55
APPENDICES
Appendix 1. Binary and categorical variables in wage regression
Source: Compiled by author
2021 2022
Binary/categorical variable No of obs No of obs
Gender
Male 116 695 111 854
Female 76 096 74 718
Education
Pre -school education 452 600
Basic education 41 904 40 915
Secondary education 93 430 89 907
Tertiary education (bachelor ’s, master ’s and doctoral) 57 005 55 150
NACE
Manufacturing 79 043 76 863
Construction 30 414 28 389
Wholesale and retail trade; repair of motor vehicles and
motorcycles
58 152 57 195
Transportation and storage 25 182 24 125
Occupation
Managers 20 015 19 421
Professionals 12 352 12 236
Technicians and associate professionals 20 311 20 142
Clerical support workers 16 274 16 386
Services and sales workers 22 958 22 787
Skilled agricultural, forestry and fishery workers 198 191
Craft and related trades workers 48 730 46 068
Plant and machine operators and assemblers 35 951 34 282
Elementary occupations 16 002 15 059
Region
Northern Estonia 98 959 95 832
Central Estonia 17 197 16 478
North -Eastern Estonia 15 507 15 427
Western Estonia 15 434 14 606
Southern Estonia 45 694 44 239
56
Appendix 2. List of variables in wage regression
Type Variable Description Coding
Dependent Wage Average monthly salary in euros,
calculated by sum of monthly salaries
and divided by months employed
Logarithm
Independent Gender Individual’s gender 1 – female
0 – male
Independent Age Age of the individual, calculated f rom
date of birth
Continuous, in years
Independent Highest education Highest level of form al education
obtained, based on International
Standard Classification of Education
(ISCED-11).
1 – Pre-school
education
2 – Basic education
3 – secondary
education
4 – tertiary education
(bachelor’s, master’s
and doctoral)
Independent Work experience Work experience of the individual
calculated from date of employment
Continuous, in years
Independent Field of activity Level 2 (sub-major gr oups) of the
Estonian Classification of Economic
Activities (EMTAK) following
Statistical Classification of Economic
Activities (NACE)
1 – Manufacturing
2 – Construction
3 – Wholesale and
retail trade; repair of
motor vehicles and
motorcycles
4 – Transportation
and sto rage
Independent Occupation Level 1 (major groups) of th e
Classification of Occupations 2008,
partly following International Standard
Classification of Occupations (ISCO-
08).
1 – Managers
2 – Professionals
3 – Technicians and
associate
professionals
4 – Clerical support
workers
5 – Services and
sales workers
6 – Skilled
agricultural, forestry
and fishery workers
7 – craft and related
trades workers
8 – Plant and
machine operators
and assemblers
9 – Elementary
occupations
57
Appendix 2 continued
Source: Compiled by author
Type Variable Description Coding
Independent Location of work The Nomenclature of Te rritorial Units
for Statistics (NUTS) is derived from
Estonian Administrative and Settlement
Classification (EHAK).
1 –Northern Estonia:
Harju county
2 – Central Estonia:
Järva, Lääne-Viru,
and Rapla county
3 – North-Eastern
Estonia: Ida-Viru
county
4 – Western Estonia:
Hiiu, Lääne, Saare,
and Pärnu county
5 – Southern Estonia:
Jõgeva, Põlva, Tartu,
Valga, Viljandi, and
Võru county
58
Appendix 3. List of variables in logistic regression
Source: Compiled by author
Type Variable Description Coding
Dependent Labour tax
evasion
Based on the result from wage regression
and parent company country.
1 – tax evasion
0 – no tax evasion
Independent Firm size Average number of employees reduced to
full -time
Continuous
Independent Field of
activity
Level 2 (sub-major groups) of the Estonian
Classification of Economic Activities
(EMTAK) following Statistical
Classification of Economic Activities
(NACE)
1 – Manufacturing
2 – Construction
3 – Wholesale and retail
trade; repair of motor
vehicles and motorcycles
4 – Transportation and
storage
Independent Turnover Sales Logarithm
Independent Debt to
asset
Total liabilities/total assets Continuous
Independent Short-term
debt to
assets
Short-term liabilities/current assets Continuous
Independent Cash to
assets
Cash/total assets Continuous
Independent Turnover to
assets
Turnover/total assets Continuous
Independent COGS to
turnover
Cost of goods sold/turnover Continuous
59
Appendix 4. Robustness checks for wage regression
ln(wage) ln(wage)
2021 2022 Intercept 6.974*** (0.011) 7.165*** (0.010) Gender -0.204*** (0.002) -0.200*** (0.002) Age 0.029*** (0.001) 0.028*** (0.001) Age²/100 -0.028*** (0.001) -0.027*** (0.001) Experience 0.026*** (0.001) 0.026*** (0.001) Experience²/100 -0.060*** (0.002) -0.060*** (0.002) Construction -0.114*** (0.003) -0.113*** (0.002) Wholesale and retail trade -0.066*** (0.002) -0.048*** (0.002) Transportation and storage -0.098*** (0.003) -0.070*** (0.003) Central Estonia -0.104*** (0.003) -0.108*** (0.003) North-Eastern Estonia -0.246*** (0.003) -0.234*** (0.003) Western Estonia -0.150*** (0.003) -0.151*** (0.003) Southern Estonia -0.116*** (0.002) -0.120*** (0.003) Professionals 0.122*** (0.005) 0.099*** (0.004) Technicians and associate professionals -0.062*** (0.005) -0.094*** (0.003) Clerical support workers -0.262*** (0.005) -0.296*** (0.004) Services and sales workers -0.435*** (0.005) -0.473*** (0.004) Skilled agricultural, forestry and fishery workers -0.516*** (0.028) -0.362*** (0.023) Craft and related trades workers -0.397*** (0.004) -0.428*** (0.003) Plant and machine operators and assemblers -0.399*** (0.005) -0.442*** (0.003) Elementary occupations -0.499*** (0.005) -0.538*** (0.004) Observations 265 604 267 458 R² 0.302 0.324 Note: Significance level * p < 0.05, ** p < 0.01, *** p < 0.001. Robust stardard errors in parentheses.
60
Appendix 5. Non-exclusive licence A non-exclusive licence for reproduction and publication of a graduation thesis 4
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07.05.2024
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