100+ datasets found
  1. f

    Table_1_Nexus Among Economic Growth, Education, Health, and Environment:...

    • figshare.com
    • frontiersin.figshare.com
    xlsx
    Updated Jun 1, 2023
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    Suleman Sarwar; Majid Ibrahim Alsaggaf; Cao Tingqiu (2023). Table_1_Nexus Among Economic Growth, Education, Health, and Environment: Dynamic Analysis of World-Level Data.XLSX [Dataset]. http://doi.org/10.3389/fpubh.2019.00307.s001
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    xlsxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Frontiers
    Authors
    Suleman Sarwar; Majid Ibrahim Alsaggaf; Cao Tingqiu
    License

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

    Area covered
    World
    Description

    The aim of current study is to examine the nexus among economic growth, education, health issues, and carbon emission for the panel of 161 countries. Education and health have confirmed insignificant coefficients for economic growth and carbon emission, which mention that higher education and better health conditions are not useful for boosting economic development and for controlling environmental degradation process. Empirical estimations have reported that higher capital investment leads to increase the economic process and carbon emission. Higher educational standard and capital investment helps to control the health issues, in the long- and short-run. On contrary, higher carbon emission creates health issues. The given results can provide support to the economic, social, and environmental policy makers during policy decisions. For example, the study suggests green financing and low carbon economy concept; the government and industries have to increase the investment on modern, energy efficient, and green technologies, which are useful for economic development, as well as to control the environmental degradation process.

  2. Data File for: "Institutions, Entrepreneurship, and Regional Differences in...

    • figshare.com
    xls
    Updated Mar 15, 2022
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    Joshua Hall; Russell Sobel (2022). Data File for: "Institutions, Entrepreneurship, and Regional Differences in Economic Growth" [Dataset]. http://doi.org/10.6084/m9.figshare.19364714.v1
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    xlsAvailable download formats
    Dataset updated
    Mar 15, 2022
    Dataset provided by
    Figsharehttp://figshare.com/
    figshare
    Authors
    Joshua Hall; Russell Sobel
    License

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

    Description

    Data file for:Joshua Hall and Russell Sobel, “Institutions, Entrepreneurship, and Regional Differences in Economic Growth,” Southern Journal of Entrepreneurship 1(1) 2008: 69-96.

  3. Industrial production growth worldwide 2019-2024, by region

    • statista.com
    Updated Sep 19, 2023
    + more versions
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    Jose Sanchez (2023). Industrial production growth worldwide 2019-2024, by region [Dataset]. https://www.statista.com/topics/6139/covid-19-impact-on-the-global-economy/
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    Dataset updated
    Sep 19, 2023
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Jose Sanchez
    Description

    In July 2024, global industrial production, excluding the United States, increased by 1.5 percent compared to the same time in the previous year, based on three month moving averages. This is compared to an increase of 0.2 percent in advanced economies (excluding the United States) for the same time period. The global industrial production collapsed after the outbreak of COVID-19, but increased steadily in the months after, peaking at 23 percent in June 2021. Industrial growth rate tracks the output production in the industrial sector.

  4. m

    Journal of Iran's Economic Essays; "Jostarhaye Eghtesadiye Iran; No. 17

    • data.mendeley.com
    Updated Nov 7, 2024
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    Journal of Economic Essays; an Islamic approach (2024). Journal of Iran's Economic Essays; "Jostarhaye Eghtesadiye Iran; No. 17 [Dataset]. http://doi.org/10.17632/6zk62khdw2.1
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    Dataset updated
    Nov 7, 2024
    Authors
    Journal of Economic Essays; an Islamic approach
    License

