End poverty in all its forms everywhere : Poverty in the Pacific is focused on hardship and lack of economic opportunity and social exclusion. While food and extreme poverty remains relatively low, an estimated one in four Pacific islanders are likely to be living below their country’s basic-needs poverty line (BNPL). Children are especially vulnerable to poverty and inequality because of their dependency on adults for care and protection, and for food. Deprivation and lost opportunities in childhood can have detrimental effects that may persist throughout a child’s life. If a child does not receive adequate nutrition, stunting may result, and intellectual development may be impaired. Poorly nourished children are more vulnerable to disease, tend to perform worse in school, and less likely to be productive adults.

Find more Pacific data on PDH.stat.

Description for the Dataset

Title: Comprehensive Socio-Economic and Environmental Dataset of Bangladesh 1980-2023

Description:
This dataset provides an extensive overview of Bangladesh's socio-economic, demographic, and environmental indicators over time. It encompasses a wide array of features, including literacy rates, population statistics, economic growth metrics, trade balances, environmental indicators, healthcare spending, and poverty rates. The dataset aims to facilitate research and analysis on Bangladesh's development trends, policy impacts, and sustainability challenges.

Key Features: - Population and Demographics: Includes total population, growth rates, population density, birth/death rates, infant mortality rates, fertility rates, urban and rural population distributions, and migration statistics.
- Economic Indicators: GDP, GNP, GNI, trade balances, export and import metrics, inflation rates, unemployment rates, labor force participation, and foreign direct investment.
- Poverty and Social Metrics: National, rural, and urban poverty rates, literacy rates, healthcare spending, and maternal mortality rates.
- Environmental Metrics: Tree cover loss, carbon emissions, renewable energy usage, deforestation causes, and greenhouse gas emissions.
- Infrastructure and Development: Access to electricity and clean water, arable land, private vehicles, and tourism spending.
- Crime and Defense: Crime rates, homicide rates, and military spending.
- Education: Education spending as a percentage of GDP and youth unemployment rates.

Intended Use:
This dataset is designed for data analysis, trend forecasting, and machine learning applications. It is suitable for researchers, policymakers, and analysts studying socio-economic development, environmental sustainability, and public policy in Bangladesh.

Source and Methodology:
The dataset aggregates publicly available statistics from reliable sources, including government reports, international organizations, and research publications. It has been curated and processed to ensure consistency and usability.

Potential Applications:
- Analyzing the impact of socio-economic policies on literacy and poverty rates.
- Forecasting demographic and economic growth trends.
- Exploring the relationship between environmental changes and economic activities.
- Studying the effects of urbanization and migration on rural-urban dynamics.

License:
CC BY-SA 4.0

Keywords:
Bangladesh, Socio-Economic Indicators, Environmental Metrics, Development Trends, Poverty Rates, Literacy Rates, GDP, Carbon Emissions, Renewable Energy, Migration.

United States US: Government-Financed GERD: % of GDP data was reported at 0.650 % in 2022. This records a decrease from the previous number of 0.654 % for 2021. United States US: Government-Financed GERD: % of GDP data is updated yearly, averaging 0.786 % from Dec 1981 (Median) to 2022, with 42 observations. The data reached an all-time high of 1.245 % in 1985 and a record low of 0.650 % in 2022. United States US: Government-Financed GERD: % of GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Gross Domestic Expenditure on Research and Development: OECD Member: Annual.

For the United States, from 2021 onwards, changes to the US BERD survey questionnaire allowed for more exhaustive identification of acquisition costs for ‘identifiable intangible assets’ used for R&D. This has resulted in a substantial increase in reported R&D capital expenditure within BERD. In the business sector, the funds from the rest of the world previously included in the business-financed BERD, are available separately from 2008. From 2006 onwards, GOVERD includes state government intramural performance (most of which being financed by the federal government and state government own funds). From 2016 onwards, PNPERD data are based on a new R&D performer survey. In the higher education sector all fields of SSH are included from 2003 onwards.

Following a survey of federally-funded research and development centers (FFRDCs) in 2005, it was concluded that FFRDC R&D belongs in the government sector - rather than the sector of the FFRDC administrator, as had been reported in the past. R&D expenditures by FFRDCs were reclassified from the other three R&D performing sectors to the Government sector; previously published data were revised accordingly. Between 2003 and 2004, the method used to classify data by industry has been revised. This particularly affects the ISIC category “wholesale trade” and consequently the BERD for total services.

