Socio-Economic conditions and individual health in Italy
Abstract
The last economic crisis has produced a fall of the population living conditions and negative consequences on the population’s health mainly in Southern Europe countries. This has led to a renewed interest in the study of the relationship between individual health and socio-economic status. In the past, many scholars have shown the existence of positive relationship between these two variables. Therefore, following the literature, the aim of the paper is to investigate the socio-economic determinants of individual health for Italian population taking into account different aspects of individual life. Using data drawn from Self-Reported Health Status for the year 2013, and applying the Instrumental variables method (IV), the paper shows two important results: 1) The perceived individual health is strongly correlated to income, economic status and life satisfaction, while the impact of aging is very low 2). Education level is negatively correlated to individual health. This second result can be explained if we consider individual labor condition. In particular we noted that the difficulty to access to the labor market determines that individual’s expectation about job, wage, quality of work are inconsistent with the “reality”. The consequence is that, individuals better educated have higher expectations regarding their lives, but often, these expectations do not find a right correspondence in the Italian labour market, and this situation produces negative psychological effects, which contribute to reduce the general individual health.
Keywords: Health, Education, Well-Being, Single Equation Models JEL Classification: I140, I240, I310, C210
Introduction
In the last forty years, with the birth of the “capability” approach, the study of the
relationship between health and individual well-being has become crucial. The capability
approach offers an alternative view of human development (Nussbaum and Sen 1993), in
which individual well-being does not depend only on economic growth. In fact, according to
Sen’s theory, individual well-being depends on the” functioning”, namely a “combination of
things that a person is able to do or be” (Nussbaum and Sen 1993, 30). In this view,
individual “health” – beside income and education – represents a fundamental issue in
enabling the extension of the “capabilities space”. The importance of these three dimensions
of life (income, education and health) are summarised by Human Development Index (HDI),
through which, despite the many criticisms, it is possible to measure the level of human
development in different countries. According to these premises, good individual health –
intended not only as “physical health” – is both the first step in order to live a fulfilling life
and an indispensable feature in increasing the level of countries’ human development.
However, it is crucial to have a clear understanding of what we mean when we use the word
“health”. It is not always easy to give a unique definition of health (Venkatapuram 2013),
since the term includes different dimensions. On one hand, we have papers addressing the
problem of health taking into account the physical dimension ( life expectancy for example)
while, on the other hand, others papers focus on the psychological dimension (for example
taking into account people suffering of depression or anxiety). In our view, it is worth
considering that “health” is not just a matter of being alive, but is also a matter of how
individuals feel both physically and psychologically. The definition of “health” we use in this
paper includes three dimensions – physical, psychological and relational – that together
Therefore, through the study of health distribution, we are able to both get information
about the health condition of the population of a society or country as well as analyse what
factors can affect individual health. This is very important as people show differences in
health, and these differences should be studied in order to produce political solutions and
reduce the health gap among individuals or social groups.
However, in literature, it is very common to think that differences in health can
depend both on by material well-being of individuals (Lynch et al. 2004; Deaton 2013;
Cooper et al. 2015; Bailey et al. 2015) and on non-monetary and/or psychological aspects
(Wilkinson 1992). For example, to be rich is not a sufficient condition to be happy and live a
life in good health. Indeed, it is not rare in advanced countries to see people with a high living
standard being dissatisfied with their lives. Psychological factors, such as to have a high life
satisfaction and/or be able to achieve individual objectives, are very important in determining
the level of a population’s health. Furthermore, the lack of opportunities or the inequality of
opportunity in the Senian sense – that is the existence of barriers prohibiting access to
education, the labour market and political participation – could compromise the health of
individuals belonging to the most disadvantaged social groups.
Hence, the purpose of this paper is to analyse the socio-economic determinants of
self-reported health regarding the Italian population considering that, if on one side it is
undeniable that the differences in health status among individuals or social groups can
depend on intrinsic characteristics, on the other side, we must keep in mind that these
differences could be also correlated to economic, social and psychological inequalities, which
could be reduced though public policies. Moreover - unlike previous works in the same field
analysing this relationship (Pirani and Salvini 2012) - in this paper we focused not only on
the usually economic and social variables used in previous work in this field (as income and
variables (life satisfaction) can interact positively (or negatively) with the self-perceived
individual health. Individual occupation and life satisfaction are - in our opinion - two
variables that affect the psychological wellbeing of individual, and hence, they represent a
fundamental aspect of general individual health status. Investigates how the individual labor
status can affect the general health is very important and in the past has been noted that
people in precarious working condition or in unemployment state tend to show a lower level
of health.