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

    Area covered
    Iran
    Description

    Journal of Economic Essays; an Islamic approach (JEE) is an open-access double-blind peer-reviewed academic quarterly journal that publishes original research, written with an Islamic approach in various Economic areas including Islamic economics and finance, Iran's economy, Fiqh (Islamic jurisprudence) and economics, philosophy of economics, economic theory, ethics and business, Islamic banking, economic development, and social security institution in Islam. JEE especially welcomes comparative articles that study the western and Muslim scholars' viewpoints about important economic issues to find the best answer to theoretical and applied questions and also to explore their differences and similarities with the aim of bringing them closer to each other. This journal accepts analytical articles that reread Muslim scholars' ideas according to new approaches in economics, moral philosophy, Islamic banking and finance, social security, econometrics, and Islamic economic institutions. Therefore, it aims to serve the interests of a wide range of thoughtful and academic readers, economists, philosophers, theologians, banking and finance experts and others interested in the multidisciplinary study of economics and Islam (including Fiqh "Islamic jurisprudence"). We hope that this publication creates an atmosphere of constructive dialogue on different economic topics between national and international scholars by which it could have a role in the growth of Islamic economic thought in Iran. In addition, Islamic solutions to the economic problems of Iran and the world will be examined and analyzed, and they will be presented to the policy makers to help improve the Islamic economic system.

    Major Topics

    The Journal publishes papers, including the following fields:

    • Islamic economics and finance;

      • Iran's economy;

      • Fiqh (Islamic jurisprudence) and economics;

      • Philosophy of economics;

      • Economic theory;

      • Ethics;

    • Islamic economic institutions

      • Islamic banking;

      • Economic development;

    • Social security.

  5. d

    Replication Data for: Historical Political Economy: Past, Present, and...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Charnysh, Volha (2023). Replication Data for: Historical Political Economy: Past, Present, and Future [Dataset]. http://doi.org/10.7910/DVN/FNNS2Z
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Charnysh, Volha
    Description

    This dataset comprises data on all articles in the new field of historical political economy (HPE) published in from 2010 to 2021 in eight top journals in political science: the American Journal of Political Science, the American Political Science Review, the British Journal of Political Science, Comparative Political Studies, Comparative Politics, the Journal of Politics, the Quarterly Journal of Political Science, and World Politics. We define political economy as work that either uses formal theory or empirically tests falsifiable arguments using quantitative methods. We classify work that uses the tools of political economy as HPE if it substantially or exclusively examines politics prior to 1945: the end of the Second World War, the onset of the Cold War, the moment when the Bretton Woods system came into effect, and the start of decolonization in Africa and Asia. We make an exception for China, where important institutional changes occurred after the founding of the People’s Republic in 1949 and the Cultural Revolution from 1966 to 1976. The dataset includes full citation, time period, region and country of study, topic, and keywords. We also code each article as one of three types: work that seeks to understand the past for its own sake, work that uses history as a way to understand the present, and work that uses history as a setting to investigate important theoretical issues.

  6. h

    ai-economy-labor-articles-annotated-processed

    • huggingface.co
    Updated Aug 11, 2025
    + more versions
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    Yacine Jernite (2025). ai-economy-labor-articles-annotated-processed [Dataset]. https://huggingface.co/datasets/yjernite/ai-economy-labor-articles-annotated-processed
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    Dataset updated
    Aug 11, 2025
    Authors
    Yacine Jernite
    Description

    yjernite/ai-economy-labor-articles-annotated-processed dataset hosted on Hugging Face and contributed by the HF Datasets community

  7. DCMS Sector Economic Estimates: Employment Oct 2020 - Sep 2021

    • gov.uk
    Updated Nov 22, 2024
    + more versions
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    Department for Digital, Culture, Media & Sport (2024). DCMS Sector Economic Estimates: Employment Oct 2020 - Sep 2021 [Dataset]. https://www.gov.uk/government/statistics/dcms-sector-economic-estimates-employment-oct-2020-sep-2021
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    Dataset updated
    Nov 22, 2024
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Digital, Culture, Media & Sport
    Description

    Details

    Last update: 10 February 2022 Next update: July 2022 Geographic coverage: UK

    Headline Findings

    There were, on average, 4.2 million filled jobs (12.7% of the UK total) in DCMS sectors (excluding Tourism) in the 12 month period between October 2020 and September 2021, a 1.7% increase compared to the preceding 12 months. Over the same period total UK filled jobs fell by 1.2%.

