Suchergebnisse

There has been a wide debate on whether democracy actually has an effect on economic outcomes, and especially on international trade. With a new estimation strategy, we analyze this relationship taking a look at the distribution of countries´ trading activity. Using a panel quantile estimation framework from Powell (2014), we find a stronger relationship at the lower quantiles, especially for the import activity. Our results suggest that the impact of democratization on trade is more important when countries trade less: the marginal benefit of democratization decreases as countries trade more. This feature supports a widely neglected issue in the literature: economies very active in the international trading network are not necessarily the most democratic countries. The results are robust to different institutional variables and even to instrumental variables estimation. Our results demonstrate that the effect of democracy on trade is underestimated using Ordinary Least Squares estimation for the group of countries for which the effect is statistically significant for, namely those countries that are active in the lower quantiles of the trading distribution. Moreover, our results complement the findings by Barro (1996) which suggest that the effects of democracy for economic growth are not uniform for all countries.

Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students.