Gross State Domestic Product Contribution to National Gross Domestic Product (GDP): Andhra Pradesh data was reported at 4.812 % in 2025. This records an increase from the previous number of 4.721 % for 2024. Gross State Domestic Product Contribution to National Gross Domestic Product (GDP): Andhra Pradesh data is updated yearly, averaging 4.447 % from Mar 2005 (Median) to 2025, with 21 observations. The data reached an all-time high of 4.929 % in 2021 and a record low of 4.133 % in 2014. Gross State Domestic Product Contribution to National Gross Domestic Product (GDP): Andhra Pradesh data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s General Election – Table IN.GEI003: Memo Items: State Economy: Gross State Domestic Product: Contribution: National Gross Domestic Product. Data prior to 2012 is based on GDP at Factor Cost

Uruguay GDP: 1983p: AP: Agriculture data was reported at 26,851.000 UYU th in 2008. This records an increase from the previous number of 23,053.000 UYU th for 2007. Uruguay GDP: 1983p: AP: Agriculture data is updated yearly, averaging 13,116.000 UYU th from Dec 1988 (Median) to 2008, with 21 observations. The data reached an all-time high of 26,851.000 UYU th in 2008 and a record low of 8,215.000 UYU th in 1990. Uruguay GDP: 1983p: AP: Agriculture data remains active status in CEIC and is reported by Central Bank of Uruguay. The data is categorized under Global Database’s Uruguay – Table UY.A024: SNA 1968: GDP: by Industry: 1983 Price: Annual.

This Dataset contains state-wise share of GDP.

Note: 1) Share of state is calculated as GSDP of state as a ratio of sum of GSDP of all states. 2) For 1960-61 and 1970-71, ratio is calculated as NSDP of state divided by sum of NSDP of all states. 3) Telangana was bifurcated from Andhra Pradesh in 2014, however the data is available since 2010-11. 4) Data for Andhra Pradesh, Bihar, Madhya Pradesh and Uttar Pradesh are given for both before and after bifurcation separately

Uruguay GDP: 1983p: AP: Livestock Production data was reported at 18,029.000 UYU th in 2008. This records an increase from the previous number of 17,147.000 UYU th for 2007. Uruguay GDP: 1983p: AP: Livestock Production data is updated yearly, averaging 16,361.000 UYU th from Dec 1988 (Median) to 2008, with 21 observations. The data reached an all-time high of 18,029.000 UYU th in 2008 and a record low of 14,542.000 UYU th in 1989. Uruguay GDP: 1983p: AP: Livestock Production data remains active status in CEIC and is reported by Central Bank of Uruguay. The data is categorized under Global Database’s Uruguay – Table UY.A024: SNA 1968: GDP: by Industry: 1983 Price: Annual.

Andhra Pradesh made up about 636 billion Indian rupees in gross valued added from fish products towards agriculture across India in fiscal year 2021. The GVA from this state made up about 40 percent of fish products nationwide. Fishing was valued at over 1.5 trillion rupees in GVA in the Indian economy that fiscal year.

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state).

For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state).

For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

This dataset provides statistics on real gross domestic product (GDP) and real GDP per capita for subnational regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Regional gross domestic product data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites.

To allow comparability over time and between countries, data at current prices are transformed into constant prices and purchasing power parity measures. Regional GDP per capita is calculated by dividing regional GDP by the average annual population of the region.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

We provide the data used for this research in both Excel (one file with one matrix per sheet, 'Allmatrices.xlsx'), and CSV (one file per matrix).

