For a quick summary of the case study, please click "US Economy Powerpoint" and download the Powerpoint.

This dataset was inspired by rising prices for essential goods, the abnormally high inflation rate in March of 7.9 percent of this year, and the 30 trillion-dollar debt that we have. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

I labeled all of the datasets to be self-explanatory based off of the title of the datasets. The US Economy Notebook has most of the code that I used as well as the four of the six phases of data analysis. The last two phases are in the US Economy Powerpoint. The "US Historical Inflation Rates" dataset could have also been labeled "The Inflation Of The US Dollar Month By Month". Lastly, the Average Sales of Houses in Jan is just a filtered version of "Average Sales of Houses in the US" dataset.

CNBC Economy Articles Dataset is an invaluable collection of data extracted from CNBC’s economy section, offering deep insights into global and U.S. economic trends, market dynamics, financial policies, and industry developments.

This dataset encompasses a diverse array of economic articles on critical topics like GDP growth, inflation rates, employment statistics, central bank policies, and major global events influencing the market. Designed for researchers, analysts, and businesses, it serves as an essential resource for understanding economic patterns, conducting sentiment analysis, and developing financial forecasting models.

Dataset Highlights

Each record in the dataset is meticulously structured and includes:

• Article Titles
• Publication Dates
• Author Names
• Content Summaries
• URLs to Original Articles

This rich combination of fields ensures seamless integration into data science projects, research papers, and market analyses.

Key Features

• Number of Articles: Hundreds of articles sourced directly from CNBC.
• Data Fields: Includes title, publication date, author, article content, summary, URL, and relevant keywords.
• Topics Covered: U.S. and global economy, GDP trends, inflation, employment, financial markets, and monetary policies.
• Format: Delivered in CSV format for easy integration with research tools and analytical platforms.
• Source: Extracted directly from CNBC’s economy news section, ensuring accuracy and relevance.

Use Cases

• Economic Research: Gain insights into U.S. and global economic policies, market trends, and industry developments.
• Sentiment Analysis: Assess the sentiment of economic articles to gauge market perspectives and investor confidence.
• Financial Modeling: Build forecasting models leveraging key economic indicators discussed in the dataset.
• Content Creation: Develop research-backed reports, articles, and presentations on economic topics.

Who Benefits?

• Researchers & Academics studying macro-economics or financial policy.
  
• Data Scientists building AI models, trend analyzers, or economic forecasting tools.
  
• Economists & Analysts need real-world news data for policy analysis.
  
• Content Strategists who write data-backed articles about economic trends.

Why Choose This Dataset?

• No need to manually scrape CNBC — data is pre-extracted and clean.
  
• High-quality economy news metadata enables detailed filtering (by date, author, topic).
  
• Ready for machine learning, sentiment analysis, or building news-based economic models.
  
• Well-suited for trend tracking, policy analysis, and economic forecasting.

Explore More News Datasets

Interested in additional structured news datasets for your research or analytics needs? Check out our news dataset collection to find datasets tailored for diverse analytical applications.

The Fuel Economy Label and CAFE Data asset contains measured summary fuel economy estimates and test data for light-duty vehicle manufacturers by model for certification as required under the Energy Policy and Conservation Act of 1975 (EPCA) and The Energy Independent Security Act of 2007 (EISA) to collect vehicle fuel economy estimates for the creation of Economy Labels and for the calculation of Corporate Average Fuel Economy (CAFE). Manufacturers submit data on an annual basis, or as needed to document vehicle model changes.The EPA performs targeted fuel economy confirmatory tests on approximately 15% of vehicles submitted for validation. Confirmatory data on vehicles is associated with its corresponding submission data to verify the accuracy of manufacturer submissions beyond standard business rules. Submitted data comes in XML format or as documents, with the majority of submissions being sent in XML, and includes descriptive information on the vehicle itself, fuel economy information, and the manufacturer's testing approach. This data may contain proprietary information (CBI) such as information on estimated sales or other data elements indicated by the submitter as confidential. CBI data is not publically available; however, within the EPA data can accessed under the restrictions of the Office of Transportation and Air Quality (OTAQ) CBI policy [RCS Link]. Datasets are segmented by vehicle model/manufacturer and/or year with corresponding fuel economy, test, and certification data. Data assets are stored in EPA's Verify system.Coverage began in 1974 with early records being primarily paper documents which did not go through the same level of validation as primarily digital submissions which started in 2008. Early data is available to the public digitally starting from 1978, but more complete digital certification data is available starting in 2008. Fuel economy submission data prior to 2006 was calculated using an older formula; however, mechanisms exist to make this data comparable to current results.Fuel Economy Label and CAFE Data submission documents with metadata, certificate and summary decision information is utilized and made publically available through the EPA/DOE's Fuel Economy Guide Website (https://www.fueleconomy.gov/) as well as EPA's Smartway Program Website (https://www.epa.gov/smartway/) and Green Vehicle Guide Website (http://ofmpub.epa.gov/greenvehicles/Index.do;jsessionid=3F4QPhhYDYJxv1L3YLYxqh6J2CwL0GkxSSJTl2xgMTYPBKYS00vw!788633877) after it has been quality assured. Where summary data appears inaccurate, OTAQ returns the entries for review to their originator.

