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The 2018 Global Dry Cleaning Index

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2018 Dry Cleaning Index

Some cities, especially capitals, are iconic for their business districts and suit-wearing industries. As necessary attire for many, we at Zipjet wondered how suit-related services themselves contribute to global trade. Does the cost of dry cleaning a suit differ around the world? Is there more competitive pricing in typically suit-wearing cities, or does it drive the price up due to demand? As part of industry market research, we undertook this study to determine not only how the cost of dry cleaning varies from nation to nation, but how much it contributes to the economy as a whole.

The study began by hand-picking 100 cities around the world, focusing on capital cities, business centres and financial districts. To source the cost of dry cleaning a suit, we looked at the average price of cleaning 2 and 3-piece suits, as both a package deal and as separate jacket and trousers in each city. Once we determined this figure, we calculated the deviation from the average, which shows how much more or less expensive the service is in comparison to all of the other cities in the study. The final index is ranked highest to lowest, based on the cost of dry cleaning a single suit.

To give the data some human perspective, we then calculated how many hours a person must work at minimum wage in order to afford the dry cleaning of one suit. Finally, to establish how much capital dry cleaning adds to the economy, we calculated how much each country spends on dry cleaning suits within a year. This was determined by multiplying the cost of dry cleaning with the total number of professionals who typically wear suits such as bankers, lawyers, insurance brokers, governmental workers and teaching staff, to give an indication of the total national cost.

“For traditionally business-oriented cities, such as Oslo, Helsinki and Zurich, our study shows that citizens are paying between 13-30% more to dry clean their suits than the rest of the world. Although you could consider this a ‘suit tax’, our data also shows that as salaries are higher in these nations, it would only take around 1 - 3 hours of working at minimum wage to afford such a service in these cities.” says Founder and Managing Director of Zipjet, Florian Färber. “We hope therefore that this index might serve as a useful tool for young professionals searching for a lucrative yet affordable new city to call home. Geneva and Copenhagen, for instance, are great examples of how the index acts as a useful indicator of overall affordability, as the data illustrates that despite high dry cleaning costs, the cities also offer higher wages.”

