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2019 Cambridge Business & Economics Conference ISBN : 9780974211428

2019 Cambridge Business & Economics Conference (CBEC)

The consequences of petty corruption on health care system performance:

evidences from the Italian regions

Elisabetta ReginatoDepartment of Economics and Business Studies, University of Cagliari

Viale S. Ignazio 17, I-09123, Cagliari, ItalyTel.: +39 0706753352 Email: [email protected]

Isabella Fadda1

Department of Economics and Business Studies, University of CagliariViale S. Ignazio 17, I-09123, Cagliari, Italy

Tel.: +39 0706753355 Email: [email protected]

Paola PagliettiDepartment of Economics and Business Studies, University of Cagliari

Viale S. Ignazio 17, I-09123, Cagliari, ItalyTel.: +39 0706753355 Email:[email protected]

Aldo PavanDepartment of Economics and Business Studies, University of Cagliari

Viale S. Ignazio 17, I-09123, Cagliari, ItalyTel.: +39 0706753357 Email:[email protected]

Keywords: petty corruption, health care, performance, Italian regions

1 Corresponding authorNovember 24-25, 2019Cambridge, UK 1


The consequences of petty corruption on health care system performance:

evidences from the Italian regions

1 Introduction

Although there is no single, universally-accepted definition of corruption, a widely accepted one is

that used by Transparency International, according to which corruption is: “the abuse of entrusted

power for private gain”. Similar definitions can be found in public sector corruption literature where

this phenomenon is described in terms of use/abuse/misuse of public office/powers for private gain

(among others: Campos & Pradhan, 2007; Golden & Picci, 2005; Kaufmann, 2002; Kolstad & Wiig,

2009; Lambsdorff, 2005; Lindstedt & Naurin, 2010; Tanzi, 1998; Treisman, 2007).

Corruption can take many different forms, ranging from petty to grand corruption. The former is

related to lower-level bureaucrats who control access to public services. The latter is practiced by high

level leaders and senior public servants, it involves huge amounts of money or political influence and

it is usually associated with procurement and investment decisions (Hussmann, 2011; Mungiu-Pippidi,

2016).

The Health care sector, more than others in the society, is prone to corruption due to uncertainty

surrounding the demand for health care services, large numbers of dispersed actors and asymmetric

information among them (Savedoff and Hussman, 2006; Holmberg and Rothstein, 2011; Vian, 2015).

In addition, this sector is particularly vulnerable to abuse because so much public money is allocated

to health spending in many countries and private actors are entrusted with important public roles. As

for the latter, when private actors act dishonestly, they are not formally abusing "public office for

private gain", but they are abusing the public’s trust (Savedoff and Hussman, 2006: 4).

A large number of studies shows the adverse impact of corruption on standard measures of

population health such as child and maternal mortality rates and life expectancy from birth (Azfar,

2005; Gupta et al, 2001; Hanf et al., 2011, Hanf et al., 2013; Lewis 2006; Muldoon et al., 2011; Rose,

2006). Conversely, the consequences of corruption, on the performance of health care systems have

hardly been researched (Klomp and Hann, 2008), especially with regard to the sub-national level. This

level of analysis is particularly fit to measure these aspects, mostly when health service performance

and corruption present vast regional differences (Charron et al., 2015, Charron et al., 2014, Charron,

2013). Then overlooking such substantial differences within countries may lead to distorted inferences

and poorly designed policy prescriptions.

November 24-25, 2019Cambridge, UK 2


The present study aims at contributing to fill this gap in the literature by exploring the relationship

between corruption and health care system performance in a “within-country” perspective. In

particular the study focuses on petty corruption and uses the case of Italian National Health Service

(NHS), composed of twenty regional health care systems (RHSs), to assess to what extent corruption

and the different dimension of RHSs' performance are associated. The Italian case is relevant since

RHSs regional performance are considerably different (Fattore, 1999; Del Vecchio et al, 2015;

Reginato, 2016; Toth, 2014). Moreover, in the European context Italy has among the widest regional

variation in the spread of corruption (Charron, 2013, Charron et al., 2014; Charron et al., 2015); this

particularity demonstrates the futility of attempting to depict corruption with a national aggregate

measure (Seligson, 2006), and emphasises the significance of its analysis.

The paper is structured as follows: Section 2 provides the background of literature on corruption in

health care sector; Section 3 describes the corruption phenomenon in Italian NHS; Section 4 illustrates

research question and method; Section 5 presents the data analysis; Section 6 provides the discussion

of results and proposes some conclusions and implications for future research in this field.

2 Background

Corruption is a systemic feature of any kind of public or private health systems due to several

organisational factors such as a specific mix of uncertainty, large numbers of dispersed actors, and

asymmetric information among them (Savedoff and Hussman, 2006; Vian 2008; Vian, 2015).

The uncertainty surrounding the demand for health care services regards not knowing who will fall

ill, when and what kinds of illnesses people get and how effective treatments are. When people fall ill,

they cannot judge whether the prescribed treatment is appropriate, or they cannot search for the best

service quality, if they ignore the precise nature of their needs. This uncertainty in selecting

monitoring, measuring and delivery of health care services makes it difficult to detect and assign

responsibility for abuses.

