Does Health cause Schooling or Does
Schooling cause Health?
Tiago Neves Sequeira
Abril, 2007
Texto para Discussão – Nº E – 01/2007
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Does Health cause Schooling or Does Schooling cause Health?∗
Tiago Neves Sequeira†
Abstract
Using a panel data approach we investigate whether schooling cause health or health
cause schooling. We found evidence that supports the influence of the level of health in
increases in education and the influence of education growth in health improvements. Both
effects are present in poor countries but not in rich ones.
Keywords: Education, Health.
JEL Classification: I00, J24, O15, O50.
∗
I thank Ana Balcão Reis for useful comments and suggestions. I gratefully acknowledge the excellent research
assistance of Margarida Rodrigues. I also acknowledge financial support from the FCT, under the Project
POCTI/EGE/60845/2004. The usual disclaimer applies.
†
Departamento de Gestão e Economia, Universidade da Beira Interior and INOVA research centre, Faculdade
de Economia, Universidade Nova de Lisboa and (Address: Universidade da Beira Interior, Departamento de
Gestão e Economia, Estrada do Sineiro, 6200-209 Covilhã, Portugal, [email protected]).
1
1
Introduction
There are three main reasons to be concerned with causality between education and health.
First, both health and education may be simply outcomes of a separate cause such as time
preference which cause a correlation but not causality. Second, education may affect health
by improving the productivity of health services or by improve basic sanitation care. Third,
poor health early in life limits educational attainment while poor health on the job may limit
training and wage growth (for a survey on these reasons and on theories linking education to
health, see Hunt-McCool and Bishop, 1998). Macroeconomic theory has focused human capital
accumulation as dependent on health and demographic features. However, the direction of
causality is not yet clear. Zhang et al. (2001, 2003) and Kalemli-Ozcan et al. (2000) assumed
that a decrease in the mortality rate and the simultaneous decrease in fertility tend to increase
parental investment in each child. Bloom, Canning and Sevilla (2001) and Cropper (2000)
argue that mortality rate is correlated with the health status of the population and thus a
decrease in mortality induces increases in the human capital quality and thus accumulation of
human capital becomes more productive. Meltzer (1992) and Preston (1980) estimates that
high mortality rate reduces expected value of future returns from education. These references
pointed out a causality from health to education. Nevertheless, one can also think that education
improves the health status of the population. Both evidence and theories on the inverse causality
are more recent and rare. For instance, Arendt (2005) uses a panel data analysis using school
reforms in Denmark for identification but the analysis on the effect of education in health
“remain inconclusive”. Tamura (2006) found positive effects of education in adult survival
and also positive influence of health in education, between 1850 and 1990 for a sample of 92
countries. We add to these literature the evaluation of causality between health and education,
controlling for income and overall education in a broad cross-section of near a hundred countries
and adressing the differences between poor and rich countries. To this end, we use dynamic
panel data estimators, that control for different types of endogeneity, namely simultaneity bias.
Our results tend to confirm the causality from health to education growth and the causality
2
between education growth and health growth. These results are driven essentially by what is
happening in poor countries.
2
Data on Education and Health
We distinguish two different indicators of Education: the first, that measures quantity of schooling (years) in total population above 15 years old (tyr) and the second, that weight this quantity
with a quality measure from Hanushek (2001) (tyr ∗ QL).1 As a proxy for health, we use life
expectancy (in natural logarithms), because it is the widely used and the mostly available proxy
for health. To control for family background when explainning schooling, we introduced adult
education, measured by total years of primary education in total population above 25 years
old and income, measured by real per capita GDP using the chain index, from the Penn World
Tables. Years of Education come from the Barro-Lee (2000) database and Life Expectancy
come from World Development Indicators.
2.1
Descriptive Statistics and Correlations
Table 1 shows descriptive statistics on the variables used in this article.
Table 1 - Descriptive Statistics
N
Average
S.D.
Min.
Max.
Dependent Variables
tyr
937
4.803
2.846
0.086
12.049
tyr ∗ QL
734
0.254
0.155
0.006
0.615
Society Background
Log(GDP )
1000
8.146
1.054
5.717983
10.537120
Adult Education1
930
3.134
1.817
0.023
7.667
Health Variable
Log(Life Expectancy) 1066 4.059169841 0.220213167 3.459926377 4.385104211
1 Average Years of Primary Education in Population above 25 years
Next table shows correlations between the dependent variables and the used covariates in
the panel database. From these figures, we conclude that there is a positive strong association
1
We use the first quality measure (QL1 ) presented in Hanushek (2001).