U.S. R&D data are generally comparable, but there are some areas of underestimation:

1. i) Up to 2008, Government sector R&D performance covers only federal government activities. That by State and local government establishments is excluded;
2. ii) Except for the Government and the Business Enterprise sectors, the R&D data exclude most capital expenditures. For the Business Enterprise sector, depreciation is reported in place of gross capital expenditures up to 2014. Higher education (and national total) data were revised back to 1998 due to an improved methodology that corrects for double-counting of R&D funds passed between institutions.

Breakdown by type of R&D (basic research, applied research, etc.) was also revised back to 1998 in the business enterprise and higher education sectors due to improved estimation procedures.

The methodology for estimating researchers was changed as of 1985. In the Government, Higher Education and PNP sectors the data since then refer to employed doctoral scientists and engineers who report their primary work activity as research, development or the management of R&D, plus, for the Higher Education sector, the number of full-time equivalent graduate students with research assistantships averaging an estimated 50 % of their time engaged in R&D activities. As of 1985 researchers in the Government sector exclude military personnel. As of 1987, Higher education R&D personnel also include those who report their primary work activity as design.

Due to lack of official data for the different employment sectors, the total researchers figure is an OECD estimate up to 2019. Comprehensive reporting of R&D personnel statistics by the United States has resumed with records available since 2020, reflecting the addition of official figures for the number of researchers and total R&D personnel for the higher education sector and the Private non-profit sector; as well as the number of researchers for the government sector. The new data revise downwards previous OECD estimates as the OECD extrapolation methods drawing on historical US data, required to produce a consistent OECD aggregate, appear to have previously overestimated the growth in the number of researchers in the higher education sector.

Pre-production development is excluded from Defence GBARD (in accordance with the Frascati Manual) as of 2000. 2009 GBARD data also includes the one time incremental R&D funding legislated in the American Recovery and Reinvestment Act of 2009. Beginning with the 2000 GBARD data, budgets for capital expenditure – “R&D plant” in national terminology - are included. GBARD data for earlier years relate to budgets for current costs only.

Among respondents who report having no account, the percentage who report not having a financial institution account because they do not trust financial institutions.

Part I: GermanyBy means of a statistical analysis, this study examines the question whether there are long-term fluctuations in the course of the economic development in Germany, and if so, why they occur. On the grounds of a discussion about the hypothesises of lack of capital, overproduction and innovation, an explanatory model for the growth waves has been developed. This model, which is based on an empirical analysis, can be summarised as follows: The long-term development of the national product is mainly determined by the development of investments, which depend on the development of the profit expectations in their turn. In this respect, the development of wages, national consumption, and protection are considered important factors for the definition of long-term profit expectations. Hereby the above-mentioned model is empirically tested. Eventually some economic conclusions are drawn. Part II: An International ComparisonIn the 1970s, the process of global economic growth weakened considerably as compared to the two preceding decades. This development provoked several explanatory attempts. Within the scope of an empiric study for Germany, the slowed growth of the 1970s has been understood as being the downswing phase of a long-term cycle of development. In doing so, the diagnosed development of the national product was mainly explained by long-term fluctuations of the (functional) distribution of income and the governmental activity, which, on their part, caused long-term ups and downs concerning investment activities due to their influence on profit expectations. In fact, the article faced harsh criticism, which was directed at both the explanatory approach and the under-lying empirical method. This study calculates the deviations of streamlined national product series from the long-term trend; its results show that there have been long-term, more or less distinct fluctuations in the development of the national product of several free-market countries other than Germany. According to the available data, different index numbers were applied to the respective national production. The period examined in this study for every country reaches as far back as data are available. With regard to the results of the empirical analysis of the long-term economic development of Germany, France, Italy, Sweden, the United Kingdom, the United States, and the Soviet Union, it can be stated that - long-term fluctuations of the economic development are not merely restricted to Germany, and that a socialistic economic system presumably does not guarantee a continuous growth either;- the cyclical pattern differs from country to country;- there were parallel developments at the international level; however, these do not develop in a synchronous way. Factual classification of the tables in HISTAT:Part I: GermanyPart I: 1. Macroeconomic indicators for the Federal Republic of Germany (1960-1990)Part I: A.1 Net national product and net investments in the Federal Republic of Germany (1850-1990)Part I:A.2 Net national product, net investments, foreign trade values and national consumption (in million D-marks) in Germany (1850-1990)Part I: A.3 Stock yields and profit expectations (in percent) in Germany (1926-1977)Part I: A.4 Actual earnings of employees and unemployment rate (in percent) in Germany (1925-1990)Part I: A.5 The population (in 1,000) in the Federal Republic of Germany and in the German Reich (1850-1913) Part II: International comparisonPart II: A.1.Macroeconomic annual production of selected states (1830-1979)Part II: A.2 Investments of selected states (1830-1979)Part II: A.3 Unemployment rate of selected states (in percent) (1887-1979)

Global Economic, Environmental, Health, and Social indicators Ready for Analysis

📝 Description

This comprehensive dataset merges global economic, environmental, technological, and human development indicators from 2000 to 2020. Sourced and transformed from multiple public datasets via Google BigQuery, it is designed for advanced exploratory data analysis, machine learning, policy modeling, and sustainability research.

