Appendix A. Estimating the Opportunity Cost of Low Social Mobility
In order to make the impact of social mobility tangible, we estimate the impact of social mobility on economic growth which we use as a basis to turn social mobility into a dollar value. The idea is to quantify the value of the missed opportunities of lacking social mobility in monetary terms. As we do not claim to estimate a causal effect, we use a framework that allows us to approximate the potential increase in GDP growth associated with a social mobility score increase.
To do so, we follow the logic of Hall and Jones (1999) who suggest that social infrastructure has an impact on output per worker in part directly (via the quality of input [human capital]) and in part indirectly (through productivity). While these authors interpret social infrastructure mainly in terms of openness and lack of rent-seeking, we argue that the drivers of social mobility measured by the GSMI include many aspects that matter for fostering human capital such as meritocracy, education quality, adequate safety nets, appropriate reward of talent, quality of institutions etc. As such it is a broader measure of “social infrastructure” than the one proposed by Hall and Jones, yet it shares many of the underlying ideas. A good social infrastructure does not only impact human capital directly but may also encourage “the adoption of new ideas and new technologies”.
An obvious methodological problem to estimate the impact of social mobility in an economic growth framework is endogeneity. Clearly, there is a simultaneous effect going from social mobility to economic growth and from economic growth to social mobility.
Good institutions that drive economic outcomes are not randomly assigned to countries, but they are determined endogenously by many factors, including the wealth of a country itself.
Despite these complications, our hypothesis is that relative social mobility is a fundamental determinant of economic growth and follow the general idea of Hall and Jones to relate “social infrastructure” with GDP per capita. However, instead of using instrumental variable regression, we simply control for the initial level of income of countries and estimate the following cross-country linear regression:
Although the inclusion of the initial level of income does not solve the endogeneity problem, it makes sure that the estimate of the relation between of GSMI on growth is conditional to the current level of income of countries.
The coefficient of the variable social mobility index is positive and significant at 95%. The marginal effect of social mobility on GDP per capita growth is 0.003 over seven years, or 0.04% per year for each unit, which means that an increase of 10 units in the index (i.e. from 50 to 60) would increase growth by 0.4% per year. For instance, a country that is growing at 1% would grow at 1.4% if it manages to increase its social mobility by 10 points. The results are the following:
You can find below an estimate of the opportunity costs of low social mobility each year and to each economy ranked in the GSMI and to the global economy. All else being equal, if all countries increased their GSMI performance by 10 points, the resulting growth would convert into an additional $514 billion for the global economy each year (in PPP terms). This would represent an extra 4.41% GDP growth for the global economy by 2030.
Appendix B. Methodology and Technical Notes
Section A of this appendix presents the methodology and detailed structure of the Global Social Mobility Index. Section B presents the methodology used to compute progress scores. Finally, Section C provides detailed descriptions and sources for each indicator included in the index.
A. Computation and Composition of the Social Mobility Index
The computation of the Global Social Mobility Index is based on successive aggregations of scores, from the indicator level (the most disaggregated level) to the overall Social Mobility Index (the highest level). At every aggregation level, each aggregated measure is computed by taking the average (i.e. arithmetic mean) of each score, each indicator is given equal weights within their pillar. The overall Social Mobility Index score is the average of the scores of the 10 pillars. Each pillar, therefore, accounts for 10% of the overall score.
For individual indicators, prior to aggregation, raw values are transformed into a progress score ranging from 0 to 100, with 100 being the ideal state, as described in Section B.
Complete Indicator List and Index Components
B. Computation of Progress Scores and Frontier Values
To allow the aggregation of indicators of different nature and magnitude, each indicator entering the Global Social Mobility Index is converted into a unit-less score, called “progress score”, ranging from 0 to 100 using a min-max transformation. Formally, each indicator is re-scaled according to the following formula:
where valuei,c is the “raw” value of country c for indicator i, worst performance (wpi,) is the lowest acceptable value for indicator i and frontier i corresponds to the best possible outcome. Depending on the indicator, the frontier may be a policy target or aspiration, the maximum possible value, or a number derived from statistical analysis of the distribution (e.g. 90th or 95th percentile). If a value is below the worst performance value, its score is 0; if a value is above the frontier value, its score is capped at 100.
In the case of indicators derived from the Executive Opinion Survey, frontieri and wpi values are always 7 and 1, respectively. These values correspond to the two extreme answers of any questions. Table 1 below provides the actual floor and frontier values used for the normalization of each individual indicator.
