science, education, and development · progress as an ongoing process. this ability requires the...

- ~~~WTP-1 24WORLD BANK TECHNICAL PAPER NUMBER 124

AF-RICA TECHNICAL DEPARTMENT SERIES

Science, Education, and Developmentin Sub-Saharan Africa

Manuel Zymelman

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WORLD BANK TECHNICAL PAPER NUMBER 124

AFRICA TECHNICAL DEPARTMENT SERIES

Science, Education, and Developmentin Sub-Saharan Africa

Manuel Zymelman

The World BankWashington, D.C.

Copyright © 1990The International Bank for Reconstructionand Development/THE WORLD BANK1818 H Street, N.W.Washington, D.C. 20433, U.S.A.

All rights reservedManufactured in the United States of AmericaFirst printing June 1990

Technical Papers are published to commmunicate the results of the Bank's work to the developmentcommunity with the least possible delay. rhe typescript of this paper therefore has not been prepared inaccordance with the procedures appropriate to formal printed texts, and the World Bank accepts noresponsibility for errors.

The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s)and should not be attributed in any manner to the World Bank, to its affiliated organizations, or tomembers of its Board of Executive Directors or the countries they represent. The World Bank does notguarantee the accuracy of the data included in this publication and accepts no responsibility whatsoeverfor any consequence of their use. Any maps that accompany the text have been prepared solely for theconvenience of readers; the designations and presentation of material in them do not imply the expressionof any opinion whatsoever on the part of the World Bank, its affiliates, or its Board or member countriesconcerning the legal status of any country, territory, city, or area or of the authorities thereof orconcerning the delimitation of its boundaries or its national affiliation.

The material in this publication is copyrighted. Requests for permission to reproduce portions of it shouldbe sent to Director, Publications Department:, at the address shown in the copyright notice above. TheWorld Bank encourages dissemination of its work and wil normally give permission promptly and, whenthe reproduction is for nonconmmercial purposes, without asking a fee. Permission to photocopy portionsfor classroom use is not required, though no tification of such use having been made will be appreciated.

The complete backlist of publications from the World Bamk is shown in the annual Index of Publications,which contains an alphabetical title list (with full ordering information) and indexes of subjects, authors,and countries and regions. The latest edition is available free of charge from the Publications Sales Unit,Department F, The World Bank, 1818 H Street, N.W., WaslLington, D.C. 20433, U.S.A., or fromPublications, The World Bank, 66, avenue d']ena, 75116 Paris, France.

ISSN: 0253-7494

Manuel Zymelman is senior advisor, Economics of Education, in the Education and Training Division,Africa Technical Department, of the World Bank.

Library of Congress Cataloging-in-Publication Data

Zymelman, Manuel.Science, education, and development in Sub-Saharan Africa / Manuel

Zymelman.p. cm. - (World Bank technical paper, ISSN 0253-7494; v.

124)Includes bibliographical references.ISBN 0-8213-1599-41. Science-Africa, Sub-Saharan. 2. Engineering-Africa, Sub

-Saharan. 3. Science-Study and teaching--Africa, Sub-Saharan.4. Engineering-Study and teaching-Africa, Sub-Saharan. 5. Scienceindicators-Africa, Sub-Saharan. I. Title. II. Series: Wor]d Banktechnical paper; no. 124.Q127.A424Z96 1990507.1'066-dc20 90-38700

CIP

- iii -

ABSTRACT

Economic development of Sub-Saharan Africa (SSA) depends to alarge extent on the ability of SSAn societies to introduce technicalchange, which in turn is the result of a confluence of many factor:scientific and technical knowledge, management, institutions, and propiereconomic and social environment. This paper deals only with one aspect oftechnical change in SSA: the production of scientific and technicalknowledge. Part I presents a quantitative view of the scientific output inSSA based on data from the Science Citation Index that provides informationon scientific publications and citations by field and by country. Sirnceuniversities are the focus of scientific research and training in SSA, PartII analyzes enrollments and outputs of universities in SSA in the area ofscience and engineering to ascertain their future role in fostering scienceand engineering. Any increase in the quantity and quality of universiLtyoutputs in science and engineering will require the upgrade of the level ofscientific and mathematics knowledge of the pool of entrants to highereducation. Part III presents a picture of science education in secondaryschools in a selected number of countries in SSA based on answers to aspecially designed questionnaire. Part IV formulates a strategy forscience and education in SSA and the possible role the World Bank in thisstrategy.

- iv -

ACKlOWLEDGL4ENS

I would like to thank Mr. Sandor Nagy for his help in

programming the tables and figures contained in the paper, to Hodan Addou

for organizing the data derived from questionnaires on secondary schools,

and to Aurora Alba for typing the manuscript.

- v -

FOREWORD

Economic development in Sub-Saharan African countries dependsforemost on the ability of their societies to establish technologicalprogress as an ongoing process. This ability requires the capacity tochoose, acquire, adapt, generate, and apply technologies. Scientificknowledge is a basic ingredient that helps develop this capacity.

The study "Science, Education, and Development in Sub-SaharanAfrica (SSA)" presents for the first time quantitative measures ofscientific output by country and field of study, and places Sub-SaharanAfrican scientific efforts in an international context. In a search forsolutions that would allow improvements in the present state of science andscience education, the study also analyzes the output of scientific andtechnological manpower of universities and secondary schools. Followingthe diagnosis of the research and training systems the study suggests astrategy to increase the quantity and, especially, the quality of scienceeducation in SSA.

This study is intended for development agencies staff,government officials, and academicians concerned with science and sci:enceeducation, especially at the university level. By focusing on the inter-relationship of science, training, and economic resources, this studv setsthe framework for a healthy debate on policies required for addressing theproblems confronting the development of science and science education inSSA.

Hans Wyss Director v

Technical DepartmentAfrica Region

- vi -

TABLE OF CONTENTS

Page Nos.

I. Scientific Output: A Quantitative View . . . . . . . 1

Science Indicators in SSA Countries . . . . . . 6Distribution of Publications by Country . . . . 7Impact Measurement . . . . . . . . . . . . . . . 7Sunmary .... 8Tables and Figures . . . . . . . . . . . . . . . 10

II. Universities and Scientific Manpower . . . . . . . . 22

Tables and Figures . . . . . . . . . . . . . . . 27

III. Science Education in Secondary Schools . . . . . . . 49

Enrollment Rates . . . . . . . . . . . . . . . . 49Summary . . . . . . . . . . . . . . . . . . . . 51Tables... ... 53

IV. A Strategy for Science Education in SSA . . . . . . 58

The Possible role of the Bank in thearea of science teaching in SSA . . . . . . . . 63

I. SCIENTIFIC OUTPUT T AVQUANTITAr-

Technical change has been credited in ecot theorv with being

a major factor in economic growth. In growth models this factor was

considered, in general, to be an autonomous process. But in real life

technical change is the result of a confluence of many factors: scientific

and technical knowledge, management, institutions, and proper economic and

social environment.

Scientific and technical knowledge are usually mentioned

together, however, for conceptual clarity it is necessary to distinguish

between them. Scientific knowledge provides the understanding of nature

and its behavior (the "know why"); technical knowledge provides the ability

to manipulate the physical world (the 'know how"). Science and technology

exist in a symbiotic relationship: technology utilizes the advances of

science to produce new and better products, while science progresses as a

result of the validation or rejection of theories through experimentation

done with instruments created with the available technology. This does not

mean that either science or technology cannot progress without advances in

the other. Historically, technology developed in many instances without

the back-up of science, but in the long run scientific advances found their

way into technology. The relationship between science and technology is

lately becoming closer and, increasingly, advances in technology depend

more and more on basic scientific knowledge.

Given that scientific knowledge is a necessary ingredient of

economic growth the question for developing countries is: is it possible

- 2 -

to achieve sustained economic growth without developing the "scientific

sector' since, after all, scientific knowledge is available worldwide. 1/

The case for developing an indigenous science establishment

rests on many fronts:

1- There is undoubtedly a minimum threshold of scientific

knowledge required for the proficient performance of technicians and

professionals (doctors, engineers, agriculturists, etc.) in the economy.

This threshold is rising continuously witlh the expansion of scientific

knowledge and improvement of technology. To impart this knowledge it is

necessary to have a functioning teaching scientific community that is able

to relate to progress in science.

2- While technological knowledge can be acquired from abroad,

to facilitate the transfer it is important to have individuals that can

understand, assimilate, and, if necessary, transform and adapt this

knowledge to local conditions. This, again, requires a minimum quantum of

people with a solid scientific background.

3- A modern technological society requires more than the

ability to perform skillfully; it requires a positive attitude to

modernization. Science, by its questioning nature, influences the way man

looks at the universe. This positive general attitude towards science can

be fostered in the population at large only by a local scientific effort.

1/ Technical knowledge is more restricted because knowhow is more easilyprotected and to a large extent can only be acquired by "learning bydoing'.

- 3 -

4- To keep the morale of those engaged in science teaching, be

it for developing technology, skills, or attitudes, it is crucial to

develop and maintain scientific activities and link the local scientists to

the international scientific community.

While it is not hard to justify the promotion of local science,

it is difficult to quantify the 'requiredw science, and determine the

quality necessary for affecting economic development. Clearly, the

assessment, if not the measurement, of science and its impact is a

requisite for a rational allocation of resources for science research and

teaching.

In principle there is a consensus as to the direct relationship

between science and socio-economic growth, 2/ however, there is less

agreement on the causality and, above all, the quantification of this

relationship, both of which hinge on the ability to measure science.

(Measures of socio-economic development are by no means easy to attain, but

advances in methodology are way ahead of those dealing with measures of

science).

Various methods have been suggested for measuring science.

Economists tend to advocate the input - output approach, although in the

end mostly inputs are measured because of the difficulty of measuring

2/ Derek J. De Solla Price *Measuring the size of science" in LittleScience, Big Science and Beyond, Columbia University Press, N.Y. 1986.

outputs. For example, it is much easier to count the number of scientists

engaged in research than to measure what these scientists produce.

The accepted indicators of scientific activity are:

for inputs:

a) human resources in science - scientists, engineers,

technicians, etc.

b) finance and expenditures

for outputs

a) scientific publications

b) citation counts

There are other wlinkedw indicators or proxies such as percent of

scientists in the population, patents, prizes, etc.

All these indicators appeal to scientists and economists alike for

they provide a quantitative measure, but their reliability is no greater

than the validity of the assumptions upon which they are based.

Strangely enough, in the case of SSA the input indicators, though

easier to measure, are quite scarce and less reliable than the output

indicators. For example, data of science inputs presented at CASTAFRICAII,

in Arusha, Tanzania 1987, covers only a few countries and are mostly from

the late 1970's. Moreover, science and technology manpower are badly

defined, and expenditures on science cover at best only part of the

scientific effort.

On the other hand output indicators, publications and citations,

provide at least comparable international data.

However, output indicators also suffer from many drawbacks. Data

Banks such as the Science Citation Index (SCI) 3/ reflects the scientific

mainstream research and does not include all published research, especially

research published in non or semi-scientific journals, or the efforts of

those that produce something new that is not picked up in the literature.

Moreover, most developed countries' journals are reluctant to increase the

space provided to results from laboratories in third world countries where

criteria for publications may be different, and there are too few local

journals in developing countries where results could be published.

Conditions in developing countries also militate against science

publications: a) topics of research in developing countries are not as

fashionable in developed countries; public health, tropical medicine,

hybridization of corn cannot compete for attraction with molecular biology;.

3/ The Data for output indicators is provided by the Institute ofScientific Information (ISI) which compiles bibliographical information onall scientific fields and publishes the Science Citation Index (SCI). Thisindex covers eight major areas of science: Clinical Medicine, Biomedicalresearch, Biology, Chemistry, Physics, Earth and Space, Engineering andtechnology, and Mathematics. (These areas are further disaggregated into106 special fields). Since 1981, the index covers over 3,000 scientificjournals selected from a universe of over 26000 journals to provide auniform minimum level of acceptable quality. The extent of the activity ofa given country in a scientific field is measured by dividing the country'snumber of publications in the field by the Region's or the World'spublication in the same field. The Activity Index (AI), the ratio of thepercentage of a country's research in a field by the percentage of theWorld's or Region's research in the same field, allows the comparison ofthe emphasis of different countries on fields of research.

The number of citations over a period of time to articlespublished in a given year referred to a given standard, for example averagecitations in a given field, allows international comparisons of the impactof the research on other scientists. But for all its statistical andcomputational advantages the data should be approached with caution.

- 6 -

b) ability to write in the "scientific" style and in the acceptable foreign

language may be lacking; c) conditions conducive to publishing results,

secretarial services, adequate rewards and professional recognition and

advancement, may be lacking. For all of these reasons the number of

publications and citations from developing countries may understate the

actual scientific activities. Nevertheless, while these measures may

understate some outputs, the relative situation of countries within a

developing region, or compared to industrialized countries will not change

very much.

Science Indicators in SSA Countries

Scientific research in SSA is very young- 4/. Nevertheless, since

independence, parallel to the growth of the national universities, research

developed gradually to the point where there is now a relatively stable,

albeit small, scientific production in a selected number of fields.

Table I-1 presents data on the ntumber of scientific publications in SSA by

field for the period 1981-1986.

The share of these publications in the world's total is

approximately .4Z which is much lower that the proportion of the SSA

population in the world population (8.5Z) (Table I-2). The distribution of

the publications by field shows a heavy concentration in the life sciences:

clinical medicine, biomedical research, and biology. Clinical medicine and

biology alone comprise 72Z of the total. T'his compares with 42Z for Latin

America and 44Z for the world as a whole. In SSA the activity index (AI),

4/ For a description and concise history of scientific research in Africasee Jacques Gaillard et Roland Waast 'La recherche scientifique en Afrique"AfriQue Contemporaine 148,3-30 (1988).

