Table 12 Post-lasso logit results for occupation titles

Farmers

–

− 1.659

Managers_Advisors_agri

8.401

8.409***

Gardeners

7.655

4.671

Forestry_and_Hunting

–

35.82***

Mineral_Oil_gas_quarries

− 53.81

− 802.7

Stone_preparers

− 19.91

8.153**

Building_material_makers

− 0.106

4.811

Glass_makers

0.660

–

Chemical_workers

0.514

5.279

Plastics_processors

1.183**

5.085

Paper_makers

1.250*

3.429

Printer

− 2.301**

− 2.578

Wood_preparers

0.490

8.695**

Metal_producers

− 2.051

2.718

Molders

− 0.0944

10.89

Metal_molders_non_cut

1.713**

6.417**

Metal_molders_metal_cut

− 0.563

6.845**

Metal_surface

− 0.938

9.343**

Metal_connectors

–

6.742*

Smiths

− 0.126

22.52

Sheet_metal

− 1.213

4.570

Locksmiths

− 0.679

6.237**

Mechanics

–

1.452

Toolmakers

–

5.917

Precision_fitters

0.0494

1.964

Electricians

1.218**

4.070

Assemblers_and_Metal

0.464

7.806**

Spinners

3.049***

81.25***

Textile_makers

1.329

6.014

Textile_processer

1.164

4.415

Textile_finisher

–

8.741

Leather_processing

1.920

6.967**

Bakery_goods_makers

− 0.851

− 66.70

Food_preparers

–

7.421**

Beverage_or_Luxury_food

–

− 35.40

Butchers_Fish_processing

0.0194

–

Nutrition

1.808**

− 97.69

Bricklayers_Concrete

− 1.383

− 43.54*

Carpenters_Roofers

− 1.756

–

Building_laborer

− 9.803

− 14.81

Building_finishers

–

5.584

Room_equip_Upholsterers

− 1.480

5.450

Carpenters

0.544

3.819

Painters_lacquerers

–

5.066*

Goods_examiner

0.858

6.393**

Assistants

0.446

7.493***

Machinists

2.980***

5.982*

Engineers

0.716

5.862**

Chemists_Physicists_Math

0.938

3.323

Technicians

0.786

6.277**

Technical_specialists

2.660**

6.270*

Wholesale_and_retail

− 0.782

4.660*

Bank_Insurance_spec

8.615*

4.088

Services_agents

− 1.967

3.764

Surface_transport

− 0.636

2.821

Water_Air_transport

1.147

1.952

Communication

− 5.685

6.454**

Warehouse_managers_transport

1.220**

6.318**

Management_consultants

3.296***

6.891**

MPs_officials

− 0.103

7.174**

Accountants_Data_processing

1.963**

5.442*

Office_specialists_auxiliary

2.663***

6.797**

Watchpersons

3.318*

5.514

Protective_services

− 15.31

7.332

Legal_professionals

31.43***

25.63

Journalists_Librarians

− 1.487

18.81**

Artists (e.g., for commercials)

–

7.739**

Health_occupations

− 15.47

–

Physicians_Pharmacists

–

2.622

Teachers

3.937

− 5.775

Humanities_Scientists

1.140

6.108

Attending_on_guests

–

3.677

Body_care_occupations

7.945

–

Housekeeping

− 55.16**

–

Cleaning

− 2.209

4.896

Job_seeker

–

8.441*

Workforce

2.287***

4.095

Nonpenalized in lasso

 # of establishments

0.482***

0.222***

 Employment growth

0.0689*

0.0367

 Share of women

0.535*

− 0.193

 (Log) wage bill

0.0825

0.552***

 Mean wage growth

0.0334

− 0.0249

 (Log) employees

Yes

Yes

 Region FE

Yes

Yes

 Industry FE

Yes

Yes

 Year FE

Yes

Yes

 Observations

57,452

51,022

 MPSE \(\lambda\)

2.97

2.57