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This study determines the best cities to find a job based on 14 factors relating to economic strength, standard of living, immigration and opportunities for youth and women. To choose the cities for the study, OECD nations were analyzed according to their available business infrastructure statistics in order to determine a final list of 100 cities.Scores are normalized such that 0=the lowest value in the final dataset and 10=the highest value in the final dataset. For columns where a low value is better, the score is inverted such that a high score is always better.The score is inverted for the following columns:“Cost of Living”“Healthcare Expenditures”“Gender Wage Gap”“Women's Liberty & Legislation”Therefore, the higher the score, the better the city ranks for that factor in comparison to the other cities in the index. For example, a score of ‘9’ for “Gender Wage Gap” indicates that the city has a low wage gap relative to the other cities in the ranking.The equation for normalization is as follows:score = 10 *x - min(X)max(X) - min(X); or scoreinverted = 10 -10 *x - min(X)max(X) - min(X)for inverted scoresMetropolitan areas - Definition and SelectionThe analysis was performed on metropolitan areas as defined by Eurostat/OECD. The shape and size of the metropolitan areas are determined through a data-driven process that identifies high-density urban centres and extends them to incorporate the commutable area surrounding the centre. The aim of the exercise is to create so-called “Functional Urban Areas” that represent the entire labour and commercial market of a city. In the United States, metropolitan areas are constructed through a similar process that also attempts to capture the extent of media markets. ‍ While the analysis was primarily limited to OECD countries, the high interest in Dubai, Hong Kong and Singapore as popular expat destinations motivated their additional inclusion. To ensure that our ranking reflects comparable values, cities from a number of countries were excluded due to incomplete or outdated data. Countries that were excluded through this process include:IcelandIsraelLatviaLithuaniaLuxembourgNew ZealandTurkeyFor Population and GDP/Capita, we used the most recent data from the Eurostat/OECD dataset for “Functional Urban Areas”. The full dataset can be found here:For Population, we used the indicator “POP: Total populations of the metropolitan area (persons)”. All values are 2014 estimates.For GDP/Capita, we used the indicator “GDP_PC: GDP per capita (US$)”, expressed in US$, constant prices and constant PPPs, OECD base year (2010). Values are either 2012 or 2013 estimates.Employment as a share of the working age population. A higher score indicates a higher rate of employment.OECD “Metropolitan areas” database;

https://stats.oecd.org/Index.aspx?Datasetcode=CITIES OECD “Regional Labour” database;For total employment, we used the most recent data from the “Metropolitan areas” database as a starting point. When selecting the data from the database, dimensions were set as follows: Indicator “UNEMP_R: Unemployment as a share of the labour force (%)”As most values in this database are 2014 or 2013 estimates, data from the metropolitan areas were extrapolated to 2016 using regional unemployment rates found in the Regional Labour database from OECD. When selecting the data from the database, dimensions were set as follows: Indicator “UNEM_RA_15_64: Unemployment Rate (% unemployed over labour force 15-64)”GDP/Capita compound annual growth rate over the period 2011 - 2016. A higher score indicates a higher compound annual growth rate.OECD “Metropolitan areas” database;

https://stats.oecd.org/Index.aspx?Datasetcode=CITIES OECD “Regional Labour” database;For GDP, we used the most recent data from the “Metropolitan areas” database as a starting point. When selecting the data from the database, dimensions were set as follows:Indicator: “GDP_PC: GDP per capita (US$)”, expressed in US$, constant prices and constant PPPs, OECD base year (2010).As most values in this database are 2014 or 2013 estimates, data from the metropolitan areas were extrapolated to 2016 using GDP growth rates found in the Regional Economy database from OECD. When selecting the data from the database, dimensions were set as follows:Indicator “GDP”Measurement: “PC_REAL_PPP: USD per head, constant prices, constant PPP, base year 2010”The formula for the five-year compound annual growth rate is (obs2016/obs2011)(1/5)Establishment birth rate, as a percentage of establishments. A higher score indicates a higher establishment birth rate.OECD “Regional Business demographics” database;

