30 December 2009

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

27 November 2009

Release of 2008 education data by UIS

The UNESCO Institute for Statistics (UIS) has announced the release of new education statistics for the year 2008. For 70 countries, new data on primary education are available at the UIS Data Centre. All indicators were calculated with new population estimates from the World Population Prospects 2008 by the UN Population Division. As part of the new data release, all historical estimates in the UIS education database were also revised.

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Friedrich Huebler, 27 November 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/11/uis.html

31 October 2009

Regional disparities in school life expectancy

The school life expectancy is the number of years of schooling a child of school entrance age can expect to receive. It is calculated as the sum of age-specific enrollment rates for the specified levels of education. The UNESCO Institute for Statistics (UIS) provides data on the school life expectancy for the following levels of education: pre-primary, primary to secondary, primary to tertiary, and tertiary.

Figure 1 displays the average school life expectancy for primary to secondary education in eight geographic regions - Arab States, Central Asia, Central and Eastern Europe, East Asia and the Pacific, Latin America and the Caribbean, North America and Western Europe, South and West Asia, and Sub-Saharan Africa - and for the world as a whole. For each region, the total, male and female school life expectancy is shown. A high value for this indicator means that school enrollment rates as well as retention rates are high and that children are likely to spend a high number of years in formal education.

The values plotted in Figure 1 are also listed in Table 1. In addition to the school life expectancy in years, Table 1 lists the gender parity index for each region. The GPI is the ratio of the female to male school life expectancy. Values below 1 mean that girls have a lower school life expectancy than boys, while GPI values above 1 mean the opposite. A GPI of 1 indicates gender parity. All data in Figure 1 and Table 1 were obtained from the UIS Data Centre and are for the years 2007 and 2008.

Figure 1: School life expectancy in years, primary to secondary education, 2007/2008
Graph with total, male and female school life expectancy by geographic region
Data source: UNESCO Institute for Statistics, Data Centre, October 2009.

Children in North America and Western Europe have the highest school expectancy. On average, boys and girls alike can expect to spend about 12.3 years in school. In Latin America and the Caribbean, the average school life expectancy is 11.7 years. In three other regions children are also likely to receive more than 10 years of primary and secondary education: Central Asia (10.8 years), Central and Eastern Europe (10.5 years), and East Asia and the Pacific (10.4 years). In Sub-Saharan Africa (8.1 years), South and West Asia (9.1 years), and in the Arab States (9.3 years) the average school life expectancy is lower than in the other regions.

Table 1: School life expectancy in years, primary to secondary education, 2007/2008
Region Total Male Female GPI
Arab States 9.3 9.8 8.8 0.90
Central Asia 10.8 10.9 10.6 0.98
Central and Eastern Europe 10.5 10.6 10.3 0.96
East Asia and the Pacific 10.4 10.3 10.5 1.02
Latin America and the Caribbean 11.7 11.6 11.8 1.02
North America and Western Europe 12.3 12.3 12.3 1.00
South and West Asia 9.1 9.4 8.7 0.92
Sub-Saharan Africa 8.1 8.7 7.6 0.87
World 9.8 10.0 9.5 0.95
Note: GPI is the gender parity index (female / male school life expectancy).
Data source: UNESCO Institute for Statistics, Data Centre, October 2009.

Sub-Saharan Africa, the Arab States, and South and West Asia have not only the lowest school life expectancy, they are also the worst performers in terms of gender parity. As the graph shows, there is a relatively large gap between the male and female school life expectancy in these regions, with GPI values ranging from 0.87 in Sub-Saharan Africa to 0.92 in South and West Asia. On average, girls receive one year less education than boys in these three regions. In Sub-Saharan Africa, the school life expectancy is 7.6 years for girls and 8.7 years for boys.

In the other regions, there is little or no difference between the school life expectancy of boys and girls. In Central and Eastern Europe, the GPI is 0.96, with a school life expectancy of 10.6 years for boys and 10.3 years for girls. North America and Western Europe have reached gender parity. In East Asia and the Pacific, and in Latin America and the Caribbean, the school life expectancy is higher for girls than for boys; in both regions, the GPI is 1.02.

Compared to the beginning of the decade, the school life expectancy has increased in all regions, especially in Sub-Saharan Africa. However, the gap between the best- and worst-performing countries is still large. In addition, gender disparity continues to be a problem, especially in regions where the school life expectancy is low.

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Friedrich Huebler, 31 October 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/10/sle.html

29 September 2009

NER, GER and universal primary education

The net enrollment ratio (NER) in primary education is one of the official indicators for the Millennium Development Goal of universal primary education. The primary NER is the share of children of primary school age that are enrolled in primary school.

