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Cities With the Best Work-Life Balance 2020

By comparing data on work intensity, legislation, and livability, study reveals a ranking of cities based on their success in promoting work-life balance to their citizens, and how they have been impacted by the COVID-19 pandemic.

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As an innovative company in the age-old security industry, we at Kisi know first-hand how much of a difference it makes to work smarter rather than harder. Like everyone this year, we’ve had to adjust to remote working, while also trying to maintain a healthy balance between our work and life commitments.

We first explored the topic of work-life balance in our 2019 study by determining the cities whose residents had the most well-rounded work-life balance. This was gauged by looking at work intensity, livability and the well-being and rights of inhabitants. Considering that work and economic conditions have changed drastically in many cities since then, we decided to go further in this year’s expanded edition by exploring whether some cities were more impacted than others by the pandemic. The resulting index offers a look into how COVID-19 has changed and continues to affect work-life balance in major cities around the world.

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 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. To begin the study, a list of in-demand metropolises worldwide with sufficient, reliable, and relevant datasets were selected. Fifty cities were finalized to include those known for attracting professionals and families for their work opportunities and diverse lifestyle offerings, as well as those which frequently top liveability indexes.

Firstly, we assessed each city’s overall work-life score based on a series of factors such as the amount of time a person dedicates to their job — taking into consideration total working hours, commuting, and vacation days taken. This year 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 wanted to find out to what extent residents receive equal treatment, evaluating their access to state-funded health and welfare programs, as well as institutional support for equality and social inclusivity. We then determined each city’s livability score by examining its affordability as well as citizens’ overall happiness, safety, and access to wellness and leisure venues — allowing us to assess whether their residents can enjoy their environment after office hours.

Finally, we looked into the effect of the COVID-19 pandemic on a city’s work-life balance in several key areas: the restriction of movement, the severity of lockdown measures, the overall economic impact, and the projected percentage change in employment as a consequence.

The result is an index of 19 factors determining 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: Oslo

