The Interaction between Health, Human Capital and Economic Growth… · 2012-09-05 · The Interaction between Health, Human Capital and Economic Growth. Empirical Evidence. Elias

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Elias Soukiazis and Túlio Cravo

The Interaction between Health, Human Capital and Economic Growth. Empirical Evidence.

DISCUSSION PAPER Nº 42 (MAY, 2007)

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mailto:[email protected]

The Interaction between Health, Human Capital and Economic Growth. Empirical Evidence.

Elias Soukiazis and Túlio Cravo This study investigates the links between health, human capital and economic growth. We argue that good health is essential for raising the levels of human capital stock and productivity enhancing therefore, higher economic growth. Any attempt to reduce the gap between the rich and poor countries must take into account not only the state of human capital formation but also the state of health of the populations. To show this, we employ the conditional convergence approach to growth, testing the impact of human capital and health on growth, by using different indicators representing different levels of human capital and health status. We implement a dynamic panel data approach for a sample of 77 countries for the period 1980-2000. This approach is enhanced by taking into account the feed back effect between human capital (and health) and the different levels of economic development. The full sample of countries is disaggregated into three main sub groupings according to their per capita income level: low, intermediate, and high income countries. The aim is to ascertain whether different convergence processes occur among the sub groupings of countries and what levels of human capital and health better explain their growth path. Our evidence is encouraging. In the whole sample, human capital and health are found to be important determinants of income. In the high-income countries human capital is more relevant. In the low-income countries health is more important to differentiate the economies. JEL: I, I10, I20, I30 Keywords: income per capita, human capital, health status, conditional convergence, panel data. Corresponding author: Elias Soukiazis Faculty of Economics, University of Coimbra Av. Dias da Silva, 165 3004-512 Coimbra, Portugal Tel: +351239790534 Fax: +351239790514 e-mail: [email protected]

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1. Introduction

The role of human capital, emphasised by endogenous growth theory in the 90´s, is now

universally accepted as being indispensable for economic growth. Low levels of human

capital represent a barrier to development and impede improvements in productivity and

competitiveness. But human capital performance mainly has been linked to levels of

education and some other factors such as innovation, technical progress and research

and development. Few authors have recognized the importance of health and nutrition

in the growth process. Among them, Mankiw, Romer, and Weil (1992) cited the

importance of considering not only education, but also health and nutrition as

conditioning factors to growth. Barro and Sala-i-Martin (2004) examined more

systematically the relationship between economic growth and health focusing on the

link between wealth and health. Sustained growth depends on the levels of human

capital whose stock increases as a result of better education, better health, and new

learning and training activities. A labour force without a minimal level of education and

health is incapable of maintaining a state of continuous growth. Human capital is the

input associated with the human body and brain: good health, strength, brainpower,

native ability, are elements that suggest a direct link between the human body and

productivity. Recognizing the importance of health, models of economic growth have

been extended to include this factor as a human capital input.

The main argument that explains the link between health and growth states that

good health raises the levels of human capital, helps to improve the levels of schooling

and education performance, increases labour productivity (reduces incapacity, debility,

and days lost because of sick leave) and that all these are beneficial to economic growth.

Healthier workers are less vulnerable to diseases, more alert, more energetic, and

consequently more productive. In the developing world, investing in health is a way to

achieve higher levels of labour productivity. Sala-i-Martin (2005) explaining the Health

–Poverty Trap in the less developed countries (poor economies are unhealthy, and they

are unhealthy because they are poor), claims that it is impossible to generate economic

growth without solving the central health problems of these countries, and that it is not

possible to improve health without generating economic growth. Therefore, the

reciprocal interdependence between health and economic growth is apparent.

Another important element for growth is that research investment in health generates

positive externalities worldwide. Less developed and developing countries can benefit

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from important innovations in health care, even if they have not made any financial

contribution to it. A greater responsibility by the pharmaceutical industry may help

considerably to improve health in less developed countries. Another component of

investment against poverty and improving human and social conditions is undoubtedly

linked to education and workforce qualifications. Under these circumstances it is

important to devote time, financial resources, and effort to the improvement in health

care, but also in public health policies, sanitation, nutrition, and other sectors which

improve health conditions. It will also require devoting resources for innovation in the

health sector, improving human capital at a clinical level in an attempt to augment its

effectiveness and efficiency.

The main difficulty at the empirical level lies in the possible existence of

endogeneity between health and wealth in the same manner as between human capital

and wealth and the correlation between health and human capital. The feedback effects

of income on health, as well as between health and human capital, are evident. Higher

incomes make possible the consumption of health-related goods, better nutrition, and

allow for better medical care. Low income tends to cause poor health and poor health, in

turn, tends to cause low income. Analogously, better health improves the human capital

performance and higher education contributes to the improvements of health status,

through research and innovation activities. The nature of these feedback effects would

yield biased and inconsistent estimates of the structural parameters when growth models

are estimated with health and human capital as the explanatory variables.

The purpose of the paper is to test the importance of human capital and health on

growth. In doing so, we divide the paper in the following sections: section 2 explains

the interaction between health, human capital and growth; section 3 explains the

mechanism through which human capital and health affect income, based on an

extended growth model; section 4 provides empirical evidence, showing the importance

of human capital and health on growth. The final section concludes.

2. The channels that explain the mutual causation between income, health and

human capital.

(i) Health affects income

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The state of health in a country will affect its growth path through various channels1, in

a way that depends on local conditions as well. The following statements can be made:

Healthier workers are more productive for a variety of reasons, among them, increased

capacity, energy, attentiveness, resistance, creativity, and so on;

Investments in health, as in education, determine the efficiency of labour services.

Labour productivity increases with higher health (and education), influencing positively

the growth of income per capita. Economies with healthier (and more educated)

working population are more productive;

Healthier economies being more productive will generate more income a part of which

can be used to finance technology investment and innovation;

When health improves the country can produce more output with any given

combination of skills, physical capital, and technological knowledge;

Health can increase the efficiency parameter A of the aggregate production function Y

= AF(K,hL) of an economy inducing higher growth. Higher efficiency is obtained by

using new technology, innovation and R&D activities and health is important to achieve

these developments. Another obvious and most direct relation between health and

aggregate growth is through the effect of health on the labour productivity term, h.

Unhealthy citizens have lower productive performance, so the same amount of hours of

work generates less product, exactly in the same way as labour does with less skills and

education. An immediate implication of this lower performance is that unhealthy

individuals are more likely to earn lower wages, work less time, and their incomes are

more likely to grow at a lower rates.