Curated by combining and transforming data from the Google BigQuery Public Data program, this dataset offers a harmonized view of global development across more than 40 key indicators spanning over two decades (2000–2020). It supports research across multiple domains such as:

• Economic Growth
• Climate Sustainability
• Digital Transformation
• Public Health
• Human Development
• Resilience and Governance

for formulas and more details check: https://github.com/Michael-Matta1/datasets-collection/tree/main/Global%20Development

📅 Temporal Coverage

• Years: 2000–2020

• Includes calculated features:

  • years_since_2000
  • years_since_century
  • is_pandemic_period (binary indicator for pandemic periods)

🌍 Geographic Scope

• Countries: Global (identified by ISO country codes)
• Regions and Income Groups included for aggregated analysis

📊 Key Feature Groups

• Economic Indicators:

  • GDP (USD), GDP per capita
  • FDI, inflation, unemployment, economic growth index

• Environmental Indicators:

  • CO₂ emissions, renewable energy use
  • Forest area, green transition score, CO₂ intensity

• Technology & Connectivity:

  • Internet usage, mobile subscriptions
  • Digital readiness score, digital connectivity index

• Health & Education:

  • Life expectancy, child mortality
  • School enrollment, healthcare capacity, health development ratio

• Governance & Resilience:

  • Governance quality, global resilience
  • Human development composite, ecological preservation

🔍 Use Cases

• Trend analysis over time
• Country-level comparisons
• Modeling development outcomes
• Predictive analytics on sustainability or human development
• Correlation and clustering across multiple indicators

⚠️ Note on Missing Region and Income Group Data

Approximately 18% of the entries in the region and income_group columns are null. This is primarily due to the inclusion of aggregate regions (e.g., Arab World, East Asia & Pacific, Africa Eastern and Southern) and non-country classifications (e.g., Early-demographic dividend, Central Europe and the Baltics). These entries represent groups of countries with diverse income levels and geographic characteristics, making it inappropriate or misleading to assign a single region or income classification. In some cases, the data source may have intentionally left these fields blank to avoid oversimplification or due to a lack of standardized classification.

📋 Column Descriptions

• year: Year of the recorded data, representing a time series for each country.
• country_code: Unique code assigned to each country (ISO-3166 standard).
• country_name: Name of the country corresponding to the data.
• region: Geographical region of the country (e.g., Africa, Asia, Europe).
• income_group: Income classification based on Gross National Income (GNI) per capita (low, lower-middle, upper-middle, high income).
• currency_unit: Currency used in the country (e.g., USD, EUR).
• gdp_usd: Gross Domestic Product (GDP) in USD (millions or billions).
• population: Total population of the country for the given year.
• gdp_per_capita: GDP divided by population (economic output per person).
• inflation_rate: Annual rate of inflation (price level rise).
• unemployment_rate: Percentage of the labor force unemployed but seeking employment.
• fdi_pct_gdp: Foreign Direct Investment (FDI) as a percentage of GDP.
• co2_emissions_kt: Total CO₂ emissions in kilotons (kt).
• energy_use_per_capita: Energy consumption per person (kWh).
• renewable_energy_pct: Percentage of energy consumption from renewable sources.
• forest_area_pct: Percentage of total land area covered by forests.
• electricity_access_pct: Percentage of the population with access to electricity.
• life_expectancy: Average life expectancy at birth.
• child_mortality: Deaths of children under 5 per 1,000 live births.
• school_enrollment_secondary: Percentage of population enrolled in secondary education.
• health_expenditure_pct_gdp: Percentage of GDP spent on healthcare.
• hospital_beds_per_1000...

Russia Balance (Profit less Loss): BM: RE: Research and Development data was reported at 119,053.000 RUB mn in 2016. This records an increase from the previous number of 98,565.000 RUB mn for 2015. Russia Balance (Profit less Loss): BM: RE: Research and Development data is updated yearly, averaging 46,242.500 RUB mn from Dec 2003 (Median) to 2016, with 14 observations. The data reached an all-time high of 119,053.000 RUB mn in 2016 and a record low of 23,562.000 RUB mn in 2004. Russia Balance (Profit less Loss): BM: RE: Research and Development data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Investment – Table RU.OD009: Enterprises Balance (Profit less Loss): Big and Medium: Annual: by Economic Activity.

