The historical and economic dictionary contains some dictionary articles devoted to the development of the economy in our and other countries. The articles reveal the main content of terms and their meaning. This study is a collection of data that includes an extensive list of economic terms.

This study shows a review about the report of Social environment and its influence on economic health of the firm which engages in wholesale and retail trading ofclothes and footwear case.

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.

United States US: Scientific and Technical Journal Articles data was reported at 408,985.300 Unit in 2016. This records a decrease from the previous number of 429,139.000 Unit for 2015. United States US: Scientific and Technical Journal Articles data is updated yearly, averaging 403,928.200 Unit from Dec 2003 (Median) to 2016, with 14 observations. The data reached an all-time high of 440,229.700 Unit in 2014 and a record low of 321,765.900 Unit in 2003. United States US: Scientific and Technical Journal Articles data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Technology. Scientific and technical journal articles refer to the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.; ; National Science Foundation, Science and Engineering Indicators.; Gap-filled total;

Imports: Volume: Daily Average: YoY: Daily Average: Transformation Industry: Articles of Iron or Steel and Other Articles of Base Metal在2025-04-30达1.408%，相较于2025-04-27的-2.071%有所增长。Imports: Volume: Daily Average: YoY: Daily Average: Transformation Industry: Articles of Iron or Steel and Other Articles of Base Metal数据按每日更新，2020-03-15至2025-04-30期间平均值为11.281%，共250份观测结果。该数据的历史最高值出现于2020-05-10，达555.744%，而历史最低值则出现于2020-10-11，为-80.760%。CEIC提供的Imports: Volume: Daily Average: YoY: Daily Average: Transformation Industry: Articles of Iron or Steel and Other Articles of Base Metal数据处于定期更新的状态，数据来源于Special Secretariat for Foreign Trade and International Affairs，数据归类于Brazil Premium Database的Foreign Trade – Table BR.JAA012: Imports: Economic Activity: Product: Volume: Year-on-Year。

China Other Daily Sundry Article: Account Receivable data was reported at 9.647 RMB bn in Oct 2015. This records an increase from the previous number of 9.364 RMB bn for Sep 2015. China Other Daily Sundry Article: Account Receivable data is updated monthly, averaging 7.258 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 9.781 RMB bn in Jul 2015 and a record low of 2.043 RMB bn in Dec 2003. China Other Daily Sundry Article: Account Receivable 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.

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.

Exports: Volume: Daily Average: Transformation Industry: Articles of Iron or Steel and Other Articles of Base Metal在2025-04-30达356.615Ton，相较于2025-04-27的373.119Ton有所下降。Exports: Volume: Daily Average: Transformation Industry: Articles of Iron or Steel and Other Articles of Base Metal数据按每日更新，2019-03-31至2025-04-30期间平均值为442.475Ton，共262份观测结果。该数据的历史最高值出现于2021-03-21，达5,529,656.631Ton，而历史最低值则出现于2020-04-12，为216.051Ton。CEIC提供的Exports: Volume: Daily Average: Transformation Industry: Articles of Iron or Steel and Other Articles of Base Metal数据处于定期更新的状态，数据来源于Special Secretariat for Foreign Trade and International Affairs，数据归类于Brazil Premium Database的Foreign Trade – Table BR.JAA003: Exports: Economic Activity: Product: Volume。

Monthly national milk cost of production estimates for 2005-present, and annual milk cost of production estimates by State and by size of operation for 2005 to 2012.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.

The data provided in this repository is the datasets of the article “Do Government Expenditures on Productive Sectors Effectively Mitigate Poverty? Evidence from South Sulawesi, Indonesia”. The raw data are gathered from The Ministry of Finance in Indonesia and the Central Bureau of Statistic Indonesia. In addition, this article uses a panel data set of 24 districts/cities in South Sulawesi for the period of 2015 to 2020.

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.

Expanded data for article “Analysis of Current Trends in Migration Processes”. Compiled by the authors based on the data of the Federal State Statistics Service of the Russian Federation

Brazil Imports: Volume: Daily Average: Transformation Industry: Articles of Iron or Steel and Other Articles of Base Metal data was reported at 2,106.051 Ton in 12 Feb 2023. This records an increase from the previous number of 1,797.193 Ton for 31 Jan 2023. Brazil Imports: Volume: Daily Average: Transformation Industry: Articles of Iron or Steel and Other Articles of Base Metal data is updated daily, averaging 1,982.092 Ton from Mar 2019 to 12 Feb 2023, with 152 observations. The data reached an all-time high of 12,734.193 Ton in 10 May 2020 and a record low of 948.166 Ton in 11 Oct 2020. Brazil Imports: Volume: Daily Average: Transformation Industry: Articles of Iron or Steel and Other Articles of Base Metal data remains active status in CEIC and is reported by Special Secretariat for Foreign Trade and International Affairs. The data is categorized under High Frequency Database’s Foreign Trade – Table BR.JAA009: Imports: Economic Activity: Product: Volume.