Colombia Consumer Price Index (CPI): Poor: Clothes Made for Bed data was reported at 100.390 Dec2018=100 in Jan 2019. Colombia Consumer Price Index (CPI): Poor: Clothes Made for Bed data is updated monthly, averaging 100.390 Dec2018=100 from Jan 2019 (Median) to Jan 2019, with 1 observations. Colombia Consumer Price Index (CPI): Poor: Clothes Made for Bed data remains active status in CEIC and is reported by National Statistics Administrative Department. The data is categorized under Global Database’s Colombia – Table CO.I015: Consumer Price Index: COICOP: Dec2018=100: by Sub Class of Good and Services.

In 2024, the gross domestic product (GDP) of the United Kingdom grew by 0.9 percent and is expected to grow by just one percent in 2025 and by 1.9 percent in 2026. Growth is expected to slow down to 1.8 percent in 2027, and then grow by 1.7, and 1.8 percent in 2027 and 2028 respectively. The sudden emergence of COVID-19 in 2020 and subsequent closure of large parts of the economy were the cause of the huge 9.4 percent contraction in 2020, with the economy recovering somewhat in 2021, when the economy grew by 7.6 percent. UK growth downgraded in 2025 Although the economy is still expected to grow in 2025, the one percent growth anticipated in this forecast has been halved from two percent in October 2024. Increased geopolitical uncertainty as well as the impact of American tariffs on the global economy are some of the main reasons for this mark down. The UK's inflation rate for 2025 has also been revised, with an annual rate of 3.2 percent predicated, up from 2.6 percent in the last forecast. Unemployment is also anticipated to be higher than initially thought, with the annual unemployment rate likely to be 4.5 percent instead of 4.1 percent. Long-term growth problems In the last two quarters of 2023, the UK economy shrank by 0.1 percent in Q3 and by 0.3 percent in Q4, plunging the UK into recession for the first time since the COVID-19 pandemic. Even before that last recession, however, the UK economy has been struggling with weak growth. Although growth since the pandemic has been noticeably sluggish, there has been a clear long-term trend of declining growth rates. The economy has consistently been seen as one of the most important issues to people in Britain, ahead of health, immigration and the environment. Achieving strong levels of economic growth is one of the main aims of the Labour government elected in 2024, although after almost one year in power it has so far proven elusive.

Surveys

The Directorate-General for Economic and Financial Affairs (DG ECFIN) of the European Commission conducts five monthly, harmonised surveys for the economies in the European Union (EU) and in the candidate countries. They are addressed to representatives of the industry (manufacturing), services, retail trade and construction sectors, as well as to consumers. A few additional questions are asked on a quarterly and biannual basis. These surveys allow comparisons among different countries’ business cycles and have become an indispensable tool for monitoring the evolution of the EU and the euro area economies, as well as monitoring developments in the candidate countries.

The Business and consumer survey (BCS) database comprises the following surveys:

Industry survey

- Monthly questions on production, order book levels, stocks of finished products, perceived economic uncertainty, selling prices and employment.

- Quarterly questions on factors limiting production, production capacity, development of (overall and export) order books and months of production assured by them, capacity utilisation, competitive position.

- Biannual questions on investment activity, as well as structure of and factors stimulating investment (annual).

Services survey

- Monthly questions on business situation, demand, perceived economic uncertainty, employment and selling prices.

- Quarterly questions on factors limiting business and capacity utilisation.

- Biannual questions on investment activity, as well as structure of and factors stimulating investment (annual).

