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Table of Contents

1. Introduction …………………………………………………………………………...3

2. Hits and misses ………………………………………………………………………..6

2.1. Of other kinds of education ……………………………………………….6

2.2. Of quantity versus quality debate ………………………………………...7

2.3. Of ‘social capital in the creation of human capital’ ……………………...8

3. Back to education and economic growth debate: Theoretical and

Empirical Backdrop …………………………………………………………………..9

3.1. Of levels of education ……………………………………………………11

3.2. A comment ………………………………………………………………..12

4. Concerns and datasets, Variables and Estimation Method ……………………......13

4.1. Concerns and Datasets …………………………………………………..13

4.2. Explaining variables and method ………………………………………..14

5. Empirical Results …………………………………………………………………....18

5.1. Brief discussion …………………………………………………………..21

6. Concluding Remarks ………………………………………………………………..24

Reference………………………………………………………………………………....25

Appendix:

Country List……………………………………………………………………………....27

Datasets…………………………………………………………………………………..28

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Education costs money, but then so does ignorance

Sir Claus Moser

The strongest principle of growth lies in human choice

George Eliot

1. Introduction

The positive impact of education is manifold: low fertility & infant mortality, children’s

education, income distribution; a considerable pool of well-educated human resource would

draw technology from developed countries; greater educational attainment begets greater

skilled labor, which sequels in greater economy’s output in goods and services1. Profits of

education are countless. This paper will strictly focus on education and economic growth

relationship.

Now, it would be interesting to briskly understand the theoretical aspects lurking in and

around the discourse of education itself. Payne (1927) says education is a continuous process

of learning right from birth and throughout the life of an individual. Education itself could be

seen as growth. Education could take place deliberately or through informal contacts with

various industrial and commercial organizations even clubs with which an individual interacts

and even religion, social pressure and family values will have a role to play in bringing about

behavioral changes in an individual.2 Apart from formal schooling, influences like economic

activities in a society / country would also impact education. However, for quantitative

measurement of education in empirical studies or even otherwise formal schooling is

considered as ‘education’.3

Why place education against economic growth?

Lucas (1988) postulated an ‘alternative or at least a complimentary’ engine for ‘technological

change’ for economic growth by latching onto human capital. He introduced human capital

into an economic model impacting growth. His model emphasizes human capital

accumulation through schooling and emphasizes specialized human capital accumulation

through learning by doing. Essentially, he is saying that human capital just not helps one’s

1 Barro and Lee (2001). 2 See Payne (1927) for social effects of education and even for effects of different quarters of society on education. Other factors influencing human capital will be discussed in section 2.1. 3 Reader should note that few aspects highlighted here that are vital are missed in the current debate on education-economic growth relationship that is discussed under section 2.2. Note: The title of this paper is the same as the title of Barro (2000)

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growth but also has a spillover effect positively affecting the others – to reinterpret and to put

it differently: human capital promotes higher human capital accumulation4. Mankiw, Romer

and Weil (1992) model human capital through physical capital stock and human capital

investment rates. They showed that omitting human capital from the economic growth model

affects physical capital investment and population growth. Thus, human capital is at the core

of economic growth.

Romer (1989) in his theoretical cum empirical model of research stresses that, skill(s) of an

agent or an individual are of three kinds: physical skills – like eye & hand coordination and

strength; educational skills – acquired through primary and secondary education; and

scientific skills or talent which is acquired through postsecondary education. He further says

that scientific skills could be separated from the other two levels and be studied through

postsecondary education5. In this paper, I will try to pick up the thread by including human

capital part and by excluding physical skills from the above – Romer’s – discussion and place

it in the context of sub-Saharan Africa which has the most number of low income countries

(in terms of numbers). And, I will go ahead to investigate the question: Is postsecondary

education more important for economic growth than primary and secondary education for

Sub-Saharan Africa? I will empirically investigate this question by basing on a cross-country

longitudinal study (1965 to 1995).6

Why sub-Saharan Africa?

‘Sub-Saharan Africa’s growth tragedy is reflected in painful human

scars. The typical African mother has only a 30 percent chance of

having all of her children survive to age five. Average life expectancy

for a person born in 1980 in sub-Saharan Africa is only 48 years

compared to 65 in Latin America, and daily calorie intake is only 70

percent of Latin America’s and East Asia’s.’7

4 This aspect will be further discussed more specifically but very briefly under section 2.1 and section 3. 5 Romer (1989) says that postsecondary education could be separated into different category - like education for engineers, scientists and the likes. I will not be separating postsecondary education and investigating, but I will be looking at postsecondary education as a whole. 6 I have included eighty-eight countries by maximally making use of the data to include more number of countries. 7 Easterly and Levine (1997).

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More Facts:

Sub-Saharan Africa’s real GDP per capita from 1965 to 1995 grew by an average of 0,42

percent per annum. On the contrary, the seven8 fast growing countries, far in the east, had an

average real GDP per capita growth of 5,14 percent from 1965 to 1995. OECD’s real GDP

per capita grew by 2,6 percent during the same period. And, the rest for the same period grew

by 1,67 percent.9

Looking back and forth:

Tardy growth in sub-Saharan Africa has resulted in increasing poverty rates and number of

poor people in the 1990s. It is the region, which has the largest proportion of people living

below $ 1 per day. It has been forecasted by the World Bank that per capita growth of sub-

Saharan Africa would climb upwards reaching 1,6 percent and over during the period 2006 to

2015, which would mean a considerable reversal of it’s gloomy past. Still, this growth rate

would fall short of the actual growth needed to make poverty decline. Ironically, the number

of poor is expected to rise from three hundred and thirteen million to three hundred and forty

million people by 201510.