Currency
  • EUR (€)
  • GBP (£)
  • USD ($)
#
City Country Cost of Dry Cleaning a Suit
(EUR) (GBP) (USD)
% Deviation from
Average Worldwide
Hours at Minimum Wage
to Afford Dry Cleaning
Total Nationwide Annual Spend
on Dry Cleaning (EUR) (GBP) (USD)
Oslo Norway 42.16 37.02 52.03 31.04 2.3 290,242,180 254,832,634 358,158,850
Helsinki Finland 33.33 29.26 41.13 22.21 3.3 187,020,000 164,203,560 230,782,680
Gothenburg Sweden 28.46 24.99 35.12 17.34 2.7 314,689,605 276,297,473 388,326,973
Aarhus Denmark 27.74 24.36 34.23 16.62 1.9 167,069,054 146,686,629 206,163,213
Zurich Switzerland 24.95 21.91 30.79 13.84 1.5 179,997,373 158,037,693 222,116,758
Stockholm Sweden 22.17 19.47 27.36 11.05 2.1 314,689,605 276,297,473 388,326,973
Graz Austria 20.42 17.93 25.20 9.3 3.0 159,651,900 140,174,368 197,010,445
Auckland New Zealand 19.76 17.35 24.38 8.65 2.1 55,132,964 48,406,742 68,034,078
Vienna Austria 19.33 16.97 23.85 8.21 2.8 159,651,900 140,174,368 197,010,445
Amsterdam Netherlands 19.07 16.74 23.53 7.95 2.1 316,235,920 277,655,138 390,235,125
Moscow Russia 18.62 16.35 22.98 7.5 16.2 2,646,586,863 2,323,703,266 3,265,888,189
Geneva Switzerland 18.41 16.16 22.72 7.29 1.1 179,997,373 158,037,693 222,116,758
Munich Germany 17.80 15.63 21.97 6.68 2.1 857,377,267 752,777,241 1,058,003,548
Copenhagen Denmark 17.42 15.29 21.50 6.3 1.2 167,069,054 146,686,629 206,163,213
Dublin Ireland 17.24 15.14 21.27 6.12 1.8 71,539,073 62,811,306 88,279,215
Nice France 17.17 15.08 21.19 6.05 1.7 835,827,328 733,856,394 1,031,410,923
London UK 17.15 15.06 21.16 6.03 2.0 819,380,335 719,415,934 1,011,115,333
Tel Aviv Israel 17.04 14.96 21.03 5.92 2.5 120,404,376 105,715,042 148,579,000
Luxembourg Luxembourg 15.98 14.03 19.72 4.86 1.4 10,923,010 9,590,403 13,478,994
Vilnius Lithuania 15.33 13.46 18.92 4.21 6.6 50,701,200 44,515,654 62,565,281
Canberra Australia 15.19 13.34 18.74 4.07 1.3 231,199,085 202,992,797 285,299,671
Paris France 14.33 12.58 17.68 3.21 1.5 835,827,328 733,856,394 1,031,410,923
Antwerp Belgium 14.20 12.47 17.52 3.08 1.5 115,922,070 101,779,577 143,047,834
Boston USA 13.70 12.03 16.91 2.58 1.5 2,455,000,400 2,155,490,351 3,029,470,494
Toronto Canada 13.67 12.00 16.87 2.55 1.6 405,862,764 356,347,507 500,834,651
Saint Petersburg Russia 13.38 11.75 16.51 2.26 14.5 2,646,586,863 2,323,703,266 3,265,888,189
Marseille France 13.20 11.59 16.29 2.08 1.3 835,827,328 733,856,394 1,031,410,923
Toulouse France 13.13 11.53 16.20 2.01 1.3 835,827,328 733,856,394 1,031,410,923
Manchester UK 13.12 11.52 16.19 2 1.6 819,380,335 719,415,934 1,011,115,333
Lyon France 13.10 11.50 16.17 1.98 1.3 835,827,328 733,856,394 1,031,410,923
New York USA 12.71 11.16 15.68 1.59 1.5 2,455,000,400 2,155,490,351 3,029,470,494
Cologne Germany 12.59 11.05 15.54 1.47 1.5 857,377,267 752,777,241 1,058,003,548
Hamburg Germany 12.51 10.98 15.44 1.39 1.5 857,377,267 752,777,241 1,058,003,548
Osaka Japan 12.41 10.90 15.31 1.29 1.8 706,396,420 620,216,057 871,693,182
Tallinn Estonia 12.30 10.80 15.18 1.18 4.3 18,568,080 16,302,774 22,913,011
Glasgow UK 12.28 10.78 15.15 1.16 1.5 819,380,335 719,415,934 1,011,115,333
Ottawa Canada 12.18 10.69 15.03 1.06 1.4 405,862,764 356,347,507 500,834,651
Riga Latvia 12.00 10.54 14.81 0.88 4.8 28,663,200 25,166,290 35,370,389
Hong Kong China 11.86 10.41 0.74 3.3 51,472,284 45,192,665 63,516,799
Cape Town South Africa 11.76 10.33 14.51 0.65 8.4 315,290,667 276,825,205 389,068,683
Dallas USA 11.73 10.30 14.47 0.62 2.0 2,455,000,400 2,155,490,351 3,029,470,494
Birmingham UK 11.55 10.14 14.25 0.43 1.4 819,380,335 719,415,934 1,011,115,333
Berlin Germany 11.54 10.13 14.24 0.42 1.4 857,377,267 752,777,241 1,058,003,548
Los Angeles USA 11.34 9.96 13.99 0.22 1.3 2,455,000,400 2,155,490,351 3,029,470,494
Barcelona Spain 11.33 9.95 13.98 0.21 2.3 322,496,240 283,151,699 397,960,360
Leeds UK 11.18 9.82 13.80 0.06 1.3 819,380,335 719,415,934 1,011,115,333
Washington, DC USA 11.12 9.76 13.72 0 1.1 2,455,000,400 2,155,490,351 3,029,470,494
Brussels Belgium 11.10 9.75 13.70 -0.02 1.2 115,922,070 101,779,577 143,047,834
