Browse LSEG's World-Check Data for extensive risk intelligence data, aiding in compliance of regulation related to anti-bribery, corruption, and more.

Over the years, we have developed distinct competencies in numerous areas so that our clients can rely on the Business Information Reports they purchase from us. Operating on the globe, we are able to provide local/regional intelligence with the most up-to-date and accurate information.

We have local presence in each country we provide information to, through our own offices or via our global network of partners that extends to more than 227 countries worldwide.

Our International Credit Reports include, among other, data on
• Shareholders & Directors • Secretary • Registered Number & Registered Address • Date of registration • Capital • Charges • Company Activities • Shareholding and/or Director Relationships • Detrimental Data • Payment records • Financial statements • Credit Scoring Assessment

Our Due Diligence Report, include: • Relationship Checks • Global KYC Screening • Negative & Local language media checks • Site & Reputation Check • Legal cases relating to the primary subject and its related entities • General media information • Passport/ID authentication • Official Documents and Certificates

Our KYC Reports investigate the subject entity against the following Global lists, amongst other categories: • Sanction Lists • Enforcement Lists • Arms Trafficking • Drug Trafficking • Fraud • Money Laundering • Terrorism • Adverse Media • Political Exposed Persons • State Owned Entities

Our local knowledge and understanding of languages, laws, customs, culture economy and commercial parameters in every country, provide us the advantage of having reliable and relevant products and services no matter where your target company is located.

The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes almost 1,500 indicators covering more than 200 countries and territories, with data spanning several decades.

WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability.

The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization.

WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally.

Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.

A politically exposed person (PEP) is a person that has been entrusted with a prominent public function. PEPs include elected officials and members of government.

World Bank Projects & Operations provides access to basic information on all of the World Bank's lending projects from 1947 to the present. The dataset includes basic information such as the project title, task manager, country, project id, sector, themes, commitment amount, product line, and financing. It also provides links to publicly disclosed online documents.

For older projects, there is a link to the Archives catalog, which contains records of older documents. Where available, there are also links to contract awards since July 2000.

Special Notes: Each project contained in Projects & Operations has a Project Profile page which links to additional information relating to that project. Such related information includes Contract Awards and Loans/Credits/Grants. The datasets are all connected via the project id which is the common key across all the operational data.

Related Links:

• Projects & Operations Advanced Search
• Project Documents
• Procurement Notices
• Contract Awards

The 2007 World Bank Group Entrepreneurship Survey measures entrepreneurial activity in 84 developing and industrial countries over the period 2003-2005. The database includes cross-country, time-series data on the number of total and newly registered businesses, collected directly from Registrar of Companies around the world. In its second year, this survey incorporates improvements in methodology, and expanded participation from countries covered, allowing for greater cross-border compatibility of data compared with the 2006 survey. This joint effort by the IFC SME Department and the World Bank Developing Research Group is the most comprehensive dataset on cross-country firm entry data available today. This database The World Bank Group Entrepreneurship Dataaset presents data collected primarily from country business registries using the first annual World Bank Group Questionnaire on Entrepreneurship (alternative sources were tax authorities, finance ministries, and national statistics offices). For more information on the author of the database, Leora Klapper, visit: http://go.worldbank.org/DK5AHCQSO0. This data was access at the preceeding link, on October 11, 2007. Please visit the link for more information in regards to this dataset.

A soil seed bank is the collective name for viable seeds that are stored naturally in the soil. This database is the result of a comprehensive literature search, including all seed bank studies from the Web of Science from which data could be extracted, as well as an additional search of the Russian language literature. The database contains information on the species richness, seed density and/or seed abundance in 3096 records from at least 1778 locations across the world’s seven continents, extracted from 1442 studies published between 1940 and 2020. Records are grouped into five broad habitat categories (aquatic, arable, forest, grassland and wetland), including information relating to habitat degradation from, or restoration to other habitats (total 14 combinations). Sampling protocols were also extracted for each record, and the database was extensively checked for errors. The location of each record was then used to extract summary climate data and biome classification from external published databases (Karger et al. 2017, 2018 and Olson et al. 2001, respectively).

