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
As a company born and bred in Brooklyn, NY, we first evaluated the local working climate
in 50 U.S. cities. Then, to understand how American cities compare on a global scale, an
international study was conducted, including 17 notable U.S. cities and 33 international
cities known for attracting professionals and families for their work opportunities and
diverse lifestyle offerings. As the second installment of a continuous study, a
shortlist of in-demand metropolises worldwide with sufficient, reliable, and relevant
datasets were selected.
This index is not a city livability index, nor does it intend to highlight the best
cities to work. Instead, it is designed to be an indicator of how well cities strike a
balance between work and life based on a series of indicators related to time
management, access to welfare, city livability, and citizen well-being.
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.