Consumers Credit in Italy

8/2/2019 Consumers Credit in Italy

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Universita degli Studi di Milano

Finance, Business, and Marketing

Year Paper

Consumer credit in Italy. Diffusion and

territorial differences.Daniela Vandone

University of Milan

This working paper site is hosted by The Berkeley Electronic Press (bepress) and may not becommercially reproduced without the publishers permission.

http://services.bepress.com/unimi/business/art3

Copyright c2007 by the author.


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Consumer credit in Italy. Diffusion and

territorial differences.

Abstract

The analysis sets out to clarify whether households demand for consumercredit can be adequately explained by models presented in the literature (life-cycle and permanent income) or whether other factors are observable, such asthe use of debt to alleviate financial difficulties. With this in mind, the researchseeks to establish whether specific determinants characterise the consumer creditmarket in different areas of the country. The Bank of Italy Survey on House-hold Income and Wealth for 2004 (SHIW) is used to identify determinants ofconsumer credit and the possible existence of territorial specificity.


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CONSUMER CREDIT IN ITALY. DIFFUSION AND TERRITORIALDIFFERENCES

Daniela Vandone*

Abstract

The analysis sets out to clarify whether households demand for consumer credit can

be adequately explained by models presented in the literature (life-cycle andpermanent income) or whether other factors are observable, such as the use of debt to

alleviate financial difficulties. With this in mind, the research seeks to establish

whether specific determinants characterise the consumer credit market in different

areas of the country.

The Bank of Italy Survey on Household Income and Wealth for 2004 (SHIW) is used

to identify determinants of consumer credit and the possible existence of territorial

specificity.

Keywords: consumer credit, household debt sustainability, Survey on Household

Income and Wealth, territorial specificity.

JEL classification: D14, G21

*Assistant professor of Economics of Markets and Financial Intermediaries, DEAS (Department of

Economics, Management and Statistics) University of Milan (Italy), [email protected]

I would like to thank Sara Romagnoli and Riccardo Troso for their help in processing and elaboratingthe data used. I am particularly grateful to Prof. Luisa Anderloni for her encouragement and

suggestions. Obviously the usual disclaimer applies to any errors, omissions, etc..


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Contents: 1. Introduction 2. The Italian consumer credit

market 3. Theory and evidence 4. An analysis of the data

5. Conclusion 6. References 7. Appendix

1. IntroductionDemand for consumer credit on the part of Italian households has risen sharply in

recent years. Current household debt levels are relatively low in comparison to other

countries and due to a late start, the diffusion of consumer credit in Italy is stilllimited. However, an analysis of emerging territorial differences and demand side

behaviour patterns reveals markedly differing socio-economic situations, which

suggest that the reasons for indebtedness are not wholly similar to those described in

the literature. In particular, debt is likely to be used by certain types of borrowers or in

certain geographical areas not to smooth intertemporal consumption as is typically

suggested, but to offset financial difficulties.

This paper analyses the factors influencing the diffusion of consumer credit in Italy

and regional characteristics if any that distinguish one area of the country from

another. The paper is organised as follows: paragraph 2 provides an outline of the

Italian consumer credit market, paragraph 3 analyses the theoretical models described

in the literature, whilst paragraph 4 examines data taken from the Bank of Italys

Survey on Household Income and Wealth (SHIW), focusing particularly on territorial

characteristics and differences. Paragraph 5 concludes.

The research is a response to the growing market for unsecured debt in Italy and the

fact that many banking groups are highlighting the strategic importance of this

business area as a vehicle for penetrating and consolidating their presence in certain

geographical areas. Given the crucial importance of the socio-economic aspects of

household debt, it is hoped that the analytical framework adopted in the present paper

will represent a useful contribution to research into the areas of household

indebtedness and banks lending policies.

1. The Italian Consumer Credit MarketThe Italian consumer credit market has three particular features: it is small in size in

comparison to other major industrialised European countries; it is growing rapidly; it

has territorial differences that may imply the need for a reassessment of existing

explanations for the diffusion of consumer credit.


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a. Low levels of indebtedness

Consumer credit is limited both in terms of its diffusion and the share of income it

represents. On the basis of total lending granted by banks and finance companies, in

2005 the consumer credit/GDP ratio for Italy is 5.1 per cent in comparison to an EU-

15 figure of 8.3 per cent, whilst the consumer credit/disposable income ratio for Italy

is 6.2 per cent, considerably lower than levels recorded in the United Kingdom (26.3

per cent), Germany (15.7 per cent) and Spain (13.3 per cent).

Chart 1. Consumer credit as a percentage of GDP Chart 2. Consumer credit as a percentage of

disposable income

Source: figures calculated using ECRI, 2005

b. Rapid growth levels

Despite lower volumes in comparison to other major European countries, consumercredit, however, continues to grow at a faster rate.

In the period 2002-2006, the average annual increase in loans to Italian households

made available by banks and finance companies was just under 17 per cent. Although

the trend in recent years has obviously felt the effects of Italys comparatively late

start in this area, expansion in this form of debt has, significantly, risen at a faster rate

than income and tracks a tendency on the part of households to consume more and

save less.


