Annex A. Proposed indicators for the inclusive and sustainable growth dashboard for Japan
Table A.1. Proposed indicators for the inclusive and sustainable growth dashboard for Japan | |||||||
---|---|---|---|---|---|---|---|
Theme | Sub-theme | Indicator | Measure and unit | Coverage | Frequency | Source | Description |
Dimension 1 – Sustainable growth and equitable sharing of benefits from growth | |||||||
Economic growth | GDP | GDP per capita | 5-year average growth rate (%) | Total | 5 years (period average) | OECD National Accounts database | * Based on GDP per capita, at constant 2015 prices and PPPs |
Level (US dollars) | Total | Annual | OECD National Accounts database | * Based on GDP per capita, at constant 2015 prices and PPPs | |||
Income and wealth | Household income and poverty | Household disposable income | Index (2010 = 100) | Total | Annual | OECD National Accounts database | * The indicator refers to real (inflation-adjusted) household disposable income per person, Index, 2010 = 100. * Households include non-profit institutions serving households, such as non-profit sports membership clubs, as these cannot be separately identified across all countries. |
Relative poverty | Rates (%) | Total poverty; Poverty in all working-age households; In-work poverty; Poverty for working-age households with children; Poverty for working-age households without children; Poverty in age group 0-25; Poverty in age group 26-65; Poverty in age group 66 and over | Annual | OECD Income Distribution database | * Relative poverty is defined as the share of people living with less than half the median disposable income in the country. | ||
Income and wealth inequality | S80/S20 income quintile share ratio | Ratio | Total | Annual | OECD Social Protection and Well-being database | * S80/S20 ratio is the ratio of the average income of the 20% richest to the 20% poorest. * S80/S20 ratio is calculated based on 2012 methodology except JPN, which is based on 2011 methodology. | |
Wealth share | Share (%) | Top 10% cohort; Bottom 40% cohort | Annual | OECD Social Protection and Well-being database |
| ||
Megatrends | Demographics | Projection of population | Estimated number (1 000 people) | Age groups (0-19, 20-64, 65-74 and 75 and over) | Annual | National Institute of Population and Social Security Research (IPSS) | * Moderate-range projection by National Institute of Population and Social Security Research (IPSS) in 2017. |
Projection of age composition | Estimated share (%) | Age groups (0-19, 20-64, 65-74 and 75 and over) | Annual | National Institute of Population and Social Security Research (IPSS) | * Moderate-range projection by National Institute of Population and Social Security Research (IPSS) in 2017. | ||
Digitalisation | Businesses purchasing cloud services | Share (%) | 250 employees and more; 50-249 employees | Annual | OECD Information and Communications Technology Database, ICT Access and Usage by Businesses | * The ICT Access and Usage by Businesses database provides a selection of 51 indicators, based on the 2nd revision of the OECD Model Survey on ICT Access and Usage by Businesses. The selected indicators originate from two sources: (a) An OECD data collection on the following OECD and accession countries or key partners: Australia, Brazil, Canada, Colombia, Japan, Korea, Mexico, New Zealand, Switzerland and the United States, and (b) Eurostat Statistics on Businesses for the OECD countries that are part of the European Statistical system. * Cloud computing refers to ICT services that are used over the Internet to access software, computing power, storage capacity etc., where the service: (a) Is delivered from servers of service providers, (b) Can be easily scaled up or down (e.g. number of users or change of storage capacity), (c) Can be used on-demand by the user, at least after the initial set up (without human interaction with the service provider), (d) Is paid for, either per user, by capacity used, or they are pre-paid. Cloud computing may include as well connections via Virtual Private Networks (VPN). Main cloud computing services include: (a) E-mail, (b) Office software, (d) Finance or accounting software, (e) Customer relationship management (CRM) software, (c) Hosting of databases, (d) Storage of files, and (f) Computing power to run own software. * Different methodology is applied for the data of cloud service usage before 2019. | |
Environmental pressures | Total GHG emissions | Million tonnes of CO2 equivalent | Emissions including LULUCF; Emissions excluding LULUCF | Annual | OECD Environment database | * Total GHG emissions refer to total emissions of CO2 (emissions from energy use and industrial processes, e.g. cement production), CH4 (methane emissions from solid waste, livestock, mining of hard coal and lignite, rice paddies, agriculture and leaks from natural gas pipelines), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3) excluding indirect CO2. * LULUCF refers to the emissions or removals from land-use, land-use change and forestry. | |
