We analyze the influence of newly constructed globalization measures on regional growth for the EU-27 countries between 2001 and 2006. The spatial Chow-Lin procedure, a method constructed by the authors, was used to construct, on a NUTS-2 level, complete regional data for exports, imports and FDI inward stocks, which serve as indicators for the influence of globalization, integration and technology transfers on European regions. The results suggest that most regions have significantly benefited from globalization measured by increasing trade openness and FDI. In a non-linear growth convergence model, the growth elasticities for globalization and technology transfers decrease with increasing GDP per capita. Furthermore, the estimated elasticity for FDI decreases when the model includes a higher human capital premium for CEE countries and a small significant growth enhancing effect accrues from the structural funds expenditures in the EU.

In this paper a Poisson gravity model is introduced that incorporates spatial dependence of the explained variable without relying on restrictive distributional assumptions of the underlying data generating process. The model comprises a spatially filtered component -including the origin, destination and origin-destination specific variables -and a spatial residual variable that captures origin-and destination-based spatial autocorrelation. We derive a 2-stage nonlinear least squares estimator that is heteroscedasticity-robust and, thus, controls for the problem of over-or underdispersion that is often present in the empirical analysis of discrete data. It can be shown that this estimator has desirable properties for different distributional assumptions, like the observed flows or (spatially) filtered component being either Poisson or Negative Binomial. In our spatial autoregressive model specification, the resulting parameter estimates can be interpreted as the implied total impact effects and, thus, include the indirect spatial feedback effects. Monte Carlo results indicate marginal biases in mean and standard deviation of the parameter estimates and convergence to the true parameter values in finite samples. Finally, patent citation flow data are used to illustrate the application of the model.

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This paper provides Monte Carlo (MC) simulation evidence on the performance of methods used for identifying the effects of nondiscriminatory trade policy (NDTP) variables in panel structural gravity models. The benchmarked methods include a fixed effect (FE) estimator that utilizes data on intra national trade flows, the bonus‐vetus (BV) and the two‐stage fixed effect (FE‐2S) estimator. The results indicate that only the FE estimates are unbiased and consistent under very general assumptions of the data generating process. The favourable asymptotic properties of the FE estimator unfold as the number of period T increases.

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