Blue Collar Worker Index | Jungheinrich PROFISHOP

European Blue Collar Worker Index

At Jungheinrich PROFISHOP, we know firsthand how hard Blue Collar labourers work and how important they are to all economies. Particularly in the context of a pandemic, the contributions and appreciation of essential workers from a wide range of industries has been starkly brought to the public’s attention.

Unfortunately though, all too often too little attention is paid to blue collar roles as an important cornerstone of society. We at Jungheinrich PROFISHOP sought to change that by answering the following question: which European cities which offer the best options for Blue Collar Workers?

To assess this, we researched a range of factors including salary, % of unionised workers, % of industry share along with up-to-date unemployment figures to assess the best cities for those who work in blue collar industries. 50 European hubs and four popular positions were included in the study to adequately compare a wide range of locations and economies.

Each individual column is sortable, and the full methodology explaining how each factor was evaluated is at the bottom of the page. Where scores are present, a higher score denotes better performance for that particular factor.

Location Truck Driver Construction Workers Metalworkers Logistician/Warehouse Workers Employment Infrastructure
# City Country Salary (EUR) Salary (% Deviation of Purchasing Power) Salary (EUR) Salary (% Deviation of Purchasing Power) Salary (EUR) Salary (% Deviation of Purchasing Power) Salary (EUR) Salary (% Deviation of Purchasing Power) Unionised Workers (Score) Industry Share of Total Employment (%) Unemployment (%) Score (0-100)
1 Copenhagen Denmark 43,602 11.44% 43,309 10.69% 50,217 28.35% 52,692 34.68% 100.00 23.83% 8.30% 100.00
2 Basel Switzerland 63,137 37.00% 61,109 32.60% 57,512 24.80% 53,215 15.47% 55.97 24.79% 6.00% 96.79
3 Dublin Ireland 48,861 49.20% 45,750 39.70% 42,328 29.25% 40,829 24.68% 63.82 17.48% 7.20% 93.11
4 Oslo Norway 42,269 27.34% 44,366 33.66% 36,977 11.40% 38,715 16.63% 85.24 19.41% 5.90% 92.79
5 Karlsruhe Germany 32,304 4.89% 28,656 -6.95% 30,048 -2.44% 31,476 2.20% 57.34 30.36% 3.80% 90.33
6 Milan Italy 32,692 14.43% 30,259 5.91% 31,895 11.64% 28,982 1.44% 72.61 26.30% 9.00% 90.19
7 Munich Germany 31,716 2.98% 21,888 -28.93% 39,870 29.46% 36,990 20.11% 57.34 26.80% 4.70% 89.96
8 Stuttgart Germany 32,304 4.89% 28,656 -6.95% 29,412 -4.50% 32,286 4.83% 57.34 30.36% 5.00% 89.74
9 Bern Switzerland 53,967 17.10% 51,341 11.40% 54,260 17.74% 50,205 8.94% 55.97 22.32% 5.40% 86.92
10 Lausanne Switzerland 63,001 36.71% 60,975 32.31% 57,388 24.53% 53,099 15.22% 55.97 17.07% 8.60% 85.68
11 Amsterdam Netherlands 49,720 56.78% 49,870 57.25% 37,621 18.63% 42,527 34.10% 57.25 10.03% 7.00% 85.48
12 Dusseldorf Germany 30,798 0.00% 27,360 -11.16% 38,832 26.09% 37,656 22.27% 57.34 21.97% 6.60% 84.23
13 Nuremberg Germany 31,716 2.98% 21,888 -28.93% 34,080 10.66% 29,406 -4.52% 57.34 26.80% 4.50% 83.89
14 Saarbrucken Germany 36,738 19.29% 25,212 -18.14% 29,298 -4.87% 29,952 -2.75% 57.34 26.19% 6.40% 83.62
15 Geneva Switzerland 56,259 22.08% 60,964 32.29% 57,380 24.51% 53,090 15.20% 55.97 17.07% 10.50% 82.48
16 Helsinki Finland 38,942 8.38% 43,521 21.12% 38,948 8.40% 34,824 -3.08% 94.71 16.55% 8.30% 82.48
17 Zurich Switzerland 56,062 21.65% 54,145 17.49% 61,069 32.51% 53,144 15.32% 55.97 14.80% 4.70% 82.39
18 Graz Austria 30,528 -7.56% 26,412 -20.03% 34,620 4.83% 30,888 -6.47% 65.70 25.25% 6.60% 80.73
19 Hanover Germany 30,180 -2.01% 21,948 -28.74% 38,982 26.57% 31,020 0.72% 57.34 23.36% 7.10% 80.58
20 Erfurt Germany 26,838 -12.86% 21,438 -30.39% 26,142 -15.12% 28,260 -8.24% 57.34 29.73% 5.70% 80.07
21 Cologne Germany 30,798 0.00% 27,360 -11.16% 36,846 19.64% 31,596 2.59% 57.34 21.97% 7.60% 80.01
22 Essen Germany 30,798 0.00% 27,360 -11.16% 34,794 12.97% 31,626 2.69% 57.34 21.97% 6.30% 79.75
23 Bielefeld Germany 30,798 0.00% 27,360 -11.16% 33,588 9.06% 30,990 0.62% 57.34 21.97% 5.90% 79.11
24 Stockholm Schweden 41,941 23.57% 38,897 14.60% 33,801 -0.42% 34,296 1.04% 98.63 12.84% 9.20% 78.96
25 Kassel Germany 30,552 -0.80% 26,922 -12.59% 33,504 8.79% 31,368 1.85% 57.34 20.98% 5.10% 78.18
26 Brussels Belgium 23,172 -27.75% 47,000 46.55% 46,066 43.64% 48,000 49.67% 86.18 7.32% 11.20% 77.76
27 Frankfurt Germany 30,552 -0.80% 26,922 -12.59% 33,504 8.79% 31,368 1.85% 57.34 20.98% 6.30% 77.53
28 Dresden Germany 27,216 -11.63% 21,150 -31.33% 26,424 -14.20% 26,772 -13.07% 57.34 26.58% 7.40% 74.80
29 Edinburgh United Kingdom 31,423 2.48% 27,669 -9.76% 34,004 10.90% 30,447 -0.70% 63.23 16.53% 7.00% 73.53
30 Bremen Germany 30,678 -0.39% 25,434 -17.42% 29,532 -4.11% 30,156 -2.08% 57.34 18.78% 5.20% 72.51
31 Leipzig Germany 27,216 -11.63% 21,150 -31.33% 25,470 -17.30% 24,864 -19.27% 57.34 26.58% 9.30% 72.44
32 Paris France 21,274 -31.63% 31,753 2.05% 44,217 42.10% 48,087 54.54% 50.77 11.08% 10.60% 70.92
33 Kiel Germany 28,332 -8.01% 21,708 -29.51% 28,614 -7.09% 29,526 -4.13% 57.34 19.12% 4.50% 70.17
34 Hamburg Germany 33,852 9.92% 28,008 -9.06% 32,526 5.61% 31,158 1.17% 57.34 12.24% 6.30% 68.12
35 London United Kingdom 29,250 -4.60% 32,226 5.10% 33,687 9.87% 39,835 29.92% 63.23 8.84% 8.40% 67.92
36 Rome Italy 29,897 4.65% 27,675 -3.13% 29,169 2.10% 26,505 -7.23% 72.61 12.71% 11.10% 67.24
37 Barcelona Spanien 27,132 6.88% 23,679 -6.73% 25,205 -0.71% 23,265 -8.36% 54.86 19.68% 19.50% 67.16
38 Schwerin Germany 29,628 -3.80% 23,382 -24.08% 23,124 -24.92% 26,334 -14.49% 57.34 18.45% 5.60% 65.89
39 Bratislava Slovakia 17,852 -5.94% 14,773 -22.17% 17,246 -9.14% 14,733 -22.38% 52.39 18.50% 7.60% 65.01
40 Manchester United Kingdom 27,128 -11.53% 53,958 75.98% 27,043 -11.80% 30,510 -0.49% 63.23 6.71% 8.90% 64.25
41 Madrid Spanien 27,813 9.56% 24,275 -4.38% 25,841 1.79% 23,851 -6.05% 54.86 11.83% 17.10% 60.11
42 Vienna Austria 30,528 -7.56% 26,412 -20.03% 30,672 -7.13% 30,888 -6.47% 65.70 11.42% 9.90% 60.09
43 Athens Greece 24,572 6.29% 22,937 -0.78% 22,467 -2.82% 20,283 -12.27% 60.49 13.44% 21.60% 59.11
44 Warsaw Poland 13,675 -13.77% 11,217 -29.27% 13,006 -17.98% 11,459 -27.74% 54.10 15.46% 6.90% 58.01
45 Marseille France 22,426 -27.93% 19,699 -36.69% 36,029 15.79% 26,424 -15.08% 50.77 13.63% 12.10% 56.10
46 Berlin Germany 30,120 -2.20% 18,546 -39.78% 26,910 -12.62% 27,594 -10.40% 57.34 10.97% 10.90% 54.67
47 Lisbon Portugal 23,375 -3.06% 19,833 -17.75% 21,046 -12.72% 19,242 -20.20% 56.31 12.20% 15.20% 54.51
48 Riga Letvia 11,946 -41.93% 10,761 -47.69% 10,434 -49.28% 10,323 -49.82% 53.41 22.70% 10.10% 51.31
49 Prague Czechia 17,412 -14.84% 8,541 -58.23% 9,244 -54.79% 16,508 -19.26% 53.07 14.23% 4.30% 50.20
50 Budapest Hungary 10,779 -30.92% 9,008 -42.28% 10,280 -34.12% 9,921 -36.42% 50.00 15.24% 5.90% 50.00
Methodology‌

