Part 1 - AL-Tax

Chapter 3lag drops from 0.280 to 0.221 (a 21% reduction) and, when we use the nonuniformspatial-weighting matrix, the estimate increases to 0.316 (13%). Columns5 (S-2SLS) and 6 (S-ML) make both ‘corrections’: One of the two consistent estimatorsand the nonuniform spatial-weighting matrix. The results, which are verysimilar across the two estimators, suggest that, on balance, Hays overestimatedthe coefficient on the spatial lag (i.e. the simultaneity bias seemed to have dominated)and so underestimated the coefficients on the capital-mobility variableand the capital-mobility-times-consensus-democracy interaction variable (inducedbiases). In more general terms, due to the endogeneity of the spatial lag, Hays(2003) seems to have overestimated the importance of international factors (taxcompetition) at the expense of domestic (consensus democracy) and commonexternal factors (capital mobility), which is just what our simulations (Franzeseand Hays, 2004, 2006) would lead us to expect. Our reanalysis of Hays’s financialopennessmodel in Table 3.2 tells a similar story.First, nonspatial OLS produces serious omitted variable bias (column 1, Table3.2). Second, Hays (2003) probably overestimated the coefficient on the spatiallag and underestimated the coefficients on the capital-mobility and consensusdemocracyinteraction variables (columns 5 and 6 vs. column 2). In Table 3.3,finally, we include period dummies in the models to control more thoroughly forcommon shocks. Again, we expect this will cause S-OLS to underestimate thecoefficient on the spatial lag for the same reason adding unit dummies causesOLS to underestimate the coefficient on temporal lags: Hurwicz or Nickell bias. 21We expect to find an analogous spatial-Hurwicz bias in the spatial-lag estimateshere (Hurwicz, 1950). Again, the results from our reanalysis are largely consistentwith this expectation. The estimated coefficient on the spatial lag in thecapital-account-openness model drops by 50% from 0.316 to 0.157 (column 4,Table 3.1 vs. column 1, Table 3.3) with the addition of the period dummies. In thefinancial-openness model, the ρ estimate is 46% smaller with period dummies(column 4, Table 3.2 vs. column 4, Table 3.3). 223.6 ConclusionTheoretically and substantively, we expect international interdependence incapital-tax policy. Empirically, Hays (2003) and Basinger and Hallerberg (2004)demonstrated such interdependence using spatial-lag models that specify onecountry’s capital tax rate to depend on the capital tax rates in other countries.However, estimating spatial-lag models is, to be brief, a tricky business. In this67