3
between income, adult education, life expectancy and schooling growth. However we cannot
infer nothing about causality.
Table 2 - Correlations
u :
Hj,i,t
tyr
tyr ∗ QL
Society Background
Log(GDP )
0.85***
0.81***
1
Adult Education
0.94***
0.88***
Health Variable
Log(Life Expectancy) 0.85***
0.76***
1 Average Years of Primary Education in Population above 25 years
*** stands for a 1% significance level; ** for 5% and * for 10%
3
Panel Procedures
3.1
GMM Estimators for Dynamic Panel Models
In this article, we use the Dynamic Panel Data system estimator developed by Arellano and
Bover (1995) and Blundell and Bond (1998). Compared with the single cross-section analysis,
the panel analysis increases the regressions’ degrees of freedom due to the increased number
of observations and, as a GMM method, it controlls for country specific effects and for other
possible sources of endogeneity, such as omitted variable bias, measurement errors and causality.
This is quite important in this context, as Hanushek et al. (1996) argued that aggregation
implies a significant upward ommited variables bias, namely linked with different institutions
and policies throughout countries. Given the properties of this estimator, we believe we are
correcting for the problem, since it controls for both fixed and variable ommited variables. The
use of this estimator is also extremely important due to our enphasis in causality.
We estimated the following equations:
u
u
e
Hj,i,t
= α0 + α1 Hj,i,t−1
+ α2 GDPi,t + α3 Adult Edi,t + α4 Hi,t
+ vi + εi,t
(1)
e
e
e
u
Hi,t
= β 0 + β 1 Hi,t−1
+ β 2 ∆Hi,t−1
+ β 3 GDPi,t + β 4 Hj,i,t−1
+ ui + ξ i,t
(2)
4
with i = 1, ..., N being the number of countries, j = 1, 2 the indicator used for schooling
dependent variables (H u ), t = 60, 65.., 2000, vi and ui are the country specific effects and H e
the natural logarithm of life expectancy. For each t, GDP per capita and life expectancy are
e are the average
measured in the preceding five-year period (e.g. for 1960, GDPi,t and Hi,t
between 1955 to 1959). Adult Education (Adult Ed) is measured contemporaneously with the
dependent variable. The third term in equation (2) is inserted so that the moment conditions
were fullfilled.
Under the assumptions that (a) the error terms (εi,t and ξ i,t ) are not serially correlated
and (b) the explanatory variables are weakly exogenous (i.e., the explanatory variables are
assumed to be uncorrelated with future realizations of the error term), the GMM dynamic
u
1
panel uses the following moment conditions: E[Hi,t−1−s
∆εi,t ] = 0, E[Xi,t−s
∆εi,t ] = 0 and
e
2
E[Hi,t−s
∆ξ i,t ] = 0, E[Xi,t−s
∆ξ i,t ] = 0, for s ≥ 2; t = 3, ..., T ; i = 1, ..., N, where X 1 is the
complete matrix of covariates in equation 1, which includes Adult Edi , Hie and GDPi (in logs)
and X 2 is the complete matrix of covariates in equation 2, which includes Hiu and GDPi (in
logs). Because we use the system GMM estimator, there are the following additional moment
u
1
restrictions for the levels equation: E[∆Hi,t−1
(vi + εi,t )] = 0, E[∆Xi,t−1
(vi + εi,t )] = 0 and
e
2
E[∆Hi,t−1
(ui + ξ i,t )] = 0, E[∆Xi,t−1
(ui + ξ i,t )] = 0, for t = 3, ..., T. It is worth noting that these
conditions allow for the levels of explanatory variables to be correlated with the unobserved
country-specific effects. Thus, we use these moment conditions and employ a GMM procedure
to generate consistent and efficient parameter estimates. This system estimator is preferable to
the difference estimator if the dependent variable is highly persistent, as is the case for education
stocks and if the number of time-series observations are small, as is also the case. The third
term in equation (2) is inserted so that the moment conditions were fullfilled.
Consistency of the GMM estimator depends on the validity of the instruments. To address
this issue, we consider two specification tests: the first is the Hansen test of over-identifying
restrictions, which tests the overall validity of the instruments; the second is the second-order
autocorrelation test for the error term. Overall, both specification tests indicate that the instruments used are valid. When the comparison between the number of observations and the
5
number of instruments indicate an overfitting bias in the empirical model (this is, an excess of
instruments), we decrease the number of instruments. In all regressions we introduce a constant
and time-period dummies that are not presented in tables.