Primary NER Number of children of primary school age enrolled in primary school
Number of children of primary school age

If all children of primary school age are enrolled in primary school, the primary NER is 100 percent. A primary NER below 100 percent means that not all children of primary school age are in primary school; some may be out of school, some may be in preschool, in secondary school or in other forms of education. By definition, the NER cannot exceed 100 percent.

The gross enrollment ratio (GER) is a related indicator. The primary GER indicates how many children, regardless of their age, are enrolled in primary school, relative to the population of primary school age.

Primary GER Number of children enrolled in primary school
Number of children of primary school age

The value of the GER can exceed 100 percent. Values above 100 percent mean that some children above or below primary school age are in primary school. A GER above 100 percent is usually an indicator of overage enrollment, for example due to repetition or late entry.

Ideally, all children in a country enter primary school at the official primary school entrance age and graduate from the final primary grade after the official duration of primary school, for example after four or six years. In this case, the primary NER would be 100 percent and universal primary education would be achieved. If no children repeated a grade, the primary GER would also be 100 percent. If we assume that some children have to repeat a grade and remain in primary school although they have reached official secondary school age, the primary GER would be slightly above 100 percent.

However, we can demonstrate that a primary NER of 100 percent is not a necessary condition for universal primary education. Similarly, the primary GER can be below 100 percent in a country, although universal primary education has been achieved. For the demonstration we refer to data for Japan. According to the Global Education Digest 2009 by the UNESCO Institute for Statistics, Japan has achieved universal primary education with a primary NER and GER of 100 percent in 2007, the most recent year with data.

What would happen to the NER and GER if Japanese children systematically entered and graduated from primary school one year early or one year late? These hypothetical situations can be simulated with data from the World Population Prospects 2008 by the UN Population Division. Table 1 lists the estimated population of Japan between 5 and 12 years of age in the year 2009.

Table 1: Estimated population of Japan between 5 and 12 years, 2009
Age
Population
5 years 1,120,774
6 years 1,134,317
7 years 1,145,758
8 years 1,155,440
9 years 1,163,697
10 years 1,171,297
11 years 1,179,006
12 years 1,185,028
5-10 years 6,891,283
6-11 years 6,949,515
7-12 years 7,000,226
Source: UN Population Division. 2009. World Population Prospects: 2008 Revision.

Scenario 1: entry and graduation at official age

Primary school in Japan has 6 grades and the official primary school age is 6 to 11 years. If all children enter primary school at age 6 and graduate after 6 years, the primary NER and GER can be calculated as follows.

Primary NER Number of children of primary school age enrolled in primary school
Number of children of primary school age

6,949,515 / 6,949,515

100%

Because there is no overage or underage enrollment, the number of children in primary school is identical to the number of children of primary school age (6 to 11 years) and thus the primary GER is identical to the primary NER.

Primary GER Number of children enrolled in primary school
Number of children of primary school age

6,949,515 / 6,949,515

100%

Scenario 2: early entry

If all children enter and graduate from primary school one year early, the primary NER and GER are no longer 100 percent. The population of primary school age (6-11 years) is still 6,949,515, but in this age group only children between 6 and 10 are in primary school, in addition to children aged 5 years. In this scenario, children age 11 are already in secondary school. The number of children of primary school age enrolled in primary school is therefore 6,949,515 - 1,179,006 = 5,770,509 and the primary NER is no longer 100 percent but 83 percent.

Primary NER
(early entry) 
Number of children of primary school age enrolled in primary school
Number of children of primary school age

5,770,509 / 6,949,515

83.0%

The primary GER is still near 100 percent because the population in primary school (5-10 years) is similar to the population of primary school age (6-11 years).

Primary GER
(early entry) 
Number of children enrolled in primary school
Number of children of primary school age

6,891,283 / 6,949,515

99.2%

Scenario 3: late entry

Now assume that all children enter and graduate from primary school one year late. Only children between 7 and 12 years are in primary school. Of the population of primary school age (6-11 years) only those between 7 and 11 are in primary school, in addition to children aged 12 years. The number of children of primary school age enrolled in primary school is therefore 6,949,515 - 1,134,317 = 5,815,198 and the primary NER is now 83.7 percent.

Primary NER
(late entry) 
Number of children of primary school age enrolled in primary school
Number of children of primary school age

5,815,198 / 6,949,515

83.7%

As in scenario 2 with early entry, the primary GER is near 100 percent because the population in primary school (7-12 years) is close to the population of primary school age (6-11 years).

