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Cities with the Best Work-Life Balance 2021

By comparing data on work intensity, institutional support, legislation, and livability, study reveals a ranking of cities based on their success in promoting work-life balance to their citizens before, during and beyond the pandemic.

See Rankings

At Kisi, we’ve made it our goal to work smarter rather than harder. Like many others, we’ve seen first-hand the value of maintaining a healthy balance between our work and life commitments, which lead us to release our first global study on the topic in 2019. In the time since, the pandemic has underscored this delicate balance more than ever before. Working from home, taking next-to-no vacations, and being separated from loved ones has brought a collective realisation that we don’t spend enough time with our families, that time off is vital to productivity, and that where we live really matters for our well-being.

For this year’s iteration we aimed to find a way to incorporate these shifts in our cultural expectations of work-life balance, alongside the key elements from the previous study such as work intensity and city livability which remain fundamental. The resulting index shows how cities rank against their pre-pandemic statuses, painting a picture of how Covid-19 has changed and continues to affect people’s work-life balance in major cities around the world. This study is not designed to be a city livability index, nor is it intended to highlight the best cities to work in; instead, it is an indicator of a city's ability to provide a healthy work-life balance for its residents, while providing opportunities to relieve work-related stress during and outside times of crisis.

To begin the study, we selected a list of in-demand metropolises worldwide with sufficient, reliable, and relevant datasets. We then finalised a shortlist of fifty cities to include those known for attracting professionals and families for their work opportunities and diverse lifestyle offerings, as well as those which frequently top livability indexes.

As perhaps the most significant change in our work lives, we first examined how adaptable a location was for remote working, calculating the percentage of jobs that were classified as ‘teleworkable’ in each city. Next, we assessed the overall work intensity of a city based on a series of factors related to overworking, holiday allowance, and parental leave. We paid special attention to unemployment figures, as they have soared in many locations due to the economic fallout from the pandemic, as well as to the percentage of people who have had to undertake multiple jobs in order to get by as a result.

Next, we looked into the amount of Covid-related economic support residents received over the last year in each location, as this had an immense impact on their livelihoods. To determine the extent to which residents receive equal treatment, we also evaluated their access to state-funded health and welfare programs, as well as institutional support for gender equality and social inclusivity of minorities and the LGBT+ community. We then appraised each city’s livability by examining its affordability as well as its citizens’ overall happiness, safety, and access to wellness and leisure venues — allowing us to assess whether its residents can enjoy their environment after office hours under normal circumstances.

Finally, we looked into the effect of the pandemic on a city’s work-life balance in several key areas - case numbers, the severity of lockdown measures, and economic losses - which were combined to determine an overall ‘Covid Impact’ score.

The result is an index of 18 factors analysing the work-life balance of 50 cities worldwide, recognizing those who encourage a healthy balance both directly and indirectly through policies and urban infrastructure, while also bringing attention to those who have been adversely affected by the pandemic.

Top Cities in the Ranking for Work-Life Balance

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1: Helsinki

Finland

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2: Oslo

Norway

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3: Zurich

Switzerland

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4: Stockholm

Sweden

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5: Copenhagen

Denmark

Top Overworked Cities in the Ranking

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1: Hong Kong

Hong Kong

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2: Singapore

Singapore

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3: Bangkok

Thailand

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4: Buenos Aires

Argentina

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5: Seoul

South Korea

2021 Work–Life Balance Index

The tables below display the cities around the world with the best work-life balance in order from highest to lowest. Each individual column is filterable, and the full methodology explaining how each factor was evaluated is at the bottom of the page.