Why slow growth in sub-Saharan Africa?

Sachs and Warner (1997b) find that sub-Saharan Africa’s wrong economic policies –

openness to trade, inflation and national savings rate – have resulted in slow growth. They

further state that high dependence on natural resources, tropical climate causing diseases, low

life expectancy at the initial level11, and failure to enter into demographic transition – low

fertility rates and low mortality rates – has negated higher growth.

Easterly and Levine (1997) say the ethnic division and low schooling are the sources of slow

growth in sub-Saharan Africa.

In this present study, I will be comparing sub-Saharan Africa with the rest of the world and

try to see the differences or similarities in terms of different levels of education and its impact

8 As categorised by Sachs and Warner (1997b) – Hong Kong, South Korea, Indonesia, Malaysia, Singapore, Taiwan and Thailand. 9 All the GDP value are taken in constant price, chain series and are from Heston, Summers and Aten – Penn World Table Version 6.1. And the calculations restricts to the countries included in this paper by omitting other countries. 10 World Bank (2005). The figures refer to the whole of sub-Saharan Africa (countries that fall outside the scope of this paper are also included). 11 They use life expectancy during the year 1970 and their study runs through 1965 to 1990.

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on economic growth – this would be my secondary objective of my study; the prime or the

main focus of this paper being, as mentioned earlier: Is postsecondary education important for

growth for sub-Saharan Africa?

2. Hits and misses

Before moving ahead with the education and economic growth discussion – in this chapter – I

would like to turn table and put forth few aspects related to education and human capital

creation that will not be captured in this study, though equally important. A study would be

more interesting and nicer if it deliberates both major hits and misses.

2.1. Of other kinds of education: Formal education is largely seen as the basic source of

human capital. On the other hand, human capital could be accumulated through ‘learning-by-

doing’ or ‘on-the-job training’12 for enhancing productivity. Factors like health13 and aspects

of social capital influence human capital14. And, Schultz (1961) says: the leisure time spent to

enhance skills and knowledge goes ‘unrecorded’. Formal education – meaning schooling –

would help create human capital from the very earlydays of a child, whereas the existing labor

could enhance one’s skills and knowledge (i.e. human capital) by leisure-time education.

Miller (1932) observes that in a rapidly changing world the labor has to make ‘multitudinous

adjustments’ to keep apace with the changes. And, leisure provides an oppurtunitiy for the

development of newer skills for ‘the wise use of leisure’. Thus, leisure-time education also

plays an important role in human capital creation. The downside of this paper is that I solely

focus on formal education as human capital.15

Clark (1940), in his review-study of the various studies on the different regions of America

during his time, acknowledges the high relationship between the amount of schooling and

income in one’s later life, he however feels that hardly any research has been made to know

which specific schooling resulted in increased income gains. He shares the views of the fellow

researchers of his time that curriculum is very crucial as it would make a student choose a

career depending on the curriculum (for instance, curriculum on agriculture would prepare a

12 See Becker (1962) and Lucas (1988) for their detailed discussion. 13 I will not be discussing health in this section of the paper as I have included this aspect in my study, which would be discussed under section 4.2. 14 Barro (2000). 15 The reader should note that cross-country longitudinal data on leisure-time-human-capital-creation, which is very challenging, is lacking from the existing studies.

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future farmer)16. He stresses that occupational education – vocational training – at school is

also determinant for higher income gains. To be more precise and to reinterpret this theory:

which vocational training is more positive for higher income gains is important. In this paper

the data used to measure education misses on these aspects.

2.2. Of quantity versus quality debate: While just dealing with various levels of education

as one of the determinants of economic growth, what misses out is to grapple with the quality

of education and other factors that would influence qualitative educational outcome of an

individual. Lee and Barro (2000) address this issue by investigating the determinants of

educational quality. They propagate and empirically show that family characteristics –

education and income of parents – and school resources have strong relation with student

performance.

To further elaborate their research: they measure educational outcomes by test scores, dropout

out rates and repetition rates. Here, test scores mean examinations in math, science and

reading conducted in various years for up to fifty-eight countries by the International

Association for the Evaluation of Educational Achievement (IEA) and the International

Assessment of Educational Progress (IAEP). These studies cover primary or secondary

students of the same age or grade group, such as age nine, age thirteen, and pupils in the last

year of secondary education.

They look at ‘family factors’ through income and education of the family (parents).

And lastly, they measure ‘school resources’ via – pupil teacher ratios; public educational

spending per pupil; salaries of primary school teachers; repetition and dropout rates; and

length of school year. They conclude by emphasizing the importance of family background,

and the positive relation of school resources – strongest being pupil-teacher ratios – with

student performance. Even they point out ‘weaker, but likely positive, effects’ are emerging

from ‘average teacher salary and the length of the school term.’17

16 See Clark (1940) for a detailed study including an elaborate discussion of the case studies done during his period. 17 See Lee and Barro (2000) for their detailed study. They do not measure quality for higher levels of education.

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For my paper, I would be confining to quantitative educational data as the data on qualitative

aspect of education at higher levels are yet to researched and developed for a large pool of

countries.