    The Creative Industries had the most jobs with 2.3 million, followed by the Digital Sector (1.8 million) and Civil Society (0.9 million). The sector with the fewest jobs was Gambling at 76 thousand.

    Revision note

    On Friday 4th November, we removed the DCMS statistics on socio-economic background and current occupation, using data from the Labour Force Survey (LFS) for the period July to September 2021.

    This is because ONS have identified an https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/theimpactofmiscodingofoccupationaldatainofficefornationalstatisticssocialsurveysuk/2022-09-26" class="govuk-link">issue with the way their underlying survey data has been assigned to the refreshed SOC2020 codes that were used to calculate these estimates in this publication. ONS expects to resolve the issue by Spring 2023.

    No other data in this release is affected. Data covering https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1043520/DCMS_sectors_Economic_Estimates_Employment_Labour_Force_Survey_July_to_September_2016_2019_and_2020.ods" class="govuk-link">July to September 2020 for socio-economic background and current occupation is unaffected by the issue.

    About this release

    These Economic Estimates are National Statistics used to provide an estimate of employment (number of filled jobs) in the DCMS Sectors, for the period October 2020 to September 2021. The findings are calculated based on the ONS Annual Population Survey (APS).

    Content

    These statistics cover the contributions of the following DCMS sectors to the UK economy;

    • Civil Society
    • Creative Industries
    • Cultural Sector
    • Digital Sector
    • Gambling
    • Sport
    • Telecoms

    A definition for each sector is available in the accompanying technical document along with details of methods and data limitations.

    The UK Statistics Authority

    This release is published in accordance with the Code of Practice for Statistics (2018) produced by the UK Statistics Authority (UKSA). The UKSA has the overall objective of promoting and safeguarding the production and publication of official statistics that serve the public good. It monitors and reports on all official statistics, and promotes good practice in this area.

    Pre-release access

    The accompanying pre-release access document lists ministers and officials who have received privileged early access to this release. In line with best practice, the list has been kept to a minimum and those given access for briefing purposes had a maximum of 24 hours.

    Contact

    Responsible analyst: George Ashford

    For any queries or feedback, please contact evidence@dcms.gov.uk.

  8. American Civil War Era Financial Data

    • figshare.com
    zip
    Updated Jun 1, 2023
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    Jeffrey Arnold (2023). American Civil War Era Financial Data [Dataset]. http://doi.org/10.6084/m9.figshare.1513853.v1
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    zipAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Jeffrey Arnold
    License

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

    Area covered
    United States
    Description

    Miscellaneous financial and economic datasets from the period around the U.S. Civil War gathered from a variety of sources.

  9. B

    Brazil Exports: Price: Other Products: Used Clothing and Other Textile...

    • ceicdata.com
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    CEICdata.com, Brazil Exports: Price: Other Products: Used Clothing and Other Textile Articles, Including Rags [Dataset]. https://www.ceicdata.com/en/brazil/exports-economic-activity-product-price
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    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Dec 22, 2024 - Mar 16, 2025
    Area covered
    Brazil
    Variables measured
    Merchandise Trade
    Description

    Exports: Price: Other Products: Used Clothing and Other Textile Articles, Including Rags data was reported at 4,979.560 USD/Ton in 30 Apr 2025. This records a decrease from the previous number of 5,013.690 USD/Ton for 27 Apr 2025. Exports: Price: Other Products: Used Clothing and Other Textile Articles, Including Rags data is updated daily, averaging 3,424.205 USD/Ton from Mar 2019 (Median) to 30 Apr 2025, with 263 observations. The data reached an all-time high of 56,209.412 USD/Ton in 15 Sep 2024 and a record low of 215.021 USD/Ton in 08 Jan 2023. Exports: Price: Other Products: Used Clothing and Other Textile Articles, Including Rags data remains active status in CEIC and is reported by Special Secretariat for Foreign Trade and International Affairs. The data is categorized under Brazil Premium Database’s Foreign Trade – Table BR.JAA004: Exports: Economic Activity: Product: Price.