https://stats.oecd.org/Index.aspx?Datasetcode=REG_BUSI_DEMOG When selecting the data from the OECD database, dimensions were set as follows:Indicator “ESTAB_B_RA: Establishment birth rate (in % of all establishments - same sector, same size class)”Economic Sector (ISIC rev.4): “B-S_X_K642: Total economy - aggregate 3 (industry, construction and services excluding insurance activities of holding companies)”Employment size range: “Total”Mercer ranking of cities by “cost of living”. A higher score indicates a lower cost of living.Mercer 2018 “Cost of living” rankingFor Cost of Living, we used the city ranking performed by Mercer as a starting point. For cities not covered by the Mercer rankings, we used estimates of cost of living from Numbeo.com.Per capita wages and other incomes (e.g. rental income and other investments) minus taxes and social contributions.OECD “Regional Economy” database;

https://stats.oecd.org/Index.aspx?Datasetcode=REGION_ECONOM When selecting the data from the OECD database, dimensions were set as follows:SNA Classification: “Last SNA classification (SNA 2008 or latest available)”Indicator: “INCOME_DISP: Disposable Household Income”Measure: “National currency per head, current prices”, converted to USD.Out-of-pocket and spending on private healthcare services, as a percentage of disposable income.OECD “Health expenditure and financing” database;

https://stats.oecd.org/Index.aspx?Datasetcode=SHA When selecting the data from the OECD database, dimensions were set as follows:Financing scheme: “Voluntary schemes/household out-of-pocket payments”;Function: “Current expenditure on health (all functions)”;Provider: “All providers”;Measure: “NCU per capita, current prices”, converted to USD.The quality of public services and the civil service; and the government's ability to formulate and implement policy.World Bank ""World Governance Indicators""While the index is compiled by the World Bank, it is in turn an aggregate of multiple secondary indices, including the Economist Intelligence Unit Riskwire & Democracy Index; the World Economic Forum Global Competitiveness Report; Satisfaction with transportation, according to the Gallup World Poll; the Institutional Profiles Database; the Political Risk Services International Country Risk Guide; and Global Insight Business Conditions and Risk IndicatorsForeign-born population as share of total population.OECD “Database on Migrants in OECD Regions” database;When selecting the data from the OECD database, dimensions were set as follows:Indicator: “ALL_T_SH: Share of Foreign-Born Population”Place of birth: “Foreign-born”All observations from this database are for the year 2015Employment rate of foreign-born people of working age.OECD “Database on Migrants in OECD Regions” database;

https://stats.oecd.org/Index.aspx?Datasetcode=REGION_MIGRANTS When selecting the data from the OECD database, dimensions were set as follows:Indicator: “ALL_T_1564EMP_RA: 15-64 years old Employed, in % of the Population of the same age and origin”Place of birth: “Foreign-born”All observations from this database are for the year 2015Employment of labour force up to 24 years old. Higher employment - higher score.OECD “Regional Labour” database;When selecting the data from the OECD database, dimensions were set as follows:Indicator: “UNEM_RA_15_24: Youth Unemployment (15-24 years old)”Gender: “Total”To calculate employment rate, we subtracted the unemployment rate from 100%.Number of new startups founded since 1 January 2016.The percentage difference between women's average monthly wages compared to men's.World Economic Forum “The Global Gender Gap Report 2017”;We used the female-to-male ratio of indicator “Estimated earned income (PPP, US$)”A sum of ratings of cultural and legislative restrictions on women’s rights.The OECD “Social Institutions & Gender Index” database evaluates social institutions in five domains:Discriminatory Family Code, including: gender differences in legal minimum age of marriage; parental authority in marriage and divorce; inheritance rights of widows and daughtersRestricted physical integrity, including: laws on domestic violence, rape and sexual harassment; prevalence and attitude to gender-based violence; female genital mutilation; reproductive autonomySon bias: due to lack of comparable data for the countries selected in this study, this domain was not included in the index.Restricted resources and assets, including: secure access to land and non/land assets; access to financial servicesRestricted civil liberties, including: access to public space; political voice and political representationResponse to the survey question: “In your country, to what extent do companies provide women the same opportunities as men to rise to positions of leadership?""World Economic Forum “The Global Gender Gap Report 2017”;We used the indicator “Advancement of women to leadership roles”. This indicator in turn refers to the results of the World Economic Forum “Executive Opinion Survey 2016-2017”, specifically the question: “In your country, to what extent do companies provide women the same opportunities as men to rise to positions of leadership? (1 = not at all, women have no opportunities to rise to positions of leadership; 7 = extensive, women have equal opportunities of leadership)”Currency exchange correct as of 25.09.2018.