Economic Policy 63rd Panel Meeting

Hosted by the De Nederlandsche Bank Amsterdam, 22-23 April 2016

The organisers would like to thank De Nederlandsche Bank for their support. The views expressed in this paper are those of the author(s) and not those of the supporting organization.

Adult Financial Literacy and Households' Financial Assets:

The Role of Bank Information Policies

Margherita Fort (University of Bologna) Francesco Manaresi (Bank of Italy)

Serena Trucchi (University of Bologna)

Adult Financial Literacy and Households’ Financial Assets:

The Role of Bank Information Policies

Margherita Fort∗ Francesco Manaresi† Serena Trucchi‡

March 15, 2016

Abstract

We investigate the role of bank information policies in fostering the accumulation

of financial knowledge. In Italy, banks that belong to the PattiChiari Consortium

implement policies aimed at increasing transparency and procedural simplification,

without offering services at a lower cost with respect to banks outside the Consor-

tium. These policies significantly affect the level of financial literacy of between 5

to 10% of households. We use bank information policies as an instrumental variable

for financial literacy in the financial assets’ equation and show that the positive re-

lationship between the two is significantly underestimated by OLS correlation. The

estimated impact is driven by households whose head is 60+ and low educated.

Key words: Bank Information Policies; Financial Assets; Financial Literacy;

Instrumental Variables

JEL: D14; G11.

∗Universita di Bologna and CESifo and IZA. Email address: margherita.fort@unibo.it†Banca d’Italia. Email address: francesco.manaresi@bancaditalia.it.‡UCL, Universita di Bologna and CeRP-Collegio Carlo Alberto. Email address: s.trucchi@ucl.ac.uk

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1 Introduction

Both in Europe and in the U.S. there is evidence of low levels of financial literacy and

a limited knowledge of basic economic concepts, like inflation or interest compounding

(Jappelli, 2010; Lusardi and Mitchell, 2011c, 2014). Lack of financial literacy and the

mis-perception of crucial economic factors may lead individuals to make suboptimal

investment choices and, thus, reduce individual wealth and welfare. For this reason,

actions to promote financial literacy in the population have been implemented in a

growing number of countries (see OECD (2013a) for a review). These interventions

mainly rely on the role of formal financial education in schools or at the workplace.1

In this paper we study a different channel which may improve financial literacy,

which has been almost neglected by the literature: bank information policies. To assess

their effectiveness, we exploit a peculiar feature of the Italian banking system, namely

the existence of a consortium of banks (PattiChiari), which has the aim of enhancing

banking transparency. Banks that entered the PattiChiari Consortium had to provide to

their customers information materials on basic economic concepts (e.g., about compound

interests, fixed/flexible interest rates, the calculation of debt repayment instalments, etc.)

and more transparent information on current account expenditures. These activities are

in line with the OECD (2013b) suggestions for improving the effectiveness of financial

literacy programs, as the informative material was provided on a regular basis (monthly)

in a format and location that are easy to access (i.e., on the internet and/or at the local

bank branch). We argue that these information policies reduced the cost of acquiring

(basic) financial knowledge for customers without imposing any additional burden in

terms of time, effort, or resources to them. We investigate to which extent information

policies improve financial literacy. Evidence on absence of self-selection of clients into

PattiChiari banks allows us to overcome one of the main issues in the evaluation of

1There is an open debate about the effectiveness of alternative financial education programs onfinancial literacy and financial behaviour (Lusardi and Mitchell, 2014; Willis, 2011). Robust and reliableempirical evidence on the evaluation of their effects is still limited and this motivates some scepticism onthe role played by financial education in improving financial knowledge and household financial welfare(Willis, 2011).

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voluntary financial education programs (OECD, 2013a) and to interpret our findings as

causal.

Our results suggest that bank information policies do significantly affect the level

of financial literacy. In particular, financial literacy is found to be 10% higher among

PattiChiari clients (about one fourth of a standard deviation of our measure of financial

literacy). Information policies are found to be relevant for a relatively small sample of

the population (around 5-10% of it). They are particularly effective among households

whose head is 60+ and is low educated. This result adds to the literature aimed at

evaluating the effectiveness of alternative interventions on financial literacy (Fernandes

et al., 2014; Lusardi and Mitchell, 2014) by showing that providing bank’s customers

with information about simple economic concepts may represent an alternative way

to improve financial knowledge among adults. Even when formal financial education

programs affect financial literacy, biases, heuristics and non rational influences may

weaken their effectiveness on individual financial behaviour (Hastings et al., 2013). We

find that bank information policies significantly affect financial assets. In this respect,

this stuedy might offer some guidance on how program effectiveness can be improved,

as we discuss in the conclusions.

A second contribution of this paper is to provide a new instrumental variable strat-

egy to assess the effect of financial literacy on financial assets, which exploits bank

information policies for identification. The association between financial literacy and

several economic decisions (risk diversification, debt exposure and retirement planning)

has been widely documented. However, evidence about the strength of the causal re-

lationship between these factors is recent and still limited. Most studies report that

regression correlations provide a lower bound of the true effect, but estimates of this

bias vary substantially. Unobserved heterogeneity, simultaneity (Jappelli and Padula,

2013b) and measurement error (van Rooij et al., 2011b) plague empirical results and

are difficult to address. In line with previous analyses, we show that the causal effect

of financial literacy on financial assets is underestimated by OLS correlation. In par-

ticular, we find that a one standard deviation increase in financial literacy leads to an

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increase of household financial assets of around 8 thousands euros (35% of a standard

deviation in financial assets). The effect of financial knowledge on financial assets is

mainly driven by elderly and low educated respondents. Moreover, the OLS bias turns

out to be largest precisely for this group of individuals: these are indeed the households

for whom measurement error in financial literacy is likely to be larger. We offer some

insights about the channels through which this effect takes place, showing that financial

literacy promotes the propensity of households to participate to stock market (Christelis

et al., 2010; van Rooij et al., 2011b,c) and, possibly, to hold a more diversified portfolio.

Conversely, we do not find evidence of a higher attitude to plan for retirement (Lusardi

and Mitchell, 2011a) nor of a higher saving rate. Lack of adequate data, prevent us from

examining the effect of financial literacy on the quality of financial decision.

We refer to financial literacy as a broad concept, that concerns basic economic notions

that individuals should have some understanding of when making even simple day-to-

day financial decisions and help them to evaluate future scenarios (Lusardi and Mitchell,

2007b). To measure financial literacy, we follow previous literature (see, for instance,

Fornero and Monticone (2011) and Lusardi and Mitchell (2007a)) and we rely on simple

questions that have the characteristics pointed out by Lusardi and Mitchell (2011c), i.e.

simplicity, relevance, brevity and capacity to differentiate. We show our indicator to

capture one latent construct, which is labelled financial literacy.

The strategy used in this paper hinges on two main assumptions: the absence of

self-selection of, respectively, individuals as clients of a PattiChiari bank and banks

into the PattiChiari Consortium. We extensively discuss them in the paper. Results

are robust to a number of falsification checks regarding potential sorting of customers

or banks. Moreover, first-stage statistics show that the instrumental variable we use

is not weak (Staiger and Stock, 1997) and the availability of multiple instruments for

financial literacy allows us to run an over-identification test that supports the causal

interpretation of our findings.

The paper is organized as follows. We start by briefly reviewing the related liter-

ature, mainly focusing on empirical contributions (Section 2). We then describe the

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main features of the PattiChiari Consortium and services provided by banks in the

Consortium to their clients, highlighting the channels through which these services may

foster financial literacy (Section 3). We present the data used to perform our analysis

in Section 4. Section 5 illustrates our empirical approach and discusses the hypothesis

at the core of the identification strategy. We present our main findings in Section 6. In

Section, 7 we provide evidence that estimated effects are not driven by self-selection of

clients into PattiChiari banks, while in Section 8 we show that selection of banks into

the Consortium does not explain our results either. Conclusions follow.

2 Related Literature

Our paper relates to the literature that analyzes tools aimed at fostering financial liter-

acy. Lusardi and Mitchell (2014) and Hastings et al. (2013) represent the most recent

reviews on these topics. They cover a) the measurement of financial literacy; b) the

relationship between financial education, financial literacy and financial outcomes; c)

the role of interventions. Both reviews point out the shortage of rigorous evaluations

of the impact of financial education programs, making it difficult to draw inference on

their effectiveness. One of the main issues that motivates the lack of empirical evidence

is the difficulty of proving causal links (OECD, 2013a). Selective participation into pro-

grams and non-random attrition, on the one hand, and measurement issues related to

financial literacy and financial behaviour, on the other hand, make this task empirically

challenging.

Several recent papers aim to assess the impact of a financial education programs on

financial knowledge and behaviours, taking into account these issues. Results are mixed.

The meta-analysis in Fernandes et al. (2014) documents a weak effect of interventions

the improvement of financial literacy. On the contrary, Luhrmann et al. (2015) analyse

a compulsory education program in Germany, showing an effect on both financial liter-

acy and behaviour. Financial education programs targeted to high school students in

Brazil turn out to significantly affect financial knowledge and behaviour, such as sav-

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ing for purchases, financial planning, participation to economic choices (Bruhn et al.,

2013). Gibson and Zia (2012) use a randomized experiment to assess the impact of

providing financial literacy training on migrants’ financial knowledge and their use of

remittances tools. Cole et al. (2011) find financial education to increase financial liter-

acy in developing countries (India and Indonesia), with small and heterogeneous effect

on economic behaviour (namely the probability of opening a bank account). Similarly,

Mastrobuoni (2011) shows that the availability of information on social security benefits

in the U.S. has significant impact on workers’ knowledge. However, he does not detect

significant changes in financial decisions. With respect to these studies, we show that

bank information policies shape both financial literacy and financial assets, pointing to

the effectiveness of this alternative tool.

The literature on the links between financial literacy and financial decisions in eco-

nomics and behavioural finance (see the recent review by Garcia 2013) suggests that

the difficulty of processing complex information might be relevant for financial decision-

making, and promote “the widest dissemination possible of some suitable financial rules”

(Garcia, 2013). OECD (2013a) offers some guidance on how the effectiveness of financial

education programs can be improved, in line with what is suggested by papers in the

area of behavioural finance (Altman, 2012). According to these remarks, education pro-

grams should: i) offer material in formats and locations that are easy to access; ii) help

consumers to simplify financial decisions, for instance breaking them in intermediate

steps or providing rules-of-thumb or problem-solving strategies; iii) increase saliency by

providing participants with regular reminders or tools to track and visualise individual

progress. The bank information policies we consider in the paper are in line with these

suggestions.

Our paper also relates to the literature that investigates the causal effect of financial

literacy on financial outcomes. Jappelli and Padula (2013b) and Lusardi et al. (2011)

provide a theoretical framework to analyse the investment in financial literacy and its

impact on wealth in the context of inter-temporal utility maximization. These models

highlight that financial literacy and wealth are simultaneously determined and that the

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incentives to invest in financial literacy may depend on the level of wealth, raising the

issue of the potential endogeneity of financial literacy in the wealth equation.2 Endogene-

ity of financial literacy in the wealth equation may also arise because of omitted variable

bias or substantial measurement error in financial literacy (Lusardi and Mitchell, 2009;

van Rooij et al., 2011b), as we discuss in more detail in Section 5.

Some recent papers have addressed this potential endogeneity problem pursuing instru-

mental variable strategies for identification. However, empirical studies assessing a causal

link between financial literacy and financial outcomes are still rare (Hastings et al., 2013).

Finding an exogenous source of variability in this setting is difficult and most of the iden-

tification strategies adopted in observational studies so far are not free from criticism.3

These papers can be grouped into three broad categories. A first group of papers ex-

ploit as instruments pre-labour market endowment of financial knowledge (Disney and

Gathergood, 2011; Jappelli and Padula, 2013b; van Rooij et al., 2011c). van Rooij et al.