This thesis explores the devastating economic consequences that a hypothetical World War III could have on the global economy. Unlike the previous world wars, this conflict would unfold in a highly globalized, digitally interconnected world—meaning the economic damage would be even more widespread and severe.Drawing from history, the paper analyzes past wars like World War I and II, highlighting how those events caused GDP contractions, hyperinflation, destruction of infrastructure, and long-term debt. It uses these precedents to build realistic scenarios for what could happen if WWIII breaks out today. The study models short-term disruptions like stock market crashes, currency collapse, and trade blockades; medium-term issues like mass unemployment and inflation; and long-term impacts such as technological regression and widespread economic stagnation.The thesis provides regional assessments as well—evaluating how countries like the U.S., China, and nations in Europe and the Global South would fare in different war scenarios, from limited conflicts to full-scale nuclear exchanges. It also discusses secondary effects like energy and food shortages, famine, and the collapse of consumer demand in non-essential sectors.Importantly, the paper doesn’t stop at doom and gloom. It outlines strategic policy responses such as emergency fiscal controls, global debt restructuring, a possible new Bretton Woods system, and a modern-day Marshall Plan to help rebuild economies post-war.In conclusion, the research emphasizes that preventing World War III is not just a matter of global peace, but an absolute economic necessity. Even the strongest economies could collapse, and recovery could take decades—if at all. The thesis serves as both a warning and a call for proactive international diplomacy, economic safeguards, and collective accountability.

This file contains the raw data from phone interviews, along with participant ID, date and time of the call, and location of respondents. Ha’apai was the village sampled for Roviana, Nusa Baruqu for Gizo, and ND refers to Nijhum Dwip. (XLSX)

InvaCost is the most up-to-date, comprehensive, standardized and robust data compilation and description of economic cost estimates associated with invasive species worldwide1. InvaCost has been constructed to provide a contemporary and freely available repository of monetary impacts that can be relevant for both research and evidence-based policy making. The ongoing work made by the InvaCost consortium2,3,4 leads to constantly improving the structure and content of the database (see sections below). The list of actual contributors to this data resource now largely exceeds the list of authors listed in this page. All details regarding the previous versions of InvaCost can be found by switching from one version to another using the “version” button above. IMPORTANT UPDATES: 1. All information, files, outcomes, updates and resources related to the InvaCost project are now available on a new website: http://invacost.fr/2. The names of the following columns have been changed between the previous and the current version: ‘Raw_cost_estimate_local_currency’ is now named ‘Raw_cost_estimate_original_currency’; ‘Min_Raw_cost_estimate_local_currency’ is now named ‘Min_Raw_cost_estimate_original_currency’; ‘Max_Raw_cost_estimate_local_currency’ is now named ‘Max_Raw_cost_estimate_original_currency’; ‘Cost_estimate_per_year_local_currency’ is now named ‘Cost_estimate_per_year_original_currency’3. The Frequently Asked Questions (FAQ) about the database and how to (1) understand it, (2) analyse it and (3) add new data are available at: https://farewe.github.io/invacost_FAQ/. There are over 60 questions (and responses), so there’s probably yours.4. Accordingly with the continuous development and updates of the database, a ‘living figure’ is now available online to display the evolving relative contributions of different taxonomic groups and regions to the overall cost estimates as the database is updated: https://borisleroy.com/invacost/invacost_livingfigure.html5. We have now added a new column called ‘InvaCost_ID’, which is now used to identify each cost entry in the current and future public versions of the database. As this new column only affects the identification of the cost entries and not their categorisation, this is not considered as a change of the structure of the whole database. Therefore, the first level of the version numbering remains ‘4’ (see VERSION NUMBERING section).

CONTENT: This page contains four files: (1) 'InvaCost_database_v4.1' which contains 13,553 cost entries depicted by 66 descriptive columns; (2) ‘Descriptors 4.1’ provides full definition and details about the descriptive columns used in the database; (3) ‘Update_Invacost_4.1’ has details about the all the changes made between previous and current versions of InvaCost; (4) ‘InvaCost_template_4.1’ (downloadable file) provides an easier way of entering data in the spreadsheet, standardizing all the terms used on it as much as possible to avoid mistakes and saving time at post-refining stages (this file should be used by any external contributor to propose new cost data).

METHODOLOGY: All the methodological details and tools used to build and populate this database are available in Diagne et al. 20201 and Angulo et al. 20215. Note that several papers used different approaches to investigate and analyse the database, and they are all available on our website http://invacost.fr/.

