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Project no: 028412
AIM-AP
Accurate Income Measurement for the Assessment of Public Policies
Specific Targeted Research or Innovation Project
Citizens and Governance in a Knowledge-based Society
Deliverable 1.3b Public health in Italy
Due date of deliverable: July 2007
Actual submission date: September 2007
Start date of project: 1 February 2006 Duration: 3 years
Lead partner: ECV
Revision [draft]
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1
The distributional effects of public health
transfers in kind: the Italian case*
Conchita D'Ambrosio
Università di Milano-Bicocca and DIW, Berlin
and
Chiara Gigliarano
Università Politecnica delle Marche, Ancona
First version: August 2007
*We are indebted to Alberto Zanardi for many useful suggestions.
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CONTENTS
Introduction………………………………………………………………………………….. 3
1. Results from other studies…...…………………………………………………………3
2. The Italian health care system……………………....…………………………………5
3. How to measure the health related transfers in kind.……………………………….13
4. Results from the empirical analysis …………………………………..……….…. …21
5. Concluding remarks…………………………………………………………….………31
Appendix A: Additional evidence from the empirical analysis.…………….………… 32
Appendix B: Results from alternative health expenditure data….……… ………….…42
References……………………………………………………….………………….….……46
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1. Introduction
The analysis of the distributional effects of public policies is generally performed on
interventions aimed directly at the individual’s income, such as taxes and cash transfers. Equally
important is the evaluation of the impact of public expenses for services, i.e. transfers in kind, such
as education and health. The importance of this evaluation is particularly relevant for the policy
implications of monetary transfers vs. services supply.
Empirically, the evaluation of transfers in kind is more complex than the evaluation of monetary
transfers: the latter may be evaluated according to the costs incurred in producing them, an
amount that could differ from the profit enjoyed by the user because of possible inefficiencies in its
production.
Two alternatives to handle the problem are conceivable, as well discussed in Baldini et al.
(2006). On the one hand, we may look at the actual benefit occurred to the individual, i.e. whether
she has actually used health care services during the period of reference or not; in this case, the
variability of the benefits’ distribution would depend only on random effects (actual use approach).
On the other hand, we may consider the health related transfer in terms of its insurance function,
i.e. the benefit is a sort of insurance premium that individuals with particular socio-demographic
characteristics should pay in order to receive the services (insurance-based approach).
In an international perspective, several studies report on the distributional impact of health
public services in kind, applying both approaches. At the same time, much fewer are the works
specific on Italy; in Section 2 we briefly review its main results.
Aim of this report is to analyze the incidence and relevance of health related transfers in kind in
Italy and its effect on income distribution, applying the insurance-based approach.
We do not derive direct conclusions on the implication of such transfers in kind to inequality
and poverty, since we do not believe in an exercise of this type: transfers in kind to sick individuals
do not make them economically better off hence increasing their well-being.
2. Results from other studies
Quite rich is the literature on the distributional impact of transfers in kind, and health in
particular, in an international context; see, e.g., Smeeding et al. (1993), Steckmest, (1996) and
Marical et al. (2006), Garfinkel et al. (2006), Smeeding and Rainwater (2002).
Smeeding et al. (1993) study the combined effect on income distribution of non-cash incomegiven by health, education and housing, comparing seven western countries (Australia, Canada,
Netherlands, Sweden, U.K., U.S., West Germany), based on the LIS (Luxembourg Income Study)
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data set, in the years between 1979 and 1983. They show that the most advantaged types of
households are the middle-aged families with children and the very elderly, while the most
disadvantaged socio-economic groups, in many of the analyzed countries, are the young singles
and younger families without children; the combination of the three non-cash transfers considered
have a levelling effect on income distribution.
Steckmest (1996) compares the distributional effects of health and education related non-cash
benefits in four countries (Norway, U.K., U.S. and Sweden). Adding education and health transfers
into the disposable income reduces inequality; higher decline in the degree of inequality are
monitored for Sweden and US, while the distributional effects in Norway and UK are limited.
Marical et al. (2006) enrich the analysis by providing estimates of the distributive implications of
public services (health, education and housing) in all the OECD countries. It constitutes one of the
few international studies where Italy is also included. Regarding health, in particular, they apply
both the insurance-based approach and the actual use approach, using the ECHP 2001 data set.
They show that, in general, public health care services are distributed uniformly across quintiles,
inducing a reduction in the inter-quintile ratio for all the countries. The distributive effect of health
care expenditures is, on average, significantly lower with the actual use approach than with the
insurance-based approach. According to the former, in particular, inequality slightly rises in Italy
and in Denmark.
Only a few studies focus on the distributional impact of health related public transfers in Italy.
The most recent studies, to the best of our knowledge, are Citoni (2001), Turati and Sonedda
(2005), Pacifico (2006) and Baldini et al. (2006).
Citoni (2001) applies the actual use method to the Bank of Italy Survey of Household Income
and Wealth (henceforth, SHIW) 1993 data set, which provides richer information about the usage
of healthcare services than the other Bank of Italy’s data sets.
Citoni (2001) concludes that the degree of inequality in the income distribution reduces after
including healthcare transfers; the same holds also if education transfers are added to the
individual income. He underlines that different results are obtained whether the benefits areequivalized or not (in the last case the effect is much smoother or sometimes even reversed) and
the distributional patterns differ according to the type of health care expenditure considered; e.g.
public hospital care is heavily concentrated on the lowest part of the income distribution while the
opposite happens for the specialists outpatient care. The overall distributional effect is a reduction
in the Gini index, with a redistribution of income in favour of the poor and the middle class.
Turati and Sonedda (2005), on the other hand, measure the health transfers in kind through a
mixture of the two approaches cited above. They apply the insurance-based approach to the non-
employed and the actual use approach for the employed individuals (taking into account the
information on the number of days of non-working due to illness); the data set employed is the
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SHIW 2000. They conclude that inequality slightly increases after adding the health related
transfers. Moreover, they compare cash public transfers (including pensions) and transfers in kind,
concluding that the latter have a weaker redistributive effect than the former.
At the light of such controversial results, Baldini et al. (2006) and Pacifico (2006) try to better
clarify the distributional impact of health related transfers in kind in Italy, applying both the
insurance-based and the actual use method.
For the actual use approach, they apply a matching of two different datasets, the Bank of Italy
SHIW 2002 data set, that includes information on individual income, and the ISTAT survey on
living conditions and usage of health services (“Indagine multiscopo Istat sullo stato di salute e
ricorso a servizi sanitari”), related to the years 1999-2000. By matching the two datasets, based on
the propensity score approach, they assign to each individual in the Bank of Italy data a set of
variables on health conditions and consumption of health care services.
The total health expenditure is partitioned according to the kind of healthcare and assigned to
the individuals according to the consumption they have declared.
Their result affirms that inequality reduces more with the insurance-based approach than with
the actual use approach. They decompose the Reynolds-Smolensky measure of redistribution
(which is equal to the absolute difference of the Gini index before and after the introduction of the
transfers in kind) of the transfer in kind into the three components of incidence, progressivity and
reranking. Comparing transfers in kind with cash transfers (“trasferimenti assistenziali”), through
the micro-simulation model Mapp02, they conclude that the transfers in kind have a higher
redistributive effect than the cash transfers do. The Gini index reduces of about 4.1 percentage
points with the usage based approach and about 5.5 percentage points with the insurance-based
approach; the incidence of the cash transfers is higher than the one of the transfers in kind.
Beyond the difference in the levels, both the methods underline that the health related transfers in
kind have a redistributive effect in reducing the inequality in the income distribution. They conclude
that the tax system has a very low redistributive impact and a most important role is played by the
system of transfers (cash without pensions and in kind), which are much more able to act on the
income distribution. For the insurance-based method, they employ information on the total healthexpenditure for each region, related to the year 2002; this global amount is divided by the entire
population, so to obtain the per capita regional expenses. In a second stage, a class of age and
gender-related weights is employed, in order to take into account the differences in health needs
due to age and gender.
2. The Italian Health care system
In order to better understand the results from the empirical analysis, we summarize first the
main features of the Italian health care system.
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• the provision of public contribution for public hospitals.
The 20 regions, on the other hand, have the responsibility for the organization, funding and
administration of publicly financed healthcare.
Finance
The two main sources of funding, for the health care system, are constituted by the households
and the enterprises; the former pay for healthcare services and benefit from most of them, while
the latter benefit only from few services, such as public hygiene and job safety, and pay health
contributions (since 1998 converted into IRAP-Imposta Regionale sulle Attività Produttive).
