Growth Effects of Human Capital and Stages of Economic Development:

A Panel Data Investigation of Different Country Experiences

G. Agiomirgianakis*, D. Asteriou, V. Monastiriotis City University The University of Reading London School of Economics

Abstract

The empirical literature on the role of human capital (HC) for economic growth has reached strong evidence for a robust positive relationship mainly through cross-country growth regressions. This paper using a large panel of data (93 countries) investigates the growth effects of human capital taking into account the different stages of economic development across countries and identifies those factors that may affect the above effects. Our findings suggest that education has indeed a significant and positive long-run effect on economic growth that is found to be increasing with the level of education. Moreover, the significance of this growth effect depends upon the particular stage of development with countries at the lowest spectrum of economic development obtaining relatively higher growth effects from all levels of education. Next, we find that taxation and technological development are important determinants of human capital productivity. Our findings have a number of straightforward policy implications: first, investment in education – especially tertiary education - can have strong growth-enhancing effects; second, advanced and developing countries should focus more on the provision of tertiary education, while countries with medium levels of economic development need a more strategic government approach that could encourage simultaneously investment in human, as well as, in physical capital; finally, policy measures aiming at enhancing the productivity of human capital must focus their effort into technology penetration and re-distribution policies.

Keywords: Human Capital, Economic Growth, Stages of Development, Panel Data

J.E.L. Classification: C23, O15, O40, O50

* Address of Correspondence: Dr. G. M. Agiomirgianakis, City University, Department of Economics, EC1V 0HB, London, UK; Tel: (0)20-74778591; Fax: (0)20-7477 8580; E-mail:G.M.Agios@city.ac.uk. The first author would like to acknowledge financial support from CNR, Milan Italy (Ref # 9701395.ct.10.

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1. Introduction

For much of the 20th century, economic growth theory, based on standard

neoclassical analysis, assumed that capital accumulation increases an economy's

growth in the medium term but that the steady state growth is constrained by the rate

of growth of the labour force. This left technical progress, which was assumed

exogenous, as the only driving force of economic growth in the long-run.

Since the mid 1980s, however, following the inspiring work of Romer (1986)

and Lucas (1988), endogenous growth theory identified a number of additional factors

that determine the growth rate of an economy. Increasing returns to scale, innovation,

openness to trade, international R&D and human capital formation are all considered

key factors in explaining the growth process (see e.g. Lucas, 1988 and Turnovsky,

1999, for an excellent review). Consequently, human capital, and in particular that

attained through education, has been introduced as a crucial determinant of economic

growth.1

Following that, voluminous empirical studies tried to investigate quantitatively

the relation between education and economic growth (see e.g. Barro 1999 & 1991;

Barro and Sala-i-Martin, 1995; Chuang, 2000; Krueger and Lindhal, 1999; Temple,

2001 & 1999; Freire-Seren, 1999; Fuente et al, 2000; Storeslettenn et al, 2000; and

Bassanini et al 2001). The general conclusion of these studies indicates that there is a

positive correlation between economic growth and education (but see also Pritchett,

1999, for a different finding). However, most of the existing studies have been carried

out by employing cross sectional data and techniques, mostly for advanced countries

that had solved their most crucial problems of development by the first quarter of the

1 We do not formally present a theoretical model here, since the theoretical aspects of education and growth have been extensively presented in the relevant literature. For a mathematical exposition of the effects of human capital to growth, see Lucas (1988); Mankiw et al (1992); Aghion and Howitt (1998) and Barro and Sala-i-Martin (1995) among others.