Norway

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2: Helsinki

Finland

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3: Copenhagen

Denmark

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4: Hamburg

Germany

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5: Berlin

Germany

Top Overworked Cities in the Ranking

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1: Hong Kong

China

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2: Singapore

Singapore

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3: Seoul

South Korea

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4: Kuala Lumpur

Malaysia

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5: Tokyo

Japan

2020 Work–Life Balance Index

The final ranking displays 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
      • Hours Worked & Commute/Week
      • Overworked Population
      • Minimum Vacations Offered (Days)
      • Vacations Taken (Days)
      • Latest Unemployment
      • Multiple Jobholders
      • Paid Parental Leave (Days)
    • Society and Institutions
      • Social Spending
      • Healthcare
      • Access to Mental Healthcare
      • Inclusivity & Tolerance
    • City Livability
      • Affordability
      • Happiness, Culture & Leisure
      • City Safety & Stress
      • Outdoor Spaces
      • Air Quality
      • Wellness and Fitness
    • Covid-19
      • Covid Impact
      • Projected Unemployment
    Work Intensity
    Society and Institutions
    City Livability
    Covid-19
    2020
    2019
    City
    Country
    TOTAL SCORE
    01
    03
    Oslo
    Norway
    39.9
    5.0%
    25
    25
    3.8%
    8.9%
    707
    94.5
    100
    100
    81.9
    83.8
    90.1
    97.6
    100
    79.8
    82.1
    100
    92.4
    100
    02
    01
    Helsinki
    Finland
    42.1
    8.1%
    25
    30
    7.7%
    6.3%
    1,190
    97.9
    99.1
    90.1
    84.6
    86.6
    100
    97
    86.3
    80.8
    97.6
    88.2
    85
    95.8
    03
    -
    Copenhagen
    Denmark
    37.5
    6.6%
    25
    28
    6.6%
    7.3%
    364
    97.3
    92.5
    94.2
    90.1
    84.2
    92.2
    95.5
    73.3
    68
    88.4
    89.7
    89.1
    93.8
    04
    04
    Hamburg
    Germany
    41.6
    7.7%
    20
    30
    8.2%
    5.3%
    467
    94.6
    91.7
    96.8
    94.4
    86.3
    79.8
    98.8
    76.9
    73.8
    75.5
    96.5
    88.3
    93.2
    05
    06
    Berlin
    Germany
    41.3
    7.7%
    20
    30
    10.5%
    5.3%
    467
    94.6
    90.7
    96.8
    100
    90.6
    88.4
    90.8
    85.7
    70.8
    73.7
    86.1
    85.7
    90.3
    06
    02
    Munich
    Germany
    41.7
    7.7%
    20
    30
    5.3%
    5.3%
    467
    94.6
    92.4
    96.8
    93.1
    83.2
    83
    100
    74.3
    73.6
    71
    87.9
    92.4
    90.1
    07
    -
    Vienna
    Austria
    41.1
    9.3%
    25
    25
    11.3%
    4.3%
    481
    96.1
    95.9
    89.3
    93.2
    83.8
    77.9
    93.2
    91.8
    69
    88.7
    83
    83.8
    89.4
    08
    07
    Zurich
    Switzerland
    40.9
    11.8%
    20
    25
    3.2%
    7.7%
    98
    82.6
    98
    92.2
    76.1
    97.2
    92.6
    98.7
    87.9
    84.7
    93
    82.2
    95.3
    88.2
    09
    05
    Stockholm
    Sweden
    43.1
    6.5%
    25
    25
    8.5%
    8.6%
    490
    95.6
    96.8
    89.1
    96.7
    79.1
    87.6
    90
    90.4
    87
    90.9
    73.3
    84.2
    85.2
    10
    -
    Calgary
    Canada
    37.2
    11.8%
    10
    15
    14.4%
    6.2%
    364
    84.8
    95.3
    91.4
    84.5
    94.9
    83.5
    95
    80.8
    93.7
    89
    69.4
    74.6
    85
    11
    11
    Ottawa
    Canada
    37.3
    11.8%
    10
    15
    9.5%
    5.6%
    364
    84.8
    95
    91.4
    83.2
    88.4
    84.5
    93.5
    73.9
    93.7
    89.7
    68.5
    82.7
    84.6
    12
    -
    Amsterdam
    Netherlands
    36.1
    7.8%
    20
    24
    3.8%
    8.2%
    115
    83.8
    98.3
    93.3
