1,721,112 research outputs found
Income and health differentials in Italian context: differences and relationships
No full textSituations of significant health inequalities in the different socio-economic groups are documented in many countries (Cavelaars, 1988; Costa et al. 2004, WHO 2008). The most convincing empirical results are found in works with an epidemiological basis, which show how different material, social and stress conditions are able to explain differences in morbidity and mortality related to specific diseases found among population groups (Barnett et al., 2004, Kim and Kim 2015). Situations of significant inequality in mortality, morbidity and, in general, in health needs are documented also in Italy (Biggeri et al., 1998; Bar-rella Spandonaro 2002; Cadum et al. 1999; Michelozzi et al. 1999; Rapiti et al. 1999; SLto 1999, 2004; Valerio Vitullo 2000, (Testi et al. 2005, Testi e Ivaldi 2005, 2011, Ivaldi e Testi 2012) although health is a variable that always has a certain measurement complexity.
Since there are multiple variables related to health, individual correlations may change in intensity and direction depending on the context of application and are often affected by problems of interactions between the variables and of non-linearity of the relationships (Fuchs 2004).
It is therefore necessary to measure the state of health through a set of varia-bles or partial indicators, on a geographical basis, in order to take better ac-count of their multidimensional nature, trying to grasp as many gradations and components as possible, thus leading to excellent results in the evaluation of the life standards of a society by policy-makers and by ordinary citizens as well.
The indices are constructed on a geographic basis, one of the few operational tools available to measure in a concise and precise manner the health differ-ences in a given territory. (Ivaldi and Testi, 2011). Moreover, they have the ad-vantage of being inexpensive, because they can be inferred directly from the census data, and transparent, because they are based on objective infor-mation, readily available, and that use the same survey criteria (Carstairs and Morris 1991). The use of health indices on a geographical basis, however, bears the implicit assumption that the characteristics of an individual derive from the area they belong to and that there is a "context effect" (Macintyre S. et al. 2002), namely that the conditions in which a person lives are able to de-termine the risks of morbidity and mortality as well as every other condition.
The analysis of the literature offers several solutions to derive a priori what should be the most appropriate variables to be included in an indicator (Jarman 1983, 1984; Carstairs and Morris 1991, Townsend et al 1988). The choice of var-iables depends on several considerations. However, some common practices can be highlighted; in particular, the availability of data influences the choice of variables to be included and so, ultimately, the composition of the indicator it-self (Grasso 2002; Ivaldi 2006; Ivaldi e Di Gennaro 2011, Soliani et. al 2011, Munda 2012).
The specific purpose of this paper is to propose an indicator that is able to explain the geographical variability of health, linking it with the disposable in-come in order to verify the existing relationships described in the literature.
The source of the data is the 2015 publication by the Italian Institute of Statis-tics (ISTAT): "Fair and Sustainable Well-being (BES)". The database contains a set of indicators, created in 2010, following an initiative by the National Council for Economy and Labour and by the National Institute of Statistics. The BES (Fair and Sustainable Well-being, the name given to the project) is based large-ly on the OECD framework and on findings by the Stiglitz-Sen-Fitoussi Com-mission. (CNEL-ISTAT, 2012, 2013, Stiglitz et al 2009). Among the intentions of the BES there is a measurement of fairness by reference to appropriate statis-tical indicators in 12 different dimensions for a total of 128 indicators. One of the dimensions analysed is, of course, health
Income and health differentials in Italian context: differences and relationships
No full textSituations of significant health inequalities in the different socio-economic groups are documented in many countries (Cavelaars, 1988; Costa et al. 2004, WHO 2008). The most convincing empirical results are found in works with an epidemiological basis, which show how different material, social and stress conditions are able to explain differences in morbidity and mortality related to specific diseases found among population groups (Barnett et al., 2004, Kim and Kim 2015). Situations of significant inequality in mortality, morbidity and, in general, in health needs are documented also in Italy (Biggeri et al., 1998; Bar-rella Spandonaro 2002; Cadum et al. 1999; Michelozzi et al. 1999; Rapiti et al. 1999; SLto 1999, 2004; Valerio Vitullo 2000, (Testi et al. 2005, Testi e Ivaldi 2005, 2011, Ivaldi e Testi 2012) although health is a variable that always has a certain measurement complexity.
Since there are multiple variables related to health, individual correlations may change in intensity and direction depending on the context of application and are often affected by problems of interactions between the variables and of non-linearity of the relationships (Fuchs 2004).
It is therefore necessary to measure the state of health through a set of varia-bles or partial indicators, on a geographical basis, in order to take better ac-count of their multidimensional nature, trying to grasp as many gradations and components as possible, thus leading to excellent results in the evaluation of the life standards of a society by policy-makers and by ordinary citizens as well.