Retail trade survey

- Monthly questions on business activity, stocks of goods, orders placed with suppliers, perceived economic uncertainty, employment, selling prices.

Construction survey

- Monthly questions on building activity and factors limiting it, order books, employment, perceived economic uncertainty, prices charged.

- Quarterly questions on operating time ensured by current backlog.

Consumer survey

- Monthly questions on financial situation, perceived economic uncertainty, general economic situation, price trends, unemployment, major purchases and savings.

- Quarterly questions on intention to buy a car, purchase or build a home, home improvements.

Indicators

Monthly Confidence Indicators (CIs) reflecting overall perceptions and expectations are calculated separately for all four business sectors covered by the survey programme, as well as consumers. The computation is done both at country and aggregate level (EU and euro area).

A monthly Economic Sentiment Indicator (ESI) is calculated based on a selection of questions from the industry, services, retail trade, construction and consumer surveys at country level and at aggregate level (EU and euro area) in order to track overall economic activity. The ESI has been calculated since 1985.

Since 2020, the set of monthly composite indicators also contains an Employment Expectations Indicator (EEI), which helps getting a timely indication of expected changes in dependent employment. The indicator is constructed as a weighted average of the employment expectations of managers in four surveyed business sectors (industry, services, retail trade and construction).

A monthly euro area Business Climate Indicator (BCI) is available for industry.

Detailed methodological information about the BCS surveys and indicators is provided in a user guide to the Joint Harmonised EU Programme of Business and Consumer Surveys: https://ec.europa.eu/economy_finance/db_indicators/surveys/documents/methodological_guidelines/bcs_user_guide.pdf

Note: Up until April 2023, the BCS surveys included a sixth survey for the financial services sector, which was only available at EU/euro area level. This survey has been discontinued.

The Economic Indicator Service (EIS) aims to deliver economic content to financial institutions on both buy and sell-side and service providers. This new service currently covers 34,351 recurring macro-economic indicators from 135 countries ( as of December 16, 2019 ) such as GDP data, unemployment releases, PMI numbers etc.

Economic Indicator Service gathers the major economic events from a variety of regions and countries around the globe and provides an Economic Events Data feed and Economic Calendar service to our clients. This service includes all previous historic data on economic indicators that are currently available on the database.

Depending on availability, information regarding economic indicators, including the details of the issuing agency as well as historical data series can be made accessible for the client. Key information about EIS: • Cloud-based service for Live Calendar – delivered via HTML/JavaScript application formats, which can then be embedded onto any website using iFrames • Alternatives methods available – such as API and JSON feed for the economic calendar that can be integrated into the company’s system • Live data – updated 24/5, immediately after the data has been released • Historical data – includes a feed of all previous economic indicators available We are currently adding additional indicators/countries from Africa as well as expanding our coverage of Indicators in G20. The calendar includes the following. • Recurring & Non-recurring indicators covering 136 countries across 21 regions. • Indicators showing high, medium, and low impact data. • Indicators showing actual, previous, and forecast data. • Indicators can be filtered across 16 subtypes. • News generation for selected high-impact data. • Indicator description and historical data up to the latest eight historical points with a chart.

In 2025, the United States had the largest economy in the world, with a gross domestic product of over 30 trillion U.S. dollars. China had the second largest economy, at around 19.23 trillion U.S. dollars. Recent adjustments in the list have seen Germany's economy overtake Japan's to become the third-largest in the world in 2023, while Brazil's economy moved ahead of Russia's in 2024. Global gross domestic product Global gross domestic product amounts to almost 110 trillion U.S. dollars, with the United States making up more than one-quarter of this figure alone. The 12 largest economies in the world include all Group of Seven (G7) economies, as well as the four largest BRICS economies. The U.S. has consistently had the world's largest economy since the interwar period, and while previous reports estimated it would be overtaken by China in the 2020s, more recent projections estimate the U.S. economy will remain the largest by a considerable margin going into the 2030s.The gross domestic product of a country is calculated by taking spending and trade into account, to show how much the country can produce in a certain amount of time, usually per year. It represents the value of all goods and services produced during that year. Those countries considered to have emerging or developing economies account for almost 60 percent of global gross domestic product, while advanced economies make up over 40 percent.

Note that because a value of 0 wt% is automatically returned for analyses below the practical detection limit in the range 0.0–0.5 wt%, such results were assigned a value of 0.25 wt% for the purposes of plotting and the calculation of averages.

