This dataset provides key economic indicators for five of the world's largest economies, based on their nominal Gross Domestic Product (GDP) in 2022. It includes the GDP values, population, GDP growth rates, per capita GDP, and each country's share of the global economy.

Columns: Country: Name of the country. GDP (nominal, 2022): The total nominal GDP in 2022, represented in USD. GDP (abbrev.): The abbreviated GDP in trillions of USD. GDP growth: The percentage growth in GDP compared to the previous year. Population: Total population of each country in 2022. GDP per capita: The GDP per capita, representing average economic output per person in USD. Share of world GDP: The percentage of global GDP contributed by each country. Key Highlights: The dataset includes some of the largest global economies, such as the United States, China, Japan, Germany, and India. The data can be used to analyze the economic standing of countries in terms of overall GDP and per capita wealth. It offers insights into the relative growth rates and population sizes of these leading economies. This dataset is ideal for exploring economic trends, performing country-wise comparisons, or studying the relationship between population size and GDP growth.

In July 2024, the merchandise exports index worldwide, excluding the U.S., stood at 204.8. This is compared to an index value of 143 for the United States in the same month. The index was highest in emerging economies, reaching an index score of 353. Moreover, the merchandise imports index was also highest in emerging economies. The merchandise exports index is the U.S. dollar value of goods sold to the rest of the world, deflated by the U.S. Consumer Price Index (CPI).

In July 2024, global industrial production, excluding the United States, increased by 1.5 percent compared to the same time in the previous year, based on three month moving averages. This is compared to an increase of 0.2 percent in advanced economies (excluding the United States) for the same time period. The global industrial production collapsed after the outbreak of COVID-19, but increased steadily in the months after, peaking at 23 percent in June 2021. Industrial growth rate tracks the output production in the industrial sector.

Brazilian and Indian share prices became the highest performing of the major developed and emerging economies as of June 2023, with index values of 235.25 and 230.91 respectively in that month. Conversely, the lowest-performing were China and the Germany, both with index values of 86.98 and 113.04 respectively at this time. The index value is calculated with 2015 values as the baseline (i.e. 2015 = 100).

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.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

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.

The Gross Domestic Product (GDP) in World was worth 111326.37 billion US dollars in 2024, according to official data from the World Bank. This dataset includes a chart with historical data for World GDP.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

A brief description of the provided GDP data for G20 & BRICS countries from 2008 to 2021 in five lines:

The data represents the Gross Domestic Product (GDP) of G20 countries, a group of major economies, over a 15-year period from 2008 to 2022.

It shows the varying economic sizes of G20 nations, with China and the United States consistently having the largest GDP, while smaller economies like Argentina and South Africa have considerably smaller GDPs.

Notably, the COVID-19 pandemic in 2020 had a significant impact, causing some countries' GDPs to contract temporarily before rebounding in 2021.

Japan, despite its size, experienced relatively stable GDP growth, while emerging economies like India and Indonesia demonstrated notable expansion over the years.

The European Union (EU) is not individually listed but represents a significant portion of the global economy, contributing to the overall global GDP figures.

Kuwait KW: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 6.000 % in 2015. This records a decrease from the previous number of 8.700 % for 2014. Kuwait KW: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 8.400 % from Dec 1996 (Median) to 2015, with 16 observations. The data reached an all-time high of 11.000 % in 2001 and a record low of 6.000 % in 2015. Kuwait KW: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kuwait – Table KW.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage. Sample excludes Northeast states and remote islands. In addition, some districts in Assam, Bihar, Jammu and Kashmir, Jharkhand, and Uttar Pradesh were replaced because of security concerns. The excluded areas represent less than 10% of the population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in India was 3,000 individuals.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

The Gross Domestic Product (GDP) in China was worth 18743.80 billion US dollars in 2024, according to official data from the World Bank. The GDP value of China represents 17.65 percent of the world economy. This dataset provides - China GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Global Competitiveness Index (GCI), developed by the World Economic Forum, measures factors influencing productivity and long-term prosperity. The indicators are grouped into 12 pillars: Institutions, Infrastructure, Macroeconomic Environment, Health and Primary Education, Higher Education and Training, Goods Market Efficiency, Labor Market Efficiency, Financial Market Development, Technological Readiness, Market Size, Business Sophistication, and Innovation. The pillars are further organized into three subindexes: Basic Requirements, Efficiency Enhancers, and Innovation and Sophistication Factors. The weight assigned to each subindex in the overall index calculation depends on a country’s stage of development, determined by GDP per capita and the share of exports represented by raw materials.