Domestic material consumption | Tonnes | Total | Annual | OECD Environment database | * Domestic material consumption refers to the amount of raw materials directly used in an economy. It includes all domestic extractions of material resources, minus those resources that are exported to other countries, plus those resources that are imported from other countries. It captures the flows of raw materials extracted or harvested from the environment and that physically enter the economic system for further processing or direct consumption (they are used by the economy as material factor inputs). | ||
COVID-19 | Incidence | Reported new cases of COVID-19 | 7-day moving average of cases per 100 000 population | Total | Daily | WHO Coronavirus (COVID-19) Dashboard and OECD Demography and Population database | * The indicator is based on the data collected by WHO. Counts primarily reflect laboratory-confirmed cases based upon WHO case definitions although some departures may exist due to local adaptations including both domestic and repatriated cases. Case detection, definitions, testing strategies, reporting practice, and lag times (e.g. time to case notification, and time to reporting of deaths) differ between countries, territories and areas. All data represent date of reporting as opposed to date of symptom onset. All data are subject to continuous verification and may change based on retrospective updates to accurately reflect trends, changes in country case definitions and/or reporting practices. * Annual population of each year is applied to calculate daily cases in the population. |
Reported new deaths of COVID-19 | 7-day moving average of cases per million population | Total | Daily | WHO Coronavirus (COVID-19) Dashboard and OECD Demography and Population database | * The indicator is based on the data collected by WHO. Counts primarily reflect laboratory-confirmed cases based upon WHO case definitions although some departures may exist due to local adaptations including both domestic and repatriated cases. Case detection, definitions, testing strategies, reporting practice, and lag times (e.g. time to case notification, and time to reporting of deaths) differ between countries, territories and areas. All data represent date of reporting as opposed to date of symptom onset. All data are subject to continuous verification and may change based on retrospective updates to accurately reflect trends, changes in country case definitions and/or reporting practices. * Annual population of each year is applied to calculate daily cases in the population. | ||
Health risks | Excess mortality | Estimated cumulative excess mortality per 100 000 population | Total | Monthly | WHO Global excess deaths associated with COVID-19 | * Excess mortality by modelled estimates as of December 2021. | |
Estimated monthly excess mortality | Total | Monthly | WHO Global excess deaths associated with COVID-19 | * Excess mortality by modelled estimates as of December 2021. | |||
Dimension 2 – Equal opportunities and foundations of future prosperity | |||||||
Education | Expenditure on education | Public expenditure per student relative to GDP per capita | Share (%) | Primary to Tertiary education; Primary, secondary education and post-secondary non-tertiary education; Total tertiary education | Annual | OECD Education at a Glance database | * "Primary to Tertiary education" refers to ISCED2011 levels 1 to 8. "Primary, secondary education and post-secondary non-tertiary education" refers to ISCED2011 levels 1 to 4. "Total tertiary education" refers to SCED2011 levels 5 to 8. * Public expenditure per student relative to GDP per capita by educational level was calculated multiplying (A) Total expenditure on educational institutions per full-time equivalent student relative to GDP per capita by educational level and (B) Relative shares of public expenditure on educational institutions by educational level. * Direct expenditure from general government to all public and private institutions. |
Educational outcomes | PIAAC: At or below Level 1 in both literacy and numeracy | Share (%) | Total | Annual | OECD Skills Matter Additional results | * The data of Adult skills of GBR was calculated as the average of England and Northern Ireland weighted by each population. The data of Adult skills of Flanders was substituted as the data of BEL. | |
PISA: Low achievers in all three domains | Share (%) | Total | Annual | OECD PISA 2018 Results | * The three domains of PISA are reading, mathematics and science and low achievers are defined as proficiency level at or below Level 2 for each. | ||