- 7 -

(a measure of the distribution of publications by field of a country or

region compared with the same measure worldwide) for biology and clinical

medicine is quite high compared with the world as a whole or Latin America,

for example. (see Table 3 and Figure 1).

Distribution of Publications by Country

Ten countries supply almost 901 of the total scientific

publications in SSA. Almost 70% is produced in three countries: Nigeria,

Kenya and Sudan (see Table 4 and Figure 2). The number of publications by

country is related to GDP (r2 = .95); this measure has been found to be

significant in international comparisons. 5/ The distribution of

publications by field across countries is given in Table 5. This table

shows, for example, that Nigeria accounts for more than half of all

publications in most fields, while countries such as Ethiopia produce less

than 1% in 4 out of 8 fields, and Zambia less than 2? in 5 of the 8 fields.

The distribution of publications by country and Activity Indexes

(AI) (in this case a country's distribution of publications by field

compared with the regional average) are given in Tables 6 and 7.

Impact Measurement

The number of citations of a given article has been considered an

indicator of the impact of the article on the scientific community and, by

implication, the quality of the work. Although social, institutional, and

5/ See D. De Solla Price, op. cit.

-8-

political factors influence publication and especially who quotes them,

large discrepancies between the average number of citations conveys a sense

of the difference in impact. The citations measures are: (a) the number of

citations from the date of publication over a period of years; (b) the

average citations per paper; (c) the Relative Citation Index (RCI) which is

the ratio of the average citation per paper of a given country to the same

ratio for the world or a partiLcular region. Table 8 gives the number of

papers by field and country and the world for 1981, and the number of

citations for the publication that occurred during 1981-1986. Table 9

gives the average number of citations per article. Table 10 gives the RCI

for the SSA countries, the world, and Latin America.

In general, the average citation per article in all fields is much

lower in SSA than the world average. The RCI also shows that SSA countries

rank lower than Latin America. It is interesting to note that the largest

producers of research don't have the greatest RCI, and some lower producers

rank higher in specific fields.

Summary

SSA contribution to scientific world publications is very small;

less than half of one percent. Publications in the medical and biological

fields have the lion's share while very few are in the physical and

especially engineering sciences. This is a characteristic shared by most

developing countries and reflects historical relationships between the

industrialized world and developing countries, and a lack of coordination

between objectives of research and economic goals. As in most regions of

- 9 -

the world the number of publications is strongly correlated with GDP,

rather than GDP/capita. Thus three countries in SSA account for almost 70%

of all scientific production. The number of citations per publication (a

proxy for quality) is well below the world average and those of Latin

America for example. But in SSA countries quantity of publications and

citations per publication (quality) don't go hand in hand, many of the

small producers of research merit higher number of citation per publication

than the larger producers.

The facts presented above point to the necessity of promoting a

strategy which aims at aligning SSA with the rest of the world by:

(a) increasing the quantity of science research in SSA more in line

with its share of population and GDP in the world;

(b) redirecting the structure of the research more towards the

physical sciences, mathematics, and engineering;

(c) improving the quality of research.

This strategy requires increase of resources and, more importantly,

qualified personnel which is the main limiting factor in the development of

science. This leads us to look at the sources of scientific manpower

supply.

TABLE I-1

PUBLICATION BY FIELD IN SSA (1981 - 1986)

1981 1982 1983 1964 1986 1986 TOTAL

CLINICAL MEDICINE 704 654 698 666 744 713 4177BIOMEDICAL RESEARCH 156 184 166 1SO 176 165 996BIOLOGY 648 462 460 469 440 437 2844CHEMISTRY 121 114 81 87 90 94 587PHYSICS 43 46 37 32 81 32 220EARTH AND SPACE so 61 S3 57 67 e6 372ENGINEERING A TECH 07 60 S0 51 43 40 301MATHEMATICS 19 26 24 29 36 12 147

TOTAL 1724 1616 1567 1581 1625 1569 9644

DISTRIBUTED BY FIELD

Weightod1961 1962 1963 1964 1965 1986 Average

CLINICAL MEDICINE 0.41 0.40 0.44 0.43 0.46 0.46 0.43BIOMEDICAL RESEARCH 0.09 0.11 0.10 0.10 0.11 0.11 0.10BIOLOGY 0.32 0.ao 0.30 0.80 0.28 0.26 0.29CHEMISTRY 0.07 0.07 0.06 0.08 0.06 0.06 0.06PHYSICS 0.02 0.03 0.02 0.02 0.02 0.02 0.02EARTH AND SPACE 0.04 0.04 0.03 0.04 0.04 0.04 0.04ENGINEERING A TECH 0.04 0.08 0.0o 0.03 0.03 0.03 0.03MATHEMATICS 0.01 0.02 0.02 0.02 0.02 0.02 0.02

Source: Data provided by Computer Horizons, Inc.

TABLE I-2

RATIO OF PUBLICATIONS SSA/WORLD

1981 1982 1983 1984 1986 1986

CLINICAL MEDICINE 0.0060 0.0066 0.0068 0.0068 0.0069 0.0068

BIOMEDICAL RESEARCH 0.0028 0.0082 0.0029 0.0027 0.0027 0.0028

BIOLOGY 0.0189 0.0124 0.0127 0.0120 0.0128 0.0128

CHEMISTRY 0.0022 0.0021 0.0016 0.0016 0.0016 0.0017

PHYSICS 0.0009 0.0010 0.0008 0.0007 0.0006 0.0006

EARTH AND SPACE 0.0040 0.0037 0.0032 0.0003 0.0038 0.0038

ENGINEERING & TECH 0.0022 0.0017 0.0016 0.0017 0.0016 0.0016

MATHEMATICS 0.0018 0.0030 0.0026 0.0031 0.0037 0.0016

Source: Dota provided by Computer Horizons, Inc.

TABLE 1-3

DISTRIBUTION OF PUBLICATIONS BY FIELDAND ACTIVITY INDEXES FOR SSA A LA

SSA LA WORLD AI-SSA

CLINICAL MEDICINE 0.43 0.29 0.33 1.31

BIOMEDICAL RESEARCH 0.10 0.16 0.16 0.66

BIOLOGY 0.29 0.13 0.11 2.68

CHEMISTRY 0.06 0.13 0.16 0.38

PHYSICS 0.02 0.17 0.10 0.23

EARTH AND SPACE 0.04 0.06 0.06 0.77

ENGINEERING A TECH 0.03 0.04 0.07 0.44

MATHEMATICS 0.02 0.02 0.03 0.61

Source: Data for SSA and World providod by Computer Horizons, Inc.Data for Latin America pertain 1980 - 1984 only are taken fromEconomic and Social Progress in Latin America, Ch. IX, Inter-AmericanDevelopment Bank, Washington, DC, 1988.

DISTRIBUTION OF PUBLICATIONS BY FIELD IN SSA FIGURE I-1

Earth E space

3 1 1-t Chitry

*1b1,,o><^rs1c~~~~(2m)ftsic

Engineering * techb // DISTRIwriON OF PWLICATIONS aY FIELD IN THE WORLDCliuLcal =dLcLin

SSL_ogi (go^ Earth £ spaceHoll" rmeseech (IatI

Nothomma pa ginecig I tech ORM Simued rcsearch

DIsTRIeTIOW OF PWLICATIONS Sy FIELD IN LA

CbeeLstry carth 6 space ~chmietay (law)flail* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Physics

Engineering * tech

Natbamm etce t §

Biinsd r\sercIPO (.hycic Clinical ,dkne

Hathommatic st^ \ Ied

clin col c k*bd s

TABLE I-4

THE TEN LARGEST PRODUCERS OF RESEARCH

1981 1982 1983 1984 1986 1988 1981-1988 SHARE

NIGERIA 781 793 749 712 768 736 4629 0.47

KENYA 268 262 243 234 260 209 1464 0.16

SUDAN 94 88 91 107 94 84 668 0.06

ZIMBABWE 80 78 47 56 72 70 407 0.04

TANZANIA 72 68 63 64 67 69 393 0.04

SENEGAMBI 64 43 e6 69 69 67 368 0.04

ETHIOPIA 29 34 46 37 33 40 219 0.02

IVORY COAST 76 64 44 48 30 40 290 0.03

ZAIRE 16 14 19 22 22 26 119 0.01

ZAMBIA 27 26 31 40 27 22 172 0.02

OTHER 212 172 188 165 202 207 1146 0.12

TOTAL 1723 1619 1687 1632 1624 1660 9646 1.00

Sourc-: Oata provided by Computer Horizons, Inc.

- 15 -

THE TEN LARGEST PRODUCERS OF RESEARCH FIGURE 1-2

mgo.

700-

Soo

Nigeria Kenya Sudan Zlmbabve Tanzania Senegambia

700'1

to 7

'goo

400.

Mo

200M

wo100- -

400

1Ehp Ir 2 Iot 1ar m O 1the

Ethiopia Ivory Coast Zaire Zamnbia Other

TABLE I-5

COUNTRY DISTRIBUTION OF PUBLISHED RESEARCH BY FIELD (in percent)

NIGERIA KENYA SUDAN ZIMBAB. TANZA. SENEGAM ETHIO IV CST ZAIRE ZAMBIA OTHER TOTAL

CLINICAL MEDICINE 41.2 21.4 6.1 3.1 4.4 3.2 3.1 2.1 1.8 2.6 11.1 100.0

BIOMEDICAL RESEARCH 64.4 16.3 4.3 3.0 1.2 8.1 0.6 2.3 0.4 0.4 8.9 100.0

BIOLOGY 46.6 11.0 6.7 7.0 5.4 2.6 1.7 4.2 0.4 1.2 14.4 100.0

ChEMISTRY 59.1 2.9 4.4 1.2 1.9 3.4 4.1 5.6 2.4 0.7 14.5 100.0

PHYSICS 68.2 6.0 1.8 2.3 3.2 6.4 0.0 6.9 2.7 2.7 11.8 100.0

EARTH AND SPACE 54.8 7.8 4.3 3.2 3.6 6.6 0.8 3.0 1.1 3.6 12.4 100.0

ENGINEERING L TECH 72.4 6.3 4.7 4.7 4.0 1.3 0.3 1.3 i.0 i.3 2.7 100.0

MATHEMATICS 51.7 11.6 8.2 6.2 2.0 2.7 6.4 0.7 0.0 1.4 8.2 100.0

Source: Data provlide by Computer Horizons, Inc.

