Age and level of education in Nigeria

According to data from a Demographic and Health Survey (DHS) that was conducted in Nigeria in 2003, 60.1% of all children of primary school age were attending primary school at the time of the survey. Among children of secondary school age, 35.1% were attending secondary school.

These primary and secondary school net attendance rates hide the fact that many children are in school at a level that is not appropriate for their age. In Nigeria, children often enter school at an advanced age and leave school well past the official graduation age. The primary school age in Nigeria is 6 to 11 years and the secondary school age 12 to 17 years. The graph below indicates the level of school attended for all Nigerians between 5 and 24 years of age. The data from the 2003 DHS survey reveals that a small percentage of Nigerians are still in primary school when they are already 20 years old. Secondary school attendance continues past 24 years of age, the highest age for which the DHS has data on current school attendance.

Level of school attended by age, Nigeria 2003

Data source: Nigeria 2003 DHS.

Only 36.6% of all 6-year-olds were attending primary school at the time of the survey. Primary school attendance reaches its peak between 9 and 11 years of age, when around 72% of all children are in primary school. At 17 years, the official graduation age from secondary school, 7.8% of all children were still in primary school.

Among 12-year-olds, only 13.9% were attending secondary school, with other children just beginning to attend primary school. Secondary school attendance reaches its peak at 16 years of age, when 51.3% of all children are in secondary school. At 24 years, 8.7% of the population were still in secondary school.

Delayed entry into the education system is a problem that exists in many other African countries. One indicator of this is that gross enrollment rates exceed net enrollment rates by a large margin in most of Sub-Saharan Africa. The repercussions of starting school late can be severe: such children are more likely to drop out from school and to enter the labor market with limited qualifications, reducing their potential to live productive lives. At the national level, having a less educated population makes it more difficult for countries to escape from poverty. Nigeria is therefore among the countries that are struggling to reach the Millennium Development Goals of universal primary education and eradication of extreme poverty and hunger.

Related articles:
- Primary school attendance in Nigeria
- Secondary school attendance in Nigeria
- Household wealth and school attendance in Nigeria
- Primary school gross and net enrollment
- Education data from household surveys

Friedrich Huebler, 14 December 2005 (edited 21 January 2006), Creative Commons License

Secondary school attendance in Nigeria

Nigeria is the country with the largest population in Africa, estimated at 130 million in 2005. The most recent education data for Nigeria was collected in a Demographic and Health Survey (DHS) in 2003. 60.1% of all children of primary school age were attending primary school at the time of the survey.

Far fewer children continue their education at the secondary level. The official secondary school age in Nigeria is 12 to 17 years and 35.1% of the children in this age group were in secondary school according to the DHS. For boys the secondary school net attendance rate (NAR) was 37.5% and for girls it was 32.6%.

Secondary school net attendance rate, Nigeria 2003

Data source: Nigeria 2003 DHS.

The attendance rate is strongly linked to household wealth and area of residence. 63.8% of children from the richest 20% of all households were in secondary school, compared to only 14.6% of children from the poorest 20% of all households. The secondary school NAR in urban areas was 46.3% and in rural areas it was 28.7%.

At the country level, the secondary school NAR of girls was 4.9% below the male NAR. This gender disparity is a result of lower attendance rates among girls in rural areas and in poor households. In urban Nigeria, the difference between male and female attendance rates was 1.9% and the gender parity index (GPI, the ratio of female to male NAR) was close to 1, in rural Nigeria the difference was 5.8% and the GPI was 0.82. In the poorest households the gender gap was 5.5% but in the richest household the gap was reversed: the NAR of girls was 2.3% above the NAR of boys. To reach the Millennium Development Goal of gender parity, education policy has to target poor rural residents.

Secondary school net attendance rate, Nigeria 2003

	Total NAR (%)	Male NAR (%)	Female NAR (%)	Difference male- female	GPI female/ male
Urban	46.3	47.2	45.3	1.9	0.96
Rural	28.7	31.7	25.9	5.8	0.82
Richest 20%	63.8	62.6	64.9	-2.3	1.04
Poorest 20%	14.6	17.5	12.0	5.5	0.69
Total	35.1	37.5	32.6	4.9	0.87

GPI: gender parity index. - Data source: Nigeria 2003 DHS.

Related articles:
- Primary school attendance in Nigeria
- Age and level of education in Nigeria
- Household wealth and school attendance in Nigeria

Friedrich Huebler, 16 November 2005 (edited 21 January 2006), Creative Commons License

Guide to creating maps with Stata (archived version)

As of 31 August 2012, this document is no longer maintained and has been replaced by a new guide to creating maps with Stata. The archived version of the guide is only of interest to users of Stata 8, who will find instructions below. All users of Stata 9 or later versions are advised to follow the new guide. Please change your links to the new guide: http://huebler.blogspot.ca/2012/08/stata-maps.html.

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The graphs and maps on this site are created with the Stata statistical package. This article describes how to make maps like those showing Millennium Development Goal regions and UNICEF regions in Stata from a shapefile.