Tertiary graduates in STEM subjects | Index (2014 = 100) | Total | Annual | OECD Education at a Glance database | * STEM graduates are defined as the sum of the graduates (Bachelor, Master, Doctoral or equivalent levels) in the fields of Natural sciences, mathematics and statistics, Engineering, manufacturing and construction, Information and Communication Technologies (ICTs). JPN data does not include Information and Communication Technologies (ICTs) category. | ||
Ratio (%) | Total; Female; Male | Annual | OECD Education at a Glance database | * STEM graduates are defined as the sum of the graduates (Bachelor, Master, Doctoral or equivalent levels) in the fields of Natural sciences, mathematics and statistics, Engineering, manufacturing and construction, Information and Communication Technologies (ICTs). JPN data does not include Information and Communication Technologies (ICTs) category. | |||
Labour market | Labour force participation | Employment-to-population ratio | Ratio (%) | Female; Male; Age groups (20-24, 25-54, 55-64, 65+) | Annual | OECD Labour Force Statistics database | * Employment-to-population ratio is defined as the proportion of an economy's population that is employed. |
Employment structure | Temporary employment | Share of dependent employment (%) | Female; Male | Annual | OECD Labour Force Statistics database | * The definition of temporary worker has some variation depending on the country. | |
Employment by size | Share (%) | Industry (except construction); Firm sizes (250+ persons employed (Large enterprises), 50-249 persons employed, 20-49 persons employed, 10-19 persons employed, 1-9 persons employed | Annual | OECD Structural and Demographic Business Statistics (SDBS) database | * This includes incomplete data for AUT, BEL, CHL, COL, CRI, DNK, EST, FRA, GRC, GRC, HUN, IRL, LTU, LUX, LVA, NLD, SVK, and SVN mostly because of confidentiality (i.e. anonymization if less than three enterprises in a sector). CHL, COL and KOR are dropped considering the coverage of data on micro enterprises. AUS, CAN and TUR are dropped considering the substantial difference in size classes. | ||
Wage | Gender wage gap | Gender wage gap at median (%) | Female | Annual | OECD Social Protection and Well-being database | * Total age group. | |
Wage in SMEs relative to large firms | Ratio (250+ persons employed = 1) | Industry (except construction); Manufacturing; Firm sizes (50-249 persons employed, 20-49 persons employed, 10-19 persons employed, 1-9 persons employed) | Annual | OECD Structural and Demographic Business Statistics (SDBS) database | * This includes incomplete data for AUT, BEL, CHL, COL, CRI, DNK, EST, FRA, GRC, GRC, HUN, IRL, LTU, LUX, LVA, NLD, SVK, and SVN. Incomplete data in Eurostat countries are mostly because of confidentiality (i.e. anonymization if less than three enterprises in a sector). MEX is dropped from Panel B and CHL, COL and KOR are also dropped considering the coverage of data on micro enterprises. In addition, AUS, CAN and TUR are dropped considering the substantial difference in size classes. For CRI and ISR, wages & salaries of employees are used as remuneration to calculate compensation per employee. | ||
Human resource development | Training in business sector | Participation in job-related non-formal education and training | Participation ratio (%) | Total; Private sector; Public sector; Firm sizes (Enterprises of 10-49 employed persons, Enterprises of 1-9 employed persons, Enterprises of 50-249 employed persons and Enterprises of over 249 employed persons) | Annual | OECD Education and Training database |
|
Participation in formal education and training | Participation ratio (%) | Total; 25-34 years; 35-44 years; 45-54 years; 55-64 years | Annual | OECD Education and Training database |
| ||
Willingness to participate in formal and/or non-formal education | Share (%) | Total | Annual | OECD Education and Training database |
| ||
Barriers to participate in formal and/or non-formal education | Share (%) | Total | Annual | OECD Education and Training database |
| ||
Public support | Public spending on labour market programmes, training | Share of GDP (%) | Total | Annual | OECD Labour Market Programmes database |
| |
Labour market inclusiveness, job quality and workers’ health | Inclusiveness | Labour underutilisation | Ratio (%) | Total | Annual | OECD National Accounts database, Household Dashboard | * Labour underutilisation includes in the numerator the unemployed, discouraged (i.e. persons not in the labour force who did not actively look for work during the past four weeks but who wish and are available to work) and underemployed workers (i.e. full-time workers working less than usual during the survey reference week for economic reasons and part-time workers who wanted but could not find full-time work), expressed as a ratio of the labour force. |