The Italian health care system is a mixture of private, insurance-based and public health
system, since it is financed by prices, premium and taxes. Prices are paid for those health care
services that are not provided by SSN, such as particular categories of drugs and dental cure;
tickets, on the other hand, are partial prices that are paid partly by the patient and partly by the
SSN. Premiums are paid to private insurance companies that provide complementary or additional
insurance beyond SSN, while taxes are the most common way to fund the public health system.
More in details, the Italian national Health System is financed by:
• General taxation: Regional add-on to the national personal income tax (Addizionale
regionale IRPEF1), central grant (“Fondo per il fabbisogno sanitario ex D.Lvo 56/2000 “) financed
by the value-added tax (VAT2) and by other indirect tax (e.g. fuel) revenues;
• Compulsory social insurance, replaced in 1998 by a business tax, IRAP; 3
• Transfers from the State (ex FSN);
• Co-payments (or tickets) of patients for pharmaceutical and specialist ambulatory services.
After the devolution reforms of the years 1990 and 2000, the responsibility of financing the
healthcare system passed from the central government to the regions, implying that each region is
responsible for eventual deficit in its budget. The introduction of tickets and forms of co-
participation constitutes another way to reduce the regional costs for the health care system.
In order to increase their financial resources, the regions are allowed also to increase the rate
of the add-on to the national personal income tax or the rate of IRAP and to control the costs for
the staff, the pharmaceutical, specialist and hospital assistance.
1 Each region decides whether the rate for the regional add-on of the national personal income tax is a unique rate or is
different rate according to income brackets; the taxable income is the same as for the national personal income tax
IRPEF. Therefore the individuals exempt from the national tax are exempt also from the ADDIZIONAL REGIONALEIRPEF.2 In 2003, about 38.55% of the total national revenue from VAT was devoted to funding the public health system.
3 In 2003, the rate of IRAP was about 4.25% of the net value of the production; however, each region may decide the
entity of such tax rate.
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Moreover, there exists a system of “perequation” among regions, so that the economically and
fiscally weaker regions receive a contribution from the economically stronger regions.
The new fiscal federalism, introduced by the law D.L.vo 56/2000, has been established,
together with the new responsibilities for the regions in the management of the health care system
and of eventual deficits, so to guarantee the implementation of the Title V of the Italian
Constitution, reformed by the law 3/2001.
An important step in the procedure of the establishment of the health care system finance is
constituted by the agreement between State and Regions to decide the aggregate funding and
how to allocate it among the regions; this often causes huge political friction. The formula for the
allocation of financial resources has changed several times; at the moment it is based on
population size, weighed by age-specific utilization rates for hospital care, drugs and residential
care, on standardized mortality rate and on an adjustment for interregional patient flows.
In particular, with the Agreement (“Accordo Stato-Regioni”) undersigned on August 8 th, 2001,
State and regions reached an agreement on the total amount of health care financial needs for the
years 2002 to 2004.
In 2003, which is the year of interest for our analysis, the total revenues of the SSN were
constituted for 39% by IRAP, for 3% by regional IRPEF, for 43% by VAT and other indirect taxes,
for 3% by co-payments of patients (tickets), for 7% by transfers from public and private sectors and
5% from integration from the State. (source: Ministero dell’Economia e delle Finanze, 2004); see
Figure 1.
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Fig.1: Health care system finance, by resources. Year 2003.
39%
3%
43%
7%
3%5%
IRAP
ADDIZIONALE IRPEF
VAT+INDIRECT TAXES
TRANSFERS FROM PUBLIC AND PRIVATE SECTORS
OWN REVENUES
INTEGRATIONS FROM THE CENTRAL GOVERNMENT (only for Sicily and Sardinia)
Source: Ministero dell’Economia e delle Finanze (2004)
Providers
The SSN combines public financing with a mixture of public and private provision:
• Public structures, which are mainly focused on prevention, general public health and family
doctors;
• Private accredited structures related to and financed by the SSN, which are mainly focused
on diagnosis, hospital services and rehabilitation;
• Private structures not related to the SSN (financed by out-of-pocket and private insurance);
such kind of structure is not very popular in the Italian health care system.
Responsibility for delivery rests with the “Aziende Sanitarie Locali” (ASL).
The hospital structures are about 1400, of which 750 are public, 550 are accredited to SSN and
100 are private not accredited. On average, the public structures are bigger than the private ones.
In 2003, according to ISTAT (2006), the “hospital usage rate” was about 144.6 for 1000
inhabitants.
Among the OECD countries, in 2003 Italy has the highest percentage of old people (19%
versus 14% OECD mean), has the 2° position in number of doctors (41/10000 versus 29/10000
OECD mean) but the 23° position in number of nurses (54/10000).
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Expenditure
According to the Italian General Accounting Office (Ministero dell’Economia e delle Finanze,
2004), in 2003 the total public health-care expenditure4 was equal to 83.2 billion Euro, i.e. a per
capita expense equal to 1407 Euro, with a deficit of 1.9 billion Euro. The incidence of the public
health care expenditure on the gross domestic product (GDP) was about 6.3%.
According to OECD (2005), 5 moreover, the total (public and private) health expenses in Italy in
2003 were 8.8% of the GDP.
In 2003, 75% of the total health-care expenditure is publicly financed (versus 72% OECD
mean) (4.0% less than in the year 1990), 21% is financed out-of-pocket and 0.9% is financed by
private insurance (source: OECD 2005).
Figure 2 shows that, in 2003, 10% of the public total health expenditure is for the hospital
sector, 34% for pharmaceutical care, 25% for services, 13% for drugs, 6% for general medicine
and 3% for outpatient specialist care, according to the Italian General Accounting Office.
Fig. 2: Health care system expenditure, by resources. Year 2003.
10%
34%
25%
3%
13%
6%
9%
HOSPITAL STAFF SERVICES SPECIALIST PHARMACEUTICAL GENERAL MEDICINE OTHER
Source: Ministero dell’Economia e delle Finanze (2004)
4 Health expenditure is defined in Ministero dell’Economia e delle Finanze (2003, 2004) as the sum of all the costs for
the specific welfare services (see Figure 3) and net costs for the extraordinary management and for the private health
care provided in public health structures (named “intramoenia”).
5 The definition of health expenses provided by OECD (2005) is the following: “Total expenditure on health measures the
final consumption of health care goods and services (i.e. current health expenditure) plus capital investment in health
care infrastructure. This includes spending by both public and private sources (including households) on medical
services and goods, public health and prevention programmes and administration. Excluded are health-related
expenditure such as training, research and environmental health. Total expenditure on health does not include
compensation for loss in income due to health problems (sick pay and disability allowances).
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The private health care expenditure is constituted mainly by co-payment and tickets for SSN
structures and private expenditure for services from structures not related to SSN. 6
It should be noted that the Italian fiscal system includes a tax-allowance on the personal
income tax for out-of-pocket expenditure, equal to 19% of the amount exceeding 129.11 Euro.
As already pointed out, the recent reforms of the Italian health care system allowed regions to
introduce co-payments for ambulatory care and for drugs (the so-called “ticket”). Each region was
free to choose the amount and level of such tickets. There exists, however, forms of full or partial
tickets exemption, for particular categories of patients, such as chronic ills, job-invalids, blind, deaf
and dumb, war-pensioners, prisoners, elderly with low income, unemployed, pregnant women.
Figure 3 synthesizes the structure of the Italian National Health System, in terms of administration
and management, of finance and of service provision.
Fig. 3. The Italian National Health Service 2002
6 As already pointed out, in 2003, 21% of the total healthcare expenditure was out-of-pocket expenditure, i.e. paid
directly by the patients, while 0.9% of the total healthcare expenditure was paid by private insurance.