TABLE I-6

DISTRIBUTION BY FIELD BY COUNTRY

NIGERIA KENYA SUDAN ZIMBAB. TANZA. SENEGAM ETHIO IV CST ZAIRE ZAMBIA AV. SSA

CLINICAL MEDICINE 0.38 0.81 0.46 0.32 0.47 0.38 0.69 0.30 0.64 0.61 0.43

BIOMEDICAL RESEARCH 0.12 0.11 0.08 0.07 0.03 0.23 0.03 0.08 0.03 0.02 0.10

BIOLOGY 0.29 0.21 0.34 0.49 0.39 0.21 0.22 0.41 0.10 0.20 0.29

CHEMISTRY 0.08 0.01 0.06 0.02 0.03 0.06 0.11 0.11 0.12 0.02 0.08

PHYSICS 0.03 0.01 0.01 0.01 0.02 0.04 0.00 0.04 0.06 0.03 0.02

EARTH AND SPACE 0.06 0.02 0.03 0.03 0.03 0.06 0.01 0.04 0.03 0.08 0.04

ENGINEERING A TECH 0.05 0.01 0.03 0.03 0.03 0.01 0.00 0.01 0.03 0.02 0.03

MATHEMATICS 0.02 0.01 0.02 0.03 0.01 0.01 0.04 0.00 0.00 0.01 0.02


TABLE I-7

ACTIVITY INDEX (1981 - 1986)

NIGERIA KENYA SUDAN ZIMBAB. TANZA. SENEGAM ETHIO IV CST ZAIRE ZAMBIA

CLINICAL MEDICINE 0.88 1.42 1.06 0.74 1.08 0.88 1.37 0.69 1.49 1.42

BIOMEDICAL RESEARCH 1.20 1.11 0.77 0.74 0.30 2.31 0.27 0.79 0.34 0.23

BIOLOGY 0.98 0.74 1.17 1.88 1.34 0.72 0.76 1.43 0.36 0.68

CHEMISTRY 1.28 0.19 0.77 0.29 0.46 0.95 1.80 1.84 1.98 0.39

PHYSICS 1.41 0.38 0.38 0.81 0.89 2.00 0.00 2.24 2.52 1.73

EARTH AND SPACE 1.13 0.60 0.71 0.74 0.82 1.60 0.34 0.96 0.84 1.88

ENINEERIGT Al TECH 1.30 0.43 0.83 1.'4 1.01 0.38 0.;S 0.46 0.84 0.77

MATHEMATICS 0.84 0.68 1.07 1.47 0.38 0.67 1.80 0.17 0.00 0.68


TABLE I-8

1981 JOURNAL SET

NUMBER OF PAPERS IN 1981

FIELDSUBFIELDNAME NIGERIA KENYA SUDAN ZIMBAB. TANZA. GAMBIA SENEGAL ETHIOP. IV CST ZAIRE ZAMBIA WORLD

CLINICAL MEDICINE 287 165 41 18 26 9 18 23 16 10 12 116396BIOMEDICAL RESEARCH 77 33 8 9 4 2 13 1 6 1 0 69196BIOLOGY 222 61 26 48 23 0 12 6 19 1 3 34719CHEMISTRY 75 3 4 2 2 0 4 1 10 2 1 62929PHYSICS 27 2 0 0 3 0 0 0 1 4 0 48878EARTH AND SPACE 43 6 4 4 3 0 1 0 2 1 3 17684ENGINEERING A TECH 35 2 7 0 5 0 1 0 1 0 3 26069MATHEMATICS 10 2 3 1 1 0 1 2 0 0 1 9407

ALL FIELDS COMBINED 778 264 92 80 88 11 60 32 63 19 23 363176

NUMBER OF CITATIONS (1981-1986)

CLINICAL MEDICINE 468 384 86 61 49 63 165 8e 42 35 13 837889BIOMEDICAL RESEARCH 110 279 17 28 4 18 78 0 15 1 1 887867BIOLOGY 361 123 30 55 60 0 32 1 27 1 1 134866CHEMISTRY 18S 13 11 a 4 0 13 0 29 4 0 283277PHYSICS 22 4 0 0 1 0 0 0 2 3 0 321278EARTH AND SPACE 68 4 8 35 4 0 1 0 2 4 8 116600ENGINEERING A TECH 47 1 7 0 9 0 1 0 2 0 1 67918MATHEMATICS 2 1 1 2 0 0 0 4 0 0 0 18662

ALL FIELDS COMBINED 1254 809 139 177 121 71 280 91 ii9 48 24 2468722

TABLE I-9

CITATIONS PER PAPER

FIELD NIGERIA KENYA SUDAN ZIMBAS. TANZAN. GAMBIA SENEGAL ETHIOP. IV CST ZAIRE ZAMBIA WORLD

CLINICAL MEDICINE 1.69 2.48 1.59 3.12 1.96 6.81 8.81 3.78 2.82 3.84 1.11 7.28

BIOMEDICAL RESEARCH 1.43 8.67 2.04 3.11 0.89 9.68 6.91 0.00 3.00 1.00 2.00 11.82

BIOLOGY 1.63 2.01 1.18 1.14 2.16 0.00 2.83 0.19 1.41 1.00 0.33 3.88

CHEMISTRY 2.60 4.71 2.75 3.00 2.00 0.00 3.28 0.00 3.00 2.80 0.00 6.36

PHYSICS 0.81 2.33 0.00 0.00 0.33 0.00 0.00 0.00 2.15 0.88 0.00 8.57

EARTH AND SPACE 1.68 0.78 1.88 10.00 1.17 0.00 0.48 0.00 0.71 4.00 2.87 8.57

ENGINEERING A TECH 1.38 0.43 1.08 0.00 1.76 0.00 1.00 0.00 2.00 0.00 0.30 2.31

MATHEMATICS 0.21 0.60 0.40 3.00 0.00 0.00 0.00 2.87 0.00 0.00 0.00 1.97

ALL FIELD COMBINED 1.82 3.08 1.62 2.22 1.80 8.44 5.70 2.89 2.26 2.73 1.02 8.78

TABLE I-10

CITATION INDEX

NAME NIGERIA KENYA SUDAN ZIMBAB. TANZAN. GAMBIA SENEGAL ETHIOP. IV CST ZAIRE ZAMBIA WORLD L.A.1/

CLINICAL MEDICINE 0.22 0.34 0.22 0.43 0.27 0.80 1.21 0.52 0.39 0.60 0.16 1.00 0.66

BIOMEDICAL RESEARCH 0.12 0.74 0.18 0.27 0.08 0.82 0.61 0.00 0.28 0.09 0.17 1.00 0.61

BIOLOGY 0.42 0.52 0.30 0.29 0.65 0.00 0.68 0.06 0.36 0.28 0.09 1.00 0.90

CHEMISTRY 0.47 0.88 0.51 0.68 0.37 0.00 0.81 0.00 0.68 0.49 0.00 1.00 0.48

PHYSICS 0.12 0.36 0.00 0.00 0.06 0.00 0.00 0.00 0.33 0.13 0.00 1.00 0.89

EARCH A SPACE 0.24 0.12 0.29 1.52 0.18 0.00 0.07 0.00 0.11 0.81 0.41 1.00 1.12

ENGINEERING A TECH 0.59 0.19 0.47 0.00 0.78 0.00 0.43 0.00 0.87 0.00 0.13 1.00 0.37

MATHEMATICS 0.11 0.25 0.20 1.52 0.00 0.00 0.00 1.36 0.00 0.00 0.00 1.00 0.81

ALL FIELDS COMBINED 0.80 0.43 0.27 0.39 0.38 0.80 0.8O 0.46 0.39 0.40 0.16 1.00 0.80

1/ Data for L.A. is for articlos published in 1980 and Citations for the period 1980-1984.

- 22 -

II. UNIVERSITIES AND SCIElITIFIC MANPOWER

All over the world universities are the locus of scientific

research and training. Their role is largest in lesser developed nations.

Whereas in industrialized countries much research and training is done in

private industry, non profit organizations, and other government agencies,

in poor developing countries universities produce the lion's share of

research and training. Therefore, to talk of science and technology in SSA

means, to a large extent, to focus on the university, its ability to

provide training, and its capacity to produce graduates in science and

engineering.

There is a dearth of data on SSA'n universities. Fewer than 15

countries provide data on enrollments by field of study, and fewer than

half a dozen publish data on graduates by level and field of study on a

time series basis. Nevertheless, it is possible to compare various SSA

countries as a group with groups from Latin America (LA) and OECD

countries.

If we compare total enrollments in higher education per 100,000

population in the three groups (SSA, LA, OECD), the averages for SSA is 89,

for LA 1468, and for OECD 2392; a ratio of 1:16:27. The ratio for

enrollments in the natural sciences is more favorable for the SSA countries

(1:6:16) but the ratio for engineering is worse (1:25:40). (See table 1

and figures 1.1 to 1.5).

- 23 -

The picture is bleaker when one analyzes graduates. The number of

graduates per 100,000 population for SSA, LA, and OECD is 13.8, 121.4, and

411 respectively - a ratio of 1:9:30. For natural sciences, the ratio is

1:5:35, and for engineering 1:16:60 (see table 2 and figures 2.1 to 2.5).

The figures referred above encompass enrollments and graduates of

all types of post secondary education regardless of their length of

training. A better measure of the capacity to absorb and generate science

and technology would be enrollments and graduates of the upper level of

higher education, what in the Unesco classification are levels 6 and 7

(level 6 corresponds to a required minimum of three or four years post

secondary education; level 7 entails at least one more year of study than

level 6). The number of students in levels 6 and 7 per 100,000 population

is 49, 1428, and 1592 for SSA, LA, and OECD respectively, resulting in a

ratio of 1:29:32. The ratio for natural science is 1:11:23, and for

engineering 1:72:72 (see table 3 and figures 3.1 to 3.5). These ratios

deteriorate when considering graduates in the same fields. Graduates from

levels 6 and 7 per 100,000 population are 5.2, 94.2, and 273.2 for SSA, LA,

and OECD respectively, giving a ratio of 1:19:54. For the natural sciences

the ratio is 1:4:31, and for engineering 1:74:166 (see table 4 and figures

4.1 to 4.5).

The distribution of enrollments by field does not reveal large

differences between SSA, LA, and OECD. For example, 13.3Z of the students

in SSA were enrolled in the natural sciences compared with 7.4Z in LA and

8.9Z in OECD. Even in the field of engineering where SSA fares less well

- 24 -

than in other fields the percentages are 12.4Z, 14.04Z and 16Z respectively

for SSA, LA, and OECD (see table 5 and figure 5). The same lack of

differences in the distributions of enrollments persists at levels 6 and 7

where the share of natural sciences are 13.7X, 6.3% and 9.5Z, and the share

of engineering are 9.95Z, 15.16X and 12.52a for SSA, LA, and OECD

respectively (see table 6 and figure 6).

The distribution of graduates by field also shows that the share

of natural sciences is higher in the SSA than in LA and OECD (10.14%,

5.36Z, and 9.11X respectively), although it is lower for engineering (8.7Z,

13.93Z and 13.63Z) (see table 7 and figure 7). If we consider levels 6 and

7 only, the proportion of graduates in the natural sciences is way above

those of LA and OECD (18.19% for SSA, 5.97% for LA, and 11.34Z for OECD),

although these proportions are reversed for engineering (3.45%, 16.49Z and

12.8%) (see table 8 and figure 8).

The data presented above point to the following:

1. The proportion of the population in SSA enrolled in higher

education, especially in the natural sciences and engineering, is

appallingly low.

2. SSA countries graduate only 2.5 people a year per 100,000

population in the combined field of natural science and

engineering compared with 95 in industrialized countries.

3. If we consider that a minimum of three or four years higher

education is required to become a professional in science or

engineering, SSA graduates only 1.2 people per year per 100,000

- 25 -

population in the natural sciences and engineering, compared with

65 in industrialized countries. The contrast is starker in the

field of engineering: developed countries graduate 166 times more

engineers per capita than do SSA countries. This discrepancy is

due partly to the existing low efficiency - ratio of graduates to

their entering class - in SSA.

4. However, contrary to a common belief that in SSA a smaller

percentage of students choose natural sciences, there are only

minor differences in the structure of enrollments and graduates by

field of study.

Given this situation, if there is a need to produce more graduates

in science and engineering, the options are:

(a) increase total enrollments leaving the same structure of

enrollments by field of study.

(b) increasing efficiency (the ratio of graduates to entrants).

(c) changing the enrollment structure to favor science and

engineering.

Option (a) is the one being followed by most SSA countries. This option is

doomed to failure for two reasons: budgetary constraints, and poor

employment prospects for the vast majority of graduates from fields other

than science and engineering.

Option (b) - an efficiency measure - should be exercised under any

circumstances, but there is a limit beyond which it is impossible tc

increase graduates with a given stream of entrants. (Of course, if

- 26 -

efficiency is extremely low, say 10-15Z, there is plenty of room to

increase the number of graduates.)

Option (c) is the only realistic hope to increase the number of science and

engineering graduates, since the present slhare is rather small and there is

plenty of room to increase the share of science and engineering without

impinging too much on the other fields, eslpecially if higher education

continues to expand.

The relative expansion of science and engineering enrollments

requires even greater increase in the share of science and engineering in

the budget because unit costs in these fields are usually higher than in

the rest of higher education. Moreover, to implement option (b) in

conjuction of option (c), may require increeased expenditures to upgrade the

level of scientific and mathematics knowledge of the pool of entrants to

higher education when minimum proficiency skills in science and mathematics

are lacking.