Seats occupied by women on listed company board | Share (%) | Female | Annual | OECD Social Protection and Well-being database |
| ||
Women in manager position | Share (%) | Female | Annual | OECD Social Protection and Well-being database |
| ||
Job quality | Job strain | Share (%) | Total; Female; Male | Annual | OECD Social Protection and Well-being database | * Job strain is defined as a situation where the job demands experienced by workers (i.e. physical demands, work intensity, inflexible working hours) exceed the resources available to them (i.e. task discretion, training, career advancement). | |
Average annual hours actually worked per worker | Hours | Total | Annual | OECD Labour Force Statistics database | * Dependent employment | ||
Average and excessive working hours | Share of employed person working 49 or more hours per week (%); Average hours per week per employed person (hours) | Total | Annual | ILO Wages and Working Time Statistics |
| ||
Workers’ health | Work-life balance (Unpaid work) | Share of women (%); 15-64 years old total (minutes) | Female; Total | Annual | OECD Social Protection and Well-being database |
| |
Work-life balance (Time off) | Hours | Total | Annual | OECD Social Protection and Well-being database | * Time off is measured by hours per day and refers to people in full-time employment. It is the sum of personal care time (i.e. the amount of time spent sleeping, eating and drinking, on other personal care activities and on travel time associated with personal care) and leisure time (i.e. the amount of time spent practicing sports, interacting with friends and relatives, attending or participating in events, watching TV or listening to music, on other leisure activities, and on travel time associated with leisure). Only time spent on main or primary activities is included and as such, it is likely to underestimate especially the time spent on leisure activities, which are often performed in combination with other tasks (e.g. chatting on the phone with a friend while cooking). * The OECD average was calculated as an arithmetic mean of the latest values of available countries. | ||
Death of despair (Deaths from suicide, alcohol and drugs) | Persons (per 100 000 population) | Total; Female; Male | Annual | OECD Social Protection and Well-being database |
| ||
Dimension 3 – Inclusive and sustainable business and innovation | |||||||
Economic growth and labour productivity | Productivity and value added | Labour productivity (GDP per hour worked) | Index (OECD Total = 100) | Total | Annual | OECD Productivity Statistics database |
|
Level (constant USD value in 2015 PPPs) | Total | Annual | OECD Productivity Statistics database |
| |||
Labour productivity in SMEs relative to large manufacturing firms | Ratio (large manufacturing firms = 1) | 1-249 persons employed (SMEs); 1-9 persons employed; 10-19 persons employed; 20-49 persons employed; 50-249 persons employed | Annual | OECD Structural and Demographic Business Statistics (SDBS) database | * This includes incomplete data in AUS, AUT, BEL, CHE, DNK, IRL, ISR, KOR, LUX, LVA, NLD, SVN, and TUR. | ||
Within-industry labour productivity dispersion | Ratio (Productivity of the firms at the 10th percentile of the distribution relative to the firms at the 90th percentile) | Food & beverages; Textiles & apparel; Wood & paper prod.; Chemicals; Pharmaceuticals; Rubber & plastics; Metal products; Computer & electronics; Electrical equipment; Machinery and equipment; Transport equipment; Furniture & other | Period average between 2000-2014 | OECD MultiProd database | * Japan vs benchmark countries, 2000-2014. This figure reports average labour productivity dispersion within industries in Japan and within country-industry pairs in a set of benchmark countries. Dispersion is measured as the ratio of the 90th percentile to the 10th percentile of the productivity distribution. Benchmark group of countries consists of Australia, Austria, Belgium, Canada, Chile, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Luxembourg, the Netherlands, Norway, New Zealand, Portugal, Slovenia, Sweden and Switzerland. | ||
Value added per person employed | Index (2013 = 100) | Total; Agriculture, forestry and fishing; Manufacturing; Construction; Wholesale retail trade accommodation food services, transportation and storage; Information and communication; Financial and insurance activities; Professional, scientific and technical activities, administrative and support service activities; Mining and utilities | Annual | OECD Productivity Statistics database | * Index based on constant prices | ||
Industry contribution to value added growth | Rates (%) | Non-agriculture business sector excluding real estate (total); Mining and utilities; Manufacturing; Construction; Business sector services excluding real estate | Annual | OECD Productivity Statistics database |
| ||