  • Factors
    • Work Intensity
      • Remote Jobs
      • Overworked Population
      • Minimum Vacations Offered (Days)
      • Vacations Taken (Days)
      • Unemployment
      • Multiple Jobholders
      • Paid Parental Leave (Days)
    • Society and Institutions
      • Covid support
      • Healthcare
      • Access to Mental Healthcare
      • Inclusivity & Tolerance
    • City Livability
      • Affordability
      • Happiness, Culture & Leisure
      • City Safety
      • Outdoor Spaces
      • Air Quality
      • Wellness and Fitness
    • Covid-19
      • Covid Impact
    Work Intensity
    Society and Institutions
    City Livability
    Covid-19
    2021
    2019
    City
    Country
    TOTAL SCORE
    01
    01
    Helsinki
    Finland
    38.9%
    13.1%
    25
    30
    79.4
    6.3%
    1,190
    96
    85.3
    94.1
    90.1
    84.3
    100
    97.3
    90.9
    90.6
    97.7
    99
    100
    02
    03
    Oslo
    Norway
    41.7%
    10.6%
    25
    25
    87.2
    8.9%
    707
    96.6
    100
    100
    88.4
    74.1
    92.4
    90.7
    100
    89.9
    83.2
    98.9
    98.6
    03
    07
    Zurich
    Switzerland
    44.9%
    13.0%
    20
    25
    88.3
    7.7%
    98
    85.1
    93.4
    95.4
    84.2
    50
    94.3
    88.7
    89.9
    86.6
    93.4
    93.9
    91.5
    04
    05
    Stockholm
    Sweden
    44.2%
    12.5%
    25
    25
    75.2
    8.6%
    490
    91.7
    94.2
    93.5
    98.3
    82.2
    90.5
    87.8
    92.1
    80.1
    91.5
    92.1
    91.4
    05
    -
    Copenhagen
    Denmark
    41.4%
    10.3%
    25
    28
    81.4
    7.3%
    364
    100
    84.8
    96.6
    93.9
    77.9
    94
    93.4
    60.2
    81.3
    89.1
    95.3
    90.4
    06
    11
    Ottawa
    Canada
    38.2%
    15.1%
    10
    15
    80.9
    5.6%
    364
    94
    97
    94.9
    89.3
    85.9
    88.1
    76.7
    99.6
    100
    90.3
    91.2
    89.1
    07
    02
    Munich
    Germany
    36.7%
    11.7%
    20
    30
    95.3
    5.3%
    467
    88.3
    88.5
    98.1
    95.5
    83
    86.9
    84.6
    79
    85.5
    72.8
    88.4
    89.1
    08
    10
    Vancouver
    Canada
    38.2%
    14.8%
    10
    15
    78.6
    6.7%
    364
    94
    97.1
    94.9
    90.1
    83.9
    87.6
    81.8
    85
    97
    86.2
    91.5
    87.8
    09
    -
    Amsterdam
    Netherlands
    41.5%
    8.3%
    20
    24
    91.9
    8.2%
    115
    83.5
    93.9
    96
    94.2
    81.2
    92.1
    84.3
    60.8
    79.5
    91.1
    87.3
    87.6
    10
    15
    Sydney
    Australia
    37.9%
    13.9%
    20
    14
    80.5
    6.4%
    140
    81.1
    96.5
    95.9
    91.3
    75.3
    87.3
    78.4
    94.4
    96.7
    84.5
    95.9
    86.8
    11
    04
    Hamburg
    Germany
    36.7%
    11.4%
    20
    30
    83.5
    5.3%
    467
    88.3
    87.9
    98.1
    96.3
    86.6
    84.4
    75.2
    82.4
    85.5
    77
    89
    85.7
    12
    -
    Vienna
    Austria
    36.7%
    13.0%
    25
    25
    72.5
    4.3%
    481
    91.6
    83.6
    93.6
    95.8
    88.5
    83
    85.6
    81.8
    82
    89.4
    87.9
    85.6
    13
    -
    Calgary
    Canada
    38.2%
    15.5%
    10
    15
    65.6
    6.2%
    364
    94
    97.2
    94.9
    90.1
    85.3
    87.3
    76.5
    73.6
    100
    89.7
    92.2
    85.2
    14
    13
    Toronto
    Canada
    38.2%
    15.1%
    10
    15
    65.3
    5.6%
    364
    94
    97.1
    94.9
    90.3
    84
    92.5
    80.8
    87.5
    92.3
    85.9
    90.9
    84.9
    15
    18
    Melbourne
    Australia