A negative correlation might exist between various indicators of population health and

measures of income inequality (Deaton, 2003). It has been shown that policies that

increase population health will result in reducing income inequality because their main

impact will be on the less privileged people with poor health status. Consequently,

1 For more details see Howitt (2005)

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reduced income inequality due to improvements in health is likely to have a positive

impact on a country’s growth path. A reduction in inequality will increase the fraction

of people able to finance an education (their children’s education) and will lengthen the

years of schooling acquiring higher knowledge which is beneficial to growth;

Another fact to consider is that foreign direct investment avoids locations where the

labour force is uneducated, untrained and unhealthy.

(ii) Health affects human capital

The influence of better health on human capital qualifications is also apparent:

Health plays an important role in determining the rate of returns to education.

Unhealthy children show higher school absenteeism so they get less education turning

them less productive and with less ability to earn income in the future. On the other

hand, unhealthy workers might receive less training because the expected rate of return

that firms get for the training investment is expected to be lower. On the contrary,

higher health of the school age population increases the learning efficiency. School age

population, with better medical care will gain more from a given amount of education

than school age population that is malnourished and suffering from diseases. The

increase in learning efficiency due to a better health will raise the research efficiency,

allocating more resources to innovation activity. The complementary nature between

health and education is obvious. If the mutual causality between health and education is

true, providing a good education system will not be enough to achieve higher growth

unless the health system also improves. Therefore, health can be treated as another

component of human capital, analogous to the skill component;

Early childhood good health along with maternal health will make a person more

creative. Just as a healthier person will be more efficient in producing goods and

services, so will the person be more efficient in producing new ideas. One of the effects

that one would expect to come from an improvement in the state of health in a country

is an increase in the research and efficiency performance that affects the country’s

ability to generate innovations and new products;

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Higher life expectancy is the consequence of better health care and can have a direct

effect on the average skill level of the population. The increase in life expectancy

encourages the acquisition of more skills prolonging, therefore, the lifetime of the

productive workers. On the contrary, low life expectancy tends to reduce the rate of

return of investing in education, and therefore, the incentives to education and

accumulation of human capital tend to be lower. Another advantage is that, an increase

in life expectancy is likely to raise the savings rate (needed for retirement) allowing

higher investment. Doppelhofer et al. (2004) found evidence that life expectancy at birth

was one of the robust determinants of growth, implying that countries with larger life

expectancy grow faster. Also, the decline in infant mortality will raise the working age

population, and therefore production;

Cure and care activities will increase the share of healthy people in the population

improving therefore their labour efficiency.

(iii) Income affects health

The channels that explain the influence of the level of income on health2 are of the

following nature:

Low income people and low income countries are more likely to be unhealthy because

they do not have the necessary means to buy health cure and care or afford expenses to

prevent diseases;

Poor citizens are more likely to be malnourished and therefore more vulnerable to

epidemic diseases;

Poor populations are more likely to live in areas with poor sanitation conditions (e.g.

unclean water) effecting therefore their state of health;

Low income citizens are more likely to have less education and hence understand the

need for medical care, protect themselves from diseases and live in healthier conditions.

2 A detailed explanation is given in Sala-i-Martin (2005)

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Higher literacy of parents can reduce child mortality, neonatal diseases and generally

implement healthier living conditions;

Because the life expectancy (and education level) is low in poor countries, the birth rate

is high. Numerous families will end up with lower health and education as a

consequence of budget constraints;

Poor populations cannot afford to pay for drugs, vaccines or medical treatments,

therefore, the pharmaceutical corporations (multinationals) will have less incentives to

invest in R&D for diseases that affect poor people since the expected returns are

unlikely to cover the large R&D expenses;

Poor countries have very weak welfare systems unable to improve the state of health

(and education) of their citizens;

All the above arguments indicate clearly that human and health capital are important

factor inputs in growth analysis, and that feed back effects between health, human

capital and income levels have to be taken into account when it is proposed to explain

economic growth.

3. The mechanism through which human capital and health affect growth

The convergence framework which is used in this study to measure the effects of human

and health capital on growth is derived from the Solow (1956) neoclassical model based

on the production function with factor inputs and labour-augmenting technical progress

given by3:

[ ] 10 with ,1 <++<= −−− γβαγβαγβαitititititit LAMHKY (1)

where Y is real output, K is physical capital, H is human capital, M is health capital, L

is labour input, A is the level of technology, α , β and γ are the physical, human and

3 The description of the model follows closely Mankiw el al, (1992) , Islam (1995) and Knowles and Owen (1995), providing the necessary adaptations.

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health capital elasticities with respect to output, respectively. Subscript i denotes the

country and t the time period.

The model assumes that L and A grow exogenously at constant rates ni and g ,

given by and , respectively. Therefore, the number of effective

units of labour, that is, A

ntiit eLL 0= gt

iit eAA 0=

itLit, grows at rate ni+g (technology assumed constant across

countries).

The model also assumes that saving S is a constant fraction of output

( ) and 10, <<= ssYS K depreciates at a constant exogenous rateδ , therefore,

KIdtdKK δ−==& , where I is investment. Accordingly, a constant amount of capital Kδ ,

in each period t, is not used. The same argument is also valid for human and health

capital implying that human and health capital depreciate at the same rate as physical

capital.

Under the standard neoclassical assumption of constant returns to scale, the

production function, in terms of effective units of labour (lower case letters), is given by

(2) γβα mhky =

with ALYy = ,

ALHh = ,

ALKk = and

ALMm =

The dynamic specification of the model with technical progress takes the following

form:

( itiitkiit kgnysk δ++−=.

)

)

)

physical capital accumulation (3a)

( itiithiit hgnysh δ++−=.

human capital accumulation (3b)

( itiitmiit mgnysm δ++−=.

health capital accumulation (3c)

where sk, sh, and sm are the fractions of income in country i invested in physical capital,

human capital and health capital, respectively, δ is a common depreciation rate and g

the growth rate of technology which is the same for all countries..