Capital deepening | Capital deepening | Index (2010 = 100) | Total; ICT; Non-ICT | Annual | OECD Productivity Statistics database | * Capital investment in ICT is based on gross fixed capital formation (GFCF) of “information and communication equipment” and “computer software and databases”, as defined by the System of National Accounts 2008 (SNA08). | |
Trade and value chains | Openness to trade | Trade-to-GDP ratio | Share of GDP (%) | Total | Annual | OECD National Accounts database | * Calculated as the ratio of sum of exports and imports to GDP |
Services Trade Restrictiveness Index | Index | All sector average | Annual | OECD STRI Regulatory database | * The STRI database records measures on a Most Favoured Nations basis. Air transport and road freight cover only commercial establishment (with accompanying movement of people). The indices are based on laws and regulations in force on 31 October 2021. | ||
Value chains participation | Global Value Chain (GVC) participation | Share of gross exports (%) | Forward participation in GVCs (Domestic value added in foreign exports as a share of gross exports, by foreign exporting country); Backward participation in GVCs (Foreign value added share of gross exports, by value added origin country) | Annual | OECD Trade in Value Added (TiVA) database |
| |
Technology and innovation | Research and Development (R&D) | R&D expenditure | Gross domestic expenditure on R&D per capita (current USD in PPPs) | Total | Annual | OECD Science, Technology and Patents database, Main Science and Technology Indicators |
|
Share of Business enterprise R&D expenditure to gross value added (%) | Agriculture, forestry and fishing; Mining and quarrying; Manufacturing; Construction; Wholesale and retail trade, repair of motor vehicles and motorcycles; Transportation and storage; Information and communication; Financial and insurance activities; Professional, scientific and technical activities | Annual | OECD National Accounts database and OECD Research and Development Statistics database | * The data includes provisional values. | |||
Researchers in business enterprise sector | Total researchers per thousand labour force (persons) | Total | Annual | OECD Science, Technology and Patents database, Main Science and Technology Indicators | * Total researcher per thousand labour force includes provisional and estimated values. | ||
Share of female researchers in total internal R&D personnel (%) | Female | Annual | OECD Science, Technology and Patents database, Main Science and Technology Indicators | * Difference in methodology to obtain the shares of female researchers in AUT, ESP, GRC, ITA, JPN and LVA. | |||
Share of doctoral or equivalent researchers in total internal R&D personnel (%) | Doctoral or equivalent | Annual | OECD Science, Technology and Patents database | * Difference in methodology to obtain the shares of doctoral or equivalent researchers in AUT, GRC, ITA, JPN and LVA. | |||
Investment and creation of intellectual assets | Investment in intellectual property products | Share of Gross Fixed Capital Formation (%) | Total | Annual | OECD National Accounts database |
| |
Venture capital investments | Share of GDP (%) | Total | Annual | OECD Entrepreneurship Financing database | * Venture capital is made up of the sum of early stage (including pre-seed, seed, start-up and other early stage) and later stage venture capital. As there are no harmonised definitions of venture capital stages across venture capital associations and other data providers, original data have been re-aggregated to fit the OECD classification of venture capital by stages. | ||
Patent applications in climate mitigation technologies | Number of total applications | Total | Annual | OECD Environment database | * This patent data is based on the family size of "2 and greater" counting higher-value inventions that have sought patent protection in at least two jurisdiction. Number of patents have fractional values based on the country of residence of the inventors. The patent statistics presented here are constructed using algorithms developed by the OECD Environment Directorate drawing on data extracted from the OECD STI Micro-data Lab: Intellectual Property Database, http://oe.cd/ipstats. Consistent with other patent statistics provided by OECD databases, only published applications for "patents of invention" are considered (i.e. excluding utility models, petty patents, etc.). | ||
Share in total applications (%) | Total | Annual | OECD Environment database | * This patent data is based on the family size of "2 and greater" counting higher-value inventions that have sought patent protection in at least two jurisdiction. Number of patents have fractional values based on the country of residence of the inventors. The patent statistics presented here are constructed using algorithms developed by the OECD Environment Directorate drawing on data extracted from the OECD STI Micro-data Lab: Intellectual Property Database, http://oe.cd/ipstats. Consistent with other patent statistics provided by OECD databases, only published applications for "patents of invention" are considered (i.e. excluding utility models, petty patents, etc.). | |||