Frankfurt Germany 10.88 9.55 13.43 -0.24 1.3 857,377,267 752,777,241 1,058,003,548
Sydney Australia 10.78 9.46 13.30 -0.34 0.9 231,199,085 202,992,797 285,299,671
Melbourne Australia 10.47 9.19 12.92 -0.65 0.9 231,199,085 202,992,797 285,299,671
Buenos Aires Argentina 10.38 9.11 12.81 -0.74 5.7 253,470,931 222,547,478 312,783,129
Chicago USA 10.31 9.05 12.72 -0.81 1.5 2,455,000,400 2,155,490,351 3,029,470,494
Madrid Spain 9.93 8.72 12.25 -1.19 2.0 322,496,240 283,151,699 397,960,360
Rio de Janeiro Brazil 9.90 8.69 12.22 -1.22 8.0 863,616,125 758,254,957 1,065,702,298
Philadelphia USA 9.65 8.47 11.91 -1.47 1.6 2,455,000,400 2,155,490,351 3,029,470,494
Warsaw Poland 9.41 8.26 11.61 -1.71 3.2 292,278,130 256,620,198 360,671,213
Montreal Canada 9.29 8.16 11.46 -1.83 1.3 405,862,764 356,347,507 500,834,651
Athens Greece 9.23 8.10 11.39 -1.89 2.3 70,596,220 61,983,481 87,115,735
Tokyo Japan 9.10 7.99 11.23 -2.02 1.2 706,396,420 620,216,057 871,693,182
Singapore Singapore 8.89 7.81 10.97 -2.23 2.5 32,894,371 28,881,258 40,591,653
Prague Czech Republic 8.76 7.69 10.81 -2.36 3.2 80,562,514 70,733,887 99,414,142
Budapest Hungary 8.51 7.47 10.50 -2.61 3.3 84,054,552 73,799,897 103,723,317
Lisbon Portugal 8.00 7.02 9.87 -3.12 2.1 43,862,400 38,511,187 54,126,202
Santiago Chile 7.97 7.00 9.83 -3.15 4.2 83,500,770 73,313,676 103,039,950
Rome Italy 7.33 6.44 9.05 -3.79 1.0 245,911,890 215,910,639 303,455,272
Taipei Taiwan 7.32 6.43 9.03 -3.8 2.1 64,708,064 56,813,680 79,849,750
Dubai UAE 7.26 6.37 8.96 -3.86 0.5 34,221,983 30,046,901 42,229,927
Sofia Bulgaria 7.11 6.24 8.77 -4.01 9.3 38,236,488 33,571,636 47,183,826
Houston USA 7.03 6.17 8.68 -4.09 1.2 2,455,000,400 2,155,490,351 3,029,470,494
São Paulo Brazil 6.46 5.67 7.97 -4.66 5.2 863,616,125 758,254,957 1,065,702,298
San Juan Puerto Rico 6.40 5.62 7.90 -4.72 1.1 15,693,764 13,779,125 19,366,104
Amman Jordan 6.32 5.55 7.80 -4.8 5.2 17,131,437 15,041,402 21,140,193
Baghdad Iraq 6.01 5.28 7.42 -5.1 4.4 47,599,399 41,792,272 58,737,658
Karachi Pakistan 5.92 5.20 7.31 -5.2 11.2 140,808,685 123,630,025 173,757,917
Milan Italy 5.77 5.07 7.12 -5.35 0.8 245,911,890 215,910,639 303,455,272
Bucharest Romania 5.72 5.02 7.06 -5.4 2.4 79,408,806 69,720,932 97,990,467
Bangkok Thailand 5.71 5.01 7.05 -5.41 5.4 187,128,903 164,299,177 230,917,066
Manama Bahrain 5.66 4.97 6.98 -5.46 4.6 3,935,372 3,455,256 4,856,249
Kuala Lumpur Malaysia 5.56 4.88 6.86 -5.56 6.2 90,248,534 79,238,213 111,366,691
Doha Qatar 5.49 4.82 6.77 -5.63 6.8 3,989,118 3,502,445 4,922,571
Riyadh Saudi Arabia 5.35 4.70 6.60 -5.77 3.4 94,883,057 83,307,324 117,085,692
Seoul South Korea 5.29 4.64 6.53 -5.83 0.9 121,734,951 106,883,287 150,220,930
Istanbul Turkey 5.21 4.57 6.43 -5.91 2.3 179,676,455 157,755,928 221,720,746
Lagos Nigeria 5.19 4.56 6.40 -5.93 22.2 255,321,581 224,172,348 315,066,831
Mexico City Mexico 4.75 4.17 5.86 -6.37 9.9 231,341,714 203,118,024 285,475,674
Muscat Oman 4.74 4.16 5.85 -6.38 1.4 8,050,013 7,067,912 9,933,716
Busan South Korea 4.15 3.64 5.12 -6.97 0.7 121,734,951 106,883,287 150,220,930
Monterrey Mexico 4.07 3.57 5.02 -7.05 8.5 231,341,714 203,118,024 285,475,674
Beijing China 3.41 2.99 4.21 -7.71 2.5 558,914,493 490,726,924 689,700,484
Algiers Algeria 3.26 2.86 4.02 -7.86 4.4 35,511,029 31,178,684 43,820,610
Ho Chi Minh City Vietnam 3.14 2.76 3.87 -7.98 3.8 122,880,970 107,889,492 151,635,117
New Delhi India 3.13 2.75 3.86 -7.99 3.4 610,366,111 535,901,445 753,191,781
Rabat Morocco 3.10 2.72 3.83 -8.02 2.6 26,847,353 23,571,976 33,129,634
Kolkata India 2.82 2.48 3.48 -8.3 6.2 610,366,111 535,901,445 753,191,781
Mumbai India 2.80 2.46 3.46 -8.32 5.3 610,366,111 535,901,445 753,191,781
Cairo Egypt 2.80 2.46 3.46 -8.32 10.6 146,360,286 128,504,331 180,608,593
Shanghai China 2.35 2.06 2.90 -8.77 1.4 558,914,493 490,726,924 689,700,484
Colombo Sri Lanka 2.03 1.78 2.51 -9.09 7.6 21,045,660 18,478,090 25,970,345
Jakarta Indonesia 1.78 1.56 2.20 -9.34 1.4 122,870,471 107,880,273 151,622,161
Show more
Methodology