Since in the steady-state the rate of growth of all kind of capital stock is zero the

steady-state values of physical, human and health capital satisfy the following

conditions, respectively:

γβαγβγβ

δ

−−−−−

⎟⎟⎠

⎞⎜⎜⎝

⎛++

=1

11

*

gnsss

ki

mihikii (4a)

9

γβαγγαα

δ

−−−−−

⎟⎟⎠

⎞⎜⎜⎝

⎛++

=1

11

*

gnsss

hi

mihikii (4b)

γβαβαβα

δ

−−−−−

⎟⎟⎠

⎞⎜⎜⎝

⎛++

=1

11

*

gnsss

mi

mihikii (4c)

Substituting the expressions found for , and into the production function,

equation (2), we derive analogously the steady-state value of output:

*ik *

ih *im

**1111

*γβα

βγγααγβα

αγβγβ

δδ

−−−−−−−−−−

⎟⎟⎠

⎞⎜⎜⎝

⎛

++⎟⎟⎠

⎞⎜⎜⎝

⎛

++=

gnsss

gnsss

yi

mihiki

i

mihikii

γβαγ

βαβα

δ

−−−−−

⎟⎟⎠

⎞⎜⎜⎝

⎛++

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gnsss

i

mihiki (5)

From the definition of output in terms of efficient units of labour,ALYy = , and the

expression found for the level of output in the steady-state, equation (5), it is possible to

derive an expression for income per capita as shown in equation (6):

( ) )ln()()ln()ln()(lnlnln 0 mihikiiit

it sw

sw

swagn

wgtA

LY γβδγβα

+⎟⎠⎞

⎜⎝⎛++++⎟

⎠⎞

⎜⎝⎛ ++

−+=⎥⎦

⎤⎢⎣

⎡ (6)

with w=1-α-β-γ

This equation shows that income per capita is negatively related to working age

population growth because the amounts of physical, human and health capital must be

spread more thinly over the population. On the other hand, income per capita is

positively related to accumulation of physical, human and health capital.

Alternatively, income per capita can be expressed as a function of the steady-state

levels of human and health capital. Solving (4b) and (4c) with respect to shi and smi in

terms of and , and substituting in (6) we obtain *ih *

im

itiikitiit

it mhsgngtALY ε

αγ

αβ

ααδ

αα

+−

+⎟⎠⎞

⎜⎝⎛−

+−

+++−

−+=⎥⎦

⎤⎢⎣

⎡)ln()

1()ln(

1)ln()

1()ln()

1(lnln **

0 (7a)

or solving equation (4c) for smi and substituting in (6) yields

iti

hikitiit

it

m

ssgngtALY

εβα

γβα

ββα

αδβα

βα

+−−

+

⎟⎟⎠

⎞⎜⎜⎝

⎛−−

+−−

+++−−

+−+=⎥

⎦

⎤⎢⎣

⎡

)ln()1

(

)ln(1

)ln()1

()ln()1

(lnln

*

0 (7b)

The decision to estimate equation (6), (7a) or (7b) will depend on whether the available

data on human capital and health capital reflect the rate of accumulation (shi) or the level

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of human capital ( ), and analogously, the rate of accumulation (s*ih mi) or the level of

health capital ( ). *im

A cross-section estimation of equation (6) or (7a and 7b) is heavily dependent on

the assumption that , , (or and ) and nkis his mis *ih *

im i are not correlated with the error

term ( iε ). In general, this is not a convincing argument that physical, human and health

capital rates and population growth will not be influenced by the observable factors

included in . This problem is solved when panel regressions (instead of cross-section)

are used, allowing for country specific effects (fixed or random) providing, therefore, a

better control for the technology shift term (

0A

iε ).

Having all the above analysis in mind we can develop the convergence model

associated with endogenous growth theory4. Let assume that is the steady-state level

of income per capita given by equation (5) and its actual value at time t. The speed

of convergence close to the steady state is described as follows:

*iy

ty

( ) ( )[ ititit yy

dtyd lnlnln * −= λ ] (8)

where λ is the rate of convergence dependent on the population, technology and capital

depreciation rates and the output elasticities with respect to physical, human and health

capital. This equation implies that:

( ) ( ) )(lnln1ln 1*

2 tyeyety iT

iT

iλλ −− +−= (9)

where is country’s income per capita at some initial point of time and ( )1tyi 12 ttT −=

the time span.

Subtracting from both sides of equation (9) we obtain a specification

that represents a partial adjustment process:

( )1ln tyi

( ) ( ) ( ) ( )[ ]1*

12 lnln1lnln tyyetyty iiT

ii −−=− −λ (10)

In this model the growth of income per capita between the period and is

determined by the distance of its initial level and the steady-state value. Substituting for

from equation (7b) we get the following expression:

2t 1t

*iy

4 For this development, see Islam (1995)

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( ) ( ) ( ) ( ) ⎥⎦

⎤⎢⎣

⎡−++⎟

⎠⎞

⎜⎝⎛−

−−

+⎟⎠⎞

⎜⎝⎛−

+−

++=− 1**

012 lnln1

)ln()1

(ln1

)ln()1

(lnlnln tygnmhsgtAtyty iiiikiii δα

ααγ

αβ

ααη ( )

with η = (1-e-λT) (11)

In this equation the growth of income per capita is explained by its initial value

(the factor of convergence), assuming ( δ+g ) to be the same for all economies and

physical, human and health capital and also population growth rates are taken to be

equal to their respective averages over the period considered.

Estimating the convergence equation (11) by using cross country regressions

introduces a problem, since it does not take into account that countries may differ in

their production functions. The omitted factors that determine the differences in the

production functions would be correlated with the observed included variables and

under these circumstances the obtained estimates would be biased. The solution to this

problem is to use panel data estimation techniques which take into account the

differences in the production functions either in the form of fixed or random effects.

The equation that can be estimated, therefore, is the following:

( ) ( ) ( ) tiiikiiiii vma

hsa

agna

atygtAtyty ,**

1012 )ln(1

ln1

)ln(1

)ln(1

)(lnlnlnln +−

+−

+−

+++−

−−+=−γη

αβηηδηηη (12)

where ( ) 0ln1 Ae Tλ−− is the time-invariant individual country-effect term and is the

error term that varies across countries and over time. Estimating equation (12) using

panel data (instead of cross-section) we take into account for differences in the

production functions across countries by introducing specific country effects, fixed or

random

tiv ,

5. The Least Squares Dummy Variable (LSDV) approach can be used to

estimate the model with fixed effects and GLS for the model with random effects.

A simplified conventional presentation of the convergence approach, based on

equation (12), and using panel data is the following:

tititi uyby ,1,, lnln ++=Δ −γ (13)

where the rate of growth of income per capita of each economy (i is country and t time)

is related to its initial level, the only factor of convergence. The higher the distance of

the initial level of per capita income from its steady-state value, the higher the

convergence. Absolute convergence is explained because of diminishing marginal

returns to capital. The marginal product of capital is higher in countries with lower

5 Islam (1995) argues that the main usefulness of the panel approach lies in its ability to allow for differences in the aggregate production function across economies. Temple (1999) states that panel data techniques allow to control for omitted variables that are persistent over time.

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levels of capital stock and, therefore, for similar saving and population rates, they grow

faster relatively to countries with higher levels of capital stock. The constant term (γ )

represents the common steady-state value of the income per capita dependent on factors,

such as, andδ,,,,, ** gnmhs iiiki ( )0A , which are treated as exogenous. The parameter

( )Teb λ−−= 1 is known as the coefficient of convergence, while λ expresses the rate or

speed of convergence given by ( )T

b−−=

1lnλ . Finally, T is the time length that the

growth of income per capita is measured.

