Conference 2013 'Economic Development in Africa' Education and - - PowerPoint PPT Presentation

Conference 2013 'Economic Development in Africa'

Education and Inclusive Growth

SUMMARY

The outline of the paper

This paper argues that…

• that education is crucial for the distribution of

growth and inclusiveness in Africa

• but, despite the fact that (a) a relatively large

share of the budgets of African countries is spent on education, and (b) that there is a rapid increase of youth with education in Africa, the levels of educational attainment in Africa lag far behind other World regions.

Significantly, that the quality of education…

…impacts on individual outcomes, on the quality of economic growth and on human behaviour in ways that facilitate the achievement of a wide range of human development goals

but is found…

• to differ widely between countries where

children who live in African countries not only receive fewer years of education but also reach lower achievement levels

• and within countries, between boys and girls

and between the poorest and wealthiest households

Also, that transition to and completion

• f…

…lower secondary and secondary education in Africa, …compares unfavourably to other world regions …notably in gender parity …and socio economic status

And in tertiary and higher education…

• …which is also essential for growth
• …the pattern is similar
• …but worryingly, there are questions about

the relevance of education to the demands of the new economy or the mismatch between skills and jobs

It argues that political and policy

• versight…
• In financial management
• Service delivery
• And long term trends in educational

achievement….

Are by far…

• The most important and pressing challenges

to education and inclusive growth in Africa

EDUCATION AND INCLUSIVE GROWTH

Presentation

INTRODUCTION

The link between education and economic growth

The links between education and growth

• There is strong evidence that economic growth has been

accompanied by growth in both spending and participation in schooling.

• Economists have examined this association quite carefully and

come to the conclusion that, through a variety of different avenues and in a number of different ways, investment in school systems does have a strong economic pay-off.

• This is an important conclusion that is highly relevant to individual,

corporate and government decisions regarding investment. For all spheres of decision making there is good evidence that the rate of return is high, even relative to other investment opportunities.

The impact of education on income

• Human capital has a positive impact on

economic growth. Although there are exceptions, empirical evidence generally shows that human capital has a positive and statistically significant impact on the growth rate of per capita income.

• the likelihood of being wage employed

increases strongly with a secondary school education (based on Gallup World Poll data, see also Annex 2). Household surveys show the same for the likelihood of earning a higher wage (AfDB, 2012).

• There is a better change of being wage

employed for those who have completed primary education, as opposed to those with no education, and an even higher probability for those who have completed secondary education.

Male Female

10 20 30 40 50 60

No Education 1 to 8 years primary schooling 9 years of education up to secondary completed 1+ years of tertiary education

Predicted probability* of being wage employed

CHALLENGES TO INCLUSIVE GROWTH IN AFRICA

The link between education and economic growth

Despite relatively large investments in education…

• .
• 5.0 10.0 15.0 20.0

Europe & Central Asia South Asia North America Latin America & Caribbean East Asia & Pacific Middle East & North Africa Sub-Saharan Africa

Public spending on education (%

• f government expenditure)

…and a rapid increase of youth with education in Africa

• By the end of this decade

60% of boys across sub- Saharan Africa will have completed primary school compared to 56% of girls. Over the last 30 years primary school completion rates for boys have risen quite slowly (from about 48% in the early 1970s) compared to a sharper rise in primary completion rates for girls (starting at 30% in the 1970s) to almost double that today.

50 100 150 200 250

2000 2005 2010 2015 2020 2025 2030 Million 20-24 year-old cohorts by education, 2000-2030

Tertiary education Secondary education only Primary education only No education

… the levels of education in Africa compare poorly with other regions

2 4 6 8 10 12 1980 1985 1990 1995 2000 2005 2010 Years

Mean years of schooling of adults

Arab States Europe and Central Asia East Asia and the Pacific Latin America and the Caribbean South Asia Sub-Saharan Africa

EDUCATION ATTAINMENT

Educational growth in Africa, in which everyone participates lags behind

• ther world regions…

…as measured by primary school completion

48 53 54 57 58 59 60 30 36 38 43 47 52 56

20 40 60 80 100 40-44 35-39 30-34 25-29 20-24 15-19 10-14

Percent Age Range

Trends in completion of primary school, 33 African countries (population weighted)