C. Indicator Definitions and Sources
The following pages provide sources for all the individual indicators included in the Global Social Mobility Index 2020 (GSMI). The title of each indicator appears on the first line, preceded by its number to allow for quick reference. Underneath is a description of each indicator or, in the case of indicators derived from the World Economic Forum’s Executive Opinion Survey, the full question and associated answers. If necessary, additional information is provided below that. For more information about indicators derived from the Executive Opinion Survey, refer to Appendix B of the World Economic Forum’s Global Competitiveness Report 2019.
The interactive ranking tables at www.weforum.org/smr2020/rankings provide information about the source and period for each individual data point. Select the indicator of interest from the selector and click on the “info” icon next to each economy to access the information. For indicators not sourced from the World Economic Forum, users are urged to refer to the original source for any additional information and exceptions for certain economies and/or data points. “Terms of Use and Disclaimer” on page ii provide information about using the data. The data used in the computation of the GSMI represents the most recent and best data available at the time when it was collected (September–December 2019). It is possible that data was updated or revised subsequently.
Pillar 1 Health
1.1 Adolescent birth rate
Adolescent fertility rate is the number of births per 1,000 women aged 15–19 | 2017
Source: United Nations Population Division, World Population Prospects.
1.2 Prevalence of malnourishment among youth
Prevalence of moderate or severe underweight or obesity among youth and adolescent (5–19 years) population | 2016
Moderate or severe underweight is defined as more than 2 standard deviations below the median of the WHO growth reference for children and adolescents. Obesity is defined as more than 2 standard deviations above the median WHO growth reference.
Source: NCDRisc, based on “Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: A pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults”, Lancet, 2018.
1.3 Health access and quality
Health Access and Quality Index score as defined by the IHME, based on the Global Burden of Disease dataset | 2017
Drawing from established methods and updated estimates from the Global Burden of Disease (GBD) 2016, we used 32 causes that should not result in death in the presence of effective care to approximate personal healthcare access and quality by location and over time. To better isolate potential effects of personal healthcare access and quality from underlying risk factor patterns, we risk-standardized cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in the Global Burden of Diseases 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardized death rates to provide a stronger signal of the effects of personal healthcare and access on cancer survival. We transformed each cause to a scale of 0–100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best). We set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the Health Access and Quality (HAQ) Index using all scaled cause values, providing an overall score of 0–100 of personal healthcare access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development.
Source: Institute for Health Metrics and Evaluation, Global Burden of Diseases 2017.
1.4 Inequality-adjusted healthy life expectancy
Inequality-adjusted Healthy life expectancy at birth | 2017
Healthy Life expectancy Index adjusted for inequality in life expectancy as defined by UNDP’s Human Development Report 2018.
Sources: Forum calculations, UNDP and Institute for Health Metrics and Evaluation’s Global Burden of Disease 2017.
Pillar 2: Education Access
2.1 Pre-primary enrolment
Enrolment in pre-primary education; population of the age group corresponding to the given level of education | 2018 or most recent period available
This data set is based on school register, school survey or census for data on enrolment by age; population census or estimates for school-age population.
Source: UNESCO Institute for Statistics.
2.2 Quality of vocational training
Response to the survey question “In your country, how do you assess the quality of vocational training?” [1 = extremely poor, among the worst in the world; 7 = excellent, among the best in the world]. | 2018–2019 weighted average or most recent period available
Source: World Economic Forum, Executive Opinion Survey.
2.3 NEET ratio
Youth (age 15–24) not in education, employment or training| 2018 or most recent period available
Source: International Labour Organization, ILOSTAT database.
2.4 Out-of-school children
Out-of-school children as a share of children of primary school age | 2016
Source: World Bank, Ending Learning Poverty: What Will It Take?, 2019.
2.5 Inequality-adjusted education access
Inequality-adjusted average between mean years of schooling and expected years of schooling | 2017
Mean and expected years of schooling are based on household surveys data harmonized in international databases, including the Luxembourg Income Study; Eurostat’s European Union Survey of Income and Living Conditions; the World Bank’s International Income Distribution Database; ICF Macro Demographic and Health Surveys; the United Nations Children’s Fund Multiple Indicators Cluster Survey; the Center for Distributive, Labor and Social Studies; the World Bank’s Socio-Economic Database for Latin America and the Caribbean; and the United Nations University’s World Income Inequality Database.
Source: UNDP, Human Development Index, 2018 Edition.