  10. Major Land Uses

    • agdatacommons.nal.usda.gov
    • datadiscoverystudio.org
    • +4more
    bin
    Updated Apr 23, 2025
    + more versions
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    USDA Economic Research Service (2025). Major Land Uses [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Major_Land_Uses/25696407
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    binAvailable download formats
    Dataset updated
    Apr 23, 2025
    Dataset provided by
    United States Department of Agriculturehttp://usda.gov/
    Economic Research Servicehttp://www.ers.usda.gov/
    Authors
    USDA Economic Research Service
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    ERS has been a source of major land use estimates in the United States for over 50 years, and the related U.S. cropland used for crops series dates back to 1910. The Major Land Uses (MLU) series is the longest running, most comprehensive accounting of all major uses of public and private land in the United States. The series was started in 1945, and has since been published about every 5 years, coinciding with the Census of Agriculture. See the latest report in the series, Major Uses of Land in the United States, 2007.

    Data from all 14 Major Land Uses reports have been combined into a set of files showing major land use estimates by region and State from 1945 to 2007. Alaska and Hawaii were added in 1959, when they achieved Statehood. Since Alaska contains such vast acreage, 50-State totals in all categories prior to 1959 may appear to change precipitously.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

  11. C

    China CN: Import: HS 8: Furnishing Articles of Silk or Silk Waste, Not...

    • ceicdata.com
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    CEICdata.com, China CN: Import: HS 8: Furnishing Articles of Silk or Silk Waste, Not Knitted or Crocheted [Dataset]. https://www.ceicdata.com/en/china/rmb-hs63-other-made-up-textile-articles-sets-worn-clothing-and-worn-textile-articles-rags
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    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Dec 1, 2023 - Nov 1, 2024
    Area covered
    China
    Description

    CN: Import: HS 8: Furnishing Articles of Silk or Silk Waste, Not Knitted or Crocheted data was reported at 0.000 RMB mn in Mar 2025. This records a decrease from the previous number of 0.019 RMB mn for Feb 2025. CN: Import: HS 8: Furnishing Articles of Silk or Silk Waste, Not Knitted or Crocheted data is updated monthly, averaging 0.015 RMB mn from Apr 2015 (Median) to Mar 2025, with 101 observations. The data reached an all-time high of 1.665 RMB mn in Jan 2022 and a record low of 0.000 RMB mn in Mar 2017. CN: Import: HS 8: Furnishing Articles of Silk or Silk Waste, Not Knitted or Crocheted data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JKF: RMB: HS63: Other Made Up Textile Articles; Sets; Worn Clothing and Worn Textile Articles; Rags.

  12. C

    China CN: Other Daily Sundry Article: YoY: Product Inventory

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). China CN: Other Daily Sundry Article: YoY: Product Inventory [Dataset]. https://www.ceicdata.com/en/china/daily-sundry-article-other-daily-sundry-article/cn-other-daily-sundry-article-yoy-product-inventory
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    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Nov 1, 2014 - Oct 1, 2015
    Area covered
    China
    Variables measured
    Economic Activity
    Description

    China Other Daily Sundry Article: YoY: Product Inventory data was reported at 14.061 % in Oct 2015. This records an increase from the previous number of 11.419 % for Sep 2015. China Other Daily Sundry Article: YoY: Product Inventory data is updated monthly, averaging 10.963 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 43.130 % in Feb 2008 and a record low of -3.172 % in May 2013. China Other Daily Sundry Article: YoY: Product Inventory data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIM: Daily Sundry Article: Other Daily Sundry Article.