Shapefiles store geographic features and related information and were developed by ESRI for its ArcGIS line of software. The shapefile format is used by many other programs and maps in this format can be downloaded from various sites on the Internet. Another common map format is the MapInfo Interchange Format for use with the MapInfo software. Shapefile data is usually stored in a set of three files (.shp, .shx, .dbf), while MapInfo data is stored in two files (.mif, .mid). Some sources for shapefiles and other data are listed on the website of the U.S. Centers for Disease Control and Prevention (CDC) under "Resources for Creating Public Health Maps." The CDC itself provides shapefiles for all countries with administrative boundaries down to the state level. Please note that these shapefiles are not in the public domain and are intended for use with the CDC's Epi Info software only. Other sources of shapefiles can be found with a Google search.

This guide is divided into two parts. Read part 1 if you have Stata 9 or 10 and part 2 if you have Stata 8. The creation of maps is not supported in older versions of Stata.

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Part 1: Creating maps with Stata 9 or 10

To create a map with Stata 9 or 10 you need the following software.

• Stata version 9.2 or newer.
• spmap: Stata module for drawing thematic maps, by Maurizio Pisati. Install in Stata with the command "ssc install spmap".
• shp2dta: Stata module for converting shapefiles to Stata format, by Kevin Crow. Install in Stata with the command "ssc install shp2dta".
• Shapefile: For the example in this guide, download world_adm0.zip (646 KB), a shapefile that contains the boundaries of all countries of the world.

Step 1: Convert shapefile to Stata format

• Unzip world_adm0.zip to a folder that is visible to Stata. The archive contains three files called world_adm0.dbf, world_adm0.shp, and world_adm0.shx.
• Start Stata and run this command:
  shp2dta using world_adm0, data(world-d) coor(world-c) genid(id)
  Two new files will be created: world-d.dta (with the country names and other information) and world-c.dta (with the coordinates of the country boundaries). If you plan to superimpose labels on a map, for example country names, you should run the following command instead, which will add centroid coordinates to the file world-d.dta:
  shp2dta using world_adm0, data(world-d) coor(world-c) genid(id) genc(c)
  Please refer to the spmap documentation to learn more about labels because they are not covered in this guide.
• The DBF, SHP, and SHX files can be deleted.

Some shapefiles are not compatible with the shp2dta command and Stata will abort the conversion with an error message. If this is the case, you can use a combination of two other programs, shp2mif and mif2dta. These programs are explained in the instructions for Stata 8 (see Step 1 and Step 2 in part 2 of this guide).

Step 2: Draw map in Stata

• Open world-d.dta in Stata.
• The file contains no country-specific data that could be used for this example so we will create a variable with the length of each country's name. The Stata command for this is:
  generate length = length(NAME)
• Draw a map that indicates the length of all country names with this command:
  spmap length using "world-c.dta", id(id)
  Be patient because spmap is slow if a map contains many features.
• The default map is monochrome, it shows Antarctica, the legend is too small and the legend values are arranged from high to low. We can draw a second map without Antarctica, with a blue palette, and with a bigger legend with values arranged from low to high:
  spmap length using "world-c.dta" if NAME!="Antarctica", id(id) fcolor(Blues) legend(symy(*2) symx(*2) size(*2)) legorder(lohi)

You now have the map below. Darker colors indicate longer names, ranging from 4 letters (for example Cuba and Iraq) to 33 letters (Falkland Islands (Islas Malvinas)). To customize the map further, please read the Stata help file for spmap.

Map created with spmap in Stata: length of country names


The instructions above can be used to convert any shapefile to Stata format. If you have maps in MapInfo format you have to use another program called mif2dta that is described in part 2 of this guide.

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Part 2: Creating maps with Stata 8

To create a map with Stata 8 you need the following software.

• Stata version 8.2.
• tmap: Stata module for thematic mapping by Maurizio Pisati. Install in Stata with the command "ssc install tmap".
• mif2dta: Stata module for converting files from MapInfo to Stata format, also by Maurizio Pisati. Install in Stata with the command "ssc install mif2dta".
• SHP2MIF: DOS program for converting shapefiles to MapInfo format. Go to the the website of RouteWare and click on "SHP2MIF (135 Kb)" under the heading "Converters" to get ishp2mif.zip.
• Shapefile: For the example in this guide, download world_adm0.zip (646 KB), a shapefile that contains the boundaries of all countries of the world.

Step 1: Convert shapefile to MapInfo format

• Unzip ishp2mif.zip. The archive contains three files, among them SHP2MIF.EXE.
• Unzip world_adm0.zip to the same folder as SHP2MIF.EXE. The archive contains three files called world_adm0.dbf, world_adm0.shp, and world_adm0.shx.
• Open a DOS command window: Windows Start menu - Run - "command" - OK.
• Change the path in the command window to the folder that contains SHP2MIF.EXE and the three map files. Use the DOS command "cd" to change the path.
• SHP2MIF works best with short file names in the 8.3 format (name up to 8 characters, extension up to 3 characters). Rename the map files with this DOS command:
  rename world_adm0.* world.*
  The map files are now called world.dbf, world.shp, and world.shx.
• Convert the maps to MapInfo format by typing "shp2mif world" in the DOS command window. This produces two new files: WORLD.MID and WORLD.MIF.
• Close the DOS command window.
• The DBF, SHP and SHX files can be deleted.