- 27 - TABLE II-1

Enrollments: total by field and per 100,000 population

SLB-S_MUM AFRICAN COLNRIE

ALL FIELDALL FIELD NAT.SC. JAT.SC. ENO. 0. ED.S M.SC TER OTHERALL LEV. ALL LEV. ALL LEV. ALL LEV. ALL LEVEALL LEVEL ALL LEV ALL LEV ALL LEVELALL LEVU

POtL. CaOUTRY 1965 * 196W . 19S * 1968 * 198

4421 aDI 2763 82.96 207 466 194 4.89 262 5.9s 2120 47.961007 lWA 1434 142.40 194 19 27 0 0o 00 0 o.o0 1240 123.14

31660 ETH 27316 61.17 4161 12.32 286 6.e 14 4.11 194so 57.7512700 049 1s53 12.11 320 2.62 00 .00 336 2. S 82 S.946177 CIN 8601 170.00 3016 54.26 1064 20.94 46 8.68 4243 81.96

18784 KEN 21756 115.82 1437 7.66 8462 4.16 1132 6.08 10706 56.9913311 MOZ 1442 10.68 69 0.52 641 4.06 162 1.22 670 5.015700 RW 1967 34.86 364 6.39 100 1.78 164 2.U6 1389 23.6420862 SON 33412 164.19 1271 6.24 2860 12.67 2011 9.6 27570 1SS.406316 E8 12711 201.25 2346 37.18 207 3.26 2466 39.36 7670 121.44

20a7 TZA 4014 19.70 247 1.21 670 3.29 413 2.08 26C4 13.17142 UGA 10103 69.06 934 4.39 1407 9.62 447 3.19 7298 49.6u6242 DZ 460 74.6 690 11.06 544 8.72 302 4.64 3144 50.37

AVAGE: 89.18 13.36 9.10 7.00 39.53DEVIAT: 61.69 16.96 11.74 9.73 42.62"DI: 10.63 0.52 0.00 0.00 5.08

mX: 201.25 56.26 45.16 39.36 11.40

HMLE ICOM SITH-AAICAB-dLUMIm

ALL FILDALL FIELD NAT.SC. NATaC. 84. X. NID.SC X.SC OrH OltHALL LEV. ALL LEV. ALL LEV. ALL LEV. ALL LEV. ALL LFV. ALL LEV 9.1 LEW ALL ~ LEVLLLLV

----------------------------------------------------------------------- __----__-----------------------------

COTRY l6 * 196 * 1966 * 196 * Lu a

27818 CM. 391490 1422.62 677 231.64 9626 346.26 39904 146.011 249629 907.9727 CPtI 563s9 2464.52 3966 16.37 6U 246.36 31S0 111.67 419 1906.201s6 Wa 22 253.59 167 16.19 237 26.82 9e 10.6S 1626 196.91

2258 J4 8126 227.02 1260 58.60 0 0.00 64 26.52 3222 142.6975108 MX 1199100 13".61 61176 81.46 264460 382.11 146710 196.01 724782 95.o0130U HIC 29001 946.3s 1107 56.20 4681 146.20 4339 141.16 19022 622.0720 PAN 55303 2647.34 2511 121.16 1167 58.71 3666 184.34 37380 1767.94.5m22 SLY 7049 1347.46 2162 41.70 139 268.64 7116 136.06 47306 904.17351 S.I 2751 762.76 27 7.69 23 S. 5 110 31.34 2591 736.161149 TT0 3164 275.37 697 60.6 529o 46.04 0 0.00 1936 166.6729l UrY 67707 2958.09 5481 164.67 7715 259.94 1S72 529.75 567s6 1980.7316394 VON 443060 2702.57 15923 97.11 8491.5 517.906 46446 263. 43 295757 1604.06

- -------------------------------------- ---------------------- ---------------------------- ---------- -- ----

AVtA6: 1467.86 74.55 2m0. n 151.73 1010.72CEVIAT: 970.75 35.26 163.41 140.49 667.75MIN: 227.02 7.69 0.00 0.00 142.69MAX: 2988.09 164.67 SU6.71 s2".76 1960.73

OE CIATRIO

ALL PM3DALL FIlD NAT.SC. NAT.SC. END. Be0. MD .SC ID .SC X TH 0IJALL W. MLLIW. ALL LE. . ALL LFV. ALL LIV. ALL LEV . ALL LEV ALL L.. ALL LJ l L&JEL

POr%L. CMRW 196 * 1965 * 1966 t 196 * 196 a

AJS 370060 2407.77 52547 641.90 36273 236.01 23411 182.46 2577qe 1677.407349 AiT U19160 2537.62 17621 231.42 19746 261.87 2360 312.73 130606 1730.10

e6 EL 24780 2611.16 20186 206.5 360S1 6S6.56 42906 438.30 146210 15s0.75249W CAN 1127000 4524.3 S 64240 336.22 927 396.96 79421 318.67 864712 3471.766462 0e 110110 1696.70 11616 182.27 2164 363.94 101246 16.10 6401 1024.39

61421 OW 1680200 2525 .9 176580 290.70 309040 506.15 219760 387.76 642660 1,372.275114 am4 11681 2274.64 7966 15.61 196021 361.35 17520 342.36 71330 1394.60

urn1 oP 936130 2446.19 91687 240.29 119620 313.44 101620 271.06 619681 1621.41461 FI 127960 263. 71 16142 331.94 31672 631.29 16336 335.97 6382B 1312.52

s5677 O 103260 1631.42 134700 236.93 189040 262.10 1516s 266 97 587120 1041.42964 amc 161900 147.00 16424 166.77 31297 336.11 24849 249.26 107630 1092.91

119259 ePN 2347500 196.40 67025 S6.20 412260 345.70 149610 125.46 1716568 1441.0614362 NLO 390320 2717.73 137 96.U4 6425 447.26 40131 279.44 2721n 1695.2041S2 NOR 9486 2292.52 7629 169.61 130619 336.4 106o6 2526.00 62507 1309.013206 NZ.. 96406 3072.26 9768 306.49 16624 525.26 66O6 212.44 6499 2029 .07

47279 TUL 469990 994.06 2506 s3.07 90462 191.36 44041 93.16 310376 656.48

AVEAGE: 2392.51 214.19 539.21 260.73 1544.35DEVIAT: 72n.06 91.40 113.01 90.13 600.29

DMI: 994.06 58.07 191.36 96.16 636.48MAX: 4524.83 641 .9 61. 29 438.10 6471.76

* The actual nube' divided by 100,000 of the populatiop

Source: Unesco Computer Tapes

-28 -

Figure II-it Enrollmentsi by field per 100,000 populationt

Figure 1.1 :all field

2.4-

2.2

b I~~~~

Figure 1.2 anatural sciences

1408

IN I

4'

BSA LAW OCDC

- 29 -

Figure 1.3 engineering

410

350 -211

2ND

Ism

ERA LAM CCCO

Figure 1.4 : medical sciences

210

241

1401

1201

41

ERA L;M CCCO

- 310 -

Figure 1.5 :other fields

I.E

1.4 -

1.3

1.1

.3 I:!.7 t

U.'

0.5

0.4-

0.3-

0.2

0.1

CIA ~~~~LMI OCDC

- 31 - TABLE II-2

Graduates: Total by field and per 100,000 population

SUB-SAHARAN AFRICAN COLMTRIES

ALL FIELDALL FIELD NAT.SC. NAT.SC. 940. 94. MED.SC MD.SC THR OTHERALL LEV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV

POPUL. COUNTRY 1985 * 1985 * 1916 * 198S * 1985 *

33680 ETH S615 16.67 526 1.56 260 0.77 226 0.67 4603 13.6712700 "4A 439 3.46 5S 0.43 0 0.00 0 0.00 384 3.026429 WI 1SS9 24.25 41 0.64 69 1.07 64 1.00 1385 21.545700 RA 4S1 7.91 75 1.32 26 0.48 SS 0.93 297 5.21

14625 UCA 2428 16.60 232 1.59 698 4.78 126 0.86 1374 9.39

AVERAE: 15.78 1.11 1.41 0.69 10.57DEVIAT: 7.31 0.48 1.71 0.36 6.59MIN: 3.46 0.43 0.00 0.00 3.02MAX: 24.25 1.S9 4.76 1.00 21 54

MMILE INCOME SUTHAMICAN COLMRIES

ALL FIELALL FIELD NAT.SC. NAT.SC. EN0. ENO. MD.SC MD.SC OT T arIEALL LEV ALL LEV ALL LEV ALL LFV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV

----------------------------------------------------------------------- __----__------------_-------------__

POUL. COUNTRY 1985 * 1985 * 1988 * 1985 * 1965 *

27S15 COL 48736 177.13 765 2.76 10072 36.61 6029 21.91 31870 115.832279 CRI 4906 206.31 320 13.45 46 19.58 674 28.33 3449 144.98918 aJr 711 77.45 39 4.25 6 7.06 36 3.92 571 62.20

7256 JAM 1165 51.51 176 7.79 0 0.00 138 5.69 654 37.6275103 HE) 113080 160.55 4474 5.95 20040 26.68 19041 25.35 69496 92.53

3058 NIC 1636 53.50 112 3.66 227 7.42 265 8.67 1032 33.752069 PAN 3260 157.01 139 6.65 425 20.34 S06 24.17 2211 105.845232 SLV S825 73.11 44 0.64 867 16.98 454 S.66 2440 46.642968 URY 2632 86.68 130 4.38 275 9.27 1004 33.8S 1223 41.21

16394 vYE 29406 179.37 1100 8.71 8417 39.14 3797 23.16 180M 11C.36------------------------------------------------------------------ __---------__-----------------------------

AVEAOE: 121.46 5.65 18.30 18.39 79.11OEVIAT: 55.34 3.26 12.28 10.04 37.58MI: 51.51 0.64 0.00 3.92 3a.7sIAX: 206.31 13.48 39.14 33.83 144.98

OED COUNTRIES

ALL FIEDALL PIIELD NAT.SC. NAT.SC. E0. ENO. MFD.SC MED.SC OTHER OTHERALL LFV ALL LEV ALL LEV ALL LFV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV

--------------------------------------------------------------------- __------__------------------------..----

POPUL. COUNTRY 1965 * 1965 * 1968 * 198S * 1988 S

15S39 AUS 76863 478.65 10465 68.09 6116 39.79 4581 29.36 52469 341.407549 AUr 14487 191.64 822 10.69 1271 16.84 2006 26.57 10366 137.34966 BEL 462 493.36 4840 49.11 7398 75.06 8113 82.32 28275 286.88

24907 CAN 1986480 798.8 19127 76.79 22697 91.98 20486 82.26 138970 545.916482 0fE 10106 1S5.86 1500 23.14 881 13.59 2010 31.01 8712 88.125114 DW 18691 369.40 337 6.59 3113 60.67 3948 77.14 11496 224.79

Ja8 ESP 99964 261.55 7901 20.67 8731 22.64 15276 39.9a 68077 17S.08483 FIN 2160S 448.39 2315 47.60 5742 116.08 08 124.78 7680 157.93

S6"77 m 297760 S26.19 40119 71.16 46820 88.06 51532 91.41 159309 262.569648 XC 27309 277.31 2695 27.35 3712 37.69 2736 27.76 18168 184.48

119269 JPN 385670 491.09 15319 12.85 100460 84.23 30211 25.33 439890 388.68320C NZL 11274 351.98 1614 50.39 907 26.32 64 20.07 8110 253.20

47279 7tH 61096 129.23 2326 4.92 12996 27.49 S342 11.30 40430 86.51234496 USA 1630300 760.53 161960 69.07 199300 64.99 182520 77.84 128820 S48.63

AVStAOE: 411.00 38.47 58.06 53.37 263.11DEVIAT: 199.36 25.25 32.19 33.46 142.36"I: 129.23 4.92 13.59 11.30 85.51MAX: 796.88 76.79 118.08 124.76 548.63


- 32 -

Figure 11-2: graduates by field per 100,000 population

Figure 2.1 all field

4EO -

300 -

30W -

2CD - ///

250 -

4101

'Enq

07o LAM OECD

Fig,ure 2.2 s natural sciences

251-

201

Is-

I EU ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~CC

- 33 -

Figure 2.3 t engineering

san

46 -

IDA LAM CcL

Figure 2.4 :mdical sciences

4'

1

IDA LAM CCUI

- 34 -.


230

250

248

220

2200-

ionI

Io EU

1481

120

Igo'

soU

Enu

4'

.20

gmA LAM OECD

- 35 - TABLE II-3

Enrollments in levels 6+7: total by field and per 100,000 population

SJB-SAHARA AFICAN COUNTRIES

ALL FRIE.DALL FrEID NAT.SC. NAT.SC. ENO. ENO. MED.SC MED.SC OTHER OTHERLEY 6+7 LEV 6.7 LEV 6.7 LEV 6.7 LEV 6.7 LEV 6+7 LEV 6.7 LEY 6.7 LEV 6+7 LEV 6+7

PUAL_. CDlMTRY 19086 * 1965 * 1906S * 1986 * 1985 s

4421 DI 2022 45.74 207 4.68 11 2.60 262 5.93 1438 32.531007 WA 1017 100.99 194 19.27 0 0.00 0 0.00 823 81.73

33680 ETH 15486 45.96 3821 11.38 1079 6.20 1196 3.56 9388 27.8718784 KEN 9074 48.31 926 4.94 956 s.09 919 4.69 6271 33.3813311 MCZ 1442 10.83 69 0.52 541 4.06 162 1.22 670 5.035700 WA 1987 34 3 83S4 69 100 1 75 184 2 68 1359 23 846316 584 4031 76.:49 526 6.36' 22 0.35 1486 26.56 2793 44.22

20878 TZA 4014 19.70 247 1.21 670 3.29 413 2.03 2684 13.171425 UA 5176 35.39 921 6.31 256 1.75 467 3.19 3530 24.146242 ZMt 4480 74.98 690 11.06 544 8.72 302 4.84 3144 50.37

-------------- 49.3 3 7.41 3.06 5.213.6VIAT: 26.168 521 2.40 6.36 20.40

NIH: 10.83 0.52 0.00 0.00 5.03MAX: 100.99 19.27 8.72 23.56 81.73

MIDDLE IIE SWUT-MEICA IOL*4TRIU

ALL FIELDALL F1ELD NAT.SC. NAT.SC. EN. ENO. M.SC MD.SC TlR OTFERLEV 6+7 LEV 6.7 LEV 6.7 LEV 6+7 LEV 6+7 LEV 6+7 LEV 6.7 LIV 6+7 LIV 6.7 LV 6.+7

.POPUL. C9JTRY 1905 * 19086 * 1968 * 1908 * 1905 *

27515 COL 31960 1161.29 5076 18.48 75259 273.48 37344 135.72 201671 733.682879 CRI s568a 2454.52 3956 166.37 590s 248.3S 3130 131.7 46396 1906.20

918 GuY 1046 113.94 167 18.19 76 8.28 24 2.61 779 84.8675103 PM 1199175 1590.71 61178 81.46 264459 352.13 148713 198.01 72402 965.11305 NIC 25443 766.61 906 29.63 3066 99.98 3469 114.09 15492 522.962069 PAN 465o 2156.69 2515 120.39 5S32 269.60 3821 182.91 33127 1585.7852 SLV 6094 1165.79 2113 40.39 12514 259.18 6507 125.90 39780 760.3229 URY 59566 2007.81 4786 161.25 7287 246.52 11171 376.38 36342 1224.46

AVERAGE: 1428.15 79.52 217.06 158.40 973.17OEVIAT: 729.39 56.53 102.33 98.67 548.55MIN: 113.94 18.19 8.28 2.61 84.86MX: 2454.52 166.37 352.13 376.36 1908.20

GM COUNTRIES

AL. FI.DALL FIELD NAT.SC. NAT.SC. 840. 940. M.SC NE.SC OTH8t OTHERLIV 6.7 LWV 6+7 LEV 6.7 LEV 6.7 LEV 6.7 LEV 6+7 LEV 6.7 LFV 6+7 LEV 6.7 LEV 6.7

POPLL. CONTNR 1968 * 1988 * 1968 * 1986 * 1968 S

7649 AVr 177670 2356.21 17621 238.42 16762 248.80 22016 291.64 119461 1562.34986 ML 135740 137.28 10890 107.46 25062 262.40 24489 248.47 74799 78.92

24907 CAN 896472 3607.31 68244 274.00 53708 215. 04 45963 184.54 730567 2933.1466 CE 70283 1129.79 11613 1a2.24 6998 107.96 10052 155.08 44370 684.515114 DM 89488 1749.22 7960 155.81 15692 306.84 8400 163.82 57315 1120.75

3 EP 933989 2443.21 91057 240.29 119826 313.44 103502 270.75 61600o 1616.7348U PN 91647 1684.56 13124 269.67 1SS76 320.30 8198 127.39 56752 1167.02

6677 a 6 78790 1204.02 115692 208.21 81963 146.36 43397 76.96 437736 776.45119259 JPN 1696766 l592.15 66748 55.97 374102 313.71 128925 106.11 1328903 1114.3714362 NLD 16660 1175.74 13764 95.84 22478 156.49 17190 119.69 115431 803.734129 NOt 4919 12.01.72 5000 121.09 6119 140.20 2906 70.36 3ss95 862.073203 NZL 543u6 1697.94 7601 237.31 4407 137.69 2466 76.99 39911 1246.05

47279 TUR 416916 661.82 2472 52.30 72122 152.55 43260 91.52 27660 505.46

AVU --E: 1592.924 171.60 217.64 152.67 1173.35DEVIAT: 613.5570 75.70 76.63 73.02 596.23NIH: 881.82 52.30 107."9 70.36 585.46MAX: 3607.31 274.00 320.30 291.64 2933.14


-36 -

Figure I1-3: Enrollments in levelsi 6+7;total by field and per 100,000 Population

Figure 3.1 a all fields

I.E -

1.4-

1-I

1.2

t I,Fgue32t aua sine

I

I

U, -I

171

1401

121-

III-

IIU~~~BE A

-37-

Figure 3.3 engineering

322

hio

148

Igo

48

21

RIA LAM OCDC

Figure 3.4 zumdical sciences

log

148

121-

III

31

3D

IIRI

3gm LAM CC

- 33 -

Figure 3.5 other fields

1.2-

1.1

2 5.?