Patent applications (Patent_applications.csv) Patent applications from residents and no residents per million inhabitants. Data obtained from the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

High-tech exports (High-tech_exports.csv) The proportion of exports of high-level technology manufactures from total exports by technology intensity, obtained from the Trade Structure by Partner, Product or Service-Category database (Lall, 2000; UNCTAD, 2019)

Expenditure on education (Expenditure_on_education.csv) Per capita government expenditure on education, total (2010 US$). The data was obtained from the government expenditure on education (total % of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Scientific publications (Scientific_publications.csv) Scientific and technical journal articles per million inhabitants. The data were obtained from the scientific and technical journal articles and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Expenditure on R&D (Expenditure_on_R&D.csv) Expenditure on research and development. Data obtained from the research and development expenditure (% of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Two centuries of GDP (GDP_two_centuries.csv) GDP per capita that accounts for inflation. Data obtained from the Maddison Project Database, version 2018 (Inklaar et al. 2018), and available from the Open Numbers community (open-numbers.github.io).

Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing “Maddison”: new income comparisons and the shape of long-run economic development (GD-174; GGDC Research Memorandum). https://www.rug.nl/research/portal/files/53088705/gd174.pdf

Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985‐98. Oxford Development Studies, 28(3), 337–369. https://doi.org/10.1080/713688318

Unctad. 2019. “Trade Structure by Partner, Product or Service-Category.” 2019. https://unctadstat.unctad.org/EN/.

World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

The trans-Golgi network (TGN) is the sorting and package centre for trafficking cargo to the endoplasmic reticulum, plasma membrane and endosomes. Signal peptides determine the sorting and trafficking of proteins to the endosomal-lysosomal pathway or to the cell surface. The main signals that mediate targeting of MHC-II molecules to the endocytic pathway are two dileucine-based motifs, Leu23-Ile24 and Pro31-Leu33 present in the short cytoplasmic tail of Ii (Odorizzi et al. 1994). These motifs bind both the adaptor proteins AP-1 and AP-2, which are components of clathrin coats associated with the TGN/endosomes and the plasma membrane, respectively (McCormick et al. 2005).

The precise pathway of class II:Ii complex trafficking from TGN to endocytic pathway is not well understood. In one view MHC II:Ii complexes directly traffic from the TGN to lysosomes, possibly using AP-1 dependent endocytic vesicles (Peters et al. 1991, Amigorena et al. 1994). Alternatively trafficking occurs via transient expression on the cell surface followed by rapid internalization and delivery to endocytic compartments. Early immunoelectron microscopy data has shown the presence of MHC class II:Ii complex molecules primarily in TGN and lysosomes (Peters et al. 1991, Hiltbold & Roche 2002). This theory was further supported by a study examining the trafficking of sulphate-tagged class II molecules, which concluded that the rapid appearance of these molecules in lysosomes was consistent with their direct transport from the TGN to lysosomes (Hiltbold & Roche 2002, Davidson 1999). The transport of cargo MHC II:Ii complexes from the TGN to lysosomes may be mediated by small TGN vesicles coated with AP-1 and clathrin. The di-leucine-based sorting signal in the Ii cytoplasmic chain recruits AP-1 and clathrin from cytosol to TGN to form AP-1 clathrin-coated TGN-derived vesicles. This process is regulated by the small GTPase ARF-1 (Salamero et al. 1996).

Gross State Domestic Product Contribution to National Gross Domestic Product: Andhra Pradesh在2025达4.812%，相较于2024的4.721%有所增长。Gross State Domestic Product Contribution to National Gross Domestic Product: Andhra Pradesh数据按每年更新，2005至2025期间平均值为4.447%，共21份观测结果。该数据的历史最高值出现于2021，达4.929%，而历史最低值则出现于2014，为4.133%。CEIC提供的Gross State Domestic Product Contribution to National Gross Domestic Product: Andhra Pradesh数据处于定期更新的状态，数据来源于CEIC Data，数据归类于India Premium Database的General Election – Table IN.GEI003: Memo Items: State Economy: Gross State Domestic Product: Contribution: National Gross Domestic Product。

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state).

For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state).

For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state).

For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state).

For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state).

