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Table 6 Wage regressions by major occupation

From: Using tools to distinguish general and occupation-specific skills

  Male Female Service Sales Admin
# Job-specific tools .0341* (.0184) .0403** (.0193) .0350 (.0558) 2.9851*** (.4202) .1529 (.2660)
Specific squared − .0001 (.0008) − .0001 (.0010) − .0023 (.0088) − 4.2719*** (.7879) − .1347 (.2012)
Total tools − .0134 (.0114) − .0220* (.0119) − .0653* (.0340) − .2292 (.1671) − .2177* (.1077)
Total tools squared − .0002 (.0004) − .0000 (.0004) .0034 (.0028) − .1412** (.0460) .0498* (.0268)
Number of clusters 300 300 39 13 24
Observations 525,898 515,341 207,764 132,593 156,764
R-squared .344 .296 .187 .317 .178
  Construction Installation and repair Production Transportation  
# Job-specific tools .0884 (.0808) − .0666 (.1188) − .0849* (.0443) .3589*** (.0743)  
Specific squared − .0250** (.0104) .0051 (.0214) .0089 (.0057) − .0739*** (.0187)  
Total tools .0044 (.0295) .0955* (.0532) .0635** (.0255) .0274 (.1053)  
Total tools squared .0010 (.0010) − .0046 (.0029) − .0022* (.0011) − .0059 (.0128)  
Number of clusters 31 23 31 21  
Observations 58,020 39,144 56,848 81,993  
R-squared .161 .194 .237 .187  
  1. Dependent variable is natural log of earnings. Robust standard errors in parentheses. Each regression includes full set of control variables, as in Table 5. Male and Female samples include occupation indicators to control for 10 major occupation groups. Tools measured in 10 tool increments
  2. *** p < .01, ** p < .05, * p < .1