Fault Lines Widen in the Global Recovery

Economic prospects have diverged further across countries since the April 2021 World Economic Outlook (WEO) forecast. Vaccine access has emerged as the principal fault line along which the global recovery splits into two blocs: those that can look forward to further normalization of activity later this year (almost all advanced economies) and those that will still face resurgent infections and rising COVID death tolls. The recovery, however, is not assured even in countries where infections are currently very low so long as the virus circulates elsewhere.

The global economy is projected to grow 6.0 percent in 2021 and 4.9 percent in 2022.The 2021 global forecast is unchanged from the April 2021 WEO, but with offsetting revisions. Prospects for emerging market and developing economies have been marked down for 2021, especially for Emerging Asia. By contrast, the forecast for advanced economies is revised up. These revisions reflect pandemic developments and changes in policy support. The 0.5 percentage-point upgrade for 2022 derives largely from the forecast upgrade for advanced economies, particularly the United States, reflecting the anticipated legislation of additional fiscal support in the second half of 2021 and improved health metrics more broadly across the group.

Recent price pressures for the most part reflect unusual pandemic-related developments and transitory supply-demand mismatches. Inflation is expected to return to its pre-pandemic ranges in most countries in 2022 once these disturbances work their way through prices, though uncertainty remains high. Elevated inflation is also expected in some emerging market and developing economies, related in part to high food prices. Central banks should generally look through transitory inflation pressures and avoid tightening until there is more clarity on underlying price dynamics. Clear communication from central banks on the outlook for monetary policy will be key to shaping inflation expectations and safeguarding against premature tightening of financial conditions. There is, however, a risk that transitory pressures could become more persistent and central banks may need to take preemptive action.

Risks around the global baseline are to the downside. Slower-than-anticipated vaccine rollout would allow the virus to mutate further. Financial conditions could tighten rapidly, for instance from a reassessment of the monetary policy outlook in advanced economies if inflation expectations increase more rapidly than anticipated. A double hit to emerging market and developing economies from worsening pandemic dynamics and tighter external financial conditions would severely set back their recovery and drag global growth below this outlook’s baseline.

Multilateral action has a vital role to play in diminishing divergences and strengthening global prospects. The immediate priority is to deploy vaccines equitably worldwide. A $50 billion IMF staff proposal, jointly endorsed by the World Health Organization, World Trade Organization, and World Bank, provides clear targets and pragmatic actions at a feasible cost to end the pandemic. Financially constrained economies also need unimpeded access to international liquidity. The proposed $650 billion General Allocation of Special Drawing Rights at the IMF is set to boost reserve assets of all economies and help ease liquidity constraints. Countries also need to redouble collective efforts to reduce greenhouse gas emissions. These multilateral actions can be reinforced by national-level policies tailored to the stage of the crisis that help catalyze a sustainable, inclusive recovery. Concerted, well-directed policies can make the difference between a future of durable recoveries for all economies or one with widening fault lines—as many struggle with the health crisis while a handful see conditions normalize, albeit with the constant threat of renewed flare-ups.

About this dataset

Context

The dataset tabulates the Economy population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Economy. The dataset can be utilized to understand the population distribution of Economy by age. For example, using this dataset, we can identify the largest age group in Economy.

Key observations

The largest age group in Economy, PA was for the group of age 60-64 years with a population of 883 (9.75%), according to the 2021 American Community Survey. At the same time, the smallest age group in Economy, PA was the 80-84 years with a population of 232 (2.56%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Economy is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Economy total population. 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 Age. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Economy. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Economy, the median household income for the households where the householder is White increased by $16,824(43.62%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $38,573 in 2011 and $55,397 in 2021.
• Black or African American: As per the U.S. Census Bureau population data, in Economy, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/economy-in-median-household-income-by-race-trends.jpeg" alt="Economy, IN median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Economy.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

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 median household income by race. You can refer the same here

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

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.

About this dataset

Context

The dataset tabulates the population of Economy by race. It includes the population of Economy across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Economy across relevant racial categories.

Key observations

The percent distribution of Economy population by race (across all racial categories recognized by the U.S. Census Bureau): 97.60% are white and 2.40% are multiracial.

https://i.neilsberg.com/ch/economy-in-population-by-race.jpeg" alt="Economy population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Economy
• Population: The population of the racial category (excluding ethnicity) in the Economy is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Economy total population. 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 Race & Ethnicity. You can refer the same here

https://i2.wp.com/www.mon-livret.fr/wp-content/uploads/2021/10/crypto-Metaverse-696x392.png?resize=696%2C392&ssl=1" alt="">

Context

• The metaverse, a living and breathing space that blends physical and digital, is quickly evolving from a science fiction dream into a reality with endless possibilities. A world where people can interact virtually, create and exchange digital assets for real-world value, own digital land, engage with digitized real-world products and services, and much more.

