Showing posts with label DHS. Show all posts
Showing posts with label DHS. Show all posts

School attendance by grade and age in Liberia

The article "Overage pupils in primary and secondary education" of June 2011 summarized data on school attendance from 36 countries and found that overage school attendance is common in sub-Saharan Africa. The countries with the highest share of overage pupils in the sample were Haiti, Liberia, Uganda, Rwanda, Cambodia, Ethiopia, Ghana, Madagascar, and Malawi. In Liberia, 93% of all pupils in primary and secondary education are at least one year overage for their grade and 84% are at least two years overage. This article takes a closer look at Liberia by analyzing data from the same Demographic and Health Survey (DHS) from 2007 that was analyzed for the earlier article.

The official primary school age in Liberia, as defined by the International Standard Classification of Education (ISCED), is 6 to 11 years. The official secondary school age is 12 to 17 years. Given these school ages, a 6-year-old in grade 1 and a 7-year-old in grade 2 are in the right grade for their age. A 7-year-old in grade 1 would be one year overage and an 8-year-old in grade 1 would be two years overage. A 5-year-old in grade 1 would be one year underage.

The graph below shows the age distribution of pupils in primary and secondary education in Liberia. Pupils who are in the right grade for their age or underage are in a small minority. In the first twelve grades, their share never exceeds 9%. By contrast, as many as 98% of all pupils in a single grade are overage. The degree of overage attendance is astounding: 5% of all first graders are 9 or more years overage, meaning that they start primary school at age 15 or later. 19% of all first graders are at least 7 years overage and 44% are at least 5 years overage. In grade 8, 18% of all pupils are 9 or more years overage; while the official age for eighth graders is 13 years, one in five pupils in that grade in Liberia is 22 years or older.

Age distribution of pupils in primary and secondary education in Liberia, 2007
Graph with data on overage and underage pupils in primary and secondary education in Liberia
Source: Demographic and Health Survey (DHS) 2007.

What are the reasons for this high prevalence of overage school attendance? In Liberia, as in other countries of sub-Saharan Africa, many pupils enter school late for a variety of reasons that include poverty, a scarcity of educational facilities, and lack of enforcement of the official school ages. High repetition rates further exacerbate the problem of overage school attendance. Among the consequences of this age structure in school are a higher probability of dropout and reduced lifetime earnings caused by incomplete education or late entry into the labor market.

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Friedrich Huebler, 31 July 2011, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2011/07/liberia.html
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Overage pupils in primary and secondary education

Pupils can be overage for their grade for two reasons: late entry and repetition. Take for example a country where children are expected to enter primary school at 6 years of age. If a child enters grade 1 at age 7, he or she is one year overage for the grade. A child who enters grade 1 at age 8 and repeats the grade will be three years overage for the grade; two of the three years are due to late entry and the third year is due to repetition.

Children who are many years overage are less likely to complete their education. If they stay in school, they graduate later than pupils who entered school at the official starting age. These overage graduates enter the labor market late and often with lower educational attainment. As a consequence, they are likely to have lower cumulative earnings over their lifetime than persons who graduated and entered the labor market at a younger age and with higher educational attainment. For the country as a whole this in turn means reduced national income and slower economic growth.

Overage school attendance is common in sub-Saharan Africa but also occurs in other regions. The figure below shows data from 36 nationally representative household surveys that were conducted between 2004 and 2009. 34 of these surveys were Demographic and Health Surveys (DHS) and the remaining two surveys, those for Bangladesh and Kyrgyzstan, were Multiple Indicator Cluster Surveys (MICS). For each country, the graph shows the share of children in primary and secondary education who are at least one or two years overage for their grade. The entrance ages and durations of primary and secondary education used in this study are those specified by the International Standard Classification of Education (ISCED).

Percentage of children in primary and secondary education who are at least 1 or 2 years overage for their grade
Graph with data on overage children in primary and secondary education
Source: Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), 2004-2009.

In the sample of 36 countries, the share of children who are at least one year overage for their grade ranges from 5 percent in Armenia to 95 percent in Haiti. Other countries where at least three out of four pupils in primary or secondary education are overage include Liberia (93%), Uganda (86%), Rwanda (83%), Cambodia (78%), Mozambique (76%), and Ethiopia (75%). In addition to Armenia, the percentage of pupils who are at least one year overage is below 10 percent in Moldova and Egypt (8%).

The share of children in primary and secondary education who are at least two years overage for their grade ranges from 1 percent in Armenia to 85 percent in Haiti. In addition to Haiti, at least half of all pupils are two or more years overage in Liberia (84%), Uganda (67%), Rwanda (65%), Ethiopia (59%), Cambodia (55%), Malawi (51%), and Madagascar (50%). On average, the share of children who are at least two years overage is 19 percent less than the share of children who are at least one year overage.

