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

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

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

This dataset provides a detailed view of South Asian countries' socio-economic, environmental, and governance metrics from 2000 to 2023. It compiles key indicators like GDP, unemployment, literacy rates, energy use, governance measures, and more to facilitate a comprehensive analysis of each country’s growth, stability, and development trends over the years. The data covers Bangladesh, Bhutan, India, Pakistan, Nepal, Sri Lanka, Afghanistan, and Maldives.

Key Indicators Economic Metrics: Includes GDP (both total and per capita in USD), annual GDP growth rates, inflation, and foreign direct investment. These metrics offer insight into economic health, growth rate, and international investment trends across the region. Employment and Trade: Tracks unemployment rates as a percentage of the labor force and trade (as a percentage of GDP), helping assess workforce stability and international commerce engagement. Income and Poverty: Features the Gini index (for income inequality) and poverty headcount ratio at $2.15/day, showing income distribution and poverty levels. These indicators reveal disparities and poverty within each country. Population Statistics: Includes total population, annual population growth, and urban population percentage, capturing demographic trends and urbanization rates. Social Indicators: Covers literacy rates, school enrollment in primary education, life expectancy at birth, infant mortality rates, and access to electricity, basic water, and sanitation services. These data points help measure the population’s health, education levels, and access to essential services. Environmental and Energy Metrics: Tracks CO2 emissions, PM2.5 air pollution, renewable energy consumption, and forest area. This environmental data is crucial for analyzing air quality, sustainable energy use, and forest coverage trends. Governance Indicators: Includes metrics such as control of corruption, political stability, regulatory quality, rule of law, and voice and accountability. These indicators reflect each country’s governance quality and institutional stability. Digital and Technological Growth: Measures internet usage rates, research and development spending, and high-technology exports. These statistics indicate digital access, innovation, and technological progress. This dataset, sourced from the World Bank DataBank, provides a robust foundation for studying South Asia's socio-economic, environmental, and governance progress. By analyzing these diverse indicators, researchers and policymakers can gain a deeper understanding of the region’s development path and identify areas that need improvement.

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.

The gross domestic product (GDP) of Papua New Guinea (PNG) grew at an annual average rate of nearly 7% between 2007 and 2010, and is expected to perform even better in 2011. Moreover, the economy remained unaffected even at the peak of the global economic crisis, when most other major Southeast Asian and Pacific economies recorded low or negative GDP growth rates. Sound macroeconomic management in the recent past and planned initiatives such as the PNG LNG Project indicate that the economy will continue to perform well in the medium to long run. Nevertheless, the country faces a number of development challenges. Per capita GDP and its growth rate remain low. The economy is heavily dependent on the mining and resource sectors, and hence remains vulnerable to fluctuations in the global markets. A majority of the people in the labor force work in the informal sector, and opportunities for productive employment in the formal sector continue to grow very slowly. Provision of public services, including education, health, and safe drinking water and sanitation, remains inadequate, especially in the rural areas.

I built this dataset to answer one big question: Can people in developing regions be happier without being rich? I combined data from trusted global reports to compare happiness, education, and money in 14 South Asian and Middle Eastern countries.

What’s Inside?

• Happiness Scores (0–10 scale from the 2023 World Happiness Report)
• Education Stats: Literacy rates, school enrollment (offline), and % of people using online learning (UNESCO + government surveys)
• Money Metrics: GDP per person, average income, unemployment, and poverty rates (World Bank)
• Social Support: How much people feel helped by friends/family

Why These Countries?

• Places like India and Bangladesh have booming online education but low incomes.
• Gulf nations like Qatar and UAE are rich but score lower on social freedom.
• Afghanistan and Lebanon show how wars and crises crush happiness.

Cool Things You Can Do

1. Compare “happy poor” vs. “unhappy rich” countries:
   • Nepal (happiness = 5.269 | GDP = $1,380) vs. Saudi Arabia (happiness = 6.494 | GDP = $24,500)
2. Test if online education beats traditional schools:
   • UAE has 38.2% online learning access vs. Pakistan’s 11.8%
3. Find hidden patterns: Why does Sri Lanka have 92.3% literacy but high poverty (25.6%)?

Data Sources

• Happiness Scores: World Happiness Report 2023
• Education & Economy: World Bank and UNESCO (2023 estimates)
• Missing Data: Afghanistan’s GDP/income stats are blank due to Taliban rule.

Who Should Use This?

• Teachers studying education’s role in happiness
• Economists exploring “money vs. joy” debates
• Students learning data analysis with real-world problems

Pro Tip: Use maps to compare regions! Saudi Arabia’s happiness (6.494) is double Afghanistan’s (1.859).

This dataset supersedes the version in https://doi.org/10.25909/5e93ff29cd66b. Added to version 2 is the R script that runs the reforestation scenarios for the study. Version 3 contains an updated landuse constraint layer.