• Major tech giants are beginning to recognize the viability and potential of metaverses, following Facebook’s groundbreaking Meta rebrand announcement. In addition to tech companies, entertainment brands like Disney have also announced plans to take the leap into virtual reality.

• While the media hype is deafening, your average netizen isn’t fully aware of what a metaverse is, how it operates and, most importantly—what benefits and opportunities it can offer them as a user.

https://cdn.images.express.co.uk/img/dynamic/22/590x/Metaverse-tokens-cryptocurrency-explained-ethereum-killers-new-coins-digital-currency-meta-news-1518777.jpg?r=1638256864800" alt="">

What Is The Metaverse?

• In its digital iteration, a metaverse is a virtual world based on blockchain technology. This all-encompassing space allows users to work and play in a virtual reflection of real-life and fantasy scenarios, an online reality, ranging from sci-fi and dragons to more practical and familiar settings like shopping centers, offices, and even homes.

• Users can access metaverses via computer, handheld device, or complete immersion with a VR headset. Those entering the metaverse get to experience living in a digital realm, where they will be able to work, play, shop, exercise, and socialize. Users will be able to create their own avatars based on face recognition, set up their own businesses of any kind, buy real estate, create in-world content and asset,s and attend concerts from real-world superstars—all in one virtual environment,

• With that said, a metaverse is a virtual world with a virtual economy. In most cases, it is an online reality powered by decentralized finance (DeFi), where users exchange value and assets via cryptocurrencies and Non-Fungible Tokens.

What Are Metaverse Tokens?

• Metaverse tokens are a unit of virtual currency used to make digital transactions within the metaverse. Since metaverses are built on the blockchain, transactions on underlying networks are near-instant. Blockchains are designed to ensure trust and security, making the metaverse the perfect environment for an economy free of corruption and financial fraud.

• Holders of metaverse tokens can access multiple services and applications inside the virtual space. Some tokens give special in-game abilities. Other tokens represent unique items, like clothing for virtual avatars or membership for a community. If you’ve played MMO games like World of Warcraft, the concept of in-game items and currencies are very familiar. However, unlike your traditional virtual world games, metaverse tokens have value inside and outside the virtual worlds. Metaverse tokens in the form of cryptocurrency can be exchanged for fiat currencies. Or if they’re an NFT, they can be used to authenticate ownership to tethered real-world assets like collectibles, works or art, or even cups of coffee.

• Some examples of metaverse tokens include SAND of the immensely popular Sandbox metaverse. In The Sandbox, users can create a virtual world driven by NFTs. Another token is MANA of the Decentraland project, where users can use MANA to purchase plots of digital real estate called “LAND”. It is even possible to monetize the plots of LAND purchased by renting them to other users for fixed fees. The ENJ token of the Enjin metaverse is the native asset of an ecosystem with the world’s largest game/app NFT networks.

Dataset Information

• The dataset brings 198 metaverse cryptos. Pls refer to the file Metaverse coins.csv to find the list of metaverse crypto coins.

• The dataset will be updated on a weekly basis with more and more additional metaverse tokens, Stay tuned ⏳

This table contains quarterly and annual data on the production components, the expenditure categories and the income components of the gross domestic product of the Netherlands. The volume development of gross domestic product is the measure of a country's economic growth. It is customary in the national accounts and thus also in the quarterly accounts to approach gross domestic product from three points of view, from production, from expenditure and from income.

In addition, this table also presents the build-up of the national claims balance from GDP and details of variables from the first four topics are available. These can be found under Additional details.

Data available from 1995.

Status of figures: The annual data for the period 1995-2021 are final. Quarterly data from 2021 onwards are provisional. As this table has been discontinued, it will no longer be provisionally finalised.

Changes as of 24 June 2024 None, this table has been discontinued. The Central Bureau of Statistics recently revised the national accounts. New sources, methods and concepts are introduced into the national accounts, so that the picture of the Dutch economy is optimally aligned with all underlying statistics, sources and international guidelines for compiling the national accounts. For more information see section 3.

When will there be new figures? No longer applicable.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Economy. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Economy, the median income for all workers aged 15 years and older, regardless of work hours, was $39,183 for males and $23,645 for females.

These income figures highlight a substantial gender-based income gap in Economy. Women, regardless of work hours, earn 60 cents for each dollar earned by men. This significant gender pay gap, approximately 40%, underscores concerning gender-based income inequality in the town of Economy.

