IESE Cities in Motion Index
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Connected Intelligence
- Sustainable Growth and Tech Trends
Publication | Update: Oct 2020
The Cities in Motion Index – measuring the future sustainability of the world’s cities together with the quality of life of their inhabitants- created by IESE Business School assesses 180 cities representing 80 countries in relation to 10 key dimensions: the economy, human capital, technology, the environment, international outreach, social cohesion, mobility and transportation, governance, urban planning, and public management.
The 2019 edition has significantly increased the number of variables in relation to the cities, including a total of 96 indicators – 13 more than in the previous edition -. Among the new variables, there are, for example, the hourly wage, purchasing power, mortgage as a percentage of income, and whether a city is a favorable environment for the development of women. This increase in the quantity and quality of the variables used allows for a more accurate assessment of the reality of the cities that appear in the CIMI.
Additionally, the 2019 edition has also widened its geographical coverage by adding 11 new cities, including Quebec (Canada), Edinburgh (United Kingdom) and Seattle (United States), among others.
The index allows cities to help identify effective solutions since the index identifies both the strength and weaknesses, by quantifying the 10 key dimensions listed above. For example to assess human capital the analysis measures the proportion of population with secondary higher education, movement of students, number of universities, expenditure on leisure and recreation, and others.
The index ranks cities according to their total value and according to each key dimension.
Year after year, the top position in the ranking seems to be disputed by London and New York, two highly developed and smart cities. After three consecutive years with New York at the top place, London has taken back the first position, followed by New York (#2) and Amsterdam (#3). The rest of the top 10 cities in the overall ranking are Paris (#4), Reykjavik (#5), Tokyo (#6), Singapore (#7), Copenhagen (#8), Berlin (#9) and Vienna (#10).
City Ranking – Top 10
London is in first position in the overall ranking thanks to its very good performance in almost all of the dimensions. The British capital ranks in 1st position in human capital and international outreach, 3rd in mobility and transportation, 7th for the governance dimension, 8th in technology and 9th in urban planning. However, the city does not show such a good performance in the dimensions of social cohesion (#45) and the environment (#34).
This year, the Big Apple is in second place in the overall ranking, but it enjoys the leading position in the economy dimension. New York also stands out in other dimensions, such as human capital (#3), urban planning (#2), mobility and transportation (#5), international outreach (#8) and technology (#11). However, the city performs worse in the social cohesion dimension (#137).
The capital of the Netherlands completes the podium of the CIMI 2019 ranking. Being a leading center for finance and trade, the city is also an important cultural and touristic hub. Amsterdam performs well overall in the ranking and stands out especially in international outreach (#3), technology (#7), the economy (#10), urban planning (#11), and mobility and transportation (#11).
Europe clearly dominates the top spots in the ranking, with seven Western European cities in the top ten positions. Moreover, if we expand the focus to the top 50 positions, the dominance of Europe is still evident, with more than half of the cities being on this continent. The Figure below exhibits how the different regions are represented in the overall ranking in accordance to their performance. It can be observed that the group with the best performing cities, the one with darker green, is mainly made up by Western European cities (with more than half of them), followed by North America (11%) and the Asia Pacific region (11%).
Geographical Regions According to Performance in the CIMI
Creating Smarter and More Sustainable Cities
Finding the right balance among the different dimensions is a complex – and permanent – process. Urban leaders need to take an all-embracing and long-term vision to establish strategic priorities to carry out the necessary transformations. However, it is important to take into account that the perfect city does not exist and that change is slow for most cities. Collaboration among the different stakeholders and participation of the public have proven to be driving forces for change and success.
The urbanization process presents one of the most significant challenges of the 21st century, with increasing effects on the economy, social divisions and environmental impacts. However, it also creates new opportunities to design, build and operate smarter, greener and more inclusive cities.
There are several other indexes available that compare the performance of cities over time. However they all agree that a city is more powerful, prosperous, and competitive if it manages to develop its various dimensions — from the economy to the quality of life of its citizens, the use of technology, and to its cultural importance.
You can download the full report here: IESE Cities in Motion Index 2019.
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Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.