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

This dataset contains US county-level demographic data from 2016, giving insight into the health and economic conditions of counties in the United States. Aggregated and filtered from various sources such as the US Census Small Area Income and Poverty Estimates (SAIPE) Program, American Community Survey, CDC National Center for Health Statistics, and more, this comprehensive dataset provides information on population as well as desert population for each county. Additionally, data is split between metropolitan and nonmetropolitan areas according to the Office of Management and Budget's 2013 classification scheme. Valuable information pertaining to infant mortality rates and total population are also included in this detailed set of data. Use this dataset to gain a better understanding of one of our nation's most essential regions

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How to use the dataset

• Look at the information within the 'About this Dataset' section to have an understanding of what data sources were used to create this dataset as well as any transformations that may have been done while creating it.
• Familiarize yourself with the columns provided in the data set to understand what information is available for each county such as total population (totpop), parental education level (educationLvl), median household income (medianIncome), etc.,
• Use a combination of filtering and sorting techniques to narrow down results and focus in on more specific county demographics that you are looking for such as total households living below poverty line by state or median household income per capita between two counties etc.,
• Keep in mind any additional transformations/simplifications/aggregations done during step 2 when using your data for analysis. For example, if certain variables were pivoted during step two from being rows into columns because it was easier to work with multiple years of income levels by having them all consolidated into one column then be aware that some states may not appear in all records due to those transformations being applied differently between regions which could result in missing values or other inconsistencies when doing downstream analysis on your selected variables.
• Utilize resources such as Wikipedia and government census estimates if you need more detailed information surrounding these demographic characteristics beyond what's available within our current dataset – these can be helpful when conducting further research outside of solely relying on our provided spreadsheet values alone!

Research Ideas

• Creating a US county-level heat map of infant mortality rates, offering insight into which areas are most at risk for poor health outcomes.
• Generating predictive models from the population data to anticipate and prepare for future population trends in different states or regions.
• Developing an interactive web-based tool for school districts to explore potential impacts of student mobility on their area's population stability and diversity

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Food Desert.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------| | year | The year the data was collected. (Integer) | | fips | The Federal Information Processing Standard (FIPS) code for the county. (Integer) | | state_fips | The FIPS code for the state. (Integer) | | county_fips | The FIPS code for the county. (Integer)...

The statistic shows the unemployment rate in South Korea from 2020 to 2024, with projections up until 2030. In 2028, the unemployment rate in South Korea was at around 2.8 percent. See the figures for the population of South Korea for comparison. Economy of South Korea South Korea is one of the world’s richest countries as well as a member of the G20, an organization made up of the 20 strongest economies in the world. Due to excessive growth from the 1960s to the 1990s, South Korea established itself as a developed country in the world with a strong economy and high wages. Continued economic growth is attributed to a robust export-oriented economy, which was deemed necessary for the country particularly due to a scarcity in natural resources as well as overpopulation in comparison to available living space. During the global financial crisis, South Korea surprised many economists by maintaining a stable economy and even experiencing economic growth, most notably during the peak of the crisis. Through stimulus packages as well as a high level of consumption, South Korea’s strong export-oriented economy was not affected as negatively as many other developed countries. South Korea’s economy primarily revolves around the production and export of technological goods. Some primary exports of the country include electronics, ships and automobiles. South Korea also has some of the largest shipbuilding companies worldwide, which are highly profitable but also have accumulated a rather large sum of debt over the course of several years.

The United Kingdom's economy grew by 1.1 percent in 2024, after a growth rate of 0.3 percent in 2023, 5.1 percent in 2022, 8.5 percent in 2021, and a record ten percent fall in 2020. During the provided time period, the biggest annual fall in gross domestic product before 2020 occurred in 2009, when the UK economy contracted by 4.6 percent at the height of the global financial crisis of the late 2000s. Before 2021, the year with the highest annual GDP growth rate was 1973, when the UK economy grew by 6.5 percent. UK economy growing but GDP per capita falling In 2022, the UK's GDP per capita amounted to approximately 37,371 pounds, with this falling to 37,028 pounds in 2023, and 36,977 pounds in 2024. While the UK economy as a whole grew during this time, the UK's population grew at a faster rate, resulting in the negative growth in GDP per capita. This suggests the UK economy's struggles with productivity are not only stagnating, but getting worse. The relatively poor economic performance of the UK in recent years has not gone unnoticed by the electorate, with the economy consistently seen as the most important issue for voters since 2022. Recent shocks to UK economy In the second quarter of 2020, the UK economy shrank by a record 20.3 percent at the height of the COVID-19 pandemic. Although there was a relatively swift economic recovery initially, the economy has struggled to grow much beyond its pre-pandemic size, and was only around 3.1 percent larger in December 2024, when compared with December 2019. Although the labor market has generally been quite resilient during this time, a long twenty-month period between 2021 and 2023 saw prices rise faster than wages, and inflation surge to a high of 11.1 percent in October 2022.

