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イタリア

  • 大統領:Sergio Mattarella
  • 首相:Giuseppe Conte
  • 首都:Rome
  • 言語:Italian (official), German (parts of Trentino-Alto Adige region are predominantly German speaking), French (small French-speaking minority in Valle d'Aosta region), Slovene (Slovene-speaking minority in the Trieste-Gorizia area)
  • 政府
  • 統計局
  • 人口、人:60,431,283 (2018)
  • 面積、平方キロメートル:294,140
  • 1人当たりGDP、US $:34,318 (2018)
  • GDP、現在の10億米ドル:2,073.9 (2018)
  • GINI指数:No data
  • ビジネスのしやすさランク:51

Productivity

すべてのデータセット:  A G H L O P R S U W
  • A
    • 12月 2019
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 14 12月, 2019
      データセットを選択
      Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors). SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.   SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) : Annex I - Services, Annex II - Industry, Annex III - Trade and Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation. The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J). Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced. Main characteristics (variables) of the SBS data category: Business Demographic variables (e.g. Number of enterprises)"Output related" variables (e.g. Turnover, Value added)"Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments) All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below: Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section). More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003. Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.
  • G
    • 1月 2015
      ソース: University of Groningen, Netherlands
      アップロード者: Knoema
      以下でアクセス: 25 2月, 2016
      データセットを選択
      The GGDC 10-Sector Database provides a long-run internationally comparable dataset on sectoral productivity performance in Asia, Europe, Latin America and the US. Variables covered in the data set are annual series of value added, output deflators, and persons employed for 10 broad sectors. It gives sectoral detail to the historical macro data in Maddison (2003) from 1950 onwards. It consists of series for 10 countries in Asia, 9 in Latin-America and 9 in Europe and the US. The data for Asia and Latin-America are based on Marcel P. Timmer and Gaaitzen J. de Vries (2007), 'A Cross-Country Database For Sectoral Employment And Productivity In Asia And Latin America, 1950-2005', GGDC Research memorandum GD-98, Groningen Growth and Development Centre, August 2007. Data for Europe and the US is based on an update of Bart van Ark (1996), Sectoral Growth Accounting and Structural Change in Post-War Europe, in B. van Ark and N.F.R. Crafts, eds., Quantitative Aspects of Post-War European Economic Growth, CEPR/Cambridge University Press, pp. 84-164. All series derived from this database need to be referred to as: "Timmer, Marcel P. and Gaaitzen J. de Vries (2009), "Structural Change and Growth Accelerations in Asia and Latin America: A New Sectoral Data Set" Cliometrica, vol 3 (issue 2) pp. 165-190."
    • 8月 2019
      ソース: Organisation for Economic Co-operation and Development
      アップロード者: Knoema
      以下でアクセス: 12 8月, 2019
      データセットを選択
      Productivity is a key driver of economic growth and changes in living standards. Labour productivity growth implies a higher level of output for unit of labour input (hours worked or persons employed). This can be achieved if more capital is used in production or through improved overall efficiency with which labour and capital are used together, i.e., higher multifactor productivity growth (MFP). Productivity is also a key driver of international competitiveness, e.g. as measured by Unit Labour Costs (ULC).   The OECD Productivity Database aims at providing users with the most comprehensive and the latest productivity estimates. The update cycle is on a rolling basis, i.e. each variable in the dataset is made publicly available as soon as it is updated in the sources databases. However, some time lag may arise which affects individual series and/or countries for two reasons: first, hours worked data from the OECD Employment Outlook are typically updated less frequently than the OECD Annual National Accounts Database; second, source data for capital services are typically available in annual national accounts later than source data for labour productivity and ULCs.   Note to users: The OECD Productivity Database accounts for the methodological changes in national accounts' statistics, such as the implementation of the System of National Accounts 2008 (2008 SNA) and the implementation of the international industrial classification ISIC Rev.4. These changes had an impact on output, labour and capital measurement. For Chile, China, Colombia, India, Japan, Turkey and the Russian Federation the indicators are in line with the System of National Accounts 1993 (1993 SNA); for all other countries, the indicators presented are based on the 2008 SNA
  • H
    • 12月 2019
      ソース: Organisation for Economic Co-operation and Development
      アップロード者: Sandeep Reddy
      以下でアクセス: 03 12月, 2019
      データセットを選択
      Unit of measure usedIndex: Year 2015 = 100 The Hourly Earnings (MEI) dataset contains predominantly monthly statistics, and associated statistical methodological information, for the 35 OECD member countries and for selected non-member economies.  The MEI Earnings dataset provides monthly and quarterly data on employees' earnings series. It includes earnings series in manufacturing and for the private economic sector. Mostly the sources of the data are business surveys covering different economic sectors, but in some cases administrative data are also used. The target series for hourly earnings correspond to seasonally adjusted average total earnings paid per employed person per hour, including overtime pay and regularly recurring cash supplements. Where hourly earnings series are not available, a series could refer to weekly or monthly earnings. In this case, a series for full-time or full-time equivalent employees is preferred to an all employees series.