Health systems are prone to corruption because of the many involved actors who can be classified

in five categories: regulators, payers, providers, suppliers, and patients. Roles and responsibilities

within health systems are split between them in ways that might increase the number of opportunities

for corruption. The relationships between drug and medical equipment suppliers, health care providers

and policy-makers are often opaque, and these actors may take advantage of their positions for

financial gain, to increase their power and prestige or to expand their market share, while patients

often pay bribes to get privileged access to health services.



Finally, another dimension that makes health sector vulnerable to abuse is the high degree of

asymmetric information among different actors (Holmberg and Rothstein, 2011). For example,

pharmaceutical companies know more about their products than physicians who prescribe them. Thus,

the former powerful market creates risks of bribery and conflict of interest between the business

objectives of the industry and the duty to prescribe the most appropriate drugs (Lantham, 2001;

Kassirer, 2006). Providers are better informed than patients in terms of treatments and medications

and they are in the unique position of telling users what service they need. In particular the patient-

provider relationship is characterised by forms of petty corruption including, among others,

inappropriate ordering of tests and procedures to increase financial gain, use of government resources

for private practice, and informal or under-the-table payments. The latter are those payments made for

services that should be free or to obtain a preferential treatment such as skipping waiting lists, receive

better or more care, and obtaining drugs (Di Tella and Savedoff 2001; Lewis, 2006; Vian, 2006).

Over the past decade a growing number of studies is enquiring how corruption can affect health

care outcomes and what can be done to contrast it (Vian, 2008 and 2015). However, the overall extent

of the phenomenon is not known as, in this sector, it may be hard to attribute corruption to a particular

actor and is not possible to isolate it from a systemic inefficiency, human error or misjudgement

(Savedoff and Hussman, 2006; Segato et al., 2013).

Moreover, since corruption is clandestine, its measure cannot be completely reliable, in the sense

that it's difficult giving precise measures of it, and "perceptions of corruption based on individuals'

actual experiences are sometimes the best, and the only, information we have" (Kaufmann et al., 2006:

2).

The most commonly used indicators, based on the perceived levels of corruption of a country, are

the Transparency International Corruption Perception Index (CPI) and the World Bank Governance

Indicators (WGI). Criticism has been raised regarding the reliability and adequacy of these indicators

(Golden and Picci, 2005; Knack, 2006; Kurtz & Schrank, 2007). For example, according to several

scholars the perceptions contained in such indexes, most often experts’ assessments, do not

necessarily reflect "actual" corruption experienced by residents of the particular country in question

(Donchev and Ujhelyi, 2009; Ko and Samajdar, 2010; Treisman, 2007)

A more targeted measure of corruption for healthcare systems can be found in the Global

Corruption Barometer and, for the European countries, in the Eurobarometer survey on corruption in

Europe. Both surveys cover people perception of the extent of corruption in their country, and

personal experiences of corruption in terms of having paid a bribe for services used in the last 12

months. In this case, the most common critique against such surveys is that data are aggregated at the

national level; as a result corruption is assumed to be uniformly spread across each single nation, thus



overlooking the fact that for different reasons (socioeconomic, demographic, etc.) specific geographic

areas exist within a country wherein corruption is more pervasive (Seligson, 2006; Gingrich, 2013;

Charron et al., 2014).

Corruption affects all health systems, both in developed and developing countries, through the

embezzlement from health budgets, procurement corruption, improper marketing relations (generally

between physicians and pharmaceutical industry), health insurance fraud, or bribes extorted at the

service delivery level (informal payments). The present paper focuses on this latter kind of abuses.

Informal payments2 represent one of the most common forms of corruption in healthcare (European

Commission, 2013); they are a common feature of health systems in which public sector finances and

directly provides for health services, and that are characterised by an supply excess of capital and

human resources, low salaries, lack of accountability and government oversight, and an overall lack of

transparency (Allin et al., 2006; Balabanova and McKee, 2002; Savedoff and Hussman, 2006;

Thompson and Witter, 2000). This form of corruption ranges from the ex ante cash payment to the ex

post gift-in-kind and a major challenge is in distinguishing the nature of payment as a gratuity or bribe

(Lewis, 2002 e 2007; Allin et al., 2006).

Under-the-table payments can undermine official payment systems, distort the priorities of health

sector, encourage unprofessional behaviours, and most of all deter access to health services for people

with less ability to pay (Allin et al, 2006; Belli et al., 2004; Falkingham, 2004; Lewis, 2007; Mæstad

and Mwisongo, 2011). According to many studies informal payments are mainly associated with

inpatient care services, particularly surgery (Allin et al., 2006; Atasanova, 2014; Lewis, 2007; Tambor

et al.,2013; Stepurko et al., 2010).

More generally, corruption in the health sector has severe consequences on access, quality, equity

and effectiveness of health care services (Hussmann, 2011). A growing number of studies have

documented its adverse effects on health outcomes such as child and maternal mortality rates and life

expectancy from birth (Azfar, 2005; Gupta et al, 2001; Hanf et al, 2011, Hanf et al, 2013; Holmberg

and Rothstein, 2011; Lewis 2006; Rose, 2006). In these studies, corruption is often measured using the

World Bank Governance Indicators.

