World Countries is a detailed layer of country level boundaries which is best used at large scales (e.g. below 1:2m scale). For a more generalized layer to use at small-to-medium scales, refer to the World Countries (Generalized) layer. It has been designed to be used as a layer that can be easily edited to fit a users needs and view of the political world. Included are attributes for name and ISO codes, along with continent information. Particularly useful are the Land Type and Land Rank fields which separate polygons based on their areal size. These attributes are useful for rendering at different scales by providing the ability to turn off small islands which may clutter small scale views.This dataset represents the world countries as they existed in January 2015.

World Countries provides a detailed basemap layer for the country boundaries of the world. This layer has been designed to be used as a basemap and includes fields for official names and country codes, along with fields for continent and display. Particularly useful are the fields LAND_TYPE and LAND_RANK that separate polygons based on their size. These fields are helpful for rendering at different scales by providing the ability to turn off small islands that may clutter small-scale views. The data is sourced from Garmin International, Inc. with additional content from the U.S. Central Intelligence Agency (The World Factbook), and International Organization for Standardization (ISO). This layer was published in October 2024 and is updated every 12-18 months or as significant changes occur.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

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

The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Average income of small-scale food producers, PPP (constant 2011 international $)





  Dataset Description

This dataset provides country-level data for the indicator "2.3.2 Average income of small-scale food producers, PPP (constant 2011 international $)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/average-income-of-small-scale-food-producers-ppp-for-african-countries.

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.

This dataset was created in order to document self-reported life evaluations among small-scale societies that exist on the fringes of mainstream industrialized socieities. The data were produced as part of the LICCI project, through fieldwork carried out by LICCI partners. The data include individual responses to a life satisfaction question, and household asset values. Data from Gallup World Poll and the World Values Survey are also included, as used for comparison. TABULAR DATA-SPECIFIC INFORMATION --------------------------------- 1. File name: LICCI_individual.csv Number of rows and columns: 2814,7 Variable list: Variable names: User, Site, village Description: identification of investigator and location Variable name: Well.being.general Description: numerical score for life satisfaction question Variable names: HH_Assets_US, HH_Assets_USD_capita Description: estimated value of representative assets in the household of respondent, total and per capita (accounting for number of household inhabitants) 2. File name: LICCI_bySite.csv Number of rows and columns: 19,8 Variable list: Variable names: Site, N Description: site name and number of respondents at the site Variable names: SWB_mean, SWB_SD Description: mean and standard deviation of life satisfaction score Variable names: HHAssets_USD_mean, HHAssets_USD_sd Description: Site mean and standard deviation of household asset value Variable names: PerCapAssets_USD_mean, PerCapAssets_USD_sd Description: Site mean and standard deviation of per capita asset value 3. File name: gallup_WVS_GDP_pk.csv Number of rows and columns: 146,8 Variable list: Variable name: Happiness Score, Whisker-high, Whisker-low Description: from Gallup World Poll as documented in World Happiness Report 2022. Variable name: GDP-PPP2017 Description: Gross Domestic Product per capita for year 2020 at PPP (constant 2017 international $). Accessed May 2022. Variable name: pk Description: Produced capital per capita for year 2018 (in 2018 US$) for available countries, as estimated by the World Bank (accessed February 2022). Variable names: WVS7_mean, WVS7_std Description: Results of Question 49 in the World Values Survey, Wave 7.

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

Analysis of ‘World Happiness Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/unsdsn/world-happiness on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

Content

The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Inspiration

What countries or regions rank the highest in overall happiness and each of the six factors contributing to happiness? How did country ranks or scores change between the 2015 and 2016 as well as the 2016 and 2017 reports? Did any country experience a significant increase or decrease in happiness?

What is Dystopia?

Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom and least social support, it is referred to as “Dystopia,” in contrast to Utopia.

What are the residuals?

The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2014-2016 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.2 shows the average residual for each country when the equation in Table 2.1 is applied to average 2014- 2016 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.2, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.85 on the 0 to 10 scale.

What do the columns succeeding the Happiness Score(like Family, Generosity, etc.) describe?

The following columns: GDP per Capita, Family, Life Expectancy, Freedom, Generosity, Trust Government Corruption describe the extent to which these factors contribute in evaluating the happiness in each country. The Dystopia Residual metric actually is the Dystopia Happiness Score(1.85) + the Residual value or the unexplained value for each country as stated in the previous answer.