Primary GER
(late entry) 
Number of children enrolled in primary school
Number of children of primary school age

7,000,226 / 6,949,515

100.7%

Table 2 and Figure 1 summarize the primary NER and GER under the three scenarios described above. In all three scenarios there is universal primary education but in the case of early or late entry, the primary NER is far below 100 percent. On the other hand, the primary GER is equal to or near 100 percent in all three scenarios, due to the small difference between the number of children in the individual age cohorts.

Table 2: Primary NER and GER in Japan in the case of age-appropriate, early and late entry and graduation
Scenario for primary school enrollment
Primary NER (%) Primary GER (%)
Entry and graduation at official age 100.0 100.0
Entry and graduation one year early 83.0
99.2
Entry and graduation one year late 83.7
100.7

Figure 1: Primary NER and GER in Japan in the case of age-appropriate, early and late entry and graduation
Bar graph with primary school NER and GER for three scenarios of school entry and graduation

In an ideal situation, when all or almost all children enter primary school at the official entrance age and graduate after the official duration of primary school, both the NER and GER are near 100 percent. However, as demonstrated with data for Japan, a primary NER and GER of 100 percent is not a necessary condition for universal primary education. In countries where children enter school before or after the official entrance age, universal primary education can exist although the primary NER may be below 100 percent.

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Friedrich Huebler, 29 September 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/09/upe.html

31 July 2009

Global Education Digest 2009

Cover of "The State of the World's Children 2009"The UNESCO Institute for Statistics (UIS) has published the 2009 edition of its annual Global Education Digest. The GED presents the latest statistics on pre-primary, primary, secondary and tertiary education, education finance, literacy and educational attainment. The statistical tables contain data for more than 200 countries and territories. In addition to national data, the tables offer global values and regional values for the Arab States, Central and Eastern Europe, Central Asia, East Asia and the Pacific, Latin America and the Caribbean, North America and Western Europe, South and West Asia, and Sub-Saharan Africa.

The analytical chapter that precedes the statistical tables is dedicated to a different topic every year. The analytical chapter in the GED 2009 explores global trends in higher education, including levels of participation, fields of study, student mobility, and tertiary education finance. The tables with time series data from 1970 to 2005 that were introduced in the previous GED are limited to data on tertiary education this year. The GED 2008 also contained time series tables on primary and secondary education.

The data from the GED are available free of charge at the UIS Data Centre. To access the statistical tables, click on "Predefined Tables" and then "Education". For most indicators, the GED only lists data for one or two years, whereas the Data Centre provides annual statistics from 1999 to 2008. The Data Centre also offers time series data for selected primary, secondary and tertiary education indicators from 1970 to 2005.

References
  • UNESCO Institute for Statistics (UIS). 2009. Global education digest 2009: Comparing education statistics across the world. Montreal: UIS. (Download PDF, 7.0 MB)
  • UNESCO Institute for Statistics (UIS). 2008. Global education digest 2008: Comparing education statistics across the world. Montreal: UIS. (Download PDF, 6.3 MB)
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Friedrich Huebler, 31 July 2009 (edited 19 September 2010), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/07/ged.html

09 May 2009

Achievement gap between black and white students in the United States

Two previous articles on this site presented data on disparities in school attendance by ethnicity, language or religion from 17 nationally representative household surveys. Net attendance rates among the least disadvantaged groups are up to 1.7 times higher than net attendance rates among the most disadvantaged groups at the primary level of education and up to 6 times higher at the secondary level of education.

Similar gaps in access to education and in student achievement exist in the United States. The National Center for Education Statistics has published the most recent findings of its National Assessment of Educational Progress, a long term study of student achievement, in the report NAEP 2008 Trends in Academic Progress. The results of the periodic assessments by the NCES demonstrate a persistent achievement gap between black and white students.

Figure 1 summarizes the results of 12 reading assessments over the period 1971 to 2008. For each assessment, the average reading scores of black and white students aged 9, 13 and 17 years are plotted in the graph. The shaded area indicates the achievement gap between black and white students. For 2004, two scores are shown for each group because the assessment format was revised in that year. The reading scores in 1971 and 2008 are also listed in Table 1.

Figure 1: Average reading scores of black and white students, 1971-2008
Trendlines with reading scores of black and white students in the United States between 1971 and 2008
Data source: NAEP 2008 Trends in Academic Progress, p. 14-15.

Black and white students of all ages achieved higher reading scores in 2008 than in previous years. In 1971, 9-year-old white students had an average score of 214 and black students in the same age group scored 170 on average. In 2008, the average score of 9-year-olds was 228 for white students and 204 for black students. As a result, the score gap between black and white 9-year-olds fell from 44 in 1971 to 24 in 2008. For 13-year-old students the score gap fell from 39 to 21 over the same period and for 17-year-olds it fell from 52 to 29.