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Chart 3. Consumer credit and disposable income in Italy 2002-2006

Sources: calculated from Bank of Italy and ISTAT

Increased lending volumes recorded both by banks and finance companies is

presumably demand driven, with the increase in demand being explained at an

economic level by low interest rates and greater product variety. Behavioural factorsmust also be taken into account such as the gradual abandonment of traditional pay

now debt averse values in preference to the consumption of life-improving products

and services. This behaviour change by borrowers against a backdrop of falling

interest rates has been encouraged by radical social transformations, the most

significant of which for our purposes is the fall in intergenerational asset transfers and

the reduced use of informal credit solutions.

On the supply side, various factors have made consumer credit increasingly more

profitable for lenders and have encouraged its expansion. The most recent of these

include product standardisation and regularly updated credit scoring methodologies.

On the regulatory plateau, more favourable treatment of banks retail loan portfolios

under Basle 2 has also helped.

c. Territorial specificity

Although consumer credit has grown throughout Italy, the existence of marked

differences from one part of the country to another in terms of both levels and

demand-side behaviour patterns mean that research is needed into whether the reasons

lie in socio-economic differences across the country.

0

20

40

60

80

100

120

140

160

180

200

2002 2003 2004 2005 2006

Consumer credit

Disposable income


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The Bank of Italys Annual Report for 2006 in fact shows that average amountsborrowed per capita using consumer credit solutions do not differ substantially from

the North/Centre ( 1,249) and the South ( 1,274). The result is significant in that if

there is little difference from one area to another in absolute terms between per capita

consumer credit liabilities, presumably in relative terms the weight of indebtedness

for southern households is greater.

This hypothesis appears to be directly confirmed by figures from the Bank of Italys

SHIW:

- average annual salary in the South amounts to 13,797 in comparison to anational average of 16,555;

- net individual wealth in the South is almost half the figure recorded in theNorth and Centre and the difference is continuing to grow (North/Centre:

92,522; South: 47,900);

- economic poverty indicators show financial tensions to be greater in the South:the share of persons living in low-income households is 29 per cent, more than

double the national average of 13.3 per cent, whilst the share of persons in the

South living in households with consumption levels below half the national

median figure amounts to 17.5 per cent in comparison to the national figure of

7.7 per cent.

Table 1. Territorial differences

Households

with

consumer

credit

liabilities

(%)

Indebted

households:

debt/annual

available

income ratio

(%)

Consumer

credit per

capita

Average

annual

earnings

Net

individual

wealth

Persons in

low-

income

households

Median

household

income

Median

household

consumption

North-

Centre

13.00% 19.9% 1,249 16,555 92,522 13.3% 27,740 20,400

South 11.8% 24.6% 1,274 13,797 47,990 29% 17,341 15,000

Source: constructed using Bank of Italy. Annual Report 2006 and SHIW 2006.

The situation illustrated above requires further analysis of consumer credit-related

differences that exist between various parts of the country. The following paragraph

examines the Italian experience on the basis of traditional theories of individuals

indebtedness, with particular attention to the question of territorial specificity raised

above.

A set of features equipped to identify consumer credit markets on a territorial basis

could also be usefully applied to analyses of other domestic markets such as those of


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the most recent entrants to the European Union, where rapid expansion of personaldebt levels is taking place in more unstable and less developed financial

environments.

3. Theory and Evidence

The Life-Cycle and Permanent Income models assume that households choose their

own optimal level of consumption not only on the basis of current income, but also

past and future receipts in order to smooth consumption over their life time. Access to

credit would achieve just this by guaranteeing the greater economic welfare that

consumption smoothing brings with it. On the other hand, rising levels of

indebtedness could be unsustainable due to the inadequate size of current income,leading to over indebtedness and loan re-payment difficulties. With this last point in

mind, continuing expansion in consumer credit needs to be monitored as it may not

reflect increased economic welfare deriving from improved distribution of income

and consumption over time, but a worsening of the condition of household finances.

In particular, according to the Permanent Income Theory, what matters to households

in determining consumption year-by-year and therefore making decisions regarding

savings and indebtedness is their expected lifetime income. It has been repeatedly

shown (Deaton 1992; Alessie, Devereux, Weber 1997; Attanasio 1999; Magri 2002;

Casolaro, Gambacorta, Guiso 2006) that analyses using the Permanent Income model

should take into account other variables which, in addition to current income and

consumption, influence the size of debt that households decide to take on, such as

social and demographic characteristics, borrowing costs, real estate market trends,

spending on consumer durables, likelihood of income shocks and risk aversion levels.

Other cross-country analyses (Crook and Hochguertel 2006) have shown how the role

these variables play in determining debt levels differs from country to country. It can

reasonably be supposed that the differing impact and influence of these variables will

also be observable in different parts of the same country.

Studies in the area of household debt increasingly focus on identifying the causes of

financial difficulties or over indebtedness which can subsequently lead to credit

default (Boyes, Hoffman, Law 1989; Crook 2003; Avery, Caleman and Canner 2004).