The indices are constructed on a geographic basis, one of the few operational tools available to measure in a concise and precise manner the health differ-ences in a given territory. (Ivaldi and Testi, 2011). Moreover, they have the ad-vantage of being inexpensive, because they can be inferred directly from the census data, and transparent, because they are based on objective infor-mation, readily available, and that use the same survey criteria (Carstairs and Morris 1991). The use of health indices on a geographical basis, however, bears the implicit assumption that the characteristics of an individual derive from the area they belong to and that there is a "context effect" (Macintyre S. et al. 2002), namely that the conditions in which a person lives are able to de-termine the risks of morbidity and mortality as well as every other condition.
The analysis of the literature offers several solutions to derive a priori what should be the most appropriate variables to be included in an indicator (Jarman 1983, 1984; Carstairs and Morris 1991, Townsend et al 1988). The choice of var-iables depends on several considerations. However, some common practices can be highlighted; in particular, the availability of data influences the choice of variables to be included and so, ultimately, the composition of the indicator it-self (Grasso 2002; Ivaldi 2006; Ivaldi e Di Gennaro 2011, Soliani et. al 2011, Munda 2012).
The specific purpose of this paper is to propose an indicator that is able to explain the geographical variability of health, linking it with the disposable in-come in order to verify the existing relationships described in the literature.
The source of the data is the 2015 publication by the Italian Institute of Statis-tics (ISTAT): "Fair and Sustainable Well-being (BES)". The database contains a set of indicators, created in 2010, following an initiative by the National Council for Economy and Labour and by the National Institute of Statistics. The BES (Fair and Sustainable Well-being, the name given to the project) is based large-ly on the OECD framework and on findings by the Stiglitz-Sen-Fitoussi Com-mission. (CNEL-ISTAT, 2012, 2013, Stiglitz et al 2009). Among the intentions of the BES there is a measurement of fairness by reference to appropriate statis-tical indicators in 12 different dimensions for a total of 128 indicators. One of the dimensions analysed is, of course, health
Gender Inequalities and different Levels of Education in Italy
No full textGender inequality in education is a theme that has always been studied by both economists and sociologists. Our societies continuously pursue the aim of gender equality and the common vision is that women are always disadvan-taged with respect to men.
Following the publication of the 2015 BES Report by ISTAT, our analysis aims to use the education indicators the report provides to create an additive index of education and compare the index between males and females.
This article investigates levels of education in twenty Italian regions: we built an additive index for females, for males and a general one, and then, comparing the ranks for each region, we looked for differences and similarities in the ranks.
Research on gender gaps in educational performance offers different points of view to explain this phenomenon.
Decades ago, educational theory and research remained focused on social class disparities and classic studies of inequality in education typically fo-cused on disparities by social class among men (Blau & Duncan, 1967, Bour-dieu & Passeron, 1977, Collins, 1979, Karbel & Halsey, 1977) and, as Jacobs pointed out, scholars who did focus on gender issues have often treated all aspects of education as working to the disadvantage of women (Jacobs, 1996). Today, research does not always find evidence of this disadvantage: in OECD countries, for example, more women than men enter higher education (Vincent- Lancrin, 2008).
Today, the awareness that something has changed in gender inequalities leads to new questions about gender differences in education.
In order to analyse the literature about gender inequalities in education, we pro-pose an analysis that does not pretend to be exhaustive, but aims to capture different directions in which studies have been developed.
Literature focused on both gaps from kindergarten to high school and gaps in higher education.
The starting point to analyse the source of inequality is to admit that some gender differences in some cognitive tasks are well established (Buchmann, DiPrete, & McDaniel, 2008) and that, as Halpern and colleagues point out: “bio-logical hypotheses are not necessarily sexist [and] there are biological origins to any cognitive ability” ( (Halpern, Wai, & Saw, 2004).
However, biological explanations to inequality in education are not totally satis-factory and other questions can be raised about this subject.
There are questions on how traditional gender stereotypes and norms influence students’ perceptions of their own abilities and the socialisations of girls and boys within their families and schools, and it has long been known that many aspects of one’s family of origin are integrally related to both educational per-formance and attainment. Other possible explanations for gender gaps are re-lated to the environments within schools and classrooms and a debate regard-ing whether teachers systematically favour one gender over the other is still ongoing (Buchmann, DiPrete, & McDaniel, 2008).
Other studies focused on gender differences in higher education. Vincent-Lancrin writes about this subject as follows: “Until the 1990s, there were on av-erage more male than female students in OECD member countries. Women were disadvantaged by inequalities in access to higher education. Since then, inequalities to the detriment of men have emerged in almost all countries. How-ever, the faster increase in female participation in higher education has re-versed the trend in OECD member countries (but not in most of the rest of the world)”. He found out that “It is only at the doctoral level that women have not yet caught up with men, although current trends suggest that this will happen within a few years. All fields of study have therefore become feminised, even though gender segregation along subject lines still remains very pronounced” (Vincent- Lancrin, 2008).