The study began by determining a final list of 100 global cities, focusing on capitals, business districts and financial centres. The average cost of dry cleaning a suit in each of these locations was then calculated, in addition to how many hours an individual earning minimum wage must work to afford the service. To establish the overall contribution that dry cleaning adds to the global economy, the total annual capital spent on the service was then calculated for each country.

Sources

Cost of Dry Cleaning a Suit:

To calculate the cost of dry cleaning a suit, prices were sourced from on average 10 stores in each city. Final prices are an average between the cost of a 2-piece and a 3-piece suit where applicable, in addition to the average between dry cleaning a suit as a package and as separate jacket and trousers. Additionally, prices are averaged between the costs for men and women. Source: Google, Baidu. Google Finance was used as a currency converter when sourcing dry cleaning prices. All prices were converted to USD before being converted to EUR, with the exchange rate fixed at 19/03/2018 23:58:00.

% Deviation from Average Worldwide:

This figure calculates the average of all dry cleaning costs in the index, and then shows the amount by which each city’s prices are above or below the average cost, expressed as a percentage. A positive percentage indicates that a city is more expensive than the average, while a negative percentage indicates that a city is less expensive than the average.

Hours at Minimum Wage to Afford Dry Cleaning:

  • Hours to work at minimum wage to afford the dry cleaning of one suit (H) = Cost of dry cleaning a suit (C) / Minimum wage per hour (Wh), so H = C/Wh.
  • Minimum wage per hour (Wh) was calculated based on how many hours an employee must work in a month (Hm) and the minimum wage (Mw), therefore Wh = Mw/ Hm. Hm is calculated based on hours an employee must work in a week (Hw). Therefore, Hm = Hw*52/12 (based on 52 weeks and 12 months in a year).
  • Majority of minimum wage data sourced at country level, but in some cases (USA, India, China, and Russia), the data was collected at a state/province level. Note: not all countries included in the index have an official minimum wage, so the average salary in the cleaning service sector was utilised instead. Source: Eurostat, Statista, local government data, national statistic department, labour unions.

Total Nationwide Annual Spend on Dry Cleaning:

  • Total expenditure on dry cleaning suits (M) = Total number of Bank/Insurance workers + Lawyers + Teaching staff + Government workers, in thousands (N) x 1000 x Price of dry cleaning a suit (P) x Frequency that people using dry cleaning services per year (F). In millions, this equation would be equal to M = NxPxFx1.000/1.000.000 =NxPxF/1.000.
  • For the purpose of the study, the number of professionals in typically suit-wearing industries was used. This includes number of bank/insurance workers, lawyers, government workers, and teaching staff. Source: United Nations, ILO, Eurostat, local statistical departments. Please get in contact if you wish to see a breakdown of the sources for this factor.
  • Based on online surveys, for the purpose of this factor in the study, we assume that an individual dry cleans one suit 6 times per year.
  • Population country and city, source: United Nations, Eurostat and CIA Factbook. When data was from available from these sources, local census data or national statistics were substituted.

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