    37.9%
    13.9%
    20
    14
    78.2
    6.4%
    140
    81.1
    95.9
    96
    91.3
    80.2
    96.7
    74.6
    90.4
    92.9
    85.1
    95.5
    84.9
    16
    -
    Auckland
    New Zealand
    35.5%
    14.9%
    20
    15
    86.4
    7.3%
    126
    86.7
    90.1
    94
    93.5
    80.6
    87.3
    81
    76.4
    93.2
    72.6
    98.7
    84.4
    17
    06
    Berlin
    Germany
    36.7%
    11.7%
    20
    30
    77.9
    5.3%
    467
    88.3
    86.8
    98.1
    100
    88.2
    91.1
    75.3
    82.7
    82.2
    75.3
    88.3
    84
    18
    32
    Singapore
    Singapore
    51.3%
    25.1%
    7
    14
    94.2
    2.7%
    119
    94
    92.8
    94.6
    65.7
    79.4
    75.1
    100
    93.3
    71.8
    79.6
    95.3
    83.8
    19
    -
    Dublin
    Ireland
    38.7%
    13.6%
    20
    21
    81.4
    2.7%
    182
    76.7
    76.1
    94.5
    91.7
    81.2
    89.3
    82.7
    62.3
    91.5
    88.5
    95.2
    83.4
    20
    12
    London
    UK
    43.5%
    15.4%
    28
    25
    81.3
    3.5%
    287
    73.6
    92.6
    97.7
    98.7
    77.8
    86
    73.2
    71.1
    84.9
    78.4
    76.3
    81.4
    21
    -
    Brussels
    Belgium
    42.3%
    7.0%
    20
    24
    74.1
    3.8%
    361
    94.2
    89.5
    92
    91.1
    86.5
    76.2
    75.9
    71.5
    78.9
    81.4
    79.8
    80.6
    22
    -
    Madrid
    Spain
    31.7%
    13.2%
    22
    30
    53.5
    2.3%
    127
    66.6
    91.8
    93.2
    98.2
    90.6
    78.5
    79
    74.6
    82.6
    90.2
    83.2
    80.1
    23
    39
    Tokyo
    Japan
    35.6%
    18.3%
    10
    10
    95.3
    3.6%
    770
    79.3
    95
    88.9
    61.3
    82.9
    68.2
    86.4
    65.5
    77.8
    80
    100
    79.3
    24
    -
    Salt Lake City
    USA
    43.3%
    14.2%
    10
    9.9
    95
    6.5%
    0
    66.6
    83.9
    80.6
    86.8
    87
    84.2
    76
    79.3
    99.7
    84.7
    96
    79.2
    25
    08
    Barcelona
    Spain
    31.7%
    13.0%
    22
    30
    53.6
    2.3%
    127
    66.6
    91.5
    93.2
    98
    90.8
    75.2
    75.9
    75.7
    82.2
    87.7
    82.6
    78.7
    26
    19
    Portland
    USA
    38.5%
    13.7%
    10
    10.1
    82.9
    5.4%
    0
    75.6
    84.8
    86.2
    90.9
    84
    84.6
    76.1
    94.9
    98.8
    71.7
    94.9
    77.2
    27
    09
    Paris
    France
    37.7%
    14.7%
    25
    30
    73.4
    5.3%
    490
    89.5
    84.5
    96
    89.5
    80.7
    81.8
    69
    67.6
    72.9
    87.2
    80.7
    77
    28
    25
    Denver
    USA
    42.6%
    12.3%
    10
    10.2
    79.7
    5.6%
    0
    74.1
    83.9
    84.1
    90.3
    83.9
    84.5
    75.5
    76.4
    96.2
    76.6
    92.7
    75.3
    29
    23
    Seattle
    USA
    42.3%
    13.6%
    10
    10.4
    82.1
    5.4%
    0
    75.2
    85
    87.6
    89.6
    75.2
    85.6
    66
    93.3
    96.5
    73.1
    94.6
    74.6
    30
    -
    Seoul
    South Korea
    37.5%
    19.5%
    15
    14
    88.2
    1.7%
    93
    72
    93.2
    86.2
    65.5
    86.2
    61.3
    84.5
    80.1
    50.8
    79.8
    96
    72.9
    31
    22
    Boston
    USA
    44.4%
    12.7%
    10
    10.7
    79.7
    5.5%
    0
    68.6
    85.4
    93.6
    89.8
    77.3
    86.9
    70.7
    97.1
    93.4
    66
    83.8
    72.7
    32
    14
    Budapest
    Hungary
    30.9%
    13.7%
    20
    24.1
    94.1
    1.4%
    1,127
    71.1
    57.6
    72.1
    65.7
    97.4
    56
    79.3
    77.5
    77.5
    74.2
    76.8
    72.1
    33