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20th century. Hence, the relationship between the growth effects of human capital and

the level of economic development has largely been overlooked2. This is hardly a

minor omission. Maybe more than any other factor of production, human capital

cannot enhance productivity in isolation of the wider socio-economic and technical

conditions under which the production of goods and services is organised. This has

been widely recognised in the literature of economic development (Otani and

Villanueva, 1990; Deolalikar et al, 1997; McMahon, 1999; Bourguignon and Verdier,

2000; Funke and Strulik, 2000) but has received little attention in the economic

growth literature.3

In our empirical analysis, we employ advanced estimation techniques for

dynamic panel data on a diverse set of countries, with different levels of economic

development and different trends in terms of GDP growth.4 We isolate the human

capital effect on growth at different stages of economic development and try to

identify the factors that make human capital more productive in some countries than

in others. More specifically, our analysis is threefold; first, it examines how and by

how much does education affect economic development; second, it investigates how

the above effect is changing at different stages of economic development and, finally,

it tries to identify factors determining the size of this effect across countries. Our

analysis provides some answers to the above issues that have significant policy

implications at both the national and supra-national levels, when designing

educational and skill-acquisition strategies with the aim of encouraging or inducing

economic growth.

2 Despite that as noted by Zolotas (1977) the adaptation and exploitation of foreign technology into less-developed countries requires a given level of technical knowledge in the labour force that often is not available. 3 Among the few exceptions are Ghosh and Wolf (1998), Judson (1998) and Sorensen (1999). 4 See Appendix 2 for a list of the countries used in the empirical analysis.

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The paper is organized as follows. Section 2 discusses the empirical

methodology of dynamic panel data (Pooled Mean Group) estimation, as well as, the

estimation of country-specific long-run coefficients. Section 3, presents the empirical

results. Finally, Section 4 concludes the paper with the policy implications of our

findings.

2. Empirical Methodology

As already stated, the aim of this study is to examine the long-run effects of

education on economic growth in a panel of data. Our panel consists of 93 (N=93)

countries, over a period of 28 years (T=28) from 1960-1987. This set of data has

complex dynamics and is characterised by strong trends and non-stationarity.

Therefore, the empirical method to be adopted here needs to be technically

appropriate for the estimation of consistent long-run parameters. This implies that

traditional panel data approaches (e.g., fixed and random effects models) cannot be

used in this case. In the type of data set we are considering, T is sufficiently large to

allow individual country estimation.

Our analysis deviates from the conventional integration/co-integration approach

for two reasons. First, there are only a few tests of co-integration in a panel data

context, while it is also well known that tests of order of integration in panel data do

not reliably distinguish between series that contain a unit root and those that are

stationary with a “near-unit root”. Second, long-run parameters may be consistently

estimated using the traditional autoregressive-distributed lag (ARDL) approach

(Pesaran and Shin, 1998). Moreover, as Pesaran, Shin and Smith (1999) have shown,

this approach yields consistent and asymptotically normal estimates of the long-run

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coefficients irrespective of whether the underlying regressors are I(1) or I(0).5 Further,

it compares favourably in Monte Carlo experiments with conventional methods of

cointegration analysis.

The above considerations have led us to the adoption of two recently developed

methods for the statistical analysis of dynamic panel data: the Mean Group (MG) and

the Pooled Mean Group (PMG) estimation.6 Indeed, these methods are particularly

suited to the analysis of panels with large time and cross-section dimensions, as they

allow us to distinguish between short- and long-run effects and, hence, remove

business-cycle and other influences from the sample. Consequently, the derived long-

run coefficients describe more accurately the true structural relationships and which

are, of course, of more interest to economic theory and policy makers alike. These

methods are described in detail in appendix 1.

In our empirical investigation we split our sample countries into three groups

of low, middle and high-income countries, according to the World Bank

classification. The classification is based on the official country groupings produced

by the World Bank, as derived from the World Development Indicators 2000 CD-

ROM. This allows us to investigate the differentiated impact that human capital has

on economic growth, at different stages of economic development. However, we also

estimate our model for the whole sample, first imposing a common long-run

coefficient on human capital, and then allowing this coefficient to vary across

countries. We further use these country-specific estimates of the long-run coefficients

of human capital as our dependent variable in a cross-sectional regression, trying to

5 In our analysis the GDP and the Investment variables are clearly trended for all countries and can be assumed to be I(1), hence become stationary after first differencing. Although the order of integration of the education variables is inconclusive, we treat these variables as being I(0), to be consistent with theory and the relevant empirical literature (see e.g. Asteriou and Agiomirgianakis 1999). 6 Note that in the traditional pooled estimators, such as the fixed and random effects, the estimation of long-run parameters is prohibited, and only the intercepts are allowed to differ across countries while

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associate country differences in these coefficients to specific socio-economic

characteristics.