Other studies, using the concept of bribery reported in the Global Corruption Barometer, have

found a positive link between informal payments and death rates for women giving birth (Fagan,

2010) or infant mortality (Muldoon et al, 2011).

2 "Informal payments are payments to individuals or institutions in cash or in kind made outside official payment channels for services that are meant to be covered by the public health care system" (Lewis, 2000: 1)


2019 Cambridge Business & Economics Conference ISBN : 9780974211428There is little evidence on the impact of informal payments on the quality of health services. Informal

payments are proven to increase patients’ costs and may thus induce them to forego or delay care

(Azfar and Gurgur, 2008; Belli et al., 2004; Falkingham, 2004; Lewis, 2000; Rose, 2006), while their

impact on quality is uncertain. Some scholars argue that the quality of services is better for those who

pay informally; this is evidenced by decreased waiting time, longer length of hospital stay and

patients’ subjective ratings of quality (Thompson and Xavier, 1999). It is also true that benefits are

restricted mainly to those patients and to the physicians. The latter, in fact, keep the payment for

themselves rather than using it to improve infrastructure or the availability of supplies (Allin et al.,

2006). Other studies contend that payments may have a negative impact on the quality, through

artificial lowering of service standards, delays of treatment, or for leading to unnecessary additional

services (Balabanova and McKee, 2002; McPake et al., 1999; Gaal et al., 2005).

Overall, the relationship between petty corruption and health care sector performance in terms of

population health status, access to care and quality needs more investigation, especially at the sub-

national level.

3. Health performance and corruption in Italian NHS

The Italian NHS was instituted in 1978 to provide universal coverage free (or almost free)3 of

charge at the point of use, and it is financed by general taxation (Borgonovi, 1988). At the beginning

of the 1990s, also because of the numerous corruption scandals which involved it, the NHS underwent

a comprehensive reform process through which it was newly designed and articulated on a regional

basis. Currently it' is institutionally decentralised. The central level defines the general objectives and

fundamental principles of the NHS and finances the essential levels of health services4 (ELSs). The

RHSs are responsible for the actual planning and delivery of the ELSs to the whole Italian population

through a network of health care organisations and public and private accredited hospitals (Anselmi,

2000; Pavan and Olla, 2000). Overall, in the last two decades Italian regions have enjoyed increasing

and wider autonomy in the way the local NHS functions. This autonomy is such that it has led to the

existence no longer of a single national health service, but rather of 20 different RHSs (Mapelli,

2012). Italy has significantly improved the quality of health care in recent years; indexes for life

expectancy at birth, avoidable hospital admissions, and survival after severe cardio-circulatory

problems are among the best in the EU and OECD countries. The Italian NHS delivers good health

3 In-patient care and general practitioner (GP) services are free of charge, but co-payments are generally required on pharmaceuticals, diagnostic procedures and specialist visits. 4 Essential levels of health services are minimum health services that have to be guaranteed to all citizens; they are defined and financed from central government.November 24-25, 2019Cambridge, UK 6

2019 Cambridge Business & Economics Conference ISBN : 9780974211428services with a lower incidence of healthcare public expenditure on GDP (9,1%) compared to the EU

weighted average (9.9%). In terms of resources, with respect to the EU average, in Italy there are more

doctors and fewer nurses per inhabitant, while equipment levels have increased significantly in the last

years (OECD/EU, 2016, OECD, 2015). These reassuring results, however, mask profound differences

in the RHSs' performance with a relevant gap between northern and southern Italian regions (Fattore,

1999; Del Vecchio et al., 2015; Pavolini and Vicarelli 2012; Reginato, 2016). As a result, many people

move between RHSs in search of better-quality care, with northern RHSs being net importers of

patients (Toth, 2014).

Unfortunately, one of the Italian healthcare most negative aspects is expressly corruption. Although

the efforts made in recent years to contrast this phenomenon, it still remains a serious challenge for the

country (Commissione Europea, 2014). In the last CPI (Transparency International, 2016) Italy

obtained a score equal to 47 out of 100 ranking at the 60th place out of 176 countries.

According to the Eurobarometer surveys on corruption, conducted in 2013 and in 2017,

respectively the 97% and the 89% of the Italian respondents consider corruption a widespread

phenomenon and the 44% (2013) and 45% (2017) perceive it as prevalent in the health sector.

Furthermore, according to the 2013 and 2017 Eurobarometer, the 4% of Italians supported informal

payments to access health services (European Commission, 2014, 2017).

Even if the country’s system is seriously affected by corruption, the different geographical areas are

not involved in the same way. According to some studies, which analyse and asses the levels of

governance in the European regions, including corruption among the various indicators, Italy is the

country with the highest degree of variation at regional level.

In Italian NHS the risk of corruption depends mainly on (Dirindin, 2013, Segato et al., 2014):

the weakness of the regulatory framework governing the system. Corruption opportunities arise from an inadequate and not fully implemented set of constitutional, state, regional, and local rules, which do not clarify functions and powers, and uses complex or ambiguous languages.

The asymmetric information between patients and health care providers on the one hand, and between the health system and the drug and medical equipment suppliers on the other. The first information gap can lead to inappropriate health behaviours and improper consumption. The second one can generate opportunities for abuse when spending decisions are driven by the prospect of private gain. Additionally, the non-fungibility of the health materials is another cause of corruption risk, because it enables public evidence procedures to be bypassed.