If equation (13) is extended to include explicitly other structural factors

(population growth, human, health and physical capital, R&D, trade, etc.) to control the

steady-steady value, we have the case of conditional convergence given by the

following equation:

(14) tijtijtiiti uXcyby ,,1,, lnlnln +++=Δ −γ

Two main differences distinguish the conditional from the absolute convergence

hypothesis. The first is that economies converge to different steady-states, represented

by iγ . The second is that there are some activities that in the long run, exhibit increasing

returns to scale characteristics, such as, human capital, technology, innovation, among

others (Barro and Sala-i-Martin, 1992; 2004). These activities with increasing returns

characteristics counterbalance the diminishing returns to scale property of capital stock

in the production function. The increasing returns to scale activities are included in the

vector . The hypothesis of absolute convergence is accepted when jtiX , iγ = γ and =0,

otherwise, convergence is conditional, meaning convergence after controlling for

differences in the steady states across countries

jc

6.

4. The estimation approach.

Equation (14) which is a reduced form of equation (12) accommodating the idea of

conditional convergence can be written alternatively as:

with φ = b+1 (15) tijtijtiiti uXcyy ,,1,, lnlnln +++= −φγ

The variables7 that constitute this growth equation are the following:

6 For empirical evidence, see in Sala-i-Martin (1994, 1996). 7 A more detailed explanation of the variables and the source of the data is given in the Appendix 1.

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i) yi,t is real GDP per capita for each country in the sample at the end of each

period;

ii) yi,t-1 is real GDP per capita at the first year of each period, the convergence

factor;

The vector Xj (I/Y, ni+g+δ, HUMAN, ART, PAT, PAT/ART, IMORT, LEXP) includes the

following conditioning factors:

iii) I/Y is the investment ratio as percentage of GDP and is a proxy for capital

accumulation;

iv) (ni+g+δ) with ni the population growth over the age of 25, δ the depreciation

rate and g the rate of technical progress. The depreciation and technology

rates are taken as constant across countries and (g+δ) is assumed to be 5%, a

common assumption in growth equations;

v) HUMAN is average years of schooling of the population over the age of 25.

This variable is commonly used in growth equations to express basic levels

of education;

vi) ART is the publication ratio defined as the number of articles published in

scientific journals, per million of inhabitants over the age of 25. This variable

attempts to capture higher levels of human capital owing to scientific work

that can not be detained in the years of schooling conventional variable.

Bernardes and Albuquerque (2003) consider that the number of published

papers may be taken as an index of the state of the educational system

reflecting some kind of efficiency.

vii) PAT is the patents ratio defined as the number of patents per million of

inhabitants over the age of 25. Patents production is a proxy for innovation

owing to the research and development activity. As Griliches (1990) argues

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the patent ratio variable can be seen as an index of innovation and can be

used as a proxy for technological capability. Nelson and Rosenberg (1993)

consider that this variable expresses the capability of transforming science

into innovation. In general terms, this variable can be assumed to reflect the

result obtained from research and development activities. R&D activities are

performed by researchers generating innovation and different kind of

knowledge.

viii) IMORT is infant mortality (with less than one year old) per 1000 births and

can be taken as a health-status indicator;

ix) LEXP is life expectancy at birth, a health-status indicator more relevant to the

production of output.

To study the convergence process across countries, conditioned by the human and

health capital variables described above, four main samples are considered. We were

able to collect coherent data for 77 countries8, and this is the full sample labeled

“world”. The whole sample is divided into three sub-groupings according to their level

of income per capita. The first grouping enhances the 24 high-income countries of the

sample, the second is the sample of 26 countries with intermediate-income, and the third

is the sample of the 27 low-income countries. The purpose of this division is twofold:

first, to detect different convergence processes among the various groups that have

different levels of development; second, to find what levels of human capital and health

contribute mostly to the improvement of the standards of living among the groups of

countries with dissimilar levels of development.

A panel data approach is used to estimate the conditional convergence equation

(15). The period of analysis spans from 1980 to 2000, but the data are organized in five-

year intervals to avoid business cycle influences. Data on investment ratio, human

capital (ART, PAT and ART/PAT) and health (IMORT, LEXP) correspond to the last

year of the respective time spans, 1985, 1990, 1995 and 2000, respectively. The GMM

dynamic system method is used to estimate the growth equation assuming that all

conditioning factors are endogenous. The time-dummy variables are used as additional

instruments in the GMM regressions to capture structural changes occurred over time, 8 A list of the counties is given in the Appendix 2.

15

and the Hansen-test reveals if all instruments are valid. For comparison reasons we also

report the results obtained from the fixed and random effects regressions (although the

Hausman-test in all estimations favours the regressions with fixed effects), but we have

to interpret these results with caution since the estimated parameters can be biased and

inconsistent due to the endogenous nature of the regressors.

Table 1 reports the results obtained from the estimation of the growth equation

considering the whole set of 77 countries. Convergence is found to run at approximately

2.5% per annum and it is statistically significant in all methods of estimation.

Investment contributes positively to raise income per capita as the theory predicts and it

is statistically significant in all cases. On the other hand, population growth does not

have any significant effect on income and in the GLS case its effect on income is

negative. These are common findings in most empirical studies in growth literature, and

in line with the Solow´s model that predicts that real income is higher in countries with

higher saving/investment rates and lower in countries with higher values of n. Higher

population growth (assuming g+δ the same for all countries) lowers income because the

available capital must be spread more thinly over the working age population (Mankiw

et al, 1992).

Among the human capital variables, the variable ART (number of published articles

divided by the population with age over 25) is the most significant and with a

substantial positive effect on income per capita. According to the GMM regressions, the

most efficient ones, every 1% increase in the publication rate will raise income by

0.13% in the set of 77 countries that constitute the whole sample. The publication rate

can be assumed to depict the qualitative characteristics of the educational system

associated with higher efficiency and contributing to the accumulation of knowledge9.

On the other hand, the variable HUMAN (average years of school attainment of the

population over age 25) which is a conventional measure of the stock of human capital

has not any significant effect on income. Therefore, higher levels of human capital

(reflected in the publication rate) differentiate better the economies in terms of the

educational level10. It is apparent that economies with higher levels of human capital,

show higher standards of education or at least that they make better use of the acquired 9 Our evidence is in line with Barro(2001) claiming that the quality of schooling is much more important than the quantity (enrolment rates), so measures of the efficiency of human capital must be considered to explain growth. 10 Soukiazis and Cravo (2007) provide more detailed evidence on the type of human capital that more properly explains the growth performance across countries.