The factors that influence debt demand and/or which may lead to difficulties in debtrepayments can be grouped into two distinct categories: household-related factors and

institutional-related factors.

Household-related factors include the socio-demographic and economic

characteristics of individual borrowers and their family nucleus. Age, gender,

education, available income, net wealth and type of occupation are variables that

determine the level of indebtedness and condition attitudes towards debt repayments.


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Young adults, with expectations of rising income, have a higher demand for credit,which over time drops as income levels rise to cover costs and is accompanied by a

generally more prudent attitude to debt.

The impact of household wealth on indebtedness is not clear cut. Magri (2002) shows

that rises in net wealth are accompanied by falls in the demand for credit as

consumption is increasingly covered autonomously. However, for households in the

middle net wealth bracket, an increase in the demand for credit may stem from

significant rises in lifestyle-improving consumption choices.

The demand for credit is also positively influenced by education levels: firstly,

increased qualifications probably enable higher future earnings along with greater job

security and, secondly, provide loan applicants with the skills necessary to evaluate

effectively the factors involved in borrowing (Grant 2003, Del Rio and Young 2005).Expectations of rising future income can reasonably be expected to boost the demand

for credit: should future income not be expected to rise, there would be no demand for

credit as there would be no need to advance financial resources via debt. Magri (2002)

highlights the effect with a dummy variable on education levels. The effect of

permanent income on the demand for and supply of credit has been studied also by

Ferri e Simon (2000), Crook (2005), Cox e Jappelli (1993). On the question of current

income levels, evidence is not always concordant. Del Rio and Young (2005) show

that very low income levels are typically volatile, so increasing the possibility of

credit exclusion. With regards to intermediate income levels, the marginal utility of

consumption is high and an increase in income may generate a rise in consumption

and a subsequent increase in the need for unsecured debt. The high income bracket is

normally associated with falls in the demand for credit. Turning to Italy, Fabbri and

Padulla (2004), in line with many studies carried out in the United States, show a

positive relation between debt and current income; Magri (2002) however illustrates a

negative relation, attributable to the fact that higher income households utilise debt

less for the purchase of the home and consumer durables.

Situations of financial difficulty are classified in the literature on the basis of the

shocks that may lead to debt repayment problems. Negative shocks on household

balance sheets include job loss, illness or divorce. The statistically most significant

variable to predict debt repayment difficulties is the debt/income ratio. Del Rio and

Young (2005) illustrate a positive relation between the level of debt to income ratioand the likelihood of repayment problems. Analogously, Rinaldi and Sanchez-

Arellano (2006) show how rises in the debt/income ratio are associated with increased

delays in debt repayments, whilst underlining also that if the increase is accompanied

by an increase in available income, the negative effect deriving from greater

indebtedness is wiped out.

The models referred to generally include dummy variables to take into account

unexpected events that can negatively impact household balance sheets, such as job


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loss or illness. The extent these shocks determines financial difficulties variessignificantly from country to country and is crucially dependant on the institutional

setting.

Institutional-related factors are those that characterise and distinguish domestic credit

markets. The principal variables analysed in the literature are the size of the informal

credit market (loans from family and friends), the extent of information sharing

amongst lenders regarding borrowers credit risk (credit register bureaus) and the

efficiency of the legal system to enforce financial contracts. These institutional factors

are relevant since they may influence borrowers willingness to repay outstanding

debt and, clearly, also the amount: the probability of repayment depends not only on

the effective capacity of borrowers to avoid default (a household-related factor), but

also on the effectiveness of institutional factors, such as enforcement procedures thatdeter those from opportunistically seeking bankruptcy or other forms of debt relief.

The importance of institutional factors typically emerges in cross-country analyses.

There is evidence that, although the set of individual variables impacting debt demand

is common from country to country, significant cross-country differences exist with

regards as to how households respond to negative shocks: in reaction to the same

shocks, borrowers in some institutional settings do not repay their debts, whilst in

other contexts they do. Such differences can be explained not only by behavioural

deviancy on the part of individual borrowers, but also by differences in the

institutional setting in which default takes place.

With specific regard to the legal system, Duygan and Grant (2006) show that the

probability of payment arrears and bankruptcy increases with the raising of the cost

and the lengthening of the time required to enforce the financial contract. They also

find that a sudden reduction in income due to, for example, a job loss leads more

probably to default in those countries where the legal protection offered to lenders is

weak. Grant and Pedulla (2006) point out, however, that the impact of this

institutional factor relates significantly only to secured debt such as mortgages, but is

irrelevant in the case of unsecured debt forms as consumer credit.

On the question of the different forms of customer information sharing in place

amongst lenders, Jappelli and Pagano (2006) show that the existence of credit register

bureaus reduces the incentive on the part of a borrower to ask for a loan from more

than one lender at the same time, thereby running the risk of over borrowing;information sharing reveals the total borrowers debt position system wide. Similarly,

Duygan and Grant (2006) show that the likelihood of borrowers insolvency decreases

when the financial system can discover previous cases of default as over borrowing

persists even in cases of income shocks such as job loss.