0.4-

5.1

aI.x )XXXXXXXXI .

SEA LAM OECD

- 39 - TABLE II-4

Graduates from level 6+7: total by field and per 100,000 population

SU-SAHARAN AFRICAN cOUlNRIES

ALL FIELDALL FIELD NAT.SC. NAT.SC. Ei. EC. MED.SC MED.SC OTHER OTFfRLed 6.7 LEV 657 LEV 6+7 LEV 6.7 LEv 6+7 LEV 6.7 LEV 6.7 LEV 6.7 LEV 6+7 LEV 6+7

POPUL. COINTRY 1985 * 1988 . 1988 * 1985 * 1985 a

33680 E7H 2067 8.14 488 1.45 136 0.40 163 0.48 1280 3.8012700 " 406 3.20 42 0.33 0 0.00 0 0.00 364 2.876429 MI 265 4.12 41 0.64 19 0.30 0 0.00 205 3.195700 RWA 211 3.70 SO 0.86 0 0.00 31 0.54 130 2.28

14625 UCA 1301 8.90 232 1.59 46 0.31 12S 0.84 900 6.15

AV -A-E: 5.21 0.98 0.20 0.37 3.66OEVIAT.: 2.10 0.48 0.17 0.33 1.34MIN: 3.20 0.33 0.00 0.00 2.28MAX: 8.90 1.59 0.40 0.84 6.15

MIDDLE DICOME SOUTH-AMERICAN COU4TRIES

ALL FIBEDALL FIELD NAT.SC. NAT.SC. ENO. ENO. D.SC D.5C OTHt OTHYLEV 6+7 LEV 6+7 LEV 6+7 LEV 6.7 Liv 6+7 LEV 6+7 LEV 6+7 LEV 6.7 LEV 6+7 LEV 6+7

POPUL COUNTRY 1985 * 1985 198 * 1985 * 195 a

27515 COL 36328 132.03 483 1.68 6959 25.29 5396 19.61 23510 65.442379 CRI 4197 176.42 282 11.85 402 15.90 670 23.96 2943 123.71

918 Om 203 22.11 39 4.25 34 3.70 0 0.00 130 14.1675103 MX 113047 150.52 4474 5.96 20040 26.66 19041 25.35 69492 92.533088 NIC 1001 32.73 107 3.50 107 3.50 102 3.34 685 22.402069 PAN 2163 103.54 116 5.5 131 6.27 250 11.97 166e 79.75S232 SLV 1821 34.81 44 0.84 53 10.57 147 2.81 1077 20.58

2968 RY 164 62.10 20 0.67 267 9.00 56 19.14 988 33.2916394 VEN 21916 133.66 662 4.04 5170 31.54 3698 22.56 12366 75.56

AVERA: 94.22 4.26 14.83 14.30 60.82DEVIAT.: 54.31 3.23 10.06 9.41 36.78MIN: 22.11 0.67 3.50 0.00 14.16MAX: 176.42 11.865 31.54 25.35 123.71

OEM COUNTRIES

ALL FIaDALL FIELD NAT.SC. NAT.SC. ENC. END. MD.SC MED.SC OT)1 OTHERLiV 6.7 LEV 6+7 LiV 6.7 LiV 6-7 LEV 6+. LEV 6+7 Liv 6.7 LEV 6+7 Li- 6.7 Liv 6.7

POPUL COUITRY 1968 * 1986 * 1988 * 1965 * 1985 *

15369 AU 48676 316.72 8734 58.83 5105 33.22 3212 20.90 31625 205.777549 AJT 9684 128.28 822 10.89 1006 13.35 1669 22.11 6185 81.93986 BEL 23711 240.57 2306 25.40 4461 46.46 3163 32.09 13761 139.62

24907 CAN 136716 556.94 14812 59.47 11074 44.46 9837 39.49 102995 413.526482 CE 9141 141.02 1342 20.70 881 13.59 2010 31.01 4906 75.725114 G6W 10209 199.63 337 6.59 1702 33.28 1710 33.44 6460 126.32

3822 ES 99162 250.40 7901 20.67 8731 22.84 15234 39.85 67296 176.044848 FIN 9637 196.17 1375 28. 27 125 25.70 1184 23.94 548" 120.2586377 am 180841 320.24 32014 S6.79 25156 41.03 9507 16.86 115862 205.519848 06C 16051 162.99 2693 27.35 1813 18.41 1794 18.22 9751 99.02

119259 JPN 402217 337.26 15181 12.73 84973 71.25 23072 19.35 278991 233.94320S NZL 8971 280.06 1614 50.39 SW 26.72 642 20.04 5859 182.92

47279 TU 46547 98.45 2250 4.76 8745 18.50 5207 11.01 3034 64.18234496 USA 1373740 885.83 144820 61.76 133581 56.97 11331 46.34 981978 418.76

AVEAE: 273.26 31.47 33.20 26.90 181.68DEVZAT.: 141.41 20.31 16.41 10.26 108.33MN: 98.45 4.76 13.35 11.01 64.18MAX: 585.85 61.76 71.25 48.34 418.76


- 40 -

Figure II-4: graduates from levels 6+7;total by field and per 100,000 population

Figure 4.1 : all fields211

2|0 -

Ion

log -

140 -

123

as E

41

Figure 4.2 :natural scieinces

32-

23

25

24-

14-

12

43

I

NSA LAM 0CCC

-41-

Figure 4.*3 engineering34.-

12

21 21-24-

22-2U

14-

12

24.-

32-

211

214

124-

'I~~~~~~~~~EE

14

2-

NA ~~~~~~LAM C~CC

- 42 -.


I9 n - _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

io IDIra'I6 ED

ISOD14a 110 120

IDa IonD

onD

noa7' anD

ED 40 ID 28

ID LAM OCDC

- 43 - TABLE II-5

Distribution of enrollments by field in percent

SUI.-SAKAN AFICA C0WMIES

MATC. SC AT.. SC C . EcN. MM. SC MID. S Tr O 0rlTHERALL LIV. ALL LV. ALL L&ELALL LEIL ALL LEV ALL LEV ALL LIVELALL LEVEL TOTAL

C ------ 19 - I 1968 I 1986 T 1968 5 T

8DI 207 7.44T 194 6.973 262 9.41S 2120 76.1o 100.00#194 1S.503 0 0.001 0 0.001 1240 85.473 100.001

ETH 4151 15.163 23S3 681S 136 5.061 19450 71.153 100.00O320 20.813 0 0.001 336 21.853 682 57.35X 100.003

aim 3016 34.273 1004 12.323 4568 5.201 4243 46.213 100.003KEN 1437 8.613 8462 36.993 1132 S.203 10708 49.206 100.003MOZ 69 4.791 541 37.523 162 11.231 670 44.4 " 100.003RWA 364 18.325 100 5.033 16 6.25 1389 68.396 100.006SON 1271 3.60W 2560 7.721 2011 6.021 27570 82.47 100.003SE4 2346 16. 473 207 I.63n 2466 19.561 7670 60.343 100.003TZA 247 6.153 670 16.69S 413 10.29S 264 66.673 100.003UCA 934 9.243 1407 13.933 467 4.62S 7295 72.21% 100. 0W2m 690 14.74% 54 11.62% 302 6.46 3144 67.183 100.003

AVEAO: 13.331 12.39# 6.703 6U.573OEVIAT: 8.15S 12.103 5.84T 12.258

IN: 3.603 0.003 0.003 46.443MAX: 34.273 38.993 21.65 80.47%

m410DLE INCOW SuT-AEICAN CmMTRIES

NAT.SC. NAT.SC. ENO. ENc. MED.SC Y ED.SC OTHER OTFetAU. LEV. AU. LEV. AU. LEV. AU. LEV. AL L LEV AU. LEV AU. LeVELALL LE.E TOTAL.

CaSArRY 19685 195 3 1965 S 1965 S

COL 6477 1.6an 95260 24.34# 39904 10.193 249629 63.613 100.003c0I 3958 6.786 5909 10.12# 3130 5.361 453 77.743 100.00#

is?167 7.173 237 10.16# 98 4.21S 126 76.441 100.006JAM 1260 24.56# 0 0.00 644 12.S 222 62. 66 100.001MEX 61176 5.103 264460 22.06# 148710 12.403 724732 60.44" 100.003NIC 1107 3.62# 4532 15.63S 4339 14.963 19026 U.59# 100.001PAN 2551 4.56# 11567 20.923 365 8.973 37350 67.545 100.003SLY 2182 3.10# 15693 19.71% 7118 10.103 47306 67.10# 100.003$A 27 0.963 23 0.84# 110 4.006 2891 94.16# 100.003770 697 22.031 529 16.723 0 0.003 1956 61.253 100.003LAY 541 6.251 7715 6.80 15725 17.933 58788 67.081 lOO.COVA 1592S 3.59# 64915 19.17% 4845 10.493 295757 66.753 100.001

AVEPA0E: 7.473 14.04% 9.10# 69.40#CIVIAT: 7.33S 7.726 4.92# 9.2S#M11: 0.98# 0.00# 0.00# 80 448KUX: 24.56# 24.34# 17. 93 94. 18

OECD COLNTRIE

AU. LEV. AU. LEY. AU. LEV. AU. LEV. AU. LI-V AU. LI-V AU. LEVE.ALL 1.249. TOTALNAT.SC. NAT.SC. BCN. EC. * MFED .C X MED.C OTIt OTHt T

CVLWY 1965 196 S 196 * 1966 #S

AUS 52547 14.20# 36273 9.80N 23431 6.333 257799 69.67 100.003AUT 17621 9.205 19748 10.313 23606 12.323 13060S 6U.173 100.00#ML. 20356 S. 253 502 14. "S 42908 17.331 141210 59.863 100.003CAN 84240 7.47# 9827 8.753 79421 7.065 864712 76.73S 100.00OC>E lU15 10.73S 21846 19.6US 10248 9.313 66401 60.301 100.003oBD 178550 11.523 309040 19.94J 219750 14.18# 842360 54.373 100.001ag 7968 6.65 19502 16.77M 17520 15.061 7133O 61.323 100.003ESP 91657 9.82# 119820 12.013 103620 11.086 $19653 68.263 100.003FIN 16142 .861S 31672 24.75S 18336 12.773 6382 49.87# 100.001Cm 134700 13.053 159040 15.40# 151640 14.S" 567120 5W.661 100.00#

164I24 9.053 33297 18.313 24549 13.303 107630 59.173 100.003JFW 67025 2. 88 41220 17. S 1496410 6.373 1716586 78.21# 100.003N4O 13764 3.533 642SS 16.4SS 40155 10.23 272186 69.73S 100.003NOR 7829 8.27 13869 14.65 1063S 11.255 62307 U.82# 100.003NZL 9765 9.94# 16624 17.103 6808 6.923 64991 66.04S 100.00#TUN 25069 5.341 90462 19.255 44043 9.373 310676 66.047 100.003

AVEAQE: 6.923 16.003 11.11% U3.971DEVIAT: 3.13U 4.061 3.29m 6.85a142: 2.865 8.75# 8.333 49.87#14X: 14.203 24.75# 17.333 76.733


- 44 -

Figure II-5: Distribution of enrollments by field in percent

.DX

4lX -

3rx -

3aD -

Dx'iDA ac ,

Figure 6: Distribution of enrollments in levels 6+7by field in percenat

?DX O

em IE

lox i

SEA LAM CC

- 45 - TABLE II-6

Distribution of enrollments in levels 6+7 by fields in percent

SJ-S4AHR AF1CA CNTrRIES

NAT.SY. NAT.SC. SC. E9. MED.SC MD.SC OTHE 0THLEV 5.7 LEV 6.7 LE 6.7 LEV U.7 LEV 8.7 LEV 8+7 LUV 8.7 LEV 8.7 TOTAL

CDUITRY 1966 S 1988 I 1988 3 19" S 3

8DI 207 10.24S 115 5.69S 262 12.98 1436 71.125 100.001SWA 194 19.061 0 0.00S 0 0.001 on 60.92 100.00mETl4 3821 24.671 1079 6.97# 1196 7.741 981 60.62 100.001KIN 926 10.235 956 10.541 919 10.131 6271 69.11S 100.001maZ 69 4.791 S41 37.52 162 11.23X 670 46. 4 100.001RWA 364 16.323 100 S.01 16 6.26 189 61.391 100.O0s58 526 10.931 22 0.481 14 30.601 2798 67.61S 100.001TZA 247 6.15s 670 16.69S 413 10.291 264 ".471 100.001UCA 92S 17.633 261 4.9SS 487 9.021 3SU0 66. 100.00S*mo 690 14.74S 544 11.623 302 6.46 S144 67.16 100.00O

AVER -: S1.70- 9.951 10.-91 S-.---OEVAT.: 5. 97S 10.381 7.486 :.6INIH: 4.791 0.005 0.01 46.48SMAX: N*24.671 37.52 30SS05 60.90.