Closer inspection of the data reveals that most of this reduction in the achievement gap occurred during the 1970s and 1980s. Since the 1990s, the gap between black and white students has remained relatively stable. Although the reading scores of black students continue to improve, they no longer grow fast enough to close the gap with white students.

Table 1: Average reading scores of black and white students, 1971 and 2008
Year Age Average reading score Score gap
Black White
1971 9 years 170 214 44
2008 9 years 204 228 24
1971 13 years 222 261 39
2008 13 years 247 268 21
1971 17 years 239 291 52
2008 17 years 266 295 29
Data source: NAEP 2008 Trends in Academic Progress, p. 14-15.

The NAEP report shows a similar achievement gap between black and white students in the area of mathematics. In addition, there is a similar but smaller gap between white and Hispanic students in reading and mathematics. In spite of long-running efforts to improve the education system for all parts of the population, minority students consistently lag behind white students in the United States.

Reference
  • Rampey, Bobby D., Gloria S. Dion, and Patricia L. Donahue. 2009. NAEP 2008 Trends in Academic Progress. Washington: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. (Download PDF file, 1.1 MB)
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Friedrich Huebler, 9 May 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/05/usa.html

05 April 2009

New version of programs to integrate Stata and external text editors

Version 3.1 of the rundo and rundolines programs to integrate Stata and external text editors has been released. The new version corrects a bug in version 3 of the rundo program, which had pointed to the wrong INI file. As a result of this bug, changed settings in rundo.ini had no effect on the execution of the program. Version 3.1 also updates the code of the rundo and rundolines programs to version 10.1 of Stata. Older versions of Stata are still supported.

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Friedrich Huebler, 5 April 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/04/stata.html

15 March 2009

Disparities in secondary school attendance by ethnicity, language or religion

Members of ethnic, linguistic or religious minorities face barriers to access to education in many countries. In an article on primary school attendance by ethnicity, language or religion the presence of such disparities was demonstrated with data from Multiple Indicator Cluster Surveys. The MICS are nationally representative household surveys supported by UNICEF that collect data on school attendance and other household member characteristics. In the most recent round of MICS surveys, carried out in 2005 and 2006, 17 countries collected data on school attendance by ethnicity, language or religion: Albania, Belize, Gambia, Georgia, Guinea-Bissau, Guyana, Kazakhstan, Kyrgyzstan, Lao PDR, Macedonia, Montenegro, Serbia, Sierra Leone, Thailand, Togo, Uzbekistan, and Viet Nam.

The school attendance data from the MICS surveys can be used to generate an education parity index that measures relative disparity across different groups of disaggregation, as described in the article on primary school attendance. To calculate the index, the attendance rate of the group with the lowest value is divided by the attendance rate of the group with the highest value. The result is a value between 0 and 1, where 1 means that children from different ethnic, linguistic or religious groups have the same secondary school attendance rate. Values closer to 0 indicate increasing disparity.

As an example, Thailand collected data on school attendance that can be linked to the mother tongue of the household head. The secondary school net attendance rates (NAR) for two groups of children identified in the 2005-06 MICS data are shown in Table 1.

Table 1: Secondary school attendance in Thailand
Mother tongue of household head
Secondary NAR (%)
Thai 81.2
Other language 65.8
Total 79.8
Data source: MICS 2005-06.

Among children from households whose head speaks Thai, the secondary NAR is 81.2 percent. Among children from households headed by someone with a different mother tongue, the secondary NAR is 65.8 percent. The secondary school parity index for Thailand is then calculated as follows.

Secondary school parity index = Lowest secondary NAR / Highest secondary NAR

= Secondary NAR of speakers of another language /
   Secondary NAR of speakers of Thai

= 65.8 / 81.2

= 0.81

The parity index is a relative, not an absolute measure of disparity. The value 0.81 means that the secondary NAR of speakers of another language is, relatively speaking, 19 percent below the secondary NAR of Thai speakers. The absolute gap between children from the two groups is 15.4 percent, the difference between 81.2 and 65.8.

The secondary school parity index for all 17 countries with data is shown in Figure 1. The index ranges from a high of 0.98 in Viet Nam to a low of 0.17 in Serbia. The low value for Serbia is explained by extremely low secondary school attendance among the Roma ethnic group. The secondary school NAR for Roma children is 14.8 percent, compared to 85.9 percent for Serbians and 88.6 percent for children from other ethnic groups. In addition to Serbia, six other countries have index values at or below 0.5: Lao PDR, Macedonia, Guinea-Bissau, Togo, Belize, and Montenegro. In these countries, children from the most advantaged ethnic, linguistic or religious group have secondary school net attendance rates that are at least twice as high as the attendance rates of children from the most disadvantaged group. In Viet Nam, Kazakhstan, Albania, and Uzbekistan, on the other hand, disparities in access to secondary education are relatively small.

