Table 6
Worker-level wage regression models

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Worker-level wage regression models: Part 1
  Model 1 Model 2 Model 3
  coefficient standard error coefficient standard error coefficient standard error
Establishment characteristics  
Export (yes = 1) 1 0.183 * 0.029 0.061 * 0.024 0.044 * 0.016
Foreign-controlled (yes = 1) 1 0.211 * 0.029 0.106 * 0.028 0.067 * 0.020
Log of plant size (employment) 0.073 * 0.015 0.060 * 0.011
Log of capital-to-labor ratio 0.102 * 0.030 0.086 * 0.023
Multi-plant status (yes = 1) 1 0.216 * 0.023 0.133 * 0.016
Metropolitan (yes = 1) 1 0.060 * 0.025 0.079 * 0.016
Worker characteristics  
Age 0.023 * 0.000
Years of education 0.053 * 0.002
Male (yes = 1) 1 0.406 * 0.009
Non-visible-minority (yes = 1) 1 0.205 * 0.023
Immigrant (yes = 1) 1 -0.066 * 0.015
Constant 10.20 * 9.79 * 7.84 * 0.056
Worker-level wage regression models: Part 2
  Model 1 Model 2 Model 3
Weighted Yes Yes Yes
F-statistics 61.2 47 637.4
R-squared 0.031 0.083 0.287
Number of observations 410,239 410,239 410,239
1.
yes=1 indicates the presence of the attribute.
Note(s):
Standard errors are heteroscedasticity-consistent and are also robust to possible clustering at the plant level; * significant at the .05 level; **significant at the .10 level.
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