Pillar 3: Education Quality and Equity
3.1 Children below minimum proficiency by age 10
Share of children at the end of primary school who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML), in the context of Sustainable Development Goal 4.1.1 monitoring | 2016
Source: World Bank, Ending Learning Poverty: What Will It Take?, 2019.
3.2 Pupil-to-teacher ratio in pre-primary education
Average number of pupils per teacher in pre-primary school | 2018 or most recent period available
The pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. Data on education is collected by the UNESCO Institute for Statistics from official responses to its annual education survey.
Source: UNESCO Institute for Statistics.
3.3 Pupil-to-teacher ratio in primary education
Average number of pupils per teacher in primary school | 2018 or most recent period available
The pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. Data on education is collected by the UNESCO Institute for Statistics from official responses to its annual education survey.
Source: UNESCO Institute for Statistics.
3.4 Pupil-to-teacher ratio in secondary education
Average number of pupils per teacher in secondary school | 2018 or most recent period available
The pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education. Data on education is collected by the UNESCO Institute for Statistics from official responses to its annual education survey.
Source: UNESCO Institute for Statistics.
3.5 Harmonized learning outcomes
Composite score representing achievement on 7 international and regional achievement tests| 2018
Harmonized learning outcomes are produced using a conversion factor to compare international and regional standardized achievement tests. These tests include PISA, TIMSS, PIRLS, SACMEQ, LLECE, and PASEC. The harmonized learning outcomes score highlights levels of student learning in reading, mathematics and science in over 100 countries based on data from four international learning assessments and three regional learning assessments. All mean scores were calculated on a scale with a center point of 500 except 2004–2010 PASEC (0 to 100 scale), 1997 LLECE (250 centre point), and PIAAC (0 to 500 scale).
Source: World Bank Harmonised Learning Outcome Dashboard.
3.6 Social diversity in schools
Score on the PISA index of social inclusion | 2018
The PISA index of social inclusion is calculated as 100*(1-rho), where rho stands for the intra-class correlation of socio-economic status. The intra-class correlation, in turn, is the variation in student socio-economic status between schools, divided by the sum of the variation in student socio-economic status between schools and the variation in student socio-economic status within schools, and multiplied by 100.
Source: OECD, PISA 2018 Database.
3.7 Lack of education materials among disadvantaged students
Proportion of students in schools whose principal reported a lack in educational material | 2018
Source: OECD, PISA 2018 Database.
Pillar 4: Lifelong Learning
4.1 Extent of staff training
Response to the survey question “In your country, to what extent do companies invest in training and employee development?” [1 = not at all; 7 = to a great extent] | 2018–2019 weighted average or most recent period available
Source: World Economic Forum, Executive Opinion Survey.
4.2 Active labour market policies
Response to the survey question “In your country, to what extent do labour market policies help unemployed people to reskill and find new employment (including skills matching, retraining, etc.)?” [1 = not at all; 7 = to a great extent] | 2018–2019 weighted average or most recent period available
Source: World Economic Forum, Executive Opinion Survey.
4.3 Access to basic services through ICTs
Response to the survey question “In your country, to what extent do information and communication technologies (ICTs) enable access for all individuals to basic services (e.g. health, education, financial services, etc.)?” [1 = not at all; 7 = to a great extent] | 2018–2019 weighted average or most recent period available
Source: World Economic Forum, Executive Opinion Survey.
4.4 Firms offering formal training
Percentage of firms offering formal training programmes for their permanent, full-time employees | 2018 or most recent period available
Source: World Bank, Enterprise Surveys (http://www.enterprisesurveys.org/).
4.5 Digital skills among active population
Response to the survey question “In your country, to what extent does the active population possess sufficient digital skills (e.g. computer skills, basic coding, digital reading)?” [1 = not all; 7 = to a great extent] | 2018–2019 weighted average or most recent period available
Source: World Economic Forum, Executive Opinion Survey.
Pillar 5: Technology Access
5.1 Internet users
Internet users as a percentage of the population based on household surveys | 2019
Source: International Telecommunication Union, World Telecommunication/ICT Indicators database (June 2019 edition).
5.2 Fixed-broadband Internet subscriptions
Number of fixed-broadband internet subscriptions per 100 population | 2018 or most recent period available
This indicator refers to the number of subscriptions for high-speed access to the public internet (a TCP/IP connection), including cable modem, DSL, fibre, and other fixed (wired)-broadband technologies—such as Ethernet, LAN and broadband over powerline communications.
Source: International Telecommunication Union, World Telecommunication/ICT Indicators database (June 2019 edition).