Figure 1: Secondary school parity index: School attendance by ethnicity, language or religion
Bar graph showing secondary school parity index in 17 countries
Data source: MICS 2005-2006.

The attendance rates used to calculate the secondary school parity index are summarized in Table 2. The table also shows whether the national agencies that implemented a survey chose ethnicity, language or religion to identify minorities. A comparison with data on primary school attendance makes clear that disparities at the secondary level of education are much larger than disparities at the primary level, where the parity index for the same group of countries has a range from 0.59 to 0.99.

Table 2: Disparities in secondary school attendance by ethnicity, language or religion
Country Year Characteristic Primary NAR (%) Parity index
Min. Max.
Albania 2005 Religion 77.1 83.7 0.92
Belize 2006 Language 36.9 79.2 0.47
Gambia 2006 Ethnicity 27.5
48.2 0.57
Georgia 2005 Ethnicity 69.0
90.6 0.76
Guinea-Bissau 2006 Language 4.3
13.8 0.31
Guyana 2006 Ethnicity 56.0
81.1 0.69
Kazakhstan 2006 Language 90.8
96.0 0.95
Kyrgyzstan 2006 Language 79.3
92.4 0.86
Lao PDR 2006 Language 10.0
45.6 0.22
Macedonia 2005 Ethnicity 17.4
73.7 0.24
Montenegro 2005 Ethnicity 46.5
92.9 0.50
Serbia 2005 Ethnicity 14.8 88.6 0.17
Sierra Leone 2005 Religion 17.8 24.4 0.73
Thailand 2005-06 Language 65.8
81.2 0.81
Togo 2006 Ethnicity 22.9
53.1 0.43
Uzbekistan 2006 Language 87.1
95.4 0.91
Viet Nam
2006 Ethnicity 93.8
95.7 0.98
Data source: MICS 2005-2006.

Data source
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Friedrich Huebler, 15 March 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/03/elr2.html

01 March 2009

Disparities in primary school attendance by ethnicity, language or religion

In many parts of the world, members of ethnic, linguistic or religious minorities face barriers to access to education. One example is Nepal, where caste and ethnicity are closely linked to primary and secondary school attendance rates. Because of the importance of this issue, "Minorities and the right to education" was the thematic focus of the first United Nations Forum on Minority Issues, which took place in Geneva on 15 and 16 December 2008.

The presence of disparities in national education systems can be demonstrated with data from Multiple Indicator Cluster Surveys (MICS), nationally representative household surveys that are carried out with the support of UNICEF. The MICS data collection process is explained in the Multiple Indicator Cluster Survey Manual 2005 (UNICEF 2006). MICS surveys conducted in 2005 and 2006 collected data on school attendance by ethnicity, language or religion in the following countries: Albania, Belize, Gambia, Georgia, Guinea-Bissau, Guyana, Kazakhstan, Kyrgyzstan, Lao PDR, Macedonia, Montenegro, Serbia, Sierra Leone, Thailand, Togo, Uzbekistan, and Viet Nam.

Minority Rights Group International (MRG) defines minorities as "non-dominant ethnic, religious and linguistic communities, who may not necessarily be numerical minorities. ... [These groups] may lack access to political power, face discrimination and human rights abuses, and have 'development' policies imposed upon them" (MRG 2009). The MICS data alone are not sufficient to identify groups that can be considered minorities as defined by MRG because the size of particular groups in relation to the entire population of a country does not indicate whether these groups are discriminated in any way. This article therefore examines differences in school attendance between all ethnic, linguistic or religious groups for which data are available. Disparities between these groups can provide insights into whether any part of a country's population faces discrimination or is otherwise disadvantaged.

With the school attendance data from the MICS surveys it is possible to generate an education parity index that measures relative disparity across different groups of disaggregation, following the methodology developed by Huebler (2008) for data on school attendance by sex, area of residence, and household wealth. The education parity index has a range of 0 to 1, where 1 indicates parity between all groups of disaggregation. This methodology can also be applied to primary school attendance rates by ethnicity, language or religion. To calculate the index, the attendance rate of the group with the lowest value is divided by the attendance rate of the group with the highest value, yielding a value between 0 and 1. The value 1 means that children from different ethnic, linguistic or religious groups have the same primary school attendance rates. Smaller values indicate increasing disparity.

