Therefore we include the square term of judicial efficiency in the regression to

Therefore we include the square term of judicial

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increase makes expatriates more attractive thus lead to more high-tech firms. Therefore, we include the square term of judicial efficiency in the regression to capture the potential nonlinear effects also for the technological intensity analysis. Therefore, we run the following regressions to confirm the predictions stated in the previous paragraph: D ( ForeignOwnership ijs ) = β 1 JudicialEfficiency s + β 2 ( JudicialEfficiency s ) 2 +( X ij )+ µ j + ϵ ijs and R & DIntensity js = β 1 Judicial Efficiency s + β 2 ( Judicial Efficiency s ) 2 + ( X ij ) + ϵ ijs 46 Following Laeven and Woodruff (2007), we also used the share of indigenous population in 1900s and the indicator of crop production as instrumental variables for judicial efficiency. However, these instrumental variables are weak in the sense that the F value for the first stage regression is only 2.5 and lead to effects quantitatively too large, though qualitatively consistent with our theory. 36
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Note that we cannot control for industry effects in the latter regression because R & DIntensity is defined at the U.S. industry level, allowing variations only at the industry level. Note also that the sample for the foreign ownership equation now includes all the plants, i.e. non-foreign plants from ESIDET. Table 8 shows the results. The first to fourth columns show the results of the Probit estimation of the effect of judicial efficiency on likelihood of foreign ownership, while the fifth and sixth columns show the results of OLS estimation of the effect of same uncertainty on R & DIntensity defined at the U.S. industry level of the foreign firms that the state attracts. The first to the forth columns suggest that one standard deviation (0.56 point) increase in the judicial efficiency from the mean is associated with about 3 to 4 percent increase in the likelihood of the plants in the area being classified as foreign owned (the mean is 21 percent). The fifth and the sixth columns suggest that one standard deviation (0.56 point) increase in the judicial efficiency is associated with about 1.4 percent decrease on R & DIntensity (defined at the U.S. industry level) of the foreign firms that the state attracts. The magnitude of β 1 and β 2 suggests that the highest point for predicted entry level and the lowest point for predicted R & DIntensity level is above the maximum potential judicial efficiency, so that entry is always increasing in judicial efficiency and R & DIntensity is always decreasing in judicial efficiency for the values of judicial efficiency we observe. 47 This is quantitatively very large because the average R & DIntensity is 2.8 percent. These results are consistent with the theory prediction that an increase in judicial quality for low values of judicial efficiency leads to more entry of foreign firms, but with a low-tech bias. The opposite is true when the increase in judicial quality happens for high values of judicial efficiency. 6 Robustness check Finally, we run regressions of the following form to examine more systematically whether plants with foreign expatriates are more likely to have higher total sales, export sales and domestic sales.
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  • Spring '17
  • JAMES FENSKE

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