The paper is organised as follows: in the second section we review literature about the
relationship between socio-economic determinants and health. In the third section we analyse
data about the health distribution in the Italian population. In the fourth section we show our
empirical strategy and discuss the results. In the fifth section we discuss the main results.
Finally, in the sixth section some conclusions are drawn.
1. Review of Literature
In recent years many scholars have tried to study which factors determine health,
focusing mainly on the social and economic conditions of individuals or groups (Rose 1992;
Feinstein 1993; Smith 1999; Masseria 2009; Bailey et al. 2015; Cooper et al. 2015; Fiorillo
and Nappo 2016). The main idea is that, for people that are at the top of the hypothetical
social ladder, the level of health should be higher than those who are at the bottom of it. This
means that, people with a low socio-economic status characterised, for example, by a
long-term unemployment conditions, low income and/or low education level, should have a lower
health level compared to those who have better socio-economic conditions. In this view, the
differences in health would depend especially on the individuals’ living conditions (Higgins
et al. 2008; Borgonovi and Pokropek 2016). Other authors have also hypothesised the
reinforce each other in worsening the conditions of the most vulnerable individuals
(Feinstein, 1993; Smith, 1999; Masseria, 2009). In particular, most studies analysing the
relationship between socio-economic status and individual health focus mainly on the effects
of four kinds of variables on health: income, income inequality, ageing and education.
Wilkinson and Pickett (2011) and Navarro (1990) conclude that there is evidence of a
negative causal relationship between income inequality and health, and that in countries
showing a high level of inequality, the general level of health is low. In this view, the
reduction of inequality could represent a possible way of increasing the health and longevity
of the most vulnerable societies (Ecob, and Smith 1999).
The effects of low income per capita and/or unemployment status on health have been
studied by Catalano et al. (2011) and Cooper et al. (2015), who highlight that unemployment
and low income negatively affect health status as they find a negative relationship between
both low income and unemployment status and levels of health. On the other side, we must
consider that there is a “circular effect” in which “poor health can become a barrier to
obtaining a higher income or gaining re-employment” (Cooper et al. 2015, 34). Vice versa,
the relationship between income per capita and individual health is obviously correlated, as
when income increases, people can more easily access better health services and improve
their health status (Preston 1975; Rodgers 1979), even if – according to Deaton (2013) – the
effect of the increase in income per capita on health is higher for developing countries than
less developed countries. In fact, in developed countries there are many other determinants of
good health. Obviously, some authors find the reverse causality problem between income and
health (Adams et al. 2003; Meer et al. 2003; Smith 1999), because good health is
fundamental to increasing the level of income per capita. However, a large part of the
Other authors (Stern 1983; Bartley 1994; Hammarstrom 1994; Morris et al. 1994;
Martikainen and Valkonen 1996, 1998; Winkelmann and Winkelmann 1998; Grobe and
Schwartz 2003) have studied the effect of persistent unemployment on health status, and they
conclude that people who are long-term unemployed experience collateral effects such as
depression, psychological stress and unhealthy lifestyles. These reduce participation in social
life (fewer leisure activities) and reduce self-confidence, and this contributes to the worst
health outcomes. Wang and Yu (2016), analysing the effect of ageing on health inequality in
China, find that age contributes to increases in health differences among individuals, even if
the effect can be mitigated by the presence of an effective health system, with better medical
care and lifestyles. Other studies (Wang and Yu 2016) highlight the positive role of education
level on health: indeed, better educated people become more medically conscientious, take
preventative actions, and hence they should improve their lifestyle, thereby avoiding
behaviour that could compromise physical health.