Economic welfare is essential in the modern economy since it directly reflects the standard of living, distribution of resources, and general social satisfaction, which influences individual and social well-being. This study aims to explore the relationship between national income accounting different attributes and the economic welfare in Pakistan. However, this study used data from 1950 to 2022, and data was downloaded from the World Bank data portal. Regression analysis is used to investigate the relationship between them and is very effective in measuring the relationship between endogenous and exogenous variables. Moreover, generalized methods of movement (GMM) are used as the robustness of the regression. Our results show that foreign direct investment outflow, Gross domestic product growth rate, GDP per capita, higher Interest, market capitalization, and population growth have a significant negative on the unemployment rate, indicating the rise in these factors leads to a decrease in the employment rate in Pakistan. Trade and savings have a significant positive impact on the unemployment rate, indicating the rise in these factors leads to an increase in the unemployment rate for various reasons. Moreover, all the factors of national income accounting have a significant positive relationship with life expectancy, indicating that an increase in these factors leads to an increase in economic welfare and life expectancy due to better health facilities, many resources, and correct economic policies. However, foreign direct investment, inflation rate, lending interest rate, and population growth have significant positive effects on age dependency, indicating these factors increase the age dependency. Moreover, GDP growth and GDP per capita negatively impact age dependency. Similarly, all the national income accounting factors have a significant negative relationship with legal rights that leads to decreased legal rights. Moreover, due to better health facilities and health planning, there is a negative significant relationship between national income accounting attributes and motility rate among children. Our study advocated the implications for the policymakers and the government to make policies for the welfare and increase the social factors.

We present results from six randomized control trials of an integrated approach to improve livelihoods among the very poor. The approach combines the transfer of a productive asset with consumption support, training, and coaching plus savings encouragement and health education and/or services. Results from the implementation of the same basic program, adapted to a wide variety of geographic and institutional contexts and with multiple implementing partners, show statistically significant cost-effective impacts on consumption (fueled mostly by increases in self-employment income) and psychosocial status of the targeted households. The impact on the poor households lasted at least a year after all implementation ended. It is possible to make sustainable improvements in the economic status of the poor with a relatively short-term intervention.

US Covid-19 Cases, Deaths and Mobility by State/County

Analyzing the Impact of the Pandemic on Low-Income Populations

By Liz Friedman [source]

About this dataset

Welcome to the Opportunity Insights Economic Tracker! Our goal is to provide a comprehensive, real-time look into how COVID-19 and stabilization policies are affecting the US economy. To do this, we have compiled a wide array of data points on spending and employment, gathered from several sources.

This dataset includes daily/weekly/monthly information at the state/county/city level for eight types of data: Google Mobility; Low-Income Employment and Earnings; UI Claims; Womply Merchants and Revenue; as well as weekly Math Learning from Zearn. Additionally, three files- Accounting for Geoids-State/County/City provide crosswalks between geographic areas that can be merged with other files having shared geographical levels.

Our goal here is to enable data users around the world to follow economic conditions in the US during this tumultuous period with maximum clarity and precision. We make all our datasets freely available so if you use them we kindly ask you attribute our work by linking or citing both our accompanying paper as well as this Economic Tracker at https://tracktherecoveryorg By doing so you are also agreeing to uphold our privacy & integrity standards which commit us both to individual & business confidentiality without compromising on independent nonpartisan research & policy analysis!

