****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

MSCI World: Reflecting Global Economic Trends or Inflated Valuations?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset provides a historical summary of Cambodia’s key macroeconomic indicators, covering GDP, inflation, government budget, trade, liquidity, foreign reserves, and demographics. It tracks how the country’s economy evolved from 1993 to 2025p offering a foundational view for economic trend analysis, forecasting, and policymaking.

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

• EO116 Database documentation
• EO116 Last historical points

The Digital Circular Economy Market size is expected to reach a valuation of USD 14.75 billion in 2033 growing at a CAGR of 25.20%. The Digital Circular Economy market research report classifies market by share, trend, demand, forecast and based on segmentation.

Please, if you use this dataset or do you like my work please UPVOTE 👁️

This dataset provides a comprehensive historical record of inflation rates worldwide, covering the period from 1960 to the present. It includes inflation data at the national level for multiple countries and territories, making it a valuable resource for economic analysis, financial forecasting, and macroeconomic research.

Data Source: https://datos.bancomundial.org/indicador/FP.CPI.TOTL.ZG?end=2023&start=1960&view=chart

Key Features:

✅ Global Coverage – Inflation rates for countries across all continents.

✅ Long-Term Data – Over 60 years of historical records, ideal for trend analysis.

✅ Regional Classification – Data categorized by region, sub-region, and intermediate region for in-depth geographic analysis.

✅ Standardized Indicators – Based on CPI (Consumer Price Index) inflation rates from reputable sources.

Potential Use Cases:

📊 Economic Research – Analyze inflation trends and economic cycles.

📈 Financial Forecasting – Predict future inflation and its impact on global markets.

🌍 Policy & Development Studies – Examine regional disparities and economic policies.

📚 Machine Learning Applications – Train predictive models using historical inflation trends.

This dataset is an essential tool for economists, data scientists, and financial analysts looking to explore global inflation patterns and their implications on economic stability.

The World Economic Outlook (WEO) database contains selected macroeconomic data series from the statistical appendix of the World Economic Outlook report, which presents the IMF staff's analysis and projections of economic developments at the global level, in major country groups and in many individual countries.

The datasets presented here comprises of a municipal water uses dataset and a city-level climatic, urban-geologic and socio-economic characteristics dataset, for 126 cities/ towns within the Contiguous United States (CONUS). The municipal water use dataset presents monthly water use information of those 126 cities for the period 2005 to 2017, under residential, commercial-industrial-institutional (CII), master meter and total water use categories. Data for the municipal water uses dataset were collected directly from the cities/ towns/ water providers and also from open data access sites for few cities/ towns in the state of California. The city characteristics dataset presents climatic, urban-geologic and socio-economic factors of the 126 cities/ towns collected from multiple published sources indicated in the Sources section of this publication. These datasets are products of an extensive work undertaken by the authors at Colorado State University under the UWIN project to characterize the trends in municipal water uses across CONUS.

TO CITE THESE DATASETS OR THE MUNICIPAL WATER USE CHARACTERIZATION PAPER, USE THE FOLLOWING CITATION: Chinnasamy, C. V., Arabi, M., Sharvelle, S., Warziniack, T., Furth, C. D., & Dozier, A. (2021). Characterization of municipal water uses in the contiguous United States. Water Resources Research, 57, e2020WR028627. https://doi.org/10.1029/2020WR028627

London’s Economic Outlook is GLA Economics’ London forecast. The forecasts are issued every six months to assist those preparing planning projections for London in the medium term. The report contains the following: * An overview of recent economic conditions in London, the UK and the world economies with analysis of important events, trends and risks to short and medium-term growth. * The ‘consensus forecast’ – a review of independent forecasts indicating the range of views about London’s economy and the possible upside and downside risk. In this context, ‘consensus forecast’ refers to the average of the independent forecasters (Cambridge Econometrics, The Centre for Economic and Business Research, Experian Economics, and Oxford Economics) * The GLA Economics forecast for output, employment, household expenditure and household income in London. Provided below are links to the current and previous versions of GLA Economics' medium term forecast for the level and growth rate of London's GVA, employment, household income and household expenditure. Forecasts for the growth and level of employment and GVA for selected sectors of the economy are also included. * All output variables are measured in terms of output at basic prices. The price base for the latest dataset is 2011. * All growth rates are in percentage change per annum. * All employment levels are in millions. * All output levels are in £bn.

About this dataset

Context

The dataset tabulates the Economy population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Economy across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Economy was 8,962, a 0.18% decrease year-by-year from 2022. Previously, in 2022, Economy population was 8,978, a decline of 0.74% compared to a population of 9,045 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Economy decreased by 452. In this period, the peak population was 9,414 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Economy is shown in this column.
• Year on Year Change: This column displays the change in Economy population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Economy Population by Year. You can refer the same here

Country Risk Assessment helps businesses to confidently evaluate global markets by incorporating country evaluation into strategic planning. Analysing trends over time to forecast and proactively plan for potential market shifts.