Male Female

early 1970s late 1970s early 1980s late 1980s early 2000s late 1990s early 1990s

…or for those enrolling in secondary education in relation to the growth of the school-age population

Sub-Saharan Africa East Asia and the Pacific

20 40 60 80 100 120 20 40 60 80 100 120 1970 1975 1980 1985 1990 1995 2000 2005 2009 GER (%) School-age population and total enrolment (in millions) School-age population Enrolment GER 20 40 60 80 100 120 40 80 120 160 200 240 1970 1975 1980 1985 1990 1995 2000 2005 2009 GER (%) School-age population and total enrolment (in millions) School-age population Enrolment GER

…and for those who complete lower secondary education

Sub Saharan Africa East Asia and the Pacific

20 40 60 80 100 Gross graduation ration (%)

What proportion of students complete lower secondary?

Total Male Female 20 40 60 80 100

Cambodia Laos Myanmar Samoa Timor-Leste Philippines Thiland Indonesia Macao Malaysia China Hong Kong Brunei

Gross graduation ration (%)

What proportion of students complete lower secondary ?

Total Male Female

…and secondary education

Sub Saharan Africa East Asia and the Pacific

10 20 30 40 50 60 Burkina… Chad 2004 Tanzania… Senegal 2006 Malawi 1998 Mali 2006 Benin 2002 Lesotho 2008 Uganda 2008 Namibia… Mauritius… Kenya 2010 Zimbabwe… Seychelles… South… (%)

Which countries have most adults completed secondary education?

Tertiary Post-secondary Upper secondary Lower secondary Primary 20 40 60 Cambodia… Thailand… Indonasia… Malaysia… China 2000 Fiji 2007 Macao 2006 Phillipines… Tonga 2006 Hong Kong… Singapore… Korea 2005 Australia… New… Samoa 2001 Japan2002 (%)

Which countries have most adults completed secondary education?

Tertiary Post-secondary Upper secondary Lower secondary Primary

…or for men and women across all levels of education

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Chad 2004 Burkina Faso 2007 Malawi 1998 Benin 2002 Senegal 2006 Kenya 2010 Tanzania 2002 Zimbabwe 2002 Mauritius 2000 Namibia 2001 South Africa 2009 Uganda 2008 Seychelles 2002 Lesotho 2008 Mali 2006

Gender parity (GPI 0.97-1.03)

Population with at least primary education (ISCE1) Population with at least lower secondary education (ISCED 2) Population with at least upper secondary education (ISCED 3)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Cambodia2007 China 2000 Indonesia 2008 Thailand 2006 Korea 2005 Malaysia 2005 Hong Kong 2008 Macao 2006 Singapore 2008 Tonga 2006 Fuji 2007 Australia 2009 Philipines 2004 Japan 2002 New Zealand 2006 Samoa 2001

Gender parity (GPI0.97-1.03)

Population with at least primary education (ISCE1) Population with at least lower secondary education (ISCED 2) Population with at least upper secondary education (ISCED 3)

Africa has made rapid progress…

• In providing access to

tertiary and higher education

50 100 150 200 250 2000 2005 2010 2015 2020 2025 2030

Million

Tertiary education Secondary education

• nly

Primary education

• nly

No education

Source: World Bank EdStats, authors' calculations

…but it is not fast enough when compared to other countries

• In spite of the enormous

amount of resources devoted to the provision of higher education in African countries, Africa continues to be endowed with relatively low stocks of higher education human capital partly because

• f the small base from which it

started, partly because of inefficiencies in the production

• f higher education human

capital, and partly because of emigration by educated Africans.