The great political interference in the control and appointment of the top health organisations' managers. As a result, the lack of autonomy from politicians by health authorities may also have repercussions on the attribution of consultancy services, and the procurement and accreditation procedures.


2019 Cambridge Business & Economics Conference ISBN : 9780974211428 The ineffectiveness of controls which are made more difficult by the organisational complexity

and decentralisation of the health sector. Ex-post controls are often preferred to internal

prevention strategies. This involves high costs, as ex-post control mechanisms are a remedial

approach that come into play after the system has already paid direct (bribe) and indirect costs

(lower quality and higher costs for the public organisation) of corruption.

The lack of transparency in a sector where, in the name of the privacy protection, access to data

is often denied.

According to recent studies conducted by Segato et al. (2013; 2014), the areas which are most

vulnerable to corruption in Italian health care sector are: appointments, pharmaceuticals, procurement,

private health and medical negligence. In the last area petty corruption, which is the subject of the

present study, is more relevant and generate more social inequalities since it makes access to care

dependent on the patient's income. The two main types of negligence, involving abuse of office and

private gain at the detriment of the citizen, are mismanagement of waiting lists as well as of intra-

moenia (inside the walls)5 activities. Excessive waiting times are the most critical aspect of Italian

health care6 (Fattore et al., 2013).

4. Research objective and method

As previously mentioned, the purpose of the research is to investigate whether and to what extent

petty corruption is related to the different dimensions of RHSs' performance.

To fulfil the research objective the research analyses the case of the Italian RHS and uses different

sources of information to conduct a multivariate analysis. Data from the European Quality of

Government Index (EQI) Survey are used to measure the level of corruption. The EQI survey is the

largest multi-country survey mainly concerned with governance of public sector institutions at the

sub-national level. Questions are framed around the concepts of quality, impartiality, and corruption

and enquires citizens' perceptions and experiences of the three main public sector services: education,

health care, and law enforcement (Charron et al., 2015)

The EQI survey allows to obtain two measures of corruption: one is a subjective indicator which

expresses citizen’s perception about corruption in their region – perceived corruption (PC); the other is

an objective measure which assesses citizens' actual experience – experienced corruption (EC). The

former indicator is built basing on a question of the EQI survey that asked citizens to rate on a 0 to 10

scale – where 0 indicates complete disagreement – the extent to which they agreed with the statement

5 The term intra-moenia refers to the activity of hospital doctors, who have decided to provide services outside the normal working hours, using the hospital’s facilities and diagnostic equipment. The services are generally the same that the doctors provide when they work for the NHS, but the patients have to pay a fee to the doctors. The hospital receives around 25% of the fee.6 The National Plan for Limiting Waiting Lists, established in 2006, sets the maximum waiting times for the provision of services: for specialist appointments 30 days; f or diagnostic appointments days 60.November 24-25, 2019Cambridge, UK 8

2019 Cambridge Business & Economics Conference ISBN : 9780974211428“Corruption is prevalent in the public health care system in my area”. This measure is used in the

research as a proxy for general corruption, which reflects both the grand and the petty one. The

objective indicator, instead, is derived from a question which explicitly asked respondents whether in

the last 12 months, them or a member of their household, paid a bribe in any form to obtain health

services.

In order to measure RHSs' performance, three assessment areas are identified. a) population health

status (HS); b) access to care (A); c) quality of care (Q).

The first area evaluates the results of the RHSs in terms of population health status through three

indicators: life expectancy rate7 (LE); standard death rate8 (STD); Under five mortality rates9 (UFM).

The second area assesses the equity in access to RHSs and the indicators adopted for this aim are: out

of pocket expenditures10 (OOP); specialist medical examinations skipped due to costs11 (MtsC);

medical treatment skipped due to waiting times12 (MtsW).

Finally, the quality of the Italian RHSs is assessed using the index of Avoidable mortality13 (AM) as

a proxy (Fantini et al, 2012; Gay et al., 2011; Gianino et al, 2017; Kossarova et al, 2009; Nolte and

M. McKee 2004, 2008, 2011; Page et al, 2006). Avoidable mortality can be broadly defined as “deaths

from selected disease groups which are considered to be either treatable or preventable through health

care services” (Rutstein et al, 1976) The term includes both amenable and preventable mortality

(Tobias and Jackson, 2001). The former refers to premature death from a set of conditions that should

not occur in the presence of timely and effective health care (Nolte and M. McKee, 2008). The latter

includes deaths considered preventable due to primary care or public health policies (Kamarudeen,

2010).