The calculation of the parity index can be illustrated with data from Macedonia. A MICS survey conducted in 2005 collected data on school attendance by ethnic group of the household head. Four ethnic groups are identified in the data and their respective primary school net attendance rates (NAR) are shown in Table 1.

Table 1: Primary school attendance in Macedonia
Ethnic group of household head
Primary NAR (%)
Albanian 97.8
Macedonian 97.5
Roma 61.1
Other ethnic group 81.9
Total 94.9
Data source: MICS 2005.

Albanians in Macedonia have the highest primary NAR, 97.8 percent. By contrast, Roma have the lowest NAR, 61.1 percent. In other words, only 6 of 10 Roma children of primary school age are attending primary school. With these values, the primary school parity index for Macedonia can be calculated as follows:

Primary school parity index = Lowest primary NAR / Highest primary NAR

= Primary NAR of Roma / Primary NAR of Albanians

= 61.1 / 97.8

= 0.62

The value 0.62 means that the attendance rate of the most disadvantaged group, Roma, is 62 percent of the attendance rate of the least disadvantaged group, Albanians. In other words, the primary NAR of Roma is 38 percent below the primary NAR of ethnic Albanians. 38 percent is not the absolute but the relative difference in school attendance because the education parity index is a relative measure of disparity.

Applying the same formula to primary NAR values from other MICS surveys yields the values in Figure 1, which shows the parity index for primary school attendance by ethnicity, language or religion. In the 17 countries with data, the parity index ranges from a high of 0.99 in Guyana to a low of 0.59 in the Lao People's Democratic Republic. In Laos, speakers of the Lao language are significantly more likely to attend primary school than speakers of other languages, whose primary school NAR is 41 percent below the NAR of Lao speakers. Similar disparities exist in Togo, where members of the Para-Gourma ethnic group have a much lower primary school attendance rate than members of the Akposso-Akébou group, and in Macedonia.

Uzbekistan and Viet Nam are characterized by the near absence of disparities in primary school attendance between different ethnic, linguistic or religious groups, similar to Guyana. In these countries, the primary NAR of the group with the lowest attendance rate is only 1 or 2 percent below the primary NAR of the group with the highest attendance rate.

Figure 1: Primary school parity index: School attendance by ethnicity, language or religion
Bar graph showing primary school parity index in 17 countries
Data source: MICS 2005-2006.

The primary school net attendance rates used to calculate the parity index are listed in Table 2. The table also shows whether ethnicity, language or religion were chosen to identify minorities in a country. This choice was made by the national agencies that implemented the survey. Eight countries selected ethnicity, seven countries selected language, and two countries selected religion as the characteristic that best captures minority status.

Table 2: Disparities in primary school attendance by ethnicity, language or religion
Country Year Characteristic Primary NAR (%) Parity index
Min. Max.
Albania 2005 Religion 91.3 94.9 0.96
Belize 2006 Language 86.6 100 0.87
Gambia 2006 Ethnicity 53.2 72.9 0.73
Georgia 2005 Ethnicity 86.9 97.5 0.89
Guinea-Bissau 2006 Language 44.9 64.7 0.69
Guyana 2006 Ethnicity 95.7 96.8 0.99
Kazakhstan 2006 Language 95.4 98.9 0.96
Kyrgyzstan 2006 Language 86.7 95.4 0.91
Lao PDR 2006 Language 52.4 88.7 0.59
Macedonia 2005 Ethnicity 61.1 97.8 0.62
Montenegro 2005 Ethnicity 69.4 100 0.69
Serbia 2005 Ethnicity 77.9 100 0.78
Sierra Leone 2005 Religion 68.3 72.3 0.94
Thailand 2005-06 Language 94.8 98.2 0.97
Togo 2006 Ethnicity 55.2 91.1 0.61
Uzbekistan 2006 Language 94.9 96.8 0.98
Viet Nam
2006 Ethnicity 93.8 95.7 0.98
Data source: MICS 2005-2006.

References
  • Huebler, Friedrich. 2008. Beyond gender: Measuring disparity in South Asia using an education parity index. Kathmandu: UNICEF.
  • Minority Rights Group International (MRG). 2009. Who are minorities?
  • United Nations Children's Fund (UNICEF). 2006. Multiple Indicator Cluster Survey manual 2005: Monitoring the situation of women and children. New York: UNICEF.
Data source
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Friedrich Huebler, 1 March 2009 (edited 15 March 2009), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/03/elr.html

08 February 2009

Population structure and children out of school

Sub-Saharan Africa is the region with the highest percentage of children out of school. At the same time, the population of most countries in Sub-Saharan Africa is increasing and children of primary school age constitute a large and growing share of the population.

