Sri Lanka LK: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 39.800 % in 2016. This records an increase from the previous number of 39.200 % for 2012. Sri Lanka LK: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 37.800 % from Dec 1985 (Median) to 2016, with 8 observations. The data reached an all-time high of 41.000 % in 2002 and a record low of 32.400 % in 1990. Sri Lanka LK: Gini Coefficient (GINI Index): World Bank Estimate 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.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. 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.

Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Gini Coefficient data was reported at 0.377 NA in 2019. This records a decrease from the previous number of 0.393 NA for 2016. Gini Coefficient data is updated yearly, averaging 0.377 NA from Dec 1985 (Median) to 2019, with 9 observations. The data reached an all-time high of 0.402 NA in 2002 and a record low of 0.324 NA in 1990. Gini Coefficient data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s Sri Lanka – Table LK.OWID.ESG: Social: Gini Coefficient: Annual.

Sri Lanka: Gini income inequality index: Pour cet indicateur, La Banque mondiale fournit des données pour la Sri Lanka de 1985 à 2019. La valeur moyenne pour Sri Lanka pendant cette période était de 36.89 index points avec un minimum de 32.4 index points en 1990 et un maximum de 40.2 index points en 2002.

LK：基尼系数（GINI系数）：世界银行估计在12-01-2016达39.800%，相较于12-01-2012的39.200%有所增长。LK：基尼系数（GINI系数）：世界银行估计数据按年更新，12-01-1985至12-01-2016期间平均值为37.800%，共8份观测结果。该数据的历史最高值出现于12-01-2002，达41.000%，而历史最低值则出现于12-01-1990，为32.400%。CEIC提供的LK：基尼系数（GINI系数）：世界银行估计数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的斯里兰卡 – 表 LK.世行.WDI：贫困。

Notes: The inequality footprint is broken down into contributions from trade partners with a Gini index of above 0.4, 0.35–0.4, 0.3–0.35, and less 0.3. ARG Argentina, AZE Azerbaijan, BLR Belarus, BOL Bolivia, BRA Brazil, CHL Chile, CHN China, ECU Ecuador, KAZ Kazakhstan, LKA Sri Lanka, LTU Lithuania, MDG Madagascar, MEX Mexico, MYS Malaysia, PHL Philippines, PRY Paraguay, RUS Russia, THA Thailand, TJK Tajikistan, UGA Uganda, UKR Ukraine, VEN Venezuela, ZMB Zambia, ac air conditioner, ch wood charcoal, clo clothes, clov cloves, coc cocoa, cof coffee, cop copper, cot cotton, cw clocks and watches parts, dp dairy products, dum dumpers, ec electronic circuits, ff fresh fruits and juices, gen electric generators, io iron ores, jew jewellery, ma maize, med medical articles and instruments, mob mobile, mot electric motors and it's parts, mt canning meat, n.e.s. not elsewhere specified, nb niobium ore, ng natural gas, pg petroleum gas, pm printing machine, po petroleum oil, pt part of telephone, pu chemical wood pulp, rc milling rice, ref refrigerators, ros roses, rubb natural rubber, rw railway parts, sb soya bean, sc solar cell, scr monitors and projectors, sf seafood, sug cane or beet sugar, tel telephone, tob tobacco, tr live tree, tra tractors parts and accessories, tx textiles, va vanilla, veg vegetables, wd wood in rough.This Table Ranked List of Countries as in Fig. 3 but with Detail on Inequality-Implicated Commodities and the Labour Embodied in Imports from Countries that have a Gini Index above 0.4.

基尼系数在12-01-2019达0.377NA，相较于12-01-2016的0.393NA有所下降。基尼系数数据按年更新，12-01-1985至12-01-2019期间平均值为0.377NA，共9份观测结果。该数据的历史最高值出现于12-01-2002，达0.402NA，而历史最低值则出现于12-01-1990，为0.324NA。CEIC提供的基尼系数数据处于定期更新的状态，数据来源于Our World in Data，数据归类于全球数据库的斯里兰卡 – Table LK.OWID.ESG: Social: Gini Coefficient: Annual。

Abstract

This survey provides information on household income and expenditure to be able to measure the levels and changes in the living condition of the people, to observe the consumption patterns and to compute various other indicators such as poverty, food ratio, gini co-efficient of income and expenditure etc. Key objectives of the survey - To identify the income patterns in Urban, Rural, Estate Sector and Districts. - To identify the income patterns by income levels. - Average consumption of food items and non food items. - Expenditure patterns by sector and by different income levels. - To identify the incidence of poverty by sector and income levels

Geographic coverage

National coverage. For this survey a sample of buildings and the occupants therein was drawn from the whole island.

Analysis unit

Household, Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design of the survey is two stage stratified and the Urban, Rural and the Estate sectors in each district of the country are the selection domains thus the district is the main domain used for the stratification. The sampling frame is the list of housing units prepared for the Census of Population and Housing (CPH) 2011.

Selection of Primary Sampling Units Primary sampling units (PSUs) are the census blocks selected for the survey. The sampling frame, which is the collection of all the census blocks prepared in CPH 2011 in Sri Lanka, is used for the selection of the PSUs at the first stage of the selection. The PSU selection is done within all the independent- selection domains that are assigned different sample size allocations to total the targeted sample size of 2,500 PSUs. The method of selection of the PSUs at the first stage is systematic with a selection probability given to each census block proportionate to the number of housing units available in the census blocks within the selection domains (PPS). The selected PSUs are updated to include newly built housing units and to exclude demolished or vacated housing units, which are no longer considered as housing units according to the survey definitions, to capture variation of natural growth and to make necessary adjustments for the same. The PSU updating operation in field is generally done less than one month prior to the survey and it was carried out for the 12 months starting from October 2015 to September 2016 to support the scheduled 12 survey months started from January to December in 2016 for the HIES 2016.

Selection of Secondary Sampling Units Secondary Sampling Units (SSUs) or Final sampling units (FSUs) are the housing units selected at the second stage from the 2,500 PSUs selected at the first stage. From each PSU, 10 SSUs (housing units) are systematically selected giving each housing unit in the PSU an equal probability to be selected for the survey. The total sample of size 25,000 housing units is resulted at the end of the sampling process and this sample represents the whole country in different probabilities depend on the different sample sizes allocated for the selection domains.

Mode of data collection

Face-to-face [f2f]

Research instrument

All the Questionnaires are included in the final report of HIES 2016

Cleaning operations

No content available

Response rate

See page number 2 for further details

In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 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 2023. 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.