(2011c) are sceptical about the validity of the exclusion restriction for the instrumen-

tal variable they use and discuss the issue at length in the paper, adding a rich set of

controls to their baseline specification. Other studies hinge on the idea that the respon-

dent’s financial literacy is influenced by financial knowledge of peers or reference groups

(Bucher-Koenen and Lusardi, 2011; Fornero and Monticone, 2011; Jappelli and Padula,

2013a; Klapper et al., 2013; Klapper and Panos, 2011; van Rooij et al., 2011a,c). The

assumption that lies behind this identification strategy is that the respondent cannot in-

fluence the peers’ experience significantly, i.e. there is no “reflection problem” (Manski,

1993). An alternative instrumental variable strategy is proposed by Lusardi and Mitchell

(2011b). They take advantage of the fact that several U.S. states mandated high school

financial education in the past and exploit the exogenous variation in financial literacy

induced by the exposure to the mandate to identify the effect of financial literacy on

planning for retirement.

2However Lusardi and Mitchell (2007c) show that the instrumental variable estimate of the impactof wealth on financial literacy is not statistically significant. They interpret this finding to support theabsence of reverse causality in their data.

3We refer to Lusardi and Mitchell (2014) for a detailed discussion of the instruments proposed in theliterature (an insightful synthesis is provided in their Table 4, p.28).

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Our paper does not belong to any of these categories and proposes a new strategy to

identify the causal effect of financial literacy on household wealth that exploits, for the

first time, the role of bank information policies in fostering household financial knowl-

edge. Moving from the assumption that acquiring financial knowledge is a costly process

(Jappelli and Padula, 2013b), we argue that bank information policies undertaken by

a specific subset of Italian banks may decrease the cost of acquiring financial literacy

without imposing additional burden to the client in terms of time, effort or resources.4

We exploit this exogenous source of variation in the cost of acquiring financial knowledge

to investigate the effect of financial literacy on financial assets. As pointed out Lusardi

and Mitchell (2014), most studies find that the OLS estimate of the impact of financial

literacy is biased downward, but the estimates differ a lot across papers and the mag-

nitude of the bias varies largely in the literature.5 Our findings confirm the downward

bias of OLS estimate, showing that causal effect is four times larger.

Financial literacy might affect financial assets by affecting price perception, expec-

tations, and optimization. As a result of increased financial knowledge individuals: a)

may make more accurate or less biased predictions; b) may be less likely to underesti-

mate compounding effects; c) may better understand risk and this may affect how well

they diversify their portfolios (Jappelli and Padula, 2013b). Moreover, financial literacy

may be related to heterogeneity in assets returns, as shown by Deuflhard et al. (2014)

focusing on returns yielded by savings accounts. Finally, the implementation of less

generous defined-contribution pension schemes shifted the burden of adequate savings

for retirement to workers. In this framework, if financial literacy implies a better ability

to understand current pension rules and to anticipate a reduction in the value of social

security wealth, financial knowledge may promote savings, necessary to sustain an ade-

quate level of consumption during retirement (Lusardi and Mitchell, 2007a). Whatever

is the most relevant channel, all point towards financial literacy being positively corre-

4See Section 3 for a description of the activities of the Consortium, and Section 7 for statisticalevidence against selection of customers into PattiChiari banks.

5The instrumental variable estimates range from three times larger the corresponding OLS estimates(Jappelli and Padula, 2013b; van Rooij et al., 2011c) to thirteen times larger than OLS (Bucher-Koenenand Lusardi, 2011).

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lated with financial assets.

Assessing the effect of financial literacy on the quality of choices is a less debated and

eventually more demanding question because it is often unclear what a better decision is.

In this respect, Guiso and Viviano (2015) exploit a high frequency dataset with detailed

portfolio management information to investigate whether financial literacy affect the

ability of dismissing a dominated alternative. More precisely, they examine the ability

to avoid financial mistakes and to make autonomous decisions about portfolio manage-

ment. They find statistically significant but economically small effects. Even though

we lack adequate date to make a similar assessment, evidence that literate individuals

hold a more diversified portfolio is consistent with a better portfolio management. Our

evidence is, however, only suggestive.

3 The PattiChiari Consortium

This section describes the main features of the PattiChiari Consortium, how and why

banks join it, and the range of services provided by banks in the Consortium. It highlights

the channels through which these services may foster financial literacy.

The PattiChiari Consortium has been created in 2003 by the Italian Banking As-

sociation (Associazione Bancaria Italiana: ABI, henceforth). By 2010, 98 banks were

members of it, corresponding to around 64% of all bank branches in the Italian territory.

All except one of the 70 banks we can consider in the paper (see Section 8 for more de-

tails) joined the consortium in 2003; one (Cassa di risparmio della provincia di Chieti)

joined in 2004. The geographical distribution is characterized by some regions (such as

Trentino-Alto Adige and Calabria) where there are few PattiChiari branches and others

(e.g., Piedmont and Sardinia) where over 80% of branches belong to a PattiChiari bank.

The share of PattiChiari branches is however homogeneous across broader geographical

areas: about 63% in northern Italy, 67% in central Italy, 64% in southern Italy.

The Consortium, develops actions aimed at fostering bank transparency and improv-

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ing financial literacy among citizens.6 A bank that joins the Consortium has to introduce

a set of “Quality Commitments” (so called “Impegni per la qualita”). They are aimed to

improving transparency in the relation between bank and clients and, thus, do not have

any goal of advertising specific financial products nor promoting any specific behaviour,

economic choice or investment in any asset. In the period we analyse (year 2010), these

commitments refer to information about mortgage and debt and to comparability of

current accounts. In what follows, we present these two commitments in detail.

Information about mortgage and debt. Every month PattiChiari banks send by

mail a mortgage statement to their clients. This statement includes information on total

and residual amount of debt to be paid and on the residual number of installments, and

reminders information about penalties. In addition, a 24-pages booklet was available

in each PattiChiari branch. The booklet covered several topics related to mortgages

and provided definitions of more general economic concepts.7 Each section included

examples about the calculation of interest rates and installments and provided several

remarks.8 An example of its content is the following remark (our translation from

Italian): “WARNING: With a lower mortgage payment the duration of the mortgage

increases. Therefore, the total amount of interest paid on the mortgage will be higher,

because the number of installments increases”.

Comparability of current accounts. Costs, interest rates and general contract con-

ditions of bank accounts vary both across and within banks. For instance, one bank may

offer up to 12 different bank accounts tailored to a specific type of customer (eg. house-

hold, retired, younger than 18, etc.). The PattiChiari Consortium provides a search en-

6It has implemented a set of formal financial literacy programs in Italian schools and, starting in2012, in workplaces and cultural and charitable associations.

7More specifically it referred to mortgage portability, substitution or renegotiation; mortgage resolu-tion and redemption. It gives a definition for amortization plan, mortgage resolution, Euribor (Euro Inter-bank Offered Rate), IRS (Interest Rate Swap), Eurirs (Euro Interest Rate Swap), fixed-rate, adjustable-rate and mixed-rate mortgages, TAEG (synthetic indicator of the cost of the mortgage), portability,installment, spread, renegotiation, and subrogation.

8A copy of the booklet, available in Italian only, can be downloaded from the URLhttps://web.archive.org/web/20111030200724/http://www.pattichiari.it/dotAsset/12557.pdf

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gine that allows the direct comparison of specific characteristics of bank accounts across

different banks, as well as the information on the location of bank branches. Thanks to

the provision of detailed information on interest rates, we expect households to become

more aware about how current accounts work and more accustomed to concepts like

interest rate compounding.

The “Quality Commitments” described above are in line with the OECD suggestions

to improve the effectiveness of education programs (OECD, 2013b). Indeed, they pro-

vide informative material on a regular basis (costumers receive the mortgage statement

every month) in format and location that are easy to access (mail, bank’s branch and

websites). These interventions are designed to simplify costumers decision problems,

providing definitions and synthesis of basic and simple information about relevant eco-

nomic concepts, such as amortization plans and interest rates compounding, sometimes

through examples. These concepts are closely related to the standard questions used to

measure financial literacy in the literature and in our paper (see Appendix A for the

exact wording of the questions used to measure financial literacy). At the same time,

the “Quality Commitments” are not directly related to the provision of formal financial

advice and do not typically take this form. However, they can indirectly impact the

probability of consulting an advisor and delegating the portfolio choice management if,

as shown by Calcagno and Monticone (2015), it depends on financial literacy.

Why Banks Join the PattiChiari Consortium? Application to the PattiChiari

Consortium is decided at the bank level, and it involves all the branches on the Italian

territory. Any bank can apply to be a member of the Consortium, provided that it

undertakes the “Quality Commitments”. Applications are evaluated by Patti Chiari

board of directors. If a member bank does not meet the requirements, it can be fined

and excluded from the Consortium.9 The Consortium, based in Rome at the ABI’s

9After considering all merges and acquisitions among banks over the relevant period, we find thatonly 1.5% of households in our baseline sample remained with a bank that changed affiliation with thePattiChiari Consortium. In all these cases these banks exited the Consortium after belonging to it for aperiod between 6 months and 5 years. Thus, we are unable to use variation induced by banks entering

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headquarters, develops projects that are, then, implemented by each PattiChiari bank.

There is no fee for entering the Consortium, yet the bank has to incur administrative

costs in order to comply to the “Quality Commitments”. Menu costs make the commit-

ment regarding the comparability between current accounts the most demanding task

for member banks. In particular, significant menu costs had to be incurred in order to

adjust the offer of bank accounts to the PattiChiari standards (allowing their compara-

bility across banks). High entry costs may partly explain the large share of PattiChiari

partners among large banks (see Section 8). Concerning the advantages of entering in the

PattiChiari Consortium, banks improve the quality of supplied services and customer

satisfaction, and, in turn, benefit from advertising provided by the Consortium. Informa-

tive advertising can benefit both the firm that makes the investment (in our application a

bank belonging to the PattiChiari consortium) and its competitors, through an increase

in savings, stock market participation and the demand for financial services in general

(Hastings et al., 2013). Free-riding problems may induce underprovision of informative

advertising. In our case, however, the fact that over 60% of all bank branches belong to

the Consortium, according to ABI data, should mitigate the incentive to free-ride.

Descriptive statistics of bank characteristics, and their heterogeneity between PattiChiari

and non-PattiChiari banks are discussed in Section 8, where we provide statistical ev-

idence that our results are not driven by bank observable differences or their potential

self-selection in the Consortium.

4 Data

The empirical analysis is based on the Bank of Italy’s Survey on Household Income

and Wealth (SHIW), a biannual survey that collects detailed information on household

income, savings and portfolios from a nationally representative sample of Italian house-

holds.

Households’ financial assets are defined as the sum of deposits, securities, and commer-

the Consortium in different periods to test the internal validity of our identification strategy.

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cial credits at the end of the reference year (2010 for the baseline analysis).10 Several

covariates (age, gender, education) refer to the household head, defined as the household

member who is responsible for economic and financial decision. We observe financial

literacy of the household head only. Our baseline empirical analysis is based on the

2010 wave, because it includes both questions about the reasons for choosing the bank

(available only in this year) and questions to measure financial literacy. In Section 9,

we check the validity of the results in a larger sample, that also includes 2006 and 2008

waves: results are fully consistent.

The set of questions on financial literacy are included since 2006 and vary slightly across

waves. In the 2010 wave, respondents are asked three questions related to portfolio

diversification, the risk associated to fixed or adjustable interest rate and the effect of

inflation.11 Questions about diversification and inflation are similar to the ones devised

for the US Health and Retirement Study (HRS henceforth) (Lusardi and Mitchell, 2011a)

while knowledge of mortgage repayment mechanisms is not considered in the HRS. All

these questions follow the principles of simplicity, relevance, brevity and capacity to

differentiate identified by Lusardi and Mitchell (2011c). Table 1 shows that in 2010

almost three out of four respondents answered correctly to the question on inflation;

the percentage of correct answers to the question on mortgages and portfolio diversifica-

tion is, respectively, 64% and 55%. On average, respondents answered correctly to less

than two questions, and one respondent out of three correctly answered to all questions.

We measure financial literacy with the number of questions correctly answered by the

household head. We present evidence corroborating the assumption that our indicators

measure just one latent construct, that we name financial literacy (as in van Rooij et al.

(2011b)), and we show that our baseline results are confirmed when using this indicator

for financial literacy (see Appendix A for more details). Moreover, our results are robust

10More precisely assets include, along with bank accounts, Italian government securities, bonds andItalian investment funds, Italian shares and equities, managed portfolios, foreign securities and a residualcategory. Commercial credits include i) credits of the household’s members with relatives or friendsnot living with the household and ii) trade credits to costumers of respondents who are self-employed,members of a profession, individual entrepreneurs or employed in a family business.

