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Table 7 Wage regressions without job-specific tools

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

  Full sample Professional Sales, serv., and admin Blue collar
# General tools − .0442 (.1015) − .2556* (.1525) − .2221 (.2016) − .0528 (.0905)
General squared − .0161 (.0407) .0495 (.0528) .1504 (.1287) .0065 (.0334)
Total tools .0044 (.0081) .0215** (.0098) − .0914*** (.0281) .0309** (.0121)
Total tools squared − .0000 (.0001) − .0003** (.0002) .0074*** (.0027) − .0016*** (.0006)
Occupation indicators Yes No No No
Observations 1,041,239 295,419 497,121 248,699
R-squared .327 .281 .246 .207
# Tool segments − .0166*** (.0062) − .0186* (.0099) − .0073 (.0080) − .0109** (.0054)
Total tools .0093 (.0080) .0244** (.0111) − .0881*** (.0257) .0384*** (.0135)
Total tools squared − .0001 (.0001) − .0003** (.0002) .0071*** (.0022) − .0018*** (.0007)
Occupation indicators Yes No No No
Observations 1,041,239 295,419 497,121 248,699
R-squared .328 .280 .245 .208
  1. Each column reports results of two regressions. Dependent variable is natural log of earnings. Robust standard errors in parentheses. Each regression includes full set of control variables, as in Table 5. Occupation indicators control for 10 major occupation groups. Tools measured in 10 tool increments
  2. *** p < .01, ** p < .05, * p < .1