However, there are exceptions. In Albania and the Ukraine, 43 and 26 percent respectively of all children in primary and secondary education are at least one year overage. By contrast, only 5 and 2 percent respectively are at least two years overage. This means that in these two countries, a relatively large number of children enter school one year late or repeat one grade, but hardly any children enter school two years late or repeat more than one grade. Late entry and repetition are therefore less likely to have negative consequences on lifetime earnings and national income in Albania and the Ukraine than in other countries.

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Friedrich Huebler, 30 June 2011, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2011/06/age.html
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Age distribution by wealth quintile in household survey data

Household survey data may not contain precise ages for all household members. Age heaping, an unusually high share of ages ending in 0 and 5, is especially common in survey data from developing countries. Age heaping can be caused by uncertainty of survey respondents about their own age or the age of other household members, intentional misreporting, or errors during data collection and processing. Errors in age data can affect the estimation of education indicators from household survey data because these indicators are often calculated for specific age groups. Examples include the youth literacy rate and school attendance rates for the population of primary and secondary school age.

An article on age distribution in household survey data on this site demonstrated age heaping in survey data from India, Nigeria and to a lesser extent Indonesia. Data for Brazil showed little to no age heaping. To investigate whether age heaping is more common among certain segments of the population, the survey samples can be disaggregated by household wealth quintile. For this purpose, the households in the sample are first ranked by wealth, from poorest to richest. The population is then divided into five equally sized groups with 20 percent each of all household members in the sample.

Figure 1 shows the age distribution by single year of age and wealth quintile in data from Brazil. The data were collected in 2006 with a Pesquisa Nacional por Amostra de Domicílios (PNAD) or National Household Sample Survey. No preference for ages ending in 0 and 5 could be observed for the entire survey sample combined and disaggregation does not change the result. The age distribution in each quintile is smooth, with no peaks at ages ending in 0 and 5. The only obvious difference between the population in the different quintiles is that poorer families tend to have more children, indicated by a peak in the age distribution in the younger age groups.

Figure 1: Age distribution in household survey data by single-year age group and household wealth quintile, Brazil
Line graph with age distribution in survey data from Brazil by single-year age group and household wealth quintile
Data source: Brazil PNAD 2006.

Figure 2 shows the age distribution in Demographic and Health Survey (DHS) data from India. The data were collected in 2005-06. In contrast to Brazil, there is considerable age heaping in the Indian data. However, peaks around ages ending in 0 and 5 are more pronounced among poorer households. Increasing household wealth is associated with a decrease in age heaping.

Figure 2: Age distribution in household survey data by single-year age group and household wealth quintile, India
Line graph with age distribution in survey data from India by single-year age group and household wealth quintile
Data source: India DHS 2005-06.

Data from Indonesia, collected with a Demographic and Health Survey in 2007, are shown in Figure 3. At the aggregate level, the survey data from Indonesia exhibit little age heaping. However, disaggregation by wealth quintile reveals that reported ages ending in 0 and 5 are more common among poorer households.

Figure 3: Age distribution in household survey data by single-year age group and household wealth quintile, Indonesia
Line graph with age distribution in survey data from Indonesia by single-year age group and household wealth quintile
Data source: Indonesia DHS 2007.

Finally, Figure 4 displays data from a 2008 Demographic and Health Survey in Nigeria. Similar to India, there is a high percentage of ages ending in 0 and 5 in the combined survey sample. The disaggregated data show that age heaping occurs more frequently among poorer households but also exists in the richest wealth quintile.

Figure 4: Age distribution in household survey data by single-year age group and household wealth quintile, Nigeria
Line graph with age distribution in survey data from Nigeria by single-year age group and household wealth quintile
Data source: Nigeria DHS 2008.

Disaggregation of household survey data from Brazil, India, Indonesia and Nigeria has shown that age heaping occurs more frequently in data collected from poorer households. Wealthier households may have more access to birth registration and therefore may be able to verify their ages with birth certificates. Wealthier households are also likely to be smaller and survey respondents would therefore have to know and report the ages of fewer persons than respondents from larger households.

Age heaping in survey data reduces the accuracy of education indicators that are calculated for single years of age, for example for all children of primary school entrance or graduation age. However, indicator estimates for larger age groups, for example all children of primary or secondary school age, are less likely to be affected by errors in age data.

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Friedrich Huebler, 30 April 2010, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2010/04/age.html
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Age distribution in household survey data

Indicators in the field of education statistics, such as those defined in the education glossary of the UNESCO Institute for Statistics, are typically calculated for specific age groups. For example, the youth literacy rate is for the population age 15 to 24 years, the adult literacy rate for the population age 15 and over, and the net attendance rates for primary and secondary education are for the population of primary and secondary school age, respectively. The net intake rate is an example for an indicator that is calculated for a single year of age, the official start age of primary school.

