Between 2010 and 2022, Chile's data on the degree of inequality in income distribution based on the Gini coefficient reached 44.9. Although having one of the highest human development indexes in Latin America, Chile's Gini coefficient was still higher than countries like Haiti and El Salvador. Nevertheless, income distribution in this South American country has shown signs of improvement, with the Gini coefficient decreasing in recent periods.

The Gini coefficient measures the deviation of the distribution of income (or consumption) among individuals or households in a given country from a perfectly equal distribution. A value of 0 represents absolute equality, whereas 100 would be the highest possible degree of inequality.

Chile CL: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 43.000 % in 2022. This records a decrease from the previous number of 47.000 % for 2020. Chile CL: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 49.600 % from Dec 1987 (Median) to 2022, with 16 observations. The data reached an all-time high of 57.200 % in 1990 and a record low of 43.000 % in 2022. Chile CL: 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 Chile – Table CL.World Bank.WDI: Social: Poverty and Inequality. 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, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

In 2022, the percentage of income held by the richest 20 percent of the population in Chile was 49.9 percent. Between 1987 and 2022, the figure dropped by 11.5 percentage points, though the decline followed an uneven course rather than a steady trajectory.

Chile CL: Income Share Held by Highest 10% data was reported at 34.500 % in 2022. This records a decrease from the previous number of 37.300 % for 2020. Chile CL: Income Share Held by Highest 10% data is updated yearly, averaging 39.900 % from Dec 1987 (Median) to 2022, with 16 observations. The data reached an all-time high of 47.000 % in 1990 and a record low of 34.500 % in 2022. Chile CL: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

From the total national wealth in Chile in 2022, 80.6 percent belonged to the top ten percent group. Almost half of Chile's wealth, 49.8 percent, was held by the top one percent. On the other hand, the bottom 50 percent had a negative wealth, a total of -0.6 percent. That year, the average personal wealth of the top one percent was valued at over three million U.S. dollars.

Chile CL: Income Share Held by Lowest 20% data was reported at 5.900 % in 2022. This records an increase from the previous number of 4.900 % for 2020. Chile CL: Income Share Held by Lowest 20% data is updated yearly, averaging 4.450 % from Dec 1987 (Median) to 2022, with 16 observations. The data reached an all-time high of 5.900 % in 2022 and a record low of 3.300 % in 1987. Chile CL: 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 Chile – Table CL.World Bank.WDI: Social: Poverty and Inequality. 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, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

The gini index in Chile was forecast to remain on a similar level in 2029 as compared to 2024 with 0.44 points. According to this forecast, the gini will stay nearly the same over the forecast period. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=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 150 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).Find more key insights for the gini index in countries like Paraguay and Argentina.

Abstract

This file contains data on Gini coefficients, cumulative quintile shares, explanations regarding the basis on which the Gini coefficient was computed, and the source of the information. There are two data-sets, one containing the "high quality" sample and the other one including all the information (of lower quality) that had been collected.

The database was constructed for the production of the following paper:

Deininger, Klaus and Lyn Squire, "A New Data Set Measuring Income Inequality", The World Bank Economic Review, 10(3): 565-91, 1996.

This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups’ income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect intertemporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

Geographic coverage

In what follows, we provide brief descriptions of main features for individual countries that are included in the data-base. Without being comprehensive, these notes are intended to indicate some of the considerations underlying our decision to include or exclude certain observations.

Argentina Various permanent household surveys, all covering urban centers only, have been regularly conducted since 1972 and are quoted in a wide variety of sources and years, e.g., for 1980 (World Bank 1992), 1985 (Altimir 1994), and 1989 (World Bank 1992). Estimates for 1963, 1965, 1969/70, 1970/71, 1974, 1975, 1980, and 1981 (Altimir 1987) are based only on Greater Buenos Aires. Estimates for 1961, 1963, 1970 (Jain 1975) and for 1970 (van Ginneken 1984) have only limited geographic coverage and do not satisfy our minimum criteria.

Despite the many urban surveys, there are no income distribution data that are representative of the population as a whole. References to national income distribution for the years 1953, 1959, and 1961(CEPAL 1968 in Altimir 1986 ) are based on extrapolation from national accounts and have therefore not been included. Data for 1953 and 1961 from Weisskoff (1970) , from Lecaillon (1984) , and from Cromwell (1977) are also excluded.

Australia Household surveys, the result of which is reported in the statistical yearbook, have been conducted in 1968/9, 1975/6, 1978/9, 1981, 1985, 1986, 1989, and 1990.