- Full-time workers, aged 15 years and older: In Economy, among full-time, year-round workers aged 15 years and older, males earned a median income of $43,012, while females earned $35,805, leading to a 17% gender pay gap among full-time workers. This illustrates that women earn 83 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Economy.

https://i.neilsberg.com/ch/economy-in-income-by-gender.jpeg" alt="Economy, IN gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 median household income by gender. You can refer the same here

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsEconomic activityThis dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by economic activity status. The estimates are as at Census Day, 21 March 2021.Definition: People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were:in employment (an employee or self-employed)unemployed, but looking for work and could start within two weeksunemployed, but waiting to start a job that had been offered and acceptedIt is a measure of whether or not a person was an active participant in the labour market during this period. Economically inactive are those aged 16 years and over who did not have a job between 15 March to 21 March 2021 and had not looked for work between 22 February to 21 March 2021 or could not start work within two weeks.The census definition differs from International Labour Organization definition used on the Labour Force Survey, so estimates are not directly comparable.This dataset contains details for Leicester city and England overall.

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.

Name issue:

A minor naming issue, the dataset was originally just income data. While the dataset has been renamed, the link has not. This data is not just income data.

Context

This dataset was uploaded to determine a possible relation between economic status of counties, and COVID-19 cases in Maryland by-county. While the data is from only 2018, 2020 census data is not available until March, 2021. This data is built to analyze features in Maryland that concern quality of living.

Acknowledgements

These datasets come from https://commerce.maryland.gov/about/rankings-and-statistics/data-explorer. They were generated by the US Census Bureau in 2018.

Disclaimer: No infringement is intended in any way, sources are US Census Bureau, Maryland Department of Labor Licensing and Regulation, Comptroller of Maryland, and Maryland Association of Realtors. Licensing statement: (https://commerce.maryland.gov/commerce/privacy)

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Uber And Lyft Drivers Carjackings

Dataset from: "Uber And Lyft Drivers Are Being Carjacked at Alarming Rates"

About this dataset

Original Author: The dataset was first posted here By The Markup

This dataset contains information on carjackings of gig economy workers in the United States. columns include: state, city, date, company, carjacking_happened_via_the_app, driver_deceased, source, additional_sources

How to use the dataset

This dataset can be used to research gig worker carjackings in the United States. The data includes information on the date, location, and circumstances of each carjacking, as well as the name of the gig economy company involved. Researchers can use this dataset to study patterns in carjackings, identify risk factors for drivers, and develop strategies for prevention and response

Research Ideas

• Finding trends in gig worker carjackings across the United States
• Determining which cities are most at risk for gig worker carjackings
• Analyzing which companies' gig workers are most at risk for carjackings

Acknowledgements

We would like to thank the researchers at the University of Michigan for their work in compiling this dataset

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: carjackings.csv | Column name | Description | |:------------------------------------|:---------------------------------------------------------------------------------------| | state | The state in which the carjacking occurred. (String) | | city | The city in which the carjacking occurred. (String) | | date | The date on which the carjacking occurred. (Date) | | company | The company for which the driver was working at the time of the carjacking. (String) | | carjacking_happened_via_the_app | Whether or not the carjacking occurred through the use of the company's app. (Boolean) | | driver_deceased | Whether or not the driver was killed as a result of the carjacking. (Boolean) | | source | The source of the information on the carjacking. (String) | | additional_sources | Any additional sources of information on the carjacking. (String) |