Access is uneven between countries and within countries, it favours males…

Access to tertiary and higher education varies a great deal between countries and it is highly unequal 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Tunisia Cape Verde Namibia Mauritius Madagascar Morocco Tanzania Angola Uganda Rwanda Comoros Kenya Djibouti Malawi Burundi Côte d'Ivoire Burkina Faso Central African Republic Mali Mauritania Ethiopia Benin Eritrea Congo Chad

Gender parity index for tertiary gross enrolment ratio

1999 2009

… those living in Urban areas

…are more likely to gain access to, and complete tertiary education

29.3 23.3 18.5 16.2 10.8 10.8 10.4 6.0 3.8 3.3 2.6 2.2 1.9

• 5.0

10.0 15.0 20.0 25.0 30.0 35.0

Urban-rural parity index for tertiary education completion (25-34 year olds)

…from privileged backgrounds

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Likelihood of being university educated, by father's education (25-34 year olds)

Less than primary Primary Secondary University Total

…and those who are able- bodied

5.1 4.8 3.1 2.6 1.9 1.8 1.4 1.4 1.3 0.9 0.6

• 1.0

2.0 3.0 4.0 5.0 6.0

Disability parity index for tertiary education completion (25-34 year olds)

Higher Education is …insufficient

In many African countries the percentage of graduates in Science, Engineering, and Agriculture is low

5 10 15 20 25 30 35 40 45 50 Namibia Sierra Leone Swaziland Uganda Botswana Chad Angola Burundi Ghana Niger Madagascar Cameroon Zimbabwe Eritrea Burkina Faso Liberia Algeria Ethiopia Morocco Kenya Benin Djibouti Gambia

…in relation to the needs

• f both the traditional

economy

Despite it being a vast sector with much potential for employment, very few university graduates in Africa major in agriculture

10 20 30 40 50 60 70 80 90 100 % of Employment in Agriculture % University Graduates in Agriculture

…and the ‘new’ economy

Employer surveys report that African graduates are weak in problem- solving, business understanding, computer use and communication skills.

…which accounts, in part, for unemployment

The mismatch between the needs

• f the economy and the skill sets

that are produced is a disabling factor University courses and delivery remain largely orientated towards public sector employment to the expense of skills needed for the new economy

THE QUALITY OF EDUCATION

How good is educational provision?

...as determined by the % of non- readers in Africa by the end of Grade 2

…or, the

20 40 60 80 100 1 2 3 4 5 Percent Grade attained

Percentage of young women aged 20-24 who can read a simple sentence, by grade attained, selected poor countries (primary school completion rates in parentheses)

Rwanda (25%) Malawi (33%) Ethiopia (20%) Benin (17%) Uganda (42%) Zambia (54%) Mali (12%)

THE GOVERNANCE OF EDUCATION

How good is the oversight of the educational system?

The role of the state is crucial in the

• versight of the educational system …
• In the financing of education
• The management of human teachers
• The management of resources in relation to

student learning outcomes

Education policies in African countries

Bridging the gap between research and policy

Moussa P. Blimpo University of Oklahoma Email: moussa.blimpo@ou.edu

CSAE Annual Conference University of Oxford 03/17/2013

The current situation

2

• Challenges
• Manage a sector that has grown rapidly
• Improve learning outcomes
• Research
• Policy relevant by design
• Limited impact – perhaps inadequate with the true

problems

• Policies
• Still struggling with the basics
• The obvious is more policy relevant
• Drawing from SBM research in The Gambia

SBM RCT in Gambia

A collaborative work

• Main partners (Funding and expertise at different levels)
• Ministry of Basic and Secondary Education
• World Bank
• DFID
• Other Partners
• Gambia Bureau of Statistics – Contracted for data collection
• West African Examination Council – Support for test design

Background

• Four years long RCT (2008 - 2011)
• Primary GER: 90% and PCR: 75%
• Poor learning outcomes
• Banjul (Region 1) & Central River (Region 2)

excluded from this study

The study its m ain findings

Group Intervention Result after 3 to 4 years (compared with control group)

Control Management manual NA Grant Management manual Grant (approx US$500) No significant increase in attendance No effect on learning outcomes WSD Management manual Grant (approx US$500) School management training Reduced student absence (down 21%) Reduced teacher absence (down 23%) No effect on learning outcomes

Why impacts on participation but not learning?