2.3. Of ‘social capital in the creation of human capital’18: Coleman (1988) theorizes that

social capital, both within the family and community will influence in creating human capital

among the rising generation. He gives two compelling examples to underscore the importance

of family in human capital generation:

Example one:

‘John Stuart Mill, at an age before most children attend school, was

taught in Latin and Greek by his father, James Mill, and later in

childhood would discuss critically with his father and with Jeremy

Bentham drafts of his father’s manuscripts. John Stuart Mill probably

had no extraordinary genetic endowments, and his father’s learning

was no more extensive than that of some other men of the time. The

central difference was the time and effort spent by the father with the

child on intellectual matters.’19

Example two:

‘In one public school district in the United States where texts for

school use were purchased by children’s families, school authorities

were puzzled to discover that a number of Asian immigrant families

purchase two copies of each textbook needed by the child.

Investigation revealed that the family purchased the second copy for

the mother to study in order to help her child do well in school.’20

He even says that social capital lying outside family is also important. For instance, the social

network of parents would have an effect on children’s educational outcome.

Sachs and Warner (1997a) say: human-capital creation since a child is ‘partly’ resulting from

positive externalities within the family and community. If the parents are literate, especially 18 ‘Social capital in the creation of human capital’ is a phrase used by Coleman (1988). 19 Coleman (1988). 20 Ibid.

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mothers, then they ‘raise’ healthier and more literate children. Similarly, a literate community

will trigger more literate children for any give level of formal expenditure on education. This

raises a possibility ‘of low-level poverty traps, in which a low-human-capital generation is

succeeded by another low-human-capital generation, while an initially high-human-capital

generation would give rise to another high-human-capital generation’.

Social capital, certainly, has an important positive impact on human capital creation. On the

contrary, the best way to create social capital wherever it is lacking / insufficient – that would

instigate human capital creation among the younger or the rising generation – is by education

itself. Educated parents, community, society, country is a healthy environment to promote and

generate continuous human capital through generations.21

Nevertheless, there are a good number of recent-past studies solely opting education as human

capital.22 Even this paper would follow suit.

3. Back to education and economic growth debate: Theoretical and

Empirical Backdrop

In this chapter, I will brief about the theoretical and empirical debate related to education /

human capital and economic growth. First, I will put across the theoretical debate. Next, I will

place few empirical studies, which study economic growth through different levels of

education. At the end, I will comment on the different methods used to measure human capital

stock via different levels of education.

Lucas (1988) beamed that human capital has two kinds of effects: internal effect and external

effect. Human capital that helps enhance one’s own skills and productivity is called internal

effect and by external effect, he means that human capital (of let’s say an individual or a group

etc.) that would have positive influence on the productivity of others. One way to interpret

this would be to say that higher levels of human capital bring in greater productivity even

among the ‘others’. He even says that people with human capital will move from places with 21 While saying so, I do not intend to over look the positive role of social capital in human capital creation (in other words social capital could be seen as education for the raising generation) – but what I am trying to imply is that both social capital and human capital go hand in hand assisting each other. In this paper I will not be looking at human capital inclusive of social-capital-influences as the measurable cross-country data on social capital – exclusively impacting human capital creation over time – is lacking. 22 Lack of data to measure positive externalities for various levels of education could be one of the reasons.

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less human capital to places where there is considerable amount of human capital. This could

be read that, in a way, initial level of human capital paves way for the future growth process.23

While hammering on the education and economic growth debate, education is sliced in to

three levels: primary education, secondary education and postsecondary education.

Psacharopoulos (1985) says that human capital investment would be like any other kinds of

investment hence diminishing returns should be applied even to human capital investment. In

his empirical study he finds that:

‘Primary education is the most profitable educational investment

opportunity, followed by secondary education. This decline is the

result of the interaction between the low cost primary education

(relative to other levels) and the substantial productivity differential

between primary school graduated and those who are illiterate.’

He further states: rate of return to any levels of education in Africa is higher and it would be

lower in advanced countries, he reasons that relative scarcity of human-to-physical capital

would be the influencing factor in such contexts24. He shows that rate of returns to investment

on primary education is high particularly in low income countries. He is basing his study on

single observation; meaning he is using just one year data.

While unequivocally admitting the importance of primary education there would still be a

question vexingly nagging: Which level of education is more potent to pump up economic

growth when looked from a longitudinal angle (over the years) especially for a region like

sub-Saharan Africa that houses most number of low-income countries in a single region.

23 Barro (1991) shows this theory empirically, which will be dealt in the course of this paper. 24 See Psacharopoulos (1985) for the detailed empirical discussion of the rate-of-return vis-à-vis education.

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3.1. Of levels of education

While measuring human capital via education in cross country studies, primary and secondary

levels of education are used as independent variables accounting for economic growth25, at

times just secondary education is used as a proxy for human capital26.

The seminal empirical study on education and economic growth relation was by Barro (1991):

in his cross-country study, he found that initial level of per capita GDP was negatively

correlated with growth and moreover he showed that initial level of primary and secondary

education was positive for growth27.

Barro (2001), furthering his 1991 cross-country longitudinal study reiterates that relation

between initial levels of GDP and growth rates is ‘virtually nil’. But, various variables, when

held constant then there is a strong relation between growth rate and level28 - his results from

the 2001 study on education reveal that male secondary and higher levels of education are

significant for growth whereas, male primary education is insignificant for growth. He even

finds that female secondary and higher levels of education lacks significance and says that

this could be because of discrimination against women in economic activities in various

countries. On the other hand, higher female primary education rates have positive impact by

lowering fertility rates. He is measuring human capital by attainment rates in the population

group aged twenty-five and older.