Another field of research have investigated the relationship between individual
occupation status and health. Following Dooley et at (2000), we individuate three possible
individual occupation status: a) People with a permanent job, so called “adequate
employment” b) People with temporary job, called “inadequately employment” and c) People
without job. The negative effects of unemployment on individual health are well known in
literature (see Björklund, 1985, Pharr et al 2012, Arge and Kristjánsson 2015). The loss of a
job produces detrimental effects on mental health of the unemployed, increasing the
probability of developing depression and anxiety. In particular, these effects are all the more
serious the longer the period is time spent in unemployment. Indeed, Pharr et at. (2000) show
that long-run unemployed reported a worse mental health compared to people with a
temporary and permanent job. Vice versa, the study of the impact of non-permanent job on
the Italian labor market had the objective to reduce the unemployment rate by using part-time
and temporary – fixed contracts, on the other hand, the uncertainty about the future have had
negative effects on individual mental health. Many studies (Dolley et at 2000, Ehlert and
Schaffmer, 2011, Virtanen et at. 2005, Wiette 2009, Moscone Tosetti and Vittarini 2016) find
that workers with a temporary contract and low quality job can develop psychological
problem and, in general, their health status is worse compared to workers with a permanent
job. This effect is due to the prolonged Concern about the future for those people who are in a
precarious working situation. The possibility of losing the job, produces anxiety and
insecurity affecting negatively the general well being of workers.
In conclusion, it is worth to highlight that, if the objective of policymakers is to improve the
population’s health status, political institutions should improve the socio-economic
conditions of the most vulnerable in society (Rose 1992; Schuring et al. 2007).
2. Data and Empirical Analysis
In order to measure the level of individual health, the variable "General Health" is
used. The ISTAT survey defines “General Health” as “assessment coming from the
individual and not from anyone outside that” (ISTAT 2013, 265). Therefore, this variable is
nothing other than a Self-Reported Health Status (SRHS), which reflects a subjective
measure of the judgment that individuals have about their own level of health. In fact, this
variable is very useful because, following the ISTAT definition, it takes into account both the
physical and the psychological and emotional factors affecting individual health statuses. In
the ISTAT survey, each individual is called to express an opinion about their own level of
health taking into account several dimensions of health. Our dataset originally contained
information about 20,000 individuals but, due to the high rate of missing responses to some
Health”, we are able to get information about the level of health in Italy, and how it is
distributed among the population. Although the use of this measure of health can raise some
doubts since it is not objective, there are at least three good motivations for using it:
a.
Individuals determine their own level of health. Since we are interested in analysing the multidimensional aspects of individual health, the use of a self-reported healthmeasure is very useful because it avoids the risk of measuring health through external
parameters constructed a priori, and through which it could be complicated give an
acceptable valuation of individual health.
b.
It is a strong predictor of an objective measure of health (Garfinkel et al. 1976; Grant et al. 1995; Appels et al. 1996).c. Data are available in a large enough sample.
Data on "General health " are drawn from the ISTAT survey on "Living Conditions and
Social Protection", and the time reference is 2013. From this survey we considered people
aged 17–65 (the working-age population), and the dataset allows us to have an overview of
the social and economic situation in Italy. The sample is highly representative of the
population and, through it, we are able to get a lot of information about the living conditions
of the Italian population. This survey collects qualitative and quantitative information about
more than 11,000 people and we can get, for each individual, information about economic
status, monthly earnings, sex, marital status and much more information.
In the ISTAT survey, each individual is asked to express an opinion about their own
health condition, considering the three dimensions of health. Each respondent can choose
from 5 health categories (very good, good, fair, bad and very bad). In order to transform the
data from qualitative to quantitative, we use the vector c=(1,2…5) , through which we
assign a specific value to each health category. For example, we assign the value 1 for the
category, the higher the value. This method of transformation is very used in literature
(Allison and Foster 2004; Abul Naga and Yalcin 2008), and it allows us to rank people based
on the values assigned to their health status. Therefore, the lower values are assigned to the
lower health categories, while the higher values are associated to the best ones.
Using the official Eurostat data, we can note – for the year 2013 - a particularly
difficult situation for Italian people as, about 1/3 of the population are included in the lowest
categories (i.e. they do not give a satisfactory judgment about their own health status), while,
on the other hand, it seems the problem of health inequality per se does not exist (the health
inequality index is 0.195, see appendix). Analysing these results we can conclude that, in
general, there is a a significant share p of Italian population is unsatisfied with its own health.
Now, our scope is to study what factors affect the individual health in Italy and try to suggest
possible explanations.