Step 2: Convert MapInfo files to Stata format

• Move the MIF and MID files to a folder that is visible to Stata.
• Start Stata and run this command:
  mif2dta world, genid(id)
  Two new files will be created: world-Coordinates.dta (with the country boundaries) and world-Database.dta (with the country names and other information). If you plan to superimpose labels on a map, for example country names, you should run the following command instead, which will add centroid coordinates to the file world-Database.dta:
  mif2dta world, genid(id) genc(c)
  Please refer to the tmap documentation to learn more about labels because they are not covered in this guide.
• The MIF and MID files can be deleted.

Step 3: Draw map in Stata

• Open world-Database.dta in Stata.
• The file contains no country-specific data that could be used for this example so we will create a variable with the length of each country's name. The Stata command for this is:
  generate length = length(name)
• Draw a map that indicates the length of all country names with this command:
  tmap choropleth length, map(world-Coordinates.dta) id(id)
  Be patient because tmap is slow if a map contains many features.
• The default map is monochrome, it shows Antarctica and the legend is too small. We can draw a second map without Antarctica, with a blue palette, and with a bigger legend:
  tmap choropleth length if name!="Antarctica", map(world-Coordinates.dta) id(id) palette(Blues) legsize(2)
• To reduce the margins, display the graph again and set the margins to zero:
  graph display, margins(zero)

You now have the map below. Darker colors indicate longer names, ranging from 4 letters (for example Cuba and Iraq) to 33 letters (Falkland Islands (Islas Malvinas)). To customize the map further, please read the Stata help file for tmap and the tmap user's guide by Maurizio Pisati. The user's guide and additional tmap files can be downloaded in Stata with the commands "ssc describe tmap" and "net get tmap".

Map created with tmap in Stata: length of country names


The instructions above can be used to convert any shapefile to Stata format. If you have maps in MapInfo format you can skip step 1 of the instructions and start with step 2.

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Related articles

• Guide to creating maps with Stata (new version of this guide, which supersedes the version on this page)
• Guide to integrating Stata and external text editors
• Guide to creating PNG images with Stata
• Guide to reading Statalist with Gmail

External links

• Stata FAQ: How do I graph data onto a map?
• Wikipedia article on shapefiles
• Wikipedia article on MapInfo Interchange Format
• Resources for Creating Public Health Maps from the Centers for Disease Control and Prevention (CDC)

Friedrich Huebler, 6 November 2005 (edited 31 August 2012), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2005/11/creating-maps-with-stata.html

Primary school attendance in Nigeria

Nigeria is one of UNICEF's 25 priority countries for girls' education. In the year 2005, the population of Nigeria is estimated to be 130 million, which makes it the most populous country in Africa. 22 million children are 6 to 11 years old, the official primary school age in Nigeria.

The most recent data on school attendance in Nigeria comes from a Demographic and Health Survey (DHS) that was conducted in 2003. 60.1% of all children of primary school age were attending primary school at the time of the survey. Boys had a higher net attendance rate (NAR) than girls, with 63.7% compared to 56.5% for girls.

Primary school net attendance rate, Nigeria 2003

Data source: Nigeria 2003 DHS.

Children in urban areas had a higher primary NAR (69.5%) than children in rural areas (55.7%). The disparity between children from the richest and poorest households was even greater. In the richest 20% of all households, 82.9% of all children of primary school age attended primary school. In the poorest 20% of all households only two out of five children were in school (primary NAR 40.4%).

A comparison of the male and female NAR reveals that there was no gender disparity in the richest households. In urban areas, the difference between male and female NAR was also relatively small, with a gender gap of just 3.1%. In rural areas and among the poorest 20% of all households, girls were far less likely to attend school than boys; in both cases, the primary NAR of girls was about 9% below that of boys.

Primary school net attendance rate, Nigeria 2003

	Total NAR (%)	Male NAR (%)	Female NAR (%)	Difference male- female	GPI female/ male
Urban	69.5	71.0	68.0	3.1	0.96
Rural	55.7	60.2	51.1	9.0	0.85
Richest 20%	82.9	82.9	82.8	0.1	1.00
Poorest 20%	40.4	45.0	35.7	9.3	0.79
Total	60.1	63.7	56.5	7.2	0.89

GPI: gender parity index. - Data source: Nigeria 2003 DHS.

Related articles:
- Secondary school attendance in Nigeria
- Age and level of education in Nigeria
- Household wealth and school attendance in Nigeria

Friedrich Huebler, 31 October 2005 (edited 21 January 2006), Creative Commons License

UNICEF priority countries for girls' education

Two of the UN Millennium Development Goals address education:

• Goal 2: Achieve universal primary education
• Goal 3: Promote gender equality and empower women

Goal 3 states further that gender disparity in primary and secondary education was to be eliminated preferably by 2005 and at all levels of education no later than 2015. Data on school attendance in 2005 is not yet available but recent estimates make clear that a large gender gap continues to exist in many countries, especially in South Asia and Sub-Saharan Africa.

