100+ datasets found
  1. Economic Data

    • lseg.com
    Updated Nov 19, 2023
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    LSEG (2023). Economic Data [Dataset]. https://www.lseg.com/en/data-analytics/financial-data/economic-data
    Explore at:
    Dataset updated
    Nov 19, 2023
    Dataset provided by
    London Stock Exchange Grouphttp://www.londonstockexchangegroup.com/
    Authors
    LSEG
    License

    https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer

    Description

    View LSEG's extensive Economic Data, including content that allows the analysis and monitoring of national economies with historical and real-time series.

  2. k

    Most Important Economic Variables

    • datasource.kapsarc.org
    • data.kapsarc.org
    Updated Oct 6, 2024
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    (2024). Most Important Economic Variables [Dataset]. https://datasource.kapsarc.org/explore/dataset/most-important-economic-variables/
    Explore at:
    Dataset updated
    Oct 6, 2024
    Description

    Explore the most important economic variables dataset including Gross Domestic Product, Inflation, Imports, Exports, Population, National Accounts, and more. Analyze economic trends in United Arab Emirates and make informed decisions.

    Gross Domestic Product (Million US$), Inflation %, Imports of Goods and Services (cif), Population (Thousand Persons), Exports of Goods and Services (fob), Disposable Income (Million US$), Gross National Income (Million US$), Net National Income (Million US$), National Saving (Million US$), Final Consumption Expenditure (Million US$), Gross Fixed Capital Formation (Million US$), GDP, wages, CPI, Price, ITEM

    United Arab Emirates Follow data.kapsarc.org for timely data to advance energy economics research.. 2019 Data is Preliminary.

  3. United States USD Trade Weighted Index: Nominal: Other Important Trading...

    • ceicdata.com
    Updated Nov 27, 2021
    + more versions
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    CEICdata.com (2019). United States USD Trade Weighted Index: Nominal: Other Important Trading Partner [Dataset]. https://www.ceicdata.com/en/united-states/us-dollar-trade-weighted-index/usd-trade-weighted-index-nominal-other-important-trading-partner
    Explore at:
    Dataset updated
    Nov 27, 2021
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jun 1, 2017 - May 1, 2018
    Area covered
    United States
    Variables measured
    Foreign Exchange Rate
    Description

    United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data was reported at 168.237 Jan1997=100 in Nov 2018. This records an increase from the previous number of 166.528 Jan1997=100 for Oct 2018. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data is updated monthly, averaging 96.825 Jan1997=100 from Jan 1973 (Median) to Nov 2018, with 551 observations. The data reached an all-time high of 168.237 Jan1997=100 in Nov 2018 and a record low of 1.998 Jan1997=100 in Jul 1973. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M016: US Dollar Trade Weighted Index.

  4. e

    Financially important economic data

    • data.europa.eu
    unknown
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    Bundesministerium der Finanzen, Financially important economic data [Dataset]. https://data.europa.eu/data/datasets/f60f6199-0eb5-4edc-ae57-506eb93839be/
    Explore at:
    unknownAvailable download formats
    Dataset authored and provided by
    Bundesministerium der Finanzen
    License

    Data licence Germany – Attribution – Version 2.0https://www.govdata.de/dl-de/by-2-0
    License information was derived automatically

    Description

    Annual overview of important economic data

  5. f

    Estimation results of TTR/DTR/ITR-to-GDP.

