This dataset comprises 348 files, each representing a unique economic indicator for the BRICS nations—Brazil, Russia, India, China, and South Africa—spanning from 1970 to 2020. The dataset includes a wide array of economic metrics such as government consumption expenditure, GDP growth, adjusted savings, and various other national accounts data. This comprehensive dataset is ideal for economic research, financial analysis, and policy evaluation, offering a robust foundation for exploring economic trends and making data-driven decisions.

Key Features: - Diversity of Indicators: Covers a wide range of economic indicators, including net national income, government expenditure, GDP, and more. - Historical Coverage: Provides data spanning five decades, enabling both historical trend analysis and long-term forecasting. - Country Focus: Specifically tailored to the BRICS nations, offering insights into some of the world’s most influential emerging economies.

Usage

This dataset can be utilized for various purposes, such as: - Economic Analysis: Researchers can use the dataset to study economic trends and performance in BRICS countries. - Machine Learning: Data scientists can train models to predict future economic indicators or identify patterns in the data. - Policy Development: Policymakers can analyze the data to develop informed strategies for economic development.

Example Use Case: Suppose you want to analyze the trend in GDP per capita growth across BRICS nations. You could load the relevant files, clean the data, and use statistical tools or machine learning models to study the trend and make predictions.

System

This dataset is self-contained and can be integrated into broader economic research systems. The data files are in CSV format, making them easy to load and manipulate with standard data analysis tools like Python, R, and Excel.

Integration: While the dataset is standalone, it can be combined with other datasets or models for more complex analyses, such as predicting future economic performance or simulating policy impacts.

Data Provenance

The dataset is sourced from the World Bank’s BRICS Economic Indicators, a trusted and comprehensive source of economic data. The data was compiled, cleaned, and structured to facilitate easy analysis and integration into various analytical workflows.

Source: Kaggle - BRICS World Bank Indicators Dataset Coverage: The dataset includes data from Brazil, Russia, India, China, and South Africa, from 1970 to 2020.

Data Preprocessing: Each file was cleaned to remove inconsistencies, and missing values were handled appropriately to ensure the quality and reliability of the data.

Data Overview

The dataset is organized into 348 CSV files, each focusing on a specific economic indicator. Examples include: - GDP per Capita (Constant 2010 US$): Tracks the GDP per capita adjusted for inflation. - Government Final Consumption Expenditure (% of GDP): Measures government spending as a percentage of GDP. - Adjusted Net Savings: Accounts for environmental depletion and degradation in national savings.

Each file contains the following columns: - SeriesName: Describes the economic indicator. - CountryName: The name of the BRICS country. - Year: The year the data was recorded. - Value: The numerical value of the indicator for that year.

This dataset provides a rich resource for anyone looking to delve into the economic history and performance of BRICS countries, offering the data necessary to explore past trends and project future developments.

Bangladesh Economic Growth and Development Indicators (1980-2019)

Dataset Overview

This dataset provides a comprehensive record of economic indicators from 1980 to 2019, covering key metrics such as GDP, GDP per capita, GDP growth, inflation rate, unemployment rate, government debt, total investment, remittance inflows, and foreign direct investment (FDI) inflows. The data can be valuable for economic analysis, forecasting, and exploring trends in economic development over the years.

Context

Understanding long-term economic trends is crucial for policymakers, economists, and researchers. This dataset allows users to analyze macroeconomic growth patterns, the impact of inflation, employment trends, and investment dynamics across four decades.

Content

The dataset contains the following columns:

• Year: The year of observation (1980–2019).
• GDP (Billion USD): The total Gross Domestic Product (GDP) in billion U.S. dollars.
• GDP per capita (USD): The GDP divided by the population, representing the average economic output per person.
• GDP growth (%): The annual percentage change in GDP.
• Inflation rate (%): The annual inflation rate based on consumer price changes.
• Unemployment rate (%): The percentage of the labor force that is unemployed.
• Government debt (% of GDP): The total government debt as a percentage of GDP.
• Total Investment (% of GDP): The share of GDP allocated to total investment.
• Remittance (Billion USD): The total amount of remittances received from abroad in billion USD.
• FDI Inflows (Billion USD): The total Foreign Direct Investment (FDI) inflows in billion USD.