PARLIAMENT
GOVERNMENT
MINISTRY OF HEALTH
REGIONAL GOVERNMENTS
REGIONAL HEALTH DEPARTMENTS
PUBLICHOSPITALENTERPRISES
ASLs
PHARMACIES GENERALPRACTICTIONERS
PRIVATE ACCREDITEDHOSPITALS ANDSPECIALISTS
RESEARCHHOSPITALS
CITIZENS/PATIENTS
NATIONALBUDGET
REGIONALBUDGET
Co-paymentsfor ambulatorycare
List prices Capitation
Regional taxes
National taxes
Tariffs
Co-payment for ambulatory care
Tariffs
Administ rat ion and p lanning flowsFinancial flowsService flows
Source: France, Taroni, Donatin i (2005)
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Interregional mobility
The Italian health system is characterized by the free mobility of the patients throughout the
country, so that patients can choose the structure where to be cured. This implies the existence of
interregional mobility of patients. Figure 4 shows the net interregional mobility related to the year
2003; the net interregional mobility of a region A is measured as the difference between the total
revenue that region A receives from all the regions whose patients are cured in region A, minus the
money that the region A gives to those regions where its patients are cured. Figure 4 shows that
the regions with highest positive interregional mobility are Lombardia and Emilia Romagna, while
the regions with highest negative interregional mobility are Campania, Sicilia and Calabria.
A recent study on the interregional mobility for admissions at hospitals, by Assr (2003) shows
that Lombardia is the region with the highest net mobility, followed by Emilia Romagna, Lazio,
Veneto and Toscana; the regions with the lowest net mobility (negative) are Campania, Calabria,
Sicilia, Puglia and Basilicata. Only 5.5% of the health assistance in mobility refers to highly
specialized healthcare, while 58.8% of the hospital admissions are for ordinary healthcare. In this
report it is highlighted that most of the hospital admission in mobility, in the year 2003, was not due
to the inadequate healthcare service quality of the structure in the own region.
Fig. 4: Net interregional mobility (in Euro). Year 2003.
Net interregional mobility (in Euro)
-300
-200
-100
0
100
200
300
400
500
C a m p a n i a
S i c i l i a
C a l a b
r i a
P u g l i a
B a s i l i c a
t a
S a r d e g n a
M a r c h e
P i e m
o n t e
V a l l e
d ' A o s t a
L i g u r i a
T r e n t i n
o
M o l i s e
A b r u z z
o
F r i u l i - V
e n e z i a
U m b r i a
L a z i o
T o s c a n a
V e n e t o
E m i l i a
- R o m a g n a
L o m b a r d i a
Source: Ministero dell’Economia e delle Finanze (2004)
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4. How to measure the health related transfers in kind
The aim of the paper is to translate into a monetary form the benefits received by the provision
of health related public goods and study the implication on the income distribution. The mostcommon way to measure health benefits in kind is to use the costs due to produce the health
transfers as a proxy of such benefits. We hypothesize that the marginal costs to produce such
goods by the state are equal to the marginal benefits received by the agent that asks for such
goods. In case of inefficient operators in the market of such goods, the marginal cost is not
anymore an adequate proxy for the marginal benefit.
In the literature, we find different alternative methods to measure the benefits from transfers in
kind. As already discussed in the Introduction, the main methods are two: the actual use-based (or
usage-based) approach and the insurance-based approach.
The usage based method assigns to each individual a transfer (based on the costs) according
to her effective use of the healthcare service; as a result, individuals are more differentiated from
each other. There exist, however, difficulties in finding data set that include both information on
income and on the usage of the healthcare services and the general health condition of the person.
The second problem is due to the difficulty in finding unitary costs for the different kind of health
services; see Pacifico (2006) for a wider discussion.
The insurance-based approach, on the other, is easier to implement, since it does not require
information on the individual health condition nor on the usage of healthcare services; the method
assigns health related transfers to the individuals proportionally to some socio-economic
characteristics, such as gender, age and geographical area. The individual transfer may be
considered as the premium an individual should have paid in order to be insured against the risk of
illness. The amount of such premium should be determined according to such individual
characteristics. The transfer is defined as proportional to the probability of loosing good health
condition, where the probability is a function of observable variables such as age and gender.
The main disadvantage of such approach is that it does not take into account the individual’s
differences in health condition and needs nor in income, since we hypothesize that the health
conditions depend not only on demographic characteristics, such as age and gender, but also on
socio-economic characteristics. The main advantage of such approach, on the other hand, is its
easy implementation, since it needs information on the aggregate expenditure on health for the
entire country and on the population distribution by age and gender.
In this report we explore two different analyses, following in both cases the insurance-based
approach.
The first analysis is conducted for the comparative report for the AIM-AP project, which is
based on the definition of health-care benefits as the health-care amounts per capita per age group
provided by OECD; the results from such analysis are discussed in Appendix B.
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In this section we focus on the second analysis, which defines the health-care benefits as
health-care amounts per capita per age group calculated for different regions and different kinds of
health-care services.
EU-SILC 2004
The dataset employed for the analysis is the “IT-SILC XUDB 2004-versione Febbraio 2006”,
which contains the Italian data of the European Survey of Income and Living Conditions (EU-
SILC), based on the European Union Regulation (no. 1177/2003) which defines the EU-SILC
project. In particular, it contains extra variables beyond the ones common to all the European
countries that are part of the project.
This survey replaces the former European Community Household Panel (ECHP) with the main
scope to provide, through harmonized definitions and methods, comparable data, cross-sectional
and panel, in order to analyse jointly income and welfare distribution among the households and to
monitor the effect of European and national socio-economical policies.
The sample design of the Italian SILC data set consists of a two-stage sampling, according to
which, for each region, municipalities are first divided into the so-called auto-representative
municipalities (with larger population size) and the non auto-representative municipalities (smaller
in size). All the auto-representative municipalities belong to the sample and within them
households are systematically drawn from the register office records. For the non auto-
representative municipalities, a sample design with two stages is conceived, according to which a
sample of municipalities is chosen (first stage) and households are selected randomly within each
municipality, from the register office records (second stage). The probability of selecting a
municipalities is proportional to its population size, while households are drawn with the same
probability.
The Italian EU-SILC sample contains 24,204 households and 61,429 individuals (52,509 aged
15 and more years old at the end of the referring income period of time) living in 731 municipalities.
For the analysis of this paper we take into account only the individuals with strictly positive income;therefore, we reduce to 24,048 households and 61,107 individuals.
In EU-SILC 2004 information on income refer to the year 2003, while information on the living
conditions refer to the moment of the interview, i.e. the year 2004. The income variable considered
in EU-SILC 2004 for the baseline income is the total disposable income “HY020”, given by the
sum, for all household members, of gross personal income components, gross cash benefits (self-
employment, sickness, survivor, unemployment, disability), income from rental of property, family
allowances, housing allowance, interests and profits from capital investments, minus taxes on
income, wealth, social insurance contributions. Such baseline income, therefore, includes already
all the direct public transfers related to health condition, if any.
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Units of analysis are individuals living in private households with positive disposable income.
In order to take into account the differences in needs among households with different sizes,
we apply the modified OECD scale for both disposable income and health transfer in kind. 7
Since our analysis is based on the individual’s age, we should note that in EU-SILC 2004 data
set the individuals aged more than 80 years old were assigned the age of 80; therefore, we are not
able to distinguish the differences in age for the over80s.
Health care expenditure per capita per age group
The health benefits in kind are approximated by the costs that the state has to pay to produce
them. In order to obtain the per capita health expenses weighted by age and gender, we collect
first the following information:
• Data on the expenditure of National Health System (SSN), which are taken from “Relazione
Generale sulla Situazione Economica del Paese” by Ministero dell’Economia e delle
Finanze (2003, 2004).8 Such costs are partitioned into the following categories of
expenses: for staff, for services and commodities, for extraordinary costs, for general
medicine, for drugs, for specialists, for rehabilitation, for prosthesis, for hospital and for
other kind of health assistance.
• The demographic population, i.e. the number of residents in each region at the beginning of
the year 2003 (data available on www.demo.istat.it ). Figures 5, 6 and 7 show the
distribution of the population by age, respectively, for the overall population and for each
region. We observe that the distribution of the overall population is skewed to the left, with
much higher percentage of elderly than that of young people. Figure 6 shows, on the other
hand, that the regions in South Italy and Trentino Alto Adige are characterized by higher
percentages of young inhabitants (less than 44 years old) than the rest of Italy. In Figure 7,
instead, we see that higher proportion of middle aged individuals live in the bigger cities,while a higher percentage of elderly live in small villages.
7 Different equivalence scales have been applied, in literature, for the health related transfers; see, e.g. Citoni et al.
(2006).8 In particolar, we employ the Appendix SA-1 “Costi del SSN per funzioni di spesa. Anni 2001-2004”.
http://www.demo.istat.it/http://www.demo.istat.it/
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Fig. 5: Distribution (in %) of the overall Italian population in 2003, by age.