    -
    Washington
    USA
    49.8%
    13.6%
    10
    9.4
    80.9
    5.5%
    40
    71.4
    83
    93
    89.1
    76.8
    90.1
    62.9
    98.1
    93.9
    73
    90.4
    71.9
    34
    17
    San Diego
    USA
    39.5%
    13.0%
    10
    9.7
    76.2
    4.0%
    117
    83.8
    84.1
    91.8
    88.8
    80.6
    85.8
    76.1
    77.7
    87
    70.1
    89.2
    71.7
    35
    31
    Philadelphia
    USA
    40.1%
    12.8%
    10
    10.7
    79.7
    6.0%
    0
    73.3
    83.6
    87.5
    84.2
    85.7
    82
    73.8
    93.6
    87
    61.3
    90
    71.5
    36
    37
    Atlanta
    USA
    40.4%
    13.4%
    10
    10.2
    84
    3.8%
    0
    66.9
    82.5
    91.2
    84.3
    83.1
    84.4
    75
    90.9
    87
    55.3
    91.6
    71
    37
    20
    San Francisco
    USA
    44.8%
    13.2%
    10
    9.7
    80.5
    4.0%
    117
    83.8
    83.1
    91.7
    93.2
    64.1
    88.2
    63.7
    83.9
    93.9
    70.6
    87.1
    70.4
    38
    21
    New York
    USA
    42.0%
    12.4%
    10
    11.4
    73.5
    4.1%
    180
    82.7
    82.3
    92.9
    90
    70.1
    87.3
    66.2
    88.2
    90.7
    60
    75.8
    67.9
    39
    16
    Milan
    Italy
    35.0%
    13.7%
    20
    21
    85.2
    1.4%
    337
    76.7
    88.5
    92
    74.3
    85.4
    70.2
    75.3
    70.9
    64.5
    75.8
    50
    67.3
    40
    33
    Miami
    USA
    36.7%
    13.5%
    10
    9.2
    84
    3.5%
    0
    66.5
    82.3
    87.6
    86.2
    84.9
    86.9
    63.7
    66.4
    97.1
    58.1
    90.9
    67.2
    41
    -
    New Orleans
    USA
    41.6%
    15.1%
    10
    10.4
    55.1
    4.4%
    0
    71.6
    81.1
    85.6
    85.1
    86.9
    79
    74
    85.1
    99.5
    50
    85
    66.7
    42
    34
    Cleveland
    USA
    31.3%
    13.1%
    10
    10.9
    84
    6.5%
    0
    72.4
    85.9
    86.9
    83
    89.4
    76
    59.1
    79.7
    86.6
    56.7
    91.8
    66.5
    43
    29
    Chicago
    USA
    39.2%
    12.6%
    10
    10.7
    72.3
    5.2%
    0
    72.7
    83
    90.5
    88.4
    81.8
    87
    58.8
    83.4
    84.7
    62.8
    88.8
    65.4
    44
    36
    Houston
    USA
    37.3%
    16.6%
    10
    9.9
    76.6
    3.7%
    0
    68.1
    83.6
    86.3
    81.7
    87.5
    84.1
    60.1
    79.9
    89.4
    55.2
    92.1
    65.3
    45
    35
    Hong Kong
    Hong Kong
    39.5%
    29.9%
    7
    14
    82.5
    3.7%
    70
    70.1
    63.7
    90.5
    68.6
    82
    51.9
    81.6
    80.9
    50
    100
    91.8
    62
    46
    26
    Los Angeles
    USA
    39.1%
    13.5%
    10
    7
    63.7
    4.0%
    117
    83.8
    82.3
    91.7
    90.7
    80.2
    87.3
    66.4
    67.5
    81.8
    69.9
    88.7
    54.2
    47
    -
    Sao Paulo
    Brazil
    25.7%
    10.8%
    10
    30
    50
    4.7%
    181
    50
    54.7
    86.7
    73.8
    100
    73.4
    50
    60.7
    70.5
    66.6
    81.5
    52
    48
    38
    Buenos Aires
    Argentina
    28.0%
    19.9%
    14
    15
    57.8
    10.0%
    92
    59.4
    52.5
    88.7
    79.4
    99.5
    74.2
    62.6
    67.7
    79.8
    71.7
    64.2
    51.6
    49
    -
    Bangkok
    Thailand
    16.8%
    20.2%
    6
    10
    100
    3.7%
    90
    73.9
    63.6
    76.7
    70.6
    95.7
    69.2
    56.2
    50
    52.1
    93.2
    95.1
    51.3
    50
    40
    Kuala Lumpur
    Malaysia
    30.1%
    16.0%
    8
    12
    88.3
    1.1%
    98
    66.6
    50
    50
    50
    98.2
    50
    67.1
    73.4
    57.1
    74.6
    92.9
    50