As we use dynamic estimators, i.e. that accounts for previous levels of the dependent
variable, this is the same as considering explanatory variables as influencing the growth of the
dependent variable. Thus, when analysing the effect of life expectancy in education (eq. (1)),
in fact we are interpreting the effect of life expectancy on education in period t given the level
of education in t − 1. This is the same as interpreting it as the effect of life expectancy on
education rise.
4
Does Health influence Education?
In this section, we investigate the causality from health to education. This is the direction for
which there is more literature, which is mainly theorectical. To control for family background
effects, which have been pointed out as the main determinant of education (Hanushek, 1986,
2003), we introduce per capita GDP and adult primary education in population above 25 years.
However, as adult education seems to have low effects in total sample, we ommit this variable
in the two last columns in the Table.
Table 3 - Does Health influence Education?
Dependent Variable
tyr
tyr ∗ QL1
tyr
tyr ∗ QL1
Lagged Dep. Variable
0.778***
0.977***
0.896***
0.973***
(p-value)
0.000
0.000
0.000
0.000
Log(GDP )
0.324***
0.003
0.129
-0.001
(p-value)
0.003
0.459
0.153
0.649
Adult Ed
0.129
-0.003
–
–
(p-value)
0.164
0.433
–
–
Log(Lif eExpectancy)
0.218
0.046**
0.815**
0.054***
(p-value)
0.574
0.030
0.013
0.003
Number of Instrum.
82
82
97
75
Hansen J
0.224
0.481
0.356
0.317
(p-value)
AR(1) (p-value)
0.000
0.002
0.000
0.000
AR(2) (p-value)
0.520
0.516
0.564
0.505
Number of Countries
98
80
100
80
Number of Obs.
726
597
738
597
Notes: (1) p-values based on robust variance-covariance matrix in parenthises;
(2) *** stands for a 1% significance level; ** for 5% and * for 10%
6
The analysis of the table points out that only when the dependent variable weights quantity
of schooling with its quality, does the Life Expectancy positively influences education. Income
only influences quantity (tyr), when controlling for education and life expectancy. If we exclude
life expectancy, only income would influence positively education for the two dependent variables. Additionally, when we ommit the less significant Adult Education variable, we obtain a
significant effect of life expectancy in both education variables, as can be seen in the last two
columns in the table.
If we detail the results and divide the sample into poor and rich samples, we easily conclude
that the result showed above arise only in the poor sample. Thus, we consider rich countries
those that are above the median for the majority of periods considered (considering all the
period, the median is 3347 USD). Otherwise, we consider the country as poor. Results are
presented in the following table.
Table 4 - Does Health influence Education in Rich and Poor Countries?
Rich Countries
Poor Countries
Dependent Variable
tyr
tyr ∗ QL1 tyr ∗ QL1
tyr
tyr ∗ QL1 tyr ∗ QL1
Lagged Dep. Variable
0.617***
0.934***
0.971***
0.761***
1.003***
1.001***
(p-value)
0.000
0.000
0.000
0.000
0.000
0.000
Log(GDP )
0.468**
0.010
-0.000
0.125*
0.006**
0.003
(p-value)
0.014
0.372
0.963
0.050
0.024
0.372
Adult Ed
0.292*
-0.001
–
0.245***
-0.003
–
(p-value)
0.057
0.905
–
0.003
0.325
–
Log(Lif eExpectancy)
0.851
0.028
0.042
0.666***
0.031*
0.032***
(p-value)
0.381
0.435
0.240
0.004
0.053
0.003
Number of Instrum.
60
60
62
60
60
46
Hansen J
0.651
0.684
0.739
0.956
1.000
0.999
(p-value)
AR(1) (p-value)
0.000
0.000
0.000
0.000
0.003
0.003
AR(2) (p-value)
0.378
0.463
0.464
0.379
0.557
0.570
Number of Countries
56
55
55
42
25
25
Number of Obs.
420
412
412
306
184
184
Notes: (1) p-values based on robust variance-covariance matrix in parenthises;
(2) *** stands for a 1% significance level; ** for 5% and * for 10%
From the Table analysis, the main conclusion is that the positive effect of Life Expectancy on
schooling growth occurs essentially in poor countries. In fact, from the variables considered, Life
Expectancy is the most important in explaining schooling in these countries. Other interesting
fact is that in rich countries, education (weighted by quality) is only explained by its lagged
value, which means that none of the explanatory variables in the regression is statistically
7
significant in explaining differences in schooling between these countries and throughout time.