Data for 1962 (Cromwell, 1977) and 1966/67 (Sawyer 1976) were excluded as they covered only tax payers. Jain's data for 1970 was excluded because it covered income recipients only. Data from Podder (1972) for 1967/68, from Jain (1975) for the same year, from UN (1985) for 78/79, from Sunders and Hobbes (1993) for 1986 and for 1989 were excluded given the availability of the primary sources. Data from Bishop (1991) for 1981/82, from Buhman (1988) for 1981/82, from Kakwani (1986) for 1975/76, and from Sunders and Hobbes (1993) for 1986 were utilized to test for the effect of different definitions. The values for 1967 used by Persson and Tabellini and Alesina and Rodrik (based on Paukert and Jain) are close to the ones reported in the Statistical Yearbook for 1969.

Austria: In addition to data referring to the employed population (Guger 1989), national household surveys for 1987 and 1991 are included in the LIS data base. As these data do not include income from self-employment, we do not report them in our high quality data-set.

Bahamas Data for Ginis and shares are available for 1973, 1977, 1979, 1986, 1988, 1989, 1991, 1992, and 1993 in government reports on population censuses and household budget surveys, and for 1973 and 1975 from UN (1981). Estimates for 1970 (Jain 1975), 1973, 1975, 1977, and 1979 (Fields 1989) have been excluded given the availability of primary sources.

Bangladesh Data from household surveys for 1973/74, 1976/77, 1977/78, 1981/82, and 1985/86 are available from the Statistical Yearbook, complemented by household-survey based information from Chen (1995) and the World Development Report. Household surveys with rural coverage for 1959, 1960, 1963/64, 1965, 1966/67 and 1968/69, and with urban coverage for 1963/64, 1965, 1966/67, and 1968/69 are also available from the Statistical yearbook. Data for 1963/64 ,1964 and 1966/67, (Jain 1975) are not included due to limited geographic coverage, We also excluded secondary sources for 1973/74, 1976/77, 1981/82 (Fields 1989), 1977 (UN 1981), 1983 (Milanovic 1994), and 1985/86 due to availability of the primary source.

Barbados National household surveys have been conducted in 1951/52 and 1978/79 (Downs, 1988). Estimates based on personal tax returns, reported consistently for 1951-1981 (Holder and Prescott, 1989), had to be excluded as they exclude the non-wage earning population. Jain's figure (used by Alesina and Rodrik) is based on the same source.

Belgium Household surveys with national coverage are available for 1978/79 (UN 1985), and for 1985, 1988, and 1992 (LIS 1995). Earlier data for 1969, 1973, 1975, 1976 and 1977 (UN 1981) refer to taxable households only and are not included.

Bolivia The only survey with national coverage is the 1990 LSMS (World Development Report). Surveys for 1986 and 1989 cover the main cities only (Psacharopoulos et al. 1992) and are therefore not included. Data for 1968 (Cromwell 1977) do not refer to a clear definition and is therefore excluded.

Botswana The only survey with national coverage was conducted in 1985-1986 (Chen et al 1993); surveys in 74/75 and 85/86 included rural areas only (UN 1981). We excluded Gini estimates for 1971/72 that refer to the economically active population only (Jain 1975), as well as 1974/75 and 1985/86 (Valentine 1993) due to lack of national coverage or consistency in definition.

Brazil Data from 1960, 1970, 1974/75, 1976, 1977, 1978, 1980, 1982, 1983, 1985, 1987 and 1989 are available from the statistical yearbook, in addition to data for 1978 (Fields 1987) and for 1979 (Psacharopoulos et al. 1992). Other sources have been excluded as they were either not of national coverage, based on wage earners only, or because a more consistent source was available.

Bulgaria: Data from household surveys are available for 1963-69 (in two year intervals), for 1970-90 (on an annual basis) from the Statistical yearbook and for 1991 - 93 from household surveys by the World Bank (Milanovic and Ying).

Burkina Faso A priority survey has been undertaken in 1995.

Central African Republic: Except for a household survey conducted in 1992, no information was available.

Cameroon The only data are from a 1983/4 household budget survey (World Bank Poverty Assessment).

Canada Gini- and share data for the 1950-61 (in irregular intervals), 1961-81 (biennially), and 1981-91 (annually) are available from official sources (Statistical Yearbook for years before 1971 and Income Distributions by Size in Canada for years since 1973, various issues). All other references seem to be based on these primary sources.

Chad: An estimate for 1958 is available in the literature, and used by Alesina and Rodrik and Persson and Tabellini but was not included due to lack of primary sources.

Chile The first nation-wide survey that included not only employment income was carried out in 1968 (UN 1981). This is complemented by household survey-based data for 1971 (Fields 1989), 1989, and 1994. Other data that refer either only to part of the population or -as in the case of a long series available from World Bank country operations- are not clearly based on primary sources, are excluded.