  13. C

    China CN: Daily Sundry Article: YoY: Current Asset

    • ceicdata.com
    Updated Dec 15, 2024
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    CEICdata.com (2024). China CN: Daily Sundry Article: YoY: Current Asset [Dataset]. https://www.ceicdata.com/en/china/other-manufacturing-daily-sundry-article/cn-daily-sundry-article-yoy-current-asset
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    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Nov 1, 2014 - Oct 1, 2015
    Area covered
    China
    Variables measured
    Economic Activity
    Description

    China Daily Sundry Article: YoY: Current Asset data was reported at 1.669 % in Oct 2015. This records a decrease from the previous number of 2.406 % for Sep 2015. China Daily Sundry Article: YoY: Current Asset data is updated monthly, averaging 9.379 % from Jan 2012 (Median) to Oct 2015, with 46 observations. The data reached an all-time high of 12.588 % in May 2013 and a record low of 1.669 % in Oct 2015. China Daily Sundry Article: YoY: Current Asset data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIM: Other Manufacturing: Daily Sundry Article.

  14. Data from: Field 70/71 Study for Resilient Economic Agricultural Practices...

    • catalog.data.gov
    • agdatacommons.nal.usda.gov
    Updated Apr 21, 2025
    + more versions
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    Agricultural Research Service (2025). Field 70/71 Study for Resilient Economic Agricultural Practices in Ames, Iowa [Dataset]. https://catalog.data.gov/dataset/field-70-71-study-for-resilient-economic-agricultural-practices-in-ames-iowa-46647
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    Dataset updated
    Apr 21, 2025
    Dataset provided by
    Agricultural Research Servicehttps://www.ars.usda.gov/
    Area covered
    Ames, Iowa
    Description

    Field 70/71 Study for Resilient Economic Agricultural Practices in Ames, Iowa See REAP brochure Resources in this dataset:Resource Title: Ames, IA Field 70/71 Study (IAAM7071) CSV data. File Name: IAAM7071_csv_data.zipResource Description: CSV format data on Experimental Units, Field Sites, Biomass, Harvest Fraction, Residue Management, Soil Chemistry, Soil Cover, Soil Physics, Amendments, Planting, Tillage, Persons, Treatments, Weather Daily, Weather Station.

  15. s

    Data from: China's Innovation Landscape

    • researchdata.smu.edu.sg
    zip
    Updated May 31, 2023
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    G. HUANG Kenneth (2023). Data from: China's Innovation Landscape [Dataset]. http://doi.org/10.25440/smu.12062820.v1
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    zipAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    SMU Research Data Repository (RDR)
    Authors
    G. HUANG Kenneth
    License

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

    Area covered
    China
    Description

    This record contains the underlying research data for the publication "China's Innovation Landscape" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/3569The People's Republic of China has experienced three decades of sustained, strong annual economic growth as it transitions from a centrally planned economy to a free market. Currently the world's second largest economy, China recognizes scientific and technological innovation as an increasingly important strategy to fuel the next phase of its productivity growth. However, the drivers and trajectories of China's scientific and technological growth remain under-investigated. To understand elements of China's innovative activities, particularly in science and technology, an analysis of comprehensive patent data provided by the State Intellectual Property Office (SIPO) of China is presented here.

  16. Commuting Zones and Labor Market Areas

    • agdatacommons.nal.usda.gov
    • catalog.data.gov
    • +1more
    bin
    Updated Apr 23, 2025
    + more versions
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    USDA Economic Research Service (2025). Commuting Zones and Labor Market Areas [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Commuting_Zones_and_Labor_Market_Areas/25696356
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    binAvailable download formats
    Dataset updated
    Apr 23, 2025
    Dataset provided by
    Economic Research Servicehttp://www.ers.usda.gov/
    Authors
    USDA Economic Research Service
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Note: Updates to this data product are discontinued. County boundaries do not always accurately define local economies. Commuting zones and Labor Market Areas combine counties into units intended to more closely reflect the geographic interrelationships between employers and labor supply.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data download page For complete information, please visit https://data.gov.