With regards to the role informal credit markets play, Grant and Padulla (2006) show

that the effect of this institutional factor on debt repayment is economically and

statistically significant and negatively impacts attitudes towards repayment:


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individuals with access to loans from family members or friends consider the possibleexclusion from formal credit markets as less problematical as they can in case of need

utilise informal credit solutions.

4. An Analysis of the Data4.1Methodology

In order to identify determinants of consumer credit and the possible existence of

differences between parts of the country (territorial specificity), the present analysis

utilised the Bank of Italys SHIW for 20061.The analysis sets out to clarify whether demand for unsecured debt in Italy can be

adequately explained by models presented in the literature or whether other factors are

observable, such as the use of debt to alleviate financial difficulties. With this in mind,

the present research seeks to establish whether specific determinants characterise the

consumer credit market in different areas of the country.

For each household included in the survey, using the Bank of Italy database it is

possible to identify the total amount of consumer credit outstanding (CRED_CONS).

This figure is the sum of amounts owed for the purchase of non durable goods, motor

vehicles, electrical household appliances, furniture and real goods. In addition to the

consumer credit variable, a selection of other variables was chosen to provide a

profile of the households taking part in the survey and which may, it is believed, shed

light on the total amount of debt outstanding (paragraph 5.2; appendix: table 1).

The data was therefore reprocessed in order to determine the percentage of

households with unsecured debt in relation to the socio-economic characteristics

mentioned previously (appendix: table 2)2. With regards exclusively to households

1 To provide the analysis with a sufficient number of households, the entire sample of households

interviewed in 2004 was used rather than the households belonging to previous years household panel

surveys. (The same families interviewed in previous panel waves in 2002 and 2004 numbered only

3604 in comparison to an entire 2004 sample total of 8012). As the demand for credit is influenced by

current income when this is lower than permanent income, panel analysis would have provided data forthe study also of the effect of permanent income and not only of current income: by using panel survey

data, single household income changes can be monitored over time and consequently permanent

income can be calculated more accurately. Indicators estimating permanent income in non-panel based

models used by Cox and Jappelli (1993) and Ferri and Simon (2002) will play an important part in the

ongoing development of the present research.2

The probability of indebtedness was calculated with reference to each chosen variable. For instance,

with regards to the gender variable, the number of households with a man as head of household and

those in which a woman is the head of household were calculated (column 6); subsequently, the

percentage of households with consumer liabilities belonging to these two sub-groups was calculated


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with consumer credit liabilities, the same methodology was used to identify theaverage amount of debt outstanding by household type (appendix: table 3).

Although the analysis carried out is descriptive and does not take into account the

relations between variables, the evidence produced offers the grounds for further

research and allows for a detailed analysis of the question of territorial specificity.

4.2 Variables used

The variables used in tables 2 and 3 are prevalently socio-demographic and economic,

such as age (CLETA), professional qualifications (QUAL), education (STUDIO),

income (Y) and wealth (W), which provide a profile of the households taking part in

the research and which are those commonly used in the literature as consumer creditdeterminants (appendix: table 1).

Amongst household-related factors, the inclusion of a qualitative variable describing a

households financial situation (SITFIN) was considered particularly useful. This

provides a measure of whether at the end of the year a household had managed to

balance income and consumption, to save or had to borrow. Together with the

quantitative income variable (Y), SITFIN can provide insights as to whether

consumer credit is used to avoid spending restraints or to smooth intertemporal

consumption. With this in mind, the analysis also utilised a variable as a lifestyle

proxy: (VAC) indicates whether a household has taken a holiday (1) or not (0).

Institutional-related factors are combined in a variable indicating the number of banks

with which a household holds a current account (NBANC); this variable is used as a

proxy for lenders (banks and finance companies) capacity to access full information

about borrowers. An indicator of recourse to informal credit circuits (DEBIT) was

also used to establish the possible effect of loans from family and friends on the

diffusion of formal unsecured debt solutions.

The indicator of a legal systems efficiency was not used in the analysis given that in

Italy this factor prevalently influences the decision to repay secured debt, such as

mortgage loans, rather than consumer credit liabilities.

4.3ResultsIn line both with theory and previous research, consumer liabilities are concentrated

principally amongst younger households (CLETA), which borrow in order to smooth

consumption over their life cycle and by so doing achieve lifestyle improvements.

Education (STUDIO) however does not appear to be a particularly significant variable

(column 2). In columns 3, 4 and 5 the same data was then broken down by geographical area (North,

Centre and South).


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in contrast to both what is normally expected and the findings presented in theliterature, which typically report higher levels of education as an effective proxy of

rising future income and consequently positively correlated to the amount of debt

outstanding. It may be posited, however, that the limited importance of education as a

variable is a specific feature of consumer credit. Higher education levels presumably

equip individual borrowers with the necessary skills to evaluate and select financial

products and services more effectively. The education variable, it can be suggested,

has a greater impact when dealing with more sophisticated and complex financial

products involving higher sums of money, such as mortgage loans, rather than

comparatively simple consumer credit solutions characterised by lower amounts

borrowed, easier to understand formalities, and wider availability (not only at banks

and finance companies, but also directly at retail outlets for specific productpurchases).