Another empirical cross-country study, which specifically focuses on the impact of

postsecondary education,29 is by Chatterji (1998). He uses enrolment rates30 of secondary and

tertiary levels as proxy to measure human capital. Mainly his hypothesis is based on Lucas

(1988) whom Chatterji ‘interprets’ saying that externalities from education would arise from

innovative and creative managers and this would surely depend on postsecondary education

or tertiary education than just secondary education. His focus period is from 1960 to 1985.

And the number of countries studied were eighty-one. To explain31 the variables used by hi m:

log of 1960 level of GDP per capita; log of average ratio of real domestic investment to the

25 See for instance Barro (1991). 26 See, for instance, Sachs and Warner (1997a); Radelet, Sachs, and Lee (2001). 27 See Barro (1991). For his study he is using enrolment rates. 28 See Barro (2001). 29 The reader should note that the author calls postsecondary education as tertiary education. 30 Chatterji (1998) uses the data for enrolment rates from World Bank. 31 I will be even explaining the variables as my empirical model almost reflects Chatterji (1998) but with changes.

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real GDP; log of 1960 secondary education enrolment rate; percentage change in secondary

enrolment rates between 1960 and 1985; log of 1960 tertiary education enrolment rate;

percentage change in tertiary enrolment rates between 1960 and 1985; OECD dummy; and

political rights variable. His empirical study showed that tertiary education did displace

secondary education for attaining higher GDP growth rates.

3.2. A comment

A comment on various available measures of human capital via quantitative educational

data: There are chiefly three means to measure yearly human capital through quantitative

educational data – enrolment rates, completion of a given level of education and average years

of a given level of education in the total population (among fifteen years of age and older or

twenty five years of age and older).

Using enrolment rates – like Chatterji (1998) – would not mirror an accurate picture of human

capital. Because: mere higher enrolment rates would not mean greater attainment of higher

human capital. Yes, enrolment rates capture the population standing, ready to run, at the start

line, but it misses on: What happened next? How many made it to the finish line? So, is

completion of a given level of education better than looking at enrolment rates? Barro and Lee

(2000) say: successful completion of a given level of education would be a ‘straightforward’

way to mirror attainment of skills and knowledge coming with a particular level of education.

But, completion would give just give the figures of completion of a given level of schooling at

a certain point, it does not look at the aggregate human capital stock – in other words human

capital among the total population – all inclusive of those who would have reached the status

of active labor and most importantly they would be part of the existing active labor force and

the rest who are over twenty five years of age. Conversely, completion means

‘straightforward’ higher human capital attainment for a group of the population entering into

the next level meaning – the labor force. And, enrolment has positive spillover effect as it

triggers improvisation of future completion rates mostly notably in low-income countries.

For sterner measurement of the existing and active total human capital stock in a country –

among the available sources – the most accurate measure would be average years of a given

level of education in the total population (among twenty five years of age and older).

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Nehru, Swanson and Ashutosh (1995) hoist a ‘most serious concern’ over Barro and Lee

(1993) saying that their estimates are solely based on the population aged twenty five and

over. Moreover, they feel that this way of estimation would lead to a ‘serious downward bias

in the estimates of education stock’, because population between the age group of fifteen and

twenty-five would be usually large and growing over time in developing countries. They

further reason by saying that, mostly primary school going children would leave their primary

levels at twelve years of age and as a result of this there would be a thirteen years of lag or

void before these primary school leavers are captured when one follows twenty-five-years-

and-over estimation.32 Barro (2001) in his empirical education and economic growth study,

where he is measures educational attainment among the twenty-five years and older in the

total population mentions that the results using fifteen years and older in the total population

had similar results (vis-à-vis twenty five years of age and older in the total population).33

4. Concerns and datasets, Variables and Estimation Method

4.1. Concerns and Datasets:

The educational datasets used in this paper are from Barro and Lee (2001). One of the

concerns using the datasets which takes into account the average years of a given level of

schooling among twenty five years of age and older in the total population is that it is being

calculated even looking at the population who are sixty four years of age and older (largely,

the people from this group are not considered as part of the active labor force).34 Using

datasets that would just target the group between twenty-five years and older and sixty-four

years of age would give an accurate picture of the active educational stock or human capital

stock among the existing labor force. So, the main concern with my study is that the results

that I would be getting in this paper could differ from the results drawn using another datasets

that takes account of the concern I have tried to show here.

When choosing educational datasets for a large pool of countries, there is room for

discrepancies – the number of years for a given level of schooling would have changed over

time. For instance, Country ‘X’ might have had ‘Y’ number of years of primary schooling in

32 See Nehru, Swanson and Ashutosh (1995) for their detailed debate. 33 Reader should note that Nehru, Swanson and Ashutosh (1995) use average years of schooling between fifteen and sixty four years age group, but Barro (2001) is using attainment rates and says that results for both twenty years and older and fifteen years and older among the total number of population were similar. I will not be able to check, in this paper, whether average years of schooling among twenty years and older in the total number of population; and fifteen years of age and older in the total number of population has similar results or not because of time constraints. 34 Nehru, Swanson and Ashutosh (1995).

