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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