Buchmann and colleagues arrive to the same conclusions: “Trend statistics in the United States reflect a striking reversal of a gender gap in college comple-tion that once favoured males” (Buchmann, DiPrete, & McDaniel, 2008).
Our analysis starts with the awareness that inequalities still exist and that when we talk about inequalities this does not mean that women are always disadvan-taged. As the European institute for gender equality found, European countries are, on average, halfway to achieve gender equality in education. What the in-stitute highlights is that, in contrast with other domains of analysis, women are not clearly disadvantaged in education, but the situation is characterized by many nuances (EIGE, 2016).
Our work takes into account Italian regions and it has two different aims: 1) building an index of education for males and one for females (relative to 2014) and observing and discussing ranking in order to analyse whether the best-performing regions are the same for both sexes or instead well-performing re-gions for one sex are not also well-performing for the other sex; 2) building an index for both sexes together to observe how Italian regions are distributed and to analyse best-performing and worst-performing regions.
In the first part of the article we present the variables selected and we discuss the method we used to set up the index. The second part contains the results and the concluding remarks
A Proposal of a Country Risk Index Based on a Factoral Analysis: An Application to South Mediterranean and Central-East European Countries
No full textThe present paper puts forward a method of calculation of Country
Risk based on Factor Analysis and applies it to Southern Mediterranean
and Central-Eastern European countries. In this work we propose a method
for estimating Country Risk using factorial analysis (Factorial Country Risk
Index – FCRI) and apply it to southern Mediterranean countries and a
number of countries of central and Eastern Europe.
The index provided periodically by Coface (a French company leader
in export credit insurance) has been chosen as the benchmark for validating
the FCRI. in order to provide a validation parameter for the index, the
classification of the Country Risk is the chosen benchmark With the objective
of providing a validation parameter for the proposed index, classification of
Country Risk is used as a benchmark presented periodically by Coface, a
leading French company in export credit insurance on.
Finally, the reckoned indexes have been updated taking into account the
evolution engendered by the ‘Arab Spring’.
The analysis was completed through certain updates of the indices which
in particular reflect the rich developments of critical situations stemming
from the so-called ‘Arab Spring’ in the southern Mediterranean countries.
The FCRI is established starting from a quite small set of variables and
is correlated very well with the benchmark. It can be quickly revised and fits
new scenaries easily. Last but least, the FCRI is able to single out in advance
those ‘latent dimensions’ that are going to increase the risk.
The index proposed here, even if only based on a number relatively small
of variables, corresponds well to the classification testing, allows for a rapid
and satisfactory review and has adequate capacity to adapt to new scenarios,
but above all, seems to be able to give substance to the pre-figurative ‘latent
dimensions’ of risks in relatively brief periods
Indicatori di deprivazione come misura di svantaggio sociale: il caso dell’Area Metropolitana Genovese - Collana Percorsi di Scienze Economiche e Sociali
No full textA Proposal of a Country Risk Index Based on a Factoral Analysis: An Application to South Mediterranean and Central-East European Countries
No full textThe present paper puts forward a method of calculation of Country
Risk based on Factor Analysis and applies it to Southern Mediterranean
and Central-Eastern European countries. In this work we propose a method
for estimating Country Risk using factorial analysis (Factorial Country Risk
Index – FCRI) and apply it to southern Mediterranean countries and a
number of countries of central and Eastern Europe.
The index provided periodically by Coface (a French company leader
in export credit insurance) has been chosen as the benchmark for validating
the FCRI. in order to provide a validation parameter for the index, the
classification of the Country Risk is the chosen benchmark With the objective
of providing a validation parameter for the proposed index, classification of
Country Risk is used as a benchmark presented periodically by Coface, a
leading French company in export credit insurance on.
Finally, the reckoned indexes have been updated taking into account the
evolution engendered by the ‘Arab Spring’.
The analysis was completed through certain updates of the indices which
in particular reflect the rich developments of critical situations stemming
from the so-called ‘Arab Spring’ in the southern Mediterranean countries.
The FCRI is established starting from a quite small set of variables and
is correlated very well with the benchmark. It can be quickly revised and fits
new scenaries easily. Last but least, the FCRI is able to single out in advance
those ‘latent dimensions’ that are going to increase the risk.
The index proposed here, even if only based on a number relatively small
of variables, corresponds well to the classification testing, allows for a rapid
and satisfactory review and has adequate capacity to adapt to new scenarios,
but above all, seems to be able to give substance to the pre-figurative ‘latent
dimensions’ of risks in relatively brief periods
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