The growth model that we estimate includes the growth rate of investment made

per employee (∆kit) and the various educational proxies (Hit) (average years of

schooling for the labour force in all three levels of education: primary, secondary and

tertiary) as explanatory variables of GDP growth (∆yit). The source of the data on

education is Nehru and Dhareshwar (1993). Data on investment and output were

derived from the Penn World Tables (5.6).

3. Estimation Results

We initially applied the MG and PMG estimation techniques to the whole

sample, also assuming that all of the long run coefficients are constant across

countries. This hypothesis is not rejected by the data.

3.1. Whole-sample analysis

Table 1 summarises the results. In both MG and PMG cases a positive

relationship between education and growth is found, supporting the theoretical

considerations. The MG estimates provide less information than the PMG ones, but

they also have the expected signs. However, in all cases, the Hausman tests (last two

columns of Table 1) do not reject the restriction of common long-run coefficients, so

pooling the data (by using the PMG estimator) appears to be a preferable and more

informative procedure. We thus focus on the PMG estimates. The coefficient for the

growth rate of per capita capital is positive and statistically highly significant for all

three alternative specifications. More importantly, the human capital coefficients are

all other coefficients and error variances are constrained to be the same. Therefore, these methods of

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positive and also highly significant. Thus, human capital is found to have a direct

positive effect on GDP growth. This finding is in line with the predictions of

contemporary growth theories7 that identify human capital as an important

determinant of economic growth.

Interestingly, the estimated impact of human capital on growth increases with

the level of education (as represented by the different educational sectors). Increasing

the average primary education of the labour force by one year increases (other things

constant) output growth by 0.05 percentage points. This effect doubles for the case of

secondary education (0.11%) but it increases by five times for the case of tertiary

education (0.27%)8. Thus, not only there is clear evidence that human capital affects

growth positively, but, it is also observed that higher levels of education are

associated with higher rates of economic growth, with the implication that human

capital has non-linear or cumulative growth effects.

3.2. Analysis by country-groups

Having established a robust positive relationship between human capital and

growth, it is interesting to further investigate how this relationship changes for

different stages of economic development. Our sample of 93 countries includes

remarkably diverse economies, from small Third World countries (e.g. Rwanda) to

large open and highly advanced economies (e.g. USA). Despite the fact that the

assumption about the poolability of the coefficients (see Hausman tests in Table 1)

cannot be rejected, as it will become evident in the next sub-section (3.2), the growth

effects of human capital can be very different across structurally different groups of

estimation are not applicable to our case. 7 i.e. the endogenous growth theory or the augmented neoclassical model (e.g. Mankiw et al 1992). 8 Our results are in line with Barro (1999) although quantitatively different given the different samples and time periods.

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countries. To investigate this possibility, we split our sample-countries into three

groups, following the World Bank classification of high, middle and low income

countries (see Appendix 2). Using these groups we re-estimate our growth model for

the three alternative human capital variables.

These results are presented in Table 2. All regressions have been estimated with

the PMG procedure, which is clearly approved by the Hausman test (last column of

Table 2), as was the case with the whole-sample regressions. The estimated capital

growth coefficients are always significant and with the correct signs. Interestingly,

these coefficients increase in both magnitude and significance with each income

group. As expected, physical capital is more productive in high-income countries

(around 0.60%) and less so in middle-income (0.54%) and low-income countries

(around 0.46%).

The results are more interesting for the case of the human capital estimates. Not

surprisingly, for high-income countries primary education is insignificant. Secondary

education is at the margin of significance, with a positive effect, which is almost

identical to the corresponding effect obtained for the low-income group. On the other

hand, the impact of tertiary education in high-income countries is highly significant

and very large, almost one and a half times larger than that estimated from the whole-

sample regression (Table 1). For the middle-income group the first two educational

levels appear to be highly insignificant. Only tertiary education has a statistically

significant effect. Still, this effect is only significant at the 5% level and is very close

to that obtained from the whole-sample estimation (0.30% against 0.27%).