    90.7
    78.9
    89.8
    95.1
    68
    65.5
    90.5
    67.4
    91.9
    83.9
    13
    10
    Vancouver
    Canada
    38.3
    11.8%
    10
    15
    12.8%
    6.7%
    364
    84.8
    95
    91.4
    84.5
    87.2
    83.8
    95.2
    97.3
    89.6
    85.3
    65.9
    78.3
    83.7
    14
    -
    Auckland
    New Zealand
    38.9
    14.8%
    20
    15
    4.0%
    7.3%
    126
    87.2
    92.2
    90
    89.2
    84.2
    83.5
    97.6
    84.9
    85.3
    70.9
    74.5
    90.7
    82.8
    15
    13
    Toronto
    Canada
    39.6
    11.8%
    10
    15
    13.9%
    5.6%
    364
    84.8
    95.1
    91.4
    84.7
    80.1
    90.3
    93.9
    88.2
    83.1
    85
    66.2
    77.7
    81.9
    16
    25
    Denver
    USA
    39.2
    12.3%
    10
    10.2
    7.8%
    5.6%
    0
    82.6
    88.7
    73.2
    84.6
    94
    79.9
    91
    89.8
    88.5
    75.1
    67.8
    79.9
    80.4
    17
    19
    Portland
    USA
    39.9
    13.7%
    10
    10.1
    11.1%
    5.4%
    0
    91.2
    89.3
    76.8
    85.7
    91.8
    80
    93.9
    88.8
    92
    69.9
    68.8
    78.2
    79.3
    18
    17
    San Diego
    USA
    39.4
    13.0%
    10
    9.7
    12.3%
    4.0%
    117
    82.6
    89.3
    86.1
    82.5
    93.9
    81.5
    95
    92.1
    75.9
    68.2
    63.8
    76.4
    79.3
    19
    39
    Tokyo
    Japan
    44.9
    20.6%
    10
    10
    3.2%
    3.6%
    770
    91.1
    98.3
    81.4
    42.4
    85.4
    58.6
    84
    57.7
    63.2
    78.7
    95
    95.5
    78.9
    20
    23
    Seattle
    USA
    41
    13.6%
    10
    10.4
    9.3%
    5.4%
    0
    82.6
    91.4
    79.1
    83.7
    96.2
    81.2
    98.4
    91.8
    88.9
    71.4
    66
    77.3
    78.8
    21
    15
    Sydney
    Australia
    40.4
    13.9%
    20
    14
    7.3%
    6.4%
    140
    85.6
    99.9
    93.2
    86
    87.6
    83.5
    99.2
    94.8
    89.1
    83.5
    56.7
    87.9
    77.9
    22
    20
    San Francisco
    USA
    41.8
    13.2%
    10
    9.7
    11.1%
    4.0%
    117
    82.6
    87.2
    86.1
    89.3
    97.6
    84.7
    92.9
    96.8
    85.3
    68.7
    57.6
    78.3
    76.6
    23
    37
    Atlanta
    USA
    40.9
    13.4%
    10
    10.2
    8.5%
    3.8%
    0
    85.9
    78.4
    85.3
    75.8
    97.5
    79.7
    85.8
    77.7
    75.9
    52.5
    64.9
    80
    75.4
    24
    18
    Melbourne
    Australia
    40
    13.9%
    20
    14
    7.3%
    6.4%
    140
    85.6
    98.8
    93.2
    85.9
    92.6
    95.7
    96.7
    84.5
    83.9
    84.2
    49.3
    86.6
    75.3
    25
    09
    Paris
    France
    45.1
    10.1%
    25
    30
    6.9%
    5.3%
    490
    100
    91.7
    93.2
    83.6
    79.2
    76.2
    82.3
    65.2
    56.4
    86.4
    62.8
    87.1
    74
    26
    -
    Washington
    USA
    41.3
    13.6%
    10
    9.4
    7.9%
    5.5%
    40
    86.9
    80.3
    88.3
    82.8
    91.9
    87
    88.2
    89.2
    85.3
    71.3
    55
    81.7
    73.9
    27
    29
    Chicago
    USA
    40.2
    12.6%
    10
    10.7
    12.0%
    5.2%
    0
    84.3
    85.6
    84
    81.9
    90.1
    83
    79.5
    92.2
    72.7
    60.4
    58.3
    75.5
    73.9
    28
    -
    Dublin
    Ireland
    43.5
    10.8%
    20
    21
    5.3%
    2.7%
    182
    79.6
    92.1
    90.8
    87.5
    71.8
    86.1
    87.6
    65
    82.2
    87.8
    54.5
    91.1
    73.4
    29
    24
    Las Vegas
    USA
    37.9
    12.1%
    10
    9.8
    16.4%
    3.8%
    0
    84.3
    79.7
    66.4
    81.7
    92.9
    77.4
    80.6
    81.2
    88
    58.4
    54.1
    65.2
    72.6
    30
    31
    Philadelphia
    USA
    40
    12.8%
    10
    10.7
    14.1%
    6.0%
    0
    90
    86
    78.9
    75.5
    85.2
    76.5
    77.9
    90
    75.9
    58.8
    60.8
    75.7
    72.6
    31
    12
    London
    UK
    45.8
    11.4%
    28
    25
    5.0%
    3.5%
    287
    89.5
    90.8
    96.1
    97.6
    76.8
    81.7
