Cities of Opportunity

At Movinga, we’ve made it our mission to understand why, how, and where people move so that we can make relocations easier. From years of experience, it’s become clear that employment is the top reason why people are willing to relocate. As a company looking into new markets, we decided to conduct a deep-dive into the number one driver behind relocation - jobs - both to identify the hottest business hubs, and to help those curious about job opportunities overseas and at home to understand the strengths and weaknesses of each city.
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We began this study by first analysing OECD countries, as they have the most available statistics, and then added a few key trending countries which are booming with employment opportunities. The research tackles three key areas: economic strength for startups and established businesses, standard of living including disposable income and healthcare benefits, and the inclusion of youth, women and expats in the workforce. The final index ranks the top 100 cities by the total job opportunity score, from highest to lowest, revealing the score for each factor. The data can also be sorted by each individual category.

“Digitalization means that companies can now attract skilled employees from all four corners of the globe, so for talented jobseekers, there are now more career opportunities than ever before.” comments Finn Age Hänsel, Managing Director at Movinga. “However, cities which offer high salaries may be misleading, as incomes in these nations are often negated by additional healthcare costs and high tax rates. Consider too that although some cities offer great opportunities for men, the wage gap and opportunity for female career advancement can be vastly different than other countries. With these considerations in mind, we hope that this study offers a holistic view of these locations and helps jobseekers to make an informed decision about their next career move.”

Our study reveals that Boston is the best city for job seekers in 2019, followed by Munich and Calgary. Oslo is the only Nordic city in the top 10, however, Stockholm (#20) makes it to the top 20. The Swedish capital is characterized by a high standard of living as well as a high expat employment rate, not to mention a buzzing startup scene.
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Sweden’s second largest city, Gothenburg, lands on #46 and Malmö - known for its diverse cultural scene - ranks #59. The study makes it clear that Sweden, boasting one of the lowest gender wage gaps, has a lot to offer for job seekers. Besides the cities already mentioned, there are plenty of interesting mid-sized and small cities, such as Norrköping and Sundsvall.
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At Movinga, we work wholeheartedly to provide our customers smooth moving experiences and are happy to assist you on your journey to your new home. Start preparing for your move by visiting our blog - read about the factors that determine your moving costs and find learn how to choose the right moving boxes.

Methodology
‍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.
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Scoring
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”
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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 scores

Metropolitan areas - Definition and Selection
The 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:
Iceland
Israel
Latvia
Lithuania
Luxembourg
New Zealand
Turkey

Population and GDP/Capita Data
‍For 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:
https://stats.oecd.org/Index.aspx?Datasetcode=CITIES
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.


Economic Strength
Total Employment
‍Short description
Employment as a share of the working age population. A higher score indicates a higher rate of employment.
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Sources
OECD “Metropolitan areas” database;
https://stats.oecd.org/Index.aspx?Datasetcode=CITIES
OECD “Regional Labour” database;
‍https://stats.oecd.org/Index.aspx?Datasetcode=REGION_LABOUR

Details
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 Growth
‍Short description
GDP/Capita compound annual growth rate over the period 2011 - 2016. A higher score indicates a higher compound annual growth rate.

Sources
OECD “Metropolitan areas” database;
https://stats.oecd.org/Index.aspx?Datasetcode=CITIES
OECD “Regional Labour” database;
‍https://stats.oecd.org/Index.aspx?Datasetcode=REGION_ECONOM

Details
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)

New Business Registrations
Short description
Establishment birth rate, as a percentage of establishments. A higher score indicates a higher establishment birth rate.

Sources
OECD “Regional Business demographics” database;
https://stats.oecd.org/Index.aspx?Datasetcode=REG_BUSI_DEMOG

Details
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”

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Standard of Living
Cost of Living
Short description
Mercer ranking of cities by “cost of living”. A higher score indicates a lower cost of living.

Sources
Mercer 2018 “Cost of living” ranking
https://www.mercer.com/newsroom/cost-of-living-2018.html

Details
For 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.

Disposable Income
Short description
Per capita wages and other incomes (e.g. rental income and other investments) minus taxes and social contributions.

Sources
OECD “Regional Economy” database;
https://stats.oecd.org/Index.aspx?Datasetcode=REGION_ECONOM

Details
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.

Healthcare Expenditures
Short description
Out-of-pocket and spending on private healthcare services, as a percentage of disposable income.

Sources
OECD “Health expenditure and financing” database;
https://stats.oecd.org/Index.aspx?Datasetcode=SHA

Details
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.

Government Effectiveness
Short description
The quality of public services and the civil service; and the government's ability to formulate and implement policy.

Sources
World Bank ""World Governance Indicators""
http://info.worldbank.org/governance/wgi/#home

Details
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 Indicators


Immigration
Immigrant Levels
Short description
Foreign-born population as share of total population.

Sources
OECD “Database on Migrants in OECD Regions” database;
‍https://stats.oecd.org/Index.aspx?Datasetcode=REGION_MIGRANTS

Details
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 2015

Expat Employment Rate
Short description
Employment rate of foreign-born people of working age.

Sources
OECD “Database on Migrants in OECD Regions” database;
https://stats.oecd.org/Index.aspx?Datasetcode=REGION_MIGRANTS

Details
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 2015


Opportunity for Youth
Youth Employment
Short description
Employment of labour force up to 24 years old. Higher employment - higher score.

Sources
OECD “Regional Labour” database;
https://stats.oecd.org/Index.aspx?Datasetcode=REGION_LABOUR

Details
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%.

New Startups
Short description
Number of new startups founded since 1 January 2016.

Sources
www.crunchbase.com


Opportunity for Women
Gender wage gap
Short description
The percentage difference between women's average monthly wages compared to men's.

Sources
World Economic Forum “The Global Gender Gap Report 2017”;
https://www.weforum.org/reports/the-global-gender-gap-report-2017

Details
We used the female-to-male ratio of indicator “Estimated earned income (PPP, US$)”

Women's Liberty & Legislation
Short description
A sum of ratings of cultural and legislative restrictions on women’s rights.

Sources
https://www.genderindex.org

Details
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 daughters
Restricted physical integrity, including: laws on domestic violence, rape and sexual harassment; prevalence and attitude to gender-based violence; female genital mutilation; reproductive autonomy
Son 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 services
Restricted civil liberties, including: access to public space; political voice and political representation

Women's Opportunity for Advancement
Short description
Response 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?""

Sources
World Economic Forum “The Global Gender Gap Report 2017”;
https://www.weforum.org/reports/the-global-gender-gap-report-2017

Details
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.