Sri Lanka HIES: Household Income per Month: Per Capita data was reported at 20,527.000 LKR in 2019. This records an increase from the previous number of 16,377.000 LKR for 2016. Sri Lanka HIES: Household Income per Month: Per Capita data is updated yearly, averaging 4,896.000 LKR from Jun 1981 (Median) to 2019, with 11 observations. The data reached an all-time high of 20,527.000 LKR in 2019 and a record low of 180.000 LKR in 1981. Sri Lanka HIES: Household Income per Month: Per Capita data remains active status in CEIC and is reported by Department of Census and Statistics. The data is categorized under Global Database’s Sri Lanka – Table LK.H002: Household Income and Expenditure Survey: Household Income per Month.

The Gross Domestic Product per capita in Sri Lanka was last recorded at 4186.50 US dollars in 2024. The GDP per Capita in Sri Lanka is equivalent to 33 percent of the world's average. This dataset provides - Sri Lanka GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Sri Lanka LK: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 4.800 % in 2016. Sri Lanka LK: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 4.800 % from Dec 2016 (Median) to 2016, with 1 observations. Sri Lanka LK: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Monthly and long-term Sri Lanka GDP Per Capita data: historical series and analyst forecasts curated by FocusEconomics.

Sri Lanka LK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at 5.280 % in 2016. Sri Lanka LK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging 5.280 % from Dec 2016 (Median) to 2016, with 1 observations. Sri Lanka LK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Key information about Sri Lanka GDP Per Capita

• Sri Lanka Gross Domestic Product (GDP) per Capita reached 4,522.000 USD in Dec 2024, compared with 3,804.000 USD in Dec 2023.
• Sri Lanka GDP Per Capita data is updated yearly, available from Dec 1959 to Dec 2024, with an average number of 547.000 USD.
• The data reached an all-time high of 4,522.000 USD in Dec 2024 and a record low of 140.000 in Dec 1962.
• CEIC converts annual GDP per Capita into USD. The Department of Census and Statistics provides GDP per Capita in LKR based on SNA 2008, at 2015 prices. GDP per capita prior to 2015 is based on SNA 2008, at 2010 prices. GDP per capita prior to 2010 is based on SNA 1993, at 2002 prices.

• In the latest reports, Sri Lanka GDP expanded 4.876 % YoY in Jun 2025.
• Sri Lanka Nominal GDP reached 24.613 USD bn in Jun 2025.
• Its GDP deflator (implicit price deflator) increased 5.393 % in Jun 2025.
• Gross Savings Rate of Sri Lanka was measured at 27.208 % in Jun 2025.

In 2024, India’s real gross domestic product (GDP) growth rate was around **** percent, the highest in South Asia. In contrast, Nepal reported the lowest real GDP growth rate in the region at approximately **** percent that year, but it was forecasted to increase to **** percent in 2026.Economy in South Asia In general, South Asia encompasses Sri Lanka, Pakistan, Afghanistan, Bangladesh, Nepal, India and Bhutan. In 2020, India had a GDP of over *** trillion U.S. dollars, while Bangladesh and Sri Lanka followed. The Maldives and Bhutan were among the countries with the lowest GDP in the Asia-Pacific region. In South Asia, the main economic activities include the services sector as well as the industrial and manufacturing sectors.Society in South AsiaFrom the South Asian countries, Bangladesh had the highest share of people living below the poverty line. The Maldives and Sri Lanka exhibited the highest and second-highest GDP per capita among the South Asian countries in 2021.

The Personal Income Tax Rate in Sri Lanka stands at 18 percent. This dataset provides - Sri Lanka Personal Income Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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).

We conducted a baseline and three additional pre-treatment surveys for the full sample at monthly frequency between August and November 2010. We randomly allocated 498 households to a sample in which we continued to conduct monthly surveys and 297 households to a sample in which we conducted quarterly surveys. This both reduced survey costs and allows us at least a partial test of whether survey frequency affects deposit or aggregate savings behavior. For both monthly and quarterly survey groups, we conducted surveys at the defined frequencies through Nov 2011. We then conducted monthly surveys of everyone in Dec 2011 and Jan 2012 and longer term follow-up surveys in July 2012 and Jan 2013. Thus, for the monthly survey sample, we have five pre-treatment, 15 post-treatment surveys – 13 monthly plus the two semi-annual surveys; for the quarterly survey sample have four pre-treatment and eight post-treatment surveys – four at quarterly intervals plus two monthly surveys and the two semi-annual surveys. Six months into the main experiment, we began a series of unbundling experiments whose impact is described in de Mel et al. (2013). The unbundling experiment was conducted in a randomly selected and well-balanced subset of the control and weekly home visit treatment arms. To avoid confounding the primary results, we drop the 192 treatment and 150 control individuals involved in the unbundling exercise as soon as that experiment began. Appendix Table 1 details the timing of the surveys, and shows which surveys are included in the sample we use here. The result is a full 30 months of data for the core sample (92 zones; 18 months at high frequency) and the 12 months prior to the beginning of the unbundling experiment for a subsample of 64 zones. Results are very similar if we use only the sample of 92 zones, but the precision of the short-term estimates is improved by the inclusion of the additional group that receives the core treatment for six months. Our analyses uses individual-level fixed effects and we cluster standard errors at both the zone and individual level using the method developed by Cameron, Gelbach and Miller (2008) as a way of accounting for both the substantial autocorrelation present in high-frequency household data and the effect of local shocks. We undertook this project with the aim of answering the simple but vexing question: what is the root source of money that is newly brought into the formal financial sector? When people begin to use formal savings, what other behaviors in the household change to allow this liquidity to be deposited in a bank? Candidate explanations are that saved capital is substituted from cash in the mattress, that greater discipline from formal savings causes expenditures to decrease, that formal savings come at the cost of informal mutual insurance networks, or that some new source of income is engendered by the savings. The survey was designed with these sources in mind. The heart of the survey instrument is a cash flow analysis for the household and individual being sampled; the selected individual was always the respondent. In order to unpack the headwaters of formal finance, we need to be able to construct balances of financial flows at both the individual and the household level. Thus, our survey was designed to capture monthly liquidity flows in and out of both the overall household and the respondent’s personal finances. Individual members of a household plausibly have better information about their own earnings and transfers than those of other members of the household. Thus, for much of the analysis, we focus on the outcomes of the individual respondent. However, savings decisions are likely made at least partially at the household level in many households, and hence we also make use of the aggregate household income and expenditure data. The enumerators were trained to check that the sources of cash matched the uses of cash for the individual. Where the initial responses yielded differences, the enumerators pointed out the inconsistency and re-asked the income and expenditure questions. The decision to focus much of the survey attention on the activities of the participant him/herself represents a tradeoff. On the one hand, we focus on data the participant certainly knows best. On the other hand, we will be somewhat limited in answering the “headwaters” question if the changes in income, expenditure, or savings come from changes in the behavior of other members of the household. That is, if we identify that increases in savings in banks are associated with increases in transfers from the spouse, we know only indirectly whether the spouse increased his income – and if he did, we do not know how he did so – or or decreased his formal or informal savings. But the aggregate household data allow us to identify the sources of changes in savings arising from income and expenditure patterns of other household members up to a point. In addition to the detailed...

In 2024, approximately 127.8 million people lived in Guangdong province in China. That same year, only about 3.7 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2024. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.

Interaction of chronic illnesses and religion towards the mean per capita income.

One-way ANOVA results of the difference of the chronic illnesses towards income (LKR).