Basing on the existing literature the research hypothesises that petty corruption has a negative

influence on RHSs ‘performance. Thus, assuming the null hypothesis that corruption has no influence

on RHSs’ performance, the following alternative hypotheses are formulated;

1. H1 Petty corruption is negatively associated with Life expectancy rates;

2. H2 Petty corruption is positively associated with Under five mortality rates;

3. H3 Petty corruption is positively associated with Standard death rates;

7 Source EUROSTAT, “Life expectancy by age, sex and NUTS 2 region” last update 08-03-2019. https://ec.europa.eu/eurostat/web/products-datasets/product?code=tgs001018 Source Progetto MEV(i) - Mortalità Evitabile (con intelligenza). www.mortalitaevitabile.it Last update 12-04-2019.9 Source ISTAT 2018: ITALIAN DATA FOR UN-SDGs. Sustainable Development Goals of the 2030 Agenda. 10 Source ISTAT 2017: Istat. Spesa per consumi finali delle famiglie. Demografia in cifre per la popolazione. Anno 2017.11 Source Istat, Indagine sulle condizioni di vita (UDB IT - SILC) 2010, 2013, 2015.12 Source Istat, Indagine sulle condizioni di vita (UDB IT - SILC) 2010, 2013, 2015.13 Source Nebo Ricerch ePA: Indicatori regionali MEV(i) 2013, 2016 and 2019. https://www.mortalitaevitabile.it/index.php/area-download/category/11-filedati. The index is calculated as the number of deaths per 100,000 inhabitants over the population aged 0–74 yearsNovember 24-25, 2019Cambridge, UK 9

https://www.mortalitaevitabile.it/index.php/area-download/category/11-filedati

http://www.mortalitaevitabile.it/

2019 Cambridge Business & Economics Conference ISBN : 97809742114284. H4 Petty corruption is positively associated with Specialist medical examinations skipped due

to costs;

5. H5 Petty corruption is positively associated with Specialist medical examinations skipped due

to waiting times;

6. H6 Petty corruption is negatively associated with Out of pocket expenses;

7. H7 Petty corruption is positively associated with Avoidable mortality.

As already stated, the analysis is conducted at the regional level (NUTS 2). All the Italian regions

are reported in table1 together with their geographic location.Table 1: Italian RegionsRegion Area Region AreaAbruzzo South Marche CentreBasilicata South Molise SouthCalabria South Piemonte NorthCampania South Puglia SouthEmilia-Romagna North Sardegna SouthFriuli-Venezia Giulia North Sicilia SouthLazio Centre Toscana Centre

Liguria North Trentino Alto Adige North

Lombardia North Umbria CentreMarche Centre Valle d'Aosta North

The EQI’s surveys are available for the years 2010, 2013 and 2017. The analysis is conducted using

the data from these three years jointly, as single period analyses would be less reliable. As a matter of

fact, although each year’s sample consists of nearly 8.000 observations – 400 per region – data

regional grouping reduces the number of observations to 20 for each year.

As for RHSs’ performance, when data for the year 2017 are not available those for 2016 are used.

5. Data analysis

The data sample drawn from the EQI’s survey included 4095, 8510 and 8400 observations

respectively for the years 2010, 2103 and 2017. Every record contained information about the region,

experienced corruption and perceived corruption. For each year of the survey data were grouped into

regional clusters, so that 3 groups of 20 regions were obtained. As for perceived corruption (PC) the

average regional value was computed, while the frequency of the value 1 (which indicates a positive

answer to the question on having paid a bribe) was considered for experienced corruption (EC).

First the relation between the variables was analysed to understand whether and to what extent

RHSs’ performances are associated to perceived corruption and experienced corruption. For all the

variables considered in the analysis, the regional values were computed.


2019 Cambridge Business & Economics Conference ISBN : 9780974211428The correlation test (table 2) shows that the perception of corruption (PC) is positively associated

with infant mortality rate (UFM), standard death rate (STD) and avoidable mortality (AM). The association with life expectancy (LE) and out of pocket expenses (OOP) is negative.

As for EC, it can be observed that it has a positive correlation with infant mortality rate (UFM)– r = 0.227 p = 0.081, avoidable mortality (AM) – r = 0.259, p = 0.046 and MtsC – r = 0.526, p = 0.000 – while no significant correlations appear with the remaining variables.

As the paper aims at highlighting regional differences the correlation test was also performed controlling for geographic location. Two groups were formed: group one including all the northern and central regions and group two including southern regions.

When controlling for geographic location the correlation test shows that in the group 1 (table 3) only MtsC is significantly correlated to PC, while EC is significantly associated to UFM, MtsC and MtsW. In the group 2 (table 4), instead, PC appears significantly associated to all the Health Status variables and also to OOP. As for EC it only appears significantly associated to MtsC.

Basing on these results the analysis then focussed on the relation among experienced corruption (EC) and RHS’s performance related to access to care and quality.

As for the former aspect, the influence of petty corruption on specialist medical examinations skipping due to waiting times or costs were analysed.

First, the influence of corruption on MtsC and Mtsw was tested considering all the regions and then controlling for the geographic location. The regression model also included per capita income among the independent variables. Hence, when considering all the regions (table 5 and 7), the tests showed that EC significantly influences the skipping of medical treatments for economic reasons, while no influence on MtsW appears. When controlling for geographic location (table 6 and 8), MtsC appears as a significant predictor of MtsC in both groups of regions, while it does not appear to influence MtsW in neither the groups.

Finally, with regard to the quality aspect, the relation of corruption with avoidable mortality (AM) was analysed. The correlation test showed a positive correlation EC – r = 0.259, p = 0.046 – thus basing on these results, the possible influence of corruption on avoidable mortality was investigated. The regression test, however did not allow to observe a significant influence of EC when the test was performed on all the regions (table 9). When the analysis controlled for geographic location, instead, EC resulted as a significant predictor of AM in group 1 regions (table 10).