The link between the population structure and the number of children out of school is shown in the figures and table below. Data on the share of children of primary school age out of school were obtained from the Childinfo website of UNICEF. The official primary school ages in individual countries from the Data Centre of the UNESCO Institute for Statistics were combined with demographic data from the UN Population Division to calculate the share of children of primary school age in each country's population. In total, data for 177 countries were available. All values are for the year 2007.

In Figure 1, the population of primary school age as a percentage of the total population is plotted along the horizontal axis. At the lower end of the scale are Belarus, Bulgaria, Germany, Latvia, Russia, and Ukraine. In these countries, children of primary school age account for less than 4 percent of the total population. The countries with the highest share of children of primary school age are located in Sub-Saharan Africa: Uganda (21 percent), Zambia (20 percent), Lesotho, Mozambique, Somalia (19 percent), Malawi, Swaziland, and Tanzania (18 percent).

The share of children out of school is plotted along the vertical axis. For five countries, the available statistics indicate that less than 0.5 percent of children are out of school: Japan, Malaysia, Spain, Uruguay, and Uzbekistan. In eight countries, half or more of all children are not in school: Somalia (77 percent), Chad (64 percent), Niger (62 percent), Liberia (61 percent), Ethiopia (55 percent), Eritrea (54 percent), Burkina Faso (53 percent), and Haiti (50 percent). Except for Haiti and Pakistan, the 20 countries with the highest share of children out of school are located in Sub-Saharan Africa.

The color of the marker for each country in Figure 1 indicates the geographic region according to the grouping used for the UN Millennium Development Goals (MDG). The size of each marker indicates the absolute size of the population of primary school age. The big red circle is the marker for India and the big green circle is the marker for China. Other countries with a large number of children of primary school age are Indonesia in South-Eastern Asia, USA in the developed countries, and Nigeria in Sub-Saharan Africa.

The distribution of the points in Figure 1 shows that countries with a small share of children of primary school age in the total population also tend to have a small percentage of children out of school. By contrast, countries with a relatively large population of primary school age also have a higher percentage of children out of school. This positive correlation between the two variables is confirmed by a linear regression of the percent of children out of school on the percent of children of primary school age and the squared percent of children of primary school age. The predicted share of children out of school is indicated by the dark gray line. The light gray band around the prediction line indicates the 95 percent confidence interval.

Figure 1: Population of primary school age and children out of school by country, 2007
Scatter plot with country data on the share of children of primary school age and the share of children out of school in 2007
Note: Marker size indicates the number of children of primary school age in a country.
Data source: UNESCO Institute for Statistics, UNICEF, UN Population Division.

For Figure 2, the data from the 177 countries in Figure 1 were combined by MDG region. The share of children of primary school age in a region's population is plotted along the horizontal axis and the share of children out of school along the vertical axis. The colors of the markers are the same as in Figure 1. The regional values, summarized in Table 1, reflect the 177 countries for which data were available.

Figure 2: Population of primary school age and children out of school by MDG region, 2007
Scatter plot with regional data on the share of children of primary school age and the share of children out of school in 2007
Note: Marker size indicates the number of children of primary school age in a region.
Data source: UNESCO Institute for Statistics, UNICEF, UN Population Division.

At the global level, about 10 percent of the population are of primary school age. The regional values range from 4.6 percent in the Commonwealth of Independent States to 16.5 percent in Sub-Saharan Africa. The average share of children out of school across the 177 countries with data is 15.5 percent. In six regions, fewer than 10 percent of all children are out of school: Commonwealth of Independent States, developed countries, Eastern Asia, Latin America and the Caribbean, Northern Africa, and South-Eastern Asia. Sub-Saharan Africa has by far the highest share of children out of school, with 36.2 percent, followed by Southern Asia with 20 percent and Oceania with 17.1 percent.

Table 1: Population of primary school age and children out of school by MDG region, 2007
MDG region
Population of primary school age as share of total population (%) Children of primary school age out of school (%)
Developed countries 6.4 4.6
Commonwealth of Independent States 4.6
6.8
Eastern Asia 7.1
0.8
South-Eastern Asia 10.8
6.5
Oceania 14.5
17.1
Southern Asia 10.8
20.0
Western Asia 12.5 12.5
Northern Africa 11.9
5.9
Sub-Saharan Africa 16.5
36.2
Latin America and the Caribbean 10.3 7.2
World 9.8 15.5
Data source: UNESCO Institute for Statistics, UNICEF, UN Population Division.