16

skills in education. The variable PAT (patent ratio) which attempts to depict even higher

levels of human capital and efficiency does not seem to differentiate successfully the

educational system among countries since its impact on income per capita is not

significant.

Among the health variables, the infant mortality rate shows to have the expected

negative effect on income and in the fixed and random effects regressions appears to be

significant. Every 1% fall in the infant mortality rate raises income per capital between

0.13 and 0.20 per cent in the set of 77 countries of the sample. On the other hand, the

life expectancy variable has a positive and significant effect on income only in the

random effects regression. Therefore, the infant mortality rate depicts better the

differences in health conditions among countries, showing that when health improves,

the working population will increase, the country can produce more output and being

more productive will generate higher income.

Table 2 reports the estimation results by considering the sample of the 24 countries

with the higher income per capita. As commonly found, population growth is not

statistically significant but the investment ratio has its excepted positive impact on

income. The difference now is in the human capital indicator. The patent-ratio

attempting to capture higher levels of the educational system, proves to be the most

relevant variable, having a positive and significant impact on income. This is an

expected result, since this level of human capital might differentiate more properly the

efficiency of the educational system among countries with higher income. Patent

production is a proxy for innovation and technological capability transforming science

into innovation. Countries with higher innovation capability are able to generate higher

income. On the other hand, the variables used to depict differences in health conditions

are shown to be not relevant. The impacts of the mortality rate and life expectancy on

income are not significant and the sign of this impact is not clear. More appropriate

indicators11 of health status are needed here to depict the efficiency of the health system

related to care and cure conditions.

The results obtained estimating the convergence equation by considering the

sample of 26 countries with the intermediate-income, are not very robust12. As Table 3

shows, the statistical significance of the coefficients is weak, even for the investment 11 It should be desirable to use indicators like, doctors (nurses) rates, number of hospitals and medical centres, nº of treatments, spending on health, etc. but these data are not available for all countries. 12 One of the reasons could be the small size of the samples when the total sample is divided in three sub-groupings.

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ratio. The most significant impacts come from the initial income (the convergence

factor) and the infant mortality rate with its expected negative influence on income per

capita. However, convergence is found to run at 3% that is higher than in the whole

sample and in the sample of the high-income countries.

Evidence from the low-income countries is similar. The most important

contribution to growth is from the convergence factor. Convergence runs at a lower rate,

around 2%. The GMM regression with no time-dummy variables in it, gives evidence

that the impact of infant mortality is negative on income and it is statistically

significant. No evidence is found that the human capital variables used have a

significant effect on income. This is not a surprising result and it is in line with the

argument that health is important for human capital being efficient. In this perspective,

health conditions seem to be more crucial for improving the economic performance of

the less developed countries.

5. Concluding remarks

In this study the convergence approach has been employed to test the importance of

human capital and health conditions on growth. Different indicators that characterize the

states of education and health are used attempting to differentiate properly the economic

performance of the countries composing the sample. The evidence is quite encouraging.

Considering the whole sample of 77 countries, the evidence shows, that along with

the investment ratio, human capital (through the publication rate) and health status

(through the infant mortality rate) are important factors for generating higher income.

Therefore, human capital and health conditions are important factor inputs in growth

equations and cannot be omitted. Other human capital proxies, such as average

schooling rate and patents rate do not explain satisfactorily the economic performance

of the countries used in the sample. The growth rate of the working age population is

not important for economic growth too.

When the sample is sub-divided in three different groupings corresponding to high,

intermediate, and low income countries, the evidence is interesting too, but the

robustness of the results is questionable. What distinguishes the high-income countries

economic performance is attributable to human capital and less to health. It is shown,

that higher levels of human capital depicted by the patent ratio is the most appropriate

variable to differentiate the countries economic performance. Patent production is the

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outcome of innovation and technological capability which are important factors for

generating more product and wealth. Health conditions are shown to be not relevant to

differentiate the high-income countries. This is a plausible result considering that high-

income countries are more homogeneous with respect to health conditions and medical

advances but more heterogeneous in human capital qualifications and technological

development.

The evidence from the intermediate and low income countries is different. The most

important contribution to growth comes from the convergence factor (initial income)

and health conditions (infant mortality) the results being more robust in the low-income

countries. Human capital variables are not relevant to differentiate the economic

performance, especially, between the low-income countries, but health conditions are.

This is a plausible result suggesting that less developed countries have to solve

primarily the health problems before attempting to develop other sectors.

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Table 1. Conditional Convergence among countries (1980-2000). Panel Data Regressions. All countries (77). variables

GMM GMMFixed Effects

(LSDV)Random Effects

GLS

0 ln ti,y 0.4260* 0.4120* 0.4368* 0.8644* (2.98) (2.90) (8.32) (35.08)

ln(n+g+δ) 0.2820n 0.3154*** 0.0853n -0.0985 (1.61) (1.70) (0.89) (-1.50)n

ln(I/Y) 0.1846** 0.1985** 0.0780* 0.0934* (2.52) (2.52) (2.62) (4.39)

ln(HUMAN) -0.1223n 0.0569n 0.0005n -0.0020n

(-0.86) (0.53) (0.01) (-0.08) ln(ART) 0.1339** 0.1230** 0.0439** 0.0046n

(2.24) (2.05) (2.03) (0.56) ln(PAT) 0.0009n 0.0148n 0.0080n -0.0042n

(0.04) (0.72) (0.87) (-0.68) ln(IMORT) 0.0498n -0.1329n -0.2078* -0.0808*

(0.30) (-1.60) (-4.45) (-3.22) ln(LEXP) -0.0608n -0.4784 n 0.0446n 0.2280*

(-0.11) (-1.04) (0.27) (2.36) constant 5.1950* 0.0029n

(5.12) (0.01) D1990 0.0487n

(1.51) D1995 0.0797n

(1.18) D2000 0.1390n

(1.53) Implied λ 0.0244 0.0250 0.0239 0.0055

Nº of Observations 231 231 308 308 d.f 220 223 223 299

R2-overall 0.9709 0.9868 Hausman Chi2(8)=95.41

Prob>chi2=0.0000 Hansen(J) chi2(37) = 34.06

Prob > chi2 = 0.608 chi2(40) = 49.00

Prob > chi2 = 0.156

AR1 z = -2.66

Pr > z = 0.008 z = -2.76

Pr > z = 0.006

AR2 z = 0.44

Pr > z = 0.662 z = 0.09

Pr > z = 0.928

Notes: GMM is Arellano Bond dynamic panel-data estimation, one-step difference GMM results with robust standard errors. LSDV are regressions with fixed effects GLS are regressions with random effects Numbers in Brackets are t-ratio. *Coefficient significant at 1% level ** Coefficient significant at 5% level *** Coefficient significant at 10% level n coefficient statistically not significant. Hausman- test, tests the Random versus Fixed effects hypotheses. Hansen(J)- test of over-identifying restrictions in the GMM estimation. AR1and AR2 are the Arellano-Bond tests for 1st and 2nd order autocorrelation in first differences.

