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Table 8 Summary statistics—matched sample

From: FDI and onshore task composition: evidence from German firms with affiliates in the Czech Republic

 

Manufacturing

Service

MNEs

Non-MNEs

Balancing

MNEs

Non-MNEs

Balancing

Mean

Mean

Std. bias

Mean

Mean

Std. bias

Median

Median

 

Median

Median

 

(Std. dev.)

(Std. dev.)

(Var. ratio)

(Std. dev.)

(Std. dev.)

(Var. ratio)

(Log) plants per firm

0.309

0.309

0.001

0.464

0.429

0.044

 

0

0

–

0

0

–

 

(0.540)

(0.549)

(0.968)

(0.850)

(0.741)

(1.313)

(Log) size (employees)

4.895

4.908

− 0.009

3.688

3.487

0.122

 

4.942

5.159

–

3.714

3.526

–

 

(1.525)

(1.332)

(1.310)

(1.712)

(1.603)

(1.140)

(Log) wage bill (in euros)

9.415

9.441

− 0.017

8.392

8.166

0.127

 

9.487

9.638

–

8.486

8.194

–

 

(1.644)

(1.444)

(1.296)

(1.832)

(1.721)

(1.133)

Employment growth

3.064

3.123

− 0.055

2.841

2.763

0.069

 

2.833

2.891

–

2.736

2.721

–

 

(1.187)

(0.954)

(1.548)

(1.224)

(1.031)

(1.410)

Wage growth

2.093

2.071

0.025

2.436

2.416

0.022

 

2.161

2.140

–

2.257

2.257

–

 

(0.881)

(0.851)

(1.072)

(0.938)

(0.880)

(1.135)

Female employees (%)

32.56

32.45

0.005

39.83

40.93

− 0.046

 

27.12

27.71

–

36.38

36.36

–

 

(21.05)

(21.21)

(0.985)

(23.22)

(24.91)

(0.869)

Task intensities (%)

 Activities performed

  Researching, developing

1.188

1.250

− 0.047

1.274

1.356

− 0.035

 

0.837

0.840

–

0.176

0

–

 

(1.301)

(1.379)

(0.890)

(2.295)

(2.373)

(0.935)

  Teaching, training

1.858

1.803

0.040

2.400

2.542

− 0.053

 

1.610

1.593

–

1.491

1.516

–

 

(1.449)

(1.345)

(1.162)

(2.567)

(2.759)

(0.866)

  Acquiring customers, PR

0.961

0.905

0.046

1.971

2.020

− 0.020

 

0.565

0.492

–

1.116

1.186

–

 

(1.214)

(1.235)

(0.967)

(2.458)

(2.451)

(1.006)

  Analyzing, investigating

7.028

7.028

0

12.51

12.39

0.030

 

6.778

6.443

–

12.84

12.50

–

 

(3.311)

(3.396)

(0.951)

(3.839)

(3.878)

(0.980)

  Buying, selling, procurement

1.832

1.747

0.053

4.451

4.700

− 0.058

 

1.475

1.267

–

3.448

3.659

–

 

(1.552)

(1.658)

(0.876)

(4.286)

(4.345)

(0.973)

  Organizing others

9.292

9.185

0.031

13.55

13.46

0.024

 

9.401

8.992

–

13.87

13.35

–

 

(3.609)

(3.340)

(1.168)

(3.859)

(3.927)

(0.966)

  Informing, consulting

13.46

13.44

0.007

14.79

14.83

− 0.010

 

13.54

13.48

–

15.02

15.15

–

 

(3.692)

(3.838)

0.925

(4.108)

(3.972)

(1.069)

  Measuring, checking

13.72

13.90

− 0.035

5.137

5.054

0.019

 

13.63

13.96

–

4.208

4.632

–

 

(5.283)

(5.135)

(1.059)

(4.532)

(4.189)

(1.170)

  Negotiating

3.083

3.054

0.016

5.791

6.041

− 0.071

 

2.970

2.803

–

5.687

6.208

–

 