A full data descriptor for this dataset is published as a data paper in the journal Ecology. As such, the data are described according to the journal's specifications in the file MetadataS1.pdf, with additional information in data_entry_intructions.pdf. The initial version of the dataset is also published as supporting information to the data paper. The file DataS1.zip described in MetadataS1.zip contains the files gsb_db.csv, gsb_code.R and data_entry_intructions.pdf.

References: Karger, D. N., O. Conrad, J. Böhner, T. Kawohl, H. Kreft, R. W. Soria-Auza, N. E. Zimmermann, H. P. Linder, and M. Kessler. 2017. Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4:170122. https://doi.org/10.1038/sdata.2017.122

Karger, D. N., O. Conrad, J. Böhner, T. Kawohl, H. Kreft, R. W. Soria-Auza, N. E. Zimmermann, H. P. Linder, and M. Kessler. 2018. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4

Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W. W. Wettengel, P. Hedao, and K. R. Kassem. 2001. Terrestrial Ecoregions of the World: A New Map of Life on Earth A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51:933–938. Link to data: https://files.worldwildlife.org/wwfcmsprod/files/Publication/file/6kcchn7e3u_official_teow.zip

The Global Financial Inclusion Database provides 800 country-level indicators of financial inclusion summarized for all adults and disaggregated by key demographic characteristics-gender, age, education, income, and rural residence. Covering more than 140 economies, the indicators of financial inclusion measure how people save, borrow, make payments and manage risk. The reference citation for the data is: Demirguc-Kunt, Asli, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden. 2015. “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, DC. - Periodicity: Annual - Number of Economies: 140 - The Global Findex indicators are drawn from survey data collected over the 2011 and 2014 calendar year. - Update Frequency: Annual + - Update Schedule: Every three years - Access Option: API, Bulk download, Query tool

What a Waste is a global project to aggregate data on solid waste management from around the world. This database features the statistics collected through the effort, covering nearly all countries and over 330 cities. The metrics included cover all steps from the waste management value chain, including waste generation, composition, collection, and disposal, as well as information on user fees and financing, the informal sector, administrative structures, public communication, and legal information. The information presented is the best available based on a study of current literature and limited conversations with waste agencies and authorities. While there may be variations in the definitions and quality of reporting for individual data points, general trends should reflect the global reality. All sources and any estimations are noted.

The USDA Nematode Collection is one of the largest and most valuable nematode collections in existence. It contains over 49,000 permanent slides and vials, with a total repository of nematode specimens reaching several million, including Cobb-Steiner, Thorne, and other valuable collections. Nematodes contained in this collection originate from world-wide sources. The USDA Nematode Collection Database contains over 38,000 species entries. A broad range of data is stored for each specimen, including species, host, origin, collector, date collected and date received. All records are searchable and available to the public through the online database. The physical collection is housed at the USDA Nematology Laboratory in Beltsville, MD. Specimens are available for loan to scientists who cannot personally visit the collection. Please see the Policy for Loaning USDANC Specimens for more information on this process. Scientists and other workers are always welcomed and encouraged to deposit material into the collection. Resources in this dataset:Resource Title: USDA Nematode Collection Database. File Name: Web Page, url: https://nt.ars-grin.gov/nematodes/search.cfm The database portal for this collection

Global Graph Database market size is expected to reach $9.4 billion by 2029 at 23.8%, ai adoption fuels graph database market growth