Finally, for the low-income group all educational levels matter. Higher levels of

education appear more productive than lower ones. More strikingly, the productivity

of tertiary education is two and a half times that of the other two educational

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categories. Moreover, it is 1.4 times bigger than in the high-income group and 1.8

times bigger than in the middle-income group of countries. Clearly, this result reflects

the relative shortage of skills in this group. Further, the significance of the growth

effect of primary education can be taken to reflect the high illiteracy rates in this

group of countries, where (primary) education might be having a strong threshold

effect. Clearly, human capital has a differential impact on economic growth and this

impact depends upon the stage of development that each country has attained. Our

findings may also suggest the existence of diminishing returns to spending on formal

education in advanced economies also found in other studies (e.g., Hanushek and

Kim, 1995; OECD, 2001).

One can go further and draw some tentative conclusions about the evolution of

the productivity of human capital across different stages of economic development. In

Figure 1 we present a hypothetical evolution based on our estimates of Table 2. This

hypothetical evolution is effectively a smoothed trend-line of these estimates. For

example, the productivity of tertiary education starts from a high level (0.53%) at low

levels of economic development and gradually declines until it reaches a global

minimum at medium levels of development (0.30%). As countries become more

developed, human capital productivity catches-up, reaching a value of 0.39% in

advanced economies. Hence, the impact of tertiary education is at its highest in the

case of low-income countries, in contrast with other findings in the literature (e.g.,

Gemmell, 1995 and 1996; Barro and Sala-i-Martin, 1995).

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Figure 1. Human capital productivity and stages of economic development

-0.2

0

0.2

0.4

0.6

0.8

Primary Secondary Tertiary

MiddleLow High

Stage of econom ic development

Hum

an c

apita

l pro

duct

ivity

For middle-income countries education is a far less important factor, as at this

stage of development physical capital infrastructure is possibly the most important

determinant of growth. This does not necessarily refute the assumption about the

“strategic complementarity” between the two types of capital (Nickel and Nicolitsas,

1997). Rather, it suggests that their relationship can vary at different levels of

economic development, making the achievement of the appropriate combination of

physical and human capital a crucial policy responsibility9. The impact of secondary

and tertiary education catches up again for high-income countries, with some signs of

convergence. Clearly, if human capital has an important effect into economic growth

that varies from one stage of economic development to another, then this raises the

issue of what determines the evolution of human capital productivity across different

9 Sorensen (1999) suggests another reason, i.e. that R&D could be activated after a threshold and being relatively more important than education at some stage of economic development.

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stages of economic development. Thus, in the next sub-section (3.3) we turn the focus

of our analysis to the determinants of human capital productivity.

3.3. The determinants of human capital productivity

In order to further investigate the country differences in the growth effect of

human capital, we re-estimate the whole-sample regression of Table 1, this time

allowing for country-specific estimates of the long-run human capital coefficients.10

This produces a series of 93 human capital coefficients that we further have attempted

to regress on a number of social, demographic and economic indicators in a cross-

sectional regression. However, this exercise is largely constrained by data availability.

We use data derived from the World Bank World Development Indicators

database, which contains information on a wide range of socio-economic and

demographic characteristics. As our dependent variable is an average over time of the

country-specific growth effects of human capital, we average each time-series from

our World Bank based dataset, to produce a set of cross-sectional series. Missing

values is the main problem with such a procedure, as to the extent that there is a

systematic pattern in the non-availability of data, this will generate a non-random bias

in our results.11 As there is no formal way to deal with this problem, we treat the

findings obtained from this exercise with a lot of caution, as merely indicative. It is

interesting to note, however, that in our estimating sample for the final cross-section,

10 This exercise was only performed for the case of tertiary education. The country-specific estimates are presented in appendix 2. Detailed results are available upon request. 11 For example, if poorer countries have systematically missing values for the early years of our sample (e.g., the 1960s) and since, as expected, social and economic conditions have improved over the course of time in all of our sample countries, then averaging over time will artificially reduce the gap between poor and rich countries in the measured socio-economic indicators. In this respect, our averaging can only understate the impact of socio-economic characteristics on human capital productivity.