VERSION NUMBERING: InvaCost is regularly updated with contributions from both authors and future users in order to improve it both quantitatively (by new cost information) and qualitatively (if errors are identified). Any reader or user can propose to update InvaCost by filling the ‘InvaCost_updates_template’ file with new entries or corrections, and sending it to our email address (updates@invacost.fr). Each updated public version of InvaCost is stored in this figShare repository, with a unique version number. For this purpose, we consider the original version of InvaCost publicly released in September 2020 as ‘InvaCost_1.0’. The further updated versions are named using the subsequent numbering (e.g., ‘InvaCost_2.0’, InvaCost_2.1’) and all information on changes made are provided in a dedicated file called ‘Updates-InvaCost’ (named using the same numbering, e.g., ‘Updates-InvaCost_2.0’, ‘Updates-InvaCost_2.1’). We consider changing the first level of this numbering (e.g. ‘InvaCost_3.x’ ‘InvaCost_4.x’) only when the structure of the database changes. Every user wanting to have the most up-to-date version of the database should refer to the latest released version.

RECOMMENDATIONS: Every user should read the ‘Usage notes’ section of Diagne et al. 20201 before considering the database for analysis purposes or specific interpretation. InvaCost compiles cost data published in the literature, but does not aim to provide a ready-to-use dataset for specific analyses. While the cost data are described in a homogenized way in InvaCost, the intrinsic disparity, complexity, and heterogeneity of the cost data require specific data processing depending on the user objectives (see our FAQ). However, we provide necessary information and caveats about recorded costs, and we have now an open-source software designed to query and analyse this database6.

CAUTION: InvaCost is currently being analysed by a network of international collaborators in the frame of the InvaCost project2,3,4 (see https://invacost.fr/en/outcomes/). Interested users may contact the InvaCost team if they wish to learn more about or contribute to these current efforts. Users are in no way prevented from performing their own independent analyses and collaboration with this network is not required. Nonetheless, users and contributors are encouraged to contact the InvaCost team before using the database, as the information contained may not be directly implementable for specific analyses.

RELATED LINKS AND PUBLICATIONS:

1 Diagne, C., Leroy, B., Gozlan, R.E. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7, 277 (2020). https://doi.org/10.1038/s41597-020-00586-z

2 Diagne C, Catford JA, Essl F, Nuñez MA, Courchamp F (2020) What are the economic costs of biological invasions? A complex topic requiring international and interdisciplinary expertise. NeoBiota 63: 25–37. https://doi.org/10.3897/neobiota.63.55260

3 Researchgate page: https://www.researchgate.net/project/InvaCost-assessing-the-economic-costs-of-biological-invasions

4 InvaCost workshop: https://www.biodiversitydynamics.fr/invacost-workshop/

5 Angulo E, Diagne C, Ballesteros-Mejia L. et al. (2021) Non-English languages enrich scientific knowledge: the example of economic costs of biological invasions. Science of the Total Environment 775:144441. https://doi.org/10.1016/j.scitotenv.2020.144441

6Leroy B, Kramer A M, Vaissière A-C, Courchamp F and Diagne C (2020) Analysing global economic costs of invasive alien species with the invacost R package. BioRXiv. doi: https://doi.org/10.1101/2020.12.10.419432

The existence of a shadow economy is recognized as an impediment to sustainable development. By applying the Bayesian approaches, the current article investigates the linkage between financial development, green trade, and the scope of the shadow economy, aiming to contribute to a comprehensive understanding of how these factors address the challenge posed by the shadow economy in Emerging and Growth-Leading Economies (EAGLE) from 2003 to 2016. The results demonstrate that (i) The progress of the financial sector is expected to diminish the scale of the shadow economy. Specifically, the expansion of financial institutions and markets has a strong and negative influence on the shadow economy. (ii) Increased involvement in green trade is likely to result in a decreased shadow economy. Empirical findings provide evidence for effective policymaking in simultaneously promoting sustainable trade practices, strengthening financial systems, and curtailing informal economic activities for inclusive economic development.

Data of the research paper: Sectoral economic complexity and environmental degradation: a sectoral perspective on the EKC hypothesis.Files included correspond to:1. Countries_Income_Classification.xlsx: Data of all 127 countries considered in the study, with respective World Bank income classifications for each year between 1995 and 2020.2. Sectors_Products_Classification.xlsx: Data of the 12 economic sectors considered in the study, with the respective 5,011 products assigned to their respecitve sector.3. Regressions_SCI_Data.xlsx: Data of the calculated Sectoral Complexixty Index (SCI) values with the respective CO2 emissions and control variables that were used in the regressions.4. Regression_Coefficients.xlsx: Coefficients with their respective t-values obtained from the quantiles regression model used in the study.

Meta-features activity pattern.

Regional heterogeneity analysis.