This dataset tracks the average applied tariff rates in both industrial and developing countries. Data is averaged for the years 1981-2005. Figures for 2005 have been estimated. Notes: All tariff rates are based on unweighted averages for all goods in ad valorem rates, or applied rates, or MFN rates whichever data is available in a longer period. Tariff data is primarily based on UNCTAD TRAINS database and then used WTO IDB data for gap filling if possible. Data in 1980s is taken from other source.** Tariff data in these countries came from IMF Global Monitoring Tariff file in 2004 which might include other duties or charges. Country codes are based on the classifications by income in WDI 2006, where 1 = low income, 2 = middle income, 3 = high incone non-OECDs, and 4 = high income OECD countries. Sources: UNCTAD TRAINS database (through WITS); WTO IDB database (through WITS); WTO IDB CD ROMs, various years and Trade Policy Review -- Country Reports in various issues, 1990-2005; UNCTAD Handbook of Trade Control Measures of Developing Countries -- Supplement 1987 and Directory of Import Regimes 1994; World Bank Trade Policy Reform in Developing Countries since 1985, WB Discussion Paper #267, 1994 and World Development Indicators, 1998-2006; The Uruguay Round: Statistics on Tariffs Concessions Given and Received, 1996; OECD Indicators of Tariff and Non-Tariff Trade Barriers, 1996 and 2000; and IMF Global Monitoring Tariff data file 2004. Data source: http://go.worldbank.org/LGOXFTV550 Access Date: October 17, 2007

Taiwan Check Cleared: Number data was reported at 9,137,317.000 Unit in Oct 2018. This records an increase from the previous number of 5,603,591.000 Unit for Sep 2018. Taiwan Check Cleared: Number data is updated monthly, averaging 9,067,794.500 Unit from Jan 1972 (Median) to Oct 2018, with 562 observations. The data reached an all-time high of 17,872,180.000 Unit in Dec 1997 and a record low of 1,996,832.000 Unit in Apr 1972. Taiwan Check Cleared: Number data remains active status in CEIC and is reported by Central Bank of the Republic of China. The data is categorized under Global Database’s Taiwan – Table TW.KA026: Bank Clearings and Dishonored Checks.

Reservoir Assessment Tool version 3.0 is a scalable and user-friendly software platform to mobilize the global water management community. RAT uses satellite remote sensing data to monitor water surface area and water level changes in artificial reservoirs. It uses this information, along with topographical information (either derived from satellite data, or in-situ topo maps) to estimate the Storage Change (∆S) in the reservoirs. Additionally, RAT models the Inflow (I) and the Evaporation (E) of each reservoir. Finally, RAT uses the modeled I, and E, and estimated ∆S, to estimate the Outflow (O) from reservoirs. The datasets and files provided here are used by RAT 3.0 as default inputs to make it easy to set up and execute RAT for first-time users.

global_data.zip - It includes Global Database encompassing global elevation data, global reservoir and dam data, major river basins in the world and the river networks, flow direction file, and geoid model. It is used by RAT 3.0 as default input for easy execution for first-time users.

global_vic_params.zip - It contains global VIC soil and domain parameters for executing the hydrological model within RAT 3.0. It is considered a part of the Global Database but is packaged separately.

params.zip - It consists of all the default parameter files used by RAT 3.0 to execute the hydrological model within it and to execute RAT itself.

routing.zip - It consists of the Fortran code for the Routing model for easy installation for users.

test_data.zip - It consists of data used by RAT 3.0 to test whether it has been installed and initialized properly in a user's system.

DescriptionFuture Hydropower Reservoirs and Dams Database (FHReD): the database contains 3,700 records of planned hydropower dams with a capacity of

1 MW. GlObal geOreferenced Database of Dams GOOD2 Version 1: this database has been developed for WaterWorld (www.policysupport.org/waterworld). The digitalization 32613 dams has been carried in Google Earth, and it is made available to the user community in raw and unfinished form in the hope that others will contribute to its development so that it will grow in use and utility. Dams by Reported Storage Capacity (GRanD): this database provides the location and main specifications of large global dams with a storage capacity of more than 0.1km³ in point format. The current version 1.3 of GRanD contains 7,320 records of dams with a cumulative storage capacity of 6,864 km³. The development of GRanD primarily aimed at compiling the available reservoir and dam information, correcting it through extensive cross-validation, error checking and identification of duplicate records, attribute conflicts or mismatches; and completing missing information from new sources or statistical approaches. Check technical documentation following the next link.