If you add all these factors up, you get the happiness score so it might be un-reliable to model them to predict Happiness Scores.

Start a new kernel

--- Original source retains full ownership of the source dataset ---

NOTE: The included files cover the data and replication code for each of the three working papers that comprise this dissertation. By the time these files are available, it is likely that the author will have updated versions of each of these files. If you are interested in using these data, please contact the author directly or visit his website for the most updated versions. Concerns about domestic authority shape how governments conduct their foreign policies. However, this influence is often difficult to observe in highly opaque, non-democratic political systems. In the first part of the dissertation, I investigate the link between domestic authority and foreign policy in the context of diplomacy and trade in late imperial China, a period that spans the Ming (1368-1644) and Qing (1644-1911) dynasties. I argue that international diplomacy can serve leaders’ domestic political needs when it is highly visible to relevant audiences; conducted with counterparts held in relatively high esteem domestically; when certain diplomatic practices are historically associated with regime authority; or when diplomacy is wielded by leaders with relatively low levels of legitimacy. Using an original dataset of over 5,000 Ming and Qing tribute exchanges, I demonstrate that Chinese emperors newly in power conducted a disproportionately high volume of diplomatic activity. I find weaker evidence that this effect was more salient among low-legitimacy emperors. An accompanying case study illustrates how the Yongle Emperor deployed tribute diplomacy as a tool for domestic authority consolidation. Turning to the trade policies of the same period, I argue that beyond leaders, other autocratic elites who participate in foreign policy making are motivated by similar authority concerns. Extant research on non-democratic trade policy has largely neglected this group of actors. I develop a theory that predicts variation in elite policy preferences based on top-down and bottom-up authority relations with the leader and local trading communities, respectively. To assess these claims, I introduce a dataset on the maritime trade preferences of several hundred individual elite officials in late imperial China created through 10 months of archival work in Beijing and Taipei. The data suggest that coastal provincial officials became key pro-trade advocates during the Qing dynasty. The findings offer an example of how trade preferences can vary within a non-democratic regime, and how historical cases can be especially useful for empirically studying these preferences. In the third paper, the dissertation then flips the focus from the domestic politics of Chinese foreign policy to how other states’ internal politics shape their engagement with contemporary China. I argue that leaders of small developing countries can seek greater domestic authority by acquiring “prestige projects,” defined as highly visible, nationally salient international development projects. After identifying a set of Chinese government-financed prestige projects using a new dataset on Chinese development finance, I show that these projects are overwhelmingly concentrated in the world’s poorest and smallest countries, and that their implementation may be associated with higher public support for recipient governments. I also find that China’s government supplies more prestige projects to states that increase their support for Chinese diplomatic objectives.

The average for 2024 based on 138 countries was 5.56 points. The highest value was in Finland: 7.74 points and the lowest value was in Afghanistan: 1.72 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

This paper analyzes the relationship between the relative size of the small and medium enterprise (SME) Sector and the business environment in 76 countries. The paper first describes a new and unique cross-country database that presents consistent and comparable information on the contribution of the SME sector to total employment in manufacturing and GDP across different countries. We then relate the importance of SMEs and the informal economy to indicators of different dimensions of the business environment. We find that several dimensions of the business environment, such as lower costs of entry and better credit information sharing are associated with a larger size of the SME sector, while higher exit costs are associated with a larger informal economy.

Analysis of ‘World Happiness Report 2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/PromptCloudHQ/world-happiness-report-2019 on 30 September 2021.

--- Dataset description provided by original source is as follows ---

The data has been released by SDSN and extracted by PromptCloud's custom web crawling solution.

Context

The World Happiness Report is a landmark survey of the state of global happiness that ranks 156 countries by how happy their citizens perceive themselves to be. This year’s World Happiness Report focuses on happiness and the community: how happiness has evolved over the past dozen years, with a focus on the technologies, social norms, conflicts and government policies that have driven those changes.

Content

What is Dystopia?

Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive (or zero, in six instances) width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom, and least social support, it is referred to as “Dystopia,” in contrast to Utopia.

What are the residuals?

The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2016-2018 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.7 shows the average residual for each country if the equation in Table 2.1 is applied to average 2016- 2018 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.7, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.88 on the 0 to 10 scale. Table 7 of the online Statistical Appendix 1 for Chapter 2 puts the Dystopia plus residual block at the left side, and also draws the Dystopia line, making it easy to compare the signs and sizes of the residuals in different countries.