The percentage of households with unsecured debt is higher amongst the lower net

wealth bracket (W), consistent with the evidence in the literature, showing that

households with high net wealth are able to cover consumption needs autonomously

without recourse to debt. The analysis, furthermore, shows that income (Y) positively

impacts both the likelihood of indebtedness and the average amount borrowed; this

may also be due to the reduced probability of supply-side restrictions.

Interestingly, the variable describing households financial situation (SITFIN)

negatively impacts the likelihood of indebtedness and the average amount borrowed

therefore showing that a segment of the population uses consumer credit to square its

balance sheet positions. This condition may indicate financial difficulties stemming

from an inability to cover current expenditure from existing income. Clearly, the build

up of household over indebtedness requires careful monitoring not only on the part of

the banking system for the potentially negative effects on the credit quality of its retail

loan portfolio, but also by policy makers in general for the wider social and economic

repercussions the phenomenon might have. Raised awareness of the situation is

justified by elaboration of the professional qualification variable (QUAL): a not

insignificant number of unemployed borrowers have sizeable average liabilities.

Analysis of the institutional factors impacting on the size of consumer liabilities

produces interesting results. The NBANC variable positively raises the likelihood of

indebtedness and the average amount borrowed: an increase in the number of lenders(banks and finance companies) a household uses is tracked by an increase in the total

amount of consumer liabilities outstanding. This evidence is also in line with the

literature on household debt and can be explained by the fact that individual lenders

have a partial view of the customers total liabilities. The informal credit market, in

the present research expressed by the quantity of loans granted by family and friends

(DEBIT), appears to raise the likelihood of indebtedness.


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The evidence discussed so far referring to Italy as a whole (column 2 of tables 2 and3) confirms that the factors driving demand for unsecured debt are substantially those

already discussed in the literature. However, findings from this research show a group

of low income and low wealth households using more than one lender to compensate

for inadequate income.

Is this type of household concentrated more in the South in comparison to other parts

of the country?

The indicator of households geographical location illustrates relative homogeneity in

the geographical distribution of unsecured debt: total outstanding liabilities assignable

to households in the South of Italy are not substantially different from those in other

parts of the country. Using this evidence as a platform, in order to examine the

question of territorial specificity in more detail, an analysis was carried out of the dataspecific to each geographic area (columns 3, 4 and 5 of tables 2 and 3).

The results highlight features of territorial specificity which are of particular interest

in that they differ from the initial view that relative poverty indicators for the South

reveal a state of potential difficulty. On the basis of the fact that total consumer

liabilities per capita do not differ significantly from other parts of the country,

recourse to credit in the South of Italy was considered to be a means of alleviating

conditions of financial difficulties. The picture that, however, emerges from the

analysis is that, in line with the position broadly accepted in the literature, consumer

credit in the South is used to smooth intertemporal competition and lifestyles rather

than reflecting conditions of over indebtedness. Figures show in fact that in the South

consumer credit is concentrated amongst households with higher levels of education:

almost 40 per cent of principal income providers of households using consumer credit

are university graduates or have at least successfully completed high school, in

comparison to 28 per cent for the North and Centre. Furthermore, the average amount

of loans is greater for those households with higher levels of education. Also

significant is the fact that in the South the percentage of households with unsecured

debt in which the principal income provider is unemployed is lower than in other

areas of the country. When this situation arises, the average total of liabilities is

relatively low. With reference to the income and wealth variables, consumer credit is

principally concentrated in the South amongst households in the last tertial, i.e. those

with the highest income and wealth levels.In the North of Italy, however, consumer credit use is mainly concentrated amongst

households with lower levels of education and professional qualifications and, in

comparison to the South, more indebted households have low income and wealth

levels. The variable indicating households financial situation confirms that a higher

number of households in the North of Italy use debt as a means of squaring their

balance sheets. In contrast to the initial hypothesis, it is therefore likely that most

households with excessive debt levels are to be found in this part of the country.


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5. ConclusionAccording to theory, households are able to borrow against expected future income

and so the demand for credit is higher amongst younger age groups with higher levels

of education and therefore enhanced expectations of higher future income receipts.

Similar high demand for credit is also expected from households with professional

qualifications and/or higher current income; two factors that make access to

borrowing easier. On the other hand, some sections of the population utilise unsecured

debt as a means of integrating income and covering basic needs.