Reservoirs GRanD: polygon delineation of 7250 reservoirs associated to the dams registered in the GRanD database. The reservoirs boundaries were obtained from the surface water maps produced by the Joint Researchh Center (JRC) of the European Commission from Landsat imagery at 30m resolution for the period 1984-2015. In places where a reservoir was not completed or filled by the year 2015 and thus not visible in the JRC surface water data, the reservoir polygons were manually delineated based on ESRI basemaps and/or other georeferenced imagery. Some remaining dam points had no visible reservoir in any available imagery; they were annotated as not yet filled (and “no polygon”) in the point version of GRanD. Bear in mind that multiple dams can be associated to the same reservoir. LimitationsFuture Hydropower Reservoirs and Dams Database (FHReD): as here has been undergoing continuous corrections and updates of the database, this version should be used for test purposes only.GlObal geOreferenced Database of Dams GOOD2 Version 1: blank.

Dams and Reservoirs by Reported Storage Capacity (GRanD): Include all reservoirs with a storage capacity of more than 0.1 km³. Only smaller reservoirs were added if data were available. AttributesFuture Hydropower Reservoirs and Dams Database (FHReD)DAM_ID: Identification numberProject Name: Name of the hydropower dam planned or under constructionContinent: Continent on which the hydropower dam is planned/under constructionCountry: Country in which the hydropower dam is planned/under constructionMain_river: Main river stem that is closest to the hydropower dam derived from FAO continental river mapsMajor Basin: Major river basin in which the hydropower dam will be located according to FAO world map of the major hydrological basinsCapacity (MW): Maximum hydropower capacity that could be provided by the hydropower dam in MWLAT_cleaned: Latitude of the hydropower dam/scheme in geographical coordinates, either directly obtained from the source or 'georeferenced' using google earthLon_Cleaned: Longitude of the hydropower dam/scheme in geographical coordinates, either directly obtained from the source or 'georeferenced' using google earthStage: Stage of the hydropower dam: “P” (=planned) includes “pre-feasibility”, “feasibility” and “financed and assigned” stage; “U” (=under construction)Start: Planned starting year [YYYY] of construction of the hydropower damEnd: Planned year [YYYY] of completion of the construction of the hydropower dam, empty if not available; type of value: positive integer numberReference 1: Main reference used to identify most of the data contained in the database for the respective hydropower damReference 2: Additional reference used to identify most of the data contained in the database for the respective hydropower damReference 3: Additional reference used to identify most of the data contained in the database for the respective hydropower dam Dams and Reservoirs by Reported Storage Capacity (GRanD):OBJECTID: Assigned by WWF. Unique identifierRES_NAME: Name of reservoir or lake ( impounded water body)DAM_NAME: Name of dam structureALT_NAME: Alternative name of reservoir or dam (different spelling, different language, secondary name) RIVER: Name of impounded river ALT_RIVER: Alternative name of impounded river (different spelling, different language, secondary name)MAIN_BASIN: Name of main basin where the dam is locatedSUB_BASIN: Name of sub-basin where the dam is locatedNEAR_CITY: Name of nearest city ALT_CITY: Alternative name of nearest city (different spelling, different language, secondary name)ADMIN_UNIT: Name of administrative unitSEC_ADMIN: Secondary administrative unit (indicating dams or reservoirs that lie within or are associated with multiple administrative units) COUNTRY: Name of countrySEC_CNTRY: Secondary country (indicating international dams or reservoirs that lie within or are associated with multiple countries)YEAR: Year (not further specified: year of construction; year of completion; year of commissioning; year of refurbishment/update; etc.) ALT_YEAR: Alternative year (not further specified: may indicate a multi-year construction phase, an update, or a secondary dam construction)DAM_HGT_M: Height of dam in meterALT_HGT_M: Alternative height of dam (may indicate update or secondary dam construction) DAM_LEN_M: Length of