NIDLE DC9E SOVT-MmICA4 C0LWTRI1

NATIC. NAT.SC. e4. E8. M0ED.SC X ID.C OtM MMLEV 6.7 LEV 6.7 LEV 6.7 LEV 6.7 LFV 6-7 LIV 6.7 LEV 5-7 LLIV 67 TOTAL

w --TW- 198 S I-------- 196 - 1_9 - 1966 - S S

COL 5076 1.591 752=9 23.65S 37844 11.691 201671 63.1II 100.001CR1 3958 6.765 6W90 10.12S 63lO 6.36 46894 77.74S 100.001alf 167 15.971 76 7.271 24 2.29 779 74.471 100.00SPmC 61176 8.101 26449 22.061 148713 12.40# 7245 60.443 100.00SNIC 906 3.661 3066 13.04S 3469 14.US 19 U5. 100.0WsPAN 2613 6.6661 663 12.491 3621 6.471 3612 78.461 100.005SLV 2113 3.481 12514 20.23 6S6 10.601 39760 ".2 100.001URY 4766 6.01 7287 12.235 11171 16.756 36342 60.99S 100.OOS

AV650: 6.301 16.16S 10.S6S 67.971DEVIAT.: 4.10S 5.645 4.66S1 t.1SMN: 1.59S 7.271 2.29 60.44SMAX: 15.971 23.5s 16.75S 77.741

NAT.SC. NAT.3C. 8G. 8G. ME0.SC MM. SC OTPO u1l.LEV 6.7 LEV 67 LFV 67 LEV 6.7 LEV 6.7 LIV 6.7 IEV 6.7 LIV 6.7 TOTAL

C8MtRlY 196 1 1966 1 196 1 196 1 1

AUT 17821 9.911 187 10.S66 22018 12.38 ll148l 67.161 100.005ML. 1060 7.60S 2666 19.065 24489 18.041 7479 68.1N 100.005CAN 6644 7.. S 63706 5.961 46965 .125 73067 61.31 100.005OHE 1161.3 is.1LIN 6996 9.6"1 10062 18. 735 44370 60.895 100.001{t ?7968 6.91S 16692 17.541 648 9.45 S671 61.071 100.O01ESP 91867 9. 6n 119622 12.631 103602 11.065 616606 64.265 100.00O

4 13124 14.32S 1667 17.001 6198 .75 6752 61.92S OO.005CIR 115U92 17.041 8196 12.071 43397 6.391 4377 64.491 100.001iPW 66746 3. 82 374132 19.701 12926 S.79 13268 69.991 100.005

NL) 13764 6.155 22478 13.311 17190 10.1in 11848 65.361 100.005NR am5000 10.05 64119 12.335 2906 5.65" 869 71.741 100.005

Z 7601 13.965 4407 8.101 2466 4.658 39911 73.395 100.001TUR 24726 5. 93 72122 17.301 4326 10.36t 276600 64.391 100.001

AVEtAOE: 9.151 12.623 6.62 62.205DEVIAT.: 4.57S S.315 4.341 16.261NIH: 3.52 S. 981 4.5O5 35'.10MAX: 17.04U 19.701 1C.04t T1.31e


- 46 - TABLE II-7

Distribution of graduates by field in percent

SUB-SAHARAN AFICAN COUNTRIES

NAT.SC. NAT.SC. EN5. ENC . ME.SC D.SC oTHER 0TFlALL LEY ALL LEV ALL LEV ALL LAY ALL LAY ALL LEY ALL 12V ALL LEV TOTAL

coufrRY 1985 s 1985 I 198 1 1966 3 s

ETH 526 9.37% 260 4.6SS 226 4.02S 4408 81.981 lOO.OOS@44 55 12.53S 0 0.001 0 0.001 384 87.47n 100.001Fwi 41 2.631 69 4.431 64 4.111 1386 88.843 100.001RWA 75 I6.631 26 5.761 53 11.753 297 65.65s 100.00SUGA 232 *.5s6 696 2.671 126 5.11 1374 56.89 100.00O

AVERACE: 10.14S 8.701 5.011 7t.151DEVIAT: 4.5l9 lo0.181 W.6s 12. 74MN: 2.63S 0.001 O.OOS 5S.591MAX: 16.6t3 28.671 11.75S 88.841

MM0L ICOME SOUTH-AMERICAN COWMRIB

NAT.SC. NAT.SC. ENO. ENC. MD.SC MED.SC OTMt OTHRtALL LEV ALL LEV ALL LAV ALL LEV ALL LEV ALL LEV ALL LEV ALL LEV TOTAL

Z;zwTRY -1985 1988 - 1985 -- - -s s-

COL 76 1.57 10072 20.671 6029 12.371 31870 68.Se 100.001CRI 320 6.521 485 9.471 674 13.731 3449 70.27m 100.006(1)f 3a s SS 9. 14 34 5 571 O. 31 100 .00OJAM 176 s.13s 0 0.001 133 11.44 84 73.435 100.00MEX "474 3.961 20040 17.73S 19041 16.643 69498 61.471 100.00SNIC 112 6.85s 227 13.881 268 16.206 1082 63.061 100.00SPAN 139 4.245 425 12.961 505 1.405 2211 67.41S 100.00SSLV 44 1.151 887 23.191 454 11.871 2440 6.79M 100.00SURY 130 4.941 275 10.451 1004 38.133 128I 44.471 100.00OVEN 1100 3.74S 6417 21.625 3797 12.911 18092 61.523 100.001-------------------------------------------------------------- __._-----------__------------

AVERAGE: 5.36s 13.931 15.40S 65.323DEYIAT: 3.701 6.77S 8.21S 8.435MD4: 1.151 0.0OD$ 5.06 44.471MAX: 15.131 23.191 384.15S 80.311

OECD cOUNTRIES

NAT.SC. NAT.SC. ENC. ENC . M.SC MED.SC arH0t OT)RALL LEY ALL Leb ALL LEV ALL LEV ALL LEV ALL LAV ALL LBE ALL LEV TOTAL

COIrRY 1985 s 1986 1 1985 1 1966 s S

AUS 1044S 14.23S 611l 6.313 4513 S.13 52449 71.335 100.003ALrr 82a S.6s 1271 8.79 2006 13.871 1036 71.67s 100.001BA.. 4840 9.95s 739a 15.21S 8113 1.W 2875 so. i58. 100.001 CAN 19127 9.643 22897 11.543 20486 10.323 135970 68.S13 100.003CHE law1 14.855 881 8.725 2010 19.90S 5712 5s.s41 100.00OoM 337 1.783 3113 16.481 3945 20.6OM 11496 60.883 100.001ESP 7901 7.903 6731 8.731 15275 15.281 6877 68.091 100.001FIN 2615 10.623 5742 26.33S 6068 27.s3s 7610 35.225 100.001am 40119 13.471 4820 15.723 SIS32 17.811 159309 5S.50 100.003aRC 2269 9.8s1 3712 13.591 2736 10.025 1816 u.s3s 100.001JPN 15319 2.625 100450 17.151 30211 5.161 439690 75.071 100.001NZL 1614 14.323 907 0.051 643 5.703 8110 71.943 100.003TIM 2328 3.81 12998 21.271 5342 8.741° 40430 ..171 100.001USA 181960 s.86s 199300 10.891 182520 9.971 1288820 70.29s 100.001

AVEAGE: 9.113 13.631 13.413 63.861OEVIAT: 4.1 " 5.283 8.308 10.08MINd: 1.731 8.053 5.161 35.2213MAX: 14.8"S 2t.33 27.a6s 75.075


- 47 -

Figure II-7: distribution of graduates by field in percent

5as -_

illx

101S

BSA LAM CLCD

Figure Os Distribution of graduates fromm levels 6+7by field in percent

DI -201

lax

CIA LAM OCD

Fir 8 Dt E

SIS ~ ~ b fil-n ecn

331-

_01 ____i TIz; ;

- 48 - TABLE II-8

Distribution of graduates from level 6+7 by field in percent

SUS-SAMARAN AFICAN C(MTRIES

NAT.SC. NAT.Sc. ENO. ENO. MED.SC MED.SC OTHER OTHERLEV 6+7 LV 6+7 LEV 6+7 LEV 6.7 LEV 6.7 LEV 6+7 LEV 6.7 LEV 6+7 'CTAL

C ---TRS 1985 - 1985 s 1-s l s 195 ?i S

ET1 488 23.611 136 6.585 163 7.891 1280 61.931 100.00142 10.345 0 0 .00 0 0.001 364 89.665 100.001

"di 41 1.5.471 19 7.171 0 0.001 "08 77.361 100.00R?WA 50 23.705 0 0.001 31 14.691 130 61.611 100.00OUGA 232 17.835 46 3.545 123 9.45X o00 69.181 100.001

AVERAGE: 16.191 3.46X 6.411 71.95oDEVIAT.: 5 .07 3.081 5.701 10.56%MIN): 10.341 0.001 0.001 61.61.1MAX: 23.701 7.171 14.691 89.665

MIDDLE INCiE SUT-AMERICAP COUNTRIES

NAT.SC. NAT.SC. ENO. EN. ME.SC ED 1T.sc OTHER OTiLEV 6.7 LEV 6+7 LEV 6.7 LEV 6+7 LEV 6+7 LEV 6+7 LiEV E+7 LEV 6+7 TOTAL

COIRY 1988 s 1985 1 1985 s le8 s s

CDL 463 1.271 6959 19.161 s398 14.65A 23=10 64.721 100.001CRI 282 6.721 402 9.581 570 13.58S 243 70.121 100.001GJY 39 19.21S 34 16.751 0 0.001 1.30 64.045 100.001

EX 74474 3.961 20040 17.731 19041 16.841 694.92 61.471 100.001NIC 107 10.69s 107 10.691 102 10.191 a88 68.431 100.001PAN le1 5.361 131 6.061 250 11.561 166 77.021 100.00SLV 44 2.421 553 30.371 147 8.071 1077 59.141 1.00.001tRY 20 1.091 267 14.491 s58 30.821 g86 53.611 100.001YEN 662 3.021 5170 23.591 3698 16.871 12386 S.521s 100.001

AVERCE: 5.971 16.491 13.864 63. WSDEVIAT.: 5.481 7.041 7. 82 6.384MN: 1.091 6.061 0.001 53.61.1MAX: 19.21S 30.371 30.821 77.021

OECD coUNTRIES

NAT.SC. NAT.SC. ENl. ENG. MED.SC M.D.SC OTHER OTHERLEV 6+7 LEV 6+7 LEV 6.7 LEV 6+7 LAV 6+7 LEV 6.7 LEV 6+7 LEV 6.7 TCTAL

COUTRY 1985 s 1985 1 1985 s 1965 s s

AiUS 8734 17.941 5105 10.491 3212 6.601 31625 u4.97s 100.001AUr 8=2 6.491 Loos 10.411 1669 17.231 61815 6i3.87 100.001BEL 2306 9.731 4481 18.901 3163 13.341 137181 56.045 200.001CAN 14812 10.681 11074 7.981 9837 7.091 102995 74.25 100.00oCHE 1342 14.681 81t 9.64X 2010 21.99s 4901 s3.6ss 100.001ONt 337 3.30S 1702 16.671 1710 16.751 6*30 63.2ss lOO.001ESP 7901 7.97s 8731 8.801 15234 15.365 67296 67.8s1 100.001FIN 1375 14.271 1260 12.971 116.4 12.081 5848 60.681 100.001ami 32014 17.735 23156 1.2.63S 9507 6.271 115862 64.1.71 l.O.CcRC 2698 16.781 1613 11.305 1794 11.181 97151 60.751 100.001JPN 15181 3.771 84973 21.131 23072 5.74s 278991 69.365 100.00ONZL 1614 17.991 a56 9.S41 642 7.161 s589 65.311 100.001TUR 22S0 4.i31 874S 18.791 5207 11.191 30345 65.191 100.001USA 144820 10.541 133SI6 9.72f 113361 8.255 981978 71.481 100.001--------------------------------------------- _----------_ --------- _<-- - _--------

AVERAGE: 11.341 12.WS0 11.371 64.49%DEVIAT.: 5.111 4.14X 4.885 5.145tIN: 3.301 7.98e 5.271 S3.691MAX: 17.99s 21.131 21.991 74.251


- 49 -

III. SCIENCE EDUCATION IN SECONDARY SCHOOLS

Increased enrollments in scientific and technological fields at

the university level requires a growing pool of graduates from Upper

Secondary Schools (USS) with an adequate knowledge of science and

mathematics. Similarly, the change in attitudes to favor the introduction

of technology requires that a large segment of the population be exposed to

basic scientific concepts, and be able to relate these concepts to the

surrounding physical, social, and economic environment. Proxies for the

fulfillment of both requirements are: enrollment-rates for lower and upper

secondary schools; time dedicated to science and mathematics in the

curriculum; and quantity and quality of inputs - teachers, laboratories and

equipment, materials, books, teaching methods -.