Distribution (in %) of
population by age
1.582.65
9.28
39.93
26.63
10.789.16
0
5
10
15
20
25
30
35
40
45
75 years
Source: ISTAT, in www. demo.istat.it
Fig. 6: Distribution (in %) of the population in each region, by age. Year 2003.
0%
20%
40%
60%
80%
100%
P i e m
o n t e
V a l l e
d ' A o s t a
L o m b a r d i a T r
e n t i n o
V e n e t o
F r i u l i - V
e n e z i a
L i g u r i a
E m i l i a
- R o m a g n a
T o s c a n a
U m b r i a
M a r c h e L a z i o
A b r u z z o M
o l i s e
C a m p a n i a P u
g l i a
B a s i l i c a t a
C a l a b r i a S i
c i l i a
S a r d e g n a
I T A L I A
OVER75 YEARS
65-74 YEARS
45-64 YEARS
15-44 YEARS
5-14 YEARS
1-4 YEARS
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Fig. 7: Distribution (in %) of population, by age and size of municipality. Year 2003-2004.
14.26 14.19 14.27
67.07 66.78 64.47
18.67 19.03 21.26
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
>50000 inhabitants 2000-50000 inhabitants 75 4.290 3.111 0.064
Source: Ministero della salute (2006) and AA.VV. (1999).
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After collecting all such information, we proceed in the following way.
First, we group the different kinds of healthcare expenditure into seven categories (see Table 2):
I. Hospital expenses: the total hospital-related expenses plus 85% of the 75% of expenses for
commodities and services plus 85% of the 75% of expenses for staff and plus administrative costs
(proportional to the share);9
II. Expenses for specialists: the entire specialists-related expenditure plus 15% of the 75% of
commodities and services expenses plus 15% of the 75% of expenses for staff plus administrative
costs (proportional to the share);
III. Pharmaceutical expenses: the total pharmaceutical costs plus administrative costs
(proportional to the share);
IV. General medicine and other assistance: general medicine-related costs plus expenses for
rehabilitation plus expenses for prosthesis plus other kinds of assistance plus administrative costs
(proportional to the share);
V. Expenses for prevention: 40% of the 25% of the expenses for commodities and services
plus 30% of the 25% of expenses for staff plus administrative costs (proportional to the share);
VI. Expenses for national health assistance: 40% of the 25% of expenses for commodities
and services plus 30% of the 25% of expenses for staff plus administrative costs (pro-rata);
VII. Extraordinary expenses: extraordinary expenses plus administrative costs (proportional to
the share).
Table 2: Re-partition of the health care expenditure and corresponding weights.
EXPENSESFOR: HOSPITAL SPECIALISTS DRUGS
GEN ERALMEDICINE PREVENTION
HEALTH ASSISTANCE
EXTRAORDINARY
- total hospital-related
expenses;
- 85% of the75% of
expenses forcommoditiesand services;
- 85% of the75% ofexpenses for
staff;
- administrativecosts
(proportional tothe share).
- specialists-related
expenditure;
- 15% of the75% of
commoditiesand servicesexpenses;
- 15% of the75% of
expenses forstaff;
- administrativecosts
(proportional tothe share).
- pharmaceuticalcosts;
- administrativecosts
(proportional tothe share).
- generalmedicine-
related costs; - expenses for
rehabilitation;
- expenses forprosthesis;
- other kinds ofassistance;
- administrativecosts
(proportionalto the share).
- 40% of the25% of the
expenses forcommoditiesand services;
- 30% of the25% of
expenses for
staff;
- administrativecosts
(proportionalto the share).
- 40% of the25% of
expenses forcommoditiesand services;
- 30% of the25%of
expenses forstaff;
- administrativecosts (pro-
rata).
- extraordinaryexpenses;
- administrativecosts
(proportionalto the share).
WEIGHTSUSED:
HOSPITALWEIGHTS
SPECIALISTSWEIGHTS
DRUGSWEIGHTS
SPECIALISTSWEIGHTS
PER CAPITASPECIALISTS
WEIGHTSPER CAPITA
9 The total expenses for commodities and services and for staff are partitioned into the main types of health services,
according to estimations provided in Ministero dell’Economia e delle Finanze (2003). It has been estimated that 75% of
the expenses for commodities and services and for staff are for the public expenses, 85% of which are for hospital and15% of which are for healthcare of specialists. The remaining 25% of expenses for staff is partitioned into: 40% foradministrative costs, 30% for costs for prevention and 30% for health assistance. The remaining 25% of expenses forcommodities and services is partitioned into: 40% for prevention, 40% for health assistance and 20% for administrativecosts.
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Figure 8 shows the total health care expenditure in the year 2003 for each region, according to
the above partition. We see that the main source of expenses is due to the hospital care, followed
by the pharmaceutical and by the general medicine care.
Fig. 8: Health care expenditure of each region, by kind of expenditure. Year 2003.
KIND OF HEALTHCARE EXPENSES
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
V a l l e
d ' A o s t a
M o l i s e
B a s i l i c a
t a
T r e n t i n
o
U m b r i a
F r i u l i V
e n e z i a G i u l i a
A b r u z z
o
M a r c h e
S a r d e g n a
L i g u r i a
C a l a b
r i a
T o s c a n a
P u g l i a
E m i l i a
R o m a g n a
P i e m
o n t e
V e n e t o
S i c i l i a
L a z i o
C a m p a n i a
L o m b a r d i a
I T A L I A
HOSPITAL SPECIALIST PHARMACEUTICAL GENERAL MEDICINE PREVENTION GENERAL HEALTH CARE EXTRAORDINARY
Source: Ministero dell’Economia e delle Finanze (2004)
The second step is to take into account the expenses for the interregional mobility of the patients,
by subtracting from the health care expenditure of each region the corresponding total mobility;10 in
particular, we partition the total amount of net mobility into the seven categories of health
expenditure, according to the corresponding share of total expenditure, in the following way:
ij
ij i
i
E M M
E = ,
where Mij is the net mobility of region i corresponding to the kind j of health expenditure, Mi is the
total net mobility of region i, Eij is the health expenditure of region i of the type j and Ei is the total
expenditure for health care of region i.
Figure 9 shows how the total expenditure of each region changes whether we take into account
the interregional mobility or not. In general, very small changes occur and, in particular, Lombardia,
Emilia Romagna and Lazio, which are characterized by high mobility, show the highest decrease in
expenditure when the mobility is taken into account; the highest increases occur, instead, for
Campania, Calabria and Sicilia.
10 Data on the net interregional mobility are taken from Table SA3 in “Relazione Generale sulla situazione economica
del Paese” by Ministero dell’Economia e delle Finanze (2004).
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Fig. 9: Total health care expenditure of each region, with and without mobility effect.
Year 2003.
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
P i e m
o n t e
V a l l e
d ' A
o s t a
L o m b a
r d i a
T r e n
t i n o
V e n e
t o
F r i u l
i - V e n
e z i a
L i g u r
i a
E m i l i a
- R o m
a g n a
T o s c
a n a
U m b r
i a
M a r
c h e
L a z i o
A b r u
z z o
M o l i
s e
C a m
p a n i a
P u g l i
a
B a s i l
i c a t a
C a l a b
r i a S i
c i l i a
S a r d
e g n a
total expenses without mobility total expenses with mobility
Source: Ministero dell’Economia e delle Finanze (2004).
The third step, in order to obtain the per capita health expenses per age, is to assign to each
individual the regional health expenses net from mobility.
For each region, the health expenses of kind V. and VII. (see the above list) are allocated
among individuals per capita. For health costs of type I., we employ the weights for hospital
expenses, indicated in Table 1; for health costs II., IV. and VI., we use the weights for expenses for
specialists; for costs III., we use the weights for pharmaceutical expenses. Such procedure is
shown also in the last row of Table 2.
For each region and for each of the seven types of health care expenditure discussed above,
we reproduce the following Table 3, which allow us to obtain, in its last column, what we want: the
expenses per capita differentiated for age classes.
Table 3: Procedure for obtaining the expenditure per capita by age (“e”=per capita expenditure)
Agerealpopulation weight
EquivalizedPopulation Ratio %
re-partitionedpopulation
expenditure percapita,by age
75 years p8 c8 p8*c8 r8=p8*c8*100/S2 rp8=r8*S1/100 e*rp8/p8Total S1=p1+…+p8 S2=p1*c1+…+p8*c8 S3=rp1+…+rp8
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The last two columns of Table 3 show that the population in each age group is recalculated, by
using the weights of the third column (which are the coefficients of Table 1); then the per capita
expenditures weighted by age are obtained multiplying the per capita expenditure (indicated with
“e” ) by the ratio of such re-partitioned population over the real population, for each age group.