For a correct calculation of education indicators it is necessary to have precise age data. In the case of data collected with population censuses or household surveys this means that the ages recorded for each household member should be without error. However, census or survey data sometimes exhibit the phenomenon of age heaping, usually on ages ending in 0 and 5. Such heaping or digit preference occurs when survey respondents don't know their own age or the ages of other household members, or when ages are intentionally misreported.

The presence of age heaping can be tested with indices of age preference such as Whipple's index. Heaping can also be detected through visual inspection of the age distribution in household survey data. Figures 1 and 2 summarize the age distribution in survey data from Brazil, India, Indonesia and Nigeria. The data from Brazil were collected with a Pesquisa Nacional por Amostra de Domicílios or National Household Sample Survey in 2006. The data for the other three countries are from Demographic and Health Surveys conducted between 2005 and 2008.

Figure 1 shows the share of single years of age in the total survey sample. A preference for ages ending in 0 and 5 is strikingly obvious in the data from India and Nigeria. In the data from Indonesia, age heaping is also present, but to a lesser extent than for India and Nigeria. Lastly, the graph for Brazil is relatively smooth, indicating a near absence of age heaping.

Figure 1: Age distribution in survey data by single-year age group
Line graph with age distribution in survey data by single-year age group
Data source: Brazil PNAD 2006, India DHS 2005-06, Indonesia DHS 2007, Nigeria DHS 2008.

In Figure 2, single ages are combined in five-year age groups, from 0-4 years and 5-9 years to 90-94 years and 95 years and over. Compared to Figure 1, the distribution lines are much smoother, including for India and Nigeria. We can conclude that age heaping is problematic for education indicators that are calculated for single years, for example all children of primary school entrance age, but less so for indicators that are calculated for a larger age group, for example all children of primary or secondary school age or all persons over 15 years of age.

Figure 2: Age distribution in survey data by five-year age group
Line graph with age distribution in survey data by five-year age group
Data source: Brazil PNAD 2006, India DHS 2005-06, Indonesia DHS 2007, Nigeria DHS 2008.

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Friedrich Huebler, 28 February 2010 (edited 30 September 2010), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2010/02/age.html
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EFA Global Monitoring Report 2010

Cover of the EFA Global Monitoring Report 2010The Education for All Global Monitoring Report 2010 was released on 19 January 2010. The Global Monitoring Report is written annually by an independent team and published by UNESCO.

The title of this year's report is Reaching the marginalized. UNESCO estimates that 72 million children of primary school age were out of school in 2007. The report examines who these children are and why they are excluded from education. The report further argues that there is a persistent financing gap that prevents countries from reaching the goal of education for all and that, based on current trends, 56 million children of primary school age will still be out of school in 2015.

The report introduces a new database on Deprivation and Marginalization in Education that was developed by the EFA Global Monitoring Report team and the Department of Economics at the University of Göttingen. The DME database introduces a measure of "education poverty", defined as the share of the population aged 17 to 22 years with less than 4 years or less than 2 years in school. Data are presented as global snapshots and in individual country profiles. All statistics were calculated with data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).

Excerpt from Nigeria country overview in DME database
Graph with education disparity data from Nigeria
Source: Deprivation and Marginalization in Education database, country overviews.

Reference
  • UNESCO. 2010. EFA Global Monitoring Report 2010: Reaching the marginalized. Paris: UNESCO. (Download in PDF format, 12 MB)
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Friedrich Huebler, 31 January 2010 (edited 7 March 2011), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2010/01/gmr.html
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MICS Compiler by UNICEF

MICS Compiler, a new website by UNICEF, provides easy access to data from Multiple Indicator Cluster Surveys (MICS), nationally representative household surveys that are carried out with support from UNICEF. The site is similar to STATcompiler, which offers data from Demographic and Health Surveys (DHS).

MICS Compiler was launched with data from 26 surveys conducted in Africa, Asia, Eastern Europe, and Latin America and the Caribbean between 2005 and 2007. Estimates are available for 39 indicators in ten areas.
  1. Survey information
  2. Child mortality
  3. Nutrition
  4. Child health
  5. Environment
  6. Reproductive health
  7. Child development
  8. Education
  9. Child protection
  10. HIV/AIDS, sexual behavior, and orphaned and vulnerable children
Access to the data requires two steps. In the first step, users of MICS Compiler must select one or more surveys. In the second step, the indicators are selected. The results are presented in tables or graphs. As an example, the screenshot below shows a graph with the female youth literacy rate in 21 countries.

MICS Compiler by UNICEF: Female youth literacy rate in 21 countries, 2005-2006
MICS Compiler screenshot with female youth literacy rate

At present, the female youth literacy rate is the only indicator listed in the area of education but the MICS for All blog has announced plans to expand MICS Compiler with data for more indicators and more surveys. There are also plans for adding a mapping function, similar to the DHS STATmapper.

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Friedrich Huebler, 30 December 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/12/mics.html
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