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Yu kun Wang; Li Zhang (2023). Estimation results of TTR/DTR/ITR-to-GDP. [Dataset]. http://doi.org/10.1371/journal.pone.0281101.t005
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Yu kun Wang; Li Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    For a long time, governments of all countries have attached great importance to the development of underground economic activities. The reason is that the characteristics of the underground economy are hidden and the information disclosure is not sufficient, which not only distorts the economic data indicators, but more importantly, the existence of the underground economy has led to the loss of a large amount of tax base, affecting the long-term economic development of the country. Whether raising the tax burden rate boosts the tax revenue or expand the scale of the underground economy. In this paper, we use Kuznet Tax Curve (KTC) method to analyze the relationship between GDP and TTR/DTR/ITR. We find that the tax base erosion rate of indirect tax is lower than that of direct tax. In addition, we explore the relationship among economic growth, tax rate and tax revenue and adopt SUR-OLS method and Threshold approach to estimate the response of economic growth on total tax revenue(TTR), direct tax revenue(DTR) and indirect tax revenue (ITR) in Taiwan from 1991-2020. Our empirical research shows that when DTR tax rates are between 12.59% and 13%, an increase in income leads to a decrease, not an increase, in DTR, leading to severe tax base erosion. That is, the relationship between GDP and DTR presents a N-shaped relationship. However, ITR does not exist any tax rate threshold effect. Obviously, with the increase of GDP, ITR also increases. This reflects that the difference of tax structure between direct tax and indirect tax plays a key role in the relationship between tax rate and tax base erosion.

  6. f

    S1 Data -

    • plos.figshare.com
    xlsx
    Updated Jun 20, 2023
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    Yu kun Wang; Li Zhang (2023). S1 Data - [Dataset]. http://doi.org/10.1371/journal.pone.0281101.s005
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 20, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Yu kun Wang; Li Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    For a long time, governments of all countries have attached great importance to the development of underground economic activities. The reason is that the characteristics of the underground economy are hidden and the information disclosure is not sufficient, which not only distorts the economic data indicators, but more importantly, the existence of the underground economy has led to the loss of a large amount of tax base, affecting the long-term economic development of the country. Whether raising the tax burden rate boosts the tax revenue or expand the scale of the underground economy. In this paper, we use Kuznet Tax Curve (KTC) method to analyze the relationship between GDP and TTR/DTR/ITR. We find that the tax base erosion rate of indirect tax is lower than that of direct tax. In addition, we explore the relationship among economic growth, tax rate and tax revenue and adopt SUR-OLS method and Threshold approach to estimate the response of economic growth on total tax revenue(TTR), direct tax revenue(DTR) and indirect tax revenue (ITR) in Taiwan from 1991-2020. Our empirical research shows that when DTR tax rates are between 12.59% and 13%, an increase in income leads to a decrease, not an increase, in DTR, leading to severe tax base erosion. That is, the relationship between GDP and DTR presents a N-shaped relationship. However, ITR does not exist any tax rate threshold effect. Obviously, with the increase of GDP, ITR also increases. This reflects that the difference of tax structure between direct tax and indirect tax plays a key role in the relationship between tax rate and tax base erosion.

  7. BEA - Timeliness: On-time release of economic statistics

    • performance.commerce.gov
    application/rdfxml +5
    Updated Mar 6, 2025
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    Bureau of Economic Analysis (2025). BEA - Timeliness: On-time release of economic statistics [Dataset]. https://performance.commerce.gov/KPI-BEA/BEA-Timeliness-On-time-release-of-economic-statist/w24e-hbdx
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    application/rssxml, csv, xml, json, application/rdfxml, tsvAvailable download formats
    Dataset updated
    Mar 6, 2025
    Dataset provided by
    The Bureau of Economic Analysishttp://www.bea.gov/
    Authors
    Bureau of Economic Analysis
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    The importance of data as an ingredient for sound economic decision-making requires BEA to deliver data to decision-makers and other data users not only quickly but also reliably—that is, on schedule. Each fall, BEA publishes a schedule for the release of its economic data the following year; this measure is evaluated as the number of scheduled releases issued on time. BEA has an outstanding record of releasing its economic data on schedule.

  8. t

    Population

    • trurocolchester.ca
    • townofoyen.com
    • +86more
    Updated Jun 12, 2018
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    (2018). Population [Dataset]. https://trurocolchester.ca/economic-data-reports/
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    Dataset updated
    Jun 12, 2018
    Description

    Population is the sum of births plus in-migration, and it signifies the total market size possible in the area. This is an important metric for economic developers to measure their economic health and investment attraction. Businesses also use this as a metric for market size when evaluating startup, expansion or relocation decisions.