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the 1960s and over time it might have changed to ‘Z’ number of years. Over last three

decades thirty-two countries have changed at least once the typical duration of schooling at

primary or secondary levels.35 The authors of the datasets – Barro and Lee – have taken

account of the changes in their present version of the data sets.36 And moreover they assume

that, mostly, the changes in a typical schooling level would take time to become effective

because the pupils who have received education in the new system will be new entrants into

the adult population with a long time lag as the data captures the population at twenty five

years and over.37

The educational data are taken from 1965 to 1995. The data has six five-yearly observations:

meaning observation from the years – 1965, 1970, 1975, 1980, 1990 and 1995. An average of

these six observations is taken for the study.

Datasets used on real GDP per capita are from Heston, summers and Aten (2002) – Penn

World Table Version 6.1. All the data used are in constant price – chain series. The time

period covered is from 1965 to 1995. Real GDP per capita growth for 1965 to 1995 is an

average of annual real GDP per capita growth for the time period selected for the study.

4.2. Explaining variables and method

Combined rights – political rights and civil liberties: According to Gastil (1978): right to

free expression is an integral part of civil rights and to ‘play part in determining who governs

or what the laws of the community are’ is vital for political rights. And he postulates that two

are interdependent – ‘civil liberties without political rights are apt to be lost and political

rights without civil rights are meaningless.’38

He further observes that: in states with controlled rights, education is determined less by

individual preference and more by needs of the state. Moreover, individuals would be forced

‘by order’ to work in a particular industry without having any ‘recourse’.39

35 Barro Lee (2001). 36 Their previous datasets was Barro and Lee (1993). 37 The age group used in this study. 38 Gastil (1978), page 7. 39 Ibid, page 170.

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And even controlled regimes would be harsh on migration. Schultz (1962) sees migration as a

positive ‘investment’ because ‘analytically a misplaced [human] resource is equivalent to a

less productive [human] resource properly located’40.

The data on political rights and civil rights / liberties are from Freedom House from 1972 to

1995 (yearly data). The measurement used for political rights include: civil war, heavy

military involvement in politics, lingering royal power, tolerance of political discussion and

religious hierarchies.

The measurement used for civil rights / liberties include: freedom of expression, assembly,

association, education, and religion41.

Health variable – life expectancy: Mushkin (1962) observes that ‘lengthening of life

expectancy through improved health reduces the rate of depreciation of investment in

education and increases the return to it’. She further says that health programs would increase

the workforce quantitatively in terms of numbers in conjunction qualitative improvements in

health would be positive for qualitative output of labor. The amount of labor or people added

to the workforce resulting from reduction in number of deaths is a reflection of improvement

of health status. She gives an arresting comparison of life expectancy in USA with Asia and

Africa to emphasize the role life expectancy:

‘The number of potential workers that may be added through health

programs is especially large in non-industrial nations. Average life

expectancy at birth in many nations of Asia and Africa – nations that

include almost two-thirds of the population – was until recently about

thirty years. This may be contrasted with almost seventy years of life

expectancy achieved in the United States.42’

40 See Schultz (1962). 41 Reader should note that the Freedom House’s methodology is reviewed periodically on the methodology used in the survey. Over the years, there have been modest methodological changes. However, the time series data are not revised retroactively, and any changes are introduced incrementally so as to ensure the comparability of the ratings from year to year. Chatterji (1998) uses just political rights in his regression. Whereas, civil liberties are directly related to education though political rights are equally important. Both should go hand in hand in this very context. Omission of any would demean the other. 42 The reader should observe that – when Mushkin (1962) is saying that the life expectancy in Asia and Africa was about thirty years and that of USA was almost seventy years – the year when the article was written was 1962.

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Moreover, Weisbrod (1959) postulates that ‘human value’ is at peak at the age of twenty-five

and would be productive till one reaches sixty eight. And, he stresses that living longer would

result in greater productive output during these years. Thus, he says health programs to

‘prevent’ or ‘postpone’ deaths would be crucial.

Thus, life expectancy would be a good indicator that would reflect the status of health in a

country. It has a positive impact on labor productivity; education brings in human capital to

the labor. And as educational outcomes are intrinsically associated with health status of a

society, it would be interesting to use life expectancy as one of the variables. Here, instead of

life expectancy one can use percentage GDP investment on health. Mushkin (1962) observes

that health and education are complementary and investment of either is investment on

people.

But, what is more interesting is that life expectancy mirrors the result of that investment.

Moreover, life expectancy could be interpreted as additional measure of human capital43.

Initial real GDP per capita (at 1965):

As Barro (1991) showed that initial level of GDP is negatively correlated to growth when

other variables are held constant, for my study I have included initial level of real GDP per

capita with other variables explained in this section particularly to know whether initial low

level of GDP is negative and hence positive for achieving higher growth in the context of sub-

Saharan Africa. Moreover, it would be interesting to compare the same with the rest of the

world.

Initial level of schooling for the three different levels of schooling (at 1965):

I have included this variable to see the empirical results whether higher human capital levels

at the initial stage or at the start is positive for higher growth or not. And more specifically:

which level of schooling is more positive for growth or to investigate whether there is or are

any level(s) of initial schooling which is negative or positive for growth.

I have grouped the countries, mainly, into: sub-Saharan Africa, OECD and the remaining.