UNICEF has identified 25 priority countries to reduce the number of girls currently out of school. The 25 countries are Afghanistan, Bangladesh, Benin, Bhutan, Bolivia, Burkina Faso, Central African Republic, Chad, Democratic Republic of Congo, Djibouti, Eritrea, Ethiopia, Guinea, India, Malawi, Mali, Nepal, Nigeria, Pakistan, Papua New Guinea, Sudan, Tanzania, Turkey, Yemen and Zambia.

UNICEF priority countries for girls' education


The countries, highlighted in the map above, were selected if they met the following criteria:

• female primary school net enrollment rate below 70%,
• gender gap in primary education above 10%,
• more than 1 million girls out of school,
• included in the Education for All Fast Track Initiative of the World Bank,
• affected by crises like HIV/AIDS and military conflict.

During the past weeks I presented data on primary school attendance, secondary school attendance, educational attainment and primary school completion in India, the largest of the 25 countries listed above. Over the coming weeks I will assess the progress of other countries toward the Millennium Development Goals.

Friedrich Huebler, 24 October 2005, Creative Commons License

Primary school completion in India, 1950-2000

The share of the Indian population who attended school has increased steadily over the past 50 years. Data on educational attainment in India shows that about 80% of all persons born around 1990 have attended at least primary school. Of the generation born between 1950 and 1970, only about 55% ever attended school.

Not every child who enters primary school completes that level of education. High dropout rates are a particular concern in Sub-Saharan Africa, the region with the lowest school life expectancy values worldwide. In India, on the other hand, most children who enter the first grade stay in primary school until they graduate after five years of education. The graph below compares the percentage of Indians who attended primary school (independent of the number of years) with the percentage who completed primary education between 1950 and 2000. The data, from a 2000 Multiple Indicator Cluster Survey (MICS), is disaggegrated by sex and area of residence.

Primary school attendance and completion by year of birth, India 1950-2000

Data source: India 2000 MICS.

The population is grouped by year of birth and for each cohort three values are shown:

• the percentage who attended primary school or higher (blue area)
• the percentage who completed primary school (green area)
• the percentage of primary entrants who completed primary school (ratio completed primary/attended primary, red line)

In urban areas, virtually all residents who attend primary school also complete that level of education. In rural India, completion rates are slightly lower but even here children typically stay in school until they graduate. 85% to 90% of primary school students born between 1950 and 1970 went on to graduate. Since 1970, this share has increased to more than 93% for India as a whole and in urban areas this value is as high as 97%. (The gap between primary school attendance and completion rates since 1990 is explained by the fact that children born since then were not old enough to have completed primary school at the time of the survey.)

The education system in India succeeds at keeping children in school. Children currently out of school are therefore likely to complete at least the primary level of education once they take the crucial step of enrolling in the first grade. A disaggregation of primary school attendance rates in India makes clear that many of the children out of school are girls from poor rural households. Educators and policy makers have to focus their efforts on this group of children to reach the Millennium Development Goal of universal primary education.

Related articles

• Education disparity in South Asia

Friedrich Huebler, 18 October 2005 (edited 12 October 2008), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2005/10/primary-school-completion-in-india.html

Educational attainment in India, 1950-2000

India is well on the way to achieving the Millennium Development Goal of universal primary education by the year 2015. In spite of rapid population growth during the last 50 years, India has managed to expand its education system to reach an ever larger part of the population.

The increase in educational attainment since 1950 can be traced with data from a Multiple Indicator Cluster Survey (MICS) that was conducted in India in 2000. This nationally representative household survey collected data on the education of all household members aged 5 years or older. The data indicates whether a person attended school, at which level, and for how many years. By grouping all household members by year of birth we can calculate the percentage of a cohort that has attended - but not necessarily completed - a certain level of education. The graph below plots these values for all persons born between 1950 and 2000, disaggregated by sex and area of residence.

Educational attainment by year of birth, India 1950-2000

Data source: India 2000 MICS.

For each cohort two values are shown:

• the percentage who attended primary school or higher (blue area)
• the percentage who attended secondary school or higher (red area)

Until the 1970s, attendance rates for the population as a whole remained stable. About 55% of the population born between 1950 and 1970 attended primary school or higher. For persons born around 1990, this value has increased to roughly 80%.

School attendance rates were historically highest among urban males. Already in the 1950s, 90% of this group attended school at the primary level or higher. In contrast, the most disadvantaged group are women living in rural India. In the 1950s, only 25% of this group attended at least primary school. Women born since the 1970s are much more likely to benefit from education. The percentage of rural women who attended primary school or higher has increased from about 30% in the 1960s to about 70% in the 1990s.

Secondary school attendance rates have increased at a similar pace but at a lower lever. Most children who attend primary school continue their education at the secondary level. In 2000, the primary school net attendance rate in India overall was estimated to be 75% for boys and 69% for girls. The secondary school net attendance rate was estimated to be 54% for boys and 46% for girls. There has been much progress toward gender equality over the last 50 years but women and girls are still disadvantaged in Indian society.