When accounting for quality, in the sample of poor countries there is evidence of divergence
in education as the coefficient on the lagged dependent variable is above unity. Income has a
positive influence years of schooling in both groups but also the education measure that accounts
for quality in poor countries, when taking adult education into account. Adult education has
a positive effect only on years of schooling, effect that disapears when accounting for quality of
schooling. If the effect of Life Expectancy was excluded results on other determinants do not
change significantly from what is presented in the Table.
5
Does Education cause Health?
Only two of the references that studied the relationship between education and health had
focused the direction of causality from education to health. Whether one of them (Arendt,
2005) found inconclusive results, Tamura (2006) reported a positive influence. In this section,
we add to these literature new results with other techniques and data.
Table 5 - Does Education influence Health?
Dependent Variable
Log(Lif eExpectancy)
u :
Measure of Hj,i,t
tyr
tyr ∗ QL1
tyr
tyr ∗ QL1
(p-value)
0.972***
0.000
0.515***
0.005
0.019**
0.001
-0.006*
0.055
0.957***
0.000
0.919***
0.000
0.009**
0.035
0.030
0.420
∆Schooling
Schooling
–
–
0.960***
0.000
0.648***
0.000
0.011***
0.007
–
–
0.035***
0.006
87
1.014***
0.000
0.878***
0.000
0.005
0.226
–
–
0.035**
0.014
77
Lagged Dep. Variable
(p-value)
Lag. Dif. Dep Variable
(p-value)
Log(GDP )
(p-value)
Schoolingt−1
(p-value)
–
–
Number of Instrum.
88
77
Hansen J
0.113
0.223
0.115
0.284
(p-value)
AR(1) (p-value)
0.086
0.049
0.086
0.017
AR(2) (p-value)
0.554
0.985
0.737
0.952
Number of Countries
103
80
100
80
Number of Obs.
570
455
567
455
Notes: (1) p-values based on robust variance-covariance matrix in parenthises;
(2) *** stands for a 1% significance level; ** for 5% and * for 10%
8
Table 5 shows evidence according to which the education quantity decreases health and
education that takes quality into account has no influence in health. However, schooling growth
has a positive and significant effect on health improvements. This is a crucial aspect of causality:
while health affects schooling growth in levels it is affected by schooling in growth and not in
levels. When education growth is taken in account, GDP becomes a non-significant explanatory
variable for life expectancy. Elliminating this variable from regression would not influence the
robust positive influence of schooling growth on health improvements. When we divide the
sample into one of poor and another of rich countries, we reach similar results to those presented
in the previous table for the Poor sample and no significance of education or its growth in the
Rich sample. We report in the following table results on the regressions of health on education
growth, from which we can conclude that the results presented above are solely driven by the
poor countries.
Table 5 - Does Education influence Health in Rich and Poor Countries?
Dependent Variable
Log(Lif eExpectancy)
Rich Countries
Poor Countries
u :
Measure of Hj,i,t
tyr
tyr ∗ QL1
tyr
tyr ∗ QL1
Lagged Dep. Variable 0.942***
0.942***
1.078***
1.110***
(p-value)
0.000
0.000
0.000
0.000
Lag. Dif. Dep Variable 0.330***
0.338***
0.399
1.103
(p-value)
0.003
0.001
0.256
0.000
Log(GDP )
0.001
0.001
-0.025
-0.015
(p-value)
0.901
0.863
0.912
0.943
∆Schooling
0.024
0.024
0.014
0.036**
Schooling
(p-value)
0.360
0.343
0.234
0.043
Number of Instrum.
56
56
35
35
Hansen J
0.382
0.391
0.341
0.993
(p-value)
AR(1) (p-value)
0.001
0.001
0.077
0.047
AR(2) (p-value)
0.163
0.160
0.603
0.102
Number of Countries
56
55
44
25
Number of Obs.
320
314
247
140
Notes: (1) p-values based on robust variance-covariance matrix in parenthises;
(2) *** stands for a 1% significance level; ** for 5% and * for 10%
6
Conclusions
This article adds evidence based on a panel data approach to the discussion of causality between
education and health. We found evidence that supports the influence of the level of health in
9
increases in education and the influence of education growth on health improvements. Both
effects are present in poor countries but not in rich ones.