dam in meters ALT_LEN_M: Alternative length of dam (may indicate update or secondary dam construction) AREA_SKM: Representative surface area of reservoir in square kilometers; consolidated from other ‘Area’ columns in the following order of priority: ‘Area_poly’ over ‘Area_rep’ over ‘Area_max’ over ‘Area_min’; exceptions apply if value in ‘Area_poly’ column seems unreliable or rounded; see also notes below AREA_POLY: Surface area of associated reservoir polygon in square kilometersAREA_REP: Most reliable reported surface area of reservoir in square kilometersAREA_MAX: Maximum value of other reported surface areas in square kilometersAREA_MIN: Minimum value of other reported surface areas in square kilometersCAP_MCM: Representative maximum storage capacity of reservoir in million cubic meters; consolidated from other ‘Cap’ columns in the following order of priority: ‘Cap_max’ over ‘Cap_rep’ over ‘Cap_min’; exceptions apply if value in ‘Cap_max’ column seems unreliable or rounded; see also notes belowCAP_MAX: Reported ‘maximum storage capacity’ in million cubic meters; see notes belowCAP_REP: Reported ‘storage capacity’ in million cubic meters (value may refer to different types of storage capacity, see notes below) CAP_MIN: Minimum value of other reported storage capacities in million cubic metersDEPTH_M: Average depth of reservoir in meters; calculated as ratio between storage capacity (‘Cap_mcm’) and surface area (‘Area_skm’); values that are somewhat higher than the dam height (‘Dam_hgt_m’) may still be reasonable, e.g. if the storage capacity refers to the maximum volume yet the reservoir polygon represents a low-fill status; values capped at 1,000 indicate exceedingly high values, which may be due to inconsistencies in the data DIS_AVG_LS: Long-term (1961-90) average discharge at reservoir location in liters per second; value derived from HydroSHEDS flow routing scheme combined with WaterGAP2 runoff estimates at 15s resolution at point location of dam DOR_PC: Degree of regulation (DOR) in percent; equivalent to “residence time” of water in the reservoir; calculated as ratio between storage capacity (‘Cap_mcm’) and total annual flow (derived from ‘Dis_avg_ls’); values capped at 10,000 indicate exceedingly high values, which may be due to inconsistencies in the data and/or incorrect allocation to the river network and the associated discharges ELEV_MASL: Elevation of reservoir surface in meters above sea level; value derived from HydroSHEDS DEM at 15s resolution at point location of damCATCH_SKM: Area of upstream catchment draining into the reservoir in square kilometers; value derived from HydroSHEDS at 15s resolution at point location of dam CATCH_REP: Reported area of upstream catchment draining into reservoir in square kilometersDATA_INFO: Supporting information on certain data issues: ‘Capacity from statistics’ = capacity derived from Eq. 1 or Eq. 2 ‘Capacity estimated’ = capacity estimated from other available information ‘No polygon’ = no polygon availableUSE_IRRI: Used for irrigation (‘Main’; ‘Major’; or ‘Sec’ = Secondary use) USE_ELEC: Used for hydroelectricity production (‘Main’; ‘Major’; or ‘Sec’ = Secondary use)USE_SUPP: Used for water supply (‘Main’; ‘Major’; or ‘Sec’ = Secondary use) USE_FCON: Used for flood control (‘Main’; ‘Major’; or ‘Sec’ = Secondary use)USE_RECR: Used for recreation (‘Main’; ‘Major’; or ‘Sec’ = Secondary use)USE_NAVI: Used for navigation (‘Main’; ‘Major’; or ‘Sec’ = Secondary use)USE_FISH: Used for fisheries (‘Main’; ‘Major’; or ‘Sec’ = Secondary use) USE_PCON: Used for pollution control (‘Main’; ‘Major’; or ‘Sec’ = Secondary use)USE_LIVE: Used for livestock water supply (‘Main’; ‘Major’; or ‘Sec’ = Secondary use)USE_OTHR: Used for other purposes (‘Main’; ‘Major’; or ‘Sec’ = Secondary use); other purposes may include new or a mix of the above purposesMAIN_USE: Main purpose of reservoir: Irrigation; Hydroelectricity; Water supply; Flood control; Recreation; Navigation; Fisheries; Pollution control; Livestock; or OtherLAKE_CTRL: Indicates whether a reservoir has been built at the location of an existing natural lake using a lake control structure; currently this

This dataset contains updates on export and import activities, including detailed records of transactions, commodities, volumes, and values across various countries provided by Volza FZ LLC.