11See Appendix A for the exact wording of the questions and details on differences across waves.

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to the use of different measures of financial literacy (see Section 6).

We use information on the date of entrance and exit of each Italian banking group

in the PattiChiari Consortium and the answer to questions about the banks used by the

household to build an indicator of whether the household is client of a bank that is part

of the PattiChiari Consortium. Our baseline analysis focuses on households having at

least one bank account. We say that a bank belongs to the Consortium in year 2010 if

the bank was part of it by January 1st of 2010. It is important to highlight that financial

literacy and financial assets are both collected at the interview, and the latter refers to

December 31st 2010. Therefore, we observe financial literacy and financial assets one

year after the moment we measure the PattiChiari treatment and, thus, the effect we

estimate is not contemporaneous but delayed by one year.

Households in the SHIW dataset report their “main” bank -as defined by the household

head- and up to seven additional banks where they hold a current account. Answers

can be chosen from a list of 87 financial institutions (encompassing more than 85% of

total credit granted in Italy). If the bank is not listed among the possible answers,

its name will be missing. For each household, we construct a dummy (Patti) that is

equal to one if the main bank used by the respondent belong to the Consortium at the

beginning of 2010 and zero if it does not; we exclude households who do not have any

bank account or for which the name of the bank is missing. Descriptive statistics show

73% of respondents in our sample to be clients of a bank belonging to the PattiChiari

Consortium (Table 1).12

We include in the sample households whose head is aged 30 or above because we

want to exclude cases in which individuals may be still enrolled in full-time education

(at the university) and thus not financially independent from the family of origin. After

excluding from our sample outliers and respondents who do not report the reason for

choosing the bank, the final sample size in our baseline regression (2010 wave data) is

4865 observations (descriptive statistics are shown in Table 1).13

12Our results are robust to alternative measures for being client of a PattiChiari bank and to differentway of dealing with missing values. This issue is discussed in Section 6.

13We exclude the upper and lower 5% tail of the financial assets distribution. Results are similar with

14

For some complementary analysis we merge this data with data from the 2002 SHIW

wave: due to the rotating panel of the survey, only 1027 households have been interviewed

in 2002 and 2010. Notably, no measure of financial literacy is available in the 2002 SHIW

wave.

We collect information on bank branches active in Italy in 2010 from the SIOTEC

database. This database, administered by the Bank of Italy, is based on compulsory

information provided by each branch to the Supervisory Agency. The only financial

institution excluded by SIOTEC is BancoPosta (the financial arm of the Italian post

office), for which we obtained the number of branches from the notes to its 2010 balance-

sheet.14

We use data from a nationally representative sample of bank accounts (the Survey on

Bank Fees and Expenditures, SBFE hereafter) to check that the fees and costs charged

by PattiChiari and non-PattiChiari banks are on average similar. This dataset, ad-

ministered by the Bank of Italy’s Banking and Financial Supervision Area, is currently

the most comprehensive source of information on bank costs in Italy and it collects in-

formation on fixed bank account costs, debit/credit card fees, costs of bank transfers

and withdrawals. We use the 2010 wave and we rely on data on 6717 current accounts

surveyed in SBFE and belonging to banks present in the SHIW dataset. Note that

the SBFE samples household’s bank accounts rather than banks, bank branches or cus-

tomers. This is a sensible choice because each bank may offer several types of accounts,

with different costs. The data at our hand suggest that SBFE might oversample accounts

of PattiChiari banks.

1% trimming. There are tied values and we discuss this point in more detail in Section B.3.14We were able to retrieve information on total assets in 2010 for 70 out of the 87 bank names presented

in the questionnaire from the Supervisory Register of the Bank of Italy. For the remaining 17 banks,it was not possible to recover such information because of different reasons. In 14 cases banks includedin the questionnaire did not have an autonomous balance-sheet: e.g., Unicredit Banca and UnicreditPrivate Banking share the same balance-sheet data in our data sources; the Banca Popolare di Intra waspart of VenetoBanca. Finally, for 3 banks neither the Supervisory Register nor other sources, such asBankscope, reported any information for year 2010.

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5 Empirical Strategy

To identify the effect of bank information policies on financial literacy, we consider the

following linear model:

flip = α0 + α1Pattiip +Xipα2 + δp + νip (1)

where flip is financial literacy of the head of household i living in province p in 2010;

Pattiip is a dummy equal to 1 if the household is client of a bank belonging to the

PattiChiari Consortium; Xip is a vector of individual-level observable characteristics.15

δp is a province fixed-effect (province effects are denoted by µp or γp in equations (2)

and (3), respectively).

Similarly, to estimate the effect of bank information policies on financial assets, we

consider the equation:

wip = θ0 + θ1Pattiip +Xipθ2 + µp + ξip (2)

where wip is household’s financial assets in 2010.

The parameters of interest are α1 and θ1, which capture the effect of being client

of a bank in the PattiChiari Consortium on financial literacy and financial assets, re-

spectively. Provided that Pattiip is exogenous in equation (1) and (2), OLS estimates

of these parameters are consistent and have a causal interpretation. We expect α1 and

θ1 to be statistically significant and positive and we argue that the variable Pattiip is

exogenous in both equations. Investing in financial literacy is costly for individuals and

the investment decision is made comparing marginal cost and marginal benefit (Jappelli

and Padula, 2013b), where the latter may depend on financial wealth w and individuals’

characteristics X. We argue that the Quality Commitments undertaken by a PattiChiari

15In the baseline specification, controls include gender of the household head, a second-order polyno-mial in age, years of education of the household head, household current labor income, household size,marital status, size of the municipality (3 categories: 20,000-40,000; 40,000-500,000; more than 500,000inhabitants). We experimented with several subsets of these controls, and the inclusion of additionalcovariates is discussed in Section 6 and Appendix B.

16

bank lower the cost of acquiring financial education without directly affecting the ben-

efits related to additional financial literacy. Thus, other things being equal, we expect

PattiChiari clients to accumulate more financial literacy and end up with a higher stock

of it. For this interpretation to be valid, the following three assumptions must be fulfilled.

First, net of information policies, banks belonging to the PattiChiari Consortium may

not differ from other banks with respect to characteristics that may directly affect house-

hold financial assets (such as costs and fees charged, credit rationing, mortgage policies,

etc.). Second, individuals must not self-select as clients of a specific bank according to

its information policy. In other words, the choice of the bank must not depend on vari-

ables that, conditional on province (or municipality) of residence, directly affect financial

literacy or wealth. Third, banks must not join the Consortium in order to attract clients

that are wealthier or more financially literate. All these assumptions should hold within

province in a given year, because all our regressions include province fixed effects. These

three assumptions are partly testable on the basis of the data at our hands and deserve a

careful discussion. We discuss client selection in Section 7, while we focus on differences

in observable characteristics of banks in Section 8 . The evidence presented supports

the validity of the assumptions above and thus the causal interpretation of our estimates.

We also examine the effect of financial literacy on financial assets by considering the

model:

wip = β0 + β1flip +Xipβ2 + γp + εip. (3)

OLS estimates of β1 in Equation (3) may be biased because of: (i) individual unobserved

heterogeneity (i.e. ability, patience or preferences) correlated with both financial literacy

and the value of financial assets; (ii) reverse causality; (iii) measurement error. In the

first case, the sign of the bias is theoretically ambiguous and depends on the sign of the

correlation between flip and εip in equation (3). The theoretical arguments in Jappelli

and Padula (2013b) suggest that reverse causality should lead to an upward bias in

the estimate of β1, since individual wealth may affect the incentive to increase financial

17

education both through a change in the opportunity-cost of time and through a change

in the relative benefit from knowing how financial tools work.16 Finally, measurement

error in financial literacy may be substantial because respondents may guess the answer

at random or they may misunderstand the question.17 In these cases, OLS estimate

may be biased downward (Jappelli and Padula, 2013b). Overall, these remarks suggest

that: (i) OLS estimates may be biased and (ii) the sign of the bias cannot be assessed

theoretically since it depends on the relevance of each of the channels described above.

To address these issues, we pursue an instrumental variable approach and use equation

(1) as the first stage equation for the financial asset equation (3).18

The instrumental variable strategy relies on the three assumptions illustrated above (and

discussed more in details in Sections 7 and 8), since it rests on consistent estimation of

α1 and θ1 with OLS and an exclusion restriction (Angrist et al., 1996).

6 Findings

Table 2 shows estimate results of the baseline specification, which includes, along with

household covariates and province fixed effects, controls for motivations for choosing

the bank.19 The association between financial literacy and financial assets is positive

(column OLS). One standard deviation increase in financial literacy (i.e. one more correct

answer) is associated with an increase in financial assets by more than 2 thousand euros

16Lusardi and Mitchell (2007c), however, present empirical results that suggest that reverse causalityis not a relevant issue in American data (HRS).

17Evidence from the U.S. (Lusardi and Mitchell, 2009) and the Netherlands (van Rooij et al., 2011b)suggests that survey data on this issue are sensitive to how the question is worded.

18In principle, we might rely on the panel component of our dataset and include household fixed effectsin equation (3). There are three arguments against this choice. First, financial assets and financial literacyare both highly stable over the short time span we consider (2006-2010) in this application. Second,respondents may learn, or at least receive a stimulus to acquire knowledge, from previous interviews:the variation in correct answers may capture a retest effect rather than increases in financial literacy.This would affect estimates delivered by a fixed effect identification approach, while it does not drive ourresults, as we show in Appendix B. Third, using the longitudinal panel would cut sample size by halfbecause of attrition and survey design. Furthermore, in case of reverse causality or measurement error,coefficients in equation (3) are biased also when including fixed effects.

19Unless otherwise stated, estimated standard errors are robust to both heteroskedasticity and serialcorrelation at the province level, the largest level at which we have enough clusters (103) to obtainconsistent estimates of the second moment of the estimator distribution.

18

(about 12% of the average financial assets holdings in our baseline sample). As discussed

in Section 5, this estimate is unlikely to reflect a causal relationship but represent an

important benchmark for our analysis.

Moving to the first stage results (column FS in Table 2), being a client of a PattiChiari

bank leads an individual to give 0.24 additional correct answers, that is approximately

one fourth of a standard deviation of financial literacy, nearly 12% of the average num-

ber of correct answers given in the sample. The effect of bank information policies on

financial literacy is sizeable compared to other explanatory variables in our regressions:

the impact of being client of a bank that belong to the PattiChiari Consortium is com-

parable to the effect of increasing education by five years.20 The F-test on excluded

instruments is about 44, showing that, when we interpret this equation as the first step

of our IV strategy, the instrument is not weak.21

The instrumental variable estimates we present in the paper apply to the sub-

population of compliers (Angrist et al., 1996), i.e. individuals forces to change their

treatment status because of the assignment to the treatment. In our case the compliers

are the individuals whose knowledge of financial instruments increases because of the

information policies of the PattiChiari banks. When both the instrumental variable and

the treatment variable are binary, the proportion of compliers can be estimated and cor-

responds to the first stage estimate (Angrist et al., 1996), i.e. the effect of the instrument

on the endogenous regressor. In our application, the instrumental variable is binary, a

dummy variable indicator of being PattiChiari client. In Table 3 we consider four al-

ternative measures of financial literacy and we estimate the effect of being PattiChiari

20As discussed in Section 2, evidence on the causal impact of interventions on financial literacy is stilllimited. Assessing the effect of bank information policies relative to other interventions is, therefore, notstraightforward. Klapper and Panos (2011) consider the role of newspapers in Russia, and find that a 1%increase in their number raise average financial literacy in the population by nearly 4%. Comparatively,increasing the number of universities by 1% would increase financial literacy by 0.15%. Carpena et al.(2011) assess the effect of a video-based financial literacy program in India, finding it increases financialawareness by about 8%. The relative size of the effect we find seems, thus, slightly larger than the one ofprevious information/training instruments in Russia and India. More recently, Luhrmann et al. (2015)studies the effect of a financial education program for teenagers in Germany, finding it both significantlyincreases their financial knowledge and reduce their propensity to buy on impulse.

21Staiger and Stock (1997) indicate a rule of thumb suggesting that the F-statistic should be greaterthan 10 to rule out weak identification problems.

