Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: School Fee data was reported at 21,419.000 IDR in 2018. This records a decrease from the previous number of 22,069.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: School Fee data is updated yearly, averaging 12,080.500 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 22,069.000 IDR in 2017 and a record low of 3,366.000 IDR in 2003. Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: School Fee data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC001: Average Monthly Expenditure per Capita.

In 2022, Canada had the highest share of adults with a university degree, at over 60 percent of those between the ages of 25 and 64. India had the smallest share of people with a university degree, at 13 percent of the adult population. University around the world Deciding which university to attend can be a difficult decision for some and in today’s world, people are not left wanting for choice. There are thousands of universities around the world, with the highest number found in India and Indonesia. When picking which school to attend, some look to university rankings, where Harvard University in the United States consistently comes in on top. Moving on up One of the major perks of attending university is that it enables people to move up in the world. Getting a good education is generally seen as a giant step along the path to success and opens up doors for future employment. Future earnings potential can be determined by which university one attends, whether by the prestige of the university or the connections that have been made there. For instance, graduates from the Stanford Graduate School of Business can expect to earn around 250,000 U.S. dollars annually.

Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: Stationary data was reported at 1,414.000 IDR in 2018. This records a decrease from the previous number of 1,534.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: Stationary data is updated yearly, averaging 920.000 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 1,859.000 IDR in 2014 and a record low of 465.000 IDR in 2003. Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: Stationary data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC001: Average Monthly Expenditure per Capita.

This map shows the purchasing power per capita in Indonesia in 2022, in a multiscale map (Country, Province, County, District, and Subdistrict). Nationally, the purchasing power per capita is 36,387,408 Indonesian rupiah. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Indonesian rupiah (IDR) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaPurchasing power per capita indexCounts of population by education levelThe Purchasing Power Index compares the demand for a specific purchasing category in an area, with the national demand for that product or service. The index values at the national level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the national average; an index of 80 implies that demand is 20 percent lower than the national average.The source of this data is Michael Bauer Research. The vintage of the data is 2022. This item was last updated in November, 2022 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This layer shows the purchasing power per capita in Indonesia in 2022, in a multiscale map (Country, Province, County, District, and Subdistrict). Nationally, the purchasing power per capita is 36,387,408 Indonesian rupiah. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Indonesian rupiah (IDR) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaPurchasing power per capita indexCounts of population by education levelThe Purchasing Power Index compares the demand for a specific purchasing category in an area, with the national demand for that product or service. The index values at the national level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the national average; an index of 80 implies that demand is 20 percent lower than the national average.The source of this data is Michael Bauer Research. The vintage of the data is 2022. This item was last updated in November, 2022 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This research tests three hypotheses: 1) Internet diffusion has a significant effect on within-province income inequality in Indonesia; 2) Internet diffusion has a significant nonlinear effect on within-province income inequality in Indonesia; and 3) Internet diffusion has a significant effect on within-province income inequality in Indonesia differing by per capita income and education level. An important finding in this research is that Internet diffusion has a significant effect on income inequality as indicated by the positive influence of the Internet on income inequality between regions. In addition, the influence of the internet tends to be nonlinear and interacts differently with the heterogeneity of each province. The attached data is in .dta and .do format which can be processed with STATA by running the file. All data on each variable was downloaded from the BPS website which was processed into STATA. Several variables such as the Williamson index, real GDP per capita, and proportion of secondary and tertiary sectors were processed by researchers based on basic data from the website. The research method uses IV-2SLS with the Driscoll-Kraay standard error correction model. IneqPaper14.dta is a data set and Paper_2013-2019.do contains commands for processing data set files. IneqPaper15_2013-2019.dta is the final processed data.