    Methodology

    The Cities with the Best Work-Life Balance 2021 ranking reveals the cities with the best social, cultural and structural systems in place in order to provide their residents with the most well-rounded work-life balance, in terms not only of work intensity, but also livability, well-being and rights. As the third yearly iteration since 2019, and the second since the pandemic began, the study also takes into account how the shift to remote-working and the impact of Covid-19 has changed and continues to affect work-life balance in major cities around the world.

    After reviewing hundreds of global metropolises, a shortlist of 50 of in-demand cities with sufficient, reliable, and relevant datasets were selected. This included cities known for attracting professionals and families for their work opportunities and diverse lifestyle offerings. As the third iteration of a continuous study, this index includes 10 more cities than in 2019.

    This index is not designed to be a city livability index, nor is it intended to highlight the best cities to work in. Instead, it is designed to be a guideline which supports the fulfillment of residents’ lives by improving the aspects of life which help relieve work-related stress and intensity.

    Factors and Scoring

    The study was divided into three categories - Work Intensity, Society and Institutions, City Livability - comprising the following 18 factors which contribute to work-life balance during and beyond the pandemic:

    • Work Intensity: Remote Jobs (%), Overworked Population (%), Minimum Vacations Offered (Days), Vacations Taken (Days), Unemployment (Score), Multiple Jobholders (%), Paid Parental Leave (Days)

    • Society and Institutions: Covid Support (Score), Healthcare (Score), Access to Mental Healthcare (Score), Inclusivity & Tolerance (Score).

    • City Livability: Affordability (Score), Happiness, Culture & Leisure (Score), City Safety (Score), Outdoor Spaces (Score), Air Quality (Score), Wellness and Fitness (Score), Covid Impact (Score)

    Each factor consists of one or more indicators which were scored and averaged. The equation for scoring is as follows:

    z-Score =

    x - mean(X)

    Standard deviation(X)

    in short

    x - μ

    σ



    For columns where a low value is better, the score is inverted such that a high score is always better:

    z-Score inverted = -1 *

    x - mean(X)

    Standard deviation(X)

    in short -1 *

    x - μ

    σ



    Data is normalized to a [50-100] scale, with 100 being the best score. Therefore, the higher the score, the better the city ranks for that factor in comparison to the other cities in the index. The formula used is min-max normalisation:

    score = (100 - 50) *

    x - min(X)

    max(X) - min(X)

    + 50



    The final score was determined by calculating the sum of the weighted average score of all of the indicators. Below you can find a detailed description of each factor within the study, and the source used.

    Work Intensity

    Remote Jobs (%)

    The quantity of jobs that are workable from home as a percentage of all jobs. Locations with a greater share of teleworkable jobs (aka remote working) may provide residents with the ability to comply with strict social distancing requirements while also maintaining regular employment and income.

    Data taken from the "How many jobs can be done at home?" (Dingle & Neiman, 2020) white paper, which uses job classification survey data to identify jobs that can be performed from home, and applies this to job statistics to estimate the share of all jobs these jobs account for. US cities employ data at a Metropolitan Statistical Area level, while country-level data was taken for all other cities. Where data was unavailable, values for some countries were modeled using GDP per capita and percentage of population with university degrees.

    Sources: Dingel, J. I., & Neiman, B. (2020). How many jobs can be done at home?. Journal of Public Economics, 189, 104235; World Bank – GDP per capita, PPP (current international $), latest data; World Bank – Percentage of population age 15+ with tertiary schooling. Completed Tertiary, latest data.

    Overworked Population (%)

    The percentage of full-time employees working more than 48 hours per working week. The International Labour Organisation (ILO) recommends a workweek of 40-hours and considers weekly work of over 48 hours "excessive".¹ The “Overworked Population” is considered to be the percentage of full-time employees working more than 48 hours per working week.

    For cities in the United States and in the European Union, average number of hours of work was incorporated into the country-level data to approximate percentages on a city-level. For all other cities, country-level data was used to evaluate the average working hours per week.

    Sources: ILO-STATISTICS – Labour force survey, latest available data; US Bureau of Labor Statistics – Current Employment Statistics survey (State & Metro Area), 2019; EUROSTAT - Average number of usual weekly hours of work in main job by sex, age and NUTS 2 regions.

    Minimum Vacations Offered (Days)

    The minimum number of compensated vacation days an employee is legally entitled to after at least one year of service. Data was taken at a national level for a full-time, five-day workweek (excluding public holidays). In the US, under the Fair Labor Standards Act, no such federal or state-level regulations exist that require employers to pay employees for time not worked, including holidays.² Despite this, time off agreements are often negotiated between employer and employee. Data for US cities is based on the average number of reported paid holiday days for a private industry employee after their first year of service (10 days per annum).³

    Sources: International Labour Organisation; European Commission – EURES Living and Working Conditions; Thomson Reuters – Practical Law database; Various national labour departments.