Due to their current population structure and demographic trends, countries in Sub-Saharan Africa have to provide schools and teachers for a relatively larger number of children than countries in other regions. The Millennium Development Goal of universal primary education by 2015 is therefore more difficult to meet for countries in Sub-Saharan Africa than for countries with a relatively small and constant or shrinking population of primary school age.

Data sources
Related articles
External links
Friedrich Huebler, 8 February 2009 (edited 9 February 2009), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/02/coos.html

01 February 2009

The State of the World's Children 2009

Cover of "The State of the World's Children 2009"The 2009 edition of The State of the World's Children was released by UNICEF in January. The main theme of this year's report is maternal and newborn health. Women in developing countries are at a much higher risk of dying from complications during pregnancy or delivery than women in developed countries. For example, the lifetime risk of maternal death is 1 in 7 in Niger and 1 in 17400 in Sweden.

The statistical annex to The State of the World's Children contains tables with national, regional and global data on nutrition, health, HIV and AIDS, education, demography, economy, women, and child protection. In the area of education, the annex lists data for the following indicators:
  • Primary school enrollment and attendance rate
  • Secondary school enrollment and attendance rate
  • Survival rate to the last grade of primary school
  • Youth and adult literacy rate
The survival rate to the last grade of primary school replaced the survival rate to grade 5 that was reported in previous years. This change was made to match the official list of Millennium Development Goal indicators, in which the survival rate to grade 5 was replaced by the survival rate to the last grade as a new indicator for MDG 2, universal primary education by 2015.

The publication of The State of the World's Children was accompanied by an update of UNICEF's Childinfo website, where additional data and analysis can be found. For example, the education section of the Childinfo site lists new national estimates for the number of children out of school, among other statistics.

External links
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Friedrich Huebler, 1 February 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/02/sowc.html

24 January 2009

Educational attainment in Brazil since 1920

Brazil is likely to reach the Millennium Development Goal of universal primary education by 2015. According to the UNESCO Institute for Statistics (UIS), 94 percent of all children of primary school age (7 to 10 years) were enrolled in primary school in 2005. Data from the 2006 National Household Sample Survey (Pesquisa Nacional por Amostra de Domicílios, PNAD), analyzed in an article on school attendance in Brazil, show that 99 percent of all children between 7 and 10 years were in pre-primary, primary or secondary education.

PNAD data can also be used to demonstrate how the education system in Brazil has expanded over the past decades. The PNAD survey collected information on the highest level of education attended for all persons in the sample. By comparing the highest level of education of persons born in different years it is possible to show the change in school attendance patterns over time. The following graph displays the highest level of education for persons born between 1920 and 2000. Household members born in 2000 were 5 or 6 years old at the time of the survey in 2006.

Highest level of education attended by year of birth, Brazil 1920-2000
Highest level of education attended by year of birth, Brazil 1920-2000
Data source: Brazil National Household Sample Survey (PNAD), 2006.

Only 59 percent of all Brazilians born in 1920 ever attended school, and three out of four persons who attended school never went beyond primary education. Primary, secondary and tertiary school attendance rates increased steadily over the following decades. By the 1960s, nine out of ten Brazilians were able to receive a formal education. 91 percent of all persons born in 1960 attended at least primary school, 58 percent in this age group attended at least secondary school, and 14 percent went to a university.

The expansion of the primary education system began to slow down in the 1960s, after it had already reached a high level of coverage, but secondary school attendance rates continued to grow at a rapid pace. Among persons born in 1990, 98 percent attended primary school and 90 percent attended secondary school. Among persons born in 1994, 99 percent attended primary school. The peak value for participation in secondary education is 91 percent for persons born in 1988.

Fewer Brazilians have tertiary education, but almost one fifth of the population born around 1980 had attended a university or other institution of higher education by the time the PNAD survey was conducted in 2006. The peak value for participation in tertiary education is 19 percent for persons born in 1981.

Data sources
  • Brazilian Institute of Geography and Statistics (IBGE), National Household Sample Survey (Pesquisa Nacional por Amostra de Domicílios, PNAD), 2006
  • UNESCO Institute for Statistics (UIS), Data Centre, January 2009
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Friedrich Huebler, 24 January 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/01/brazil.html

08 January 2009

Update to Stata guide

I revised the guide to integrating the Stata statistical package with an external text editor. The programs described in the guide allow the execution of Stata commands directly from an external editor. The programs have been confirmed to work under Windows Vista, using the same installation procedure as for Windows XP. Most graphs and maps on this site were created with Stata.

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External links
Friedrich Huebler, 8 January 2009, Creative Commons License
Permanent URL: http://huebler.blogspot.com/2009/01/stata.html