Further inclusion or exclusion of countries or a region will be discussed under the Empirical

Results section.44

43 Sachs and Warner (1997b). 44 See Country List section for the countries used and for the source of country groups.

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For my study, the dependent variable would be average real GDP per capita growth rate from

1965 to 1995. And the three main independent variables would be average years of primary

schooling, secondary schooling and postsecondary schooling levels of education in the total

population – separately (among twenty five years of age and older in the total population –

1965 to 1995). Other variables would be: log of life expectancy during 1990, combined rights

(political rights, civil liberties / rights 1972 to 1995), log of 1965 level of real GDP per capita,

log of 1965 average primary education, log of 1965 average secondary education, log of 1965

average tertiary education.45

Before explaining the regression results I will list all the variable codes that are used in the

regression Table-1 and Table-2 and explain them:

LogGDP65 = Log on initial real GDP per capita in 1965

LogPRY65 = Log of average years of primary education in 1965 among twenty five years of

age and older in the total population (starting point or the initial level of this study)

LogSE65 = Log of average years secondary education in 1965 among twenty five years of

age and older in the total population (starting point or the initial level of this study)

LogPSE65 = Log of average years postsecondary education in 1965 among twenty five years

of age and older in the total population (starting point or the initial level of this study)

LogLIEEX = Log of life expectancy at 1990

PolCivRigts = Average political and civil rights (1972 to 1995)

AvPRY = Average years of primary education in the total population – among twenty-five

years of age and older in the total population (1965 to 1995).

AvSE = Average years of secondary education in the total population – among twenty-five

years of age and older in the total population (1965 to 1995).

45 The educational data captures 1965 to 1995 except for Benin and Rwanda where the base year is 1970. And, for China, Congo, Egypt and Gambia, the base year is 1975. On Political rights and civil rights/liberties: data for Mozambique starts from 1975. Cyprus (Greece) is taken as Cyprus as Cyprus (Turkey) has lot of missing data. Data for Hong Kong has just one observation from Gastil (1978), pp. 15. For South Africa, the data for 1972 has combined ratings for the ‘black’ and ‘white’ populations (average) – this method is followed as the Freedom House data for the year 1972, for South Africa, has separate ratings for the ‘black’ and ‘white’ populations. Reader should note that the combined rights have been reversed for the regression: six points would mean highest freedom and zero means the lowest freedom. On life expectancy: For UK, USA, Hong Kong, Bangladesh and Spain, the data are from 1991. And, Italy is during 1992. Source – all the data: U.S. Census Bureau, International Data Base. As the data for life expectancy was sparse most notably for Taiwan and Hong Kong so, I had to settle for 1990. The country group – Sub-Saharan Africa and South Asia – follows Barro and Lee (2001) and as in their datasets and OECD countries are taken from World Bank’s website.

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AvPSE = Average years of postsecondary education in the total population – among twenty-

five years of age and older in the total population (1965 to 1995).

5. Empirical Results

In this chapter I will first explain the results for postsecondary education, secondary education

and primary education and economic growth. Then, I will discuss results related to the initial

levels of education. Next, I will discuss the role of life expectancy, combined rights and initial

level of real GDP per capital. In the end of this chapter, I will be discussing and speculating

related to the main results.

Postsecondary, secondary and primary education:

Instead of going straight ahead by running regression for sub-Saharan Africa – as I wanted to

compare sub-Saharan Africa with the rest of the world – I ran a regression for OECD

countries and non-OECD countries (sub-Saharan Africa included). The results for OECD

show that primary education is negative for growth. Postsecondary education, though

positive, was not significant. It was the same with secondary education for OECD countries.

One possible reason could be that OECD countries would have evenly reached greater levels

in higher levels of education – kind of steady state. And thus, there is no significance in the

results (see regression 1a, table 1).

What is surprising is that for the rest of the countries (non OECD countries), postsecondary

education is negatively correlated to economic growth and is significant at 90 percent

confidence level. And primary and secondary education, though positive for economic

growth, are not significant (see regression 1b, table 1).

The following regression shows that for sub-Saharan Africa post secondary education is

positive and significant at 90 percent confidence level. Secondary education is negatively

correlated to economic growth though not significant. Primary education, though positive, is

not significant for growth (see regression 2a, table 1).

In the next regression – for countries other than OECD and sub-Saharan Africa –

postsecondary education is again surprisingly, negatively related to growth though not in a

significant way. Secondary education is positive but not significant. Maybe it is because sub-

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Saharan Africa was moved out, primary education became ‘likely’ significant (see regression

2b, table 1) for economic growth for these countries.

Among the sub-Saharan African countries, Botswana and Mauritius were high performers in

economic growth. Their combined real GDP per capita from 1965 to 1995 grew at 4.3 percent

per annum. These two countries could be outliers in sub-Saharan Africa region. So, for my

next regression I just included the rest of the non-performing countries of sub-Saharan Africa

by excluding the two outliers. The results show that the positive significance level of

postsecondary education as seen in regression 2a jumped one step ahead by increasing the

significance level to 95 percent confidence level. This means that postsecondary education is

more significant than it was previously observed. For sub-Saharan Africa – seen through the

regression results – one can say that postsecondary education is highly significant for

economic growth. Secondary education still remained negative though not significant. And,

primary education was positive but not significant (see regression 4, table 2)

Strictly speaking, for non OECD and non sub-Saharan African countries the regression results

were not telling much about the significance of different levels of education (both at negating

growth or promoting growth). So, to know the significance of education for later group of

countries, I had to scoop out the region other than sub-Saharan Africa that has the most

number of poor people, i.e. South Asia from the regression. The results improved further from

the results seen in the regression 2b, table 1. Primary education which was ‘likely’ positive

became significant for economic growth at 95 percent confidence level, postsecondary

education remained negative but not significant and secondary education was the reverse –

positive but not significant (see regression 3, table 2).