Country Risk Assessment is an estimate of the average credit risk of a country’s businesses. It is drawn up based on macroeconomic, financial and political data. It offers: - An indication of a country’s potential influence on businesses’ financial commitments. - Insight into the economic and political environment that could impact credit risk.

Dataset Structure and Content: Assessment Coverage: 20 sample companies with country risk evaluations Geographic Diversity: Multiple countries represented via ISO-3166 alpha2 country codes.

Risk Classification System: The dataset employs a standardized A-E rating scale to categorize country risk levels: A1: Very good macroeconomic outlook with stable political context and quality business climate (lowest default probability) A2: Good macroeconomic outlook with generally stable political environment A3: Satisfactory outlook with some potential shortcomings A4: Reasonable default probability with potential economic weaknesses B: Uncertain economic outlook with potential political tensions C: Very uncertain outlook with potential political instability D: Highly uncertain outlook with very unstable political context E: Extremely uncertain outlook with extremely difficult business conditions (highest default probability)

Application Context: This sample demonstrates how country risk assessments can be systematically documented and tracked over time. Each assessment includes comprehensive evaluations of the macroeconomic environment, political stability, and business climate factors that directly influence payment behavior and default probabilities. The dataset structure allows for both current and historical tracking, enabling trend analysis and comparative risk evaluation across different national markets. It serves as a representative example of how comprehensive country risk data can be organized and utilized for strategic business decision-making. Note: This is sample data intended to demonstrate the structure and capabilities of a country risk assessment system.

Learn More For a complete demonstration of our Country Risk Assessment capabilities or to discuss how our system can be integrated with your existing processes, please visit https://business-information.coface.com/economic-insights to request additional information.

Market Overview The global evening economy market experienced a significant decline due to the COVID-19 pandemic, but is expected to rebound strongly in the coming years. The market is projected to grow from USD XXX million in 2025 to USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The increasing urbanization, rising disposable income, and growing preference for nightlife and entertainment are the key drivers of the market growth. The market is segmented into four major types: eating and drinking economy, entertainment economy, nightlife economy, and others. The eating and drinking economy segment holds the largest market share due to the increasing popularity of fine dining, casual dining, and fast food restaurants. Regional Trends The Asia Pacific region is expected to dominate the evening economy market throughout the forecast period. The region is home to some of the world's largest and most vibrant cities, such as Tokyo, Shanghai, and Seoul. These cities offer a wide range of evening entertainment options, from live music and theater to nightclubs and bars. North America and Europe are also major markets for the evening economy, with cities such as New York City, London, and Paris attracting millions of visitors each year. The Middle East and Africa region is expected to witness the fastest growth in the coming years, driven by the increasing disposable income and the growing number of young people in the region.

Global Economic, Environmental, Health, and Social indicators Ready for Analysis

📝 Description

This comprehensive dataset merges global economic, environmental, technological, and human development indicators from 2000 to 2020. Sourced and transformed from multiple public datasets via Google BigQuery, it is designed for advanced exploratory data analysis, machine learning, policy modeling, and sustainability research.

Curated by combining and transforming data from the Google BigQuery Public Data program, this dataset offers a harmonized view of global development across more than 40 key indicators spanning over two decades (2000–2020). It supports research across multiple domains such as:

• Economic Growth
• Climate Sustainability
• Digital Transformation
• Public Health
• Human Development
• Resilience and Governance

📅 Temporal Coverage

• Years: 2000–2020

• Includes calculated features:

  • years_since_2000
  • years_since_century
  • is_pandemic_period (binary indicator for pandemic periods)

🌍 Geographic Scope

• Countries: Global (identified by ISO country codes)
• Regions and Income Groups included for aggregated analysis

📊 Key Feature Groups

• Economic Indicators:

  • GDP (USD), GDP per capita
  • FDI, inflation, unemployment, economic growth index

• Environmental Indicators:

  • CO₂ emissions, renewable energy use
  • Forest area, green transition score, CO₂ intensity

• Technology & Connectivity:

  • Internet usage, mobile subscriptions
  • Digital readiness score, digital connectivity index

• Health & Education:

  • Life expectancy, child mortality
  • School enrollment, healthcare capacity, health development ratio

• Governance & Resilience:

  • Governance quality, global resilience
  • Human development composite, ecological preservation

🔍 Use Cases

• Trend analysis over time
• Country-level comparisons
• Modeling development outcomes
• Predictive analytics on sustainability or human development
• Correlation and clustering across multiple indicators

⚠️ Note on Missing Region and Income Group Data

Approximately 18% of the entries in the region and income_group columns are null. This is primarily due to the inclusion of aggregate regions (e.g., Arab World, East Asia & Pacific, Africa Eastern and Southern) and non-country classifications (e.g., Early-demographic dividend, Central Europe and the Baltics). These entries represent groups of countries with diverse income levels and geographic characteristics, making it inappropriate or misleading to assign a single region or income classification. In some cases, the data source may have intentionally left these fields blank to avoid oversimplification or due to a lack of standardized classification.