    Vacations Taken (Days)

    The average number of used paid vacation days offered to full-time employees in a single year. City-level data was used where available. For US cities, data was calculated by subtracting the unused vacation days from the average number of days offered. The percentage of unused vacation days in the US was sourced at a state-level. For non-US cities, country-level data was taken on the number of vacation days used.

    Sources: US Travel Association – State-by-State Time Off, 2019; Expedia – Vacation Deprivation study, 2018/17; UBS – Prices and Earnings study, 2018.

    Unemployment (Score)

    The unemployment rate for the metropolitan area or region in the first quarter of 2021. This factor is expressed as a score where the higher the score, the lower the unemployment rate. For cities that have not published their unemployment rate, the rate was estimated using the quarter-to-quarter trend of the country. In rare instances, national figures were used. Unemployed persons are considered those of the labour force who are jobless, looking for a job, and available for work.

    Sources: Official statistical websites of each metropolitan area/region/country.

    Multiple Jobholders (%)

    The percentage of employed people holding more than one job at any one time. The holding of more than one job at a time can be a sign of engaging in precarious work, and is often used as a financial coping tactic for those in economically vulnerable positions, including minorities. Multiple job-holding can also expose workers to longer hours, lower wages, and compromise their labour protections. The ILO has voiced concern about incidence of multiple job-holding, describing it as a possible “sign of persons engaged in irregular low-productive work, with an overlap to working poverty and an inability to earn sufficient income on the main job alone.”⁴

    Unfortunately, detailed geographical data on the number of multiple-jobholders is underreported and not regularly published. However the latest available data where possible was compiled from official statistics and independent research. All US and Canadian data is at a state and province level, respectively, while other cities use national data. Values for Hong Kong and Bangkok are modelled estimates using national figures for the percentage of part-time workers as a proportion of the workforce.

    Sources: Eurostat – Job-holder survey, 2018; Bureau of Labor Statistics – Multiple-jobholding rates, 2015; Statistics Canada – Multiple jobholders, 2019; Singapore Ministry of Manpower – Labour Force report, 2018; Australian Department of Social Services – HILDA survey, 2019; Stats NZ – Household labour force survey, 2019; Statistics Korea – Economically Active Population survey, 2019; The World Bank – Malaysia Economic Monitor, 2019; Japan Labor Issues Journal – Atsushi Kawakami: “Who Holds Multiple Jobs?...”, 2019; Latin American Perspectives – “Precarious Work in Argentina 2003-2017”, 2020; United Nations – Economic and Social Council Brazil report, 2001.

    Paid Parental Leave (Days)

    The number of paid family leave days from work afforded to employees by law. The sum comprises the legislated number of days for paid maternal, paternal and parental leave, and reflects the number of days compensated, regardless of benefits provided or level of compensation. At the federal level, the US does not mandate paid leave for parents, but some states have recently passed relevant legislation (these include the states of California, New York, Hawaii, and the District of Columbia). National data is used, except for US cities, which use state-level data.

    Sources: OECD – Employment statistics database, latest available; data, ILO – Maternity and paternity at work study, 2014; Thomson Reuters – Practical Law, 2020; Official local government websites.

    Society and Institutions

    Covid Support (Score)

    The degree of income support provided by governments to workers affected by the economic effects of Covid-19. This factor is expressed as a score where the higher the score, the greater the support. It takes into account government programmes to replace income lost due to Covid, length of unemployment benefits, consumer confidence, household spending and general wage levels; as well as overall spending by governments to dampen the impact of Covid on the economy. In addition, the level of covid cases and deaths was taken into account.

    Sources: Thomas Hale, Noam Angrist, Rafael Goldszmidt , Beatriz Kira , Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, and Helen Tatlow. (2021). “A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).” Nature Human Behaviour https://doi.org/10.1038/s41562-021-01079-8; IMF - Fiscal Monitor Database of Country Fiscal Measures in Response to the COVID-19 Pandemic (April, 2021): Above the line spendings Additional - spending or foregone revenues - Non-health sector (as % of gdp); OECD - Household Dashboard: Real Household Final Consumption expenditure per capita, Consumer Confidence; year to year comparison (Q2 2020/Q2 2019 and Q3 2020/Q3 2019); OECD - Benefits and wages database, SSA Country profiles, local authorities: Length of unemployment benefits for a 30 year old single; no children; work prior to unemployment; five years of contribution; average salary, full-time; contract terminated because of shortage of work; ILO - Mean nominal monthly earnings; Worldometers - Total Cases per million, total deaths per million.

