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