Enrollment Rates

The Secondary School enrollment rate in SSA is very low. Less

than 20X of the secondary school age group attend school compared with

almost 90Z in Europe and 54Z in Latin America. Within this total enrollment

rate, that of Lower Secondary School (LSS) is less than 25Z while that of

the USS is only 102 (see Table III-1). Of this 102 less than 402 follow the

'scientific stream" in countries where there is a choice between

"humanities" and "science and mathematicsw (see Table III-2). Moreover, of

those taking the "scientific stream" on the average only 582 pass the

science and mathematics exam in their last year (see Table III-2), and of

those admitted in the university less than 442 choose a career in Science

- 50 -

and Technology fields. It means that of every ten students admitted in USS

only one will end up choosing a career in scientific and technology fields

(assuming no dropout along the way).

A low enrollment rate in USS means that in SSA the chances of

spotting natural scientific talent 6/ in the general population is 7 to 8

times lower than in Europe, and less than half than that in Latin America.

A low enrollment in LSS means that in SSA the proportion of the secondary

school population exposed to a systematic program to foster positive

attitudes towards technology is four times smaller than in Europe, and that

over the long run more than three quarters of the population will remain

untouched by modern knowledge and ideas. But even for those lucky enough

to enroll in post primary education the quantity and quality of science

education leaves much to be desired. On average students in LSS receive

8.6 hours/week of science and mathematics compared with 10.2 hours/week and

10.7 hours/week for Europe and Latin America respectively (see Table III-

3). 7/ In USS the number of hours of science and mathematics/week for the

"science" streams is 13.4 and for the non science streams 8.5. 8/

Teachers 8/ - Most of the qualified science teachers teach in the

USS. The unqualified teachers teach mostly in LSS. In general, a science

6/ D. De Solla Price, op. cit.

7/ The length of LSS varies from country to country. Francophone countriesrequire 4 years, most anglophone 3 years. Length of the school year alsovaries, but in general the school year in Europe is longer.

8/ Data were calculated from a non-statistical survey of 60 schoolsdistributed among 10 countries.

- 51 -

teacher must possess a secondary school diploma plus 3 or 4 years

specialized training at the University or a Teacher Training College (see

Table III-4). The percentage of females among these teachers is less than

25Z, and this has a negative effect on enrollment of females in the science

streams.

Laboratories, equipment, and books in USS. 8/ - In general, they are

deficient. Consumables are in short supply. Very few have qualified

technicians to take care of the equipment. Science books are available but

in short supply. Most students have no access to scientific magazines.

Science Teaching Methods in USS. 8/ - Reading and copying from textbooks

are the main teaching vehicles. Lecturing is the principal teaching mode,

on average it takes up more than 502 of the time of the teacher. Where

laboratories exist, practical work takes up on the average less than 20Z of

total time allocated to science. There is little technical support from

central authorities. The ratio of science inspectors to teachers is less

than 1/300.

In Summary:

The secondary school is an integral component of the scientific

structure. It supplies the candidates to be trained in science and

technology in Higher Education institutions, and is the vehicle for shaping

positive attitudes towards modernization. On both accounts the Secondary

School System in SSA is falling short of the required task. On average

less than 40X of the school population in USS are in the science stream,

and of those, given the high rate of failure, almost 502, only 44% of the

- 52 -

successful ones will go into science, engineering and medicine. It can be

easily seen that to increase appreciably the pool of applicants to science

and technology at the university level will take a concentrated effort to

increase enrollment levels and the proportion of those taking science

streams, and also to lower the percent of examination failures in the

sciences and mathematics. Concomitantly, enrollment in LSS will have not

only to expand to accommodate the expansion of USS, but more importantly,

to help expose a larger population to science and technology. To succeed,

this quantitative expansion,at both the LSS and the USS, must be

accompanied by an improvement of quality through the provision of better

teachers, equipment, materials, and educational infrastructure.

TABLE III-1- 53 -

ENROLLMENiT RATESTOTAL LOWER AND UPPER GENERAL SECONDARY SCHOOLS IN SSA

------------------------------------------------------ _--___-----------__----__------

COUNTRY LOWER ENR.RATE UPPER ENR.RATE TOTAL ENR.RATE-------------------------------------------------------------- __----_--------__------

BENIN 20.673 6.37x 14.96%

BOTSWANA 41.17X 10.163 29.48%

BURKINA FASO 7.46S 2.65X 6.66%

BURUNDI 3.38X 0.96! 2.37X

CAMEROON 26.76X 10.46X 20.15X

CENTRAL AFRICAN REP 12.00X 6.36X 9.34X

CHAD 6.91X 3.18X 6.39X

COTE D'IVOIRE 22.74X 6.s8x 18.34X

ETHIOPIA 21.13X 9.61X 13.73X

GABON 31.96X 9.82X 22.73X

GHANA 60.23X 3.79X 37.74X

GUINEA 10.23X 6.08! 8.64X

GUINEA-BISSAU 7.79X 1.70X 5.46X

KENYA 21.66X

MADAGASCAR 26.89X 11.83X 20.156

MALAWI 4.113 3.94X 4.02X

MALI 8.68X 1.77X 6.34!

MAURITANIA 16.15X 12.84X 14.67!

MAURITIUS 62.15X 62.11X 67.31X

MOZAMBIQYE 10.71! 2.82X 6.11!

NIGER 8.20X 1.64X 5.54X

RWANDA 1.33X

SENEGAL 16.63X 8.26X 13.37!

SOMALIA 8.465

SUDAN 23.61X 11.76X 17.99X

SWAZILAND 54.91X 23.63X 43.36X

TOGO 31.91X 7.13X 21.98X

UGANDA 12.93X 2.18X 9.623

TANZANIA 4.64X 0.633 3.39X

ZAMBIA 27.74X 11.12X 18.14X

ZIMBABWE - 63.94!-------------------------------------------------------------------- __-------__------

AVERAGE FOR SSA 22.33X 8.79X 17.60%---------------------------------------------------------- __----__-----------__------

EUROPE('87) 89.40%---------------------------------------------------------- __-----------------__------

LATIN AMERICA('87) 63.70%------------------------------------- ____-____----------------------__-------__------

1] Since enrollmnt data of lower and upper secondary school in SSApertain only to general education, and general education is, on theaverage, around 90X of the total enrollment, enroll-mnt rates for Lower,Upper and Total Secondery education would be 24.6, 9.7Z, and 19.4!respectively.Source: Calculated from Unoeco Statistical Yoerbook, 1989

TABLE III-2

STUDENTS IN SCIENTIFIC STREAM IN UPPER SECONDARY SCHOOLS IN SELECTED SSA COUNTRIES

--------------------------------------------------------------- __------------__-------------------------------------------------------

8URKINA FASO ETHIOPIA IV COAST KENYA MADAGASCAR MALAWI SENEGAL UGANDA ZAMBIA

Porportion of StudentsIn Science Streams: (N.A.) (N.A.) (N.A.) 21.99% 60.61% (N.A.) 60.87X (N.A.) 30.25X

Passing Rate in ScienceA Moth of Students Inthes Streams: 66.25% 60.00 (N.A.) 68.15X 24.72% 68.0OX 66.76X 71.83x 76.22X

Percontage of ScienceSbtudent In --t Y-arof Upper Secondary SchoolAdmitted to University: 38.10% (N.A.) 36.00x 15.68% 19.88X (N.A.) 62.60X (N.A.) (N.A.)

Percentage of thouAdmitted going IntoScience and Technology: $5.00% (N.A.) (N.A.) (N.A.) 48.90% (N.A.) 48.10X (N.A.) (N.A.)

Source: Special Qu tionnalre

TABLE III-3Page 1 of 2

WEEKLY CLASS HOURS AND SHARE OF SCIENCE AND MATHEMATICS IN THE TOTAL,IN LOWER AND UPPER SECONDARY SCHOOLS IN SELECTED SSA COUNTRIES

(GRADES 7-9)

TOTAL TIME PERCENTAGECOUNTRIES (Hours) SCIENCE MATHEMATICS SCIENCE AND MATHEMATICS

------------------------------------------------------------- __--------------__----------------------------------

BOSTWANA 26.9 3.3 4.0 27.1

BURKINA fASO 28.0 3.0 6.0 28.6

CAMEROON 30.0 9.9 6.0 49.7

ETHIOPIA 20.0 2.6 2.6 26.0

GHANA 30.1 3.1 4.0 23.7

KENYA 27.7 3.6 4.7 29.6

MALAWI 28.8 3.3 3.8 24.7

MALI 32.6 6.0 6.0 30.7

MAURITIUS 16.6 3.3 3.3 39.7

LJ1NIGER 24.0 3.0 6.0 33.3

RWANDA 32.9 2.7 6.4 24.7

UGANDA 26.3 6.9 4.7 46.8

TANZANIA 30.0 6.0 4.0 83.3

Average for SSA: 27.1 4.3 4.3 32.1

EUROPE: 27.6 6.4 3.8 37.1

LATIN AMERICA: 27.2 6.7 4.0 39.3

Source: The Place of Science and Technology in School Curricula: A Global Survey, Unosco,1986.

TABLE III-3Page 2 of 2

UPPER SECONDARY : SCIENCE STREAM---------------------------------------------- _---------------__-------------__--------------------

TOTAL TIME PERCENTAGECOUNTRIES (Hours) SCIENCE MATHEMATICS SCIENCE AND MATHEMATICS

ETHIOPIA 19.6 7.2 3.2 63.06%

KENYA 30.3 6.7 4.0 36.31X

MADAGASCAR 34.0 8.2 6.8 41.18X

SENEGAL 36.0 10.3 6.7 48.67X

ZAMBIA 28.0 8.0 7.0 63.57X

AGERACE FOR SSA: 29.4 8.1 6.3 46.34X

UPPER SECONDARY : NON-SCIENCE STREAM------------------------------- -------- ------------------------------------------------------------ G

TOTAL TIME PERCENTAGECOUNTRIES (Hours) SCIENCE MATHEMATICS SCIENCE AND MATHEMATICS------------------------------------------------------------- __--------------__--------------------

ETHIOPIA 19.4 4.4 3.2 39.18X

KENYA 29.6 6.4 4.0 31.76X

MADACASCAR 36.7 6.0 3.0 26.23X

MALAWI 29.2 6.4 4.4 3S.99X

SENEGAL 05.0 2.1 a.0 16.24%

ZAMBIA 28.0 4.0 4.4 3o.oox

AGERAGE FOR SSA: 29.6 4.8 3.7 29.73X

Source: Special Questionnaire

TABLE III-4

EDUCATION AND TRAINING REQUIREMENTS OF SCIENCE TEACHERS IN SELECTED SSA COUNTRIES

--------------------------------------------------------------- __------------__-----------------------------------------------------------

I ETHIOPIA I KENYA M UALAWI UGANDA I ZAMBIA

I LOWER UPPER I LOWER UPPER I LOWER UPPER I LOWER UPPER I LOWER UPPER I

Secondery School Diploma: | I I I I I+courses: I I I I I * I

Toacher Training Certificate: I 30.oox I I I 1 70.00% I10+2: * 10+3:1 I , I I I12+1 : I I I I I12+2: I * I a I * I

(sp.clalization:ocionce) I | I I I *

College Diploma (Educ): I 70.00X | I I I 20.00%12-2: a I a at * a I12+3:1 ~aa 12+4: | I I I at

(sp.cialization:nclnce) I I a I I I

university diploma 100.0OXB.ED.: I

w/ 1YR.T.T: I a I(speciallzation:mclenc*) I a a a I

B.SC.: I a a I e3YEARS: 1 * I I I I4YEARS: I a ,

w/ 1YR.T.T: I a Iw/dipl.ED.: I | | I a a

(specialization:ocTenco) I a a a * I e

a (Alternative Education and Training Attainments Required for Science Teaching)

Source: Special Questionnaire

- 58 -

IV. A STRATEGY FOR SCIENCE EDUCATION IN SSA

Economic development in SSA countries depends foremost on the

ability of their societies to establish technological progress as an

ongoing process. This ability requires the capacity to choose, acquire,

adapt, generate, and apply technologies. 9! Scientific knowledge is a

basic ingredient that helps develop this capacity.

The type of scientific knowledge which a country requires at a

given time is a function of the technological goals of the society. For

example, the level of scientific knowledge required to choose among

technologies is different from the one needed to adapt technologies or to

generate a new technology. But even the lowest level of technological

aspirations demands a local institutional scientific foundation. In SSA

this scientific base is needed mainly as an important input to the

educational and training processes required to produce the specialized

manpower demanded by the economy, and to influence the population at large

to accept and encourage technological change. But to function efficiently

in these two endeavors the scientific establishment needs to keep up with

scientific progress, keep in contact with the scientific international

community, and provide its members with opportunities for scientific

advancement. For most of SSAn countries, these demands on the scientific

9/ Technologies are processes by which knowledge -- cognitive,psychomotor, intuitive, etc. -- is used in the practical solution ofproblems.

- 59 -

community are a tall order indeed. Not only are budgetary resources

constrained but, more importantly, qualified technical scientific personnel

are in scarce supply.

