Health is one of the most important assets for human beings, since it allows people to fully use their capacity. Poor or compromised health reduces the well-being of individuals, by affecting their future incomes, wealth and consumption. For policy implicHealth, Human capital, Development

Carlei Vittorio

Colantonio Emiliano

Furia Donatella

Mattoscio Nicola

English

Research Papers in Economics

424 
 
EU INTEGRATION, HEALTH STANDARDS AND ECONOMIC DEVELOPMENT 
 
Mattoscio Nicola  
University of Chieti-Pescara, Faculty of Scienze Manageriali, Viale Pindaro 42, 65127, Pescara 
(Italy), mattoscio@unich.it, 39 085 4537583 
Colantonio Emiliano  
University of Chieti-Pescara, Faculty of Scienze Manageriali, Viale Pindaro 42, 65127, Pescara 
(Italy), colantonio@unich.it, +39 085 4537577 
Carlei Vittorio  
University of Chieti-Pescara, Faculty of Scienze Manageriali, Viale Pindaro 42, 65127, Pescara 
(Italy), carlei@unich.it, +39 085 4537577 
Furia Donatella  
University of Chieti-Pescara, Faculty of Scienze Manageriali, Viale Pindaro 42, 65127, Pescara 
(Italy), dfuria@unich.it, +39 085 4537974 
 
Abstract  
Health is one of the most important assets for human beings, since it allows people to fully use their 
capacity.  Poor  or  compromised  health  reduces  the  well-being  of  individuals,  by  affecting  their  future 
incomes, wealth and consumption. For policy implications, it is notorious how health can affect not just the 
economic outcomes of a person, but of an entire country. Keeping this view in mind, the aim of the paper is 
to analyze clusters and distances among the EU’s Member States in terms of health standards, by using the 
Self-Organizing Map (SOM) artificial neural network methodology. 
  
Key words: Health, Human capital, Development 
 
JEL codes: H1, I1, O1 
 
1. Introduction 
Less developed economies have generally shown to achieve poorer health standards than 
those in advanced countries. The effect of income differences on health performance is 
one the most debated questions in literature. In order to investigate this further, one needs 
to assess the objective of policies aimed at increasing health levels of countries. In this 
respect, a number of programmes are designed to reduce morbility and mortality of the 
population.  In  addition,  a  secondary  goal  of  these  policies  is  to  increase  the  level  of 
economic  development  by  improving  health  standards.  For  example,  the  report  of  the 
WHO’s Commission on Macroeconomics and Health (2001) states: «Improving the health 
and longevity of the poor is an end in itself, a fundamental goal of economic development. 
But  it  is  also  a  means  to  achieving  the  other  development  goals  relating  to  poverty 
reduction. The linkages of health to poverty reduction and to long-term economic growth 
are powerful, much stronger than is generally understood. The burden of disease in some 
low-income regions [...] stands as a stark barrier to economic growth and therefore must 
be addressed frontally and centrally in any comprehensive development strategy». 
Economists have identified several channels by which health may impact on the output of 
an economy. To give an example, a healthier population is generally more productive, 
since  people  in  good  health  may  work  hardly,  longer  and  be  more  concentrated. 
Furthermore, improvement in  health standards  is an  indirect incentive  for investing in 
education. Such an investment will be most likely amortized during a longer working life. 
Finally, healthy students tend to have lower rates of absenteeism and a better cognitive 
performance, thereby succeeding in achieving higher education standards, all else being 
equal. 425 
 
In accordance with the prevailing literature, our hypothesis is that health represents one of 
the main factors of human development. The major objective of this work is to examine 
the existence of clusters and distances among the EU’s Member States  with respect to 
health  services  provided  to  people.  In  this  regard,  a  brief  overview  of  the  relevant 
literature on the relation between health and economic outcomes is provided (see par. 2). 
In the second section, a data analysis is performed using a Self-Organizing Map (SOM) 
neural network methodology, in order to identify multidimensional similarities and gaps 
among the EU’s Member States (see par. 3). Finally, brief concluding remarks are made 
on the main results achieved by the analysis (see par. 4). 
 