    84.7
    73.8
    73
    77.1
    54.5
    89.1
    72.5
    32
    -
    Madrid
    Spain
    43.5
    7.2%
    22
    30
    12.6%
    2.3%
    127
    93.1
    92.3
    88.5
    96.9
    70
    72
    88.2
    99.1
    69.7
    89.6
    43
    73.9
    72.4
    33
    34
    Cleveland
    USA
    39.2
    13.1%
    10
    10.9
    11.7%
    6.5%
    0
    90
    84.4
    78
    73.7
    87
    68.9
    52.6
    83.5
    75.3
    54
    67.5
    72.5
    72.4
    34
    33
    Miami
    USA
    40.9
    13.5%
    10
    9.2
    13.2%
    3.5%
    0
    92.4
    85.6
    79.1
    78.7
    81.1
    82.9
    81.3
    86
    89.7
    55.4
    56.8
    77.4
    72.2
    35
    08
    Barcelona
    Spain
    42.3
    7.2%
    22
    30
    12.8%
    2.3%
    127
    93.1
    92.1
    88.5
    96.5
    65.2
    67.8
    78.7
    79.3
    82.3
    87
    46.6
    73.6
    71.8
    36
    -
    Brussels
    Belgium
    42.7
    8.1%
    20
    24
    14.7%
    3.8%
    361
    98.1
    93.4
    86.6
    86
    86.8
    69.1
    62.7
    71.3
    65
    80.2
    50.1
    82.3
    70.5
    37
    26
    Los Angeles
    USA
    42.5
    13.5%
    10
    7
    16.8%
    4.0%
    117
    82.6
    86.7
    86.1
    85.3
    88.9
    83.4
    80.6
    80.2
    68.7
    68
    58.4
    71.8
    70.3
    38
    36
    Houston
    USA
    43.7
    16.6%
    10
    9.9
    9.4%
    3.7%
    0
    82.6
    81.8
    77
    71.8
    96.6
    79.3
    80.6
    70.1
    79.1
    52.3
    62.8
    77.4
    69.6
    39
    22
    Boston
    USA
    40.5
    12.7%
    10
    10.7
    15.5%
    5.5%
    0
    82.6
    92.7
    89.2
    83.8
    86.7
    83
    93.5
    92.8
    84.6
    63.8
    41.6
    74.9
    68.6
    40
    21
    New York
    USA
    40.6
    12.4%
    10
    11.4
    16.4%
    4.1%
    180
    85.9
    87.7
    88
    84.2
    84.5
    83.4
    79
    92
    81
    57.5
    35
    74.3
    65.9
    41
    32
    Singapore
    Singapore
    51.1
    25.1%
    7
    14
    2.8%
    2.7%
    119
    56.2
    88.6
    90.9
    48.2
    82.2
    67.6
    92
    74.3
    54.9
    78.3
    76.4
    96
    64.7
    42
    -
    New Orleans
    USA
    40.9
    15.1%
    10
    10.4
    12.9%
    4.4%
    0
    91.2
    76
    75.8
    77.1
    83
    72.7
    81.6
    82.1
    93.3
    46.8
    38.8
    74.5
    63
    43
    -
    Bangkok
    Thailand
    50.7
    20.2%
    6
    10
    0.9%
    3.7%
    90
    68
    51.8
    60.8
    56
    38.3
    60
    75.2
    46.3
    27.9
    92.7
    83.3
    100
    59.6
    44
    -
    Sao Paulo
    Brazil
    46
    12.3%
    10
    30
    13.5%
    4.7%
    181
    85.1
    47.3
    77.6
    60.5
    35
    65.4
    30.6
    57.9
    53.2
    64.4
    64.7
    75.2
    58
    45
    35
    Hong Kong
    China
    50.1
    29.9%
    7
    14
    3.8%
    3.7%
    70
    83.5
    83.2
    83.9
    52.6
    69.4
    37.4
    83
    81.8
    25
    100
    72.1
    93
    57.8
    46
    38
    Buenos Aires
    Argentina
    43
    19.9%
    14
    15
    11.5%
    10.0%
    92
    71.8
    47.8
    81.1
    69.6
    56.5
    66.4
    63.9
    77.3
    66
    69.9
    48
    78.2
    55.6
    47
    40
    Kuala Lumpur
    Malaysia
    52
    21.0%
    8
    12
    2.4%
    1.1%
    98
    15.9
    47.1
    15.9
    25
    78.3
    35
    69.5
    35
    34.8
    73
    82.1
    95.2
    53.1
    48
    14
    Budapest
    Hungary
    46
    4.2%
    20
    24.1
    3.9%
    1.4%
    1,127
    87.9
    68.9
    53
    48.1
    58.7
    42.8
    68.9
    85.7
    62.7
    72.6
    93.5
    91.2
    15.4
    49
    16
    Milan
    Italy
    43.7
    9.8%
    20
    21
    4.0%
    1.4%
    337
    97.2
    96.2
    86.5
    61.1
    58.4
    61.3
    74
    87.6
    44.9
    74.2
    51.2
    89.1
    12.1
    50
    -
    Seoul
    South Korea
    47.7
    23.1%
    15
    14
    5.3%
    1.7%
    93
    71.5
    90.2
    76.8
    48.2
    88.9
    49.7
    55.9
    81.1
    26.1
    78.5
    94.8
    93.7
    1

