Weather Conditions and Economic Analysis

Investigating the Relationship between Weather and Economic Activity

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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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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 United States recorded a Government Debt to GDP of 124.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - United States Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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).

Between 2019 and 2020, the number of unemployed people worldwide increased from 191.93 million to 235.21 million, the biggest annual increase in unemployment in this provided time period. In 2022, the number of people unemployed decreased down to 205.25 million.

The data collected aim to test whether English proficiency levels in a country are positively associated with higher democratic values in that country. English proficiency is sourced from statistics by Education First’s "EF English Proficiency Index" which covers countries' scores for the calendar year 2022 and 2021. The EF English Proficiency Index ranks 111 countries in five different categories based on their English proficiency scores that were calculated from the test results of 2.1 million adults. While democratic values are operationalized through the liberal democracy index from the V-Dem Institute annual report for 2022 and 2021. Additionally, the data is utilized to test whether English language media consumption acts as a mediating variable between English proficiency and democracy levels in a country, while also looking at other possible regression variables. In order to conduct the linear regression analyses for the dats, the software that was utilized for this research was Microsoft Excel.The raw data set consists of 90 nation states in two years from 2022 and 2021. The raw data is utilized for two separate data sets the first of which is democracy indicators which has the regression variables of EPI, HDI, and GDP. For this table set there is a total of 360 data entries. HDI scores are a statistical summary measure that is developed by the United Nations Development Programme (UNDP) which measures the levels of human development in 190 countries. The data for nominal gross domestic product scores (GDP) are sourced from the World Bank. Having strong regression variables that have been proven to have a positive link with democracy in the data analysis such as GDP and HDI, would allow the regression analysis to identify whether there is a true relationship between English proficiency and democracy levels in a country. While the second data set has a total of 720 data entries and aims to identify English proficiency indicators the data set has 7 various regression variables which include, LDI scores, Years of Mandatory English Education, Heads of States Publicly speaking English, GDP PPP (2021USD), Common Wealth, BBC web traffic and CNN web traffic. The data for years of mandatory English education is sourced from research at the University of Winnipeg and is coded in the data set based on the number of years a country has English as a mandatory subject. The range of this data is from 0 to 13 years of English being mandatory. It is important to note that this data only concerns public schools and does not extend to the private school systems in each country. The data for heads of state publicly speaking English was done through a video data analysis of all heads of state. The data was only used for heads of state who had been in their position for at least a year to ensure the accuracy of the data collected; with a year in power, for heads of state that had not been in their position for a year, data was taken from the previous head of state. This data only takes into account speeches and interviews that were conducted during their incumbency. The data for each country’s GDP PPP scores are sourced from the World Bank, which was last updated for a majority of the countries in 2021 and is tied to the US dollar. Data for the commonwealth will only include members of the commonwealth that have been historically colonized by the United Kingdom. Any country that falls under that category will be coded as 1 and any country that does not will be coded as 0. For BBC and CNN web traffic that data is sourced by using tools in Semrush which provide a rough estimate of how much web traffic each news site generates in each country. Which will be utilized to identify the average number of web traffic for BBC News and CNN World News for both the 2021 and 2022 calendar. The traffic for each country will also be measured per capita, per 10 thousand people to ensure that the population density of a country does not influence the results. The population of each country for both 2021 and 2022 is sourced from the United Nations revision of World Population Prospects of both 2021 and 2022 respectively.

COVID19-Algeria-and-World-Dataset

A coronavirus dataset with 104 countries constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the COVID-19. The assumptions for the different factors are as follows:

• Geography: some continents/areas may be more affected by the disease
• Climate: cold temperatures may promote the spread of the virus
• Healthcare: lack of hospital beds/doctors may lead to more human losses
• Economy: weak economies (GDP) have fewer means to fight the disease
• Demography: older populations may be at higher risk of the disease

The last column represents the number of daily tests performed and the total number of cases and deaths reported each day.

Data description

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Countries in the dataset by geographic coordinates

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• Europe: 33 countries
• Asia: 28 countries
• Africa: 21 countries
• North America: 11 countries
• South America: 8 countries
• Oceania: 3 countries

Statistical description of the data

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Data distribution

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Download

The dataset is available in an encoded CSV form on GitHub.