Related articles

• Primary school completion in India, 1950-2000
• Education disparity in South Asia
• Educational attainment in sub-Saharan Africa

Friedrich Huebler, 10 October 2005 (edited 12 October 2008), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2005/10/educational-attainment-in-india-1950.html

Guide to reading Statalist with Gmail

Statalist is a mailing list for users of the Stata statistical package. To subscribe to Statalist, send an e-mail message to majordomo@hsphsun2.harvard.edu, with the text "subscribe statalist" (without quotes) in the body of the message.

In Gmail all messages are displayed with a variable-width font by default. This can make Statalist messages hard to read if they contain tables that only lign up properly with a fixed-width font. Here is a typical Stata table:

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         age |     11258    10.46394    2.277534          7         14
      school |     11209    .7712552    .4200433          0          1

In Gmail the same table would be displayed like this:

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         age |     11258    10.46394    2.277534          7         14
      school |     11209    .7712552    .4200433          0          1

Gmail has no setting to change the font but users of the Firefox browser can overcome this limitation by editing the file userContent.css in their Firefox profile. If the file userContent.css does not exist you can create a text file with that name and save it in your profile folder. This and other profile files can also be edited easily with the chromEdit extension for Firefox.

Adding the lines below to userContent.css overrides the Gmail settings for non-HTML mail and all simple text messages are then displayed with a fixed-width font.

div.msg div.mb {
   font-family: monospace !important;
   font-size: 12px !important;
}
textarea.tb {
   font-family: monospace !important;
   font-size: 12px !important;
}
td.ct {
   font-family: monospace !important;
   font-size: 12px !important;
}

The procedure is described in more detail on the site of Michael Gauthier, where I learned about this approach.

Update (22 June 2008)

As of June 2008, the instructions above are obsolete. Google has added a "Fixed width font" option to Gmail. To activate this feature, follow these steps:

• In Gmail, click on "Settings".
• In the Settings menu, click on "Labs".
• Look for the "Fixed width font" option and select "Enable".
• Click on "Save Changes".

To read a message in fixed width font, open the message, click on the "Reply" drop-down menu in the upper right corner of the message, and select "Show in fixed width font".

Related articles

• Guide to integrating Stata and external text editors
• Creating maps with Stata
• Creating PNG images with Stata

Friedrich Huebler, 3 October 2005 (edited 22 June 2008), Creative Commons License

Secondary school attendance in India

A Multiple Indicator Cluster Survey (MICS) that was conducted in India in 2000 showed that 72.1% of all children of primary school age (6-10 years) were attending primary school. (A previous post on this site has more details on primary school attendance in India.) Only two thirds of these children continue their education at the secondary level. Among children of secondary school age (11-17 years), 49.7% were in secondary school at the time of the MICS survey.

A comparison of primary and secondary school net attendance rates (NAR) reveals that virtually all primary school children from the richest 20% of all households go on to attend secondary school. For children from the poorest 20% of all households, the NAR drops from 64.0% at the primary level to 32.3% at the secondary level. Rural children are also much less likely to stay in school than urban children.

Secondary school net attendance rate, India 2000

Data source: India 2000 MICS.

The gender disparity that can be observed at the primary level is even more pronounced at the secondary level. The secondary school NAR among boys was 53.7%, compared to 45.7% for girls. A disaggregation of the data shows that this disparity at the country level is a consequence of gender discrimination in rural areas and among poor households. In urban areas and among the richest households, the gender parity index (GPI) - the ratio of female to male NAR - is at or close to 1. Rural girls and girls from the poorest household, on the other hand, are much less likely to attend secondary school than their male peers.

Secondary school net attendance rate, India 2000

	Total NAR (%)	Male NAR (%)	Female NAR (%)	Difference male- female	GPI female/ male
Urban	65.1	65.0	65.1	-0.1	1.00
Rural	44.2	49.6	38.7	10.8	0.78
Richest 20%	78.1	79.1	77.1	1.9	0.98
Poorest 20%	32.3	38.3	26.2	12.0	0.69
Total	49.7	53.7	45.7	8.0	0.85

GPI: gender parity index. - Data source: India 2000 MICS.

Friedrich Huebler, 25 September 2005 (edited 27 January 2007), Creative Commons License

National wealth and school enrollment

The first and second goal of the Millennium Development Goals are the eradication of extreme poverty and hunger and the achievement of universal primary education. In previous posts I presented evidence on the connection between poverty and education in India and the United States. At the global level, this link is also evident. Middle and high income countries typically have higher levels of school enrollment than low income countries.

This relationship can be demonstrated by plotting school net enrollment ratios (NER) against gross domestic product (GDP) per capita as a measure of national wealth. The graphs below present the primary and secondary school NER in 2002/03 (the latest year with data) in relation to GDP per capita in 2002. School enrollment figures are from the Global Education Digest 2005 by UNESCO and the GDP per capita from the World Development Indicators 2005 by the World Bank. The GDP data was adjusted with purchasing power parities (PPP) to take the price levels in different countries into account. In addition, the GDP per capita is plotted on a logarithmic scale so that the relationship with NER can be seen more easily.