    Healthcare (Score)

    The measure of a city’s healthcare system based on access, quality and satisfaction. Country-level data was obtained from the Universal Health Coverage (UHC) index for access and quality indicators, while US cities also incorporate state-level data from the Health Access and Quality (HAQ) study. Additional data was taken from healthcare access indexes developed by the World Health Organisation and the European Commission. Satisfaction survey results were taken at a city level.

    Sources: Lozano, R., Fullman, N., Mumford, J. E., Knight, M., Barthelemy, C. M., Abbafati, C., ... & Cárdenas, R. (2020). Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1250-1284; Fullman, Nancy, et al. "Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016." The Lancet 391.10136 (2018): 2236-2271; European Commission — DRMKC - INFORM Risk Index (‘Access to health care’ indicator); 2021., WHO - World Health Data Platform, Universal Health Coverage Index; latest data; Numbeo – Healthcare Index; data as of April 2021.

    Access to Mental Healthcare (Score)

    The accessibility and effectiveness of governments in implementing mental health policies aimed to care for individuals with mental health illnesses. This factor uses national data on governance, access to treatment, and the environment necessary for treatment. This factor also incorporates suicide rates and city-level survey data on healthcare quality.

    Sources: EIU/Jannsen – Asia- Pacific Mental Health Integration Index, 2016; EIU/Jannsen – Europe Mental Health Integration Index, 2014; Institute for Health and Metrics Evaluation – Health Access and Quality Index, 2016; Numbeo – Healthcare Index, 2020; Local statistics departments.

    Inclusivity & Tolerance (Score)

    The degree to which a city supports gender and LGBT+ equality, inclusivity and tolerance through legislation and opportunity. The score combines the following ‘Gender Equality’ (degree of gender parity), as well as the ‘LGBT+’ (inclusiveness and tolerance) factors:

    Gender Equality:

    Gender equality scores were developed using data on the level of difference in economic opportunity and participation, educational attainment, health, and political empowerment between men and women. City-level data was used for US cities, with country-level data used for non-US cities.

    Sources: Economist – Glass Ceiling Index, 2020; World Economic Forum – Gender Gap Index, 2020; Council on Foreign Relations – Women's Workplace Equality Index, 2020; OECD – Social Institutions & Gender Index, 2019.

    LGBT+

    For LGBT+ scores, the comprehensiveness of equality and protection (an emphasis on work rights) legislation, health access, as well as political representation for the LGBT+ community were examined. The percentage of the population that identifies as LGBT+ was also included, as environments in which a higher number of citizens feel comfortable openly identifying as a minority is also a potential indicator of a tolerant and supportive community.

    Sources: SPARTACUS – Gay Travel Index, 2020; Gallup – Daily Tracking polls, 2015/2017; Out Leadership – State LGBT+ Business Climate Index, 2019; Local statistics departments, latest available data.

    City Livability

    Affordability (Score)

    Monthly living costs as a proportion of the average household income, after tax. A basket of estimated monthly costs includes: rent, basic utilities costs, groceries, internet connection, leisure activities, clothes, and eating out. A higher score indicates a higher level of remaining monthly income (if any) after accounting for these deductions.

    Sources: OECD – Employment Database, 2018; Numbeo – Cost of Living Index, data as of April, 2021.

    Happiness, Culture & Leisure (Score)

    The degree to which residents are able to enjoy their environment after office hours, measured through the average perceived level of happiness as well as the accessibility and variety of a city’s cultural and lifestyle offerings. The score combines both of the following ‘Happiness’ and ‘Culture & Leisure’ factors.

    Happiness

    Score includes the average perceived level of happiness at a city level. In the rare absence of city-level data, national data was used. The score is calculated from survey responses evaluating the perceived happiness with one’s own life, as well as the degree of positive and negative effects a respondent experiences.

    Sources: Sustainable Development Solutions Network – World Happiness Report, 2020; Walethub – Happiest Cities, 2019.

    Culture & Leisure

    The vibrancy and variety of cultural and lifestyle offerings in a city. The score combines cultural city rankings, the number of persons employed in the cultural and creative industries, and the amount of leisure facilities and activities available, such as the number of sports stadiums, restaurants, parks, shops, entertainment and nightlife venues per capita. Cities with an exceptional number of activities received supplementary points.