  17. Data from: Tennessee Valley Study for Resilient Economic Agricultural...

    • catalog.data.gov
    • agdatacommons.nal.usda.gov
    Updated Apr 21, 2025
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    Agricultural Research Service (2025). Tennessee Valley Study for Resilient Economic Agricultural Practices in Auburn, Alabama [Dataset]. https://catalog.data.gov/dataset/tennessee-valley-study-for-resilient-economic-agricultural-practices-in-auburn-alabama-c8d63
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    Dataset updated
    Apr 21, 2025
    Dataset provided by
    Agricultural Research Servicehttps://www.ars.usda.gov/
    Area covered
    Auburn, Alabama, Tennessee
    Description

    Tennessee Valley Study for Resilient Economic Agricultural Practices in Auburn, Alabama Maize ( Zea mays L.) produced in narrow rows can increase yields and accelerate canopy closure. Costly equipment modifications make narrow rows impractical, but a twin-row configuration may boost pro duction with fewer equipment modifications. Four field experiments were conducted to measure weed biomass, leaf area index (LAI), and yield for a conventional (CN) and a glyphosate-resistant (GR) hybrid across three plant densities (low 4.0–4.4 plants m - 2 ; medium 5.9–6.4 plants m - 2 ; and high 7.9–8.4 plants m - 2 ) and two row configurations (single vs. twin) in a conservation tillage system during the 2005 grow ing season. The experimental design was a split–split plot with a RCB arrangement of whole plots where hybrids were assigned to main plots, row configurations to subplots, and plant density to sub-subplots with four replications. Row configuration had little effect on weed biomass compared to plant density and hybrid. Leaf area index increased with higher plant density at all locations. In general, LAI increased with the twin-row configuration, but LAI also varied with hybrid based on interactions between hybrid and plant density or row configuration. Row configuration had little impact on maize yields, while plant density had the most effect on yields. Plant density also interacted with hybrid or row configuration at multiple locations, although maize yields did not always increase with higher plant density. Conventional hybrids may also provide an alternative to GR hybrids, particularly at lower plant densities. Maize yield increases with twin rows were minimal and may not justify twin row conversion under dryland condi tions, but growers that already utilize twin-row equipment will not suffer yield decreases by planting twin rows. Resources in this dataset:Resource Title: Auburn, AL Tennessee Valley Study (ALAUTVS) CSV data. File Name: ALAUTVS_csv_data.zipResource Description: CSV format data on Experimental Units, Field Sites, Residue Management, Persons, Treatments, Weather Daily, Weather Station.

  18. f

    Data from: Perceptions of Scholars in the Field of Economics on...

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Jun 21, 2016
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    Ratnavelu, Kuru; Kumar, Sameer (2016). Perceptions of Scholars in the Field of Economics on Co-Authorship Associations: Evidence from an International Survey [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001538094
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    Dataset updated
    Jun 21, 2016
    Authors
    Ratnavelu, Kuru; Kumar, Sameer
    Description

    Scholars (n = 580) from 69 countries who had contributed articles in the field of Economics during the year 2015 participated in a survey that gauged their perceptions of various aspects of co-authorship, including its benefits, motivations, working relationships, order of authorship and association preferences. Among the main findings, significant differences emerged in the proportion of co-authored papers based on age, gender and number of years the researchers had spent in their present institution. Female scholars had a greater proportion of co-authored papers than male scholars. Respondents considered improved quality of paper, contribution of mutual expertise, and division of labor as the biggest benefits of and motivation for co-authorship. Contrary to common perceptions that Economics researchers used a predominantly alphabetical order of authorship, our study found that a considerable percentage of respondents (34.5%) had practiced an order of authorship based on the significance of the authors’ contribution to the work. The relative importance of tasks differed significantly according to whether researchers co-authored as mentors or co-authored as colleagues. Lastly, researchers were found to associate, to varying degrees, with other researchers based on socio-academic parameters, such as nationality, ethnicity, gender, professional position and friendship. The study indicates that Economics authors perceive co-authorship as a rewarding endeavor. Nonetheless, the level of contribution and even the choice of association itself as a co-author depends to a great extent on the type of working relationship and socio-academic factors.