The first graph makes clear that low income countries have significantly lower levels of primary school enrollment. Most countries with a GDP per capita of $2,500 or less have net enrollment ratios below 80%. Almost all countries above this level of GDP have NER values of more than 80%.

Primary school net enrollment ratio and GDP per capita, 2002

Data sources: (1) Primary school NER: Global Education Digest 2005, UNESCO Institute for Statistics. - (2) GDP per capita: World Development Indicators 2005, World Bank.

The link between national wealth and school enrollment is even more obvious at the secondary level of education. Virtually all countries with a secondary school NER below 60% have a GDP per capita of less than $10,000. In contrast, all countries with a per capita income of more than $15,000 have NER levels near or above 80%.

Secondary school net enrollment ratio and GDP per capita, 2002

Data sources: (1) Secondary school NER: Global Education Digest 2005, UNESCO Institute for Statistics. - (2) GDP per capita: World Development Indicators 2005, World Bank.

Related articles

• National wealth and years of education
• Household wealth and years of education
• Poverty and educational attainment in the United States
• Poverty and educational attainment in the United States, part 2

Friedrich Huebler, 18 September 2005 (edited 28 August 2008), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2005/09/national-wealth-and-school-enrollment.html

Poverty and educational attainment in the United States, part 2

Poverty and education are closely linked. In India, for example, children from the poorest households are least likely to attend primary school. Such a link between poverty and education also exists in industrialized countries. In the United States, the states with the highest poverty rates are also those with the lowest share of high school graduates. The graph below plots the percent of the population living below the poverty level against the percent of the population above 25 years of age without complete high school education. The data is from the 2004 American Community Survey and all states are marked with their U.S. postal abbreviation.

Percent of population below poverty level and percent of population 25 years and over who did not graduate from high school, United States 2004

Data source: 2004 American Community Survey.

The regression line emphasizes the close link between poverty and lack of education in the United States. Mississippi has the poorest and least educated population, with 22 percent living below the poverty level and 23 percent not having graduated from high school. Other states with a low share of high school graduates and high poverty rates are Alabama, Arkansas, Kentucky, Louisiana, New Mexico, Texas, and West Virginia. In contrast, Alaska, Connecticut, Minnesota, and New Hampshire have the least poor and most educated population in the United States. In the country as a whole, 13 percent of the population live below the poverty level and 16 percent did not graduate from high school.

Related articles

• Poverty and educational attainment in the United States
• Achievement gap between black and white students in the United States
• National wealth and school enrollment
• Household wealth and years of education
• Educational attainment in sub-Saharan Africa

Friedrich Huebler, 12 September 2005 (edited 9 May 2009), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2005/09/poverty-and-educational-attainment-in_12.html

Poverty and educational attainment in the United States

On August 29, 2005, hurricane Katrina, possibly the deadliest storm in U.S. history, caused extensive damage in the southeastern United States. The victims of Katrina tended to be black, poor and less educated and many did not have the means to leave the area before the storm arrived.

Alabama, Louisiana and Mississippi, the states that were particularly hard hit, are among the poorest in the nation, according to data from the 2004 American Community Survey by the U.S. Census Bureau. In Mississippi, 21.6% of the population lived below the poverty line, more than in any other U.S. state. Louisiana was the second poorest state, with 19.4% of the population living below the poverty line. Alabama was the ninth poorest of the 50 states, with 16.1% below the poverty line. New Orleans, the city with the highest number of storm victims, had a poverty rate of 23.2%. In comparison, 13.1% of the entire U.S. population lived below the poverty line in 2004. In terms of education, the three states affected most by the hurricane also rank near the bottom of the 50 U.S. states, as the graph below shows.

Percent of population 25 years and over who graduated from high school, United States 2004

Data source: 2004 American Community Survey.

The 2004 American Community Survey found that 83.9% of the U.S. population aged 25 years and over graduated from high school, which means that they have at least 12 years of education. In the graph above, the states are ranked by the share of the population who completed high school. Mississippi had the lowest percentage of high school graduates, 77.3%. Alabama with 79.5% and Louisiana with 79.9% rank fifth and sixth in terms of the lowest share of high school graduates.

The victims of hurricane Katrina are mostly from the margins of U.S. society, poor, with little education and limited economic opportunities. Local, state and national government agencies have the responsibility to assist those in need but there has been widespread criticism of the government's slow response to the disaster. Many survivors have lost their homes and other belongings and are unable to provide for themselves. They will depend on help from the government and charitable organizations like the Red Cross for many months and years to come.

Related articles

• Poverty and educational attainment in the United States, part 2
• Achievement gap between black and white students in the United States
• National wealth and school enrollment
• Household wealth and years of education
• Educational attainment in sub-Saharan Africa

Friedrich Huebler, 4 September 2005 (edited 9 May 2009), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2005/09/poverty-and-educational-attainment-in.html

Primary school attendance in India

Today I will begin a series of posts that take a closer look at gender disparity in school attendance. Goal 3 of the Millennium Development Goals calls for the promotion of gender equality and the empowerment of women. The targets listed under this goal include the elimination of gender disparity in primary and secondary education, preferably by 2005, and at all levels of education no later than 2015.