Potential Uses

• Economic Forecasting: Analyze past trends to predict future economic growth.
• Macroeconomic Analysis: Study the relationship between GDP growth, inflation, and government debt.
• Investment Research: Assess how investment levels correlate with GDP growth.
• Policy Insights: Understand economic cycles and evaluate policy effectiveness.
• Machine Learning: Train models to forecast economic indicators.

Acknowledgment

This dataset is compiled for research and educational purposes. If using this dataset, please provide appropriate credit.

Feel free to explore and analyze the dataset! 🚀

Real gross domestic product (GDP) in the United States is expected to grow by just over two percent in 2025. Beyond that, growth is projected to ease, slipping from roughly 2.8 percent in 2024 to around 1.8 percent by 2030. The softer outlook points to an economy that is still expanding, but at a more subdued pace. Is U.S. debt sustainable? The U.S. economy continues to grapple with growing levels of public debt. The national debt is anticipated to reach approximately 122.5 percent of GDP in 2025, reflecting ongoing fiscal pressures. The U.S. is not alone in it high debt-to-GDP ratio. Other developed economies, including Japan, Singapore, and Italy, currently maintain even higher public debt burdens. Such levels could constrain future economic growth and narrow the range of policy options available to governments. Consumer sentiment in flux The University of Michigan’s Consumer Sentiment Index, a key gauge of confidence in the economy. In November 2025, it stood at 51, its lowest level since June 2022. Based on monthly surveys of households, it tracks consumers views on personal finances, buying conditions, and the broader economic climate.

This dataset examines growth trends for the global economy and how they affect developing countries. The reports include three-year forecasts for the global economy and long-term global scenarios which look ten years into the future.

The forecast process starts with initial assumptions about advanced-economy growth and commodity price forecasts. These are used as conditioning assumptions for the first set of growth forecasts for EMDEs, which are produced using macroeconometric models, accounting frameworks to ensure national account identities and global consistency, estimates of spillovers from major economies, and high-frequency indicators. These forecasts are then evaluated to ensure consistency of treatment across similar EMDEs. This is followed by extensive discussions with World Bank country teams, who conduct continuous macroeconomic monitoring and dialogue with country authorities. Throughout the forecasting process, staff use macroeconometric models that allow the combination of judgement and consistency with model-based insights.

This collection includes only a subset of indicators from the source dataset.

This statistic shows the results of a 2013 survey among millennials (those aged between 18 and 30) about which country they think will be the biggest driver in the next 10 years for the growth of the global economy. The survey was conducted globally. ** percent of the surveyed millennials believe that India will be the country that drives the growth of global economy the most in the next 10 years.

Abstract

The Future of Business Survey is a new source of information on small and medium-sized enterprises (SMEs). Launched in February 2016, the monthly survey - a partnership between Facebook, OECD, and The World Bank - provides a timely pulse on the economic environment in which businesses operate and who those businesses are to help inform decision-making at all levels and to deliver insights that can help businesses grow. The Future of Business Survey provides a perspective from newer and long-standing digitalized businesses and provides a unique window into a new mobilized economy.

Policymakers, researchers and businesses share a common interest in the environment in which SMEs operate, as well their outlook on the future, not least because young and innovative SMEs in particular are often an important source of considerable economic and employment growth. Better insights and timely information about SMEs improve our understanding of economic trends, and can provide new insights that can further stimulate and help these businesses grow.

To help provide these insights, Facebook, OECD and The World Bank have collaborated to develop a monthly survey that attempts to improve our understanding of SMEs in a timely and forward-looking manner. The three organizations share a desire to create new ways to hear from businesses and help them succeed in the emerging digitally-connected economy. The shared goal is to help policymakers, researchers, and businesses better understand business sentiment, and to leverage a digital platform to provide a unique source of information to complement existing indicators.

With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Geographic coverage

Argentina Australia Belgium Brazil Canada Colombia Egypt France Germany Ghana India Indonesia Ireland Israel Italy Kenya Mexico Nigeria Pakistan Philippines (the) Poland Portugal Russian Federation (the) South Africa Spain Taiwan Turkey United Kingdom of Great Britain and Northern Ireland (the) United States of America (the) Viet Nam

Analysis unit

The study describes small and medium-sized enterprises.

Universe

The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

Twice a year in over 97 countries, the Facebook Survey Team sends the Future of Business to admins and owners of Facebook-designated small business pages. When we share data from this survey, we anonymize responses to all survey questions and only share country-level data publicly. To achieve better representation of the broader small business population, we also weight our results based on known characteristics of the Facebook Page admin population.