• 1. Improvement in the control schools?
• 2. WSD brought back poorer-performing students?
• 3. Management practices – are these the right ones?
• 4. Heterogeneous impacts – only works in some cases?
• 5. Low teacher quality – coming to school ‘doesn’t matter’?

6

Teacher ‘quality’

What does research say?

7

• Teacher characteristics such as diplomas and

experience have low predictive power

• Value added approach on students’ learning outcomes
• Hence the idea of performance pay teacher incentives

Teacher ‘quality’

Experience for Gambia

8

• Research was also research of questions as

much as answers

• Teacher content knowledge (1049 teachers from

176 primary schools)

• Difficulty level ranging from grade 1- 6, ensured

that timing is not a constraint

Selected questions: Literacy

48.38 40.73 69.58 54.07 85.28 10 20 30 40 50 60 70 80 90 100 MYSTERIOUS: Pleasant/Stange/Quiet/ Frightening STARTLED : Began/Scattered/Frightened/Deafened EVEN: Sandy/ Level/ Rocky/Hard ENORMOUS: Heavy/Hard/Huge/Rotten The children worked in ___ silence during the test. (Complete, Common, Company, Count ) % of Teachers who Answered Correctly Questions

Selected Literacy Questions (Full Sample)

Selected math questions

83.42 77.82 63.98 36.80 50.74 54.86 48.09 37.00 29.44 45.04 10 20 30 40 50 60 70 80 90 100 14 + 139 + 9 252 ÷ 7 864 ÷ 24 1 1/2 – 3/4 1/2 x 1/3 1/10 ÷ 1/5 75% of 36 1/4 x 1/6 ÷ 1/8 1/4 x 5 1/4 + 1/2 + 1/8 % of Teachers who Answered Correctly Questions

Selected math Questions (Full sample)

Strong policy response

• Hire ‘better’ trained teachers
• Reform at the teacher training college to

emphasize content knowledge as much as pedagogy

• Train existing teachers

11

Similar basic problems in other areas E.g. Class size

12

• What does research say?
• Research shows class size does matter
• In fact good to marginally expand classes of good teachers
• Reducing class size not the only response (e.g. double shifts)
• Innovation in teaching large classes and other ideas should be explored

Summary

13

• Potential gap between the research frontier and the policy

frontier

• Research: Complex and bigger questions
• Policy: The basics, the fundamentals
• Teacher quality and class size as examples
• Same in other areas such as Information management
• Basic EMIS
• Taking advantage of basic statistics: Census vs. Random sample
• New technologies – Not to hand laptops to kids but to enable local

and central authorities to monitor and manage.

F R A N C I S T E A L C E N T R E F O R T H E S T U D Y O F A F R I C A N E C O N O M I E S U N I V E R S I T Y O F O X F O R D C S A E C O N F E R E N C E E C O N O M I C D E V E L O P M E N T I N A F R I C A O X F O R D 1 7 - 1 9 T H M A R C H 2 0 1 3

Education for Inclusive Growth in Africa

An MSc Exam Question (set long ago)

Investing in education involves two levels of wasted resources. First those devoted to the education. The second the resources to provide jobs for the educated. Discuss.

Africa in comparative perspective

250 300 350 400 US$ 2000 Prices 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year

Source: World Bank: World Development Indicators

Africa's GDP: 1960-2007

2,000 4,000 6,000 8,000 Income in US$

Middle-East Latin America South-East Asia South Asia Sub-Saharan Africa China

2000 1980 2000 1980 2000 1980 2000 1980 2000 1980 2000 1980

Note:Sub-Saharan Africa excludes South Africa. Source: PENN World Tables. Incomes are weighted by population and expressed in 1996 PPP$.

Changes in Incomes per Capita: 1980-2000

5 10 15 Annualised 5 Year Average Growth Rates

South-East Asia South Asia China Sub-Saharan Africa

2009 2000 1990 1980 2009 2000 1990 1980 2009 2000 1990 1980 2009 2000 1990 1980

Note:sub-Saharan Africa excludes South Africa. Source: World Development Indicators 2010. Growth rates are weighted by population.