Education statistics published by UNESCO are mostly based on administrative records of enrollment. Data from household surveys are an alternative source of statistics on school attendance. Because household surveys collect data on various characteristics of children and the households they live in it is possible to analyze gender disparity in more detail than with national enrollment data on primary or secondary education.

With household survey data a child is counted as being in school if one of the following two conditions is met:

1. The child attended school during the week preceding the survey.
2. The child was in school anytime during the year preceding the survey. This condition is added to account for children who were not in school because they were ill, because they were on vacation, or for other reasons.

In India, one of the most recent surveys is a Multiple Indicator Cluster Survey (MICS) that was conducted in 2000. Overall, 72.1% of all children of primary school age (6-10 years) were attending primary school according to the data from the India MICS (see also the graph in a previous post). The graph below presents the primary school NAR for boys and girls disaggregated by the area of residence and by household wealth.

Primary school net attendance rate, India 2000

Data source: India 2000 MICS.

In the country as a whole, more boys than girls go to primary school. The NAR of boys is 6.6% higher than the NAR of girls (see the table below). This gender disparity at the country level is clearly driven by the patterns of school attendance in rural areas and in poor households. In urban areas and among the richest 20% of the population, boys' and girls' attendance rates are virtually identical. Rural families and those from the poorest 20% of the population, on the other hand, are likely to send only boys to school when they cannot afford education for all their children. To reach the Millennium Development Goal of gender parity, policy makers have to focus their efforts on rural India and on households that suffer from poverty.

Primary school net attendance rate, India 2000

	Total NAR (%)	Male NAR (%)	Female NAR (%)	Difference male- female	GPI female/ male
Urban	76.8	77.3	76.2	1.1	0.99
Rural	70.7	74.7	66.5	8.2	0.89
Richest 20%	81.9	81.8	81.9	-0.1	1.00
Poorest 20%	64.0	67.7	60.2	7.5	0.89
Total	72.1	75.3	68.7	6.6	0.91

GPI: gender parity index. - Data source: India 2000 MICS.

Friedrich Huebler, 29 August 2005 (edited 27 January 2007), Creative Commons License.

Education data from household surveys

Education statistics published by the UNESCO Institute for Statistics rely on two sources of data: (a) administrative records of enrollment that are provided by national governments and (b) population statistics from the United Nations Population Division. For example, the primary school net enrollment rate is calculated as follows:

• Primary school NER = children of primary school age enrolled in primary school / total population of primary school age
  

The numerator uses national enrollment data and the denominator population data from the UN Population Division. This combination of data from two different sources can lead to errors in the calculation of indicators like the NER. Estimates by the UN Population Division may not match the actual population figures in a country. Local schools, whose records are aggregated at the national level, may misstate enrollment to obtain financing and governments may do the same to meet objectives like the Millennium Development Goals. A further limitation of education statistics based on administrative records is that they provide little information about the characteristics of students beyond age and sex. In spite of these concerns, the data published by UNESCO remains the most comprehensive collection of global education statistics.

Household surveys are an alternative source of data on education. Because survey data is collected at the household level, it is possible to capture a large amount of additional information on children and the households and communities they live in. In contrast to records of enrollment, household surveys also provide data on children out of school that can be analyzed to guide policies aimed at increasing school enrollment rates.

Estimates derived from surveys use a single source of data, which minimizes problems that arise from the combination of data from different sources. However, the estimates are affected by two other types of errors: sampling errors and nonsampling errors. Sampling errors arise because surveys cover only a part of the population and estimates are therefore not identical to those that would be obtained by surveying the entire population of a country. Nonsampling errors are the result of mistakes during data collection and data processing, for example incorrect responses by survey respondents and mistakes during data entry. Sampling errors can be minimized by selecting a sufficiently large sample and nonsampling errors can be minimized through training of survey staff.

Two household survey programs that focus on developing countries are the Demographic and Health Surveys (DHS) and the Multiple Indicator Cluster Surveys (MICS). The DHS were initiated by the U.S. Agency for International Development (USAID) in the 1980s to provide data on population and health trends. The MICS program was developed by UNICEF in the 1990s to monitor the situation of women and children.

Both the DHS and the MICS collect data on school attendance. As an example I show estimates for the primary school net attendance rate (NAR) from the 2000 MICS in India. The primary school NAR is calculated as follows:

• Primary school NAR = number of children of primary school age that are attending primary school / number of children of primary school age
  

Similar to the NER, the maximum value of the NAR is 100%. In the India MICS, 72.1% of all children of primary school age (6-10 years) were found to be in primary school. Attendance rates were higher among boys than girls (75.3% vs. 68.7%). NER data from UNESCO limits the analysis to a breakdown by sex but with survey data we can also analyze other disparities. In India, for example, urban children (NAR 76.8%) are more likely to attend primary school than rural children (NAR 70.7%). Household wealth is also an important determinant of school attendance. Among the poorest 20% of all households, only 64.0% of children attend primary school, compared to 81.9% of children from the richest 20% of all households.