This paper has investigated the factors that determine the diffusion of consumer credit

and the specific features that distinguish one area of the country from another.On the basis of the evidence produced by the research, two major aspects emerge,

which provide a different perspective of the territorial features of the Italian market

and also offer a useful starting point for further research. First, demand for consumer

credit in the South does not appear to be concentrated amongst low income and low

wealth households. Indeed, consistent with the life-cycle/permanent income model,

debt is principally used by households to smooth intertemporal income and

consumption in order to improve their overall economic condition. These households

do not appear to have specific difficulties in meeting debt repayments. Second, in the

north of Italy, however, a more careful monitoring of debt levels and distribution is

required given the significant amount of consumer liabilities held by households in

financial difficulty, which use debt recurrently to integrate monthly pay cheques and

maintain living standards. This can result in their being exposed to excessively high

levels of indebtedness.

Another question to be addressed is the extent to which the spatial distribution of

consumer credit is influenced by supply-side factors. Indeed, access to credit in the

South of Italy may be more difficult as a result of the greater risks the area presents,

which consequently force lenders to reduce the supply of credit and/or apply more

severe pricing policies for marginal segments of the population, reserving supply to

prime borrowers, i.e. those with higher credit ratings.

Bearing this in mind, it is reasonable to posit that consumer credit aggregates have

been influenced by the strategies adopted by major banking groups in theirpenetration and consolidation of the southern Italian market. In fact, over the last few

years there have been considerable and deep-seated changes to banking in the South,

with banks from the North and Centre playing a dominant role (Bongini and Ferri

2005). The process of aggregation via merger and acquisition may initially have led to

a dispersion of information concerning local borrowers and the loosening of ties with

some parts of the traditional customer base, negatively impacting in particular local

enterprises. Consequently, banks new to the area may have concentrated on those


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segments of the credit market, typically lending to households, where credit scoringprocedures are standardised and apparently more efficient. It appears likely that the

convenience of such a choice was confirmed by a favourable combination of

advantageous lending rates on unsecured debt and relatively low credit risk levels,

which led banks to focus lending on households.

The logical continuation of this work should be an analysis of the interactions

between credit supply strategies and the differences in borrowers behavioural

patterns from one geographical area to another. It is hoped that such research will

shed light on the structure and competitive nature of the Italian consumer credit

market.


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6. References

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and liquidity constraints: a cohort analysis, in European Economic Review, vol. 41,

n.1

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Taylor and M. Woodford, Elsevier Science

Avery R.B., P.S. Caleman, G.B. Canner (2004), Consumer credit scoring: do

situational circumstances matter? in Bank for International Settlements, WorkingPapers, no. 146.

Banca dItalia (2006), Survey on Household income and Wealth 2004, Supplements to

the Statistical Bulletin, n.7

Bongini P., G. Ferri (2005),Il sistema bancario meridionale, Editori Laterza

Boyes W.J., D.L. Hoffman, S.A. Law (1989), Econometric analysis of bank scoring

problems, in Journal of Econometrics, vol. 40

Casolaro L., L. Gambacorta, L. Guiso (2006), Regulation, formal and informal

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Cox D., T. Jappelli (1993), The effect of borrowing constraints on consumer

liabilities, in Journal of Money Credit and Banking, vol. 25, n.2

Crook J. (2005), The measurement of household liabilities: conceptual issues and

practices, Credit research Centre, working paper, University of Edinburgh

Crook J. (2003), The demand and supply for household debt: a cross country

comparison, Credit research Centre, working paper, University of Edinburgh

Crook J., S. Hochguertel (2006),Household debt and credit constraints: comparative

micro evidence from four OECD countries, Finance and Consumption Workshop,

European University Institute, Florence, 12 June


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Deaton A. (1992), Understanding consumption, Oxford University Press

Del-Rio, A., G. Young, (2005), Unsecured debt in BHPS: determinants and impact on

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7. Appendix

TABLE 1. Database variables and principal descriptive statistics

Table 1 illustrates the statistical characteristics of the variables used in the analysis. The database refers

to 2004: 8,012 households, 20,581 persons, of which 13,341 salary earners.

CRED_CONS is the total amount of household liabilities payable to banks and financecompanies for the purchase of real goods, motor cars and bikes, bicycles, furniture, electrical

appliances and non durables.

CLETA is the variable indicating the age group of a households principal income provider(capofamiglia) with values assignable to each age group as follows: up to 30 years of age

(value of 1); from 31 to 40 (value of 2); from 41 to 50 (value of 3); from 51 to 65 (value of 4);

over 65 (value of 5).

NCOMP: the number of household members.

NPERC: the number of household members earning an income.

QUAL is the variable indicating the professional qualifications of salary earners, with valuesassignable to each group as follows: unemployed principal income provider (value of 1);

salaried employee (value of 2); self-employed (value of 3).

AREA3 is the variable indicating the geographical area with the following values: North(value of 1); Centre (value of 2); South (value of 3).

STUDIO is the variable indicating the education level of the households principal incomeprovider, with values assignable to each group as follows: no educational qualification (value

of 1); completed only primary school (value of 2); completed junior high school at the age of

14 (now 15) (value of 3); technical/professional school leaving diploma e.g. nursing, etc.

(value of 4); high school leaving diploma (value of 5); university diploma (value of 6);

university degree (value of 7); post-graduate qualification (value of 8).

VAC is the variable indicating whether the household took a holiday or day trips during 2004;1 indicates that it did, whilst 0 indicates that it did not.