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How to use the dataset

This dataset provides US COVID-19 case and death data, as well as Google Community Mobility Reports, on the state/county level. Here is how to use this dataset:

• Understand the file structure: This dataset consists of three main files: 1) US Cases & Deaths by State/County, 2) Google Community Mobility Reports, and 3) Data from third-parties providing small business openings & revenue information and unemployment insurance claim data (Low Inc Earnings & Employment, UI Claims and Womply Merchants & Revenue).
• Select your Subset: If you are interested in particular types of data (e.g., mobility or employment), select the corresponding files from within each section based on your geographic area of interest – national, state or county level – as indicated in each filename.
• Review metadata variables: Become familiar with the provided variables so that you can select which ones you need to explore further in your analysis. For example, if analyzing mobility trends at a city level look for columns such as ‘Retailer_and_recreation_percent_change’ or ‘Transit Stations Percent Change’; if focusing on employment decline look for columns such pay or emp figures that align with industries of interest to you such as low-income earners (emp_{inclow},pay_{inclow}).
• Unify dateformatting across row values : Convert date formats into one common unit so that all entries have consistent formatting if necessary; for exampe some entries may display dates using YYYY/MM/DD notation while others may use MM//DD//YY format depending on their source datasets; make sure to review column labels carefully before converting units where needed..

• Merge datasets where applicable : Utilize GeoID crosswalks to combine multiple sets with same geographical coverageregionally covering ; example might be combining low income earnings figures with specific county settings by reference geo codes found in related documents like GeoIDs-County .
  6 . Visualise Data : Now that all the different measures have been reviewed can begin generating charts visualize findings . This process may include cleaning up raw figures normalizing across currency formats , mapping geospatial locations others ; once ready create bar graphs line charts maps other visual according aggregate output desired Insightful representations at this stage will help inform concrete policy decisions during outbreak recovery period..

  Remember to cite

Research Ideas

• Estimating the Impact of the COVID-19 Pandemic on Small Businesses - By comparing county-level Womply revenue and employment data with pre-COVID data, policymakers can gain an understanding of the economic impact that COVID has had on local small businesses.
• Analyzing Effects of Mobility Restrictions - The Google Mobility data provides insight into geographic areas where...

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

Economic welfare is essential in the modern economy since it directly reflects the standard of living, distribution of resources, and general social satisfaction, which influences individual and social well-being. This study aims to explore the relationship between national income accounting different attributes and the economic welfare in Pakistan. However, this study used data from 1950 to 2022, and data was downloaded from the World Bank data portal. Regression analysis is used to investigate the relationship between them and is very effective in measuring the relationship between endogenous and exogenous variables. Moreover, generalized methods of movement (GMM) are used as the robustness of the regression. Our results show that foreign direct investment outflow, Gross domestic product growth rate, GDP per capita, higher Interest, market capitalization, and population growth have a significant negative on the unemployment rate, indicating the rise in these factors leads to a decrease in the employment rate in Pakistan. Trade and savings have a significant positive impact on the unemployment rate, indicating the rise in these factors leads to an increase in the unemployment rate for various reasons. Moreover, all the factors of national income accounting have a significant positive relationship with life expectancy, indicating that an increase in these factors leads to an increase in economic welfare and life expectancy due to better health facilities, many resources, and correct economic policies. However, foreign direct investment, inflation rate, lending interest rate, and population growth have significant positive effects on age dependency, indicating these factors increase the age dependency. Moreover, GDP growth and GDP per capita negatively impact age dependency. Similarly, all the national income accounting factors have a significant negative relationship with legal rights that leads to decreased legal rights. Moreover, due to better health facilities and health planning, there is a negative significant relationship between national income accounting attributes and motility rate among children. Our study advocated the implications for the policymakers and the government to make policies for the welfare and increase the social factors.

Abstract

The Global Findex 2025 reveals how mobile technology is equipping more adults around the world to own and use financial accounts to save formally, access credit, make and receive digital payments, and pursue opportunities. Including the inaugural Global Findex Digital Connectivity Tracker, this fifth edition of Global Findex presents new insights on the interactions among mobile phone ownership, internet use, and financial inclusion.

The Global Findex is the world’s most comprehensive database on digital and financial inclusion. It is also the only global source of comparable demand-side data, allowing cross-country analysis of how adults access and use mobile phones, the internet, and financial accounts to reach digital information and resources, save, borrow, make payments, and manage their financial health. Data for the Global Findex 2025 were collected from nationally representative surveys of about 145,000 adults in 141 economies. The latest edition follows the 2011, 2014, 2017, and 2021 editions and includes new series measuring mobile phone ownership and internet use, digital safety, and frequency of transactions using financial services.