5.3 Mobile-broadband subscriptions
Number of active mobile-broadband subscriptions per 100 population | 2018 or most recent period available
This indicator includes standard mobile-broadband subscriptions and dedicated mobile-broadband data subscriptions to the public internet.
Source: International Telecommunication Union, World Telecommunication/ICT Indicators database (June 2019 edition).
5.4 3G mobile network coverage
Percentage of the population covered by at least a 3G mobile network | 2018 or most recent period available
Source: International Telecommunication Union, World Telecommunication/ICT Indicators database (June 2019 edition).
5.5 Rural electricity access
Percentage of rural population with access to electricity | 2017 or most recent period available
Source: World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework, led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program.
5.6 Internet access in schools
Response to the survey question “In your country, to what extent is the Internet used in schools for learning purposes?” [1 = not at all; 7 = to a great extent] | 2018–2019 weighted average or most recent period available
Source: World Economic Forum, Executive Opinion Survey.
Pillar 6: Work Opportunities
6.1 Unemployment among labour force with basic education
Percentage of the labour force with a basic level of education who are unemployed | 2018 or most recent period available
Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).
Source: International Labour Organization, ILOSTAT database.
6.2 Unemployment among labour force with intermediate education
Percentage of the labour force with an intermediate level of education who are unemployed | 2018 or most recent period available
Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).
Source: International Labour Organization, ILOSTAT database.
6.3 Unemployment among labour force with advanced education
Percentage of the labour force with an advanced level of education who are unemployed | 2018 or most recent period available
Advanced education comprises short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).
Source: International Labour Organization, ILOSTAT database.
6.4 Unemployment in rural areas
Number of persons who are unemployed as a percentage of the total number of employed and unemployed persons (i.e., the labour force) in rural areas| 2017 or most recent period available
Source: International Labour Organization, ILOSTAT database.
6.5 Female labour force participation
Ratio of female to male labour force participation rate, calculated by dividing female labour force participation rate by male labour force participation rate, and multiplying by 100 | 2019
Labour force participation rate is the proportion of the population ages 15 and older who are economically active: all people who supply labour for the production of goods and services during a specified period.
Source: International Labour Organization, ILOSTAT database.
6.6 Workers in vulnerable employment
Contributing family workers or own-account workers as a percentage of total employment | 2019
Source: International Labour Organization, ILOSTAT database.
Pillar 7: Fair Wages Distribution
7.1 Incidence of low pay
Percentage of workers earning less than two-thirds of gross median earnings of all full-time workers | 2018 or most recent period available
Source: International Labour Organization, ILOSTAT database.
7.2 Bottom 40% to top 10% of labourincome share
Ratio in between the labour income share of the decile (1–4) to the top 10th decile | 2017
Source: International Labour Organization, The Global Labour Income Share and Distribution, 2019
7.3 Bottom 50% to top 50% labour income share
Ratio in between the labour income share of the decile (1-5) to the top 5 deciles (5-10) | 2017
Source: International Labour Organization, The Global Labour Income Share and Distribution, 2021.
7.4 Mean income/consumption of bottom 40% as a percentage of national mean income/consumption
Based on real mean per capita consumption or income measured at 2011 purchasing power parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet) | 2012-2017
Source: World Bank, Global Database of Shared Prosperity and Median Income/Consumption.
7.5 Adjusted labour income share
Labour share of income as a % of GDP, including own-account workers and contributing family workers | 2017
For more information on the methodology used, please visit https://www.ilo.org/ilostat-files/Documents/Labour%20income%20share%20and%20distribution.pdf
Source: International Labour Organization, The Global Labour Income Share and Distribution, 2021.
Pillar 8: Working Conditions
8.1 Workers’ rights
Score adapted from the International Trade Union Confederation (ITUC) Global Rights Index, which measures the level of protection of internationally recognized core labour standards. The scale of this indicator ranges from 0 (no protection) to 100 (high protection) | 2019
Dimensions of labour protection include civil rights, the right to bargain collectively, the right to strike, the right to associate freely, and access to due process rights. The indicator does not consider firing regulations. We distinguish between countries where workers have “non-access to rights” (Greece, Hong Kong SAR, Kuwait, Qatar, Saudi Arabia, and the United Arab Emirates) and countries experiencing “breakdown of institution” (Afghanistan, Libya) or murders (Guatemala). We assign a score of 10 to the former case and 3 to the latter. More details about the methodology of the Global Rights Index can be found at https://survey.ituc-csi.org/ITUC-Global-Rights-Index.html.