References
[1] Arellano, M. and S. Bond (1991), “Some Tests of Specification for Panel Data: Monte Carlo
evidence and an Application to Employment Equations”, Review of Economic Studies, 58:
277-297.
[2] Arellano, M. and O. Bover (1995), “Another Look at the Instrumental Variables Estimation
of Error-Components Model”, Journal of Econometrics, 68: 29-52.
[3] Arendt, J. (2005), “Does Education cause Better Health? A panel data analysis using
shool reforms for identification”, Economics of Education Review, 24 (2), 149-160.
[4] Barro, R. and J. Lee (2000), International Data on Educational Attainment: Updates
and Implications, Working-Paper 42, Centre for International Development at Haward
University, April.
[5] Bloom, D., D. Canning and J. Sevilla (2001), The Effect of Health on Economic Growth:
Theory and Evidence, NBER working Paper 8587.
[6] Blundell, R. and S. Bond (1998), “Initial Conditions and Moment restrictions in Dynamic
Panel Data Models”, Journal of Econometrics, 87:115-143
[7] Hanushek, E. (1986), “The Economics of Schooling: Production and Efficiency in Public
Schools”, Journal of Economic Literature, 24, September: 1141-1177.
[8] Hanushek, E., S. Rivkin and L. Taylor (1996), “Aggregation and the Estimated Effects of
School Resources”, Review of Economics and Statistics, 78, 4: 611-627.
[9] Hanushek, E. (2003), “The failure of Input-Based Schooling Policies”, The Economic Journal, 113:64-98.
10
[10] Hanushek, E. and D. Kimko (2001), “Schooling, Labor Force Quality and Economic
Growth”, American Economic Review, 90, 1(5): 1184-1208.
[11] Hunt-McCool, J. and D. Bishop (1998), “Health Economics and the Economics of Education: Specialization and Division of Labor”, Economics of Education Review, 17 (3),
237-244.
[12] Kalemli-Ozcan, S., H. Ryder and D. Weil (2000), “Mortality Decline, Human Capital
Investment and Economic Growth”, Journal of Development Economics, 62 (1), 1-23.
[13] Psacharopoulos, G. (1994), “Returns to Investment in Education: A Global Update”,
World Development, vol. 22(9), 1325-1343.
[14] Summers, R. and A. Heston (2002), Penn World Table, Center for International Comparisons, University of Pennsylvania.
[15] Tamura, R. (2006), “Human Capital and Economic Development”, Journal of Development
Economics, v.79(1), 26-72.
[16] Zhang, J. et al. (2003), “Rising Longevity, education, savings and growth”, Journal of
Development Economics, v.70, 83-101.
[17] Zhang, J. et al. (2001), “Mortality Decline and Long-Run Economic Growth”, Journal of
Public Economics, v.80(3), 485-507.
[18] World Bank (2004), World Development Indicators 2004, CD Database.
11
A
Variable Description
tyr
tyr ∗ QL1
Log(GDP )
Adult Education
Life Expectancy
Measure
Source
Dependent Variables
total years of educatiom
Barro-Lee (2000)
The last weighted by a measure of quality Barro-Lee (2000) and Hanushek (2001)
Society Background
Real Chain Index GDP per capita
PWT
Primary Years of Education above 251
Barro-Lee (2000)
Health Variable
Life Expectancy at birth2
WDI
WDI - World Development Indicators Database, World Bank (2004); PWT - Penn World
Table, Summers and Heston (2002)
Detailed Definitions:
1. PYR : Average years of primary schooling in the total population above 25 years old.
2. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing
patterns of mortality at the time of its birth were to stay the same throughout its life.