For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

Gross State Domestic Product (GSDP): 2004-05p: Andhra Pradesh data was reported at 2,645,209.200 INR mn in 2015. This records an increase from the previous number of 2,467,237.300 INR mn for 2014. Gross State Domestic Product (GSDP): 2004-05p: Andhra Pradesh data is updated yearly, averaging 1,949,939.599 INR mn from Mar 2005 (Median) to 2015, with 11 observations. The data reached an all-time high of 2,645,209.200 INR mn in 2015 and a record low of 1,347,668.834 INR mn in 2005. Gross State Domestic Product (GSDP): 2004-05p: Andhra Pradesh data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under Global Database’s India – Table IN.GEI002: Memo Items: State Economy: Gross State Domestic Product. Rebased from 2004-2005 base to 2011-2012 base. Replacement series ID: 374618267

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state).

For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state).

For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

All data are expressed as a percentage, except for GDP per capita, net wages, total population, life expectancy, expected years of education, average years of schooling, life and non-life premium, total premium, bank deposits, financial assets and deposits of insurance companies, which are expressed in absolute terms.

Source of data:

Data on Life and Non-life premium, Total (gross) premium, Premium reserve data, Financial assets and Deposits of insurance companies are collected from the official reports of insurance supervision agencies: Insurance Supervision Agency in Montenegro (http://www.ano.me/en/), Croatian Financial Services Supervisory Agency (https://www.hanfa.hr/en/), National Bank of Serbia (https://www.nbs.rs/internet/english/, Insurance Supervision Agency of North Macedonia (http://aso.mk/en/?lang=en) and Financial Supervisory Authority in Albania (https://amf.gov.al/).

The economic indicators for the observed Western Balkan countries (GDP per capita, unemployment rate, inflation rate, net earnings and average effective deposit interest rate) are taken from the website Eurostat (https://ec.europa.eu/eurostat) and Statista (https://www.statista.com/)

All demographic indicators, except for the expected and average years of schooling and education index, were collected from the Eurostat and UNDP database (https://ec.europa.eu/eurostat/data/database; http://hdr.undp.org/en/countries/profiles/ ).

Data on expected and average school years were taken from the UNESCO Institute for Statistics (http://uis.unesco.org) , while the education index was obtained as a result of a calculation based on a formula published on the UNDP website (http://hdr.undp.org/en/content/education-index).

Data on bond yield were collected from the website of European Commission (https://ec.europa.eu/), i.e. from EC reports - EU Candidate Countries’ & Potential Candidates’ Economic Quarterly (CCEQ), except two data for Serbia (2006 and 2007) which were estimated by Makima extrapolation.

Bank deposits data are taken from the official reports of banks' regulatory institutions: Central bank of Montenegro (https://www.cbcg.me/en), National bank of Serbia (https://www.nbs.rs/en/indeks/), Croatian National bank (https://www.hnb.hr/en/home), National bank of the Republic of North Macedonia (https://www.nbrm.mk/pocetna-en.nspx); Bank of Albania (https://www.bankofalbania.org/home/)

Description of columns:

f1-GDPper capita; f2- Unemployment (%); f3-Inflation rate (%); f4- Net Wages €; f5- Deposit rate (%); f6- Population; f7- Female (%); f8- Population <15 (%); f9- Population 15-64 (%); f10- Dep old (%); f11- Dep young (%); f12- Urban population (%); f13-Life exp. (years); f14- Preschool enroll rate (%); f15- Elem school enroll rate (%); f16-High school enroll rate (%); f17- University enroll rate (%); f18- Expected years of schooling; f19- Avg. years of schooling; f20- Education Index (%); f21- Fertility rate (number of children to a woman); f22- Birth rate (per 1000 inhabitants); f23- Health costs (% GDP); f24-premium reserve per GDP,

i1- life premium €; i2- non-life premium €; i3- total premium €; i4- bond yield (%); i5a- bank deposits ( national currency); i5b- bank deposits €; i6a-financial assets in insurance (national currency); i6b- financial assets in insurance €; i7a- deposits of insurers (national currency); i7b –deposit of insurers €