Bayesian inference and model comparisons are easily performed quite accurately using Gibbs sampling, even if (1) the likelihood is analytically intractable and (2) nonstandard prior probability density functions (pdfs) are required. In this study Bayesian model comparisons are performed among five competing theories of endogenous protection. Tariff and nontariff barrier data from 1983 between the United States and five OECD partner countries-Japan, France, Germany, Italy, and the United Kingdom-are used in the analysis. Posterior odds based on two priors show special-interest models to be more likely than other models in determining U.S. protection.

The extreme bounds analysis in Leamer (1982) is extended to the Tobit model with censoring. The extension involves the simple modification of replacing the censored values of the dependent variable by their expectation conditional on the MLEs and using the negative of the Hessian evaluated at the MLEs in place of the inverse of the covariance matrix. The application of this method to a data set that pools U.S. nontariff barriers across industries and countries arrives at the surprisingly robust (to changes in the prior) conclusion that U.S. nontariff barriers against its largest trading partners are already significantly retaliatory.

Grossman and Helpman (1994) present a theory of endogenous protection by explicitly modeling government-industry interactions for which mere “black-box” models previously existed. They obtain a Ramsey pricing-type solution to the provision of protection which emphasizes the role of inverse import penetration ratios and import elasticities. On the lobbying side, the model makes predictions about lobbying competition and lobbying spending according to deadweight costs from protection. The model not only makes for richer theory in terms of rigor and elegance, but its predictions are directly testable. Whether the Grossman-Helman model stands up to real-world data is investigated in this paper. Predictions from both the protection side and lobbying side are tested using cross-sectional U.S. nontariff barrier data. We also compare the “second-generation” Grossman-Helpman model with a more traditional specification. Our results call for serious consideration of this model in the political economy literature.

In popular discussion much has been made recently of the susceptibility of government policies to lobbying by foreigners. The general presumption has also been that such interactions have a deleterious effect on the home economy. However, it can be argued that, in a trade policy context, bending policy in a direction that would suit foreigners may not in fact be harmful: If the policy outcome absent any lobbying by foreigners is characterized by welfare-reducing trade barriers, lobbying by foreigners may result in reductions in such barriers and raise consumer surplus (and possibly improve welfare). Using a new data set on foreign political activity in the US, this paper investigates the relationship between trade protection and lobbying activity empirically. The approach taken in this paper is primarily a structural one. To model the role of foreign and domestic lobbies in determining trade policy, we develop first a theoretical framework building on the wellknown work of Our analysis of the data suggests that foreign lobbying activity has significant impact on trade policy -and in the predicted direction: Tariffs and non-tariff barriers (NTBs) are both found to be negatively related with foreign lobbying activity. We consider also extended specifications in which we include a large number of additional explanatory variables that have been suggested in the literature as determinants of trade policy (but that emerge from outside of the theoretical structure described above) and confirm the robustness of our findings in this setting.

In this paper, we examine the role of political connections in mitigating the detrimental impact of policy uncertainty on banks. Our estimates show that banks are more cautious when facing policy uncertainty, but that the effect is partially alleviated when banks are politically connected. For an increase of one standard deviation in policy uncertainty, connected banks maintain a loss provision to loan volume ratio that is almost seven percent lower compared to their unconnected peers. These findings are robust to a geographical regression discontinuity setting, as well as to a placebo test. Lastly, the mitigating role of political connections is driven mainly by smaller banks and periods of stricter banking regulations.