Growth Rates (in constant price LCU) 1980, 1990, 2000, 2009

2 4 6 Annualised 5 Year Average Growth Rates

South-East Asia South Asia China Sub-Saharan Africa

2005 2000 1990 1980 2005 2000 1990 1980 2005 2000 1990 1980 2005 2000 1990 1980

Note:sub-Saharan Africa excludes South Africa. Source: Barro and Lee Data for Education Levels. Growth rates are weighted by population.

Growth Rates of Years of Education 1980, 1990, 2000, 2005

1 2 3 4 5 Annualised 5 Year Average Growth Rates

Sub-Saharan Africa

2005 2000 1990 1980

Note:sub-Saharan Africa excludes South Africa. Source: Barro and Lee Data for Education Levels. Growth rates are weighted by population.

Growth Rates of GDP and Years of Education 1980 to 2005

GDP Years of Education

Levels and changes in education

TGO GBR RWA NPL BGD TUR GNB PER KEN ZMB BOL ARG PRT ITA COG NOR TUN EGY GMB BEN DZA AUT ESP ZAF MUS BEL IDN AUS TZA MOZ HKG JPN URY UGA SLV DOM VEN IND PHL PAK MWI MLI ECU JAM NLD NIC COL POL NER CAN CHN JOR HND NZL CMR USA ISR SWE IRN LKA KOR MYS BRA GRC CRI GTM HUN BRB PRY FRA SEN ZWE PAN DNK GHA THA ISL LSO CHL SWZ CHE IRL TTO FIN MEX SYR

6 7 8 9 10 11 Ln of GDP Per Capita 5 10 15 Years of Education Ln of Real GDP in US$(1996 PPP) Linear prediction

Source: Barro and Lee (2000) Education Data and PENN World Tables (6.1).

ARG AUS AUT BEL BEN BGD BOL BRA BRB CAN CHE CHL CHN CMR COL CRI DNK DOM DZA ECU EGY ESP FIN FRA GBR GHA GMB GNB GRC GTM HKG HND HUN IDN IND IRL IRN ISL ISR ITA JAM JOR JPN KEN KOR LKA LSO MEX MLI MOZ MUS MWI MYS NER NIC NLD NOR NPL NZL PAK PAN PER PHL POL PRT PRY RWA SEN SLV SWE SYR TGO THA TTO TUN TUR TZA UGA URY USA VEN ZAF ZMB ZWE

• .5

.5 1 1.5 Change in Ln of GDP Per Capita 1 2 3 4 Change in Years of Education Growth of GDP Linear prediction

Source: Barro and Lee (2000) Education Data and PENN World Tables (6.1).

What do we mean by inclusive growth?

 Clearly we can mean many things but central to them

all I think is that growth should benefit the poor, preferably the poorest.

 We may care about inequality as well and we will

certainly care if the poorest incomes are growing at 0.5 or 5 per cent.

 Given we know what we are after how do we get it?

How do we achieve inclusive growth?

 In one sense the answer is easy.  We increase the price of the assets owned by the

poor.

 In the case of SSA that is land and unskilled labour.  Some policies may do both but most will not.  The answer is usually posed in terms of increasing

the productivity of those assets.

 Indeed that is (sometimes implicitly) how investing

in education is seen to link to poverty reduction.

Earnings and Education across 26 countries (none of them African)

1 1.5 2 2.5 3 Ln Hourly Wages 5 10 15 20 25 Years of Education Ln pppUS$ Predicted

An International Earnings Function

What is the pattern in Africa?

50 100 150 US$ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Note: The wages are calculated for an average worker over the period from 1992 to 2003 with the average characteristics of the sample for gender,age and tenure.

Ghana: Wages in US$ per Month by Years of Education

100 200 300 US$ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Note: The wages are calculated for an average worker over the period from 1992 to 2003 with the average characteristics of the sample for gender,age and tenure.

Kenya: Wages in US$ per Month by Years of Education

50 100 150 200 US$ 2 3 5 6 8 9 10 11 12 13 14 15 16 18

Note: The wages are calculated for an average worker over the period from 1992 to 2003 with the average characteristics of the sample for gender,age and tenure.