    Methodology

    The Best Cities for Work-Life Balance 2020 assesses a city’s implementation of smarter working policies and their capacity to simultaneously equip residents with the ability to enjoy their leisure time. In comparison to the 2019 edition, the expanded study also takes into account how COVID-19 has changed and continues to affect work-life balance in major cities around the world.

    City Selection

    A shortlist of in-demand metropolises worldwide with sufficient, reliable, and relevant datasets were selected. 50 cities were then finalized, including those known for attracting professionals and families for their work opportunities and diverse lifestyle offerings. As the second installation of a continuous study, this index includes 10 more cities than the previous year.

    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 focuses on three broad categories with the following factors outlined below which make a city successful at achieving a well-rounded work-life balance:

    • Work-Intensity: Hours Worked & Commute/Week, Overworked Population (%), Minimum Vacations Offered (Days), Vacations Taken (Days), Latest Unemployment (%), Multiple Jobholders (%), Paid Parental Leave (Days).
    • Society & Institutions: Social Spending (Score), Healthcare (Score), Access to Mental Healthcare (Score), Inclusivity & Tolerance (Score).
    • City Livability: Affordability (Score), Happiness, Culture & Leisure (Score), City Safety & Stress (Score), Green Spaces and Weather (Score), Air Quality (Score), Wellness and Fitness (Score).

    To assess the impact of the COVID-19 pandemic on cities’ current and future work-life balance, the study also includes a COVID Impact (Score) and Projected Unemployment (Score).

    Where scores are out of 100, the higher the score, the better. For the total score, a value of 100 does not mean a city is perfect in terms of work-life balance and has zero room for improvement. Rather, it means that the city has the healthiest work-life balance out of all the cities in the index. On the other end of the spectrum, a score of 1 indicates that the city performs the poorest in comparison to the other cities in the study. However, this does not necessarily mean that the city has a poor work-life balance in the greater global context.

    The data collected was then analyzed for each factor, resulting in a weighted average to create a final score for each category. This was then aggregated into a final work-life balance score for each city. The scores for each category at a city-level (Work Intensity, Society & Institutions, City Livability) can be provided upon request.

    The final score for overall work-life balance was determined by calculating the sum of the weighted average score of all of the indicators.

    Work Intensity

    Hours Worked & Commute/Week

    The average number of hours a full-time employee spends working and commuting to and from their workplace per working week.

    Hours worked

    Employed persons include individuals undertaking full-time work as their main job. An employee is considered to work full-time if they work for 35 hours or more a week. US cities employ data at a Metropolitan Statistical Area level, while the latest country-level data was taken for all other cities.

    Sources: US Bureau of Labor Statistics – Current Employment Statistics survey (State & Metro Area), 2019; ILO-STATISTICS – Labour force survey, latest available data.

    Commuting time

    Commuting duration data is based on self-reported times gathered through surveys, and includes the mean travel time to and from work or school for all forms of transport. One-way commuting durations were multiplied by ten to get the estimated weekly commute times for a five-day work week.

    Sources: US Census Bureau – American Community Survey, 2017; Eurostat – Eurofond travel survey, 2015; Numbeo – Traffic Index‍; various media sources.

    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 For non-US cities, country-level data was used to evaluate the average working hours per week. For US cities, average number of hours of work was incorporated into the country-level data to approximate percentages on a city-level.

    Sources: ILO-STATISTICS – Labour force survey, latest available data; US Bureau of Labor Statistics – Current Employment Statistics survey (State & Metro Area), 2019.

    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 labor departments. Latest available data.

    Vacations Taken (Days)

    The average number of used paid vacation days offered to full-time employees in a single year. This section uses city-level data 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. All cities use 2018 data, except for Zurich, Stockholm, Oslo, Amsterdam, Helsinki, Copenhagen, Vienna, Buenos Aires, Dublin, Dubai, and Brussels, which use 2017 data.

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

    Unemployment (%)

    The most recently available unemployment rate for the metropolitan area or region. National figures were used in rare instances. Unemployed persons are considered those of the labor force who are jobless, looking for a job, and available for work.

    Subnational unemployment figures often take longer to report and are less available than national figures. As unemployment rates are seasonal and labor departments have their own standards for the regularity of reporting unemployment figures, so the most recently available data was used to offer a snapshot of the job market as close to mid-2020 as possible. Further details about the collection can be provided upon request.

    Sources: US Bureau of Labor Statistics – Local Area Unemployment Statistics, data as of September, 2020; Local, subnational and national government statistical departments, data as of September 2020.

    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. Research has not concluded that high levels of multiple job-holding point directly to economic strain or exploitation. But in places where institutions and protections are weak, certain workers may be more exposed to the negative effects of such employment. We have included this indicator in our study to provide an oft-neglected angle on conditions for a work-life balance.

    Unfortunately, detailed geographical data on the number of multiple-jobholders is underreported and not regularly published, and some data presented here is dated (Brazil, for example). However, it was deemed more beneficial to report the data than to omit it, and therefore the latest available data compiled from official statistics and independent research was included. 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 from work days 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

    Social Spending (Score)

    Government social expenditure as a percentage of national GDP, represented as a score. National data is taken, except for the US cities, which use state-level data. Social spending includes policy areas such as unemployment, housing, family, support for the elderly, health, and active labor market programmes.