12
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actuación pública mediante cuestiones de respuesta cerrada y abierta:
Teoría y práctica
- Ramón Álvarez Esteban, José Luis Burguete e Pablo Gutiérrez Rodríguez
Nº M-03/2004
Supply chain relationships in local government in the United Kingdom: Na
exploratory study
- Terry Robinson e Lesley Jackson
Nº M-02/2004
Marketing territorial: Um instrumento necessário para a competitividade
das regiões rurais e periféricas
- Anabela Dinis
Nº M-01/2004
A natureza do Marketing do ensino superior público português: Análise
exploratória
- Luísa Lopes e Mário Lino Raposo
2003
Nº E-05/2003
A Avaliação do Capital de Risco segundo a Teoria das Opções
- Paulo Peneda Saraiva
Nº E-04/2003
Microeconomia simplificada para iniciantes dos curso de gestão, economia
e marketing
- Carlos Osório
Nº E-03/2003
O canal do crédito, o sobreendividamento e as crises económicas
- José Alberto Fuinhas
Nº E-02/2003
Housing Market in Portugal revisited: a spatial analysis for 275 counties
- Pedro Guedes Carvalho
Nº E-01/2003
Giz e tecnologias de informação e comunicação:uma avaliação de um ano
de Microeconomia (I & II) para três licenciaturas
- Carlos Osório
2002
Nº 11/2002
Competitive Balance in the Portuguese premier league of professional
soccer
- António Marques
Nº 10/2002
Risco de Perda Adicional, Teoria dos Valores Extremos e Gestão do Risco:
Aplicação ao Mercado Financeiro Português
- João Monteiro
- Pedro Marques Silva
Nº 08/2002
Alguns considerandos sobre o canal do crédito
- José Alberto Fuinhas
Nº 07/2002
Externalities of the Microsoft’s Network Goods
- João Leitão
- Carlos Osório
Nº 06/2002
A admissibilidade teórica do canal do balanço
- José Alberto Fuinhas
Nº 05/2002
A admissibilidade teórica do canal do crédito bancário
- José Alberto Fuinhas
Nº 04/2002
O canal do crédito e a política monetária
- José Alberto Fuinhas
Nº 03/2002
Parcerias estratégicas da banca portuguesa em portais digitais
- João Leitão, Carlos Osório e Daniela Gomes
(Publicado na Revista Portuguesa e Brasileira de Gestão, Volume 1, N.º 3, Outubro/Dezembro
de 2002, INDEG/ISCTE e Fundação Getulio Vargas, Lisboa)
Nº 02/2002
Os canais de transmissão da política monetária
- José Alberto Fuinhas
Nº 01/2002
O canal do crédito bancário na economia portuguesa: análise econométrica do
período de 1977 a 1998
- José Alberto Fuinhas
2001
Nº 12/2001
Housing and Labor Markets Connections: recent developments in the
portuguese economy
- Pedro Guedes Carvalho
Nº 11/2001
Desenvolvimento Rural na Sociedade do Conhecimento
- Pedro Guedes Carvalho, João Leitão e Andrea R. Silva
(Publicado na Revista Portuguesa e Brasileira de Gestão, Volume 1, N.º 2, Julho/Setembro de
2002, INDEG/ISCTE e Fundação Getulio Vargas, Lisboa )
Nº 10/2001
A gestão do portafólio de títulos e a eficácia do canal do crédito bancário
- José Alberto Fuinhas e José R. Pires Manso
(Publicado na Revista de Gestão e Economia, nº2, DGE/UBI, Janeiro 2002)
Nº 09/2001
Digital Challenges for the Small and Medium Enterprises of the Textile and
Clothing Industry in Portugal
- Carlos Osório e João Leitão
(Publicado na Revista de Gestão e Economia, nº2, DGE/UBI, Janeiro 2002)
Nº 08/2001
Aluguer e Venda de Bens Duráveis em Caso de Monopólio
- Carlos Osório e Paulo Maçãs
Nº 07/2001
A Dinâmica dos Termos de Troca e da Balança Comercial: Curva S na
Europa?
- Alda Rito, Alexandra Ferreira e Tiago Sequeira
(Publicado na revista Prospectiva e Planeamento, Departamento de Prospectiva e
Planeamento, Ministério do Planeamento, vol.7, 2001, pp. 187-230)
Nº 06/2001
A Determinação do Preço dos Bens Duráveis em Duopólio
- Carlos Osório, Paulo Maçãs e João Leitão
Nº 05/2001
Crescimento Económico no Pós-guerra: os Casos de Espanha, Portugal e
Irlanda
- Tiago Sequeira
Nº 04/2001
O Comércio Ibérico e o Comércio Portugal-UE: que diferenças?
- Ricardo Pinheiro Alves
Nº 03/2001
Universidade e Protecção da Propriedade de Activos Intelectuais:
Fundamentos Económicos e Aspectos Críticos
- Alcino Couto
Nº 02/2001
Marketing Interno: Uma Abordagem Teórica
- Mário Franco, Luís Mendes e Anabela Almeida
Nº 01/2001
O Efeito da Publicidade Experimentável na Fixação do Preço dos Bens
Duráveis
- Carlos Osório, Paulo Maçãs e João Leitão