19

client on each of these binary indicators: these are also alternative estimates of the pro-

portion of compliers in the population. We consider four definitions: a dummy equal to

1 if all three questions have been answered correctly and zero otherwise or we consider

the correct answers of each of these three questions separately. Being a PattiChiari

client is strongly correlated with all these definitions and leads to about 13% increase

in the probability of answering all questions correctly, 14% increase in the probability

of answering correctly the question on inflation and an 11% increase in the probability

of answering the loan or portfolio question correctly, with respect to the sample average

(see Table 1). These estimates suggest that the compliers represent a fraction of our

sample ranging between 5% and 10% of the population.

Financial assets of households who have the main current account in a PattiChiari

are 1.9 thousand euros higher than clients of other banks, roughly 10% of the average

value of assets in our sample (column ITT in Table 2). The resulting instrumental vari-

able estimates (column IV of Table 2) are larger than what simple association suggests:

on average, a one standard deviation increase in financial literacy (i.e. an additional

correct answer provided, about a 50% increase with respect to the average financial

literacy), conditional on other covariates, leads to an increase of household assets of 8

thousand euros, that roughly correspond to 35% of a standard deviation in financial

assets.22 Thus, in line with findings by Lusardi and Mitchell (2011b), the causal effect

of financial literacy is around four times higher than suggested by the corresponding

association. The downward bias in OLS estimate can be explained by a negative corre-

lation between financial literacy and the error term in the financial assets equation or

by the presence of substantial measurement error in financial literacy. First, individual

specific unobservables may be negatively correlated with financial literacy and positively

associated with financial assets (or viceversa). For instance, wealthy individuals may

have a higher opportunity cost of time and/or expect (more efficient) professional port-

folio management to yield a greater return, in absolute terms. For these reasons, they

22This effect apply to the sub-population of individuals whose knowledge of financial instrumentsincreases because of the information policies of the PattiChiari banks and cannot be generalized to theaverage sample household without further assumptions (Angrist, 2004).

20

may decide not to directly invest in their own financial literacy but, on the contrary,

to delegate portfolio management decisions to a professional advisor. This channel is

consistent with downward bias of the OLS estimate: if professional advisors are indeed

able to generate larger returns the observed association between financial literacy and

financial assets will be smaller in absolute terms than the true causal effect. Second, the

indicator(s) for financial literacy may suffer of measurement error, since the respondent

may misunderstand the survey questions or pick the answer randomly. This argument

is consistent with evidence on the sensitivity of answers to financial literacy questions

documented by Lusardi and Mitchell (2009) for the US and by van Rooij et al. (2011b)

for the Netherlands.

We also consider alternative measures of being a PattiChiari client, as reported in

Table 20 in Appendix C. Along with the indicator based on the main bank account we

use in the baseline specification (first panel), we rely on a dummy variable that is equal

to one whether at least one of the banks of the respondent belong to the PattiChiari

Consortium (second panel). Finally, we include records for which the name of the bank

is missing and we consider banks with missing names to signal a non-PattiChiari bank.23

Then, we create a dummy taking the value one if at least one of the accounts recorded in

the survey is with a PattiChiari bank, and zero otherwise. We expect the latter indicator

to have some degree of attenuation bias with respect to our main indicator, because some

of the banks for which the name is unavailable may belong to the Consortium. All results

are confirmed using these alternative indicators.

In the baseline regression we control for province fixed effect. But municipality-

specific factors may affect both the level of financial assets and the probability of being

client of a PattiChiari bank (e.g. different banks may decide to open branches in a

23The SHIW questionnaire includes a list of names from which the respondent may pick her bank.This list seems to include a slightly larger share of PattiChiari banks with respect to the overall universeof Italian banks. Our baseline analysis exclude records for which the name of the banks is not listedand might, in principle, be biased towards PattiChiari clients. Because of this we replicate our results:a) changing the sample and including households whose main bank has a missing name; or b) using analternative indicator of being PattiChiari client that exploits information on all the accounts held bythe household; or c) including controls for the number of current accounts held by the household; or d)restricting the attention to large or small banks only. Baseline results are confirmed.

21

particular type of municipality). We include municipality-fixed effects and replicate our

baseline results: previous findings are confirmed, as shown in Table 21 in Appendix C.

We also the robustness our findings to several falsification checks, which are reported

ans discussed in Appendix B. In particular, we explore: i) the role of retest effects and

the timing of the effect of bank information policies and financial literacy; ii) the role

of trust in institutions; iii) the sensitivity of our results to sample selection and the

functional form. Our results turn out to be robust to all these checks.

7 Evidence Against Self-Selection of Households into Pat-

tiChiari Banks

In this section we discuss evidence supporting the absence of self-selection of households

into PattiChiari or non-PattiChiari banks, that lies behind the validity of our identifi-

cation strategy.

We first provide evidence on the determinants of households choice of banks. Then we

tackle the potential reverse causality that may explain the estimated effect. We then

show that our results are robust to the use of a propensity-score matching estimator,

and we perform an overidentification test on the validity of the exclusion restriction for

the IV model. In the Appendix B.2, we also show that different measures of trust are

not different between PattiChiari and non-PattiChiari households.

It is worth emphasizing that all the results are net of province (or municipality) fixed

effects: we are, thus, exploiting only the within-province variability of the instrument

for identification (see Table 21 in Appendix C).

Why do households become customers of PattiChiari (or non-PattiChiari)

banks?

Bank clients may self-select into PattiChiari banks according to their financial knowl-

edge. Hence, respondents with higher financial literacy may value more financial con-

ditions and products supplied by the bank and, in turn, they may decide to become

22

customer of a specific bank according to its financial offerings. Even though the ho-

mogeneity between PattiChiari and non-PattiChiari banks concerning cost and credit

strategies, documented in Section 8, weakens this self-selection issue, we explore further

the determinants of household choices by exploiting information collected by the SHIW.

We rely on answers to the following question “Why did you choose [the bank you use

more often] when you and your household first began using it?”. The set of available

alternatives can be naturally grouped in the following three categories: 1) convenience,

i.e. convenience to home/work, respondent’s employer’s bank (or respondent’s business’s

bank); 2) financial/economic reasons, i.e. favourable interest rates, speed of transaction

execution, range of services, low fees for services, possibility of online banking; 3) bank

type, i.e. it is a well-known/ important bank, staff courtesy. The main reason for choos-

ing a bank turns out to be related to convenience, that is the only determinant for 63%

of PattiChiari clients and for 67% of other respondents. Financial conditions are the

only determinant for less than 15% of the respondents in both groups. We interpret

this evidence as follows: the preferred financial offerings may be readily available and

consumers typically decide their bank depending on the one that is more conveniently

located to them. In other words, the constraint on financial services is not binding

and it is satisfied by the abundance of options, hence individuals choose on the basis

of their second (binding) constraint, namely convenience. As we shown in Table 22 in

Appendix C, our results are confirmed when we omit these controls. We include them

in our baseline regression to improve the precision of our estimates.

Transparency or, more generally, information acquisition is not mentioned among

the alternative reasons for choosing the bank. For this reason, one may argue that we

cannot rule out the possibility that clients join a PattiChiari bank to get more infor-

mation. Households that are wealthy or willing to use sophisticated financial products

may prefer PattiChiari banks because of the information policies they provide. This

potential problem of self-selection is particularly hard to test, as it entails gouging

unobserved characteristics (propensity to invest) and potentially simultaneous choices

(information seeking and bank choice). Nonetheless, some indirect evidence can be ob-

23

tained by exploiting data on the source of financial information used by respondents to

make investment decisions. In the 2010 SHIW survey, household heads are asked about

the information source they have been consulted before the current investments were

performed. They can be grouped in three broad categories: formal sources (financial

intermediaries, experts and advisors), and two categories of informal ones, namely press

or websites and the residual (friends, others, do not know). Descriptive statistics of the

resulting indicators are reported in Table 1. Since this information is collected only for

respondents who hold financial assets other than bank accounts (“investor households”

hereafter), the sample size reduces to 1492 households, who are, on average, wealthier

and more financially sophisticated than the rest of the sample.24 We estimate the base-

line regressions on the subsample of “investor households”, with and without controlling

for sources of financial information. Particularly, we control for an indicator that takes

value 1 if the respondent relies only on that specific source of financial information and

0 otherwise (descriptive statistics of these variables are shown in Table 1). Results are

reported in Table 4. Conditioning on sources of information does not alter the effect

of the variables of interest. Moreover, the coefficients associated to sources of financial

information are not statistically significant. The reduced form (first-stage and intention-

to-treat) and instrumental variable estimates are shown not to be confounded by the

direct effect of sources of financial information. It is worth noting that, when we restrict

our analysis on the sample of “investor households”, both the intention-to-treat and the

effect of financial literacy on financial assets become larger in magnitude, although less

precisely estimated with respect to the entire sample. This is informative of the potential

heterogeneity of the effects in the population.

The reasons why banks join the PattiChiari Consortium relate to the advertisement

of the bank commitment to transparency (see Section 3). We claim that the effectiveness

of this advertising does not depend on wealth or financial literacy, thus entering into the

Consortium does not have the effect of attracting wealthier or more financially literate

24Average financial wealth of “investor households” is 38.22 thousand euros, compared to 19.38 thou-sand euros for the entire sample. The average number of correct answers on the questions measuringfinancial literacy is 2.2 among “investor households”, 1.9 in the broad sample.

24

clients. In other words, our identification strategy hinges on the assumption that the

effectiveness of advertisement policies implemented by the PattiChiari Consortium does

not depend on financial literacy nor wealth. While we cannot test this assumption di-

rectly due to lack of relevant data, we collect complementary evidence documenting that

individuals do not self-select as client of a bank belonging to the PattiChiari Consortium

according to their wealth, as illustrated in the next paragraph.

Reverse causality: Are wealthier clients more likely to become PattiChiari

clients?

In order to document absence of self-selection of clients according to their wealth, we

exploit the panel component of the SHIW dataset to retrieve information on household

financial assets in 2002, i.e. before the PattiChiari Consortium was created. First, we

perform a“placebo”test, regressing 2002 assets on being a PattiChiari client in 2010. We

fail to find a significant correlation between being such a client in 2010 and 2002 assets.25

Second, in order to control for “pre-treatment” differences in the asset endowment, we

estimate the baseline specification by including financial assets in 2002 among the control

variables. Results are reported in Table 23 in Appendix C. The upper panel shows the

results with the additional controls, while the lower panel reports the baseline results

estimated on the sample for which we can recover information on financial assets in

2002. The main coefficients in the two panels are similar both in terms of size, while

their statistical significance is hampered by the huge cut in sample size. Also the first

stage result (column 2) remains stable when we include financial assets in 2002 as a

regressor.

In order to rule out self-selection of wealthiest clients into PattiChiari banks as the

driver of our findings, we compare households with the same assets before the creation

of the Patti-Chiari Consortium and examine the growth in their assets between 2002

and 2010 or 2002 and 2008 or 2002 and 2006. The exercise is performed on the sample

of individuals surveyed in 2002 and at least in another year (2006, 2008 and 2010).

25The coefficient associated to the PattiChiari dummy in this test regression is 12.663 with a standarderror of 13.847.

25

We compare households with the same assets before the creation of the PattiChiari

Consortium and examine the growth in their assets between 2002 and 2010 or 2002 and

2008 or 2002 and 2006, controlling also for a vector of household characteristics and year

specific province fixed effects. We standardized growth to have zero mean and unitary

variance so that coefficients can be easily interpreted as effects in units of standard

deviation. Results are reported in Table 5. Figures confirm our results: they suggest

that OLS underestimates the effect of financial literacy on growth of financial assets and

that the causal effect of financial assets is as large as about 15% of a standard deviation

and positive. As a robustness check, we estimate a model that allows for heterogeneity

across years in the effects of financial literacy on financial assets growth (Table 24 in

Appendix C). The effect of financial literacy is positive and significant in all years and

about 4% of standard deviation smaller in 2008 (the year after the fall in stock market

prices documented by Bottazzi et al. (2013)), with respect to 2006 and 2010.