Credit score and limits Get to know company credit scores and recommended credit limits before you make a decision. Awareness about your partners’ creditworthiness, B2B customer payment experience and history, and the amounts prospects and suppliers alike can afford to owe is crucial in today’s business relations. Stay assured that you engage in smart business relationships only

Trade payment information Understand your B2B partners’ and prospects’ company payment behaviour, easily perform due diligence checks and avoid financial difficulties caused by other businesses’ inability to honour their liabilities in time. Use the Global Database Business Credit Reporting to make informed sound decisions that will bring value to your business and help it grow in the long run

Court judgments and charges Understand your potential partners’ trustworthiness and future ability to operate by checking court judgements they are involved in. Global Database’s Business Credit Reporting helps you be certain whether they run transparent businesses. Monitor their activity hassle-free while setting customized alerts to get notified as soon as a change takes place

Corporate hierarchy knowledge Screen clients and competitors and get to know their shareholder and group structure. Global Database company profiles display their corporate relationships tree to help you get a better understanding of your potential business partners’ position. Besides, you can get in touch with their higher-ups using direct emails and corporate phones available in our business directory

Up to 5 years of full financials Access up to 5 years of in-depth company financial intelligence along with key indicators and ratios readily calculated for you. No matter what you need to find out about your business partners’ or competitors’ financial situation, you will obtain this data with our global business directory

Taiwan Check Cleared: Value data was reported at 1,500,481.000 NTD mn in Oct 2018. This records an increase from the previous number of 1,134,644.000 NTD mn for Sep 2018. Taiwan Check Cleared: Value data is updated monthly, averaging 1,588,856.500 NTD mn from Jan 1972 (Median) to Oct 2018, with 562 observations. The data reached an all-time high of 5,192,491.000 NTD mn in Jul 1997 and a record low of 64,479.750 NTD mn in Feb 1972. Taiwan Check Cleared: Value data remains active status in CEIC and is reported by Central Bank of the Republic of China. The data is categorized under Global Database’s Taiwan – Table TW.KA026: Bank Clearings and Dishonored Checks.

The Sub-global Scenarios that Extend the Global SSP Narratives: Literature Database, Version 1, 2014-2021 consists of 37 columns of bibliographic data, methodological and analytical insights, from 155 articles published from 2014 to 2021 that extended the narratives of global SSPs. Local and regional scale Shared Socioeconomic Pathways (SSPs) have grown largely in addressing Climate Change Impact, Adaptation, and Vulnerability (CCIAV) assessments at sub-global levels. Common elements of these studies, besides their focus on CCIAV, are the use of both quantitative and qualitative elements of the SSPs. To explore and learn from current literature on novel methods and insights on extending SSPs, the sub-global extended SSPs literature database is constructed in the research for analyses. The database was developed in four stages: searches; screening; data extraction; and coding. The search stage incorporated three approaches: using a search string in three academic databases (Scopus, Web of Science Core Collection, ScienceDirect); a targeted search of a specific relevant database (ICONICS); and a targeted selection in Google Scholar of all papers that cited the publication of the global SSP narratives. In the screening step, criteria were assessed for full-text papers for eligibility including relevant typologies, methodologies, and other criteria. Finally, data from eligible papers was extracted and entered in a coding framework in an Excel workbook spreadsheet. The coding framework resulted in 37 columns to systematize coding of data from the 155 papers selected along several different dimensions, including categories of papers or analysis, several subcategories for SSP Applications and SSP Extensions, specific SSPs used, specific Representative Concentration Pathways (RCPs) used, typologies of extensions of qualitative and quantitative SSPs, and the types of models and nature of the extended SSPs.

United States Google Search Trends: Government Measures: Government Subsidy data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. United States Google Search Trends: Government Measures: Government Subsidy data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 0.000 Score in 14 May 2025 and a record low of 0.000 Score in 14 May 2025. United States Google Search Trends: Government Measures: Government Subsidy data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s United States – Table US.Google.GT: Google Search Trends: by Categories.