📋 Column Descriptions

• year: Year of the recorded data, representing a time series for each country.
• country_code: Unique code assigned to each country (ISO-3166 standard).
• country_name: Name of the country corresponding to the data.
• region: Geographical region of the country (e.g., Africa, Asia, Europe).
• income_group: Income classification based on Gross National Income (GNI) per capita (low, lower-middle, upper-middle, high income).
• currency_unit: Currency used in the country (e.g., USD, EUR).
• gdp_usd: Gross Domestic Product (GDP) in USD (millions or billions).
• population: Total population of the country for the given year.
• gdp_per_capita: GDP divided by population (economic output per person).
• inflation_rate: Annual rate of inflation (price level rise).
• unemployment_rate: Percentage of the labor force unemployed but seeking employment.
• fdi_pct_gdp: Foreign Direct Investment (FDI) as a percentage of GDP.
• co2_emissions_kt: Total CO₂ emissions in kilotons (kt).
• energy_use_per_capita: Energy consumption per person (kWh).
• renewable_energy_pct: Percentage of energy consumption from renewable sources.
• forest_area_pct: Percentage of total land area covered by forests.
• electricity_access_pct: Percentage of the population with access to electricity.
• life_expectancy: Average life expectancy at birth.
• child_mortality: Deaths of children under 5 per 1,000 live births.
• school_enrollment_secondary: Percentage of population enrolled in secondary education.
• health_expenditure_pct_gdp: Percentage of GDP spent on healthcare.
• hospital_beds_per_1000: Hospital beds per 1,000 people.
• physicians_per_1000: Physicians (doctors) per 1,000 people.
• internet_usage_pct: Percentage of population with internet access.
• **mobile_subscriptions_per_10...

ABOUT

The Global Population Growth Dataset provides a comprehensive record of population trends across various countries over multiple decades. It includes detailed information such as the country name, ISO3 country code, year-wise population data, population growth, and growth rate. This dataset is valuable for researchers, demographers, policymakers, and data analysts interested in studying population dynamics, demographic trends, and economic development.

Key features of the dataset:

✅ Covers multiple countries and regions worldwide
✅ Includes historical and recent population data
✅ Provides year-wise population growth and growth rate (%)
✅ Categorizes data by country and decade for better trend analysis

This dataset serves as a crucial resource for analyzing global population trends, understanding demographic shifts, and supporting socio-economic research and policy-making.

FILE INFORMATION

The dataset consists of structured records related to country-wise population data, compiled from official sources. Each file contains information on yearly population figures, growth trends, and country-specific data. The structured format makes it useful for researchers, economists, and data scientists studying demographic patterns and changes. The file type is CSV.

COLUMNS DESCRIPTION

• Country – The name of the country.
• ISO3 – The three-letter ISO code of the country.
• Year – The year corresponding to the population data, useful for trend analysis.
• Population – The total population of the country for the given year.
• Population Growth – The absolute increase in population compared to the previous year.
• Growth Rate (%) – The percentage change in population compared to the previous year.
• Decade – The decade classification (e.g., 1990s, 2000s) for grouping long-term trends.

An essential resource for all users of UK economic and labour market statistics. It draws together the expert research and analysis and range of content found in Economic Trends and Labour Market Trends to build an up-to-date, comprehensive and unique statistical picture of the UK economy and labour market.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: ELMR

These market data provide a comprehensive set of measures of changes in economic activity throughout the coastal regions of the United States. In regard to the sources of data, establishments, employment, and wages are taken from the Quarterly Census of Employment and Wages (QCEW). The data series also is known as the ES-202 data. These data are based on the quarterly reports of nearly all employers in the United States. These reports are filed with each state's employment or labor department, and each state then transmits the data to the Bureau of Labor Statistics (BLS), where the national databases are maintained. The data for the Coastal Economies have been taken from the national databases at BLS (except in the case of Massachusetts). Gross State Product (GSP) data are taken from the Bureau of Economic Analysis (BEA), which develops the estimates of GSP from a number of sources. In regard to "employment", data are reported by employers, not employees, and does not contain any information about age. There is no difference between "employed" and "employment". The source is known as the payroll survey, a survey filed by employers every 3 months showing the number of people employed at each establishment in each of the preceding 3 months. Detailed information on the geographies the data are available for can be found here: https://coast.noaa.gov/htdata/SocioEconomic/CoastalEconomy/CoastalEconomy_DataDescription.pdf

Rental Economy Solutions Market expected to rise from USD 92.72 billion in 2025 to USD 265.51 billion by 2034, at a CAGR of 12.4% during the forecast period.