The last step consists of summing, in correspondence of each age group and each region, the
seven different kinds of per capita health expenses, weighted by age and gender.
The per capita expenses that we obtain for each age group, for each region and for each type
of expenses represent the individual health transfers in kind and are shown in Figure 10.
Figure 10 shows that the regions assigning higher per capita weighted amount of health
transfers are Trentino and Valle d’Aosta, while Campania has the lowest per capita weighted
transfer.
Fig. 10: Health care amounts per capita per age groups (Euro). Year 2003.
HEALTHCARE AMOUNTS PER-CAPITA PER AGE GROUP (EURO)
500
1000
1500
2000
2500
3000
3500
4000
4500
75
ABRUZZO
VAL D'AOSTA
BASILICATA
CALABRIA
CAMPANIA
EMILIA
FRIULILAZIO
LIGURIA
LOMBARDIA
MARCHE
MOLISE
PIEMONTE
PUGLIA
SARDEGNA
SICILIA
TOSCANA
UMBRIA
VENETO
TRENTINO
Source: Own elaboration of data in Ministero dell’Economia e delle Finanze (2004).
5. Results from the empirical analysis
In the following analysis, we focus only on the change in the distribution of income after health
transfers in kind. We underline in particular which are the groups that are the most affected by
such public intervention, comparing their final income distributions.
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We do not discuss the implications of health transfers in kind on inequality and poverty, since,
according to our opinion, the increase in mean income attributed to some groups of individuals due
of their health condition cannot be considered as an improvement in their socio-economic state.
IR relevance on the overall population
When we add the healthcare transfers to the cash-income of each individual, the overall
disposable income obviously increases; the degree of such increment, i.e. the health related
transfers relevance, is described in Table 4.
Table 4 compares the income share, by quintile, of the baseline distribution (based on cash
income) with the distribution of income including the healthcare transfers in kind. The share of
income increases for the three lowest quintiles and reduces for the two highest quintiles.
Therefore, health transfers in kind are able to reduce the overall dispersion in the income
distribution.
Table 4 shows, moreover, the percentage increase in income due to health transfers, by
quintile; we observe that the relevance of such transfers decreases drastically with income. In
particular, looking at the subgroups defined by age, we note that when health transfers are added
only to the group of individuals aged between 15 and 64, which are about the 67% of the
population, the entire society benefits much more than in case of health transfers added to the
other two groups. Figure 11 provides a clear insight into such trend.
In absolute terms, same results hold when looking at the absolute equivalized health transfer
mean, shown in the last columns of Table 4. When health benefits are transferred either to the
youngest group or to the sole elderly, the absolute mean transfer decreases with income, because
young and old people are mainly located in the lowest quintiles; for the middle-aged individuals, the
average amount of the transfers increases with income, revealing that such group is mainly located
in the highest quintiles. When the health transfers in kind are added to the entire population, such
amount slightly increases when moving from the lowest to the middle-income quintiles and then
reduces from the middle to the highest income quintile.
Tab. 4: Relevance of health related transfers in kind. Year 2003.
INCOMESHARE
% INCREASE IN DISPOSABLEINCOME DUE TO TRASFERS TO
PERSONS AGEDMEAN TRASFER PER CAPITA DUE TOTRASFERS TO PERSONS AGED
QUINTILE A B 0-14 15-64 65+ ALL 0-14 15-64 65+ ALL
1 7.4 8.8 5.7 20.3 10.3 36.3 317 1127 574 2019
2 12.7 13.7 3.1 11.5 8.3 22.9 297 1088 790 2175
3 17.1 17.6 1.9 9.1 5.8 16.9 245 1167 747 2159
4 22.6 22.3 1.1 7.7 3.7 12.6 191 1301 627 2118
5 40.1 37.6 0.6 4.5 1.7 6.9 190 1352 518 2060 ALL 100.0 100.0 1.7 8.1 4.4 14.1 248 1205 650 2101
Source:EU-SILC 2004
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Fig. 11: Increase (%) in disposable income due to transfers to persons aged
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
1 2 3 4 5 ALL
0-14 15-64 65+ ALL
Source: EU-SILC 2004.
The following Figures 12 to 15 show the kernel density estimation for the income distribution
before and after health transfers in kind, both for the overall population and for subgroups defined
by age; we note that, in each graph, the income distribution is shifted to the right after the transfers,
with the most evident impact attributed to the above65 years old group.
Fig. 12: Kernel estimation of the income distribution before (red line) and after (blue line) health
transfers in kind, overall population.
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Fig. 13: Kernel estimation of the income distribution before (red line) and after (blue lin
e) health transfers in kind, for group aged 25 years old and below.
Fig. 14: Kernel estimation of the income distribution before (red line) and after (blue line) health
transfers in kind, for group aged 26 to 64 years old.
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Fig. 15: Kernel estimation of the income distribution before (red line) and after (blue line) health
transfers in kind, for group aged 65 years old and above.
Finally, Figure 16 shows the change in the relative income position (defined as the ratio of the
income mean of the subgroup over the overall income mean) before and after the introduction of
the health related transfer to the entire population; we observe clearly that the higher increase in
income position is in correspondence of the older individuals, while a slight loose in income
position happens for young and middle-aged individuals.
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Fig. 16: Relative income position before and after health related transfers. Year 2003.
Change in relative income position due to health transfers by age group
0
20
40
60
80
100
120
75 years
baseline income income+health
Source: EU-SILC 2004.
IR relevance by groups
It is interesting now to decompose the percentage increase of income by subgroups,
partitioning the population according to different socio-economic characteristics; Table 5 reveals, in
particular, in which subgroups of the population the health transfers in kind have greater impact.
Splitting the population by type of household, we observe that the highest increase in income
mean is attributed to the old singles or couples, although they do not have the lowest income
mean. The lowest increase in income mean is attributed to the couples or singles that live alone
without children or old family member, who have the highest baseline in income mean.
Among the groups based on the socio-economic conditions of the householder, the
unemployed and the pensioner benefit the most from health care transfers, the former mainly
because their baseline income mean is very low and the latter because they are older than the
other kind of householders, in particular than the white collar or self employed.
According to the education level, the least educated householders show the greatest increase
in income; the main reason is due to the fact that the householder with high level of education are
younger on average that the householders with very low level of education and income mean.
When the population is split into age levels, we observe, in both data sets, that the elderly are
the most affected by the health transfers in kind, followed by the group of the below25 years old;
this is due to the shape of the health care amount per capita per age group shown in Figure 10.
The geographical partitions, finally, show that the South of Italy and the smallest villages
register the highest health transfers relevance; the reason of such result is mainly due to the lower
income mean of such beneficiaries groups.
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Tab.5: Change in income mean by groups. Year 2003.
Income position
Characteristic of householdor household head
Pop.
share in%
Income
Mean(Baseline)
% increase
in meanequiv.Income
BaselineIncluding
health
Household type
Older single persons or couples 17.2 13916 24.2 93 102
Younger single persons or couples 14.8 17524 8.3 118 112
Couple with children up to 18 34.9 13845 12.4 93 91
Mono-parental household 2.7 11789 13.0 79 78
Other household types 30.4 15695 14.0 105 105
Socioeconomic group of HH head
Blue collar worker 20.9 12085 13.8 81 81
White collar worker 18.6 17597 9.7 118 113
Self-employed 17.3 17939 9.9 120 116
Unemployed 3.2 8003 21.1 54 57
Pensioner 29.1 15116 18.5 101 105
Other 11.0 12415 19.1 83 87
Educational level of HH head
Tertiary education 9.1 23796 8.1 160 151
Upper secondary education 28.8 16547 11.2 111 108
Lower secondary education 30.4 13315 14.1 89 89
Primary education or less 31.7 12398 21.0 83 88
Age of HH member
Below 25 24.8 13312 13.3 89 89
25-64 55.9 15845 11.5 106 104
Over 64 19.3 14251 23.6 96 104
Area
North 45.3 17090 12.3 115 113
Center 19.3 16000 13.6 107 107
South+islands 35.4 11527 17.9 77 80
City size
>50000 inhabitants 41.9 15912 13.0 107 106
2000-50000 inhabitants 39.5 14709 14.3 99 99
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Disaggregating by regions
Figure 17 orders the regions according to their income mean before the health transfers; the
poorest regions are located in the South of Italy (Calabria, Sicilia and Basilicata), while the richest
are in the North (Lombardia, Valle d’Aosta and Emilia Romagna). After health transfers in kind,
almost every region maintains the same rank, with very slight re-ranking such between Valle
d’Aosta and Emilia Romagna.