China Annual household surveys from 1980 to 1992, conducted separately in rural and urban areas, were consolidated by Ying (1995), based on the statistical yearbook. Data from other secondary sources are excluded due to limited geographic and population coverage and data from Chen et al (1993) for 1985 and 1990 have not been included, to maintain consistency of sources..

Colombia The first household survey with national coverage was conducted in 1970 (DANE 1970). In addition, there are data for 1971, 1972, 1974 CEPAL (1986), and for 1978, 1988/89, and 1991 (World Bank Poverty Assessment 1992 and Chen et al. 1995). Data referring to years before 1970 -including the 1964 estimate used in Persson and Tabellini were excluded, as were estimates for the wage earning population only.

Costa Rica Data on Gini coefficients and quintile shares are available for 1961, 1971 (Cespedes 1973),1977 (OPNPE 1982), 1979 (Fields 1989), 1981 (Chen et al 1993), 1983 (Bourguignon and Morrison 1989), 1986 (Sauma-Fiatt 1990), and 1989 (Chen et al 1993). Gini coefficients for 1971 (Gonzalez-Vega and Cespedes in Rottenberg 1993), 1973 and 1985 (Bourguignon and Morrison 1989) cover urban areas only and were excluded.

Cote d'Ivoire: Data based on national-level household surveys (LSMS) are available for 1985, 1986, 1987, 1988, and 1995. Information for the 1970s (Schneider 1991) is based on national accounting information and therefore excluded

Cuba Official information on income distribution is limited. Data from secondary sources are available for 1953, 1962, 1973, and 1978, relying on personal wage income, i.e. excluding the population that is not economically active (Brundenius 1984).

Czech Republic Household surveys for 1993 and 1994 were obtained from Milanovic and Ying. While it is in principle possible to go back further, splitting national level surveys for the former Czechoslovakia into their independent parts, we decided not to do so as the same argument could be used to

Based on the degree of inequality in income distribution measured by the Gini coefficient, Colombia was the most unequal country in Latin America as of 2022. Colombia's Gini coefficient amounted to 54.8. The Dominican Republic recorded the lowest Gini coefficient at 37, even below Uruguay and Chile, which are some of the countries with the highest human development indexes in Latin America. The Gini coefficient explained The Gini coefficient measures the deviation of the distribution of income among individuals or households in a given country from a perfectly equal distribution. A value of 0 represents absolute equality, whereas 100 would be the highest possible degree of inequality. This measurement reflects the degree of wealth inequality at a certain moment in time, though it may fail to capture how average levels of income improve or worsen over time. What affects the Gini coefficient in Latin America? Latin America, as other developing regions in the world, generally records high rates of inequality, with a Gini coefficient ranging between 37 and 55 points according to the latest available data from the reporting period 2010-2023. According to the Human Development Report, wealth redistribution by means of tax transfers improves Latin America's Gini coefficient to a lesser degree than it does in advanced economies. Wider access to education and health services, on the other hand, have been proven to have a greater direct effect in improving Gini coefficient measurements in the region.

Income share held by second 20% of Chile jumped by 8.79% from 9.10 % in 2020 to 9.90 % in 2022. 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.

This is the replication package for Astorga, Pablo. 2024. Revealing the diversity and complexity of long-term income inequality in Latin America: 1920-2011. Journal of Economic History, 84(4).This paper analyses and documents new long-term income inequality series for Argentina, Brazil, Chile, Colombia, Mexico and Venezuela based on dynamic social tables with four occupational groups. This enables the calculation of comparable Overall (4 groups) and Labor Ginis (3 groups) with their between- and within-groups components. The main findings are: the absence of a unique inequality pattern over time; country outcomes characterized by trajectory diversity and level divergence during industrialization, and by commonality and convergence post 1980; the occurrence of inequality-levelling episodes with different timing and length; and significant changes in trends, but also evidence indicating persistence.

Income share held by third 20% of Chile surged by 6.87% from 13.10 % in 2020 to 14.00 % in 2022. 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.

Chile CL: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 13.800 % in 2022. This records an increase from the previous number of 13.400 % for 2020. Chile CL: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 17.900 % from Dec 1987 (Median) to 2022, with 16 observations. The data reached an all-time high of 20.800 % in 1987 and a record low of 13.400 % in 2020. Chile CL: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. 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.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

As of 2022, the bottom 50 percent in Chile, that is, the population whose income lied below the median, earned on average 4,800 U.S. dollars at purchasing power parity (PPP) before income taxes. This is nearly 43 times less than the average income of the top ten percent, that stood at 215,000 USD that year. In relation to percentage distribution of national wealth in Chile, the top ten percent accounted for over 80 percent of the overall national wealth.