(1.863)

(1.926)

(0.936)

(3.509)

(3.553)

(0.975)

  Serving, caring

1.581

1.475

0.075

3.355

3.497

− 0.034

 

1.314

1.168

–

1.983

2.060

–

 

(1.514)

(1.340)

(1.276)

(4.243)

(4.069)

(1.087)

  Surveilling, monitoring

12.04

12.09

− 0.009

2.484

2.440

0.010

 

12

12.30

–

0.440

0.349

–

 

(5.630)

(5.379)

(1.095)

(4.466)

(4.391)

(1.034)

  Repairing

8.259

8.324

− 0.013

6.314

5.586

0.050

 

7.924

8.113

–

0.563

0.540

–

 

(5.309)

(4.823)

(1.212)

(15.35)

(13.85)

(1.229)

  Producing, manufacturing

9.063

9.201

− 0.025

1.388

1.288

0.025

 

8.679

8.873

–

0

0

–

 

(6.237)

(6.031)

(1.070)

(4.123)

(3.752)

(1.208)

 Associated knowledge

  Management

0.515

0.479

0.055

0.906

0.921

− 0.011

 

0.332

0.324

–

0.415

0.360

–

 

(0.712)

(0.593)

(1.440)

(1.376)

(1.368)

(1.012)

  Computer engineering

0.359

0.366

− 0.010

0.837

0.881

− 0.022

 

0.145

0.135

–

0

0

–

 

(0.647)

(0.711)

(0.827)

(1.953)

(2.029)

(0.927)

  Giving presentations

0.831

0.752

0.073

1.408

1.524

− 0.060

 

0.434

0.419

–

0.752

0.841

–

 

(1.107)

(1.058)

(1.094)

(1.862)

(1.986)

(0.879)

  Foreign language

0.054

0.061

− 0.034

0.025

0.033

− 0.046

 

0

0

–

0

0

–

 

(0.187)

(0.228)

(0.675)

(0.124)

(0.189)

(0.435)

  Legal/law

0.008

0.010

− 0.041

0.013

0.011

0.024

 

0

0

–

0

0

–

 

(0.027)

(0.034)

(0.615)

(0.069)

(0.078)

(0.767)

  System analysis

0.188

0.210

− 0.047

0.757

0.741

0.008

 

0.060

0.069

–

0

0

–

 

(0.420)

(0.511)

(0.675)

(1.913)

(1.917)

(0.996)

  Maths

1.419

1.492

− 0.052

1.388

1.642

− 0.103

 

1.158

1.109

–

0.518

0.456

–

 

(1.293)

(1.512)

(0.731)

(2.241)

(2.675)

(0.702)

  Other technical

1.612

1.630

− 0.009

0.638

0.772

− 0.082

 

0.879

0.931

–

0

0

–

 

(2.051)

(2.011)

(1.040)

(1.536)

(1.725)

(0.793)

  Labor legislation

0.006

0.007

− 0.066

0.001

0.0045

− 0.099

 

0

0

–

0

0

–

 

(0.018)

(0.022)

(0.670)

(0.007)

(0.052)

(0.019)

  Design

0.052

0.056

− 0.013

0.089

0.117

− 0.045

 

0

0

–

0

0

–

 

(0.266)

(0.274)

(0.943)

(0.533)

(0.681)

(0.612)

  Use of software

6.206

6.251

− 0.014

10.73

10.59

0.032

 

5.930

5.748

–

10.61

10.51

–

 

(3.044)

(3.108)

(0.959)

(4.430)

(4.617)

(0.920)

  Marketing, sales

0.339

0.303

0.044

0.653

0.643

0.006

 

0

0

–

0

0

–

 

(0.828)

(0.836)

(0.981)

(1.671)

(1.575)

(1.126)

  Finance, taxes

0.184

0.193

− 0.022

0.522

0.653

− 0.080

 

0.061

0.079

–

0

0

–

 

(0.331)

(0.494)

(0.451)

(1.370)

(1.861)

(0.542)