  9. United States Loan Officer Survey: SIP: Very Important

    • ceicdata.com
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    CEICdata.com, United States Loan Officer Survey: SIP: Very Important [Dataset]. https://www.ceicdata.com/en/united-states/senior-loan-officer-opinion-survey-reason-for-strong-demand-for-commercial--industrial-loans/loan-officer-survey-sip-very-important
    Explore at:
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Apr 1, 2015 - Jan 1, 2018
    Area covered
    United States
    Variables measured
    Loans
    Description

    United States Loan Officer Survey: SIP: Very Important data was reported at 18.200 % in Apr 2018. This records an increase from the previous number of 13.300 % for Jan 2018. United States Loan Officer Survey: SIP: Very Important data is updated quarterly, averaging 1.850 % from Jan 2008 (Median) to Apr 2018, with 42 observations. The data reached an all-time high of 33.300 % in Jul 2016 and a record low of 0.000 % in Jul 2017. United States Loan Officer Survey: SIP: Very Important data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.KA037: Senior Loan Officer Opinion Survey: Reason for Strong Demand for Commercial & Industrial Loans. Senior Loan Officer Survey Questionnaire: If demand for C&I loans has strengthened over the past three months, how important have been the increase in investment in plant or equipment on the possible reason of change?

  10. f

    Performance of unit root test.

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Yu kun Wang; Li Zhang (2023). Performance of unit root test. [Dataset]. http://doi.org/10.1371/journal.pone.0281101.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Yu kun Wang; Li Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    For a long time, governments of all countries have attached great importance to the development of underground economic activities. The reason is that the characteristics of the underground economy are hidden and the information disclosure is not sufficient, which not only distorts the economic data indicators, but more importantly, the existence of the underground economy has led to the loss of a large amount of tax base, affecting the long-term economic development of the country. Whether raising the tax burden rate boosts the tax revenue or expand the scale of the underground economy. In this paper, we use Kuznet Tax Curve (KTC) method to analyze the relationship between GDP and TTR/DTR/ITR. We find that the tax base erosion rate of indirect tax is lower than that of direct tax. In addition, we explore the relationship among economic growth, tax rate and tax revenue and adopt SUR-OLS method and Threshold approach to estimate the response of economic growth on total tax revenue(TTR), direct tax revenue(DTR) and indirect tax revenue (ITR) in Taiwan from 1991-2020. Our empirical research shows that when DTR tax rates are between 12.59% and 13%, an increase in income leads to a decrease, not an increase, in DTR, leading to severe tax base erosion. That is, the relationship between GDP and DTR presents a N-shaped relationship. However, ITR does not exist any tax rate threshold effect. Obviously, with the increase of GDP, ITR also increases. This reflects that the difference of tax structure between direct tax and indirect tax plays a key role in the relationship between tax rate and tax base erosion.

  11. United States Loan Officer Survey: SIF: Very Important

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). United States Loan Officer Survey: SIF: Very Important [Dataset]. https://www.ceicdata.com/en/united-states/senior-loan-officer-opinion-survey-reason-for-strong-demand-for-commercial--industrial-loans/loan-officer-survey-sif-very-important
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Apr 1, 2015 - Jan 1, 2018
    Area covered
    United States
    Variables measured
    Loans
    Description

    United States Loan Officer Survey: SIF: Very Important data was reported at 9.100 % in Apr 2018. This records an increase from the previous number of 7.100 % for Jan 2018. United States Loan Officer Survey: SIF: Very Important data is updated quarterly, averaging 0.000 % from Jan 2008 (Median) to Apr 2018, with 42 observations. The data reached an all-time high of 14.300 % in Jan 2016 and a record low of 0.000 % in Jul 2017. United States Loan Officer Survey: SIF: Very Important data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.KA037: Senior Loan Officer Opinion Survey: Reason for Strong Demand for Commercial & Industrial Loans. Senior Loan Officer Survey Questionnaire: If demand for C&I loans has strengthened over the past three months, how important have been the increase in accounts receivables financing needs on the possible reason of change?