A random sample of firms, representing the target population in each country, is selected to respond to the Future of Business Survey each month.

Mode of data collection

Internet [int]

Research instrument

The survey includes questions about perceptions of current and future economic activity, challenges, business characteristics and strategy. Custom modules include questions related to regulation, access to finance, digital payments, and digital skills. The full questionnaire is available for download.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Note: Response rates are calculated as the number of respondents who completed the survey divided by the total number of SMEs invited.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Other factors beyond sampling error that contribute to such potential differences are frame or coverage error (sampling frame of page owners does not include all relevant businesses but also may include individuals that don't represent businesses), and nonresponse error.

Note that the sample is meant to reflect the population of businesses on Facebook, not the population of small businesses in general. This group of digitized SMEs is itself a community worthy of deeper consideration and of considerable policy interest. However, care should be taken when extrapolating to the population of SMEs in general. Moreover, future work should evaluate the external validity of the sample. Particularly, respondents should be compared to the broader population of SMEs on Facebook, and the economy as a whole.

Across the United States, the United Kingdom, Germany, and the European Union, gross domestic products (GDP) decreased in 2020 as a result of the COVID-19 pandemic. However, by 2021, growth rates were positive in all four areas again. The United Kingdom, Germany, and the European Union all experiencing slow economic growth in 2023 amid high inflation, with Germany even seeing an economic recession. GDP and its components GDP refers to the total market value of all goods and services that are produced within a country per year. It is composed of government spending, consumption, business investments and net exports. It is an important indicator to measure the economic strength of a country. Economists rely on a variety of factors when predicting the future performance of the GDP. Inflation rate is one of the economic indicators providing insight into the future behavior of households, which make up a significant proportion of GDP. Projections are based on the past performance of such information. Future considerations Some factors can be more easily predicted than others. For example, projections of the annual inflation rate of the United States are easy to come by. However, the intensity and impact of something like Brexit is difficult to predict. Moreover, the occurrence and impact of events such as the COVID-19 pandemic and Russia's war in Ukraine is difficult to foresee. Hence, actual GDP growth may be higher or lower than the original estimates.

United States Texas Service Sector Outlook: Future Employment data was reported at 28.200 % in Nov 2018. This records a decrease from the previous number of 31.900 % for Oct 2018. United States Texas Service Sector Outlook: Future Employment data is updated monthly, averaging 24.200 % from Jan 2007 (Median) to Nov 2018, with 143 observations. The data reached an all-time high of 43.700 % in Mar 2007 and a record low of -18.200 % in Nov 2008. United States Texas Service Sector Outlook: Future Employment data remains active status in CEIC and is reported by Federal Reserve Bank of Dallas. The data is categorized under Global Database’s United States – Table US.S016: Texas Service Sector Outlook Survey.

TSOS: Future Input Prices: Decrease data was reported at 21.800 % in Apr 2020. This records an increase from the previous number of 17.300 % for Mar 2020. TSOS: Future Input Prices: Decrease data is updated monthly, averaging 3.400 % from Jan 2007 (Median) to Apr 2020, with 160 observations. The data reached an all-time high of 21.800 % in Apr 2020 and a record low of 0.400 % in Apr 2019. TSOS: Future Input Prices: Decrease data remains active status in CEIC and is reported by Federal Reserve Bank of Dallas. The data is categorized under Global Database’s United States – Table US.S017: Texas Service Sector Outlook Survey.

Dataset Overview:

This dataset offers a comprehensive time series analysis of three vital economic indicators in the United States: Gross Domestic Product (GDP), Unemployment Rate, and Consumer Price Index (CPI). Spanning from January 1974 to January 2024, this dataset provides valuable insights into the U.S. economy over the past five decades, capturing periods of growth, recession, and inflation.

Contents:

• GDP Data (gdp_data.csv): Quarterly data on the Gross Domestic Product, measured in billions of dollars, highlighting economic performance and trends over the years.
• Unemployment Data (unemployment_data.csv): Monthly data on the unemployment rate, showing fluctuations in labor market conditions and workforce participation over time.
• CPI Data (cpi_data.csv): Monthly data on the Consumer Price Index for All Urban Consumers (CPI-U), capturing changes in the price level of consumer goods and services and reflecting inflationary trends.