We also explore whether there are differences in pre-treatment (2002) financial assets

among individuals who switch between PattiChiari and non-PattiChiari banks between

2006 and 2008 or between 2008 and 2010. We focus on households sampled for at least

two consecutive waves over the period 2006-2010 and we group them in four categories

of households: households who remained clients of PattiChiari over the two waves;

households who remained clients of a non-PattiChiari bank; those who moved from a

PattiChiari to a non-PattiChiari bank; and those that made the opposite move. We

then merge to these data, data on financial assets in 2002. Although for this exercise we

can only use individuals interviewed in 2002, the distribution of the types of switch is

fairly similar to the one in the full sample: 77.9% remained clients of PattiChiari banks,

13.6% remained clients of a non-PattiChiari bank, 5.4% switched to a non-PattiChiari

bank, 3.1% switched to a PattiChiari bank. In almost all these cases (99.4%) the switch

has been voluntary, i.e. due to switch of the household’s current account from one

bank to another, rather than because the bank entered or exited the Consortium. We

then test whether there are significant between group differences in financial assets in

2002, i.e. before the PattiChiari consortium was created, using our baseline regression

26

(yet, controlling for labor income in 2002, rather than in 2010). Table 6 presents the

results for three different regression models: column (1) does not control for reasons for

choosing the main bank; column (2) controls for reasons for choosing the main bank

(baseline); column (3) controls for reasons for choosing the main bank, costs charged

by the bank and bank characteristics (i.e. bank size). We cannot detect significant

differences in financial assets between groups in any specification: clients who remained

with PattiChiari banks between 2006 and 2008 or 2008 and 2010 are not wealthier nor

poorer than those who switched to Non-PattiChiari banks, nor the two types of “stayers”

or “switchers” differ. We interpret this as evidence that there is no selection of clients

into PattiChiari banks.

Households who hold more than one bank account may be wealthier than the average

and may already diversify their portfolios using services from different banks. As having

more bank accounts increases the chances of being a PattiChiari client, this may generate

spurious correlation. To control for this possibility, we include household’s number of

bank accounts in our baseline specification. Results, presented in the third panel of

Table 18 in Appendix B, confirm our baseline findings.

Financial assets and financial literacy may correlated to the tenure status of the

respondent. To examine this issue, we include among the regressors in the baseline

specification an indicator for whether the family owns the dwelling where they live (Table

25 in Appendix C). Homeownership turns out to be positively associated with the level

of both financial literacy and financial assets, but our findings are robust to the inclusion

of this additional regressor.

A matching estimator

We estimate the effects of being client of a PattiChiari bank on financial literacy and

financial assets using a matching estimator, based on propensity score matching, and we

replicate the baseline regression analysis of the paper on the sample where we observe

clients of PattiChiari and non-PattiChiari banks that have similar probability to be a

PattiChiari client based on observables. This exercise allows us to match almost all Pat-

27

tiChiari clients with similar non-PattiChiari clients in terms of demographics, reasons

for choosing the main bank, province of residence and household labour income. After

excluding 36 households we cannot match, we replicate our baseline estimate and find

essentially the same results.

We start estimating the propensity score through a logit model, where the dependent

variable is the probability of being PattiChiari client in 2010. The balancing property is

satisfied once we include among the controls demographics of the household head (age,

age squared, gender, years of education, marital status) and household characteristics

(number of household members, household labour income), municipality population size

(inhabitants), province fixed effects, reasons for choosing the main bank and some in-

teractions between those controls.26 We perform radius matching with caliper 0.005,

with replacement, matching each treated individual with two controls: observations of

PattiChiari clients are used only once, while observations in the control group (non-

PattiChiari clients) may be used in several matches. Table 26 in Appendix C assesses

whether the matching procedure is able to balance the distribution of covariates among

PattiChiari clients and non-PattiChiari clients. For each variable, we show the mean

value in the group of clients of PattiChiari and non-PattiChiari banks in, respectively,

column 2 and 3. The last two columns report the standardized percentage bias across

covariates after matching and the p-value of a t-test of equality between the means. We

do not reject that covariates are balanced at all conventionally used levels of significance,

with few exceptions (for which we do not reject the null at 1% significance level) The

last line in the table shows that the joint test does not reject with a p-value of 0.103.

Figure 1 displays a graphical representation of the common support. Notably, most ob-

servation are on the common support (99.3%) and we are not able to match only 0.007%

of the households (36 out of 4865) with a very high propensity score. We estimate the

effect of being PattiChiari clients on financial literacy (first stage effect) and financial

26More precisely, in addition to the levels of the listed variables, we include interactions between:household head gender and marital status; household head marital status and municipality size; familysize and and municipality size; household head gender and municipality size; household head age andmunicipality size; family size and household labour income; household head marital status and householdlabour income.

28

assets (intention-to-treat effect) for the treated using a matching estimator: 2.38 with a

standard error of 1.03 for financial assets; 0.17 with a standard error of 0.06 for financial

literacy. The intention-to-treat effect is statistically significant at 5%; the first stage

estimate is statistically significant at 1%. Both estimates in line with those based on the

OLS estimator reported in Table 2 (see FS and ITT column). Table 7 reports results of

instrumental variable regression on the common support with the specification of Table

2 and on the common support with the specification that achieves covariate balancing.

An overidentification test

We exploit the panel dimension of the SHIW to perform an over-identification test. We

focus on households sampled for at least two consecutive waves over the period 2006-

2010 (i.e. either in 2006 and 2008 or in 2008 and in 2010; 3196 observations) and

we group them in the four categories of households discussed above: households who

remained clients of PattiChiari over the two waves; households who remained clients

of a non-PattiChiari bank; those who moved from a PattiChiari to a non-PattiChiari

bank; and those that made the opposite move. As discussed above, these four groups

identify three instrumental variables (plus a baseline group). All these variables can be

used to predict financial literacy in 2010. Results are summarized in the first panel of

Table 27 in Appendix C. The lower panel of Table 27 replicates the exercise focusing

on bank-switchers over the 2006-2010 period (two waves). Despite the sample size is

sensibly reduced, the main results of Table 2 are confirmed: one standard deviation rise

in financial literacy causes an increase in financial assets by 12.7 and 9.6 thousand euros

in, respectively, the upper and lower panels. Looking at the over-identification test, the

Hansen J statistic does not reject the null hypothesis of exogeneity of the instruments

at all conventional levels of significance: the p-value associated to the test is 0.43 in the

top panel of Table 27 and 0.75 in the bottom panel. In short, this evidence supports the

exogeneity of our instrument (i.e. being client of a PattiChiari bank) in the financial

assets equation.

29

8 Evidence Against Self-Selection of Banks into the Pat-

tiChiari Consortium

In this Section, we present empirical evidence supporting the absence of self-selection of

banks into the PattiChiari Consortium, one of the pillars on which the causal interpre-

tation of our findings stands. To this scope, we compare observable characteristics of

banks belonging or not to the consortium and we check the robustness of our findings

across relevant sub-samples and to the inclusion of bank-specific controls.

We start by analysing to which extent banks differ according to costs and credit

policies. Descriptive statistics on costs of bank accounts charged by banks belonging

and not belonging to the PattiChiari Consortium are reported in Table 8, together with

a Welch test on equality between means of the two groups. Differences in costs and fees

are generally not statistically significant, the only exception being average debit card fees

that are slightly larger (less than 3 euros per year) for non-PattiChiari banks. Focusing

on ATM withdrawal costs, the Quality Commitments of banks joining the PattiChiari

banks did not prescribe anything related to free access to ATM machines to customers

of other banks of the Consortium and, on average, ATM withdrawal costs are 0.31 for

PattiChiari banks and 0.29 for non-PattiChiari banks, not statistically different from

each other. Our main results are unaffected by the inclusion of these costs among the

control variables (see the upper panel of Table 30 in Appendix C).

We check whether banks that belong to the PattiChiari Consortium differ from other

banks with respect to credit rationing, exploiting information about liquidity constraints

provided by SHIW. We do not find evidence of any correlation between being PattiChiari

client and being liquidity constrained, that may signal differences in credit rationing, at

standard significance levels.27

27Following Jappelli et al. (1998), we consider a household to be “liquidity constrained” if it either (a)applied to a bank or a financial company to ask for loan or mortgages and the application was rejected,or (b) considered the possibility to apply but did not, thinking that the application would have beenrejected. At average values of the covariates, the marginal effect of being PattiChiari client on theprobability of being liquidity constrained is -0.005 with a standard error of 0.007. The model controls forprovince fixed effects, municipality size, and individual observable characteristics (quadratic polynomialin age, gender, marital status, household size, education, family labor income and reason for choosing

30

In principle, large and small banks may differ with respect to their policies about

investment or client relations, to their ability to attract clients, and to other unobserv-

ables that may affect customers wealth or financial literacy. Therefore, we examine to

which extent banks belonging or not to the PattiChiari Consortium are different accord-

ing to their size. To this purpose, Table 9 ranks banks according to their total assets,

and provides their number of branches and PattiChiari status: 49 out of the 70 banks

listed (70%) belong to the PattiChiari Consortium. Names of PattiChiari banks are

underlined, names of non-PattiChiari banks are in blue. The following two columns in

Table 9 report the decile of the empirical distribution of total assets and of the number

of branches to which each bank belongs to. Among the top-10 banks in terms of assets

(reporting more than 40,000 millions euros of assets) there are 9 PattiChiari banks and

1 non-PattiChiari bank, while all top-30 banks in terms of number of branches are part

of PattiChiari. Yet, apart from the top-sized banks, the rest of the distribution is much

more mixed. Within the sub-sample of small banks, PattiChiari and non-PattiChiari

banks are evenly distributed over both measures: among banks with below-median as-

sets, there are 20 PattiChiari and 15 non-PattiChiari banks, among bank with below-

median number of branches there are 17 non-PattiChiari and 18 PattiChiari banks.28

We test for statistical differences in the mean of these variables by PattiChiari status in

Table 10. The difference is not statistically significant.

To check whether different characteristics between PattiChiari and non-PattiChiari

banks drive our results, we perform three different exercises: 1) we replicate our exercise

excluding the ten largest banks (in terms of assets or number of branches); 2) we replicate

our baseline results using data of clients of small or large banks only; 3) we add the control

variables for bank characteristics to our baseline regression.

First, we run our baseline regression on a sample that excludes the top 10 banks (where

the incidence of PattiChiari banks is higher) in terms of, alternatively, level of total

the main bank).28The Wilcoxon rank-sum test of assets and number of branches byPattiChiari status among small

banks largely fails to reject the null of different distributions (p-values equal 0.79 and 0.82, respectively).

31

assets, or number of branches.29 Results are reported in Table 28 in Appendix C and

confirm the baseline results in Table 2.30

Second, we look for a sample of banks where the overall size distribution for PattiChiari

and non-PattiChiari banks is similar. To this purpose, we estimate the baseline model

among households having their current account in a “small bank”, which is defined,

alternatively, as having lower-than-median assets or number of branches.31 Within the

sub-sample of small banks, PattiChiari and non-PattiChiari banks are evenly distributed

over both dimension: among banks with below-median assets, there are 20 PattiChiari

and 15 non-PattiChiari banks, among bank with below-median number of branches

there are 17 non-PattiChiari and 18 PattiChiari banks.32 Results are reported in Table

29, together with results obtained in the complement samples of “large banks”. Point

estimates of the effects are generally consistent with our baseline in both sub-samples,

although the statistical significance of the results on small banks is severely hampered by

the large reduction in sample size: larger banks tend to have more customers and when

we focus on clients of small banks we are left with around 500 households. This is also

consistent with the fact that customers tend to choose their main bank for convenience

to home or work and in this exercise large banks are those who have more branches by

definition.

Finally, we test for the robustness of our results by including assets and number of

branches among the covariates. The bottom panel of Table 30 in Appendix C shows

29These banks are, respectively: UniCredit, Intesa Sanpaolo, Banca Monte dei Paschi di Siena, BancaNazionale del Lavoro, Banca Popolare del Mezzogiorno Banca popolare dell’Emilia Romagna, BancaPopolare di Milano, Cassa di Risparmio di Parma e Piacenza, BancoPosta, Banca Carige (for totalassets) and UniCredit, BancoPosta, Banca Monte dei Paschi di Siena, Intesa Sanpaolo, Banco di Napoli,Banca Popolare di Verona-S. Giminiano e Prospero, Banca Nazionale del Lavoro, Banca Popolare diNovara, Banca Popolare di Lodi, Cassa di Risparmio di Parma e Piacenza (for the number of branches).