SITFIN is the variable describing the households current financial situation. The followingvalues were assigned to households describing their financial situation in the following ways:

have to borrow (value of 1); have to use savings (value of 2); just about manage to make

ends meet (value of 3); manage to save something (value of 4); manage to save a fair

amount (value of 5).

Y is the available income variable. Distribution is divided into tertials and the variable wasadjusted as follows: income up to 18,000 (value of 1); income from 18,000 to 31,000

(value of 2 ); income over 31,000 (value of 3).

W is the net wealth variable, also adjusted into classes: net wealth up to 20,000 (value of 1);net wealth from 20,000 to 165,000 (value of 2); net wealth above 165,000 (value of 3).

NBANC is the variable indicating the number of current accounts, if any, held by the

households principal income provider with the following values assigned: no current account(value of 1); one current account (value of 2); current account held in more than one bank

(value of 3).

Variables (symbols used) Average St. Dev. Min Max

CRED_CONS 884.2 * 3,712.9 0 100,000

CLETA 56.8 15.8 18 97


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NCOMP 2.6 1,3 1 9

NPERC 1.6 0.7 1 7

QUAL n.a. n.a. 1 7

AREA3 n.a. n.a. 1 3

STUDIO n.a. n.a. 1 8

VAC n.a. n.a. 0 1

SITFIN n.a. n.a. 1 5

Y 29,866.6 26,931.0 -41,575.16 1,022,616.85

W 165,192.3 315,300.1 -220,000 9,660,113

NBANC n.a. n.a. 0 2

* The average value takes into account also households without consumer liabilities. The average value calculated on the number

of households with consumer liabilities only (1,034 households out of a total of 8,012) 6,853.60.


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Table 2. Percentage of households with unsecured debt in relation to their socio-economic

characteristics (all households interviewed)

Total(%)

North

(%)

Centre

(%)

South

(%)

Number of

households

interviewedGeographical area(AREA3)North 13.1% 13.1% - 3,819

Centre 14.5% - 14.5% - 1,628

South 11.5% - - 11.5% 2,564

Gender

Male 14.4% 14.7% 16.5% 12.6% 4,902

Female 10.5% 10.5% 11.5% 10.0% 3,110

Age group (CLETA)

Under 30 21.1% 23.6% 15.7% 20.3% 413

From 31 to 40 19.3% 20.9% 21.6% 14.8% 1,446

From 41 to 50 20.8% 21.6% 20.9% 19.3% 1,667

From 51 a 65 12.1% 9.4% 16.4% 13.1% 2,091

Over 65 2.8% 2.5% 3.8% 2.8% 2,395

Educational

qualification(STUDIO)No educational

qualification

6.2% 3.3% 6.3% 6.9% 504

Primary school

certificate

5.7% 5.4% 6.3% 5.8% 2,112

Middle school

certificate

14.4% 14.7% 17.9% 12.0% 2,305

Professional school

diploma

16.0% 16.7% 17.9% 10.4% 474

High school

diploma

19.9% 18.8% 21.0% 20.8% 1,950

University diploma 9.1% * * * 54

University degree 12.2% 10.9% 9.6% 18.1% 591Post-graduate

qualification

* * * * 22

Work status (QUAL)

Unemployed 10.8% 11.1% 12.3% 9.4% 5,410

Payroll employee 16.5% 16.2% 19.8% 14.4% 1,933

Self-employed 19.9% 19.6% 14.2% 27.3% 669


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Holidays (VAC)Yes 17.6% 16.5% 18.8% 21.1% 3,092

No 9.9% 9.3% 12.2% 9.4% 4,920

Number of banks

used (NBANC)

Without a current

account

6.4% 10.9% 5.1% 5.6% 1,871

With a current

account at one bank

14.1% 12.8% 15.6% 15.8% 5,321

With a current

account at more than

one bank

20.2% 16.7% 26.9% 27.2% 820

Informal credit

circuit (DEBIT)

Yes 20.9% 34.8% * 3.3% 139

No 12.8% 12.7% * 11.7% 7,873

Household size(NCOMP)1 member 7.1% 9.1% 5.8% 4.4% 1,973

2 members 9.9% 10.4% 10.1% 8.8% 2,239

3 members 17.6% 18.5% 19.0% 15.1% 1,700

4 members 18.7% 16.7% 27.9% 16.5% 1,568

More than 4

members

14.8% 16.1% 20.3% 12.8% 532

Number of of

earners (NPERC)

1earner 9.6% 10.3% 16.0% 12.3% 3,975

2 earners 15.5% 16.0% 16.5% 14.0% 3,1593 earners 16.5% 12.3% 17.9% 23.7% 699

More than 3 earners 25.7% 16.9% 38.6% 27.3% 179

Tertials of

households income(Y)Up to 18,000 7.3% 8.6% 6.4% 6.6% 2,572

From 18,000 to 31,000

13.5% 13.1% 13.9% 13.7% 2,630

Over 31,000 17.5% 15.7% 19.2% 21.8% 2,810

Tertials of

households net

wealth (W)