The statistic depicts France's real gross domestic product (GDP) growth rate from 2020 to 2024, with projections up until 2030. GDP refers to the total market value of all goods and services that are produced within a country per year. It is an important indicator of the economic strength of a country. Real GDP is adjusted for price changes and is therefore regarded as a key indicator for economic growth. In 2024, France's real GDP grew by about 1.07 percent compared to the previous year. Unemployment in France France has one of the largest economies in the world and is the second largest economy in the European Union, behind Germany, with whom France often partnered in order to support the structure of the European Union. France is also the fourth most populated country in Europe and has maintained slow population growth since the mid 2000s. Despite being not only a European but also a global economic power, France struggled with maintaining a low unemployment rate and experienced a significant increase in unemployment after the 2008 crash, just like many other prominent industrial countries. However, unlike these other nations, unemployment continued to rise well into the 2010s, while the employment situations in neighboring and international countries improved almost every year. The lack of working opportunities is related to the Eurozone crisis that primarily affected southern European countries, such as Spain, Portugal and Italy.

The replication file includes the original survey experiment data set, an R file for the survey experiment, and an R file for the analysis based on the ESS data and the data by Rueda and Stegmueller. ESS data are freely available online on the European Social Survey website (file name: "ESS4-2008, ed. 4.3 - Multilevel Data"). Data by Rueda and Stegmueller are available from the authors and on the Harvard Dataverse (file name: "Replication Data for: The Externalities of Inequality: Fear of Crime and Preferences for Redistribution in Western Europe").

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: Scientific Research and Development Services: Income (Loss) Before Income Taxes (QFR111547USNO) from Q4 2009 to Q2 2025 about gains/losses, R&D, legal, science, professional, finance, tax, corporate, income, services, industry, and USA.

In this paper, we show that state stability exhibits a persistent and robust non-monotonic relationship with economic development. Based on observations in Europe spanning from 1 AD to 2000 AD, regions that have historically experienced either short- or long-duration state rule on average lag behind in their local wealth today, while those that have experienced medium-duration state rule fare better. These findings support the argument that both an absence as well as an excess of state stability are bad for economic development. State instability hinders investment for growth, while too much stability is likely indicative of elite capture and subsequent stagnation of innovation.

The percentage of respondents who report not having a bank or similar financial institution account because they lack the documentation needed to open one, such as an identity card, a wage slip, or the like. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.

Abstract (en): This special topic poll, conducted April 30 to May 6, 1996, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. This poll sought Americans' views on the most important problems facing the United States, their local communities and their own families. Respondents rated the public schools, crime, and drug problems at the national and local levels, their level of optimism about their own future and that of the country, and the reasons they felt that way. Respondents were asked whether they were better off financially than their parents were at their age, whether they expected their own children to be better off financially than they were, and whether the American Dream was still possible for most people. Respondents then compared their expectations about life to their actual experiences in areas such as job security, financial earnings, employment benefits, job opportunities, health care benefits, retirement savings, and leisure time. A series of questions asked whether the United States was in a long-term economic and moral decline, whether the country's main problems were caused more by a lack of economic opportunity or a lack of morality, and whether the United States was still the best country in the world. Additional topics covered immigration policy and the extent to which respondents trusted the federal, state, and local governments. Demographic variables included respondents' sex, age, race, education level, marital status, household income, political party affiliation, political philosophy, voter registration and participation history, labor union membership, the presence of children in the household, whether these children attended a public school, and the employment status of respondents and their spouses. The data contain a weight variable (WEIGHT) that should be used in analyzing the data. This poll consists of "standard" national representative samples of the adult population with sample balancing of sex, race, age, and education. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Persons aged 18 and over living in households with telephones in the contiguous 48 United States. Households were selected by random-digit dialing. Within households, the respondent selected was the adult living in the household who last had a birthday and who was at home at the time of interview. 2009-10-29 First names were removed from the data file. A full product suite including online analysis with question text has been added. The location of the weight variable was also corrected. telephone interviewThe data available for download are not weighted and users will need to weight the data prior to analysis. The data collection was produced by Chilton Research Services of Radnor, PA. Original reports using these data may be found via the ABC News Polling Unit Website.According to the data collection instrument, code 3 in the variable Q909 (Education Level) included respondents who answered that they had attended a technical school.The original data file contained four records per case and was reformatted into a data file with one record per case. To protect respondent confidentiality, respondent names were removed from the data file.The CASEID variable was created for use with online analysis.