Fig. 17: Mean income before and after health transfers (weighted by age).
0
5000
10000
15000
20000
25000
C a l a b
r i a
S i c i l i a
B a s i l i c a
t a
P u g l i a
C a m p a n i a
M o l i s e
S a r d e g n a
A b r u z z
o
I T A L I A
M a r c h e
U m b r i a
L i g u r i a
L a z i o
V e n e t o
T o s c a n a
P i e m
o n t e
F r i u l i - V
e n e z i a
T r e n t i n
o
L o m b a r d i a
V a l l e
d ' A o s t a
E m i l i a
- R o m a g n a
income mean before health transfers income mean after health transfers
Source: EU-SILC 2004.
Figure 18, on the other hand, shows the percentage increase in disposable income by quintile,
for each region. In correspondence of the lowest quintiles, there are differences in the proportions
of income’s increase among regions; the poorest inhabitants of Trentino, Valle d’Aosta and Molise,in particular, receive higher relative increase in their income mean than the poorest inhabitants of
the other regions do. More similar level, among regions, of the percentage increase in income are
registered in correspondence of the highest income quintiles. For all the regions, the percentage
increase in income mean decreases with income. Table 6 reports the same values illustrated in
Figure 18.
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Fig. 18: Increase (in %) in disposable income, by quint ile. Year 2003.
0
5
10
15
20
25
30
35
40
45
50
1 2 3 4 5
QUINTILE
% I
N C R E A S E I N D I S P O S A B L E I N C O M E
Piemonte
Valle d'Aosta
Lombardia
Trentino
Veneto
Friuli-Venezia
Liguria
Emilia-Romagna
Toscana
Umbria
Marche
Lazio
Abruzzo
Molise
Campania
Puglia
Basilicata
Calabria
Sicilia
Sardegna
ITALIA
Source: EU-SILC 2004.
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6. Concluding remarks
In this report, we convert into monetary amounts the benefits in kind related to the public
supply of healthcare, taking into account both age and gender of the individuals.
In particular, very useful are the weights that are usually employed by the regions and the State
in order to allocate among the regions the national funds for healthcare; such coefficients weigh
the population differently according to age and gender. We are therefore able to obtain per capita
healthcare costs, weighted by age and gender, and add them to the individual cash income, in
order to obtain an accurate value of personal income that takes into account also the benefits from
the public service of health.
Health benefits are easy to predict from a life cycle analysis; health care benefits are, in fact,disproportionately high for the elderly. Since single elderly on average have low income, these
benefits represent a large proportion of the latter.
The most affected groups, indeed, that emerge from our analysis are the over65, the old
singles and couples and the households with unemployed, pensioner or less educated
householder.
Disaggregating by regions, after the health related transfers in kind, very slight re-ranking of the
regions occurs, in term of income position.
Implications on the levels of inequality and poverty are discussed in the Appendix A; in Appendix B a robustness analysis is conducted, which is alternative to the one conducted in the
main part of the report and by employs the per capita health care expenditure differentiated by age
and gender that are provided by Marical et al. (2006).
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Appendix A: Addi tional evidence from empirical analysis
In this appendix, we discuss the implications of the health related benefits on the degree of
inequality and poverty, both for the overall population and for several subgroups.
Effects on overall inequality and poverty
Different inequality measures are considered in order to capture differences along the entire
distribution of income; as absolute inequality measure,11 we consider the Gini mean difference and
as relative indices,12 we compare the Gini index and the Atkinson index for different value of the
sensitivity parameter.
Table A1 shows that inequality has decreased when including health transfers in the income ofthe entire population; this is true for all the relative measures considered. In particular, the
percentage in the reduction is high for the inequality measures that are more sensitive to changes
in the tails of the distribution, i.e. the highest reduction is registered in correspondence to the
Atkinson index with the highest value of the inequality sensitive parameter. According to the
absolute Gini measure, instead, inequality slightly increases when adding health transfers to the
whole population. Such opposite trend of the relative and absolute inequality measures is because
the relative health transfers are decreasing with income, while the absolute health transfers are
slightly increasing with income (see Table 4). The same holds also in case of health transfersadded only to the 15-64 aged, while for the remaining two groups both absolute and relative
inequality decrease.
The result is in accordance with most of the studies on health transfers in kind (see, among
others, Baldini et al., 2006).
The reduction in income inequality reflects both the greater importance of health care services
for those at the bottom of the income distribution (i.e. the ratio of health care benefits to disposable
income declines across the distribution) and a distribution of public health care services that tends
to benefit most those in the lower quintiles.
The last three rows of the Table A1 show, instead, the Foster-Greer-Thorbecke (FGT) poverty
index for three different parameters; the poverty line is not fixed, in this analysis, i.e. it has been
updated with health transfers. The result on the overall distribution is a consistent decrease in all
the poverty indices; moreover, the percentage of reduction increases as the parameter of
sensitivity increases.
11 “An index of absolute inequality demands invariance to equal additions to all incomes.” (Lambert (2001),page 111)12
An index that satisfies the scale invariance, i.e. an index that is not “affected by equiproportionate (scale)changes in all incomes” is a relative index. (Lambert (2001), page 110)
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Focusing on the subgroups, when health transfers in kind is added only to the youngest and
middle aged groups poverty decreases, while if the transfers are added only to the elderly, keeping
constant all the other groups, the headcount ratio reduces but the remaining measures of poverty
increase.
Tab. A1: Change in the overall inequality and poverty indices. Year 2003.
VALUE OF THE INDEXPROPORTIONAL CHANGE DUE TO
TRANSFERS TO PERSONS AGED
A B 0-14 15-64 65+ ALL
Gini meandifference
4846 4878 -10.9 5.3 -3.0 0.7
Gini 0.325 0.287 -1.9 -5.8 -2.9 -11.8
Atkinson 0.5 0.091 0.070 -3.9 -12.1 -4.7 -22.4
Atkinson 1.5 0.272 0.192 -5.0 -17.6 -2.4 -29.2
FGT0 0.186 0.144 -4.3 -8.9 -2.9 -22.8
FGT1 0.055 0.037 -6.5 -19.8 2.3 -33.1
FGT2 0.027 0.015 -9.8 -28.8 4.3 -43.1
Source:EU-SILC 2004
Effect on inequality and poverty by groups
Table A2 decomposes the changes in inequality by subgroups. For each different partition, the
inequality within groups is always much higher than the inequality between groups, i.e. itspercentage contribution to aggregate inequality is always greater than 90%; moreover, the
introduction of health transfers decreases both the within groups and the between groups
inequality, causing a decrease in total inequality.
When health transfers are added to the whole population, inequality decreases more for older
singles and couples and for the mono-parental households, for the families with unemployed or
pensioner householder, for families whose head has primary or no education, for the individuals
aged more than 64 years old. Moreover, inequality reduces more in the South Italy and islands and
in the towns with less than 2000 inhabitants. Such results coincide with what has been discussed
about the decomposition of the percentage increase in mean income by subgroups.
The impact of health transfers in kind on inequality can be analyzed graphically through the
Lorenz and Generalized Lorenz curves.
Figure A1 shows that before and after health transfers in kind, the group of over65 is the most
equal, while the Lorenz curves of the other two groups intersect. From Figure A2, moreover, we
see that, while there is no dominance among the groups in terms of Generalized Lorenz curves,
after the health transfers in kind such dominance occurs, with the group of over65 enjoying the
highest welfare level, followed by the 15-64 and by the 0-14 aged groups.
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Fig. A1: Lorenz curves before (a) and after (b) health transfers, by age groups (65: green)
(a) (b)
Fig. A2: Generalized Lorenz curves before (a) and after (b) health transfers, by age groups
(65: green)
(a) (b)
Figure A3 and A4 compare the Lorenz and generalized Lorenz curves of the subgroups
created by the regional partition of Italy into North, Center and South. Both before and after the
health related transfers, South of Italy is the most unequal subgroup, while no dominance occurs
for the two remaining groups. In terms of generalized Lorenz curves, both before and after the
transfers a welfare dominance exists among the groups, revealing that the highest welfare level is
enjoyed in the North, followed by the Center and, much further, by the South; after the transfers,
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35
the welfare of all the groups increases, but much more in the south, reducing, therefore, the
distance from the rest of the country.