Python code

The Python Jupyter Notebook to read and visualize the data is available on nbviewer.

Data update

The dataset is updated every month with the latest numbers of COVID-19 cases, deaths, and tests. The last update was on March 01, 2021.

Data construction

The dataset is constructed from different reliable sources, where each row represents a country, and the columns represent geographic, climate, healthcare, economic, and demographic factors that may contribute to accelerate/slow the spread of the coronavirus. Note that we selected only the main factors for which we found data and that other factors can be used. All data were retrieved from the reliable Our World in Data website, except for data on:

• Continents: www.kaggle.com/statchaitya/country-to-continent
• Geographic-coordinates: www.kaggle.com/eidanch/counties-geographic-coordinates
• Temperatures: www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data
• Share of the population over 65 years old: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS
• GDP/Capita: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

Citation

If you want to use the dataset please cite the following arXiv paper, more details about the data construction are provided in it.

@article{belkacem_covid-19_2020,
  title = {COVID-19 data analysis and forecasting: Algeria and the world},
  shorttitle = {COVID-19 data analysis and forecasting},
  journal = {arXiv preprint arXiv:2007.09755},
  author = {Belkacem, Sami},
  year = {2020}
}

Contact

If you have any question or suggestion, please contact me at this email address: s.belkacem@usthb.dz

Context

The World Happiness Ranking focuses on the social, urban, and natural environment. Specifically, the ranking relies on self-reports from residents of how they weigh the quality of life they are currently experiencing which englobes three main points: current life evaluation, expected future life evaluation, positive and negative affect (emotion). Half of the underlying data comes from multiple Gallup world polls which asked people to give their assessment of the previously mentioned points, and the other half of the data is comprised of six variables that could be used to try to explain the individuals’ perception in their answers.

Content

The data sources’ datasets were obtained in two different formats. The World Happiness Ranking Dataset is a Comma-separated Values (CSV) file with multiple columns (for the different variables and the score) and a row for each of the analyzed countries.

The rankings of national happiness are based on a Cantril ladder survey. Nationally representative samples of respondents are asked to think of a ladder, with the best possible life for them being a 10, and the worst possible life being a 0. They are then asked to rate their own current lives on that 0 to 10 scale. The report correlates the results with various life factors.

1. GDP per capita is in terms of Purchasing Power Parity (PPP) adjusted to constant 2011 international dollars, taken from the World Development Indicators (WDI) released by the World Bank on November 28, 2019. See Statistical Appendix 1 for more details. GDP data for 2019 are not yet available, so we extend the GDP time series from 2018 to 2019 using country-specific forecasts of real GDP growth from the OECD Economic Outlook No. 106 (Edition November 2019) and the World Bank’s Global Economic Prospects (Last Updated: 06/04/2019), after adjustment for population growth. The equation uses the natural log of GDP per capita, as this form fits the data significantly better than GDP per capita.
2. The time series of healthy life expectancy at birth are constructed based on data from the World Health Organization (WHO) Global Health Observatory data repository, with data available for 2005, 2010, 2015, and 2016. To match this report’s sample period, interpolation and extrapolation are used. See Statistical Appendix 1 for more details.
3. Social support is the national average of the binary responses (0=no, 1=yes) to the Gallup World Poll (GWP) question, “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”
4. Freedom to make life choices is the national average of binary responses to the GWP question, “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
5. Generosity is the residual of regressing the national average of GWP responses to the question, “Have you donated money to a charity in the past month?” on GDP per capita.
6. Perceptions of corruption are the average of binary answers to two GWP questions: “Is corruption widespread throughout the government or not?” and “Is corruption widespread within businesses or not?” Where data for government corruption are missing, the perception of business corruption is used as the overall corruption-perception measure.
7. Positive affect is defined as the average of previous-day affect measures for happiness, laughter, and enjoyment for GWP waves 3-7 (years 2008 to 2012, and some in 2013). It is defined as the average of laughter and enjoyment for other waves where the happiness question was not asked. The general form for the affect questions is: Did you experience the following feelings during a lot of the day yesterday? See Statistical Appendix 1 for more details.
8. Negative affect is defined as the average of previous-day affect measures for worry, sadness, and anger in all years.