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Sharing Economy Market Size 2025-2029

The sharing economy market size is forecast to increase by USD 1118.8 billion, at a CAGR of 32.3% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing popularity of online ride-hailing services. This trend is fueled by the convenience and affordability these services offer, enabling users to access transportation on demand. Another key driver is the adoption of blockchain technology in the sharing economy, which enhances security and trust between users, facilitating seamless transactions. However, the market also faces regulatory challenges, as governments grapple with the complexities of overseeing peer-to-peer transactions and ensuring consumer protection.
Companies looking to capitalize on the opportunities presented by the sharing economy must navigate these regulatory hurdles while maintaining a focus on innovation and user experience. Effective strategic planning and operational agility will be essential for success in this dynamic market.

What will be the Size of the Sharing Economy Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with digital platforms revolutionizing various sectors through peer-to-peer transactions and collaborative consumption. Platform governance and digital identity play crucial roles in ensuring trust and safety, while user experience and mobile applications enhance accessibility. User reviews and community marketplaces foster community building and customer loyalty. Technology adoption, including machine learning and artificial intelligence, drives operational efficiency and innovation. Trust and safety measures, such as security measures and reputation management, mitigate risks. Monetization strategies, including peer-to-peer lending and revenue streams, enable platform sustainability. Circular economy principles and sustainable consumption are gaining traction, aligning with social responsibility and economic sustainability.

Legal frameworks and network effects shape the regulatory landscape, while pricing models and network effects influence market dynamics. The future of work is evolving, with freelancing platforms and task rabbiting shaping the gig economy. Blockchain technology and smart contracts offer potential solutions for trust, transparency, and decentralized finance. Insuring against risks and managing tax implications remain critical considerations. Continuous innovation and adaptation are essential for success in the market. Platforms must prioritize user experience, trust and safety, and operational efficiency while navigating regulatory frameworks and social impact.

How is this Sharing Economy Industry segmented?

The sharing economy industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Sharing accommodation
  Sharing transport
  Sharing finance
  Others


End-user

  Individual
  Business


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The sharing accommodation segment is estimated to witness significant growth during the forecast period.

The market in the US is characterized by robust competition among digital platforms that facilitate peer-to-peer transactions in various sectors, including accommodation, freelancing, and peer-to-peer lending. Sharing economy regulations continue to evolve, shaping the market's dynamics. In the accommodation sector, individuals rent or share their living spaces through online platforms, offering cost-effective, flexible alternatives to traditional lodging. This trend is particularly popular among budget-conscious consumers, students, and those seeking affordable short-term stays. Platform governance and user experience are crucial factors in building customer loyalty and trust. Digital identity and user reviews play a significant role in ensuring trust and safety.

Payment gateways enable seamless transactions, while machine learning and artificial intelligence power personalized recommendations and pricing models. The circular economy and sustainable consumption are gaining traction, with many platforms emphasizing the social impact of their services. Operational efficiency and security measures are essential for platform monetization. Community marketplaces and community building foster network effects, driving user acquisition and revenue streams. Peer-to-peer lending platforms offer alternative financing options, while task rabb

Dataset Description: Prices of Products in Brazil

This dataset spans a decade of detailed information on product prices across various states in Brazil. The data was collected by the National Supply Company (Conab) and provides a comprehensive view of price trends, enabling in-depth analyses of economic and trade evolution.

Key Attributes: 1. Product/Unit: Name of the product along with the corresponding unit of measurement. 2. U.F. (Federative Unit): Brazilian state where prices were recorded. 3. Commercialization Level: Indication of whether the product is intended for retail, wholesale, or other commercial channels. 4. Monthly Periods: Monthly records of product prices over the past decade, allowing for detailed temporal analysis.

Key Insights: - Regional Variations: The dataset reveals significant variations in product prices between different states, highlighting the influence of regional factors on the economy. - Impact of the Pandemic: A trend of price increases, especially during the pandemic, is observed, reflecting the economic impact of this period.

Responsible Use: This dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Users are encouraged to explore, analyze, and share the data while respecting the conditions of non-commercial use and proper attribution to the source (Conab).

Limitations and Considerations: - The quality of insights obtained will depend on the user's interpretation and in-depth analysis. - Some outliers may require specific treatment as needed.

This dataset provides a solid foundation for exploring the dynamics of product prices in Brazil, contributing to understanding economic trends and regional patterns over time.