3. Econometric strategy
As we said before, the ISTAT survey includes a set of information that can be used to
study the socio-economic factors that affect the self-reported health. In order to analyse the
determinants of health, we focus on a set of main variables that represents the material,
social, psychological, physical and relational factors that could impact individual health.
Unfortunately, due to missing data, it has not been possible to use many social or relational
variables that are present in the survey (such as trust in others, satisfaction with personal
relationships and other similar variables). However, the number of observations would be
very limited and therefore the results would not be significant. Moreover, since some
variables we use in our model (such as life satisfaction) have only been included in the
Income per capita represents the “material” condition of an individual, and as we saw
before, the increase of income produces a positive effect on health. Age is considered as the
physical factor, and the hypothesis is that ageing reduces the level of the physical dimension
of health. Other variables such as education level, number of people working at local, and life
satisfaction represent the social and psychological factors that could affect individual health.
The general model we estimate is the following:
SRHSi=α+βkXp+γD+( βk+δD) Xk+ε (1)
Where SRHS is the self-reported health status of individual i, Xp is the set of
independent variables, βk are the coefficients, ϵ is the error term, and k =1,2… n
and p=1,2 … p identifies the coefficients of the single variables. γD with D=1 is the
variable dummy of the single categories when we assume that the difference in health among
the categories (for example the differences between males and females) regarding the
intercept. On the other hand,
(
βk+δD)
, with D=1 it used when we assume that thedifferences in health among the categories refer to the slope.
Since it is very likely that log income per capita is correlated with the error term and
this could bias our estimate, we need to use the method of Instrumental Variables (IV)- two
stage least square in order to check the robustness of the OLS estimator results. The
instrument we have chosen is “hours worked per week”. This variable is certainly linked to
log income per capita (Frijters et at. 2005), but at the same time, it is not correlated with error
terms since the number of hours worked depends on national or local collective bargaining
agreements.
In the next section, we show and discuss results for a general model through which we
estimate the socio-economic factors that affect SRHSs. First we estimate equation (1) through
4.
Discussion of results
The econometric approach consists of estimating the equation (1) first through the
OLS approach corrected for heteroscedasticity and then using IV. In Table 2, column 1, the
result for the simplest model – an estimate by the OLS method in which we do not use
dummy variables – is reported. The result shows that log income and life satisfaction are
positively correlated to the self-reported health. These results are already confirmed in the
existing literature, hence this is nothing new. In particular, we can note that the coefficient of
“life satisfaction” is very large, while the effect of log income on health is very low. This
result could confirm the hypothesis that, in rich countries the increase of income has only a
marginal effect on health (Deaton 2013). Ageing seems not to affect health, while the
coefficient of number of people working at the local is positive and significant. The
surprising result is that, unlike the results found in the existing literature, education level is
negatively correlated with health. However, as we said above, it is very likely that the
possible correlation of log income with error terms produces biased results of the OLS
estimate. For this reason, we use the number of hours worked per week as an IV. Column 2
(Table 2) shows very different results: the coefficient of log income becomes larger and this
shows that the increase of income remains the most important variable in enjoying good
health. Also, the coefficient of life satisfaction increases when we use IV in place of the
simple OLS, while on the other hand, ageing and number of people working at the local are
now negatively correlated with health. These results are more consistent with our expectation,
because it is normal to think that when the number of people working in the same workplace
increases, there are physical and psychological stresses that could compromise individual
health. Moreover, looking at column 3, it seems that females have a health level slightly
better than males, considering the following variables (income per capita, life satisfaction,
[Table 2. about here]
On the other hand, we can see that the effect of education level is again negatively
correlated with health. This result can be explained if we think that the effect of education
level on the perceived individuals’ health can be divided in two: the effect on physical health
and the effect on psychological health.
The literature has almost always focused on the relationship between education level
and physical health, and it is possible to conclude that the effect is positive. Indeed, higher
education levels lead people to avoid behaviours that are “dangerous” for their own health.
On the other hand, people with a higher education level have higher expectations than those
less qualified for jobs, higher wages and so on. The problem is that, in a recession period, it's
more difficult to find a good job, have a high wage and achieve one’s own goals. In other
words, the transition from school to work becomes very difficult (Ryan 2000; Matsumoto and
Elder 2010; Dorsett and Lucchino 2015), and this negatively impacts psychological health.