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which due to data availability included only three fourths of the original sample, all

three-income groups are analogously represented.12

Our results from the best fitting regression are reported in Table 3. Despite our

reservations, the performance of the regression is very satisfactory. The right-hand

side variables have been selected on the basis of their explanatory power, using a

stepwise backward deletion procedure, among a large number of possible alternatives.

The candidate variables covered a wide range of social (e.g., illiteracy, health

conditions, pollution, use of cars), economic (e.g., FDI flows, savings, tax rates,

public investment), demographic (e.g., fertility rates, age and gender composition of

population, urbanisation) and technological conditions (e.g., size of R&D sector,

high-tech exports, etc). All of these wider factors are reflected in our final selected

variables, which explain around half of the cross-country variation in human capital

productivity. Further, all specification tests suggest that the estimates are robust, as

there is no indication of heteroskedasticity (Cook-Weisberg test), non-normality

(Shapiro-Wilk test) or mis-specification (Ramsey RESET test).

Additionally, with few exceptions most of the estimated coefficients are

significant even at the 1% level. Further diagnostics suggested that, if anything, there

is little evidence of multicollinearity. Note also that, due to averaging, our results are

independent of any business cycle effects and are very unlikely to suffer from

simultaneity or endogeneity problems. Finally, it must be noted that the omission of

specific variables is only of minor importance so far as our right-hand side variables

are successful in covering the whole range of socio-economic and demographic

factors that can be reasonably assumed to affect human capital productivity. This,

however, implies that our estimates cannot be interpreted as pure and direct effects,

12 In the original sample, high, middle and low income countries represent the 31%, 38% and 31% of the sample, respectively. In the smaller cross-sectional sample the corresponding percentages are 31%,

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but rather as broad indicators of the impact that should be attributed to the wider

factors that the corresponding variables represent. For example, the estimated impact

of internet penetration can only be interpreted as an indicator of the effect that

technology penetration in everyday life (that the former attempts to control for) has on

human capital productivity and not as a direct effect of internet use.

With these remarks about the economic interpretation of our findings, these

seem to be generally consistent with the discussion of the previous sub-section. The

level of technology penetration and socio-economic development, as captured by the

use of internet and the share of savings to GDP, are found to have a positive impact

on the productivity of human capital. On the other hand, electric power consumption

enters with a highly significant negative coefficient, plausibly capturing an adverse

agglomeration effect (congestion diseconomies). It must be noted, however, that a

large number of other socio-demographic and technological indicators consistently

failed to be significant. Illiteracy, fertility rates and age dependency ratios, as well as

the use of cars and mobile phones or the levels of water pollution and CO2 emissions,

were among them.

As expected, the size of the agricultural sector was found to be negatively

related to human capital productivity. Taxation on income, profits and capital gains

also enters with a negative sign, suggesting that human capital is less productive in

countries with more progressive tax systems and, thus, more redistribution-supportive

governments. Probably the same “egalitarian government” effect is also reflected in

the obtained coefficient for health expenditure. This implicitly raises from yet another

perspective the old question about the trade-off between equality and efficiency

(Okun, 1975). More interestingly, however, it can be taken as evidence suggesting

44% and 25%.

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that educational inequality increases human capital productivity, given the rather

convincing evidence about the relationship between economic inequalities and

inequalities in access to education (Steedman, 1996; OECD and Statistics Canada,

2000). The implication of this result is that inequalities in economic and educational

opportunities might positively affect the productivity of human capital (which has

also been suggested in different strands of literature –for example, Walde, 2000;

Lloyd-Ellis, 2000). This result attributes an extra role to policy in adopting non-

conflicting educational and growth strategies.

Another interesting finding relates to the estimated effect of population density.