Primary school net attendance rate, India 2000

Data source: India 2000 MICS.

In the coming weeks I will present more education data from household surveys. By providing detailed information on the determinants of school attendance at the household level, surveys are an important tool for policy makers and for researchers and organizations that work in the area of education.

Related articles

• Education disparity in South Asia

Friedrich Huebler, 22 August 2005 (edited 12 October 2008), Creative Commons License
Permanent URL: http://huebler.blogspot.com/2005/08/education-data-from-household-surveys.html.

Pre-primary education

Pre-primary or preschool education is targeted at children below the official primary school starting age. The content of national programs of pre-primary education varies, but the common goal is to introduce children to a school-type environment. Such programs set the foundation for lifelong learning and help countries reach the Millennium Development Goal of universal primary education.

The Global Education Digest 2005 from UNESCO has statistics on participation in pre-primary education for the school year 2002/03. The net enrollment ratio (NER) is listed for 149 countries, the gross enrollment ratio (GER) for 180 countries. The NER is the share of children of preschool age who are enrolled in preschool. The GER is the number of children in preschool, regardless of age, divided by the population of preschool age.

• NER = number of children of preschool age in preschool / number of children of preschool age
• GER = number of children in preschool / number of children of preschool age

The maximum value for the NER is 100% while the GER can exceed 100%. In a previous post I demonstrated that the difference between NER and GER at the primary level can be quite large. This is often the case in Sub-Saharan Africa because of delayed entry into primary school. At the pre-primary level, the difference between NER and GER is much smaller, as a comparison of the two maps below shows.

For most countries the pre-primary NER and GER lie in the same range although there are notable exceptions, among them Australia, South Korea, and the Ukraine. For this reason, and because the GER is available for more countries, I focus on the GER to describe participation at the pre-primary level of education.

Pre-primary net enrollment ratio, 2002/2003

Data source: UNESCO Institute for Statistics (UIS). 2005. Global Education Digest 2005. Montreal: UIS.

Pre-primary gross enrollment ratio, 2002/2003

Data source: UNESCO Institute for Statistics (UIS). 2005. Global Education Digest 2005. Montreal: UIS.

The lower graph presents the pre-primary GER in the school year 2002/03. The GER is above 80% in most industrialized countries, but also in some countries of East Asia (for example Malaysia and Thailand), Latin America (for example Cuba and Mexico), and Eastern Europe (Belarus and Russia). The pre-primary GER is lowest in Africa and the Middle East, with most countries in the range below 20%. Low GER values are also observed in Central, South, and East Asia. In Sub-Saharan Africa this pattern of low participation continues at the primary level, while in East Asia primary school enrollment rates are significantly higher.

Friedrich Huebler, 25 July 2005, Creative Commons License.

Transition from primary to secondary education

Statistics on primary and secondary school enrollment from the Global Education Digest 2005 by UNESCO indicate that few children in Africa continue their education past the primary level. The percentage of children enrolled in the last grade of primary school who continue their education at the secondary level is known as the transition rate from primary to secondary education.

The following graph shows the transition rate from primary to secondary education for 147 countries for which the Global Education Digest 2005 has data. The blue circles indicate the transition rate in each country. The red marks indicate the average transition rate in each region and the 95% confidence interval for this average.

Transition rate from primary to secondary education, 2002/03

Data source: UNESCO Institute for Statistics (UIS). 2005. Global Education Digest 2005. Montreal: UIS.
Note: Regional averages are weighted by each country's population of primary school age.

Worldwide, 85% of children in the last grade of primary school go on to attend secondary school (see the table below). Only two regions have transition rates below this global average: Eastern and Southern Africa (67.1%), and West and Central Africa (52.4%). Transition rates are highest in the industrialized countries (98.2%) and in Eastern Europe and the CIS countries (96.1%). However, even in Sub-Saharan Africa some countries have transition rates above 80%.

Transition rate from primary to secondary education, 2002/03

Region	Transition rate (%)
	Male	Female	Total
East Asia, Pacific	89.8	89.7	89.7
Eastern and Southern Africa	67.4	66.6	67.1
Eastern Europe, CIS	98.8	98.7	96.1
Industrialized countries	98.1	98.1	98.2
Latin America, Caribbean	92.0	91.3	91.0
Middle East, North Africa	86.4	86.1	86.3
South Asia	84.5	89.5	86.8
West and Central Africa	54.6	49.4	52.4
World	84.4	85.4	85.1

Data source: UNESCO Institute for Statistics (UIS). 2005. Global Education Digest 2005. Montreal: UIS.
Note: Regional values are weighted by each country's population of primary school age.

The difference between male and female transition rates is negligible, except for two regions: South Asia, and West and Central Africa. In South Asia, girls are more likely to transfer to secondary school; the transition rate is 89.5% for girls and 84.5% for boys. In West and Central Africa, the opposite can be observed; here the transition rate is 54.6% for boys and 49.4% for girls. Girls in this region are thus doubly disadvantaged. Not only do they have very low primary school enrollment rates, they are also less likely to continue their education at the secondary level.

Friedrich Huebler, 17 July 2005, Creative Commons License