Why do we use these six factors to explain life evaluations?

The variables used reflect what has been broadly found in the research literature to be important in explaining national-level differences in life evaluations. Some important variables, such as unemployment or inequality, do not appear because comparable international data are not yet available for the full sample of countries. The variables are intended to illustrate important lines of correlation rather than to reflect clean causal estimates, since some of the data are drawn from the same survey sources, some are correlated with each other (or with other important factors for which we do not have measures), and in several instances there are likely to be two-way relations between life evaluations and the chosen variables (for example, healthy people are overall happier, but as Chapter 4 in the World Happiness Report 2013 demonstrated, happier people are overall healthier). In Statistical Appendix 1 of World Happiness Report 2018, we assessed the possible importance of using explanatory data from the same people whose life evaluations are being explained. We did this by randomly dividing the samples into two groups, and using the average values for .e.g. freedom gleaned from one group to explain the life evaluations of the other group. This lowered the effects, but only very slightly (e.g. 2% to 3%), assuring us that using data from the same individuals is not seriously affecting the results.

Data source: http://worldhappiness.report/ed/2019/

More such datasets can be downloaded from DataStock.

--- Original source retains full ownership of the source dataset ---

United States US: Income Share Held by Lowest 20% data was reported at 5.000 % in 2016. This records a decrease from the previous number of 5.100 % for 2013. United States US: Income Share Held by Lowest 20% data is updated yearly, averaging 5.300 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 6.400 % in 1979 and a record low of 5.000 % in 2016. United States US: Income Share Held by Lowest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Yemen YE: Income Share Held by Lowest 10% data was reported at 3.000 % in 2014. This records a decrease from the previous number of 3.300 % for 2005. Yemen YE: Income Share Held by Lowest 10% data is updated yearly, averaging 3.000 % from Dec 1998 (Median) to 2014, with 3 observations. The data reached an all-time high of 3.300 % in 2005 and a record low of 2.900 % in 1998. Yemen YE: Income Share Held by Lowest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Yemen – Table YE.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

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

United States US: Income Share Held by Lowest 10% data was reported at 1.700 % in 2016. This stayed constant from the previous number of 1.700 % for 2013. United States US: Income Share Held by Lowest 10% data is updated yearly, averaging 1.800 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 2.300 % in 1979 and a record low of 1.700 % in 2016. United States US: Income Share Held by Lowest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Household surveys:

Subnational information from different available household surveys of farmers and smallholders in developing and emerging countries.

Household data provides an overview on farmer households livelihoods, decisions, constraints, among other dimensions. One of the main purposes of this suite of data is to provide farmer information disaggregated at different subnational levels, as well as georeferenced information, when available.

The surveys available in this section provide information divided in ten main dimensions: Production, Consumption, Income, Capital, Inputs, Access to markets, Labor, Technology adoption, Infrastructure, and Social.

Currently available is the Data Portrait of Small Family Farms, more will be added.

Data Portrait:

The Data Portrait of Small Family Farms is a project developed by FAO with the objective to set the ground for a standardized definition of smallholders across countries as well as provide consistent measures of inputs, production, sociodemographic characteristics of smallholder farmers across the world. It generates an image on how small family farmers in developing and emerging countries live their lives, putting in numbers the constraints they face, and the choices they make so that policies can be informed by evidence to meet the challenge of agricultural development.

The Data Portrait of Small Family Farms makes use of household surveys developed by national statistical offices in conjunction with the World Bank as part of its Living Standards Measurement Study (LSMS).

The Data Portrait of Small Family Farms collected data for 19 countries across the world, and for some of them data was reported for more than one round, resulting in a total of 29 surveys. The following table shows the sources of the data. Country and year available information is also presented. Find the link to the table here

This dataset is the aggregation of the following datasets;

1. Household Survey (Data Portrait - Admin 1 - GADM - 1998-2012)
2. Household Survey (Data Portrait - Admin 2 - GADM - 1998-2013)
3. Household Survey (Data Portrait - Admin 3 - GADM - 1992-2013)
4. Household Survey (Data Portrait - Admin 1 - GAUL - 1992-2012)
5. Household Survey (Data Portrait - Admin 2 - GAUL - 1992-2012)

EN.POP.DNST. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.