This project's goal was to maximise the returns of investing in the lives of young people by deriving specific lessons for inclusion policies in education and disseminating the findings among key policy makers. This dataset provides information on how classrooms are formed in 127 Chilean schools.

Equality of opportunity is considered by many a basic human right. It is achieved when everybody can reach their full potential, and nobody is limited by the circumstances of their own birth. However, today millions of youths around the world face persistent gaps in opportunity. This is both a social and an economic issue, because economic potential is lost when many of our youth do not have access to safe environments, high quality education and employment opportunities. This project focuses on ways to eradicate disparities in education and their consequences for labour market opportunities. In particular, it uses data from innovative inclusion policies in Chile, a country characterized by high income inequality, to find ways to close these opportunity gaps early on, i.e., before university enrolment and labour market entry. The goal of this research is not only to provide a scientific evaluation of educational policies in Chile, but also to draw practical public policy lessons that can be useful to any country. To achieve this, the project combines exceptionally detailed data with structural modelling. Most of the data have already been or will be collected by the Chilean Ministry of Education in Chile. They will be complemented with a small data collection carried out by the candidate at a minimal cost, leveraging on established relationships with research users in-country. Structural modelling is the analysis of the mechanisms through which policies work. It is what allows us to extrapolate, from specific contexts, general conclusions that are applicable to many countries. The project addresses three related research questions. First, it evaluates an affirmative action programme called PACE (Programa de Acceso Efectivo y Acompanamiento a la Educacion Superior), which guarantees admission to university to the best students in disadvantaged high schools in Chile. The study will use a Randomized Control Trial that exploits the planned programme roll-out to scientifically evaluate programme effectiveness and to identify the key ingredients for inclusion policy success. Second, it determines if differences exist in the effectiveness of the pilot programme for PACE, the Propedeutico programme, between high schools that do and that do not stream students of similar ability into the same classes. In doing so, the study extends our understanding of the role of tracking and peers in the production of achievement. For example, findings will determine if students compete more fiercely for university admission when they are in classrooms with similar peers. Third, it evaluates the impact of higher education on disadvantaged youth. To do so, it uses cut-off rules for university admission to apply a policy evaluation technique known as regression discontinuity. The benefits of higher education on the academic and labour market outcomes of disadvantaged youths are not well understood because very few poor students are observed enrolling in university. Because these are the very students that inclusion policies target, evaluating the benefits for them is of paramount importance for policy makers and researchers. This project's goal is to maximise the returns of investing in the lives of young people. This not only reduces the vast human cost of inequality, but it also increases aggregate earnings and economic growth. Due to the candidate's network in academia and in the public sector in Chile (including in the Chilean Ministry of Education), the results of the study can have a direct and immediate impact on the educational policy discourse in Chile. Over the years, the Chilean Governments have shown willingness to enact reforms that have a strong evidence-base. Therefore, potentially hundreds of thousands of poor children in Chile can be directly affected in the short term. Other countries could then follow the Chilean example, amplifying the potential impact to millions of underprivileged and talented children around the world.

Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 0.500 % in 2022. This records a decrease from the previous number of 1.800 % for 2020. Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 0.550 % from Dec 2011 (Median) to 2022, with 6 observations. The data reached an all-time high of 1.800 % in 2020 and a record low of 0.400 % in 2017. Multidimensional Poverty Headcount Ratio: World Bank: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Income share held by fourth 20% of Chile climb by 3.05% from 19.70 % in 2020 to 20.30 % in 2022. 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.

Income share held by lowest 20% of Chile soared by 20.41% from 4.90 % in 2020 to 5.90 % in 2022. 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.

Chile CL: Income Share Held by Fourth 20% data was reported at 20.300 % in 2022. This records an increase from the previous number of 19.700 % for 2020. Chile CL: Income Share Held by Fourth 20% data is updated yearly, averaging 19.150 % from Dec 1987 (Median) to 2022, with 16 observations. The data reached an all-time high of 20.300 % in 2022 and a record low of 17.300 % in 1990. Chile CL: Income Share Held by Fourth 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Social: Poverty and Inequality. 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, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data was reported at 2.030 % in 2016. This records an increase from the previous number of 1.530 % for 2011. Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data is updated yearly, averaging 1.530 % from Dec 2006 (Median) to 2016, with 3 observations. The data reached an all-time high of 2.030 % in 2016 and a record low of 1.040 % in 2006. Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Social: Poverty and Inequality. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).;Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/financial-protection);Weighted average;This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/].