30The descriptive statistics on financial assets and financial literacy in these sub-samples are similarto the one reported in Table 1. The share of PattiChiari clients in these sub-samples is around 67%compared to 73% in Table 1.

31The median of total assets and number of branches two variables on the sample of 70 banks for whichthe information is available- see Table 9- are, respectively, 9328.5 and 307. Table 29 reports descriptivestatistics on key variables in the sub-samples (share of PattiChiari clients, financial literacy and financialassets): these are similar to those reported in Table 2 for all sub-samples.

32The Wilcoxon rank-sum test of assets and number of branches byPattiChiari status among smallbanks largely fails to reject the null of different distributions (p-values equal 0.79 and 0.82, respectively).

32

that results are not affected by these additional controls. To sum up, the distribution

ofPattiChiari and non-PattiChiari banks in terms of assets or number of branches differ

mostly in the upper tail, with PattiChiari banks being more frequent among banks with

higher total assets and larger number of branches (see Table 9). Yet, the two groups of

banks do not differ in size on average and, most importantly, our main results seem not

to be driven by bank size.

9 Mechanisms and Heterogeneity

Financial literacy can affect wealth through many different channels.33 With the data

at our hand, we can analyse three potentially relevant mechanisms that may drive the

positive effect measured in the aggregate. First, we investigate to which extent portfolio

composition depends on financial literacy of the respondent (Christelis et al., 2010; van

Rooij et al., 2011b,c). Second, we examine whether literate respondents are more prone

to plan for retirement (Lusardi and Mitchell, 2008; van Rooij et al., 2011a), that, in

turn, may enhance saving. Finally, we focus on the relation between financial literacy

and saving behaviour.

Looking at portfolio composition, we exploit information about assets held by SHIW

respondents and we examine to which extent the probability of holding equities, mutual

funds and government bonds differs according to financial literacy. We also consider a bi-

nary indicator of household participation to the stock market, either directly or through

a mutual fund. To analyze the role of retirement planning, we use answers to the follow-

ing question (not asked to retirees): “Have you ever thought about how to arrange for

your household’s support when you retire?”. Saving rate is defined as the ratio between

savings and income.

Results reported in Table 11 suggest that financial literacy leads to an increase in stock

33We refer to Jappelli and Padula (2013b) (note 5) and Lusardi et al. (2011) for a discussion of thelinks between financial literacy and financial wealth. Relevant channels relate to expectations (liter-ate individuals may be more accurate and/or less biased), preferences (financial literacy may ease theunderstanding of risk and reducing ’direct risk aversion’), perceptions (financial literacy may reduceunderestimation of compounding effects), diversification, and the cost charged on loans or mutual funds.

33

market participation and to a more diversified portfolio. While lack of availability of

more detailed data on portfolio management prevent us to examine the effect of finan-

cial literacy on the quality of financial decisions, as in Guiso and Viviano (2015), our

results are consistent with financial literacy to promote a better portfolio management,

in terms of diversification and stock market participation. Financially literate respon-

dents holding a more efficient and diversified portfolio may contribute to explain the

positive effect of financial knowledge on financial assets. This channel may be partic-

ularly relevant in Italy, a country characterized by relatively low participation rates in

the stock market (Bottazzi et al., 2013).

Turning to planning for retirement and saving rate, while the estimated coefficient is pos-

itive, we fail to detect a significant causal effect of financial literacy on both outcomes.

This finding may reflect the fact that Italy is characterized by a generous pension sys-

tem, compared to other countries, and relatively high saving rates. Undersaving to

meet retirement target in Italy might thus be a less relevant issue with respect to other

countries.

To shed more light on the heterogeneity of the impact of bank information policies and

on the effect of financial literacy on financial assets, we estimate our baseline model across

age and education groups. The results are shown in Table 12. The first and the second

panels report estimate for, respectively, low and high-educated respondent; the third and

fourth ones consider, respectively, the young and the elderly and the bottom panel refers

to the subgroup of low-educated individuals aged 60+. The effect of financial literacy

on financial assets is the largest for low-educated and old respondents (the IV coefficient

is 12.6 in the bottom panel). This finding is consistent with return of financial literacy

that are decreasing in education.34 In addition, individuals aged 60+ are expected to

be wealthier than the young and, in turn, to gain more from their financial knowledge.

The bias in the OLS estimate is the highest when 60+ and low-educated respondents are

considered. The determinants of the bias described above are, thus, more relevant in this

34This latter result is in line with the results by Cole et al. (2011) showing, in a different context, asignificant impact of financial literacy (on the demand for current accounts in India and Indonesia) onlyfor those with limited education or financial literacy.

34

sub-sample. First, education is expected to be negatively correlated with the probability

of choosing the answer at random or not understanding the questions. Moreover, the

elderly may be less concentrated when answering the questionnaire and, thus, more

subject to measurement error. Second, low-educated elderly may be more likely to

delegate their portfolio’s management, rather than investing in their financial knowledge,

further increasing the bias in the OLS estimate of the effect of financial literacy on

assets. Turning to the effectiveness of bank information policies, they significantly foster

financial knowledge in all the subgroups we consider, but the magnitude of their effect

is heterogeneous. Results in Table 12 illustrate that the marginal effect is substantially

greater for low educated respondents and for those aged 60+.

10 Concluding remarks

The interests by scholars and policy-makers, in Europe and in the U.S., on the deter-

minants of financial literacy and on the link between financial literacy and wealth has

been constantly increasing in the last years. Institutions, such as the OECD, the U.S.

Treasury Department and the European Commission, have expressed the need for im-

proved financial knowledge, emphasizing the role of formal financial education in schools

or at the workplace. However, whether these programs affect financial knowledge and

financial decisions, and who are the individuals reacting are still controversial issues.

This research contributes to the discussion focusing on a new and possibly comple-

mentary policy instrument. We start recognizing the potential role of bank information

policies in reducing the cost of acquiring financial literacy and we posit a novel question:

do bank information policies actually increase financial literacy and, in turn, household

financial wealth? To answer this question, we identify a group of Italian banks, the Pat-

tiChiari Consortium, that implements active policies aimed at increasing transparency.

Requirements to enter into the Consortium translate into the provision of information

about simple economic concepts by banks to their customers.

The fraction of individuals whose financial literacy is affected by these information

35

policies ranges between 5% to 10% while being client of a PattiChiari bank translates

into a 12% increase in financial literacy on average. We use the exogenous variation in

financial literacy induced by bank information policies to assess the effect of financial

literacy on financial assets. Our baseline estimate of the effect of about one standard

deviation increase in financial literacy is approximately 8 thousand euros in 2010 and

reflects 35% of standard deviation of financial assets in the sample. The effects of

financial literacy on financial assets growth - over a time horizon of between eight and

four years - are as large as 15% of a standard deviation in growth over the same time-

span.

We find that both the improvement in financial literacy and the increase in financial

assets are driven by a specific subgroup of households: those whose head is older and

less educated. This result is reasonable, given the type of intervention we studied: the

provision of simple and basic economic concepts, which are likely to be more relevant

for individuals who are less exposed to other sources of financial information, such as

economic newspapers or the web. Therefore, even though the effectiveness of information

policies, in terms of compliance, is limited, their impact on welfare may not be negligible

as they affect financial knowledge and behaviour of costumers who may profit more from

them. Moreover, our findings may stimulate further research broader interventions,

aimed to convey more sophisticated information.

We find that the effect of financial literacy on financial assets is mainly driven by

portfolio management and diversification. While we cannot assess whether the quality of

investment decisions improves, increased portfolio diversification in our case-study may

be possibly interpreted as intrinsically better, given Italy’s historically low rates of stock

market participation (particularly among the elderly).35

The impact on financial assets is remarkable, if compared with other studies of fi-

nancial education programs that typically find small or no effects on financial decisions.

We argue that two features, besides the afore-mentioned impact on the elderly, may ex-

35In a different setting, where households already diversify portfolios, the improvement in the qualityof financial decisions related to an increase of financial knowledge may not lead to such large effects (seeGuiso and Viviano (2015)).

36

plain this result. First, the fact that the less educated and elderly are those who benefit

from the policy. Indeed, (i) returns to financial education are likely to be decreasing

in education, and (ii) older households may under-invest their financial endowments ac-

cumulated over their lifetime. Second, individuals who receive information from banks

are treated for a rather long period of time (i.e. about 9 years, the average lenght of

the relation between client and bank), while financial education programs last at most

for some months. Third, individuals receive financial education in a setting where this

information is relevant. Indeed, it is mostly through banks that households make their

financial decisions, such as saving and diversifying their portfolio of investments.

These results show how financial education may be taught in different settings, which

may be potentially more salient than schools or workplaces. They also point to the im-

portance of targeting specific types of individuals whose marginal benefits from financial

literacy may be larger, and who are tipically neglected by standard financial education

programs.36

Finally, our analysis shows that the relationship between financial literacy and fi-

nancial assets is largely underestimated by simple OLS correlation and this is mainly

due to measurement error: this suggests that there is room to improve the way financial

literacy is measured.

Acknowledgements

We acknowledge the financial support of MIUR- FIRB 2008 project RBFR089QQC-003-

J31J10000060001. We thank E. Battistin, R. Bottazzi, T. Bucher-Koenen, M. Cervellati,

M. Giofre, M. Hurd, T. Kuchler, P. Legrenzi, B. Luppi, M. Padula, G. Pasini, E. Ret-

tore, E. Sette, M. Wakefield, Y. Winter and participants to the BoMoPaV Economics

Meeting in Padua, the Netspar International Pension Workshop in Paris, the SAVE-

PHF Conference in Munich, the CERP Annual Conference on Financial Literacy, Saving

and Retirement in an Ageing Society, the European Association of Labour Economists

36Notice, however, that these individuals may be those that have higher levels of financial assets,relative to the average individual in the population. As such, these interventions will not necessarilyreduce inequality in wealth.

37

(EALE) Annual conference in Bonn, Munich Center for the Economics of Aging (MEA)

and seminars at the University of Bologna and at the Johannes Kepler University of

Linz for useful comments on an earlier version of this work. We thank the Bank of Italy

Banking and Financial Supervision Area (notably, P. Franchini and A. Scognamiglio)

for kindly providing data on bank account costs. We are indebted with the PattiChiari

Consortium (notably, V. Panna and L. Napoleoni) for the data provided and for intrigu-

ing discussions about their initiatives. The views here expressed are those of the authors

and do not necessarily reflect those of the Bank of Italy. The usual disclaimer applies.

38

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Tables

Table 1: Descriptive Statistics.