6. Discussion and conclusions

Corruption affects all sectors, but it is an especially critical problem in the health care sector where,

quantitatively, it continues to be a widely obscure phenomenon. The difficulty of quantifying

corruption in the health sector depends on factors such as medical confidentiality, the economic and

structural dimension of the public service, the number and type of actors involved, the quantity and

nature of the services provided, and the difficulties in carrying out systemic checks.


2019 Cambridge Business & Economics Conference ISBN : 9780974211428Many studies show that corruption has a corrosive impact on the population's health status,

measured by indicators such as child and maternal mortality rates and life expectancy from birth

(Azfar, 2005; Gupta et al, 2001; Hanf et al, 2011, Hanf et al, 2013; Lewis 2006; Muldoon et al, 2011;

Rose, 2006), as well as a negative effect on the efficiency of public health spending (Rajkumar and

Swaroop, 2008).

More generally corruption can affect the different performance dimension of a health care system,

reducing the resources available for health, lowering the quality and effectiveness of health care

services, and increasing the cost of provided services. It undermines equity of access to health care by

discouraging people who have to pay in order to use health services. Surprisingly there are only a

handful of studies that have systematically analysed the relation between corruption and these

different performance dimension. Some studies have focused on the effect of petty corruption on the

equity of access or on the quality of the health service delivery (Azfar and Gurgur, 2008; Belli et al.,

2004; Falkingham, 2004; Lewis, 2002; Mæstad and Mwisongo, 2011; Balabanova and McKee, 2002;

McPake et al., 1999; Gaal et al., 2005), but no study has jointly considered the relation of corruption

with the above mentioned performance dimensions, nor has conducted a sub-national level analysis.

The present paper fills this literature gap trying to give a preliminary overview of whether and how

petty corruption is related and affects the different performance dimensions of a health system. In

order to fulfil the research objective, the Italian NHS case was investigated. The relevance of the

Italian case arises from the consideration that in the European context Italy has among the widest

regional variation in both the levels of health care service performance and corruption.

No health system is immune to abuses and frauds, but the specific institutional structures can make

particular kind of corruption more or less attractive. Thus, understanding how a country's health

system functions and analysing its vulnerabilities to the phenomenon was the first step to carry out.

According to previous studies the Italian NHS is prone to corruption because of (Dirindin, 2013;

Segato et al., 2014): the weakness of the regulatory framework governing the system; the asymmetric

information between patients and health care providers and between the latter and the drug and

medical equipment suppliers; the great political interference in the control and appointment of the top

health organisations' managers; the ineffectiveness of controls; the lack of transparency. The health

system areas most exposed to the phenomenon are appointments, pharmaceuticals, procurement,

private health and medical negligence. With respect to the latter area excessive waiting times are the

most critical aspect (Fattore et al., 2013) which are strictly related to the petty corruption related to

paying bribes to skip ahead the waiting lists inside public service institutions.


2019 Cambridge Business & Economics Conference ISBN : 9780974211428Basing on these considerations the study tried to assess whether and to what extent corruption is

associated to RHS’s performance. Three dimensions were analysed: health status, access to care and

quality of health services.

As for health status the results show that perceived corruption is negatively associated with Life

Expectancy and positively associated to Infant mortality rate and Standard death rate, while

experienced corruption is only positively associated with Infant mortality, although the relation

appears weak. However, when considering geographic location, results are mixed and in the northern

and central regions only EC is associated to one of the Health status variables – i.e. infant mortality -.

In the southern regions, instead, it is PC to be correlated to all the Health status variables, while EC

has no significant correlation with any of them. Thus, it is not possible to confirm the research

hypothesis about the relation of petty corruption with this aspect of RHS’s performances which

requires further investigation. The research findings are instead consistent with its assumption about

the importance of analysing corruption at subnational level.

With regard to Access to care, the aspects considered were: medical treatment skipped due to costs,

medical treatment skipped due to waiting list and out of pocket expenses. Out of pocket expenses

showed a strong negative association with perceived corruption, both when controlling for geographic

location and when not. Contrary to the research expectations, petty corruption does not appear related

to this variable either when geographic location was considered and when not.

As for renunciation to specialist medical examination for economic reasons the analysis showed

that this variable is always strongly and positively associated with the payment of bribes. On the

contrary the association with perceived corruption is only significant in the northern and central

regions. Based on these findings, the influence of petty corruption on this aspect of access to care, was

assessed. All the tests performed showed that petty corruption, significantly influences specialist

medical examinations skipping for economic reasons. It is thus possible to confirm the research

hypothesis about the association between petty corruption and specialist medical examination

skipping for economic reasons.

The relation between petty corruption and medical examination skipping due to waiting lists,

instead, appears less straightforward. As a matter of fact, the analysis showed that the two variables

are only significantly correlated when controlling for geographic location in the group of the northern

and central regions. In those cases, a strong negative correlation becomes apparent. Thus, it is not

possible to confirm the research hypothesis about the positive association between EC and MtsW. The

regression analysis did not highlight a significant influence of the payment of bribes on the skipping

of medical examinations either when controlling for geographic location and when not.