Up to 20,000 14.1% 16.2% 13.9% 11.6% 2,691

From 20,000 to 11.8% 12.8% 13.7% 9.8% 2,700


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165,000Over 165,000 12.8% 10.7% 15.4% 14.7% 2,621

Financial situation(SITFIN)Need to borrow 24.7% 37.5% 37.5% 14.6% 271

Need to withdraw

from savings

11.2% 13.7% 18.7% 6.1% 680

Just about manage

to make ends meet

12.0% 13.0% 12.7% 10.5% 4,226

Manage to save a

little

14.5% 13.1% 16.3% 16.4% 2,399

Manage to save a

fair amount

7.8% 6.3% 4.9% 15.9% 436

Source: our computations on Bank of Italys SHIW , 2006

insufficient number of respondents to carry out analysis


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Table 3. Average amount of unsecured debt held by households in relation to their socio-

economic characteristics (households with unsecured debt liabilities only)

Total(euro)

North

(euro)

Centre

(euro)

South

(euro)

Number of

households

with

unsecured

debtGeographical area(AREA3)North 7,258.4 7,258,4 - - 502

Centre 5,964.1 - 5,964,1 - 236South 6,877.1 - - 6,877,1 295

Gender

Male 7,470.9 8,017,5 5,824,9 7,837,7 705

Female 5,528.1 5,442,7 6,256,3 5,116,1 328

Age group (CLETA)

Under 30 7,400.3 724,3 * * 88From 31 to 40 7,219.2 8,345,6 6,031,3 5,729,4 279

From 41 to 50 6,577.9 6,348,7 6,852,8 6,816,4 347

From 51 a 65 6,944.0 7,284,1 5,894,7 7,465,8 252

Over 65 5,721.1 * * * 68

Educational

qualification(STUDIO)No educational

qualification

3,058.2 * * * 31

Primary schoolcertificate

6,917.0 7,534,8 * 7,220,5 121

Middle school

certificate

7,761.9 8,418,9 7,522,4 6,704,3 334

Professional school

diploma

5,871.0 5,811,1 * * 76

High school diploma 6,154.1 6,152,7 4,957,8 7,256,8 388University diploma * * * * 6

University degree 8,033.8 7,951,8 * 9,140,6 72

Post-graduate

qualification

* * * * 7

Work status (QUAL)

Unemployed 6,766.5 7,712,3 6,315,5 5,590,1 582

Payroll employee 6,166.3 5,766,7 5,276,2 7,936,1 318

Self-employed 8,875.8 8,875,3 * 10,417,3 133


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Holidays (VAC)

Yes 6,913.0 6,751,0 6,103,7 8,352,3 545

No 6,787.2 8,286,6 5,843,6 6,127,0 488

Number of banks

used (NBANC)

Without a current

account

4,953.4 3,161,0 * 6,070,9 121

With a current

account at one bank

6,873.6 7,270,3 5,852,5 7,033,7 747

With a current

account at more than

one bank

8,144.3 8,820,0 7,143,4 7,623,0 166

Informal credit

circuit (DEBIT)

Yes * * * * 29

No 6,959.1 7,456,8 5,987,5 6,920,8 1,004

Household size(NCOMP)1 member 5,826.2 5853.8 * * 140

2 members 6,127.8 6,663.5 4,400.4 6,248,6 222

3 members 7,910.7 8,290.2 7,323.1 7,650,8 299

4 members 6,809.0 7,715.0 5,731.7 6,743,5 294

More than 4members

6,880.3 * * 7,163,4 78

Number of of

earners (NPERC)

1earner 5,827.8 5,878.3 5,636.2 5,886.5 3812 earners 7,568.2 8,059.2 6,705.1 7,195.4 492

3 earners 7,361.2 8,023.0 5,431.8 8,076.6 115

More than 3 earners 6,438.6 * * * 46

Tertials of

households income(Y)

Up to 18,000 4,790.9 3,461.3 * 5,626.1 187From 18,000 to

31,0006,570.4 8,002.1 5,652.7 5,108.7 354

Over 31,000 7,840.9 7,913.1 6,149.3 9,784.3 493

Tertials of

households net

wealth (W)

Up to 20,000 7,827.0 8,162.0 6,746.4 7,813.2 380


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From 20,000 to 165,000

6,225.1 6,57.,5 5,785.0 6,003.5 319

Over 165,000 6,345.0 6,719.3 5,630.5 6,617.4 335

Financial situation(SITFIN)Need to borrow 10,998.7 15,120.7 * * 68

Need to withdraw

from savings

7,127.3 7,016.4 * * 76

Just about manage tomake ends meet

6,027.0 5,996.1 6,138.7 5,997.3 509

Manage to save a

little

6,889.7 7,445.0 5,319.3 7,326.9 347

Manage to save a fairamount

9,929.8 * * * 34

Source: our computations on Bank of Italys SHIW, 2006

* insufficient number of respondents to carry out analysis

Documents

Consumers Credit in Italy