Female inventors | Share in total inventors (%) | Female | Annual | OECD Social Protection and Well-being database |
| ||
Digital transformation | Firms performing big data analysis | Share (%) | All businesses (10 persons employed or more); Large (250 employees and more); Medium (50 to 249 employees); Small (10 to 49 employees) | Annual | OECD Businesses database, ICT Access and Usage |
| |
Adults with medium and high performance in problem solving in technology-rich environments | Share (%) | Total; Female; Male; Age groups (25-34, 35-44, 45-54, 55-65) | Annual | OECD Education and Training database |
| ||
Gap between age groups (points) | Gap between 35-44 and 55-65 years old; Gap between 25-34 and 55-65 years old | Annual | OECD Education and Training database |
| |||
Business dynamism | Entry and exit | Average entry rate | Ratio (%) | Total | Period average between 1998-2015 | OECD DynEmp3 database | * This reports unweighted averages of entry and exit rates across STAN a38 industries and available years for the period of 1998-2015 and a coverage table is available in Calvino and Criscuolo (2019). JPN data is based on manufacturing sector. The data exclude self-employment and the classification of sectors. Owing to methodological differences, it may deviate from officially published national statistics. Data for some countries are still preliminary. |
Average exit rate | Ratio (%) | Total | Period average between 1998-2015 | OECD DynEmp3 database | * This reports unweighted averages of entry and exit rates across STAN a38 industries and available years for the period of 1998-2015 and a coverage table is available in Calvino and Criscuolo (2019). JPN data is based on manufacturing sector. The data exclude self-employment and the classification of sectors. Owing to methodological differences, it may deviate from officially published national statistics. Data for some countries are still preliminary. | ||
Post-entry employment growth | Ratio (%) | Total | Period average between 1998-2015 | OECD DynEmp3 database | * JPN data is based on manufacturing sector. The data exclude self-employment and the classification of sectors. Owing to methodological differences, it may deviate from officially published national statistics. Data for some countries are still preliminary. | ||
Net job creation | Rates (%) | Young (< 6) Small (< 250); Old (≥ 6) Small (< 250); Young (< 6) Large (≥ 250); Old (≥ 6) Large (≥ 250) | Period average between 2000-2014 | OECD DynEmp3 database | * A benchmark group of countries consists of Austria, Belgium, Brazil, Costa Rica, Spain, Finland, France, Hungary, Italy, the Netherlands, Norway, Portugal, Sweden and Republic of Türkiye. Benchmark rates in the figure are calculated as the average of these countries. This reports the 2000-2014 average relative contribution of net job creation in each to aggregate employment change in manufacturing. It is defined as net job creation (i.e., the difference of total employment at time t and t-1) of the particular group over average total employment in two periods in the macro-sector. | ||
Dimension 4 – Responsive and inclusive governance | |||||||
Confidence in government | Population placing their trust in government | Share (%) | Total | Annual | OECD Social Protection and Well-being database | * OECD average was calculated as arithmetic means of the values of available OECD countries. This indicator is based on unofficial data from the Gallup World Poll. | |
Voter turnout | Participation in national elections | Participation rate (%) | Total | Annual | OECD Social Protection and Well-being database | * OECD average was calculated as arithmetic means of the values of available OECD countries. National elections refer to parliamentary elections, with the exceptions of Brazil, Finland, France, Korea, Mexico, the Russian Federation and the United States, where Presidential elections are considered. Australia, Belgium, Brazil, Luxembourg and Turkey enforce compulsory voting. In Chile, compulsory voting was dropped in 2012. | |
Female political participation | Female parliamentarians in the House of Representatives and the House of Councilors | Share (%) | Female | Periodical | Inter-Parliamentary Union (IPU) PARLINE database | * The House of Representatives has 465 seats. The House of Councillors has 248 seats (from July 26th, 2022). |
This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided.
© OECD 2023
The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at https://www.oecd.org/termsandconditions.