    Sources: OECD – Social Expenditure SOCX, latest available data; Eurostat – Social protection statistics, 2016; Social Investment Portal – Latin America and the Caribbean, 2016; Tax Policy Center – State and Local General Expenditures, 2017; Bureau of Economic Analysis – GDP by state, 2017; Ministry of Finance – Asia Development Bank, 2017; Pew Trusts – Federal Spending in the States, 2014; Brazilian and Hong Kong media sources, 2018/15.

    Healthcare (Score)

    The measure of a city’s healthcare system based on access, quality and satisfaction. Country-level data was obtained from the Health Access and Quality (HAQ) for access and quality indicators, while US cities use state-level data for these indicators. Satisfaction survey results were taken at a city level.

    The preparedness or resilience of healthcare systems in the wake of the COVID-19 pandemic was not assessed. Healthcare systems are, by design, meant to treat only a proportion of the population at a given time, as was popularly illustrated in the global “flatten the curve” rhetoric.

    Any inability to meet the healthcare needs of residents during an emergency of this magnitude cannot be put down to the quality of healthcare access alone. (Italy, one of the first and hardest hit countries, is world-renowned for its first-class public healthcare system). Any analysis must also take into account varying external factors, such as the timeliness and effectiveness of responses (both official policy and behavior from the wider community) to protect residents and avoid overburdening healthcare services.

    Sources: Institute for Health Metrics and Evaluation — Health Access and Quality Index, 2016; Numbeo – Healthcare Index, 2020.

    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 ‘LGBT+’ (inclusiveness and tolerance) factors.

    Gender

    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, we looked at the comprehensiveness of equality and protection (an emphasis on work rights) legislation, health access, as well as political representation for the LGBT+ community. We also included the percentage of the population that identifies as LGBT+, 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, 2020.

    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 at a national level as well as the accessibility and variety of a city’s cultural and lifestyle offerings.

    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 were given supplementary points.

    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.

    Happiness, Culture & Leisure (Score)

    The degree to which citizen’s feel safe and unburdened by city-induced stress. Both factors are equally weighted.

    Safety

    The degree of personal safety experienced by residents. The safety score combines data on violent crime rates, political violence, traffic deaths and perceived criminality.

    Sources: Economist Intelligence Unit – Safe Cities Index, 2019; Global Residence Index – STC Safety Index, 2019; Social Progress Imperative – Social Progress Index, 2019; Numbeo – Crime Index, latest data; National law enforcement databases.

    City Stress

    The degree to which a city is burdened by stress-inducing factors. The score is based on data on a city’s population density, transport and infrastructure, climate, and local economy.

    Sources: WalletHub – Stressed Cities, 2016; Zipjet – Stressful Cities Ranking, 2017.

    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. 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 Factors

    COVID Impact (Score)

    The degree of social and economic impact on account of a location’s COVID-19-related response.

    To interpret the social impact, we included mobility reports comparing the change in a movement by driving, walking and transit in a specific city. The data includes the average percent change in these forms of movement between August and a January-baseline for the year 2020. We also included similar data on the change in visitor numbers to specific categories of location, such as workplace, transit station, retail stores. Cities showing a considerable percent shift in this movement data can be expected to have experienced heavy restriction or lockdown conditions in the month of August. Additionally, we included the rate of COVID-related deaths per 100k people as a measure of the human loss of life, as well as the psychological impact of disaster faced by a specific community. This data was taken at a national level, with US cities using state-level data. City-level data was also included on the number of implemented restrictions and public health measures (such as lockdowns,visa restrictions, and school and border closures), as well as whether these measures had been lifted by the time of research. To assess the potential economic impact, we also included economic forecasts on the projected GDP growth for 2020 at a national level.

    Sources: Apple – COVID-19 Mobility Trends Reports, data as of September 2020; Google – COVID-19 Community Mobility Reports, data as of September 2020; Center for Disease Control and Prevention – CDC COVID Data Tracker, data as of September 2020; European Centre for Disease Prevention and Control – Worldwide COVID-19 death data, data as of September 2020; International Monetary Fund – World Economic Outlook Report, June 2020; ACAPS – COVID-19 Government Measures Dataset, 2020.

    Projected Unemployment (Score)

    The projected percent change in employment as a result of the COVID-19 pandemic, as a score. The projected unemployment rate for 2020 was compared to the unemployment rate of 2019. Both figures are taken from the IMF, with the inclusion of the metropolitan/regional-level data on latest available unemployment figures.

    Sources: Local statistical departments, data as of September, 2020; International Monetary Fund – World Economic Outlook Report, June 2020.

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