Acknowledgements

The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by data from the Gallup World Poll, and supported by the Ernesto Illy Foundation, illycaffè, Davines Group, Blue Chip Foundation, the William, Jeff, and Jennifer Gross Family Foundation, and Unilever’s largest ice cream brand Wall’s.

Inspiration

Find the relationship between the ladder score and the other pieces of data.

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.

Dataset Overview 📝

The dataset includes the following key indicators, collected for over 200 countries:

• Government Expenditure on Education (% of GDP): Shows the percentage of a country’s GDP allocated to education.
• Literacy Rate (Adult Total): Represents the percentage of the population aged 15 and above who can read and write.
• Primary Completion Rate: The percentage of children who complete their primary education within the official age group.
• Pupil-Teacher Ratio (Primary and Secondary Education): Indicates the average number of students per teacher at the primary and secondary levels.
• School Enrollment Rates (Primary, Secondary, Tertiary): Reflects the percentage of the relevant age group enrolled in schools across different education levels.

Data Source 🌐

World Bank: This dataset is compiled from the World Bank's educational database, providing reliable, updated statistics on educational progress worldwide.

Potential Use Cases 🔍 This dataset is ideal for anyone interested in:

Educational Research: Understanding how education spending and policies impact literacy, enrollment, and overall educational outcomes. Predictive Modeling: Building models to predict educational success factors, such as completion rates and literacy. Global Education Analysis: Analyzing trends in global education systems and how different countries allocate resources to education. Policy Development: Helping governments and organizations make data-driven decisions regarding educational reforms and funding.

Key Questions You Can Explore 🤔

How does government expenditure on education correlate with literacy rates and school enrollment across different regions? What are the trends in pupil-teacher ratios over time, and how do they affect educational outcomes? How do education indicators differ between low-income and high-income countries? Can we predict which countries will achieve universal primary education based on current trends?

Important Notes ⚠️ - Missing Data: Some values may be missing for certain years or countries. Consider using techniques like forward filling or interpolation when working with time series models. - Data Limitations: This dataset provides global averages and may not capture regional disparities within countries.

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).

In July 2024, the global merchandise imports index, excluding the U.S., stood at 192.6. This is compared to a value of 121 for the United States in the same period. In emerging economies, it reached an index level of nearly 291.4.The merchandise imports index is the U.S. dollar value of goods bought from the rest of the world, deflated by the U.S. Consumer Price Index (CPI).

This is the latest version of the Global VAR (GVAR) dataset. The GVAR is a global modelling framework for analyzing the international macroeconomic transmission of shocks, taking into account drivers of economic activity, interlinkages and spillovers between different countries, and the effects of unobserved or observed common factors. This dataset includes quarterly macroeconomic variables for 33 economies (log real GDP, y, the rate of inflation, dp, short-term interest rate, r, long-term interest rate, lr, the log deflated exchange rate, ep, and log real equity prices, eq), as well as quarterly data on commodity prices (oil prices, poil, agricultural raw material, pmat, and metals prices, pmetal), over the 1979Q2 to 2016Q4 period. These 33 countries cover more than 90% of world GDP. You can download the data, as well as a description of the compilation, revision and updating of the GVAR Database, from here: http://www.econ.cam.ac.uk/people-files/faculty/km418/research.html#gvar

It would be appreciated if use of the updated dataset could be acknowledged as: “Mohaddes, K. and M. Raissi (2018). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2016Q4. University of Cambridge: Faculty of Economics (mimeo)”.

India recorded a Government Debt to GDP of 81.59 percent of the country's Gross Domestic Product in 2023. This dataset provides - India Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Results from ML estimation of measurement models.

Unemployment Rate in the United States increased to 4.30 percent in August from 4.20 percent in July of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The United States recorded a trade deficit of 78.31 USD Billion in July of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Unemployment Rate in South Africa increased to 33.20 percent in the second quarter of 2025 from 32.90 percent in the first quarter of 2025. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Ghana decreased to 11.50 percent in August from 12.10 percent in July of 2025. This dataset provides - Ghana Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.