Usage and Applications:

• Economic History Analysis: Examine long-term trends and cycles in U.S. economic performance, including periods of recession and expansion.
• Predictive Modeling: Develop models to forecast future economic conditions based on historical data patterns.
• Policy Impact Studies: Analyze the effects of fiscal and monetary policies on GDP, unemployment, and inflation over time.

Data Sources:

The dataset is sourced from the Federal Reserve Economic Data (FRED) database, maintained by the Federal Reserve Bank of St. Louis. FRED is a comprehensive resource for economic data, widely used by researchers, analysts, and policymakers.

How to Use the Dataset:

• Exploration: Utilize tools like Pandas and Matplotlib in Python to explore and visualize the dataset.
• Time Series Analysis: Apply techniques such as ARIMA, exponential smoothing, and seasonal decomposition to analyze trends and seasonality.
• Comparative Studies: Compare economic performance across different decades and investigate interactions between GDP, unemployment, and CPI.

Note: This dataset is intended for educational and research purposes. Users are encouraged to cite the original data source (FRED) when using this dataset in publications or presentations.

In most years since 1980, global GDP growth has been relatively consistent, generally fluctuating between two and five percent growth from year to year. The most notable exceptions to this were during the Great Recession in 2009, and again in 2020 during the Covid-19 pandemic, where the global economy actually shrank in both of these years. As the world economy continues to deal with the economic impact of the pandemic, as well as the fallout from Russia's invasion of Ukraine in 2022, the future remains uncertain, however current estimates suggest that annual growth will return to steady figures of around 3 percent in 2030.

** IMF's GDP Data 📈: 1980-2028 Global Trends Explore the economic trajectories of countries worldwide with the "IMF's GDP Data: 1980-2028 Global Trends" dataset. Providing a comprehensive overview of GDP per capita, this dataset measures the average economic output per person in current U.S. dollars. With actual data from 1980 to 2023 and predictions extending to 2028, it's an invaluable asset for understanding past progress and anticipating future growth.

Nighttime Lights Economic Indicators Market Outlook

According to our latest research, the global Nighttime Lights Economic Indicators market size was valued at USD 1.84 billion in 2024, reflecting robust adoption across multiple sectors. The market is expected to grow at a CAGR of 14.2% from 2025 to 2033, reaching USD 5.31 billion by 2033. This growth is primarily driven by increased reliance on satellite and remote sensing data for real-time economic analysis, urban planning, and disaster management, as organizations worldwide seek more accurate, timely, and granular economic indicators beyond traditional data sources.

One of the primary growth factors for the Nighttime Lights Economic Indicators market is the increasing demand for high-resolution, real-time economic data to support decision-making in both the public and private sectors. Traditional economic indicators often suffer from time lags, limited spatial granularity, and potential biases, making them less suitable for rapid response or localized analysis. Nighttime lights data, captured via satellite and aerial imagery, offers a dynamic and unbiased proxy for economic activity, urbanization, and infrastructure development. This capability is particularly valuable for tracking economic growth in regions with limited statistical infrastructure or where ground-based data collection is challenging. The proliferation of advanced remote sensing technologies and the decreasing cost of satellite imagery acquisition have further democratized access to these data sources, enabling a broader range of stakeholders to leverage nighttime lights as a reliable economic indicator.

Another significant driver is the integration of advanced analytics, such as machine learning and geospatial information systems (GIS), with nighttime lights data to extract actionable insights. These technologies allow for the automated processing and interpretation of vast amounts of imagery, transforming raw visual data into meaningful economic metrics. For instance, machine learning algorithms can identify patterns in light intensity that correlate with economic output, infrastructure expansion, or disaster impact. This analytical capability is crucial for applications such as urban planning, disaster management, and environmental monitoring, where timely and precise information is essential for effective intervention. The growing sophistication of these analytical tools is expanding the utility of nighttime lights data, making it a cornerstone of data-driven policy and business strategies.

The expanding application landscape also contributes to the market’s growth trajectory. Beyond economic forecasting and urban planning, nighttime lights data is increasingly used for disaster response, environmental monitoring, and infrastructure development. Governments and humanitarian organizations, for example, utilize changes in nighttime illumination to assess the impact of natural disasters or conflicts, enabling rapid resource allocation and recovery planning. Similarly, environmental agencies monitor light pollution and its effects on ecosystems, while infrastructure developers assess growth patterns to guide investment decisions. The versatility of nighttime lights data, coupled with its global coverage, positions it as a critical resource for a wide array of stakeholders seeking to enhance situational awareness and optimize resource allocation.