Description: Interviews with experts on women's economic empowerment and the blue economy in the Indian Ocean Rim were conducted. Although women have played an important economic role in the Indian Ocean for generations, they face a multitude of barriers to full economic inclusion in the Indian Ocean's Blue Economy. This qualitative dataset focuses on the Blue Economy as a vehicle for women’s economic empowerment. The initial target interviews were 10 but 8 interviews were conducted. The realisation rate is therefore 80%. Abstract: The Indian Ocean Rim Association (IORA) committed itself to a framework of gender equality through advancing women’s economic empowerment through the Blue Economy, and as such, Women’s Economic Empowerment and Blue Economy are cross-cutting issues across IORA’s Priority Areas (IORA, 2016, IORA 2018b). The vehicle through which to facilitate women’s economic empowerment in IORA is through the key sectors of the Blue Economy, as well as IORA’s key priority areas. To this effect, the notions of both the Blue Economy and Women’s Economic Empowerment are cross-cutting issues that inform IORA’s 2017 – 2021 Action Plan. The Foreign and Commonwealth Office has supported this research to provide IORA member states with technical assistance on the topic “Strengthening women’s economic empowerment in the Blue Economy, specifically in the Indian Ocean†. Specifically, the primary objective was to provide an outcome report that provides technical support on realising the cross-cutting themes of the Blue Economy and Women’s Economic Empowerment as per IORA’s Action Plan 2017 – 2021. This project required a three-pronged approach, drawing on policy analysis; qualitative key informant elite interviews; and quantitative analysis of existing databases, such as the World Bank’s Gender Data Portal to mine and analyse relevant data that speak to factors that impact on women’s economic empowerment within the space of the Blue Economy. Although women have played an important economic role in the Indian Ocean for generations, they face a multitude of barriers to full economic inclusion in the Indian Ocean’s Blue Economy. This qualitative dataset focuses on the Blue Economy as a vehicle for women’s economic empowerment. While the quantitative data will allow one to construct a baseline of women’s inclusion and participation in the Blue Economy, there is a need to engage with those “silent†or “invisible†challenges and opportunities that impact on women’s economic empowerment in the Indian Ocean’s Blue Economy. We used interviews to supplement the qualitative metasynthesis,policy analysis and quantitative analysis. isolate key themes that impact (whether positively or negatively) on women’s economic empowerment in the Indian Ocean’s Blue Economy.

List of AMA (Employment Activities) employers with AMA participants aggregated by year and broken down by employee class, sector, employer address. Labour activities in the social economy started in April 2021, but we do not have full figures for 2021, so data is only available from 2022.Since participants may have several employees, summing up the number of participants in this dataset will not reflect the total number of unique participants.Differences with the figures in the annual report social economy can be explained in number of participants because they know another source, this dataset is based on data that companies themselves pass on to the WSE department, so that there may be no participants.

This table contains data on the components of production, the categories of expenditure and the income components of gross domestic product. Gross domestic product is an important macroeconomic concept. The volume development of gross domestic product is the measure of a country's economic growth. It is customary in national accounts to approach gross domestic product from three points of view: production, expenditure and income.

Data available from 1995 to 2022.

Status of figures: Data from 1995 to 2021 are final. Data for 2022 are provisional. As this table has been discontinued, the data will no longer be finalised.

Changes as of 24 June 2024 None, this table has been discontinued. The Central Bureau of Statistics recently revised the national accounts. New sources, methods and concepts are introduced into the national accounts, so that the picture of the Dutch economy is optimally aligned with all underlying statistics, sources and international guidelines for compiling the national accounts. For more information see section 3.

When will there be new figures? No longer applicable.

About this dataset

Context

The dataset tabulates the population of Economy by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Economy across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.4% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Economy is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Economy total population. 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 Gender. You can refer the same here

Introduction

Warren Edward Buffett (born August 30, 1930) is an American business magnate, investor, and philanthropist. He is currently the chairman and CEO of Berkshire Hathaway. He is one of the most successful investors in the world and has a net worth of over $95 billion as of October 2022, making him the world's sixth-wealthiest person.

Buffett was born in Omaha, Nebraska. He developed an interest in business and investing in his youth, eventually entering the Wharton School of the University of Pennsylvania in 1947 before transferring to and graduating from the University of Nebraska at 19. He went on to graduate from Columbia Business School, where he molded his investment philosophy around the concept of value investing pioneered by Benjamin Graham. He attended the New York Institute of Finance to focus on his economics background and soon after began various business partnerships, including one with Graham. He created Buffett Partnership, Ltd in 1956 and his firm eventually acquired a textile manufacturing firm called Berkshire Hathaway, assuming its name to create a diversified holding company. In 1978, Charlie Munger joined Buffett as vice-chairman.

Buffett has been the chairman and largest shareholder of Berkshire Hathaway since 1970. He is noted for his adherence to value investing, and his personal frugality despite his immense wealth. (Reference- Wikipedia)

Context

Every year Warren Buffet shares his views about the market and the company's (Berkshire Hathway) stock performance for the year. And, also express his views on the investment opportunity going forward, based on the economic condition.

This dataset is a collection of letters written by him from 1977 - 2021.

If you are into Financial analytics or like analyzing financial data, this dataset can provide the type of data analysis from an NLP standpoint based on the facts and inference made by The Great Warren Buffet. A detailed analysis using the text data can provide some pattern towards what is good with the current economy or what is the risk expected by an investor going forward, and a lot more.

I am really excited to share this dataset on Kaggle and expect diversified analysis using this dataset. Hope you will have fun working with this dataset. Happy Learning!

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