  12. F

    Relative Importance Weights: Manufacturing (NAICS)

    • fred.stlouisfed.org
    json
    Updated Jul 16, 2025
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    (2025). Relative Importance Weights: Manufacturing (NAICS) [Dataset]. https://fred.stlouisfed.org/series/RIWGMFS
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 16, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Relative Importance Weights: Manufacturing (NAICS) (RIWGMFS) from Jan 1972 to Jun 2025 about contributions, IP, production, manufacturing, industry, indexes, and USA.

  13. f

    Estimation of Tax Kuznets curve of Tax-to-GDP.

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Yu kun Wang; Li Zhang (2023). Estimation of Tax Kuznets curve of Tax-to-GDP. [Dataset]. http://doi.org/10.1371/journal.pone.0281101.t006
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Yu kun Wang; Li Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    For a long time, governments of all countries have attached great importance to the development of underground economic activities. The reason is that the characteristics of the underground economy are hidden and the information disclosure is not sufficient, which not only distorts the economic data indicators, but more importantly, the existence of the underground economy has led to the loss of a large amount of tax base, affecting the long-term economic development of the country. Whether raising the tax burden rate boosts the tax revenue or expand the scale of the underground economy. In this paper, we use Kuznet Tax Curve (KTC) method to analyze the relationship between GDP and TTR/DTR/ITR. We find that the tax base erosion rate of indirect tax is lower than that of direct tax. In addition, we explore the relationship among economic growth, tax rate and tax revenue and adopt SUR-OLS method and Threshold approach to estimate the response of economic growth on total tax revenue(TTR), direct tax revenue(DTR) and indirect tax revenue (ITR) in Taiwan from 1991-2020. Our empirical research shows that when DTR tax rates are between 12.59% and 13%, an increase in income leads to a decrease, not an increase, in DTR, leading to severe tax base erosion. That is, the relationship between GDP and DTR presents a N-shaped relationship. However, ITR does not exist any tax rate threshold effect. Obviously, with the increase of GDP, ITR also increases. This reflects that the difference of tax structure between direct tax and indirect tax plays a key role in the relationship between tax rate and tax base erosion.

  14. F

    Relative Importance Weights: Energy, Total

    • fred.stlouisfed.org
    json
    Updated Jun 17, 2025
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    (2025). Relative Importance Weights: Energy, Total [Dataset]. https://fred.stlouisfed.org/series/RIWB50089S
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 17, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Relative Importance Weights: Energy, Total (RIWB50089S) from Jan 1972 to May 2025 about contributions, energy, IP, production, industry, indexes, and USA.

  15. f

    Literature review of Taiwan’s underground economy in recent years.

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Yu kun Wang; Li Zhang (2023). Literature review of Taiwan’s underground economy in recent years. [Dataset]. http://doi.org/10.1371/journal.pone.0281101.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Yu kun Wang; Li Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Taiwan
    Description

    Literature review of Taiwan’s underground economy in recent years.

  16. F

    Relative Importance Weights: Equipment: Industrial Equipment

    • fred.stlouisfed.org
    json
    Updated Jul 16, 2025
    + more versions
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    (2025). Relative Importance Weights: Equipment: Industrial Equipment [Dataset]. https://fred.stlouisfed.org/series/RIWB52131S
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 16, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Relative Importance Weights: Equipment: Industrial Equipment (RIWB52131S) from Jan 1972 to Jun 2025 about contributions, IP, equipment, production, industry, indexes, and USA.

  17. United States LS: Increase in Customer Financng Needs(SIF): Not Important

    • ceicdata.com
    Updated Apr 6, 2018
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    CEICdata.com (2018). United States LS: Increase in Customer Financng Needs(SIF): Not Important [Dataset]. https://www.ceicdata.com/en/united-states/senior-loan-officer-opinion-survey-reason-for-strong-demand-for-commercial--industrial-loans/ls-increase-in-customer-financng-needssif-not-important
    Explore at:
    Dataset updated
    Apr 6, 2018
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Apr 1, 2015 - Jan 1, 2018
    Area covered
    United States
    Variables measured
    Loans
    Description