The negative sign obtained here is casting some doubt on the hypothesis that human

capital accumulation and productivity increase with the density of human

interaction,13 Finally, the share of physicians in the population has a strong positive

effect. Surprisingly this is the only technology-related indicator that added any

significant information on the model, as the R&D-related variables (i.e., R&D

spending and employment shares) were almost always insignificant. As a final note, it

must be emphasised that also insignificant were a number of variables related to

education (e.g., government spending on education and school enrolments). We

interpret this as an indication that our human capital productivity estimates (the

dependent variable) are unbiased and independent from any possible external human

capital effects (see Sakellariou, 2001).

In this section we have tried to investigate the determinants of human capital

from a large number of candidate variables. Our findings suggest that the level of

technology penetration, socio-economic development and health conditions, as

captured by the use of internet; the share of savings to GDP and the number of

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physicians are found to have a positive impact on the productivity of human capital.

On the other hand, population density, taxation, health expenditure and the size of the

agriculture sector are found to be negatively related.

4. Conclusions

The growth effect of education is an issue that has been studied extensively in

the last two decades. This paper has provided some further evidence to the results

obtained so far in the literature, using a large panel of data for three groups of

countries at different stages of economic development. Given the long time-

dimension of the data, our investigation, by utilising contemporary panel data

estimation techniques, not only took into account country-specific (fixed) effects but

also, and more importantly, allowed each country to follow its own short-run

dynamics. Hence, the obtained coefficients for the human capital variables can

arguably be considered as revealing a long-run structural relationship between human

capital and growth.

Our findings suggest that human capital affects growth positively and that

higher levels of education are associated with higher rates of economic growth,

suggesting the existence of a non-linear or cumulative growth effect for human

capital. Moreover, human capital has a differential impact on economic growth and

this impact depends upon the stage of development that each country has attained.

We have also tried to investigate the determinants of human capital among a

large number of candidate variables. Our findings suggest that the level of technology

penetration, socio-economic development and health conditions, as captured by the

use of internet; the share of savings to GDP and the number of physicians are found to

13 See e.g. Lucas, 1988. At this point it should be noted that Beauchemin (2001, p.412), finds that countries with large school-aged populations, generate slow flows of resources into public education

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have a positive impact on the productivity of human capital. On the other hand,

population density, taxation, health expenditure and the size of Agriculture sector are

found to be negatively related.

We conclude that government intervention in providing higher (university)

education can be a crucial factor for economic development and sustained economic

growth. Nevertheless, strong attention must be placed on the factors identified above,

in order for the beneficial growth-effects of human capital accumulation to be

maximised.

resulting a fall into education quality and economic growth.

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International Differences in Wage Inequality”, European Journal of Political Economy, 16(3), pp.445-68.

Zolotas X (1977) International Monetary Issues And Development Policies, Bank of

Greece, Athens.

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Appendix 1: The MG and PMG estimation methods

MG estimation derives the long-run parameters for the panel from an average of the long-run parameters from ARDL models for individual countries. For example, if the ARDL is the following

ititiitiiti ezdxLbyLa ++= )()( (1) for country i, where i=1,….,N, then the long-run parameter for country i is

)1()1(

i

ii d

b=θ (2)

and the MG estimator for the whole panel will be given by

∑=

=N

iiN 1

ˆ1 θθ (3)

It can be shown that MG estimation with sufficiently high lag orders yields super-consistent estimators of the long-run parameters even when the regressors are I(1) (see Pesaran, Shin and Smith, 1999). The PMG method of estimation, introduced by Pesaran, Shin and Smith (1999) occupies an intermediate position between the MG method, in which both the slopes and the intercepts are allowed to differ across country, and the classical fixed effects method, in which the slopes are fixed and the intercepts are allowed to vary. In PMG estimation, only the long-run coefficients are constrained to be the same across countries, while the short-run coefficients are allowed to vary. Setting this out more precisely, the unrestricted specification for the ARDL system of equations for t=1,2,…T time periods and i=1,2,…N countries for the dependent variable y is

it

m

ji

n

jjtiijjtiijit xyy εµδλ ++′+= ∑ ∑

= =−−

1 0,, (4)