20

Table 2. Conditional Convergence among countries (1980-2000). Panel Data Regressions. High-income countries (24). Variables



GLS

0 ln ti,y 0.3500*** 0.4123** 0.5417* 0.8253* (1.84) (2.23) (4.81) (14.82)

ln(n+g+δ) 0.0856n -0.0007n -0.1554n -0.0917n

(0.30) (-0.00) (-1.02) (-0.96) ln(I/Y) 0.3034* 0.3813* 0.3511* 0.1749*

(6.74) (8.03) (5.67) (3.09) ln(HUMAN) 0.1783n 0.5343* 0.3904** 0.0456n

(1.23) (3.67) (2.33) (0.74) ln(ART) 0.0519n -0.0511n -0.0490n -0.0033n

(0.90) (-1.02) (-1.26) (-0.19 ln(PAT) 0.1233* 0.1014** 0.0709** 0.0078n

(3.70) (2.53) (2.33) (0.71) ln(IMORT) 0.1161n 0.0448n 0.0126n -0.0667***

(1.30) (0.85) (0.18) (-1.68) ln(LEXP) -2.1587n 2.0156n 1.6196n -0.3906n

(-1.14) (1.48) (1.39) (-0.63) constant -4.876n 2.703n

(-0.97) (1.01)

D1990 0.1027*

(3.26) D1995 0.1527**

(2.54) D2000 0.2247**

(2.70) Implied λ 0.0279 0.0250 0.0192 0.0071




Prob > chi2 = 0.998 chi2(40) = 17.71 Prob > chi2 = 0.999

AR1 z = -0.92 Pr > z = 0.356

z = -1.06 Pr > z = 0.290

AR2 z = -0.80 Pr > z = 0.425

z = -0.69 Pr > z = 0.492

Notes: GMM is Arellano Bond dynamic panel-data estimation, one-step difference GMM results with robust standard errors. LSDV are regressions with fixed effects GLS are regressions with random effects Numbers in Brackets are t-ratio. *Coefficient significant at 1% level ** Coefficient significant at 5% level *** Coefficient significant at 10% level n coefficient statistically not significant. Hausman- test, tests the Random versus Fixed effects hypotheses. Hansen(J)-test of over-identifying restrictions in the GMM estimation. AR1and AR2 are the Arellano-Bond tests for 1st and 2nd order autocorrelation in first differences.

21

Table 3. Conditional Convergence among countries (1980-2000). Panel Data Regressions. Intermediate-income countries (26). Variables



GLS

0 ln ti,y 0.2950*** 0.2951n 0.2899* 0.7178* (1.69) (1.48) (3.31) (12.67)

ln(n+g+δ) 0.3658n 0.2671n 0.1883n -0.1598n

(0.89) (0.68) (0.61) (-1.18) ln(I/Y) 0.1172n 0.1251n 0.1075*** 0.0962*

(1.49) (1.65) (1.95) (2.70) ln(HUMAN) 0.0197n -0.0513n -0.0930n -0.0658n

(0.12) (-0.41) (-0.60) (-0.85) ln(ART) 0.0931*** 0.0707n 0.0525n 0.0275**

(1.72) (1.63) (1.41) (1.95) ln(PAT) -0.0186n -0.0188n -0.0018n -0.0081n

(-0.91) (-0.92) (-0.10) (-0.74) ln(IMORT) -0.5131n -0.3236*** -0.3674* -0.1663*

(-1.62) (-2.01) (-3.16) (-2.91) ln(LEXP) -0.3803n -0.0736n -0.2838n -0.4703n

(-0.56) (-0.15) (-0.57) (-1.40) constant 8.822* 4.3817*

(3.08) (2.55) D1990 -0.0937n

(-1.38) D1995 -0.0898n

(-0.72) D2000 -0.1520n

(-0.84) Implied λ 0.0304 0.0304 0.0306 0.0116




Prob > chi2 = 1.000 chi2(40) = 15.61 Prob > chi2 = 1.000

AR1 z = -1.71 Pr > z = 0.086

z = -1.58 Pr > z = 0.113

AR2 z = 0.13 Pr > z = 0.893

z = 0.39 Pr > z = 0.694


22

Table 4. Conditional Convergence among countries (1980-2000). Panel Data Regressions. Low-income countries (27). Variables



GLS

0 ln ti,y 0.5538* 0.5124** 0.5559* 0.8292* (3.12) (2.66) (6.16) (18.24)

ln(n+g+δ) 0.1594n 0.1675n 0.1162n -0.0332n

1.02 (0.96) (0.87) (-0.32) ln(I/Y) 0.0165n -0.0049n -0.0165n 0.0384n

0.26 (-0.07) (-0.34) (1.17) ln(HUMAN) -0.2065n 0.0241n 0.0474n 0.0150n

-1.42 (0.30) (0.58) (0.45) ln(ART) 0.0209n 0.0005n -0.0031n -0.0131n

0.46 (0.01) (-0.08) (-0.91) ln(PAT) 0.0038n 0.0158n 0.0035n -0.0129n

0.18 (0.83) (0.27) (-1.22) ln(IMORT) 0.0452n -0.2709** -0.1964*** -0.0699n

0.21 (-2.06) (-1.74) (-1.21) ln(LEXP) 0.1987n -0.1873n 0.0698n 0.3492*

(0.52) (-0.89) (0.31) (2.61) constant 4.1847* 0.0075n

(2.63) (0.01) D1990 0.0627***

(1.84) D1995 0.0841n

(1.26) D2000 0.1892***

(2.13) Implied λ 0.0187 0.0205 0.0186 0.0070




Prob > chi2 = 0.985 chi2(40) = 19.59

Prob > chi2 = 0.997

AR1 z = -2.79

Pr > z = 0.005 z = -2.58

Pr > z = 0.010

AR2 z = 0.41

Pr > z = 0.681 z = 0.07

Pr > z = 0.941


23

Appendix 1.