Fig. A3: Lorenz curves before (a) and after (b) health transfers, by geographical area (North
Italy: blue, Center Italy: red, South Italy: green)
(a) (b)
Fig. A4: Generalized Lorenz curves before (a) and after (b) health transfers, by geographical
area (North Italy: blue, Center Italy: red, South Italy: green)
(a) (b)
A last observation regards the impact of health benefits on poverty levels, by subgroups, is
synthesized by Table A3. According to each of the measures FGT0, FGT1 and FGT2, poverty
reduces in each of the groups defined by different partitions. Each of the poverty measures
reduces mostly for old single or couples, for pensioner householders, for householders with
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primary or less education, for the over64, for North and Center of Italy and for the very small
villages.
Tab. A2: Change in inequality, by subgroups. Year 2003.E F G H
Mean LogDeviation
(MLD) Baseline
% contribution toaggregate inequality
Characteristic of household or household head
% changein
inequality
BaselineIncome
andHealth
Household type
Older single persons or couples (at least one 65+) 0.138 -35.6 12.3 10.7
Younger single persons or couples (none 65+) 0.208 -20.0 16.0 17.3
Couple with children up to 18 0.205 -25.8 37.0 37.2
Mono-parental household 0.273 -30.7 3.8 3.6
Other household types 0.181 -25.3 28.5 28.8
% Within groups inequality 0.188 -26.2 97.7 97.6% Between groups inequality 0.004 -23.7 2.3 2.4
Socioeconomic group of HH head
Blue collar worker 0.133 -25.9 14.3 14.4
White collar worker 0.121 -17.8 11.7 13.0
Self-employed 0.292 -21.3 26.2 27.9
Unemployed 0.379 -41.6 6.2 4.9
Pensioner 0.129 -30.6 19.4 18.2
Other 0.236 -33.2 13.5 12.2
% Within groups inequality 0.176 -26.7 91.2 90.5
% Between groups inequality 0.017 -20.2 8.8 9.5
Educational level of HH head
Tertiary education 0.209 -16.3 9.8 11.1
Upper secondary education 0.164 -20.6 24.5 26.3
Lower secondary education 0.183 -26.8 28.8 28.5
Primary education or less 0.161 -32.5 26.4 24.1
% Within groups inequality 0.173 -25.6 89.5 90.1
% Between groups inequality 0.020 -30.2 10.5 9.9
Age of HH member
Below 25 0.216 -27.0 27.7 27.4
25-64 0.196 -23.6 56.8 58.8
Over 64 0.140 -35.3 14.0 12.3
% Within groups inequality 0.190 -26.2 98.6 98.4
% Between groups inequality 0.003 -19.1 1.4 1.6
Area
North 0.155 -23.3 36.5 37.9
Center 0.161 -24.7 16.0 16.3
South+islands 0.214 -30.1 39.3 37.2
% Within groups inequality 0.177 -26.4 91.9 91.5
% Between groups inequality 0.016 -22.3 8.1 8.5
City size
>50000 inhabitants 0.204 -24.6 44.3 45.2
2000-50000 inhabitants 0.184 -26.3 37.7 37.6
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The remaining graphs illustrate the differences in percentage reduction of inequality and
poverty among regions, after the introduction of the health benefits. In Figure A5 we observe that
the regions may be grouped into three clusters, according to the levels of their pre and post Gini
index: regions with high pre and high post Gini, regions with low pre and low post Gini and regions
with middle pre and middle post Gini index.
Fig. A5: Gini index before and after health transfers, for each region. Year 2003.
ITALY
Piemonte Aosta
Lombardia
TrentinoVeneto
Friuli
Liguria
Emilia-Romagna
Toscana
Umbria
Marche
Lazio
Abruzzo
Molise
Campania
Puglia
Basilicata
Calabria
Sicilia
Sardegna
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.3
0.31
0.32
0.23 0.25 0.27 0.29 0.31 0.33 0.35 0.37
GINI BEFORE HEALTH TRANSFERS
G I N I A F T E R
H E A L T H
T R A N S F E
R S
Source:EU-SIL 2004
Fig. A6: Gini index before and after health transfers, by region. Year 2003.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
F r i u
l i - V e n
e z i a
V e n e
t o
B a s i l i c a
t a
T r e n
t i n o
M a r
c h e
T o s c
a n a
M o l i
s e
L i g u r i a
U m b r
i a
E m i l i a
- R o m
a g n a
L o m b a
r d i a
P i e m
o n t e
S a r d
e g n a
V a l l e
d ' A
o s t a
P u g l i
a L a
z i o
I T A L
Y
A b r u
z z o
S i c i l i a
C a l a b
r i a
C a m
p a n i a
Gini baseline Gini baseline+health
Source: EU-SILC 2004.
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Figures A6 and A7 show a slight re-ranking of the regions in terms both of Gini index and of
FGT2 poverty index. From Figure A8 we note that, among all the regions, Molise, Basilicata,
Trentino register the highest decrease in inequality, while Trentino, Abruzzo and Molise in poverty.
Fig. A7: Foster-Greer-Thorbecke (FGT2) index of poverty before and after health transfers, byregion. Year 2003.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
T r e n
t i n o
V e n e
t o
F r i u l
i - V e n
e z i a
E m i l i a
- R o m
a g n a
L o m b a
r d i a
M a r
c h e
T o s c
a n a
U m b r
i a
V a l l e
d ' A
o s t a
P i e m
o n t e
A b r u
z z o
L i g u r i a
L a z i o
I T A L
Y
S a r d
e g n a
M o l i
s e
B a s i l i c a
t a
P u g l i
a
C a m
p a n i a
S i c i l i a
C a l a b
r i a
FGT2 baseline FGT2 baseline+health
Source: EU-SILC 2004.
Fig. A8: Decrease (in %) of Gini and FGT2 after health transfers, by region. Year 2003.
-70
-60
-50
-40
-30
-20
-10
0
I T A L Y
P i e m
o n t e
V a l l e
d ' A o s t a
L o m b a r d i a
T r e n t i n
o
V e n e t o
F r i u l i - V
e n e z i a
L i g u r i a
E m i l i a
- R o m a g n a
T o s c a n a
U m b r i a
M a r c h e
L a z i o
A b r u z z
o
M o l i s e
C a m p a n i a
P u g l i a
B a s i l i c a
t a
C a l a b
r i a
S i c i l i a
S a r d e g n a
Gini FGT2 Source:EU-SIL 2004
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Tab. A4: Inequality and poverty changes, by region. Year 2003.
GINI ATKINSON 0.5 ATKINSON 1.5 FGT0 FGT1 FGT
REGION Baseline%change Baseline
%change Basel ine
%change Basel ine
%change Basel ine
%change Basel ine
%c
Piemonte 0.309 -11.0 0.081 -20.3 0.223 -23.4 0.166 -27.6 0.040 -42.3 0.016
Valle d'Aosta 0.317 -12.5 0.086 -23.3 0.252 -29.6 0.102 -40.7 0.028 -45.7 0.014Lombardia 0.303 -10.5 0.082 -19.1 0.220 -23.5 0.088 -29.7 0.022 -38.0 0.011
Trentino 0.280 -14.6 0.064 -27.2 0.198 -34.1 0.094 -55.3 0.018 -59.3 0.007
Veneto 0.274 -12.2 0.064 -22.3 0.196 -32.2 0.088 -27.9 0.020 -45.2 0.008
Friuli-V.G. 0.273 -11.3 0.062 -21.1 0.185 -25.5 0.086 -35.2 0.019 -40.9 0.008
Liguria 0.295 -12.1 0.075 -23.2 0.253 -34.2 0.128 -29.3 0.038 -35.3 0.019
Emilia R. 0.296 -10.9 0.076 -19.9 0.213 -23.7 0.077 -29.1 0.019 -37.4 0.009
Toscana 0.282 -11.3 0.075 -13.1 0.206 -17.1 0.125 -29.0 0.031 -34.7 0.013
Umbria 0.296 -11.6 0.075 -13.1 0.206 -17.1 0.125 -29.0 0.031 -34.7 0.013
Marche 0.281 -12.2 0.068 -22.3 0.191 -24.8 0.122 -39.5 0.025 -39.7 0.011
Lazio 0.319 -11.4 0.086 -22.1 0.260 -28.6 0.152 -26.3 0.043 -35.0 0.021
Abruzzo 0.329 -12.9 0.096 -22.8 0.234 -24.0 0.169 -31.0 0.042 -42.9 0.018
Molise 0.288 -15.0 0.070 -29.4 0.227 -35.1 0.204 -28.4 0.067 -38.8 0.035
Campania 0.351 -12.5 0.107 -24.1 0.303 -28.9 0.355 -15.6 0.118 -27.4 0.060
Puglia 0.319 -13.2 0.088 -26.6 0.303 -39.3 0.329 -14.6 0.111 -30.6 0.055
Basilicata 0.279 -14.6 0.065 -28.2 0.208 -32.4 0.314 -31.3 0.082 -36.8 0.038
Calabria 0.350 -13.3 0.107 -25.9 0.322 -34.8 0.392 -13.0 0.133 -29.2 0.067
Sicilia 0.349 -14.4 0.102 -27.9 0.322 -37.1 0.396 -20.8 0.129 -32.2 0.065
Sardegna 0.312 -13.0 0.084 -24.5 0.254 -32.3 0.223 -29.2 0.057 -36.7 0.027
ITALY 0.325 -11.76 0.091 -22.41 0.272 -29.23 0.186 -22.76 0.055 -33.08 0.027
Source: EU-SILC 2004.