where xij is the (k×1) vector of explanatory variables for group i and µi represents the fixed effects. In principle the panel can be unbalanced and m and n may vary across countries. This model can be reparametrised as a VECM system

it

m

ji

n

jjtiijjtiijtiitiiit xyxyy εµγγβθ ++′+∆+′−=∆ ∑ ∑

−

=

−

=−−−−

1

1

1

0,,1,1, )( (5)

where the βI s are the long-run parameters and θi s are the error correction parameters. The pooled group restriction is that the elements of β are common across countries, so that

it

m

ji

n

jjtiijjtiijtitiiit xyxyy εµγγβθ ++′+∆+′−=∆ ∑ ∑

−

=

−

=−−−−

1

1

1

0,,1,1, )( (6)

All the dynamics and the ECM terms are free to vary. Estimation of this model is by maximum likelihood. Again it is proved that under some regularity assumptions, the parameter estimates of this model are consistent and asymptotically normal for both stationary and non-stationary I(1) regressors. Both MG and PMG estimations require selecting the appropriate lag length for the individual country equations. This selection was made using the Schwarz Bayesian Criterion.

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Appendix 2: List of countries considered in the empirical analysis and estimated country-specific long-run coefficients for tertiary education

High-income Middle-income Low-income Name Code Estimate Name Code Estimate Name Code Estimate

Australia AUS 0.850 Algeria DZA 0.172 Angola AGO 0.859Austria AUT 0.704 Argentina ARG 0.755 Bangladesh BGD 0.111Belgium BEL 0.990 Bolivia BOL 0.728 Cameroon CMR 0.568Canada CAN 0.392 Brazil BRA 0.889 China CHN 0.588Cyprus CYP 0.965 Chile CHL 0.596 Cote d'Ivoire CIV 0.600Denmark DNK 0.642 Colombia COL 0.116 Ethiopia ETH 0.259Finland FIN 0.960 Costa Rica CRI 0.678 Ghana GHA 0.102France FRA 0.711 Dominican Rep. DOM 0.246 Haiti HTI 0.224Germany DEU 0.783 Ecuador ECU 0.943 Honduras HND 0.704Greece GRC 0.714 Egypt EGY 0.700 India IND 0.726Iceland ISL 0.900 El Salvador SLV 0.473 Indonesia IDN 0.510Ireland IRL 0.758 Guatemala GTM 0.851 Kenya KEN 0.547Israel ISR 0.199 Guyana GUY 0.459 Madagascar MDG 1.155Italy ITA 0.908 Iran, Isl. Rep. IRN 0.450 Malawi MWI 0.406Japan JPN 0.666 Iraq IRQ 0.273 Mali MLI 1.004Kuwait KWT 0.224 Jamaica JAM 0.778 Mozambique MOZ 0.925Luxembourg LUX 0.487 Jordan JOR 0.740 Myanmar MMR 0.642Malta MLT 0.457 Korea, Rep. KOR 0.639 Nicaragua NIC 0.225Netherlands NLD 0.820 Libya LBY 0.457 Nigeria NGA 0.225New Zealand NZL 0.459 Malaysia MYS 0.522 Pakistan PAK 0.371Norway NOR 0.454 Mauritius MUS 0.479 Rwanda RWA 0.127Portugal PRT 0.879 Mexico MEX 0.737 Senegal SEN 0.491Singapore SGP 0.511 Morocco MAR 0.542 Sierra Leone SLE 0.791Spain ESP 0.779 Panama PAN 0.590 Sudan SDN 0.250Sweden SWE 0.795 Paraguay PRY 0.545 Tanzania TZA 0.467Switzerland CHE 0.579 Peru PER 1.051 Uganda UGA 0.333Taiwan TWN 0.459 Philippines PHL 0.492 Zaire ZAR -0.058United Kingdom GBR 0.656 South Africa ZAF 0.259 Zambia ZMB 0.426United States USA 0.499 Sri Lanka LKA 0.492 Zimbabwe ZWE 0.444

Thailand THA 0.613 Trinidad-Tobago TTO 0.570 Tunisia TUN 0.845 Turkey TUR 0.756 Uruguay URY 0.481 Venezuela VEN 0.160

Notes: The classification of countries is based on the World Bank country tables by income, obtained from the World Development Indicators 2000 CD-ROM. Our list includes only those countries for which data were available and were thus included in our empirical analyses. In our sample there are 29 High Income; 29 Low Income and 35 Middle Income Countries.