Description of the variables used in the estimation and the data source.

yi,t is real GDP per capita in international dollars (constant prices) and population

(millions of inhabitants) were collected from Heston et al (2002) “Penn World Tables, 6.1”. Available at http://pwt.econ.upenn.edu/ I/Y is investment ratio as percentage of GDP, at 1996 constant prices was taken from “Penn World Tables, 6.1”. HUMAN is average years of schooling and the population over age 25 were taken from Barro & Lee (2000). Available at http://www.cid.harvard.edu/ciddata/ciddata.html ART is publication ratio defined as the number of articles published in scientific journals, per million of inhabitants over age 25. The source of the data is the Institute for Scientific Information (ISI), and we have used the “Science Citation Index”, which excludes papers from arts and humanities. Available at http://isi15.isiknowledge.com PAT is patents ratio defined as the number of patents per million of inhabitants over age 25. The source of the data is USPTO (United States Patent and Trademark Office), available at http://www.uspto.gov. Number of “utility patent” applications (per year) registered in the USPTO by country of origin. The country of origin of an application is based on the residence of the first-named inventor. Some countries have no patents for some years, therefore, the value of 0.1 was assumed to avoid missing data misspecification when we implement the log transformation. IMORT is infant mortality rate per 1000 lives births, and LEXP is life expectancy at birth, total years. The source of the data is World Bank (HNPStats-the World Bank´s Health, Nutrition and Population data platform).

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Appendix 2. List of the countries Income per capita in PPP constant dollars in 2000 High Income (24) Intermediate income (26) Low Income (27) USA 33308.40 Norway 27043.97 Canada 26922.21 Hong Kong 26703.36 Denmark 26627.16 Switzerland 26421.59 Ireland 26378.97 Australia 25534.80 Japan 24671.66 Netherlands 24313.17 Finland 23798.48 Belgium 23784.25 Austria 23681.31 Sweden 23661.82 Germany 22861.05 France 22371.36 United Kingdom 22188.23 Italy 21794.18 New Zealand 18824.46 Spain 18054.65 Israel 16939.58 Portugal 15955.13 Republic of Korea 15881.34 Greece 14624.61

Mauritius 13927.68 Trinidad & Tobago 11147.76 Argentina 10994.77 Hungary 10443.70 Malaysia 9936.61 Chile 9919.99 Uruguay 9612.94 Mexico 8766.09 South Africa 7541.10 Brazil 7185.02 Thailand 6856.97 Turkey 6837.72 Tunisia 6777.45 Venezuela 6420.10 Panama 6066.10 Iran 5990.68 Costa Rica 5863.37 Colombia 5380.13 Dominican Republic 5270.84 Algeria 4893.68 Paraguay 4682.22 Peru 4583.03 El Salvador 4434.91 Romania 4286.71 Egypt 4184.31 Syria 4093.71

Guatemala 3913.66 Jordan 3892.17 China 3746.98 Jamaica 3691.87 Indonesia 3637.30 Ecuador 3467.21 Philippines 3423.65 Sri Lanka 3299.80 Bolivia 2721.66 Zimbabwe 2484.42 India 2480.26 Honduras 2054.11 Cameroon 2042.03 Pakistan 2006.61 Bangladesh 1684.58 Lesotho 1594.02 Nepal 1460.13 Ghana 1349.37 Kenya 1244.44 Benin 1214.21 Mozambique 1036.92 Mali 969.61 Rwanda 895.54 Zambia 891.85 Niger 875.15 Togo 870.59 Malawi 783.43

Source: Heston, Alan., Summers, Robert and Aten, Bettina.. “Penn World Table”, Version 6.1. Center for International Comparisons at the University of Pennsylvania (CICUP), October (2002).

25

References Barro, R., (2001), “Human Capital: Growth, History, and Policy - A Session to Honor Stanley Engerman. Human Capital and Growth,” American Economic Review 91, 12-17. Barro, Robert J. and Sala-i-Martin, Xavier, (1992), “Convergence,” Journal of Political Economy 100, 223-251. Barro, R., and X. Sala-i-Martin, (2004), “Economic Growth”, 2nd edition. Cambridge, Mass.: The MIT Press. Bernardes, Américo and Albuquerque, Eduardo, (2003), “Cross-over, Thresholds, and Interaction Between Science and Technology: lessons for less-developed countries,” Research Policy 32: 865-885. Casasnovas, G.,Rivera, B., and Currais, L., (2005), “Health and Economic Growth: Findings and Policy Implications” , The MIT Press. Griliches, Zvi, (1990), “Patent Statistics as Economic Indicators: A Survey,” Journal of Economic Literature, Vol. XXVIII, 1661-1707. Deaton, A. (2003), “Health, Inequality, and Economic Development”, Journal of Economic Literature, 41:113-158. Doppelhofer, G., R.. Miller, and X. Sal-i.Martin (2004), “Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach”, American Economic Review 94 (4): 813-835. Heston, Alan., Summers, Robert and Aten, Bettina., (2002), “Penn World Table”, Version 6.1. Center for International Comparisons at the University of Pennsylvania (CICUP), October. Howitt, P. (2005), “Health, Human Capital, and Economic Growth: A Schumpeterian Perspective”, in “Health and Economic Growth: Findings and Policy Implications” chap.1., edited by Casasnovas G.,Rivera B., and Currais, L., Cambridge, Mass.:The MIT Press. Islam, Nazrul, (1995), “Growth Empirics: A panel data approach,” The Quarterly Journal of Economics, 110, 1127-1170. Knowles, S. and Owen, D. (1995), “Health capital and cross-country variation in income per capita in the Mankiw-Romer-Weil model”, Economics Letters, 48, 99-106. Mankiw, G., D. Romer, and D. Weil, (1992), “A Contribution to the Empirics of Economic Growth”, Quarterly Journal of Economics, 107(2): 407-437. Nelson, Richard and Rosemberg, Nathan, (1993), “Technical Innovation and National System”, in Nelson, Richard. (Ed), National Innovation System: A comparative analysis. Oxford University Press, Oxford, 3-21.

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Sala-i-Martin, Xavier, (1994), “Economic Growth: Cross-sectional Regressions and the Empirics of Economic Growth,” European Economic Review. 38, 739-747. Sala-i-Martin, Xavier, (1996), “The Classical Approach to Convergence Analysis,” The Economic Journal, 106, 1019-1036. Sala-i-Martin, X. (2005), “On the Health-Poverty Trap”, in “Health and Economic Growth: Findings and Policy Implications” chap.4., edited by Casasnovas G.,Rivera B., and Currais, L., Cambridge, Mass.:The MIT Press. Solow, Robert, (1956), “A Contribution to the Theory of Economic Growth,” Quarterly Journal of Economic, LXX 65-94. Soukiazis Elias and Tulio Cravo (2007), “What type of human capital better explains the convergence process among countries”, Review of Development Economics (forthcoming). Temple, Jonathan, (1999), “The New Growth Evidence,” Journal of Economic Literature, XXXVII, 112-156.