The Gross Domestic Product (GDP) in Japan was worth 4026.21 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Japan represents 3.79 percent of the world economy. This dataset provides - Japan GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Weather Conditions and Economic Analysis

Investigating the Relationship between Weather and Economic Activity

By [source]

About this dataset

This dataset consists of detailed information about the weather conditions in different cities from one of the official weather websites. It includes several variables including temperature, humidity, pressure, wind speed and direction, precipitation levels, cloud cover etc. which can be used to analyze the correlation between economic activities in these cities and their weather conditions. For example, this data can help us understand how certain types of business like tourism, retail or leisure activities are affected by changes in temperature and humidity levels. Additionally, it allows us to identify which specific kind of weather has more economic impact in a certain region and thus create accurate forecasts which could further improve commercial performances. All in all, this dataset is an invaluable source of information for people interested in understanding the relation between climate dynamics and economic outcomes

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How to use the dataset

• City Name: This column provides the name of the cities covered in this dataset.
• Weather Condition: This column lists the weather conditions associated with each city, such as sunny, cloudy, windy, etc.
• Temperature (C): This column provides the temperature (in Celsius) of each city as provided by official weather sources.
• Population: This column lists the population size (in millions) of each city covered in this dataset.
• GDP Per Capita: This column presents GDP per capita (measured in US Dollars) for each city included in our dataset 6 Economic Activity Index: This index measures economic activity levels for a particular state or region and can be used to analyze how different weather conditions affect economic activities such as tourism, retail, and leisure activities

How to use this dataset?

This dataset can be used to explore relationships between different factors that might influence economic activity levels at a regional level—namely population size and wealth as well as weather condition—or across countries over time and certain seasons or months to identify trends in regional differences between regions regarding their respective economics activities levels due to varying climates or meteorological events . Some specific analysis that could be done includes:

• Use City Name & Weather Condition columns together to calculate correlations between types of weather patterns/conditions seen throughout different locales; temperatures could also potentially be included for more comprehensive data exploration/analysis on climate dynamics - research on how “cold” vs “warm” periods affect local economies overall would also benefit from including these two columns together;

• Analyze Population & Economic Activity Index together - use these variables together to see if any correlation exists between populations sizes within a given region versus their respective economic performance level; other related variables such as GDP Per Capita could also potentially provide valuable insight into how economic activity varies depending on population density;

• Using all 6 columns together would enable even more comprehensive analysis e..g comparing temperatures & storm information versus expected tourist visits data or analyzing effects/correlations between strong winds & droughts versus changes seen within agricultural outputs . With careful combination of all 6 columns you could easily create some interesting models & computations for understanding broad implications which climate dynamics have upon global economics ; conversely you may explore individual cities too!

Research Ideas

• Use this dataset to analyze the correlation between weather conditions and consumer sentiment by comparing customer purchasing decisions in different cities under different weather conditions.
• Use this dataset to identify the optimal temperature for selling certain products, so that retailers can optimize their prices accordingly.
• Use this dataset to study how changes in weather influencers the types of transportation used by the population of a certain city, and help suggest improvements to public systems for better customer experience in changing climate situations

Acknowledgements

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

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The average for 2023 based on 193 countries was -0.07 points. The highest value was in Liechtenstein: 1.61 points and the lowest value was in Syria: -2.75 points. The indicator is available from 1996 to 2023. Below is a chart for all countries where data are available.

Cameroon CM: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data was reported at 12.200 % in 2018. This records an increase from the previous number of 7.100 % for 2014. Cameroon CM: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data is updated yearly, averaging 8.200 % from Dec 1991 (Median) to 2018, with 7 observations. The data reached an all-time high of 12.200 % in 2018 and a record low of 6.000 % in 1991. Cameroon CM: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Social: Health Statistics. Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.

The Global Competitiveness Index (GCI) 4.0, developed by the World Economic Forum, tracks the performance of 140 countries across 12 pillars of competitiveness: Institutions, Infrastructure, ICT Adoption, Macroeconomic Stability, Health, Skills, Product Market, Labor Market, Financial System, Market Size, Business Dynamism, and Innovation Capability. A country’s performance on the overall GCI and each of its components is reported as a ‘progress score’ on a 0-to-100 scale, where 100 represents the ‘frontier’—an ideal state where an issue no longer constrains productivity growth. The GCI 4.0 framework was introduced in the 2018 edition of the Global Competitiveness Report.

The Gross Domestic Product (GDP) in Nigeria was worth 187.76 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Nigeria represents 0.18 percent of the world economy. This dataset provides the latest reported value for - Nigeria GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.