India

  • Presidente:Droupadi Murmu
  • Primer Ministro:Narendra Modi
  • Capital:New Delhi
  • Idiomas:Hindi 41%, Bengali 8.1%, Telugu 7.2%, Marathi 7%, Tamil 5.9%, Urdu 5%, Gujarati 4.5%, Kannada 3.7%, Malayalam 3.2%, Oriya 3.2%, Punjabi 2.8%, Assamese 1.3%, Maithili 1.2%, other 5.9% note: English enjoys the status of subsidiary official language but is the most important language for national, political, and commercial communication; Hindi is the most widely spoken language and primary tongue of 41% of the people; there are 14 other official languages: Bengali, Telugu, Marathi, Tamil, Urdu, Gujarati, Malayalam, Kannada, Oriya, Punjabi, Assamese, Kashmiri, Sindhi, and Sanskrit; Hindustani is a popular variant of Hindi/Urdu spoken widely throughout northern India but is not an official language (2001 census)
  • Gobierno
  • Instituto Nacional de Estadística
  • Población, personas:1.435.228.798 (2024)
  • Área, km2:2.973.190
  • PIB per cápita, US$:2.411 (2022)
  • PIB, mil millones US$:3.416,6 (2022)
  • Índice de GINI:32,8 (2021)
  • Ranking de Facilidad para Hacer Negocios:62

Todos los conjuntos de datos: A E G I M N R S W
  • A
  • E
  • G
  • I
    • agosto 2022
      Fuente: International Centre for Tax and Development
      Subido por: Knoema
      Acceso el: 16 agosto, 2022
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      Data cited at: ICTD/UNU-WIDER, ‘Government Revenue Dataset’, 2018, https://www.wider.unu.edu/project/government-revenue-dataset' ICTD Government Revenue Dataset, 2018 A major obstacle to cross-country research on the role of revenue and taxation in development has been the weakness of available data. Government Revenue Dataset (GRD), developed through the International Centre for Tax and Development (ICTD), is aimed at overcoming this obstacle. It meticulously combines data from several major international databases, as well as drawing on data compiled from all available International Monetary Fund (IMF) Article IV reports. It achieves marked improvements in data coverage and accuracy, including a standardized approach to revenue from natural resources, and holds the promise of significant improvement in the credibility and robustness of research in this area. Dataset contains Central, General and merged government revenue data reported as % of GDP.
    • mayo 2023
      Fuente: International Monetary Fund
      Subido por: Felix Maru
      Acceso el: 29 mayo, 2023
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  • M
  • N
  • R
    • diciembre 2023
      Fuente: Organisation for Economic Co-operation and Development
      Subido por: Knoema
      Acceso el: 13 enero, 2024
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      A key set of information for policy analysis is i) how much revenue is collected; ii) in what ways is it collected; iii) from which institutional units of the economy are revenues raised for each particular financing scheme; and iv) which financing schemes receive those revenues. This dataset provides information about the contribution mechanisms the particular financing schemes use to raise their revenues. Understanding the nature of the flows is of importance from the perspective of both health and public finance policy. For example, the classification of revenues make it possible to distinguish between public and private funding of health care finance. Understanding how resources are raised by financing schemes is important for many countries, as many health systems are struggling with the issue of funding. The classification of revenues of financing schemes is suitable for tracking the collection mechanisms of a financing framework. Furthermore, the new classification makes it possible to analyse the contribution of the institutional units to health financing.
  • S
    • diciembre 2023
      Fuente: Reserve Bank of India
      Subido por: Raviraj Mahendran
      Acceso el: 19 diciembre, 2023
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      Note: FY2017-2018, 2018-2019, 2019-2020 and 2020-21 have been considered as 2017, 2018, 2019 and 2020 respectively. Capital Disbursements and Receipts, Expenditure and Revenue of India
  • W
    • febrero 2022
      Fuente: International Monetary Fund
      Subido por: Knoema
      Acceso el: 06 abril, 2022
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      The IMF’s World Revenue Longitudinal Data set (WoRLD) is a compilation of government tax and non-tax revenues from the IMF’s Government Finance Statistics and World Economic Outlook, and drawing on the OECD Revenue Statistics and Revenue Statistics in Latin American and the Caribbean.