  • L
    • 9月 2014
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 25 11月, 2015
      データセットを選択
    • 9月 2014
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 26 11月, 2015
      データセットを選択
    • 4月 2018
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 11 4月, 2018
      データセットを選択
      Labour productivity per hour worked is calculated as real output (deflated GDP measured in chain-linked volumes, reference year 2010) per unit of labour input (measured by the total number of hours worked). Measuring labour productivity per hour worked provides a better picture of productivity developments in the economy than labour productivity per person employed, as it eliminates differences in the full time/part time composition of the workforce across countries and years.
    • 12月 2019
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 14 12月, 2019
      データセットを選択
      Gross domestic product (GDP) is a measure for the economic activity. It is defined as the value of all goods and services produced less the value of any goods or services used in their creation. GDP per person employed is intended to give an overall impression of the productivity of national economies expressed in relation to the European Union (EU28) average. If the index of a country is higher than 100, this country's level of GDP per person employed is higher than the EU average and vice versa. Basic figures are expressed in PPS, i.e. a common currency that eliminates the differences in price levels between countries allowing meaningful volume comparisons of GDP between countries. Please note that 'persons employed' does not distinguish between full-time and part-time employment. Labour productivity per hour worked is calculated as real output per unit of labour input (measured by the total number of hours worked). Measuring labour productivity per hour worked provides a better picture of productivity developments in the economy than labour productivity per person employed, as it eliminates differences in the full time/part time composition of the workforce across countries and years.
    • 8月 2019
      ソース: Organisation for Economic Co-operation and Development
      アップロード者: Knoema
      以下でアクセス: 12 8月, 2019
      データセットを選択
      The productivity and income estimates presented in this dataset are mainly based on GDP, population and employment data from the OECD Annual National Accounts. Hours worked are sourced from the OECD Annual National Accounts, the OECD Employment Outlook and national sources. The OECD Productivity Database aims at providing users with the most comprehensive and the latest productivity estimates. The update cycle is on a rolling basis, i.e. each variable in the dataset is made publicly available as soon as it is updated in the sources databases. However, timely data issues may arise and affect individual series and/or individual countries. In particular, annual hours worked estimates from the OECD Employment Outlook are typically updated less frequently (once a year, in the summer) than series of hours worked from the OECD Annual National Accounts.
  • O
  • P
    • 4月 2019
      ソース: University of Groningen, Netherlands
      アップロード者: Knoema
      以下でアクセス: 23 5月, 2019
      データセットを選択
      PWT version 9.1 is a database with information on relative levels of income, output, input and productivity, covering 182 countries between 1950 and 2017.
    • 7月 2018
      ソース: EU KLEMS Project
      アップロード者: Sandeep Reddy
      以下でアクセス: 13 3月, 2019
      データセットを選択
      EU KLEMS Growth and Productivity Accounts
    • 10月 2019
      ソース: Organisation for Economic Co-operation and Development
      アップロード者: Knoema
      以下でアクセス: 08 10月, 2019
      データセットを選択
      The OECD Productivity Database aims at providing users with the most comprehensive and the latest productivity estimates. The update cycle is on a rolling basis, i.e. each variable in the dataset is made publicly available as soon as it is updated in the OECD Annual National Accounts database. However, timely data issues may arise and affect individual series and/or individual countries. Sectors differ from each other with respect to their productivity growth. Understanding the drivers of productivity growth at the total economy level requires an understanding of the contribution of each sector. Data of real gross value added, labour compensation, hours worked and employment are sourced from the OECD Annual National Accounts.