    United States LS: Increase in Customer Financng Needs(SIF): Not Important data was reported at 38.500 % in Oct 2018. This records an increase from the previous number of 30.800 % for Jul 2018. United States LS: Increase in Customer Financng Needs(SIF): Not Important data is updated quarterly, averaging 42.050 % from Jan 2008 (Median) to Oct 2018, with 44 observations. The data reached an all-time high of 100.000 % in Apr 2009 and a record low of 12.500 % in Oct 2011. United States LS: Increase in Customer Financng Needs(SIF): Not Important data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S023: Senior Loan Officer Opinion Survey: Reason for Strong Demand for Commercial & Industrial Loans. Senior Loan Officer Survey Questionnaire: If demand for C&I loans has strengthened over the past three months, how important have been the increase in financing needs on the possible reason of change?

  18. F

    Relative Importance Weights: Final Products

    • fred.stlouisfed.org
    json
    Updated Jul 16, 2025
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    (2025). Relative Importance Weights: Final Products [Dataset]. https://fred.stlouisfed.org/series/RIWB50002S
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 16, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Relative Importance Weights: Final Products (RIWB50002S) from Jan 1972 to Jun 2025 about contributions, IP, production, industry, indexes, and USA.

  19. F

    6) To the Extent That the Price or Nonprice Terms Applied to Hedge Funds...

    • fred.stlouisfed.org
    json
    Updated Jun 26, 2025
    + more versions
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    (2025). 6) To the Extent That the Price or Nonprice Terms Applied to Hedge Funds Have Tightened or Eased Over the Past Three Months (as Reflected in Your Responses to Questions 4 and 5), What Are the Most Important Reasons for the Change?| A. Possible Reasons for Tightening | 6. Worsening in General Market Liquidity and Functioning. | Answer Type: First In Importance [Dataset]. https://fred.stlouisfed.org/series/CTQ06A6MINR
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 26, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for 6) To the Extent That the Price or Nonprice Terms Applied to Hedge Funds Have Tightened or Eased Over the Past Three Months (as Reflected in Your Responses to Questions 4 and 5), What Are the Most Important Reasons for the Change?| A. Possible Reasons for Tightening | 6. Worsening in General Market Liquidity and Functioning. | Answer Type: First In Importance (CTQ06A6MINR) from Q1 2012 to Q2 2025 about Hedge Fund, ease, marketable, general, change, liquidity, 3-month, price, and USA.

  20. United States Loan Officer Survey: SAR: Very Important

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). United States Loan Officer Survey: SAR: Very Important [Dataset]. https://www.ceicdata.com/en/united-states/senior-loan-officer-opinion-survey-reason-for-strong-demand-for-commercial--industrial-loans/loan-officer-survey-sar-very-important
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Apr 1, 2015 - Jan 1, 2018
    Area covered
    United States
    Variables measured
    Loans
    Description

    United States Loan Officer Survey: SAR: Very Important data was reported at 9.100 % in Apr 2018. This records a decrease from the previous number of 13.300 % for Jan 2018. United States Loan Officer Survey: SAR: Very Important data is updated quarterly, averaging 0.000 % from Jan 2008 (Median) to Apr 2018, with 42 observations. The data reached an all-time high of 20.000 % in Apr 2017 and a record low of 0.000 % in Jul 2017. United States Loan Officer Survey: SAR: Very Important data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.KA037: Senior Loan Officer Opinion Survey: Reason for Strong Demand for Commercial & Industrial Loans. Senior Loan Officer Survey Questionnaire: If demand for C&I loans has strengthened over the past three months, how important have been the increase in accounts receivables financing needs on the possible reason of change?

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Close
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LSEG (2023). Economic Data [Dataset]. https://www.lseg.com/en/data-analytics/financial-data/economic-data
Organization logo

Economic Data

Explore at:
Dataset updated
Nov 19, 2023
Dataset provided by
London Stock Exchange Grouphttp://www.londonstockexchangegroup.com/
Authors
LSEG
License

https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer

Description

View LSEG's extensive Economic Data, including content that allows the analysis and monitoring of national economies with historical and real-time series.

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