2. Health and economic outcomes 
Most studies examining the relation between health and economic outcomes – both at the 
micro and macroeconomic level – are based on two distinct typologies of indicators: input 
and output of national health services.  
Inputs are usually considered to be both economic and non-economic factors affecting an 
individual’s health during his entire life, such as health expenses, physicians and nurses 
density in population, beds availability in hospitals, etc. As far as outputs are concerned, 
scholars usually  consider  a number  of different  features  that  are  related to inputs  and 
personal genetic information (mortality, morbility, life expectancy at birth, etc.).  
A second strand of research has tried to the question whether, and to what extent, health 
disparities have an impact on income differences, by examining input and output data at 
the aggregate level rather than the individual. The first pioneering works on this subject 
(see,  for  example,  World  Bank  1980,  Hicks  1979  and  Wheeler  1980)  drew  some 
preliminary conclusions highlighting the importance of health in growth and development 
dynamics. More recently, Barro (1997), among others,  showed how an increase in life 
expectancy is correlated with economic growth: using data relative to the period after the 
Second  World  War,  he  found  that  an  increase  of  10%  in  life  expectancy  may  raise 
economic  growth  by  an  annual  0,4%.  According  to  Fogel  (1997),  the  growth  of  the 
amount of calories available on average per worker in the past two centuries has played a 
non-neglectable role in rising GDP growth rates in countries like the United Kingdom and 
France. Similar conclusions to the studies of Barro and Fogel were also reached in Bloom 
and  Williamson  (1998),  Gallup  and  Sachs  (2001),  Arora  (2001)  and  Bhargava  et  al. 
(2001).  Though primarily  focused  on econometric estimations, all  the  works reviewed 
above show how health may impact strongly on economic trends.  
 
3. Health standards in the UE: a non-linear clustering through SOM Neural Networks 
In this paragraph, we discuss the results of a data analysis based on a SOM, used to better 
identify clusters and gaps among the EU’s Member States in terms of health standards. A 
SOM is a type of artificial neural network that is trained using unsupervised learning to 
derive  a low-dimensional (typically two-dimensional), discretized  representation of the 
input space of training samples, called  a map. This makes SOM useful for visualizing 
low-dimensional views of high-dimensional data, similarly to multidimensional scaling. 
The model was first described as an artificial neural network by Finnish professor Teuvo 
Kohonen  (1995),  after  whom  was  named  “Kohonen  map”.  Like  most  artificial  neural 
networks, SOMs operate in two modes: training and mapping. Training builds the map by 
using input examples. It is a competitive process, also called vector quantization. Mapping 
automatically  classifies  a  new  input  vector.  This  spatial  organizing  process,  used  for 
important statistic features of input data, is also known as feature mapping. SOM creates 
feature mappings by means of an unsupervised learning technique. 426 
 
For the aim of this paper, we considered a set of 21 variables for each of the 27 EU’s 
Member  States  (see  tab.  1).  The  observations  refer  to  the  most  recent  year  available 
(mainly 2006). At any rate, the variables show a certain degree of stability in the last five 
years.  
The positions found by the Kohonen map for the countries considered, with respect to the 
agglomerations produced by the SOM Neural Network, show two main results. 
In connection with the variables used in this study, some well-defined groups are formed 
(see Fig. 1):  
- Group 1, comprising Eastern European countries. In particular, Bulgaria, Estonia, Latvia, 
Poland and Romania belong to the same codebook, while Lithuania, Cyprus, Slovakia, 
Hungary and Czech Republic constitute a second subgroup. Slovenia, instead, is an outlier 
with respect to the other countries, and locates close to Mediterranean countries; 
- Group 2, including some Mediterranean countries, such as Portugal, Malta, Spain and 
Greece. Italy represents an outlier and appears to be as an outpost between Mediterranean 
countries and Central and Northern European countries; 
- Group 3, which comprises the main countries of Central Europe and is located in the top-
right part of the Kohonen map; 
-  Group  4,  including  countries  of  continental  and  non-continental  Europe  as  well  as 
Scandinavia. This group is placed in the top-left part of the Kohonen map. 
The location of countries along the directrix which goes from the top to the lower part of 
the map represents a clear correlation with the overall quality of national health services in 
the  countries  considered.  However,  it  must  be  mentioned  that,  even  within  the  well-
performing  countries located in the  top part  of the  map, it  is possible  to identify two 
distinct profiles that split the group of continental Europe in two separate branches.  
 