    Note: all data collected for this factor reflects pre-pandemic conditions where cultural and lifestyle offerings were available without restriction. This was designed to measure the vibrancy of a city's offerings under normal circumstances, with the hope that the existing cultural framework of a location will allow it to return to a similar level in the future.

    Source: US Bureau of Economic Analysis – State Arts and Cultural Production Employment, 2016; European Commission – Cultural and Creative Cities Monitor, 2019; Mori Foundation – Global Power City Index, 2018; TimeOut – ‘48 best cities in the world in 2019’; Wallethub – Funnest Cities in the US rankings, 2019; OSM Overpass Turbo API – Searches included: bars; clubs; pubs; restaurants; cafes; galleries; museums; and cinemas, latest data; TripAdvisor – Searches included: Nightlife, Museums, Concerts & Shows, Outdoor Activities, Nature & Parks, latest data; World Stadiums – Database, latest data.

    City Safety (Score)

    The degree of safety provided by a city in more than a dozen key areas, including environmental, social and infrastructural security. Indicators include statistics on injuries and fatalities, damage caused at an economic level, public opinion data, and data on the vulnerability of a location to particular hazards.

    Sources: Germanwatch – Global Climate Risk Index, 2021/2020; United Nations Office on Drugs and Crime – database; Economist Intelligence Unit – Safe Cities 2019; European Commission/Disaster Risk Management Knowledge Centre – INFORM RISK report 2021; Igarape Institute – Fragile Cities index, 2017; Numbeo - Crime Index; Vision of Humanity – Global Peace Index, 2020; World Health Organisation - Global Health Observatory database, latest available data.

    Green Spaces and Weather (Score)

    The prevalence and accessibility of a city’s urban green infrastructure as a score, including its proximity to residents and the percentage of land allocated to green space. Data on weather and daylight conditions that could affect the use of public outdoor spaces was also incorporated. This includes average temperatures, the annual number of rainy days, annual sunshine hours, and cloudlessness.

    Significant weighting is placed on the green spaces indicator, as the existence of favourable weather alone is not a condition for a good score in this section. Data is collected at a city level.

    Sources: United States Forest Service – iTree survey tool; The Trust for Public Land – ParkScore index, 2020; OECD – Green area survey, 2018; European Environmental Agency – Urban green infrastructure database, 2017; Weather Spark – Weather analysis data, 2020.

    Air Quality (Score)

    Annual median particulate matter (PM2.5/PM10) pollution for the year 2019, represented as a score. Data from pre-pandemic conditions was used in order to assess a city’s air quality under normal circumstances, with a view that pollution levels may return to similar levels in the future should measures not be taken to reduce them.

    Daily average data was taken across all days of a single year, with the median pollution level representing the overall score. Data was taken at a city level.

    Sources: AQICN – Air Quality Index historical database, 2019; World Health Organisation – Global Ambient Air Quality Database, 2018.

    Wellness and Fitness (Score)

    The general state of a community’s physical fitness and health as represented by a population’s average life expectancy, as well as levels of inactivity, obesity, and the number of fitness studios and gyms per capita. National data was used for life expectancy at birth, while US cities use city-level data. Adult obesity rates and the prevalence of physical inactivity were taken at a national level, with US cities using state level data. Data on the number of gyms per capita is taken at a city level.

    Sources: World Health Organisation – Global Health Observatory data repository, latest data; US Center for Disease Control and Prevention – Adult Physical Inactivity Prevalence, 2020; Opportunity Insights – US life expectancy data, 2016; The State of Childhood Obesity – Adult Obesity Rates, 2019; OSM Overpass Turbo API – Searches included: ‘leisure=fitness_centre’ and ‘leisure=sports_centre’.

    COVID Impact (Score)

    The degree of social and economic impact on account of a location’s Covid-19-related response. This factor is expressed as a score where the higher the score, the lower the impact. Three dimensions of the impact were taken into account: public health, economic and social. The impact on public health is quantified through cases and deaths relative to population; the impact on economy through year-on-year GDP growth in 2020 and 2021; and the impact on society through the severity of limiting measures put in place to contain the pandemic, as well as changes in mobility patterns as a measure of the effect of these restrictions.

    Sources: Oxford COVID-19 Government Response Tracker, International Monetary Fund; Apple – Covid-19 Mobility Trends Reports.

    About Kisi: Founded in 2012 in Brooklyn, Kisi is a cloud-based access control system providing mobile-first security solutions. Businesses of every size and industry use Kisi to secure their spaces, streamline operations and build a vibrant office culture.

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