Regionally, the market exhibits strong growth in Asia Pacific and North America, driven by robust investments in space technology, urbanization, and digital infrastructure. Asia Pacific, in particular, is witnessing accelerated adoption due to rapid urban expansion in countries such as China and India, where traditional economic data collection faces significant challenges. North America benefits from advanced satellite networks and a mature ecosystem of analytics providers, supporting widespread integration of nighttime lights data across sectors. Europe follows closely, leveraging the data for sustainable development and climate monitoring initiatives. Meanwhile, Latin America and the Middle East & Africa are gradually increasing their adoption, supported by international collaborations and technology transfer initiatives. These regional dynamics highlight the global relevance and transformative potential of nighttime lights economic indicators in shaping the future of economic analysis and planning.

Job Growth Statistics: Statistics on job growth are essential in understanding the state and trajectory of an economy because they offer insight into the shifting dynamics of labor markets. By measuring net job addition or subtraction over a certain timeframe, employment growth statistics allow policymakers, companies, and individuals to make well-informed decisions regarding workforce planning, investment decisions, or career choices. Statistics on job growth provide a key measure of economic development as they show whether an economy is expanding, contracting, or remaining stable. Positive employment growth numbers often signal healthy economies with increased consumer spending and company confidence. Conversely, negative or stagnant job growth indicates a slowdown or recession. Furthermore, statistics on employment growth may also be used to highlight developing markets and professions for policymakers as well as job seekers in finding prospective development areas. As such, employment data provides an essential means of measuring an economy's current state and future direction, as well as helping shape policies and initiatives within it. Editor’s Choice From 2020-2030; job growth in the US is anticipated to be 5.3%. Nurse practitioners are predicted to experience the highest job growth; between 2021-2031 at 45.7%; 2019 alone saw sectors producing goods create 188,000 new jobs. Leisure and hospitality job creation decreased by 47% year-on-year between April 2020 and March 2021. President Clinton created 19 million new employment opportunities between June and July of 2022 and 528,000 nonfarm payroll employees were gained; yet by April 2020 20.5 million jobs had been lost from the economy as a whole. By 2031, it is projected that employment opportunities across the nation will reach 166.5 million; over that same timeframe childcare service workers have seen their ranks decline by 336,000. Since the COVID-19 outbreak, healthcare employment levels have suffered a dramatic decrease. By some accounts, over one and a half million employees may have left healthcare jobs since 2016. (Source: zippia.com)

Good health, nutrition, a place to live, education… Many of the things we care most about require goods and services produced by people: the care that nurses and doctors give; the food we eat; the homes we live in; the education that teachers provide.

Economic growth means an increase in the quantity or quality of the many goods and services that people produce.

The history of economic growth is, therefore, the history of how societies left widespread poverty behind. In places that have seen substantial economic growth, few now go without food, almost all have access to education, and parents rarely suffer the loss of a child. The work of historians shows this was not the case in the past.

Similarly, the history of economic growth is also the history of how large global inequalities emerged – in nutrition, health, education, basic infrastructure, and many other dimensions. In some countries, the quantity and quality of the goods and services underpinning these outcomes grew substantially over the past two centuries; in others, they did not.

Of course, economic growth does not reflect everything we value. On Our World in Data we provide thousands of measures that try to capture these many different dimensions, covering topics such as biodiversity, pollution, time use, human rights and democracy.

Economic growth is, however, central to shaping people's overall living conditions. Just as in the past, the future of global poverty and inequality will depend on whether, and which, countries are able to substantially grow their economy. As such, it is one of the most important aspects of understanding our world today and what is possible for the future.

On this page, you can find all our data, and writing on the topic. Work on visualization for better understanding this matter. Good luck

By Max Roser, Pablo Arriagada, Joe Hasell, Hannah Ritchie and Esteban Ortiz-Ospina

We use the yield curve to predict future GDP growth and recession probabilities. The spread between short- and long-term rates typically correlates with economic growth. Predications are calculated using a model developed by the Federal Reserve Bank of Cleveland. Released monthly.