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List of the Discussion Papers published by CEUNEUROP

Year 2000 Alfredo Marques - Elias Soukiazis (2000). “Per capita income convergence across countries and across regions in the European Union. Some new evidence”. Discussion Paper Nº1, January. Elias Soukiazis (2000). “What have we learnt about convergence in Europe? Some theoretical and empirical considerations”. Discussion Paper Nº2, March. Elias Soukiazis (2000). “ Are living standards converging in the EU? Empirical evidence from time series analysis”. Discussion Paper Nº3, March. Elias Soukiazis (2000). “Productivity convergence in the EU. Evidence from cross-section and time-series analyses”. Discussion Paper Nº4, March. Rogério Leitão (2000). “ A jurisdicionalização da política de defesa do sector têxtil da economia portuguesa no seio da Comunidade Europeia: ambiguidades e contradições”. Discussion Paper Nº5, July. Pedro Cerqueira (2000). “ Assimetria de choques entre Portugal e a União Europeia”. Discussion Paper Nº6, December.

Year 2001 Helena Marques (2001). “A Nova Geografia Económica na Perspectiva de Krugman: Uma Aplicação às Regiões Europeias”. Discussion Paper Nº7, January. Isabel Marques (2001). “Fundamentos Teóricos da Política Industrial Europeia”. Discussion Paper Nº8, March. Sara Rute Sousa (2001). “O Alargamento da União Europeia aos Países da Europa Central e Oriental: Um Desafio para a Política Regional Comunitária”. Discussion Paper Nº9, May.

Year 2002 Elias Soukiazis e Vitor Martinho (2002). “Polarização versus Aglomeração: Fenómenos iguais, Mecanismos diferentes”. Discussion Paper Nº10, February. Alfredo Marques (2002). “Crescimento, Produtividade e Competitividade. Problemas de desempenho da economia Portuguesa” . Discussion Paper Nº 11, April. Elias Soukiazis (2002). “Some perspectives on the new enlargement and the convergence process in Europe”. Discussion Paper Nº 12, September. Vitor Martinho (2002). “ O Processo de Aglomeração nas Regiões Portuguesas”. Discussion Paper, Nº 13, November.

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Year 2003 Elias Soukiazis (2003). “Regional convergence in Portugal”. Discussion Paper, Nº 14, May. Elias Soukiazis and Vítor Castro (2003). “The Impact of the Maastricht Criteria and the Stability Pact on Growth and Unemployment in Europe” Discussion Paper, Nº 15, July. Stuart Holland (2003a). “Financial Instruments and European Recovery – Current Realities and Implications for the New European Constitution”. Discussion Paper, Nº 16, July. Stuart Holland (2003b). “How to Decide on Europe - The Proposal for an Enabling Majority Voting Procedure in the New European Constitution”. Discussion Paper, Nº 17, July. Elias R. Silva (2003). “Análise Estrutural da Indústria Transformadora de Metais não Ferrosos Portuguesa”, Discussion Paper, Nº 18, September. Catarina Cardoso and Elias Soukiazis (2003). “What can Portugal learn from Ireland? An empirical approach searching for the sources of growth”, Discussion Paper, Nº 19, October. Luis Peres Lopes (2003). “Border Effect and Effective Transport Cost”. Discussion Paper, Nº 20, November. Alfredo Marques (2003). “A política industrial face às regras de concorrência na União Europeia: A questão da promoção de sectores específicos” Discussion Paper, Nº 21, December.

Year 2004 Pedro André Cerqueira (2004). “How Pervasive is the World Business Cycle?” Discussion Paper, Nº 22, April. Helena Marques and Hugh Metcalf (2004). “Immigration of skilled workers from the new EU members: Who stands to lose?” Discussion Paper, Nº 23, April.

Elias Soukiazis and Vítor Castro (2004). “How the Maastricht rules affected the convergence process in the European Union. A panel data analysis”. Discussion Paper, Nº 24, May.

Elias Soukiazis and Micaela Antunes (2004). “The evolution of real disparities in Portugal among the Nuts III regions. An empirical analysis based on the convergence approach”. Discussion Paper, Nº 25, June.

29

Catarina Cardoso and Elias Soukiazis (2004). “What can Portugal learn from Ireland and to a less extent from Greece? A comparative analysis searching for the sources of growth”. Discussion Paper, Nº 26, July. Sara Riscado (2004), “Fusões e Aquisições na perspectiva internacional: consequências económicas e implicações para as regras de concorrência”. Documento de trabalho, Nº 27, Outubro.

Year 2005 Micaela Antunes and Elias Soukiazis (2005). “Two speed regional convergence in Portugal and the importance of structural funds on growth”. Discussion Paper, Nº 28, February. Sara Proença and Elias Soukiazis (2005). “Demand for tourism in Portugal. A panel data approach”. Discussion Paper, Nº 29, February. Vitor João Pereira Martinho (2005). “Análise dos Efeitos Espaciais na Produtividade Sectorial entre as Regiões Portuguesas”. Discussion Paper, Nº 30, Abril. Tânia Constâncio(2005). “Efeitos dinâmicos de integração de Portugal na UE” Discussion Paper, Nº 31, Março. Catarina Cardoso and Elias Soukiazis (2005). “Explaining the Uneven Economic Performance of the Cohesion Countries. An Export-led Growth Approach.” Discussion Paper, Nº 32, April. Alfredo Marques e Ana Abrunhosa (2005). “Do Modelo Linear de Inovação à Abordagem Sistémica - Aspectos Teóricos e de Política Económica” Documento de trabalho, Nº 33, Junho. Sara Proença and Elias Soukiazis (2005). “Tourism as an alternative source of regional growth in Portugal”, Discussion Paper, Nº 34, September. Year 2006 Túlio Cravo and Elias Soukiazis (2006). “Human capital as a conditioning factor to the convergence process among the Brazilian States”, Discussion Paper, Nº 35, February. Pedro André Cerqueira (2006). “Consumption Smoothing at Business Cycle Frequency”. Discussion Paper Nº36, May. Elias Soukiazis and Túlio Cravo (2006). “What type of human capital better explains the convergence process among countries”. Discussion Paper, Nº37, May.

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Stuart Holland (2006). “After the European Constitution: Twin Action Proposals” Discussion Paper, Nº38, June.

Elias Soukiazis and Rodrigo Martins (2006). “Elections, Political Spillovers and Economic Performance in the EU”. Discussion Paper, Nº39, July. Pedro André Cerqueira (2006). “International Real Business cycle and R&D”. Discussion Paper, Nº40, November. Year 2007 Vitor Castro(2007). “The impact of the European Union Fiscal Rules on Economic Growth”. Discussion Paper Nº 41, March. Elias Soukiazis and Túlio Cravo (2007). “The Interaction between Health, Human Capital and Economic Growth. Empirical Evidence”. Discussion Paper Nº 42, May.

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