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Table 1: Pooled Mean Group and Mean Group Estimates

(Dependent Variable: ∆yit) sample size (N x T) =2511

PMG Estimates MG Estimates Hausman Test

Coef. s.e. t-ratio Coef. s.e. t-ratio h p-val

∆kit 0.32 0.04 8.01 0.36 0.08 4.33 1.73 0.19

Hit=primary 0.05 0.02 2.52 0.11 0.24 0.44 0.06 0.82

Joint Hausman test: 2.14 0.17

∆kit 0.45 0.04 11.04 0.34 0.09 3.73 0.20 0.66

Hit=secondary 0.11 0.04 2.75 0.32 3.02 0.11 0.10 0.75

Joint Hausman test: 0.32 0.72

∆kit 0.29 0.10 2.90 0.45 0.04 10.91 0.12 0.71

Hit=tertiary 0.27 0.13 2.07 0.34 2.85 0.12 0.44 0.53

Joint Hausman test: 0.28 0.87

Notes: Error correction coefficients and short run dynamics and individual country estimates are not reported for economy of space.

Table 2: PMG Results for the sub-group estimates Variable coeff s.e. t-stat h-test

A. High Income Countries Capital growth 0.6130 0.0354 17.312 0.65Primary 0.0265 0.0496 0.535 0.77

Capital growth 0.5976 0.0365 16.360 0.82Secondary 0.2122 0.1228 1.728 1.20

Capital growth 0.5940 0.0396 15.008 0.56Tertiary 0.3914 0.0445 8.792 0.55

B. Middle Income Countries Capital growth 0.5441 0.0501 10.860 0.44Primary 0.0825 0.1305 0.632 0.59

Capital growth 0.5493 0.0495 11.098 0.79Secondary 0.7660 0.6136 1.248 0.87

Capital growth 0.5349 0.0499 10.723 0.98Tertiary 0.2998 0.1460 2.054 1.02

C. Low Income Countries Capital growth 0.4664 0.0564 8.275 0.62Primary 0.2087 0.1115 1.872 0.44

Capital growth 0.4614 0.0559 8.246 0.55Secondary 0.2127 0.1177 1.808 0.89

Capital growth 0.4624 0.0560 8.261 0.95Tertiary 0.5343 0.1642 3.254 0.94Notes: Short-run Coefficients and MG estimates are not presented for economy of space. SBC (Schwarz) has been used to select the lag orders for each country. All the long-run parameters have been restricted to be the same across countries. The mean group estimates have been used as initial estimate(s) of the long-run parameter(s) for the pooled maximum likelihood estimation.

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Table 3. The determinants of human capital productivity

Variable Coefficient Std. Error Statistic Probability Electric power consumption (kwh per capita)

-4.34E-05 1.32E-05 -3.298 0.002

Population density (people per sq km)

-0.0001 5.36E-05 -2.203 0.031

Taxes on income, profits and capital gains (% of total taxes)

-0.0032 0.0013 -2.529 0.014

Internet hosts (per 10,000 people)

0.0011 0.0004 2.531 0.014

Genuine domestic savings (% of GDP)

0.0086 0.0028 3.101 0.003

Agriculture, value added (% of GDP)

-0.0090 0.0028 -3.238 0.002

Physicians (per 1,000 people)

0.0891 0.0390 2.288 0.026

Health expenditure, total (% of GDP)

-0.0303 0.0143 -2.117 0.038

Constant 0.9057 0.1296 6.989 0.000

R-squared 0.49 - Heteroskedasticity (Cook-Weisberg) 0.661 0.417 Normality (Shapiro-Wilk) 0.004 0.498 Ramsey RESET test (omitted variables) 1.280 0.291

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