Fig. 1 – Kohonen Map 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A close examination of the feature mapping highlights a second important aspect: the 
differences among countries, as evidenced by the identification of groups done before, is 
based on only a few variables of the whole set used in the analysis. The main distinction 
may be mainly summarized in terms of current expenses, rather than the provision of basic 
health care (see Fig. 2, where warm colours are associated with high values).  427 
 
 
 
Fig. 2 –Expenditures on health 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The  stock  variables,  such  as  beds  availability  in  hospitals  or  physicians  and 
pharmaceutical  personnel  density  in  population  (which  represent  a  good  proxy  for 
measuring  the  provision  of  basic  health  care),  do  not  allow  to  distinguish  between 
Western European countries and Eastern European countries (see Fig. 3).  
 
Fig. 3 – Health endowment 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
However,  there  are  a  few  output  variables,  primarily  associated  with  morbility  and 
mortality, that exhibit relatively high values with regard to countries of Eastern Europe 
(see Fig. 4).  
 428 
 
 
Fig. 4 – Mobility and Mortality 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Hence,  in  this  case,  the  observed  differences  in  output  terms  may  be  not  due  to  the 
provision of basic health care, but rather to investments in health (these investments may 
actually affect the quality of the provision of basic health care and its effectiveness). 
 
Concluding remarks 
Health capital has a significant effect on economic development. For policy implications, 
it is notorious how health can affect not just the economic outcomes of a person, but of an 
entire nation. It is important to include investment in health as a macroeconomic policy 
tool, due to the fact that differences in economic development between countries have 
been significantly explained by health disparities. This shows that investments in health 
improve economic development and are among the few feasible options to be used for the 
elimination of poverty traps. 
This paper was aimed at highlighting the distances among the EU’s Member States, by 
focusing the attention on their health standards. By using SOM artificial neural networks, 
we  showed  that  recently  acceded  EU  countries  should  reduce  the  gaps  from  more 
advanced  countries,  especially  in  terms  of  health  investments.  Further  investigation  is 
required to better explore the dynamics of the above mentioned phenomena. 
 
Bibliography 
1. Arora S. (2001), Health, Human Productivity, and Long-Term Economic Growth, Journal of 
Economic History, 61, 699-749. 
2. Barro R. (1997), Determinants of economic growth. A cross country empirical study, MIT 
Press, Cambridge, MA. 
3. Bhargava A., Jamison D. T., Lau L. J. and Murray C. J. L. (2001), Modeling the effects of 
health on economic growth, Journal of Health Economics, 20, 423-440. 
4. Bloom D. E. and Williamson J. G. (1998), Demographic transitions and economic miracles in 
emerging Asia, The World Bank Economic Review, 12, 419-455. 
5.  Fogel  R.  W.  (1997),  New  findings  on  secular  trends  in  nutrition  and  mortality:  some 
implications  for  population  theory,  in  Rosenzweig  M.  R.  and  Stark  O.  (Eds.),  Handbook  of 
population and family economics, Elsevier, Amsterdam, pp. 433-481. 
6. Gallup J. L. and Sachs J. D. (2001), The economic burden of malaria, American Journal of 
Tropical Medicine and Hygiene, 64, 85-96. 429 
 
7. Hicks N. L. (1979), Growth vs. basic needs: Is there a trade-off?, World Development, 7, 985- 
994. 
8. Kohonen T. (1995), Self-Organizing Maps, Springer Verlag, Berlin, Heidelberg. 
9. Livi Bacci M. (1989), Popolazione e alimentazione: saggio sulla storia demografica europea, Il 
Mulino, Bologna. 
10. Mitchell B. R. (1975), European Historical Statistics, 1750-1970, Columbia University Press, 
New York. 
11. Wheeler D. (1980), Basic needs fulfillment and economic growth, Journal of Development 
Economics, 7, 435-451. 
12. World Bank (1980), Poverty and Human Development: World Development Report 1980, 
The World Bank, Washington D.C. 
13.  World  Health  Organization  (2001),  Macroeconomics  and  health:  Investing  in  health  for 
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   430 
 
 
 
Tab. 1 – Data base 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   

EU INTEGRATION, HEALTH STANDARDS AND ECONOMIC DEVELOPMENT

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