2019 Cambridge Business & Economics Conference ISBN : 9780974211428Finally, with respect to quality, the analysis pointed out, the existence of a positive association with

petty corruption, only when all the regions were considered in the analysis, while no significant

correlation appeared when controlling for geographic location. As for the influence, the test showed

that petty corruption only affects avoidable mortality in the northern and central regions. In this last

respect the study can only provide partial evidence of the detrimental effect of petty corruption due to

bribery on the quality of services, that in the extant literature has been scarcely demonstrated

(Balabanova and McKee, 2002; McPake et al., 1999; Gaal et al., 2005).

Taken together our results suggest that petty corruption undermines the population health status and

the equity of health services. This complements cross-country findings on the subject and adds to the

expanding list of ways corruption undermines welfare.

The study has some limitations that should be addressed in future research. First, the number of

variables to be included in each aspect of RHS’s performance should be increased to better capture the

observed phenomenon. Also, expanding the number of observations would increase the reliability of

the findings. In this respect extending the analysis to other countries could add value. Perceived

corruption is used here as a proxy for general corruption, which includes both grand and petty

corruption. However, as no health specific measures are currently available for grand corruption, the

index adopted in the research represents, by now, the only available alternative.



Annex 1

Table 2: Correlation table, all observations

60 60 0.5694 AM -0.0749 1.0000 60 OOP 1.0000 OOP AM

60 60 60 60 60 60 60 0.0436 0.0455 0.0076 0.0084 0.1801 0.0017 0.1149 AM 0.2614* 0.2592* -0.3412* 0.3375* 0.1754 0.3975* -0.2057 60 60 60 60 60 60 60 0.0000 0.1684 0.0005 0.0668 0.0009 0.6182 0.0052 OOP -0.6693* -0.1802 0.4362* -0.2382* -0.4178* 0.0657 -0.3564* 60 60 60 60 60 60 60 0.1133 0.1309 0.0024 0.0696 0.0002 0.0000 MtsW 0.2065 -0.1972 -0.3851* 0.2359* 0.4594* -0.7266* 1.0000 60 60 60 60 60 60 0.1299 0.0000 0.0623 0.8167 0.0025 MtsC 0.1977 0.5255* 0.2422* 0.0306 -0.3838* 1.0000 60 60 60 60 60 0.0138 0.8429 0.0000 0.0000 STD 0.3164* 0.0261 -0.9495* 0.5162* 1.0000 60 60 60 60 0.0052 0.0806 0.0000 UFM 0.3567* 0.2274* -0.5553* 1.0000 60 60 60 0.0069 0.3600 LE -0.3454* -0.1203 1.0000 60 60 0.0028 EC 0.3795* 1.0000 60 PC 1.0000 PC EC LE UFM STD MtsC MtsW



Table 3: Correlation table, northern and central regions

36 36 0.0186 AM 0.3902* 1.0000 36 OOP 1.0000 OOP AM

36 36 36 36 36 36 36 0.2496 0.1146 0.9156 0.0030 0.5618 0.0079 0.1451 AM 0.1969 0.2676 -0.0183 0.4813* -0.1000 0.4355* -0.2478 36 36 36 36 36 36 36 0.0054 0.7656 0.6811 0.0195 0.9443 0.4372 0.1537 OOP -0.4545* 0.0515 -0.0709 0.3877* -0.0121 0.1336 -0.2427 36 36 36 36 36 36 36 0.5302 0.0345 0.0106 0.4133 0.0009 0.0000 MtsW -0.1081 -0.3534* -0.4206* -0.1406 0.5282* -0.7972* 1.0000 36 36 36 36 36 36 0.0488 0.0013 0.0272 0.2187 0.0014 MtsC 0.3308* 0.5163* 0.3681* 0.2101 -0.5134* 1.0000 36 36 36 36 36 0.2442 0.6889 0.0000 0.1900 STD -0.1992 -0.0691 -0.9698* 0.2235 1.0000 36 36 36 36 0.4631 0.0249 0.1098 UFM -0.1262 0.3733* -0.2711 1.0000 36 36 36 0.2975 0.9451 LE 0.1785 -0.0119 1.0000 36 36 0.0707 EC 0.3048* 1.0000 36 PC 1.0000 PC EC LE UFM STD MtsC MtsW

-> var16 = 1



Table 4: Correlation table, southern regions

24 24 0.5720 AM -0.1214 1.0000 24 OOP 1.0000 OOP AM

24 24 24 24 24 24 24 0.5823 0.7785 0.0598 0.3434 0.4436 0.0873 0.0264 AM -0.1182 0.0606 -0.3897* -0.2022 0.1641 0.3565* -0.4525* 24 24 24 24 24 24 24 0.0037 0.9300 0.0000 0.2446 0.0000 0.1932 0.2316 OOP -0.5693* 0.0189 0.8017* -0.2470 -0.8285* 0.2751 -0.2537 24 24 24 24 24 24 24 0.1353 0.1890 0.5531 0.0564 0.2111 0.0000 MtsW 0.3139 -0.2777 -0.1274 0.3946* 0.2648 -0.7886* 1.0000 24 24 24 24 24 24 0.5258 0.0040 0.1777 0.0395 0.0474 MtsC -0.1362 0.5650* 0.2846 -0.4228* -0.4086* 1.0000 24 24 24 24 24 0.0044 0.4698 0.0000 0.0036 STD 0.5604* -0.1549 -0.9365* 0.5704* 1.0000 24 24 24 24 0.0806 0.1005 0.0128 UFM 0.3637* -0.3433 -0.5002* 1.0000 24 24 24 0.0195 0.5549 LE -0.4732* 0.1268 1.0000 24 24 0.3801 EC 0.1876 1.0000 24 PC 1.0000 PC EC LE UFM STD MtsC MtsW



Table 5: regression analysis – corruption and medical examination skipping due to excessive costs.