Higher education - mostly universities and technical institutes -

has been in the past, and will continue to be in the future, a major if not

the only source of scientific manpower. How can this institution adjust to

fulfill the function of providing the necessary scientific skills, in

quantity and quality, that a continuous technological progress demands?

Presently, the numbers of students in, and graduates from,

scientific and technological departments in SSAn-universities do not

correspond to the needs of the countries. Moreover, the gap in scientific

and technical manpower per unit of population between SSA and develope!d

countries, and even other developing areas, is widening. This is

especially true for graduates with advanced degrees (that require more than

3 or 4 years of post-secondary education) that form the core of those that

choose and adapt technologies and teach and train future professionals in

scientific and technical areas. The reason for this growing gap is the

small number of students that choose science and engineering as their

careers, the small graduation rate, and the even smaller number of those

that continue to higher levels of training in these fields. How can we

reverse this trend7

It is possible to do so by: (1) increasing the total Higher

Education student population (leaving the share of science and technology

constant), (2) increasing the share of science and technology in the

student population, or (3) both.

- 60 -

Given the budgetary constraints of Higher Education in the near

future, option (1) of increasing the total number of students to such an

extent as to increase significantly the number of graduates in the sciences

and technology - a path taken in the past by most SSA countries - is

foreclosed. The only available option is option (2) - increasing the share

of science and technology. But a shift in the relative shares in favor of

science and technology implies an increase in the total budget even if the

total number of students remains constant because, in general, cost per

student in science and technology are higher than in other areas. To this

increase of budget we must also add the cost of improving quality of

instruction in science and technology. But the increase in budget to

improve quality of instruction has also a positive savings effect. While

it increases cost per student, it also increases the probability of

promotion and lowers repetition rates. Thus, by increasing the number of

graduates from the same pool of admissions it may lower the cost per

graduate, where the graduate is, presumably, of a higher quality. The

enhancement of quality has also the addit:Lonal effect of encouraging

graduates from one level to seek further training at the next higher level.

It is actually at these higher level where the need for graduates in SSA is

greatest.

However, even considering the positive impact of quality

enhancement on lowering cost per graduate, we must assume that pressures on

the budgets for Higher Education will increase. Given the limitations on

the budget how can this demand be satisfied? If we want to maintain the

enrollment levels in Higher Education, there is no other alternative than

- 61 -

to restructure the budget to favor instruction, and within instruction

science and technology, over line items of expenditure that are not

directly linked to the educational process. Student maintenance, bourses,

and scholarships are obvious candidates for the budgetary ax since their

shrinkage will barely affect educational outputs, though it may produce

political fallout.

Rearrangement of budgets and the creation of places in science and

technology provide opportunities for students to go into these area of

specialization, but will the students avail themselves to these

opportunities? Present conditions militate against a strong shift in

students' preferences toward careers in science and technology. First,

science, mathematics, and technology subjects are considered to be harder

to cope with than other university curricula. Second, economic rewards,

i.e., level of salaries of graduates, do not favor science and technology

over other disciplines. The reason why heretofore graduates from science

and technology are not doing better than other graduates is that employment

opportunities for all types of university graduates were, and continue to

be, mostly confined to the public sector where salary scales conform to

levels and not types of education. Even in the private sector salary

differentials are not large because the low quality of science and

technology education blurs differences in the knowledge graduates from

different disciplines possess. Furthermore, the productivity of a specific

kind of knowledge depends not only on the supply of this knowledge but. also

on its demand. This has created a chick and egg situation: there is no

sufficient demand for scientists and engineers because the rate of

technological change is low, but this rate is low because there are nct

- 62 -

enough scientists and engineers. A way to break this vicious circle is to

provide a supply that is larger and of a better quality than the one that

could be fully employed in the short run, even at the risk of under

employment in the short term. Because maLrkets are sometimes slow to react

to factors such as quality, whose impact on profitability is not

immediately evident, entrepreneurs may have to be educated to the long run

gains of employing well qualified science and technology professionals at a

reasonable remaneration. While salary differentials are one way of

channeling students into careers another way is to reduce or increase the

costs of different careers to the individual. Subsidies in the form of

scholarships can be provided for students in "desired" programs while

discouraging measures, such as tuition fees, can be introduced for students

in "unwanted" programs.

Economic incentives are ways to channel applicants into socially

desired programs of science and technology, but this presupposes an

adequate pool of graduates from secondary schools with a suitable

background in science and mathematics. In SSA this pool is rather small.

On the average less than 40Z of graduates of secondary schools choose a

science and mathematics stream, and the rate of success of these streams is

less than 60Z, and of those admitted to Higher Education less than 452 go

into science and technology fields. Sometimes these percentages are

determined by administrative fiat, not always decided on the basis of

national socio-economic goals, but more often, when the choice resides with

the student, enrollments by course are the result of a combination of

factors: poor quality of teachers, curriculum, and textbooks and materials;

- 63 -

greater demand on the student's time and efforts by science and mathematics

courses; lack of a "visible" reward for the required extra efforts.

To overcome the effect of these factors it will be necessary

foremost to improve the quality of science and mathematics for a large

number of secondary school students. Given the budgetary constraints and

increasing demand for secondary school education, it is not possible to

offer quality science and mathematics to all students. The only solution

is, first, to target the quality teaching of these subjects to the

population that is going to make the best use of this instruction, while

providing a good quality but of a different curricula of science and

mathematics for the majority of students which in any case are not going

into the science and technology fields, and second, to enhance the status

of those engaged in science and mathematics by increasing the awareness of

the student body, teachers, and parents of the crucial roles that science

and mathematics plays in the economic development of the country. This

increased status is necessary to reward, albeit partially, students in

science streams for the increased time and effort required by science and

math curricula.

The possible role of the Bank in the area of science teaching in SSA

In the last 20 years the role of the Bank in science education

at the Secondary and Higher Education levels in SSA was quite limited. Of

the almost 170 education projects only 12 make specific reference to the

need of improving science education, although many more projects for

secondary and technical schools include science laboratories as components.

It is interesting to note that most of these 12 projects date from the late

- 64 -

1960s to the middle 1970s; the interest in science teaching diminished

noticeably since then. (A similar pattern was followed by other

international agencies such as AID). It is now high time for the Bank to

address the problem of science teaching but not only as one part of a

general curricula. Rather, science education should be considered per se

an important vehicle for the development of science and technology - the

basic ingredients of economic growth. To achieve this there is a need to

reassess the old strategies and formulate new ones:

1 - Science and math education should be reviewed as a major input to

help the individual fulfill his role in society, especially his function in

the economy. To this effect it is importaint to define what type of science

and math is required for the proficient performance in the economy, and

what type of science and math is required to function socially. These

distinctions in the type of science and math required have methodological

(pedagogical) as well as financial implications. Where hitherto the Bank

strategy "to improve the quality of science teaching," led to the

construction of the same type of science laboratories for everybody, for

the future, it is important to target first the audience according to the

distinctions made above, and only then decide what inputs should be

required in terms of type of curricula, teachers, and materials.

2 - For those occupations that require a more rigorous and advanced

science teaching, it is important to provide a quality education

commensurate with international standards. This is very costly because of

the need for high quality complementary inputs: teachers, materials,

curricula, laboratories. With limited physical and financial resources in

mosts SSA countries efforts can only be confined to a few "elite" schools.

- 65 -

The trend in the past for the Bank was to foster 'equality" which in

practice meant spreading the same resources over a larger number of

schools. In the case of science and math teaching, to create the right

environment and have a minimum quantum of quality resources it will be

necessary to concentrate resources in a few "points of excellence."

Criteria for equality could be shifted to geography and socio-economic

class rather than focusing on schools and students.

3 - For those for whom science and math is required to function as

individuals in society, a different curriculum should be developed, pferhaps

combining knowledge of environmental and health concerns and scientific

methodology. To be effective this type of science and math teaching

should also be made more culture bound. Certainly, the type of

laboratories and materials required for this group would be different from

the previous one.

4 - Regarding Higher Education the Bank will have to shift its focus from

the financial aspects of Higher Education to the primary function of

institutions of higher learning: education, training, quality, and

relevance. For too long the emphasis of Bank's policies concerned only

cutting costs, shifting financial burdens from government to the public,

users fees, etc., while all along little attention was given to the basic

problems of low quality teaching and, hence, low quality of graduates.

Indeed, present strategy has not lead to increased efficiency of Higher

Education.

This shift in strategy is especially important for science and

technology sectors of Higher Education. A major focus for the Bank should

- 66 -

be to ensure that the quality of graduates meets international standards.

This means that within the Higher Education Science and Technology will

have to have a privileged position.

5 - To accelerate science and technology development in higher education,

quantitatively and qualitatively, will require extra resources. These can

be obtained by shifting resources from noni-educational endeavors such as

student maintenance and scholarships. It is sometimes argued that these

resources be shifted to basic education. While this may be warranted in

some cases, the choice is not obvious in many others where the contribution

of science and technology to economic development may outweigh that of an

increase in literacy rates.

6 - Finally, whatever strategy to foster science and technology education

is adopted, it is fundamental that the problem be approached in a

comprehensive way. Improvement of secondary school science cannot be done

independently of the state of science in the universities, while

improvements in science teaching in the university cannot ignore what's

going on in secondary schools, the source from which the university draws

its students.

Distributors of World Bank PublicationsARGENTINA FLYLAND MALAYSIA For, ,i$. osr,oC t. Hi-H.- SRL Akorldoo Kiej.k.tqp. Uorderity MsIoy. Cprortiv 1vt oto.e S.b1,iptoo Se.rleGIei. G-eros P. O. , 125 ll-khp, Ltmiled P.O. BSo 41095Flord. 165, 4th Flor -Of 453/465 SP-C0101 P.O. Bo 1127, J]l- P-tn Sm Crmigholl1333 B_eo A Helild 10 KulA Ltpw Jdu 3024

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RECENT WORLD BANK TECHNICAL PAPERS (continued)

No. 93 Batstone, Smith, and Wilson, Thle Safe Disposal of Hazardous Wastes: The Special Needs and Problems ofDeveloping Countries

No. 94 Le Moigne, Barghouti, and Plusquellec, Technological and Institutional Innovation in Irrigation

No. 95 Swanson and Wolde-Semait, Africa's Public Enterprise Sector and Evidence of Reforms

No. 96 Razavi, The New Era of Petroleum Trading: Spot Oil, Spot-Related Contracts, and Futures Markets

No. 97 Asia Technical Department and Europe, Middle East, and North Africa Technical Department,Improving the Supply of Fertilizers to Developing Countries: A Summary of the World Bank'sExperience

No. 98 Moreno and Fallen Bailey, Alternative Transport Fuels from Natural Gas

No. 99 International Commission on Irrigation and Drainage, Planning the Management, Operation, andMaintenance of Irrigation and Drainage Systems: A Guide for the Preparation of Strategies andManuals (also in French, 99F)

No. 100 Veldkamp, Recommended Practices for Testing Water-Pumping Windmills

No. 101 van Meel and Smulders, Wind Pumping: A Handbook

No. 102 Berg and Brems, A Case for Promoting Breastfeeding in Projects to Limit Fertility

No. 103 Banerjee, Shrubs in Tropical Forest Ecosystems: Examples from India

No. 104 Schware, The World Software Industry and Softzware Engineering: Opportunities and Colnstrainlts forNewly Industrialized Economies

No. 105 Pasha and McGarry, Rural Water Supply and Sanitation in Pakistan: Lessons from Experience

No. 106 Pinto and Besant-Jones, Demand and Netback Values for Gas in Electricity

No. 107 Electric Power Research Institute and EMENA, The Currenit State of Atmospheric Fluidized-BedCombustion Technology

No. 108 Falloux, Land Information and Remote Sensingfor Renewable Resource Managemenit in Sub-SaharanAfrica: A Demand-Driven Approach (also in French, 108F)

No. 109 Carr, Technologyfor Small-Scale Farmers in Sub-Saharani Africa: Experience with Food Crop Productionin Five Major Ecological Zones

No. 110 Dixon, Talbot, and Le Moigne, Dams and the Environment: Considerations in World Bank Projects

No. 111 Jeffcoate and Pond, Large Water Meters: Guidelinles for Selection, Testing, and Maintenance

No. 112 Cook and Grut, Agroforestry in Sub-Saharant Africa: A Farmer's Perspective

No. 113 Vergara and Babelon, The Petrochemical Industry in Developing Asia: A Review of the CurrentSituation and Prospects for Development in the 1990s

No. 114 McGuire and Popkins, Helping Women Improve Nutrition in the Developing World: Beating the ZeroSum Game

No. 115 Le Moigne, Plusquellec, and Barghouti, Dam Safety and the Environment

No. 116 Nelson, Dryland Management: The "Desertification" Problem

No. 117 Barghouti, Timmer, and Siegel, Rural Diversification: Lessons from East Asia

No. 118 Pritchard, Lending by the World Bankfor Agricultural Research: A Review of the Years 1981 through1987

No. 119 Asia Energy Technical Department, Flood Control in Bangladesh: A Plan for Action

No. 120 Plusquellec, The Gezira Irrigation Scheme in Sudan: Objectives, Design, and Performance

No. 121 Listorti, Environmental Health Components for Water Supply, Sanitation, and Urban Projects

No. 122 Dessing, Support for Microenterprises: Lessons for Sub-Saharan Africa

No. 123 Barghouti and Le Moigne, Irrigation in Sub-Saharan Africa: The Development of Public and PrivateSystems

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