  Native language (German)

2.982

2.976

0.002

6.132

5.830

0.058

 

2.313

2.237

–

4.969

4.186

–

 

(2.574)

(2.796)

(0.848)

(5.037)

(5.295)

(0.905)

  Regulations

1.289

1.325

− 0.016

0.348

0.287

0.051

 

0.437

0.471

–

0

0

–

 

(2.262)

(2.281)

(0.984)

(1.230)

(1.155)

(1.134)

  Other specialized

0.511

0.452

0.037

0.110

0.120

− 0.016

 

0

0

–

0

0

–

 

(1.760)

(1.447)

(1.480)

(0.603)

(0.617)

(0.953)

  Medical

0.073

0.038

0.052

0.027

0.026

0.005

 

0

0

–

0

0

–

 

(0.881)

(0.369)

(5.716)

(0.172)

(0.387)

(0.197)

Untargeted variables

 Tasks per worker

5.145

5.092

0.042

5.457

5.490

− 0.014

 

5.119

5.024

–

5.491

5.333

–

 

1.227

1.300

0.891

2.207

2.410

0.839

 Number of different tasks

21.54

21.70

− 0.033

17.02

16.22

0.134

 

22

23

–

18

17

–

 

5.251

4.822

1.186

6.143

5.892

1.087

 Broad occupational categories (shares)

  Production

   Unqual. manual occ.

37.06

35.64

0.057

4.801

4.923

− 0.009

 

37.96

35.46

–

0

0

–

 

24.98

24.44

1.044

13.27

14.28

0.864

   Qual. manual occ.

20.13

21.87

− 0.090

3.794

3.435

0.035

 

14.29

16.89

–

0

0

–

 

19.39

19.43

0.996

10.20

10.17

1.005

   Technicians

9.088

9.228

− 0.014

5.632

4.604

0.085

 

7.105

7.216

–

0

0

–

 

9.767

9.848

0.984

13.20

10.78

1.498

   Engineers

3.501

3.688

− 0.031

3.721

5.224

− 0.114

 

1.739

1.462

–

0

0

–

 

5.275

6.703

0.619

10.95

15.00

0.533

  Services

   Unqual. service occ.

6.240

6.222

0.002

18.13

19.67

− 0.058

 

4.054

4.138

–

5.302

5.463

–

 

8.357

9.038

0.855

25.57

27.15

0.887

   Qual. service occ.

0.243

0.511

− 0.120

0.638

1.269

− 0.132

 

0

0

–

0

0

–

 

1.142

2.953

0.150

2.502

6.289

0.158

   Semiprof.

0.154

0.123

0.031

0.239

0.247

− 0.004

 

0

0

–

0

0

–

 

1.042

0.902

1.335

1.688

1.978

0.728

   Professions

0.255

0.300

− 0.039

0.799

0.849

− 0.013

 

0

0

–

0

0

–

 

0.814

1.413

0.332

3.817

3.566

1.146

  Administration

   Unqual. admin. occ.

3.538

3.478

0.0102

9.993

9.271

0.045

 

1.639

1.566

–

3.125

3.120

–

 

5.795

6.125

0.895

16.96

14.80

1.314

   Qual. admin. occ.

16.94

16.17

0.061

46.10

43.17

0.101

 

14.25

13.18

–

42.60

40.48

–

 

12.40

12.89

0.925

29.03

28.93

1.007

   Managers

2.812

2.695

0.022

5.891

7.254

− 0.110

 

1.660

1.515

–

2.395

2.159

–

 

5.008

5.658

0.783

10.76

13.86

0.602

  1. Table 8 describes the summary statistics for 738 (540) MNEs in the manufacturing (service) sector and their matched non-MNEs 2 years prior to the FDI event. For each variable, we report the mean, median and standard deviation. In comparing MNEs and non-MNEs, we also report the standardized bias and the variance ratio between the two groups. The wage bill is denoted in constant 2010 euros. Employment or wage growth is measured as the log difference between \(t-2\) and \(t-6\)