The Global Findex 2025 is an indispensable resource for policy makers in the fields of digital connectivity and financial inclusion, as well as for practitioners, researchers, and development professionals.

Geographic coverage

National Coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most low- and middle-income economies, Global Findex data were collected through face-to-face interviews. In these economies, an area frame design was used for interviewing. In most high-income economies, telephone surveys were used. In 2024, face-to-face interviews were again conducted in 22 economies after phone-based surveys had been employed in 2021 as a result of mobility restrictions related to COVID-19. In addition, an abridged form of the questionnaire was administered by phone to survey participants in Algeria, China, the Islamic Republic of Iran, Libya, Mauritius, and Ukraine because of economy-specific restrictions. In just one economy, Singapore, did the interviewing mode change from face to face in 2021 to phone based in 2024.

In economies in which face-to-face surveys were conducted, the first stage of sampling was the identification of primary sampling units. These units were then stratified by population size, geography, or both and clustered through one or more stages of sampling. Where population information was available, sample selection was based on probabilities proportional to population size; otherwise, simple random sampling was used. Random route procedures were used to select sampled households. Unless an outright refusal occurred, interviewers made up to three attempts to survey each sampled household. To increase the probability of contact and completion, attempts were made at different times of the day and, where possible, on different days. If an interview could not be completed at a household that was initially part of the sample, a simple substitution method was used to select a replacement household for inclusion.

Respondents were randomly selected within sampled households. Each eligible household member (that is, all those ages 15 or older) was listed, and a handheld survey device randomly selected the household member to be interviewed. For paper surveys, the Kish grid method was used to select the respondent. In economies in which cultural restrictions dictated gender matching, respondents were randomly selected from among all eligible adults of the interviewer’s gender.

In economies in which Global Findex surveys have traditionally been phone based, respondent selection followed the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies in which mobile phone and landline penetration is high, a dual sampling frame was used.

The same procedure for respondent selection was applied to economies in which phone-based interviews were being conducted for the first time. Dual-frame (landline and mobile phone) random digit dialing was used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digit dialing was used in economies with limited or no landline presence (less than 20 percent). For landline respondents in economies in which mobile phone or landline penetration is 80 percent or higher, respondents were selected randomly by using either the next-birthday method or the household enumeration method, which involves listing all eligible household members and randomly selecting one to participate. For mobile phone respondents in these economies or in economies in which mobile phone or landline penetration is less than 80 percent, no further selection was performed. At least three attempts were made to reach the randomly selected person in each household, spread over different days and times of day.

Research instrument

The English version of the questionnaire is provided for download.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in: Klapper, Leora, Dorothe Singer, Laura Starita, and Alexandra Norris. 2025. The Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9.

Colombia Consumer Price Index (CPI): Poor: Maintenance & Repairs Made in the Workshop data was reported at 100.420 Dec2018=100 in Jan 2019. Colombia Consumer Price Index (CPI): Poor: Maintenance & Repairs Made in the Workshop data is updated monthly, averaging 100.420 Dec2018=100 from Jan 2019 (Median) to Jan 2019, with 1 observations. Colombia Consumer Price Index (CPI): Poor: Maintenance & Repairs Made in the Workshop data remains active status in CEIC and is reported by National Statistics Administrative Department. The data is categorized under Global Database’s Colombia – Table CO.I015: Consumer Price Index: COICOP: Dec2018=100: by Sub Class of Good and Services.

This dataset includes Alaska commercial loan data from 1976-2016. These data were used for the State of Alaska Salmon and People (SASAP) project to examine fisheries related loans. The goal of the commercial fishing loan program is to provide long-term, low interest loans to improve the quality of Alaska seafood products. The program is operated by the Alaska Department of Commerce, Community, and Economic Development; Division of Economic Development. The loans promote development of resident fisheries and maintenance of commercial fishing vessels and gear and are available to individuals who have been Alaska residents for the past 2 years. Loans are available for purchases made within the 12 months prior to loan application or to refinance vessel or gear loans made by other lenders more than 12 months before loan application. Interest rates are fixed at the time of loan approval. The fisheries related loans data used by SASAP are associated with hatcheries (fisheries enhancement) and individual fishermen, and can be identified using the "Fund Abbreviation" column, with relevant codes being CF (Commercial Fishing) and FE (Fisheries Enhancement). The purposes of these codes can be looked up in the provided list of current and historical loan codes.