Turkey DI: Profit or Loss data was reported at 1,422,135.000 TRY th in Feb 2020. This records an increase from the previous number of 671,588.000 TRY th for Jan 2020. Turkey DI: Profit or Loss data is updated monthly, averaging 1,274,264.000 TRY th from Jan 2012 (Median) to Feb 2020, with 98 observations. The data reached an all-time high of 6,759,626.000 TRY th in Dec 2019 and a record low of 110,729.000 TRY th in Jan 2013. Turkey DI: Profit or Loss data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.KB067: Aggregate Profit-Loss Account: Investment and Development Banks.

One of the most influential ideas in the study of political instability is that income shocks provoke conflict. “State prize” theories argue that higher revenues increase incentives to capture the state. “Opportunity cost” theories argue that higher prices decrease individual incentives to revolt. Both mechanisms are central to leading models of state development and collapse. But are they well-founded? We examine the effects of exogenous commodity price shocks on conflict and coups, and find little evidence in favor of either theory. Evidence runs especially against the state as prize. We do find weak evidence that the intensity of fighting falls as prices rise—results more consistent with the idea that revenues augment state capacity, not prize-seeking or opportunity cost. Nevertheless, the evidence for any of these income-conflict mechanisms is weak at best. We argue that errors and publication bias have likely distorted the theoretical and empirical literature on political instability.

Turkey DI: Non Interest Income: Other Non Interest Income data was reported at 71,723.000 TRY th in Feb 2020. This records an increase from the previous number of 58,592.000 TRY th for Jan 2020. Turkey DI: Non Interest Income: Other Non Interest Income data is updated monthly, averaging 198,434.000 TRY th from Jan 2012 (Median) to Feb 2020, with 98 observations. The data reached an all-time high of 707,993.000 TRY th in Oct 2018 and a record low of 17,039.000 TRY th in Jan 2013. Turkey DI: Non Interest Income: Other Non Interest Income data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.KB067: Aggregate Profit-Loss Account: Investment and Development Banks.

This data has been created by the Regional Australia Institute for the [In]Sight competitive index released in 2012. Modelled on the World Economic Forums Global Competitiveness Report [In]Sight was developed in collaboration with Deloitte Access Economics and combines data from sources including the Australian Bureau of Statistics and the Social Health Atlas of Australia. Both employment rates and the levels of labour force participation are key inputs into the creation of an efficient labour market. Generally long-term unemployment indicates the presence of inherent structural problems which may adversely impact competitiveness. Low labour force participation may reflect low education levels in the region a lack of economic opportunities or an atypical age structure (such as a skew towards retirement age persons).

Russia Loss Amount: OKVED2: BM: Same Period PY=100: Year to Date: Scientific Research and Development data was reported at 154.500 Same Period PY=100 in Nov 2018. This records an increase from the previous number of 134.600 Same Period PY=100 for Oct 2018. Russia Loss Amount: OKVED2: BM: Same Period PY=100: Year to Date: Scientific Research and Development data is updated monthly, averaging 118.200 Same Period PY=100 from Jan 2017 (Median) to Nov 2018, with 23 observations. The data reached an all-time high of 179.100 Same Period PY=100 in Apr 2018 and a record low of 83.600 Same Period PY=100 in Aug 2017. Russia Loss Amount: OKVED2: BM: Same Period PY=100: Year to Date: Scientific Research and Development data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.OD007: Enterprises Balance (Profit less Loss): Big and Medium: ytd: Loss: Same Period PY=100.

Note, 8/11/2020: Please see http://dev.cityofchicago.org/open%20data/data%20portal/2020/08/11/city-owned-property.html for information about changes to this dataset. -- Property currently or historically owned and managed by the City of Chicago. Information provided in the database, or on the City’s website generally, should not be used as a substitute for title research, title evidence, title insurance, real estate tax exemption or payment status, environmental or geotechnical due diligence, or as a substitute for legal, accounting, real estate, business, tax or other professional advice. The City assumes no liability for any damages or loss of any kind that might arise from the reliance upon, use of, misuse of, or the inability to use the database or the City’s web site and the materials contained on the website. The City also assumes no liability for improper or incorrect use of materials or information contained on its website. All materials that appear in the database or on the City’s web site are distributed and transmitted "as is," without warranties of any kind, either express or implied as to the accuracy, reliability or completeness of any information, and subject to the terms and conditions stated in this disclaimer.