Source: World Economic Forum calculations based on International Trade Union Confederation, 2019 Global Rights Index (https://www.ituc-csi.org/rights-index-2019).
8.2 Labour-employer cooperation
Response to the survey question “In your country, how do you characterize labour-employer relations?” [1 = generally confrontational; 7 = generally cooperative] | 2018–2019 weighted average or most recent period available
Source: World Economic Forum, Executive Opinion Survey.
8.3 Meritocracy at work
Response to the survey question “In your country, to what extent is pay related to employee productivity?” [1 = not at all; 7 = to a great extent] | 2018–2019 weighted average or most recent period available
Source: World Economic Forum, Executive Opinion Survey.
8.4 Employees working more than 48 hours per week
Share of workers working more than 48 hours per week (full-time and part time contracts) based on national labour surveys | 2017 or most recent period available
Source: International Labour Organization, ILOSTAT database.
8.5 Collective bargaining coverage
The number of employees whose pay and/or conditions of employment are determined by one or more collective agreement(s) as a percentage of the total number of employees | 2016 or most recent period available
Collective bargaining coverage includes, to the extent possible, workers covered by collective agreements in virtue of their extension. Collective bargaining coverage rates are adjusted for the possibility that some workers do not have the right to bargain collectively over wages (e.g. workers in the public services who have their wages determined by state regulation or other methods involving consultation), unless otherwise stated in the notes. The statistics presented result from an ILO data compilation effort (including an annual questionnaire and numerous special enquiries), with contributions from J. Visser.
Source: International Labour Organization, ILOSTAT database.
Pillar 9: Social Protection
9.1 Adequacy of guaranteed minimum income benefits
The income of jobless families relying on minimum-income safety-net benefits as a percentage of the median disposable income for a couple with two children (with one partner is out of work). | 2018 or most recent period available
This can be compared with a poverty line defined as a fixed percentage of median income. For instance, if the poverty threshold is 50% of median income, a value of 30% means that benefit entitlements alleviate poverty risks of 60%. This ratio includes housing benefits.
Source: OECD.
9.02 Social protection effective coverage
Share of the population effectively covered by a social protection system, including social protection floors | 2016 or most recent period available
This indicator also provides the coverage rates of the main components of social protection: child and maternity benefits, support for persons without a job, persons with disabilities, victims of work injuries, and older persons.
Source: International Labour Organization, ILOSTAT database.
9.3 Social protection spending
Total public social protection expenditure [all functions] as a percent of GDP (%) | 2015 or most recent period available
Source: International Labour Organization, ILOSTAT database.
9.4 Social safety net protection
Response to the survey question: “In your country, to what extent does a formal social safety net provide adequate protection to the general population (e.g., protection against job loss, disability, old age, poverty)?” [1 = not at all—it doesn’t provide any protection; 7 = to the full extent—it provides full protection] | 2018–2019 weighted average or most recent period available
Source: World Economic Forum, Executive Opinion Survey.
Pillar 10: Inclusive Institutions
10.1 Incidence of corruption
Score on the Corruption Perceptions Index, which measures perceptions of corruption in the public sector, where the scale ranges from 0 (highly corrupt) to 100 (very clean) | 2018 edition
This is a composite indicator and the index aggregates data from a number of different sources that provide perceptions of business executives and country experts of the level of corruption in the public sector. More details about the methodology can be found at https://www.transparency.org/cpi.
Source: Transparency International, Corruption Perceptions Index 2018 (https://www.transparency.org/cpi2018 ).
10.2 Government and public services efficiency
Score on the Government Effectiveness pillar from the World Bank’s World Governance Indicators | 2018 edition
For more information, see https://info.worldbank.org/governance/wgi/
Source: World Bank, World Governance Indicators 2019 database.
10.3 Inclusiveness of institutions (gender, race & religion)
Score based on three pillars from the Haas Inclusiveness Index score of each country | 2018 edition
The Inclusiveness Index is an annual equity index that identifies and captures the degree of inclusion and marginality. The index is a diagnostic instrument intended to help us redefine the ways in which we think about a true democratic and inclusive society. More details on this index can be found at https://haasinstitute.berkeley.edu/.
Source: Haas Institute for a Fair and Inclusive Society (UC Berkeley), Inclusiveness Index.
10.04 Political stability and protection from violence
Score on the Political Stability and Absence of Violence/Terrorism pillar from the World Bank World Governance Indicators | 2018 edition
For more information, see https://info.worldbank.org/governance/wgi/Home/.
Source: World Bank, World Governance Indicators 2019.
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