial Regression-Based Model Specifications for Exogenous and Endogenous Spatial Interaction," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 15-36, Springer.
    3. Daniel A. Griffith & Manfred M. Fischer, 2016. "Constrained Variants of the Gravity Model and Spatial Dependence: Model Specification and Estimation Issues," Advances in Spatial Science, in: Roberto Patuelli & Giuseppe Arbia (ed.), Spatial Econometric Interaction Modelling, chapter 0, pages 37-66, Springer.
    4. Debarsy, Nicolas & LeSage, James, 2018. "Flexible dependence modeling using convex combinations of different types of connectivity structures," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 48-68.
    5. Debarsy, Nicolas & LeSage, James, 2018. "Flexible dependence modeling using convex combinations of different types of connectivity structures," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 48-68.
    6. Tamás Krisztin & Manfred M. Fischer, 2015. "The Gravity Model for International Trade: Specification and Estimation Issues," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(4), pages 451-470, December.
    7. Manfred M. Fischer & Jinfeng Wang, 2011. "Spatial Interaction Models and Spatial Dependence," SpringerBriefs in Regional Science, in: Spatial Data Analysis, chapter 0, pages 61-70, Springer.
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    Cited by:

    1. James Paul LeSage, 2020. "Fast MCMC estimation of multiple W-matrix spatial regression models and Metropolis–Hastings Monte Carlo log-marginal likelihoods," Journal of Geographical Systems, Springer, vol. 22(1), pages 47-75, January.

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    More about this item

    Keywords

    Bayesian; MCMC estimation; socio-cultural distance; origin-destination flows; treatment of time invariant variables; panel models;
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