In short: postsecondary education is very significant for economic growth for sub-Saharan

Africa and it is negative for the rest of the countries (when OECD and even when South Asia

are excluded). And, primary education is equally significant for growth for the rest of the

countries (when OECD and South Asia are excluded).

Initial levels of education:

Initial level of primary education is virtually negative, it is significantly negative for countries

other than OECD, sub-Saharan Africa and South Asia (except for OECD countries). In other

- 20 -

words initial higher level of primary education is not positive for economic growth46. To

make it even simpler: higher levels primary education at the initial level is not needed for

economic growth. One of the possible reasons could be that countries have seen massive

improvements in primary education during 1965 to 1995. The rate of primary school

education has been effectively paced up that the starting point did not matter in achieving

greater number of primary educated population especially among the countries other than sub-

Saharan Africa and South Asia that were excluded from the regression47. And this could be

seen in the regression 3: where the initial level of primary education (LogPRY65) is

significant and negatively correlated to growth, thus making primary education (AvPRY)

significant and positively correlated to growth. Coming back to sub-Saharan Africa: though

the LogPRY65 is negatively correlated it is not significant. It means that outside sub-Saharan

Africa48, countries have been doing well in primary schooling for achieving growth.

LogSec65 is positively correlated to growth but not significant and LogPSE65 is positive for

sub-Saharan Africa but insignificant49.

Life expectancy, combined rights and initial level of real GDP per capita

Life expectancy is positive and significantly correlated to growth except for OECD countries -

may be because OECD countries have already achieved very high life expectancy. Life

expectancy for sub-Saharan Africa is positive and significant but not as significant as it is for

other countries. This shows that life expectancy in sub-Saharan Africa is still low even in

1990 and increasing life expectancy could increase growth. Combined rights are positive for

growth – greater the country is liberal; greater the growth rates. LogGDP65 is ‘virtually nil’ in

all the regressions supporting the fact that could be seen most importantly in Barro (1991).

This supports the neoclassical model of ‘convergence’: initial level of GDP is negative for

growth. In other words, initial low level of GDP per capita would not be a hurdle for growth.

Thus low level of initial GDP per capita would not negate to ‘catch up’ with the richer or the

advanced countries.50

46 OCED excluded. 47 Ibid. 48 And excluding South Asia and OECD as in the regression 3, table 2. 49 LogPSE65 is negative for countries other than OECD sub-Saharan Africa and South Asia. 50 To put it in an even simpler but in a raw manner, low level of GDP is good for long term higher growth.

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5.1. Brief discussion

Why is postsecondary education significant for sub-Saharan Africa and the reverse for the

rest51?

Here, I will be spending lines speculating the possible explanation and the possible weakness

of the empirical findings.

Average years of primary schooling among twenty five years and older in the total population

in sub-Saharan Africa during 1965 to 1995 is 1,81 times lower than the rest of the world and

for secondary schooling it is 2,7 times lower and for postsecondary schooling it is 5,15 times

lower than the rest of the countries.52 Looking at these figures and to answer the question:

maybe the huge gap in postsecondary education gives room for sub-Saharan Africa to achieve

greater economic growth through filling this gap. On the other hand, even there is a handsome

gap for secondary education that is negative for sub-Saharan Africa. This begs future

research.

But the next obvious question would be why is that the other countries have negative, though

not significant, relationship with growth vis-à-vis postsecondary education? Why is this

missing link? These questions could be better answered by breaking down the educational

variables in to male and female education separately and by doing an empirical study to know

regional differences and gender differences most notably focusing on East Asia that has

achieved the highest growth rates and comparing it with sub-Saharan Africa, Latin America,

and South Asia – the three regions that have had slow growth rates respectively.

If the same study is done taking into account the quality aspect of education, then it would

give a more accurate picture of the different levels of education and economic growth

relation. Moreover, the results would be quite different for different kinds of measurements

used for education – like enrolment rates versus aggregate human capital / educational stock.

And even it could differ if different sources of educational datasets are used. These important

aspects demand future research in bringing about harmonious datasets covering quality

aspects of the education for all levels of schooling.

51 OECD is omitted from the discussion hereafter. 52 Educational data is from Barro and Lee (2001) datasets. And the calculations restricts to the countries included in this paper by omitting other countries. Sub-Saharan Africa’s calculation includes Botswana and Mauritius.

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Regression 1a

Regression 1b

Regression 2a

Regression 2b

Dependant Variable GDP6595 GDP6595 GDP6595 GDP6595

Constant 12,871 (22,491)

-21,535 (6,331)

-6,750 (9,518)

-29,901 (11,827)

LogGDP65 -3,255***

(,491) -2,236***

(,340) -2,307***

(,577) -2,172***

(,453) LogPRY65 2,264

(1,626) -,714* (,471)

-,222 (,930)

-,870 (,645)

LogSE65 -,293 (,409)

,491 (,396)

,477 (,547)

,202 (,614)

LogPSE65 ,004732 (,251)

,156 (,286)

,166 (,413)

,008932 (,419)

LogLIEEX 8,459 (12,482)

22,172*** (3,656)

13,453** (5,619)

26,185*** (7,307)

PolCivRigts ,321 (,297)