Variable Mean Std. Dev. NFinancial literacy (number of correct answers out of three) 1.93 0.98 4865Financial literacy, binary indicator 1(three out of three answers correct) 0.34 0.47 4865Financial literacy, binary indicator 1(question on inflation correct) 0.74 0.44 4865Financial literacy, binary indicator 1(question on loan correct) 0.64 0.48 4865Financial literacy, binary indicator 1(question on portfolio diversification correct) 0.55 0.5 4865Financial literacy (number of correct answers out of two present in 2006 & 2008) 1.38 0.71 4865Instrumental variable, binary indicator 1(Household is PattiChiari client) (patti) 0.73 0.45 4865Instrumental variable, binary indicator 1(Household is PattiChiari client) (patti atl) 0.75 0.43 4865Financial assets (thousands) 19.4 22.4 4865Savings rate+ 0.17 0.27 4865Planning for retirement 0.46 0.50 2653Ownership of equities and mutual funds 0.12 0.32 4865Ownership of equities 0.06 0.23 4865Ownership of mutual funds 0.08 0.27 4865Ownership of government bonds 0.11 0.32 4865Male household head 0.56 0.50 4865Married 0.64 0.48 4865Nb. hh components 2.47 1.21 4865Years of education of the household head 9.81 4.46 4865Household labor income 26.3 16.3 4865Municipality 20.000-40.000 inh. 0.26 0.44 4865Municipality 40.000-500.000 inh. 0.44 0.50 4865Municipality 500.000+ inh. 0.10 0.30 4865High trust∗ 0.56 0.50 4865Length of relationship with the bank: less 2 years 0.04 0.18 4757Length of relationship with the bank: 2-4 years 0.07 0.26 4757Length of relationship with the bank: 5-10 years 0.16 0.37 4757Length of relationship with the bank: more than 10 years 0.73 0.45 4757Length of relationship with the bank∗∗: years 9.03 2.30 4757Number of current accounts 1.2 0.46 4865Homeownership 0.74 0.44 4865Motivations for choosing the main bank∗∗∗

Only one reason: related to convenience 0.54 0.50 4865Only one reason: related to financial/economic reason 0.07 0.25 4865Only one reason: related to bank characteristics 0.02 0.15 4865Two reasons: both related to convenience 0.12 0.32 4865Two reasons: related to convenience & financial/economic reasons 0.10 0.30 4865Two reasons: related to convenience & bank characteristics 0.10 0.31 4865Two reasons: both related to financial/economic reasons 0.03 0.16 4865Two reasons: financial/economic reasons & bank characteristics 0.02 0.15 4865Two reasons: both related to bank characteristics 0.00 0.05 4865Sources of information∗∗∗∗

Only intermediaries or experts 0.657 0.475 1492Only press or websites 0.016 0.126 1492Only friends, others, do not know 0.26 0.439 1492

Notes: Unless otherwise stated all descriptive statistics refer to the 2010 estimation sample.+ Calculated as the ratio between winsorized saving and income.∗ Our measure of trust relies on the answer to the question “Do you trust your principal bank? Please assign a score of 1 to 10,where 1 means ’I don’t trust it at all’ and 10 means ’I trust it completely’ and the intermediate scores serve to graduate yourresponse”. Respondents who trust their main bank are those who choose a value above the median of the distribution of answers(8).∗∗ Recoded from the categorical variable collected in the survey: for each category we impute the mid-point.∗∗∗ Each respondent can give at most two answers to the question on why the main bank is chosen. The 13 alternatives among whichthe respondent can choose are grouped into 3 broad categories: convenience (convenience to home/work, respondent’s employer’sbank (or respondent’s business’s bank)), financial/economic reasons (favourable interest rates, speed of transaction execution,range of services, low fees for services, possibility of online banking) and bank type (it is a well-known, important bank, staffcourtesy). The figures show the percentage of respondents who choose only one alternative, two alternatives in the same group ortwo alternatives in different categories.∗∗∗∗ Based on the question about the information source consulted before the current investments were performed. Available onlyfor households who hold financial assets other than bank accounts. The figures show the average number of respondents who relyonly on that specific source of financial information.

Table 2: Effect of Financial Literacy on Financial Assets. Financial literacy is thenumber of correct answers out of the three questions asked in 2010. 2010 SHIW Data.

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Financial literacy 2.231*** 8.273***(0.630) (2.979)

Client of PattiChiari 0.235*** 1.941***(0.035) (0.723)

Age 0.832*** 0.054*** 0.939*** 0.494**(0.136) (0.006) (0.134) (0.197)

Age squared -0.005*** -0.000*** -0.006*** -0.001(0.001) (0.000) (0.001) (0.002)

Male 1.017 0.170*** 1.347** -0.063(0.642) (0.025) (0.652) (0.705)

Married 2.479*** 0.024 2.497*** 2.295***(0.625) (0.037) (0.645) (0.596)

Nb. hh components -1.501*** 0.051*** -1.391*** -1.812***(0.292) (0.017) (0.292) (0.342)

Years education 0.918*** 0.045*** 0.999*** 0.626***(0.095) (0.004) (0.091) (0.178)

Municipality 20.000-40.000 inhabitants 1.040 0.071 1.256 0.673(1.415) (0.070) (1.456) (1.388)

Municipality 40.000-500.000 inhabitants 0.212 -0.009 0.157 0.235(1.086) (0.088) (1.114) (1.173)

Municipality 500.000+ inhabitants -0.308 0.151 -0.027 -1.275(2.061) (0.165) (2.024) (2.679)

Household labor income 0.414*** 0.000 0.413*** 0.409***(0.033) (0.001) (0.034) (0.032)

Only one motivation: services 1.126 0.092 1.647 0.887(1.207) (0.060) (1.204) (1.310)

Only one motivation: bank characteristics 1.804 -0.108 1.489 2.382(1.870) (0.067) (1.847) (1.970)

Two motivations: two convenience 4.626*** 0.056 4.774*** 4.314***(1.258) (0.052) (1.292) (1.165)

Two motivations: convenience & financial conditions 1.312 0.132* 1.786 0.698(1.155) (0.069) (1.173) (1.208)

Two motivations: convenience & bank characteristics 4.595*** 0.067 4.665*** 4.113***(1.306) (0.057) (1.303) (1.261)

Two motivations: two financial conditions 2.818 0.184** 3.470* 1.949(1.741) (0.077) (1.832) (1.608)

Two motivations: financial conditions 1.644 0.139 2.013 0.862& bank characteristics (2.232) (0.087) (2.242) (2.228)

Two motivations: two bank characteristics 4.698 0.037 4.701 4.391(3.831) (0.172) (3.869) (3.844)

Province FE Yes Yes Yes YesN. of observations 4865 4865 4865 4865

F-test on the excluded instruments 43.849

Notes: the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α + βflip +Xipγ+λp+εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financial literacy of thehousehold head, Xip is a vector of household controls (age, age squared, gender, marital status, years of education of the householdhead and number of household components, household labour income in 2010) and λp is a province fixed effect. Financial literacyis instrumented with a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiari Consortium and 0otherwise. Heteroskedasticity-robust standard errors clustered at the province level in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p <0.01.

Table 3: Robustness: Different measures for financial literacy.

OLS FS ITT IVDependent variable: Fin. assets Fin. literacy Fin. assets Fin. assets

Number of answers correct out of 3Financial literacy (nb/3) 2.231*** 8.273***

(0.630) (2.979)Client of PattiChiari 0.235*** 1.941***

(0.035) (0.723)N. of observations 4865 4865 4865 4865F-test on the excluded instr. 43.849

Binary indicator for 3 answers correct out of 3

Financial literacy (3 correct) 4.512*** 43.378**(1.086) (20.156)

Client of PattiChiari 0.045*** 1.941***(0.016) (0.723)

N. of observations 4865 4865 4865 4865F-test on the excluded instr. 8.152

Binary indicator for inflation question correct

Inflation correct 1.921 19.096***(1.204) (7.315)

Client of PattiChiari 0.102*** 1.941***(0.019) (0.723)

N. of observations 4865 4865 4865 4865F-test on the excluded instr. 29.128

Binary indicator for loan question correct

Loan correct 1.604** 27.207**(0.798) (10.831)

Client of PattiChiari 0.071*** 1.941***(0.013) (0.723)

N. of observations 4865 4865 4865 4865F-test on the excluded instr. 28.297

Binary indicator for portfolio question correct

Portfolio correct 5.287*** 31.491**(1.136) (12.929)

Client of PattiChiari 0.062*** 1.941***(0.019) (0.723)

N. of observations 4865 4865 4865 4865F-test on the excluded instr. 10.715

Notes: the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α + βflip +Xipγ+λp+εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financial literacy of thehousehold head, Xip is a vector of household controls (age, age squared, gender, marital status, years of education of the householdhead and number of household components, household labour income in 2010) and λp is a province fixed effect. Financial literacyis instrumented with a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiari Consortium and 0otherwise. Heteroskedasticity-robust standard errors clustered at the province level in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p <0.01.

Table 4: Effect of Financial Literacy on Financial Assets - Sample of “Investor House-holds”, i.e. of households who invested some savings in financial markets (bonds, equities,investment funds, etc.)

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Panel A: “Investor Households”, Baseline Specification as in Table 2

Financial literacy 2.473** 38.036*(1.022) (19.587)

Client of PattiChiari 0.134** 5.082***(0.062) (1.306)

N. of observations 1492 1492 1492 1492F-test on the excluded instr. 4.710

Panel B: “Investor Households”, Controlling for Sources of Financial Information

Financial literacy 2.314* 39.034*(1.022) (21.977)

Client of PattiChiari 0.121** 4.717***(0.060) (1.279)

Source of InformationOnly Intermediaries and Experts -0.430 0.069 -0.374 -3.052

(2.256) (0.118) (2.119) (5.463)Only Press and Websites -1.183 0.208 -0.364 -8.493

(5.231) (0.136) (5.278) (7.867)Only Friends and Other -4.245 -0.077 -4.161 -1.172

(2.771) (0.120) (2.675) (5.044)

N. of observations 1492 1492 1492 1492F-test on the excluded instr. 3.992

Notes: “Investor households”: household that invested some savings in financial markets (bonds, equities, investment funds, etc.).

Each panel of the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α+βflip+Xipγ + λp + εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financial literacy ofthe household head, Xip is a vector of household controls (as in the baseline specification of Table 2), and λp is a province fixedeffect. Financial literacy is instrumented with a dummy taking the value 1 if the household’s main bank belongs to the PattiChiariConsortium and 0 otherwise. Heteroskedasticity-robust standard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 5: Effect of financial literacy on financial assets growth between 2002 and 2006 or2008 or 2010. SHIW repeated cross-section 2006-2010. Financial literacy is the numberof correct answers out of the two common across waves.

OLS FS ITT IVDependent variable: Fin. ass. growth Fin. lit. Fin. ass. growth Fin. ass. growth

Financial literacy 0.014*** 0.153***(0.005) (0.052)

Client of PattiChiari 0.179*** 0.027***(0.040) (0.008)

N. of observations 3181 3181 3181 3181F-test of excluded instruments 19.755

Notes: Each column reports estimates of a separate regression. OLS, ITT and IV columns are regression where the dependent

variable is(yitp−yi2002p−E[yitp−yi2002p])

V ar[yitp−yi2002p], i.e. standardized growth in financial assets between 2002 and time t for household i

in province p. FS columns are regressions where the dependent variable is financial literacy. All models include the same vector ofhousehold controls: age, age squared, gender, marital status and years of education of the household head; number of householdcomponents, household labour income, reasons for choosing the main bank and size of the municipality. All models include andyear-specific province fixed effects. In IV columns, financial literacy is instrumented with a dummy that takes the value 1 if thehousehold’s main bank belongs to the PattiChiari Consortium and 0 otherwise. Heteroskedasticity-robust standard errors clusteredat the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 6: Differences Among Bank Switchers in Household Financial Assets in 2002,before the PattiChiari Consortium was created. Data: panel data SHIW, Bank Switchersbetween 2006 and 2008 and between 2008 and 2010.

(1) (2) (3)Dependent variable: Fin. ass. in 2002 Fin. ass. in 2002 Fin. ass. in 2002

Switchs to Client of PattiChiari -10.787 -9.917 -7.642(6.927) (6.188) (5.109)

Stays Client of Non-PattiChiari -12.536 -8.936 -7.722(13.066) (16.437) (17.156)

Switchs to Client of Non-PattiChiari -3.632 -15.928 5.209(19.331) (24.442) (21.381)

Baseline Controls Y Y YMotivation for Choosing Bank N Y YBank Costs and Characteristics N N YN. of observations 1666 833 812

Null Hypothesis: No Difference Tests p-values

Stays PattiChiari Client vsSwitchs to Non-PattiChiari 0.851 0.516 0.808

Stays Non-PattiChiari Client vsSwitchs to PattiChiari 0.884 0.956 0.997

Stays Non-PattiChiari vs Stays PattiChiari 0.340 0.588 0.654

Switchs to Non-PattiChiari vsSwitchs to PattiChiari 0.704 0.813 0.592

Notes: the table reports OLS regression results of the model yip = α + βZip + Xipγ + λp + εip where yip is financial assetsof household i, living in province p, in year 2002; Zip is a vector of dummies indicating whether the household switched mainbank between waves or not and the type of switch, Xip is a vector of household controls and λp is a province fixed effect. Morespecifically, all regressions include controls for characteristics of the household head (age, age squared, gender, marital status,years of education) and the household (number of household components, household labour income in 2002), in analogy with thespecification in Table 2. Column (2) adds motivations for choosing the main bank to the list of controls; column (3) adds motivationsfor choosing the main bank, bank costs and bank characteristics to the list of controls. Column (1) uses data on all switchers2006-2008 and 2008-2010; column (2) and (3) use records with no missing data on all the added controls. Heteroskedasticity-robuststandard errors clustered at the province level in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 7: Instrumental Variable estimates of the effect of financial literacy on financialassets. SHIW 2010.