The Blue-Collar Index 2020 evaluates the best cities for blue collar workers in Europe. Each city is reviewed under a criterion of eleven factors, using data relating to the financial prosperity, economic well-being, and unionisation of craftworkers. Four professions classified as “Blue Collar Work” were selected to draw comparisons between wage earners in the sample. The cumulative dataset aims to offer relevant and up-to-date information pertaining to the standard of living for this sector of work, targeting essential workers at the bedrock of many thriving enterprises.

1.0 City Selection

Cities were shortlisted based on their popular demand as attractive destinations to live and reputation as economic hubs. 50 cities were finalised across Europe, based on the accessibility of readily available, transparent data. This study utilises metropolitan areas as specified by the OECD.

2.0 Factors and Scoring

The study is divided into two sections, culminating to a total score to position each city. For factors measured as a score, a higher value signifies better performance. A value of 100 indicates that the city performs the highest within the sample but is not an indication that it is unable to improve.


A weighted score is calculated from factors provided, resulting in a final score for each city. Further data for each city and factor can be provided upon request.

2.1 Professions
2.1.1 Salaries
2.1.2 Purchasing Power Parities (PPP)
2.2 Unionised Workers (Score)
2.3 Share of Industrial and Construction Workers in Total Employment (%)
2.4 Estimated Unemployment Rate (June 2020)
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