  • R
    • 12月 2019
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 14 12月, 2019
      データセットを選択
      The unit labour cost (ULC) measures the average cost of labour per unit of output. It is calculated as the ratio of labour costs to labour productivity. The ULC represents a link between productivity and the cost of labour in producing output. The input data are obtained through official transmissions of national accounts' country data in the European system of accounts - ESA2010 - transmission programme. Nominal ULC (NULC) is calculated as: (D1/EEM) / (B1GM/ETO), where: D1 = Compensation of employees, all industries, in current prices EEM = Employees, all industries, in persons (following the domestic concept) B1GM = Gross domestic product at market prices in millions, chain-linked volumes reference year 2010 ETO = Total employment, all industries, in persons (following the domestic concept). The MIP scoreboard indicator is the Nominal unit labour cost - 3 years % change. In the MIP domain are also published annual figures on: NULC - 1, 3, 5 and 10 years % change and index 2010=100Real labour productivity - 1, 3, 5 and 10 years % change and index 2010=100ULC performance related to the Euro area – 1 and 10 years % change And quarterly data: NULC – 1 year % change and index 2010=100Real labour productivity – 1 year % change and index 2010=100
    • 7月 2019
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 03 7月, 2019
      データセットを選択
      This metadata refers to two datasets based one and the same data collection:Material flow accounts (env_ac_mfa): detailed material input flows into the national economy (in tonnes)Resource productivity (env_ac_rp): various ratios of gross domestic product (GDP) over domestic material consumption (DMC)   1. Economy-wide material flow accounts (EW-MFA) compile material flow inputs into national economies. EW-MFA cover all solid, gaseous, and liquid material inputs, except for water and air, measured in mass units per year. Like the system of national accounts, EW-MFA constitute a multi-purpose information system. The detailed material flows provide a rich empirical database for numerous analytical purposes. Further, EW-MFA are used to derive various material flow indicators such as:Domestic extraction (DE): total amount of material extracted for further processing in the economy, by resident units from the natural environment;Imports (IMP): imports of products in their simple mass weight;Direct material input (DMI): measures the direct input of material into the economy; it includes all materials which are of economic value and which are availble for use in production and consumption activities (DE+IMP);Exports (EXP): exports of products in their simple mass weight;Domestic material consumption (DMC): measures the total amount of material actually consumed domestically by resident units (DE+IMP-EXP). Note: IMP and EXP are distinguished into extra-EU-trade and total trade.   2. Resource productivity (GDP/DMC) is defined as the ratio of gross domestic product (GDP) over domestic material consumption (DMC) and commonly expressed in Euro per kilogram of material. The data set env_ac_rp employs different types of GDP for calculating this ratio, depending on the analytical perspective (see item 4). The term designates an indicator that reflects the GDP generated per unit of resources used by the economy. This is typically a macro-economic concept that can be presented alongside labour or capital productivity.
    • 7月 2019
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 03 7月, 2019
      データセットを選択
      Resource productivity is gross domestic product (GDP) divided by domestic material consumption (DMC). DMC measures the total amount of materials directly used by an economy. It is defined as the annual quantity of raw materials extracted from the domestic territory of the focal economy, plus all physical imports minus all physical exports. It is important to note that the term 'consumption', as used in DMC, denotes apparent consumption and not final consumption. DMC does not include upstream flows related to imports and exports of raw materials and products originating outside of the focal economy. For the calculation of resource productivity, Eurostat uses GDP either in unit 'EUR in chain-linked volumes' (to the reference year 2010 at 2010 exchange rates) or in unit 'PPS' (Purchasing Power Standard). Consequently, the indicator is expressed: i) in euro per kg, for comparing the changes in one country over time; ii) in PPS per kg, for comparing different countries in one specific year. It is also calculated as an index on year 2000, for comparing countries in different years. More information on resource productivity can be found in Statistics Explained.