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Appendix B: Results from alternative health expenditure data
For comparative purpose, within the AIM-AP project, we apply the same method, insurance-
based, on the same data set, EU-SILC 2004, but using different source for the health care
expenditure amounts per capita per age group, the one used in Marical et al. (2006). The health
care costs per capita per age group employed in this second analysis are illustrated in Table B1.
The definition of total health expenditures considered is the following: “total expenditure on
health measures the final consumption of health care goods and services plus capital investment in
health care infrastructure. This includes spending by both public and private sources on medical
services and goods, public health and prevention programmes and administration. Excluded are
health-related expenditure such as training, research and environmental health” (OECD (2005)).
Total expenditure in 2003 is about 82,382 millions Euros, according to the definition ofhealthcare expenditure provided by the OECD.
Tab. B1 Health care costs per capita per age group (with correction factor) 2003
AGE HEALTHCARE COST
0-4 1027.72
5-9 682.85
10-14 596.57
15-19 640.78
20-24 730.74
25-29 818.1430-34 884.24
35-39 932.98
40-44 990.84
45-49 1086.49
50-54 1251.70
55-59 1508.19
60-64 1895.15
65-69 2370.35
70-74 2938.46
75-79 3556.47
80-84 4197.7285+ 3949.44
Source: Marical et al.(2006)
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Tab. B2: Relevance of health transfers in kind. Year 2003.
INCOME SHARE
% INCREASE IN DISPOSABLEINCOME DUE TO TRASFERS TO
PERSONS AGED
MEAN TRASFER PER CAPITADUE TO TRASFERS TO
PERSONS AGED
QUINTILE A B 0-14 15-64 65+ ALL 0-14 15-64 65+ ALL1 7.44 8.58 4.79 18.32 11.23 34.33 266 1018 624 1908
2 12.73 13.68 2.69 10.59 9.17 22.45 255 1004 869 2128
3 17.12 17.59 1.65 8.52 6.42 16.59 210 1089 821 2120
4 22.60 22.45 0.98 7.41 4.08 12.47 166 1250 688 2103
5 40.10 37.70 0.54 4.33 1.87 6.74 161 1295 559 2015
ALL 100.00 100.00 1.42 7.57 4.77 13.74 211 1129 711 2049
Source:EU-SILC 2004
Tab. B3: Inequality and poverty changes. Year 2003.
VALUE OFTHE INDEX
PROPORTIONAL CHANGEDUE
TO TRANSFERS TOPERSONS AGED
A B 0-14 15-64 65+ ALL
Gini 0.325 0.290 -1.7 -5.1 -3.0 -10.9
Atkinson 0,5 0.091 0.072 -3.4 -10.9 -4.8 -20.8
Atkinson 1,5 0.272 0.198 -4.6 -16.3 -2.3 -27.0
FGT0 0.186 0.149 -3.9 -7.3 -1.8 -19.9
FGT1 0.055 0.040 -6.0 -17.6 3.8 -28.4
FGT2 0.027 0.017 -8.8 -26.0 5.8 -37.6
Source:EU-SILC 2004
The results are very similar to the ones obtained from the analysis discussed throughout the
report and in Appendix A, even if the levels of the impacts are slightly different.
In particular, the health related transfers in this second analysis induce lower increase in
income share for the lower quintiles and lower decrease in income share for the higher quintiles
than the first analysis does; therefore, the reduction in inequality due to health transfers will be
lower with this second method, as shown in Table B3.
Table B2 shows, moreover, that the percentage increases in income mean are lower with thissecond method, if compared to the method described in the report; we should note that when
transfers are added to the sole elderly, inequality increase much more with the second than with
the first approach.
Finally, looking at the Tables B4 and B5, this analysis underlines that the groups that benefit
the most from the health related transfers are the same underlined by the previous analysis; higher
reduction in inequality and poverty is registered with this second approach, for the groups that
include old individuals, such for the age-related, household type-related and educational level-
related groups.
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Tab. B4: Mean income and inequality changes, by subgroups. Year 2003.
A B D E F G H
% contribution toaggregate inequality
Characteristic of household orhousehold head
Pop.share
in %
IncomeMean
(Baseline)
%increasein meanequiv.
Income
Mean LogDeviationBaseline
% changeIn
inequality
BaselineIncome
andHealth
Household type
Older single persons or couples 17.2 13916 26.7 0.138 -37.5 12.3 10.1
Younger single persons or couples 14.8 17524 8.5 0.208 -20.0 16.0 16.9
Couple with children up to 18 34.9 13845 10.6 0.205 -22.9 37.0 37.6
Mono-parental household 2.7 11789 10.0 0.273 -24.6 3.8 3.8
Other household types 30.4 15695 13.6 0.181 -23.4 28.5 28.8
% Within groups inequality ./. ./. ./. 0.188 -24.5 97.7 97.2
% Between groups inequality ./. ./. ./. 0.004 -6.7 2.3 2.8
Socioeconomic group of HH head
Blue collar worker 20.9 12085 12.1 0.133 -22.4 14.3 14.7
White collar worker 18.6 17597 8.5 0.121 -15.1 11.7 13.0
Self-employed 17.3 17939 9.0 0.292 -18.8 26.2 28.0
Unemployed 3.2 8003 19.1 0.379 -38.1 6.2 5.1
Pensioner 29.1 15116 19.8 0.129 -31.2 19.4 17.6
Other 11.0 12415 19.9 0.236 -31.6 13.5 12.1
% Within groups inequality ./. ./. ./. 0.176 -24.7 91.2 90.4
% Between groups inequality ./. ./. ./. 0.017 -17.3 8.8 9.6
Educational level of HH head
Tertiary education 9.1 23796 7.5 0.209 -14.7 9.8 11.1
Upper secondary education 28.8 16547 10.4 0.164 -17.8 24.5 26.5
Lower secondary education 30.4 13315 13.1 0.183 -23.7 28.8 29.0Primary education or less 31.7 12398 22.0 0.161 -30.5 26.4 24.2
% Within groups inequality ./. ./. ./. 0.173 -23.1 89.5 90.6
% Between groups inequality ./. ./. ./. 0.020 -32.3 10.5 9.4
Age of HH member
Below 25 24.8 13312 11.3 0.216 -23.6 27.7 27.9
25-64 55.9 15845 10.9 0.196 -21.8 56.8 58.5
Over 64 19.3 14251 25.9 0.140 -36.8 14.0 11.7
% Within groups inequality ./. ./. ./. 0.190 -24.5 98.6 98.1
% Between groups inequality ./. ./. ./. 0.003 2.2 1.4 1.9
Area
North 45.3 17090 12.1 0.155 -22.0 36.5 37.5Center 19.3 16000 13.3 0.161 -22.8 16.0 16.3
South+islands 35.4 11527 17.2 0.214 -27.3 39.3 37.7
% Within groups inequality ./. ./. ./. 0.177 -24.4 91.9 91.5
% Between groups inequality ./. ./. ./. 0.016 -20.7 8.1 8.5
City size
>50000 inhabitants 41.9 15912 12.7 0.204 -22.9 44.3 45.0
2000-50000 inhabitants 39.5 14709 14.0 0.184 -24.4 37.7 37.5
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