The maps in this dataset were produced from existing datasets to determine the climate mitigation potential of reforestation in Southeast Asia under various constraints, namely biophysical, financial, land-use and operational constraints through to the year 2030. This was done for three main forest types: peatswamp, mangrove and terrestrial forests. All calculations were based on data dated between 2013–2019 and at a resolution of 0.01 degrees (~1 km).

Biophysical constraints. Biophysical constraints were firstly determined by identifying degraded forest areas: maximum threshold of 35 MgCha^-1 above-ground carbon for terrestrial forests^1,2, indications of clearings for peatswamp forests^3,4 and changes in Landsat pixels over time for mangrove forests⁵ from a pantropical above-ground carbon layer⁶. We then focus on degraded areas that are low in biomass due to natural biophysical settings, by masking out ‘forest’ or ‘woodland’ areas that were previously identified as degraded from the Potential Natural Vegetation (PNV) map⁷. We also masked out current landcover areas that would preclude reforestation, such as bare ground, industrial land, large scale agriculture, water and urban areas^8,9. Lastly, we estimated the climate mitigation potential of each raster cell in the biophysical constraint layer based on the different forest types and subtypes according to the PNV map and IPCC classifications^3,5,7,10. This was calculated as the sum of carbon dioxide likely to be sequestered due to aboveground biomass growth and avoided business-as-usual (BAU) flux annualised to 2030 (see Table S3 for details and key references). Climate mitigation potential for areas of smallholder agriculture – defined as agricultural areas of less than 2 ha – identified within the layer nevertheless, were taken as forests and its carbon gain was calculated as the difference between croplands and natural forests¹¹.

Financial constraints. Financial constraints were determined by two components: direct cost of reforestation and the opportunity cost based on revenue lost from agricultural production. Direct costs of reforestation (including planning, planting and maintenance) across Southeast Asia were specified by forest type^12,13 and adjusted to each country based on relative hourly wages¹⁴ and gross domestic product per capita¹⁵. The opportunity cost based on revenue lost from agricultural production in Southeast Asia were derived from spatially explicit crop rents of the 17 most economically important crops based on production in 2017, considering only crops produced in >1% of the country’s land area¹⁶. The maximum crop rent for each cell was then identified, indicating the maximum agriculture revenue lost due to reforestation. All costs were adjusted to 2018 USD. The low estimate of reforestation costs was based purely on direct cost. The moderate estimate was based on both direct and opportunity cost from foregone agricultural rent weighted by crop development potential index¹⁷. The high estimate was based on the direct and full opportunity cost. We thus calculated the cost of reforestation per ton of carbon dioxide equivalent mitigated, utilising the biophysical constraints layer and omitting all areas > 100 USD MgCO2e^-1 to limit reforestation to cost-effective areas^18,19,20.

Land-use constraints. There are two levels of land-use constraints: more permissive one, which only excluded reforestation on smallholder agriculture lands (any raster cell that possessed agriculture lands ≤ 2 ha) with high estimated yield¹⁷, and a less permissive one which excluded reforestation on all smallholder agriculture lands.

Operational constraints. Four operational constraints were applied to account for the practical considerations that may influence the long-term viability of reforested sites. These include proximity to seed sources (SS), protection status (PA), deforestation risk (DR) and accessibility for monitoring and management (AM). SS was determined by utilising a 2-km buffer from the nearest existing forest edge as a proxy for propagule sources^21-24 to support natural regeneration. Reforestation and thus climate mitigation potential is thus constrained to areas in relative proximity to seed sources. For PA, we constrained reforestation to legally protected areas²⁵, namely those of IUCN categories I-VI, estimating the climate mitigation potential in areas with some form of protection status. For DR, we constrained reforestation to areas with acceptable likelihood of transition to deforested areas i.e. ≥ 0.5 probability of deforestation²⁶ (medium to high potential) from a spatially explicit layer predicting tree cover loss to 2029, estimating the climate mitigation potential in areas with acceptable deforestation risk. We also considered AM to account for the need for continued monitoring and management associated with post-planting site upkeep, thus, limiting reforestation areas to within a day’s travelling time to the nearest cities²⁷ and estimated the climate mitigation potential for these areas.

Uncertainties across estimations of climate mitigation potential were derived from the range of values associated with the aboveground carbon gain and the BAU flux reported in our literature review (see Table S3 for details), where the minimum and maximum climate mitigation potential across each forest type were calculated for each specific study^10,28 or collated across a number of studies^29-31. This produced a total of 111 maps, which represented the mean, minimum and maximum climate mitigation potential of each of the constrained reforestation estimations.

Four reforestation scenarios were then analysed using the derived outputs, namely 1) an independent scenario where each constraint is considered separately 2) full contingent scenario with all constraints are sequentially applied, 3) moderate contingent scenario 1, where we consider a moderate cost estimate, and 4) moderate contingent scenario 2 which applies a more permissive land-use constraint.

Further details for this dataset are presented in Zeng et. al.

τ (tau) = Kendall’s rank correlation coefficient.p =  p value.