    • 7月 2019
      ソース: Eurostat
      アップロード者: Knoema
      以下でアクセス: 03 7月, 2019
      データセットを選択
      Resource productivity is gross domestic product (GDP) divided by domestic material consumption (DMC). DMC measures the total amount of materials directly used by an economy. It is defined as the annual quantity of raw materials extracted from the domestic territory of the focal economy, plus all physical imports minus all physical exports. It is important to note that the term 'consumption', as used in DMC, denotes apparent consumption and not final consumption. DMC does not include upstream flows related to imports and exports of raw materials and products originating outside of the focal economy. For the calculation of resource productivity, Eurostat uses GDP either in unit 'EUR in chain-linked volumes' (to the reference year 2010 at 2010 exchange rates) or in unit 'PPS' (Purchasing Power Standard). Consequently, the indicator is expressed: i) in euro per kg, for comparing the changes in one country over time; ii) in PPS per kg, for comparing different countries in one specific year. It is also calculated as an index on year 2000, for comparing countries in different years. More information on resource productivity can be found in Statistics Explained.
  • S
    • 4月 2019
      ソース: Organisation for Economic Co-operation and Development
      アップロード者: Knoema
      以下でアクセス: 12 4月, 2019
      データセットを選択
      STAN Indicators provides annual indicators related to production and employment structure, labour productivity and labour costs, investment, business research and development expenditures and international trade patterns. Data are presented for OECD countries and cover the time-period 1970-2011, although the time coverage may vary across countries and indicators. Series are provided for a wide range of economic activities (according to an ISIC Rev.4 based hierarchy) compatible with the list in the underlying STAN Database in ISIC Rev. 4. STAN Indicators belong to the STAN family datasets; they are primarily drawn from STAN Database for Structural Analysis (STAN), STAN Bilateral Trade (BTDIxE) and STAN Research & Development Expenditures in Industry (ANBERD). Indicators are compiled to respond to the needs of analysts and researchers interested in measuring economic performance, productivity growth, competitiveness and structural changes. They also complement the OECD publications, Science Technology and Industry Scoreboard and Economic Globalisation Indicators.
  • U
    • 8月 2019
      ソース: Organisation for Economic Co-operation and Development
      アップロード者: Knoema
      以下でアクセス: 06 8月, 2019
      データセットを選択
      Early Estimates of Quarterly Unit Labour Cost (ULC) indicators for the total economy provide current edge data on ULCs and their components labour productivity and labour compensation per employed person.  Recent and more longer terms trends in productivity and competitiveness on the total economy level and by sector or activity can be found in the OECD Compendium of Productivity Indicators.Data of quarterly GDP, labour compensation and employment are sourced from the OECD Quarterly National Accounts and the Main Economic Indicators Databases.  Early Estimates of Quarterly ULCs are available for all OECD member countries (except Chile, Iceland, Mexico), as well as for the zone aggregates Euro area and OECD Total. Unit labour costs (ULCs) measure the average cost of labour per unit of output. They are calculated as the ratio of total labour costs to real output. Different from the estimates of annual ULC above, the Early Estimates of Quarterly ULC use employment and not hours worked as measure of labour input (see below "Other aspects, Recommended uses and limitations"). Quarterly ULCs can be decomposed into the components labour compensation per employee and output per person employed (employment-based labour productivity). The OECD estimates of total labour costs adjust for labour compensation of self-employed persons Every effort has been made to ensure that data are comparable across countries. The adjustment for the self-employed assumes that labour compensation per person is equivalent for the self-employed and employees. This assumption may be more or less valid across different countries and economic activities.  EEQ ULCs are also fully compatible with the ULC series published by the ECB which provides ULC series for 21 EU OECD member countries and Euro area. Those for nine Non-EU member OECD countries are compiled by the OECD following a methodology that is fully consistent with that used by the ECB.
  • W
    • 2月 2018
      ソース: World Input-Output Database
      アップロード者: Knoema
      以下でアクセス: 16 2月, 2018
      データセットを選択
      Data cited at: World Input-Output Database http://www.wiod.org/home Topic: Socio - Economic Accounts Publication: http://www.wiod.org/database/seas16 License: https://creativecommons.org/licenses/by/4.0/   Basic data on output and employment, World Input-Output Database (WIOD) database, February 2018 released. The Socio-economic accounts contain industry-level data on employment, capital stocks, gross output and value added at current and constant prices. The industry classification is the same as for the world input-output tables. Reference: Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R. and de Vries, G. J. (2015),  "An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production",  Review of International Economics., 23: 575–605

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