  19. Food Expenditure Series

    • agdatacommons.nal.usda.gov
    • data.globalchange.gov
    • +4more
    bin
    Updated Apr 23, 2025
    + more versions
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    USDA Economic Research Service (2025). Food Expenditure Series [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Food_Expenditure_Series/25696386
    Explore at:
    binAvailable download formats
    Dataset updated
    Apr 23, 2025
    Dataset provided by
    Economic Research Servicehttp://www.ers.usda.gov/
    Authors
    USDA Economic Research Service
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The ERS Food Expenditure Series annually measures total U.S. food expenditures, including purchases by consumers, governments, businesses, and nonprofit organizations. The ERS Food Expenditure Series contributes to the analysis of U.S. food production and consumption by constructing a comprehensive measure of the total value of all food expenditures by final purchasers. This series annually measures total U.S. food expenditures, including purchases by consumers, governments, businesses, and nonprofit organizations. Because the term expenditure is often associated with household decisionmaking, it is important to recognize that ERS's series also includes nonhousehold purchases. For example, the series includes the dollar value of domestic food purchases by military personnel and their dependents at military commissary stores and exchanges, the value of commodities and food dollars donated by the Federal government to schools, and the value of food purchased by airlines for serving during flights.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

  20. C

    China CN: Export: HS 8: Articles of Apparel or Clothing of Paper, Cellulose...

    • ceicdata.com
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    CEICdata.com, China CN: Export: HS 8: Articles of Apparel or Clothing of Paper, Cellulose Wadding [Dataset]. https://www.ceicdata.com/en/china/rmb-hs48-paper-and-paperboard-articles-of-paper-pulp-of-paper-or-of-paperboard
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    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Jan 1, 2024 - Dec 1, 2024
    Area covered
    China
    Description

    CN: Export: HS 8: Articles of Apparel or Clothing of Paper, Cellulose Wadding data was reported at 17.247 RMB mn in Mar 2025. This records an increase from the previous number of 10.203 RMB mn for Feb 2025. CN: Export: HS 8: Articles of Apparel or Clothing of Paper, Cellulose Wadding data is updated monthly, averaging 6.510 RMB mn from Jan 2015 (Median) to Mar 2025, with 123 observations. The data reached an all-time high of 20.680 RMB mn in Jun 2023 and a record low of 0.312 RMB mn in Feb 2020. CN: Export: HS 8: Articles of Apparel or Clothing of Paper, Cellulose Wadding data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JKF: RMB: HS48: Paper and Paperboard; Articles of Paper Pulp, of Paper or of Paperboard.

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Suleman Sarwar; Majid Ibrahim Alsaggaf; Cao Tingqiu (2023). Table_1_Nexus Among Economic Growth, Education, Health, and Environment: Dynamic Analysis of World-Level Data.XLSX [Dataset]. http://doi.org/10.3389/fpubh.2019.00307.s001

Table_1_Nexus Among Economic Growth, Education, Health, and Environment: Dynamic Analysis of World-Level Data.XLSX

Related Article
Explore at:
xlsxAvailable download formats
Dataset updated
Jun 1, 2023
Dataset provided by
Frontiers
Authors
Suleman Sarwar; Majid Ibrahim Alsaggaf; Cao Tingqiu
License

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

Area covered
World
Description

The aim of current study is to examine the nexus among economic growth, education, health issues, and carbon emission for the panel of 161 countries. Education and health have confirmed insignificant coefficients for economic growth and carbon emission, which mention that higher education and better health conditions are not useful for boosting economic development and for controlling environmental degradation process. Empirical estimations have reported that higher capital investment leads to increase the economic process and carbon emission. Higher educational standard and capital investment helps to control the health issues, in the long- and short-run. On contrary, higher carbon emission creates health issues. The given results can provide support to the economic, social, and environmental policy makers during policy decisions. For example, the study suggests green financing and low carbon economy concept; the government and industries have to increase the investment on modern, energy efficient, and green technologies, which are useful for economic development, as well as to control the environmental degradation process.

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