,144 (,141)

,821*** (,259)

-,006582 (,181)

AvPRY -,561 (,404)

,511 (,368)

,212 (,966)

,597* (,402)

AvSE ,390 (,258)

,826 (,612)

-2,004 (1,887)

1,217 (,774)

AvPSE ,650 (,870)

-3,677** (2,337)

32,956** (16,251)

-3,027 (2,633)

No. Of Countries 22 66 21 45 Adjusted R Square ,889 ,632 ,725 ,532 (OECD) (non-

OECD) Sub-Saharan

Africa (OECD

filter used)

Countries other than

Sub-Saharan Africa

(OECD filter used)

Note: The dependent variable is the average real GDP per capita from 1965 to 1995. *** indicates 95 percent and more significance level, ** indicates 90 percent and more significance level and * indicates 85 percent and more significance level. Standard errors are in parenthesis.

Table: One

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Regression

3 Regression

4 Dependant Variable GDP6595 GDP6595

Constant -29,964 (13,375)

-1,206 (10,268)

LogGDP65 -2,529***

(,481) -2,461***

(,703) LogPRY65 -2,349***

(1,034) -,296 (,953)

LogSE65 ,864 (,637)

,652 (,566)

LogPSE65 -,140 (,435)

,141 (,434)

LogLIEEX 27,260*** (7,844)

11,199** (5,892)

PolCivRigts ,142 (,211)

,535 (,353)

AvPRY 1,186*** (,504)

,341 (,978)

AvSE ,769 (,755)

-2,595 (2,149)

AvPSE -3,469 (2,615)

35,451*** (19,931)

No. of Countries 40 19 Adjusted R Square ,616 ,563 Countries

other than Sub-Saharan

Africa, OECD and South Asia

Sub-Saharan Africa

(excluding Botswana

and Mauritius)

Note: The dependent variable is the average real GDP per capita from 1965 to 1995. *** indicates 95 percent and more significance level, ** indicates 90 percent and more significance level. Standard errors are in parenthesis.

Table: Two

- 24 -

6. Concluding Remarks

This paper primarily investigated whether postsecondary education is more important for

economic growth for sub-Saharan Africa. But missed on, most prominently, the qualitative

educational outcomes and the kind / type of education within a given level of education which

is more vital than merely looking at three levels of education. Moreover, postsecondary

education is costly. With these caveats, the study could be best read as: yes, postsecondary

education is positive and significant for economic growth for sub-Saharan Africa.

William Shakespeare in the early seventeenth century wrote a play – a comedy – All’s Well

That Ends Well. It is, most importantly, regarded as one of his three problematic plays: in

short, it means that all the themes, debates, resolutions and poetic justice / justice remains

untouched or inconclusive. Using the same logic: ‘well, all’s well’. But, what lacks is justice

for the field I chose to deal in this paper – why does postsecondary education negates (though

not significantly) growth in countries other than sub-Saharan Africa where there was or is

higher economic growth than sub-Saharan Africa? And, why primary education is significant

for countries other than sub-Saharan Africa? And other questions like: which kind of

education – vocational education or life sciences or engineering or economics or medicine or

social sciences – is more important for economic growth? What in postsecondary education is

important for low-income countries like sub-Saharan Africa? How could discrimination

against women be done away with for bringing in comprehensive educational development

and economic growth in the developing world? How does religion in the regions that are

ethno linguistically fragmented like sub-Saharan Africa would negate quality of education?

These questions demand future research.

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Appendix: Country List (Countries used in the study):

Region: No. of countries:

Sub-Saharan Africa53

21

OECD54

22

South Asia55

5

The Rest

41 Country: Benin

Botswana Cameroon

Central African. Rep. Congo

Gambia Ghana Kenya

Lesotho Mali

Mauritius Mozambique

Rwanda Senegal

Sierra Leone South Africa

Togo Uganda Zaire56 Zambia

Zimbabwe

Canada United States

Japan Austria Belgium Denmark Finland France Greece Iceland Ireland Italy

Korea Netherlands

Norway Portugal Spain

Sweden Switzerland

United Kingdom Australia

New Zealand

Bangladesh India Nepal

Pakistan Sri Lanka

Barbados Costa Rica

Dominican Rep. El Salvador Guatemala Honduras Jamaica Mexico

Nicaragua Panama

Trinidad & Tob. Argentina Bolivia Brazil Chile

Colombia Ecuador Guyana

Paraguay Peru

Uruguay Venezuela

China Indonesia Malaysia

Philippines Thailand

Fiji Hong Kong Singapore Taiwan Algeria Egypt

Tunisia Iran

Israel Jordan Syria

Cyprus Turkey

53 Country classification follows Barro and Lee (2001) datasets. 54 OECD classification follows the World Bank’s website, which is called as ‘High income OECD countries’ that excludes Turkey and Mexico. 55 As per Barro and Lee (2001) datasets. 56 Former Democratic Republic of Congo.

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Barro, Robert J. and Lee, Jong-Wha. (2001). ‘International data on educational attainment:

updates and implications’. Oxford Economic Papers, 53(3), (2001), pp. 541-63.

Freedom House. Country ratings from 1972 through 2003.

http://www.freedomhouse.org/ratings/allscore04.xls (accessed on April 23, 2004).

Heston, Alan, Summers, Robert and Aten, Bettina. (2002) Penn World Table Version 6.1,

Center for International Comparisons at the University of Pennsylvania (CICUP).