United States TSOS: Future Input Prices: Number Change data was reported at 53.800 % in Apr 2020. This records a decrease from the previous number of 55.900 % for Mar 2020. United States TSOS: Future Input Prices: Number Change data is updated monthly, averaging 49.000 % from Jan 2007 (Median) to Apr 2020, with 160 observations. The data reached an all-time high of 61.100 % in Apr 2019 and a record low of 28.400 % in Jun 2008. United States TSOS: Future Input Prices: Number Change data remains active status in CEIC and is reported by Federal Reserve Bank of Dallas. The data is categorized under Global Database’s United States – Table US.S017: Texas Service Sector Outlook Survey.

The greatest economy in Africa is that of Nigeria. Gross domestic product (GDP), which represents the total cost of goods and services generated in Nigeria, is a key indicator of the country's economic development. In this data analysis, we will decide on the potential future of Nigeria's growth and examine some potential variables that could slow down the country's pace of development. We will also examine the role that leadership may play in the country's economy's expansion.

To read more https://rb.gy/rlqdk

Abstract

The Future of Business Survey is a new source of information on small and medium-sized enterprises (SMEs). Launched in February 2016, the monthly survey - a partnership between Facebook, OECD, and The World Bank - provides a timely pulse on the economic environment in which businesses operate and who those businesses are to help inform decision-making at all levels and to deliver insights that can help businesses grow. The Future of Business Survey provides a perspective from newer and long-standing digitalized businesses and provides a unique window into a new mobilized economy.

Policymakers, researchers and businesses share a common interest in the environment in which SMEs operate, as well their outlook on the future, not least because young and innovative SMEs in particular are often an important source of considerable economic and employment growth. Better insights and timely information about SMEs improve our understanding of economic trends, and can provide new insights that can further stimulate and help these businesses grow.

To help provide these insights, Facebook, OECD and The World Bank have collaborated to develop a monthly survey that attempts to improve our understanding of SMEs in a timely and forward-looking manner. The three organizations share a desire to create new ways to hear from businesses and help them succeed in the emerging digitally-connected economy. The shared goal is to help policymakers, researchers, and businesses better understand business sentiment, and to leverage a digital platform to provide a unique source of information to complement existing indicators.

With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Geographic coverage

When the survey was initially launched in February 2016, it included 22 countries. When the survey was initially launched in February 2016, it included 22 countries. The Future of Business Survey is now conducted in over 90 countries in every region of the world.

Analysis unit

The study describes small and medium-sized enterprises.

Universe

The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

Twice a year in over 97 countries, the Facebook Survey Team sends the Future of Business to admins and owners of Facebook-designated small business pages. When we share data from this survey, we anonymize responses to all survey questions and only share country-level data publicly. To achieve better representation of the broader small business population, we also weight our results based on known characteristics of the Facebook Page admin population.

A random sample of firms, representing the target population in each country, is selected to respond to the Future of Business Survey each month.

Mode of data collection

Internet [int]

Research instrument

The survey includes questions about perceptions of current and future economic activity, challenges, business characteristics and strategy. Custom modules include questions related to regulation, access to finance, digital payments, and digital skills. The full questionnaire is available for download.

The questionnaire was pretested by the target audience, as well as experts from the area of research interest. Additionally, steps were taken to translate the survey in order to reduce sensitivities to cultural response bias: - Respondents were given the option to respond to the survey in any of fifteen languages native to the countries in which it was conducted. - Translations were done only by native speakers, with two rounds of additional online checks in the context of the survey environment. - Translators were provided with context material for this survey (e.g., the Facebook for Business website) in order to understand the context of the survey. They were also instructed to take the English survey at least two times before starting with the translations. - Translations were discussed in a group in order to ensure a common understanding of questions and items. - The tone (formal vs. informal) of the survey was based on cultural conventions, e.g., Facebook usually uses an informal tone, while in cultures such as the Japanese this is very uncommon and thus a formal tone was used there.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Note: Response rates are calculated as the number of respondents who completed the survey divided by the total number of SMEs invited.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Other factors beyond sampling error that contribute to such potential differences are frame or coverage error (sampling frame of page owners does not include all relevant businesses but also may include individuals that don't represent businesses), and nonresponse error.

Note that the sample is meant to reflect the population of businesses on Facebook, not the population of small businesses in general. This group of digitized SMEs is itself a community worthy of deeper consideration and of considerable policy interest. However, care should be taken when extrapolating to the population of SMEs in general. Moreover, future work should evaluate the external validity of the sample. Particularly, respondents should be compared to the broader population of SMEs on Facebook, and the economy as a whole.

Explore the Creator Economy Market: insights on trends, opportunities, and tools for creators to grow their brands and monetize their content effectively.