Nigeria: Wages in US$ per Month by Years of Education

20 40 60 80 US$ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Note: The wages are calculated for an average worker over the period from 1992 to 2003 with the average characteristics of the sample for gender,age and tenure.

Tanzania: Wages in US$ per Month by Years of Education

One answer to the exam question: Implications of convexity

 The convexity of the earnings function will provide a

powerful incentive to pursue higher education even though the returns to lower levels of education are modest.

 So if that is the shape of the earnings function in can

explain two striking features of our data:

 no close links between increases in education and changes in

incomes

 greatly accelerating demand for tertiary education while most

get no return from the education.

(a) GDP in 20 0 0 (b) Changes in GDP Between 198 0 and 20 0 0

Incomes and physical capital

RWA MLI ZAF UGA JAM NPL GHA SEN KOR URY TZA NIC CMR BEL NLD ESP PAN BEN GBR FRA CPV USA DZA GMB SLV HND ISL HKG TTO DOM TCD LSO ROM NZL IND THA IRL MEX ITA NER TGO LKA PER PRT ZMB EGY PAK MOZ SYR CIV PRY GNB MDG BOL CHL AUS TUN GNQ MWI JPN GRC IDN NGA GIN PHL GAB ETH MYS ZWE MARECU NOR DNK JOR SWE CHN SYC COM CAN COG CHE CRI ARG COL TUR ISR KEN GTM BRB BRA BDI MUS LUX BGD BFA VEN FIN AUT IRN

6 7 8 9 10 11 Ln of GDP Per Capita 6 8 10 12 Ln of Capital per Capita Ln of Real GDP in US$(1996 PPP) Linear prediction

Source: PENN World Tables (6.1) with Inputed Capital Stock. ARG AUS AUT BDI BEL BEN BFA BGD BOL BRA BRB CAN CHE CHL CHN CIV CMR COG COL COM CPV CRI DNK DOM DZA ECU EGY ESP ETH FIN FRA GAB GBR GHA GIN GMB GNB GNQ GRC GTM HKG HND IDN IND IRL IRN ISL ISR ITA JAM JOR JPN KEN KOR LKA LSO LUX MAR MDG MEX MLI MOZ MUS MWI MYS NER NGA NIC NLD NOR NPL NZL PAK PAN PER PHL PRT PRY ROM RWA SEN SLV SWE SYC SYR TCD TGO THA TTO TUN TUR TZA UGA URY USA VEN ZAF ZMB ZWE

• .5

.5 1 1.5 Change in Ln of GDP Per Capita

• 1
• .5

.5 1 1.5 Change in Log of Capital Stock per Capita Growth of GDP Linear prediction

Source: PENN World Tables (6.1) with Inputed Capital Stock.

Jobs in urban Africa

Another answer: Outcomes in Ghana 2004/ 05

 Small firm less than 10 employees

Self- employment Small Firm Large Firm Public Sector No employment None

0.62 0.09 0.01 0.00 0.27

Junior Secondary

0.52 0.09 0.05 0.02 0.32

Senior Secondary

0.42 0.08 0.08 0.06 0.36

University

0.29 0.06 0.11 0.17 0.37

Outcomes in Tanzania 2004/ 05

 Small firm less than 10 employees Self- employment Small Firm Large Firm Public Sector No employment None

0.79 0.05 0.05 0.01 0.10

Junior Secondary

0.72 0.07 0.05 0.10 0.05

Senior Secondary

0.62 0.06 0.05 0.24 0.04

University

0.41 0.03 0.03 0.50 0.03

Two possible answers

 Yes.  Policy in Africa has consisted of taxing the rural poor

to subsidise the urban (educated) better off in jobs of marginal social value and (in the case of the public sector) of zero value.

 The low return on education for almost all relative to

much higher returns on other activities is what underlies SSA’s historically very low growth rates. OR

The other answer

 No.  Higher productivity across both land and labour is

the key to raising incomes of the poorest.

 Such productivity rises depend on the application of

human capital.

 The problem is that much investment in education

has failed to produce human capital due to its low quality.

 Investing in high quality education is - and will

remain - the key to inclusive growth.

So which do you think correct?

You choose