The negative effect of education level on health is likely due to the mismatch between
“individual expectations” and “real life” overcoming the positive effect of education level on
physical health. However, Table 2 shows the average effect of “education” on health, without
distinguishing between specific levels of education.
To verify the hypothesis of mismatch between individual expectations and the
difficulties in the transition from school to work, we estimate – using the IV method – the
differences in health distinguishing by education level. Remembering that the reference
category is represented by people with a lower education level, we can see that looking at
Table 3, column 1, the level of self-perceived health is reduced with the increase of the
education level.
However, column 1 does not allow us to capture the differences in health among
people with varying levels of education that have permanent jobs. Column 2 (Table 3) shows
that compared to people with a permanent job, people with lower education level and
temporary jobs have greater self-perceived health. This happens as people with a low
education level, either consciously or unconsciously, do not have high expectations about
wage or type of job, and therefore tend to be satisfied with the fact that they have a job. Vice
versa, people with a high education level, having greater expectations due to the skills
acquired during their career, suffer from unmet expectations’. Finally, looking at column 3,
we add another category: people in unemployment separated by level of education. Column 3
allows us to evaluate the difference in self-perceived health between people with a permanent
job and people in unemployment, taking into account education level. We can note that there
is a strong difference in self-perceived health between more educated people with and
without a permanent job. These people have great difficulty finding a job despite a high skill
level, and very often feel excluded from labour market, which leads to psychological
problems (depression, mistrust etc.) thereby reducing the level of individual health.
Of course the mismatch problem between individual expectations and “real life” is
also due to the negative phase of the business cycle but, we believe that the Italian
government should do much more to ensure easier access to the labour market, and at the
same time reduce precarious working conditions in order to ensure that more educated people
see their efforts rewarded.
5. Concluding remarks
According to the capability approach, health represents a determinant of human
live a fulfilling life and expand the space of capabilities. The differences in health
(considering physical, psychological and emotional dimensions) among individuals or social
groups depends on a set of social and economic variables.
Using data on “General Health” drawn from the ISTAT survey on “Living
Conditions and Social Protection”, the first step of this paper has been to study how health is
distributed among the Italian population. Data show that more than 80% of the Italian
population states that their own health condition is “very bad” or “bad”. This means that the
level of self-perceived health for Italian population is very low.
The second step of the paper has been to analyse the socio-economic determinants of
health. Using the IV approach, we have estimated a general model in which the self-reported
health was a function of a set of variables such as income per-capita, age, education level, life
satisfaction and number of people working at the local. The results show that, on the one side,
income per capita and life satisfaction are positively correlated to individual health, while on
the other side the effect of age and number of people at the local on health is negative.
However, these results are consistent with our expectations. The “strange” result is the
negative effect of education level on health: with increasing education level individual health
levels seem to decrease. One possible explanation is the mismatch between individual
expectations and the difficulties in the transition from work to school: those with higher
levels of education have higher expectations than other people less qualified for jobs, wages
and so on. The problem is that, very often people leaving school have difficulties finding a
job or wage consistent with their expectations. The negative effect produced from the
mismatch between “individual expectations” and “real life” can overcome the positive effect
of education level on physical health, and for this reason the level of individual health is
In conclusion, in order to avoid this effect of education on individual health, the
Italian government should implement reform to facilitate entry into the labour market, limit
the precarious working conditions and allow individuals to live a life consistent with their
expectations.
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Appendix
The calculation of health inequality is based on the median-based dominance condition elaborated by Allison and Foster (2004).
Suppose that there are n health categories such that: HC=c1, c2 ,…cn with c1<c2<…<cn .
Suppose that we have a probability density distribution p= p1, p2 …, pn
Where p1 represents the percentage of population in health category c1 , p2
represents the percentage of population in health category c2 and so on.
With P we indicate the vector of cumulative distribution P=P1, P2 …, Pn , such that
∑
i=1 n
Pi=1
Since, following Naga and Yalcin (2008), we use the median of health distribution as a reference, the health inequality index in Italy in 2013 is given by the equation:
i=¿ HI2013=1−
(
2∑
¿ n⌊ Pi−0.5⌋−1 n−1)
Where HI2013 is the Health inequality, Pi is the share of population in the i health category, n−1 is the number of categories minus 1.
Using ISTAT data and the above equation, we found that the health inequality for the Italian population for 2013 is 0.195