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure. In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression. The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists. The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population. The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways. First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data. Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes. Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work. Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes. Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status. Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Indonesia Average Monthly Expenditure per Capita: Urban: Goods and Services: School Fee and Non/Formal Education Cost: Non-Formal Education Cost data was reported at 2,757.000 IDR in 2018. This records a decrease from the previous number of 2,865.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Urban: Goods and Services: School Fee and Non/Formal Education Cost: Non-Formal Education Cost data is updated yearly, averaging 997.000 IDR from Dec 2003 (Median) to 2018, with 15 observations. The data reached an all-time high of 6,998.000 IDR in 2008 and a record low of 213.000 IDR in 2006. Indonesia Average Monthly Expenditure per Capita: Urban: Goods and Services: School Fee and Non/Formal Education Cost: Non-Formal Education Cost data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC002: Average Monthly Expenditure per Capita: Urban.

0.56 (score, 0-1, 1 - is the best) in 2017. zero represents- the lowest income per capita, lowest educational attainment, and highest TFR observed across all GBD geographies from 1970 to 2016, and one represents the highest income per capita, highest educational attainment, and lowest TFR.

Indonesia Average Monthly Expenditure per Capita: Rural: Goods and Services: School Fee and Non/Formal Education Cost: Other Cost of School Contribution data was reported at 1,200.000 IDR in 2018. This records a decrease from the previous number of 3,770.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Rural: Goods and Services: School Fee and Non/Formal Education Cost: Other Cost of School Contribution data is updated yearly, averaging 875.000 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 4,045.000 IDR in 2013 and a record low of 173.000 IDR in 2003. Indonesia Average Monthly Expenditure per Capita: Rural: Goods and Services: School Fee and Non/Formal Education Cost: Other Cost of School Contribution data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC003: Average Monthly Expenditure per Capita: Rural.

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Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: Development School Contribution and Admission Fee data was reported at 5,782.000 IDR in 2018. This records an increase from the previous number of 4,905.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: Development School Contribution and Admission Fee data is updated yearly, averaging 1,949.500 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 7,227.000 IDR in 2014 and a record low of 796.000 IDR in 2003. Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: Development School Contribution and Admission Fee data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC001: Average Monthly Expenditure per Capita.

India has the most universities worldwide. According to data from July 2023, there were an estimated ***** universities in India. Indonesia had the second most universities, counting *****, followed by the United States with ***** universities.

Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: Non-Formal Education Cost data was reported at 1,786.000 IDR in 2018. This records an increase from the previous number of 1,774.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: Non-Formal Education Cost data is updated yearly, averaging 488.000 IDR from Dec 2003 (Median) to 2018, with 14 observations. The data reached an all-time high of 1,786.000 IDR in 2018 and a record low of 111.000 IDR in 2003. Indonesia Average Monthly Expenditure per Capita: Goods and Services: School Fee and Non/Formal Education Cost: Non-Formal Education Cost data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC001: Average Monthly Expenditure per Capita.

Indonesia Average Monthly Expenditure per Capita: Urban: Goods and Services: School Fee and Non/Formal Education Cost: Development School Contribution/Admission Fee data was reported at 8,826.000 IDR in 2018. This records an increase from the previous number of 7,522.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Urban: Goods and Services: School Fee and Non/Formal Education Cost: Development School Contribution/Admission Fee data is updated yearly, averaging 3,142.000 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 11,965.000 IDR in 2014 and a record low of 1,131.000 IDR in 2012. Indonesia Average Monthly Expenditure per Capita: Urban: Goods and Services: School Fee and Non/Formal Education Cost: Development School Contribution/Admission Fee data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC002: Average Monthly Expenditure per Capita: Urban.

Abstract

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.

The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Geographic coverage

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).

The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.

Analysis unit

Country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.

The naming conventions for the adult mortality-related are as follows. Variables are named:

GGG_MC_AAAA

GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:

All - All Fem - Female Mal - Male Rur - Rural Urb - Urban Rurm - Rural/Male Urbm - Urban/Male Rurf - Rural/Female Urbf - Urban/Female Noed - No education Pri - Some or completed primary only Sec - At least some secondary education Noedm - No education/Male Prim - Some or completed primary only/Male Secm - At least some secondary education/Male Noedf - No education/Female Prif - Some or completed primary only/Female Secf - At least some secondary education/Female Rch - Rural as child Uch - Urban as child Rchm - Rural as child/Male Uchm - Urban as child/Male Rchf - Rural as child/Female Uchf - Urban as child/Female Edltp - Less than primary schooling Edpom - Primary or more schooling Edltpm - Less than primary schooling/Male Edpomm - Primary or more schooling/Male Edltpf - Less than primary schooling/Female Edpomf - Primary or more schooling/Female Edltpu - Less than primary schooling/Urban Edpomu - Primary or more schooling/Urban Edltpr - Less than primary schooling/Rural Edpomr - Primary or more schooling/Rural Edltpmu - Less than primary schooling/Male/Urban Edpommu - Primary or more schooling/Male/Urban Edltpmr - Less than primary schooling/Male/Rural Edpommr - Primary or more schooling/Male/Rural Edltpfu - Less than primary schooling/Female/Urban Edpomfu - Primary or more schooling/Female/Urban Edltpfr - Less than primary schooling/Female/Rural Edpomfr - Primary or more schooling/Female/Rural

M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").

C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").

AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values: 1554 - Ages 15-54 1524 - Ages 15-24 2534 - Ages 25-34 3544 - Ages 35-44 4554 - Ages 45-54

Other variables that are in the databases are:

period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04) svycountry - Name of country for DHS countries ccode3 - Country code u5mr - Under-5 mortality (from World Development Indicators) cname - Country name gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators) gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators) pop - Population (from World Development Indicators) hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010) region - Region

Indonesia Average Monthly Expenditure per Capita: Urban: Goods and Services: School Fee and Non/Formal Education Cost: Text Books data was reported at 2,904.000 IDR in 2018. This records a decrease from the previous number of 3,321.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Urban: Goods and Services: School Fee and Non/Formal Education Cost: Text Books data is updated yearly, averaging 2,622.000 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 4,813.000 IDR in 2014 and a record low of 1,167.000 IDR in 2003. Indonesia Average Monthly Expenditure per Capita: Urban: Goods and Services: School Fee and Non/Formal Education Cost: Text Books data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC002: Average Monthly Expenditure per Capita: Urban.

Indonesia Average Monthly Expenditure per Capita: Rural: Goods and Services: School Fee and Non/Formal Education Cost: Development School Contribution/Admission Fee data was reported at 2,108.000 IDR in 2018. This records a decrease from the previous number of 7,522.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Rural: Goods and Services: School Fee and Non/Formal Education Cost: Development School Contribution/Admission Fee data is updated yearly, averaging 755.000 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 11,350.000 IDR in 2013 and a record low of 261.000 IDR in 2004. Indonesia Average Monthly Expenditure per Capita: Rural: Goods and Services: School Fee and Non/Formal Education Cost: Development School Contribution/Admission Fee data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC003: Average Monthly Expenditure per Capita: Rural.

Indonesia Average Monthly Expenditure per Capita: Rural: Goods and Services: School Fee and Non/Formal Education Cost: Stationary data was reported at 1,142.000 IDR in 2018. This records a decrease from the previous number of 1,776.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Rural: Goods and Services: School Fee and Non/Formal Education Cost: Stationary data is updated yearly, averaging 774.500 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 2,291.000 IDR in 2013 and a record low of 371.000 IDR in 2003. Indonesia Average Monthly Expenditure per Capita: Rural: Goods and Services: School Fee and Non/Formal Education Cost: Stationary data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC003: Average Monthly Expenditure per Capita: Rural.

Japan TE: Indonesia: AC: School or Company Dormitory data was reported at 249,151.654 JPY/Person in Jun 2017. This records an increase from the previous number of 21,688.662 JPY/Person for Mar 2017. Japan TE: Indonesia: AC: School or Company Dormitory data is updated quarterly, averaging 24,762.167 JPY/Person from Mar 2014 (Median) to Jun 2017, with 4 observations. The data reached an all-time high of 249,151.654 JPY/Person in Jun 2017 and a record low of 0.000 JPY/Person in Mar 2014. Japan TE: Indonesia: AC: School or Company Dormitory data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q025: Tourism and Leisure: Characteristics of Visitors and Trips: Total Trip Expenditure per person by Nationality.