_cons .3588863 .1315364 2.73 0.008 . Reddito 5.34e-06 6.19e-06 0.86 0.392 .1012187 EC 2.144784 .4530117 4.73 0.000 .5559727 MtsC Coef. Std. Err. t P>|t| Beta

Total 1.99042816 59 .033736071 Root MSE = .15797 Adj R-squared = 0.2603 Residual 1.42232042 57 .02495299 R-squared = 0.2854 Model .568107743 2 .284053871 Prob > F = 0.0001 F(2, 57) = 11.38 Source SS df MS Number of obs = 60

Table 6: regression analysis – corruption and medical examination skipping due to excessive costs – controlling for geo-graphic location

_cons -.0948822 .3315412 -0.29 0.778 . Reddito .0000404 .0000235 1.72 0.100 .289999 EC 1.939037 .5656285 3.43 0.003 .5785801 MtsC Coef. Std. Err. t P>|t| Beta

Total .641602621 23 .027895766 Root MSE = .13504 Adj R-squared = 0.3463 Residual .382955168 21 .01823596 R-squared = 0.4031 Model .258647453 2 .129323726 Prob > F = 0.0044 F(2, 21) = 7.09 Source SS df MS Number of obs = 24

-> var16 = 2

_cons .6827729 .405399 1.68 0.102 . Reddito -.0000117 .0000198 -0.59 0.559 -.0875567 EC 2.356725 .6806021 3.46 0.002 .5137354 MtsC Coef. Std. Err. t P>|t| Beta

Total 1.33694901 35 .038198543 Root MSE = .17147 Adj R-squared = 0.2303 Residual .970268644 33 .02940208 R-squared = 0.2743 Model .36668037 2 .183340185 Prob > F = 0.0050 F(2, 33) = 6.24 Source SS df MS Number of obs = 36

-> var16 = 1



Table 7: regression analysis – corruption and medical examination skipping due to waiting lists. All the regions

_cons .380575 .0655791 5.80 0.000 . Reddito -.0000112 3.09e-06 -3.61 0.001 -.4439841 EC -.6087225 .2258544 -2.70 0.009 -.33107 MtsW Coef. Std. Err. t P>|t| Beta


Table 8: regression analysis – corruption and medical examination skipping due to waiting lists – controlling for geo-graphic location

_cons .6164321 .2065011 2.99 0.007 . Reddito -.0000292 .0000146 -1.99 0.060 -.3834041 EC -.541145 .3523028 -1.54 0.139 -.2956372 MtsW Coef. Std. Err. t P>|t| Beta


-> var16 = 2

_cons .284463 .1801328 1.58 0.124 . Reddito -6.08e-06 8.79e-06 -0.69 0.494 -.1118844 EC -.6668994 .302415 -2.21 0.035 -.3566995 MtsW Coef. Std. Err. t P>|t| Beta


-> var16 = 1



Table 9: regression analysis – Corruption and Avoidable mortality.

_cons 174.1006 18.94067 9.19 0.000 . Reddito -.0013847 .0008919 -1.55 0.126 -.2040437 EC 98.14596 65.23169 1.50 0.138 .1977268 AM Coef. Std. Err. t P>|t| Beta

Total 32953.3726 59 558.531739 Root MSE = 22.746 Adj R-squared = 0.0737 Residual 29491.4296 57 517.393501 R-squared = 0.1051 Model 3461.94304 2 1730.97152 Prob > F = 0.0423 F(2, 57) = 3.35 Source SS df MS Number of obs = 60

Table 10: regression analysis – Corruption and Avoidable mortality – controlling for geographic location

_cons 163.1692 63.98404 2.55 0.019 . Reddito .0002289 .0045364 0.05 0.960 .0110015 EC 30.60144 109.1605 0.28 0.782 .061125 AM Coef. Std. Err. t P>|t| Beta

Total 14317.4998 23 622.499993 Root MSE = 26.061 Adj R-squared = -0.0911 Residual 14263.1754 21 679.198831 R-squared = 0.0038 Model 54.324388 2 27.162194 Prob > F = 0.9609 F(2, 21) = 0.04 Source SS df MS Number of obs = 24

-> var16 = 2

_cons 59.30276 45.50003 1.30 0.201 . Reddito .0040718 .0022203 1.83 0.076 .2931587 EC 132.0518 76.38749 1.73 0.093 .2763414 AM Coef. Std. Err. t P>|t| Beta

Total 14506.849 35 414.4814 Root MSE = 19.245 Adj R-squared = 0.1064 Residual 12222.2157 33 370.370173 R-squared = 0.1575 Model 2284.63328 2 1142.31664 Prob > F = 0.0592 F(2, 33) = 3.08 Source SS df MS Number of obs = 36

-> var16 = 1



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