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Panel A : Baseline model on common support

Financial literacy 2.289*** 8.550***(0.629) (3.005)

Client of PattiChiari 0.233*** 1.996***(0.036) (0.724)

F-test excluded instr. 43.216N. of observations 4829

Panel B: Propensity score model controls on common support

Financial literacy 2.180*** 7.933***(0.629) (3.044)

Client of PattiChiari 0.221*** 1.751**(0.035) (0.695)

F-test excluded instr. 38.917N. of observations 4829

Notes: Each column reports estimates of a separate regression. OLS, ITT and IV columns are regression where the dependentvariable is financial assest. FS columns are regressions where the dependent variable is financial literacy. All models includethe same vector of household controls: age, age squared, gender, marital status and years of education of the household head;number of household components, household labour income, reasons for choosing the main bank and size of the municipality. Allmodels include province fixed effects. In IV columns, financial literacy is instrumented with a dummy that takes the value 1 if thehousehold’s main bank belongs to the PattiChiari Consortium and 0 otherwise. Heteroskedasticity-robust standard errors clusteredat the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 8: Bank Costs by PattiChiari Status - Year 2010 (euro)

Type Basic Debit Credit Avg. Cost Avg. Cost Avg. Cost Avg. Cost Obsof Bank Account Card Card Bank Transf. Bank Transf. of Withdrawal of Withdrawal

Fees Fees Fees (desk) (online) (desk) (ATM)

non-Patti-Chiari 34.9 7.09 15.67 1.88 0.35 0.05 0.31 917(30.3) (6.28) (20.5) (5.26) (4.03) (0.90) (1.93)

Patti-Chiari 36.3 4.23 14.06 2.05 0.52 0.07 0.29 5800(38.6) (5.20) (12.93) (6.09) (4.66) (1.62) (1.82)

Welch t-test stat. 1.25 13.1 2.32 0.84 1.17 0.66 0.34(p-value) (0.18) (0.00) (0.28) (0.20) (0.19) (0.32) (0.38)

Source: Bank of Italy Survey on Bank Fees and Expenditures. Notes: an observation is an individual bank account. Due to confidentiality reasons, for each bank, only means and

standard deviations of each variable has been provided, together with the number of bank accounts surveyed. The means and standard deviations provided in this table are, thus,

combined assuming observations are independent between banks. The Welch t-test for equality of means assumes unequal standard deviations between the two groups.

Table 9: Bank Total Assets, Number of Branches, and PattiChiari status in 2010

Bank Name PattiChiari Total Assets No. of Decile Decile(mln) Branches by Assets by Branches

UniCredit 1 929488 11266 10 10Intesa Sanpaolo 1 658757 4060 10 10

Banca Monte dei Paschi di Siena 1 244279 4677 10 10Banca Nazionale del Lavoro 1 98022 1847 10 10Banca Popolare del Mezzogiorno 1 60515.92 174 10 4

Banca popolare dell’Emilia Romagna 1 58498 429 10 6

Banca Popolare di Milano 1 54053 703 10 8

Cassa di Risparmio di Parma e Piacenza 1 46339 1082 9 9

BancoPosta 0 43223.27 7700 9 10Banca Carige 1 40010 807 9 9

Banca Popolare di Vicenza 1 35533 520 9 7

Veneto Banca 0 33057 335 9 6Cassa di Risparmio di Firenze 1 31677 637 9 8

Credito Emiliano 1 29998 976 9 9Banca Popolare di Verona-S. Geminiano e S. Prospero 1 29689 2153 8 10

Banco di Napoli 1 28153 2424 8 10

Credito Valtellinese 1 26761 164 8 3Banca Popolare di Sondrio 0 26282 336 8 6

Deutsche Bank 1 24859 358 8 6Banca Popolare di Bergamo 1 24456 957 8 9

Banca delle Marche 1 21486 501 8 7Cassa di Risparmio del Veneto 1 19625 1000 7 9

Banca Popolare di Lodi 1 19556 1175 7 9

Banco di Brescia 1 17622 738 7 8Banca Popolare di Novara 1 17075 1205 7 9

Banco di Sardegna 1 13930 550 7 7

Unipol Banca 0 12052 332 7 6Banca Mediolanum 1 11622 6 7 1FinecoBank Banca 1 11250 3 6 1Banca popolare dell’Etruria e del Lazio 0 10903 304 6 5Cassa di risparmio in Bologna 1 10161 596 6 8

Banca Popolare Commercio e Industria 1 10130 598 6 8

Aletti and Co. 0 9940 42 6 1Banca Carime 1 9784 700 6 8Banca Fideuram 1 9556 108 6 2Banca Popolare di Ancona 1 9101 310 5 6

Banca Popolare FriulAdria 1 8501 313 5 6

Cassa di Risparmio di Bolzano 0 8210 198 5 5Banco di Desio e della Brianza 0 8163 166 5 4Banca Regionale Europea 1 8132 569 5 8

Banca Sella 1 7979 528 5 7Cassa di risparmio di Ferrara 0 7573 170 5 4Banca Popolare di Bari 0 7286 221 4 5Banca di Credito Cooperativo di Roma 0 7259 152 4 3Cassa di risparmio di Asti 0 6118 210 4 5Banca dell’ Adriatico 1 5613 463 4 7Cassa di risparmio della provincia di Teramo 0 5489 187 4 4Banca Di Credito Sardo 1 5401 117 4 2Banca Popolare dell’Alto Adige 0 5248 140 4 3Banca della Campania 1 5155 210 3 5

Cassa dei Risparmi di Forli e della Romagna 1 4769 171 3 4

Banca Nuova 1 4711 135 3 3Cassa di risparmio di Venezia 1 4650 434 3 7

Cassa di Risparimio di Rimini 0 4551.51 179 3 4Cassa di Risparmio di Biella e Vercelli 1 4523 211 3 5

Banca Popolare di Puglia e Basilicata 1 4460 191 3 5

Banca Agricola Popolare di Ragusa 0 4388 101 2 2Cassa di Risparmio del Friuli Venezia Giulia 1 4300 447 2 7

Credito Siciliano 1 4059 157 2 3SpA-Generbanca 1 3808 70 2 1

Cassa di risparmio della provincia di Chieti 1 3574 116 2 2

Banca Popolare Pugliese 1 3263 103 2 2

Banca Popolare di Spoleto 1 3029 165 2 4

Banca Monte Parma 0 3005 91 1 2Cassa di risparmio di San Miniato 0 2986 132 1 2IW Bank 0 2874 2 1 1Banca di Piacenza 0 2793 62 1 1Allianz Bank 0 2769 19 1 1Cassa di risparmio di Pistoia e della Lucchesia 1 2515 145 1 3

Cassa di risparmio della Spezia 1 2055 152 1 3

Notes: data from Bankscope, Bank of Italy - SIOTEC database, and PattiChiari Consortium.

Table 10: Average Total Assets and Number of Branches by PattiChiari status in 2010

Variable PattiChiari non-PattiChiari p-value No. of Obs.

Total Assets (bns.) 54948.631 10198.561 0.209 70(160923.038) (10734.01)

No. of Branches 922.417 433.704 0.197 87(1689.874) (1455.516)

Notes: Data: SIOTEC, 2010; Banks Listed in SHIW 2010. Sample size for total assets and number of branches differs because forsome banks the information on total assets in 2010 is missing.

Table 11: Effect of Financial Literacy on Other Outcomes

OLS FS ITT IVDependent variable: Fin. assets Fin. literacy Fin. assets Fin. assets

Ownership of stock and fundsFinancial literacy 0.019*** 0.134***

(0.006) (0.047)Client of PattiChiari 0.235*** 0.031***

(0.035) (0.010)N. of observations 4865 4865 4865 4865F-test on the excluded instr. 43.849

Ownership of equitiesFinancial literacy 0.006 0.026

(0.004) (0.028)Client of PattiChiari 0.235*** 0.006

(0.035) (0.007)N. of observations 4865 4865 4865 4865F-test on the excluded instr. 43.849

Ownership of mutual fundsFinancial literacy 0.016*** 0.091*

(0.005) (0.047)Client of PattiChiari 0.235*** 0.021**

(0.035) (0.011)N. of observations 4865 4865 4865 4865F-test on the excluded instr. 43.849

Ownership of government bondsFinancial literacy 0.028*** 0.300***

(0.007) (0.070)Client of PattiChiari 0.235*** 0.070***

(0.035) (0.015)N. of observations 4865 4865 4865 4865F-test on the excluded instr. 43.849

Retirement PlanningFinancial literacy 0.039*** 0.197

(0.014) (0.122)Client of PattiChiari 0.213*** 0.042

(0.048) (0.026)N. of observations 2653 2653 2653 2653F-test on the excluded instr. 19.967

Saving rateFinancial literacy 0.002 0.023

(0.007) (0.043)Client of PattiChiari 0.235*** 0.005

(0.035) (0.010)N. of observations 4865 4865 4865 4865F-test on the excluded instr. 43.849

Notes: The table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model DVip = α + βflip +Xipγ + λp + εipwhere DVip is a dependent variable measured for household i, living in province p, in year 2010; flip is financialliteracy of the household head, Xip is a vector of household controls (as in the baseline specification of Table 2), and λp is aprovince fixed effect. Financial literacy is instrumented with a dummy taking the value 1 if the household’s main bank belongs tothe PattiChiari Consortium and 0 otherwise. In the top four panels the dependent variable is a dummy equal to 1 if the householdowns equities, mutual funds or government bonds; in the fifth panel is a dummy equal to 1 if the household head reports to haveplanned for retirement; in the bottom panel is the winsorized saving rate (ratio between savings and income). Heteroskedasticity-robust standard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 12: Heterogeneity in the Effect of Financial Literacy on Financial Assets. Financialliteracy is the number of correct answers out of the three questions asked in 2010. 2010SHIW Data.

OLS FS ITT IVDependent variable: Fin. assets Fin. literacy Fin. assets Fin. assets

Education <= mediana

Financial literacy 1.985*** 9.426**(0.628) (3.747)

Client of PattiChiari 0.256*** 2.412**(0.044) (0.998)

N. of observations 2563 2563 2563 2563F-test on the excluded instr. 33.677Mean 15.354 St. dev 18.971

Education > mediana

Financial literacy 3.034*** 7.333Client of PattiChiari 0.127*** 0.928

(0.046) (1.110)N. of observations 2122 2122 2122 2122F-test on the excluded instr. 7.542

Age < 60b

Financial literacy 1.544** 5.154(0.769) (6.261)

Client of PattiChiari 0.167*** 0.860(0.044) (1.096)

N. of observations 2419 2419 2419 2419F-test on the excluded instr. 14.507

Age 60+b

Financial literacy 2.665*** 10.309**(0.709) (4.032)

Client of PattiChiari 0.256*** 2.635**(0.048) (1.121)

N. of observations 2367 2367 2367 2367F-test on the excluded instr. 28.385

Low education, age 60+Financial literacy 1.722** 12.595**

(0.808) (5.153)Client of PattiChiari 0.232*** 2.918**

(0.052) (1.204)N. of observations 1647 1647 1647 1647F-test on the excluded instr. 19.857

Notes: the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α + βflip +Xipγ + λp + εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financial literacyof the household head, Xip is a vector of household controls, and λp is a province fixed effect. Financial literacy is instrumentedwith a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiari Consortium and 0 otherwise.Heteroskedasticity-robust standard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.a: This group includes respondents with compulsory schooling (years of education lower or equal to 8).b: 60 is the median age in our baseline sample.

Figure 1: Propensity Score Matching: Distribution of estimated propensity score. SHIW2010 data.

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

A Measuring Financial Literacy

The three questions available in the 2010 wave for eliciting financial literacy are: i) un-

derstanding inflation “Imagine leaving 1,000 euros in a current account that pays 1%

interest and has no charges. Imagine also that inflation is running at 2%. Do you think

that if you withdraw the money in a year’s time you will be able to buy the same amount

of goods as if you spent the 1,000 euros today? Yes/No, I will be able to buy less (correct

answer)/No, I will be able to buy more/Do not know”; ii) understanding mortgages

“Which of the following types of mortgage do you think will allow you from the very start

to fix the maximum amount and number of installments to be paid before the debt is ex-

tinguished? Floating rate mortgage/Fixed rate mortgage (correct answer)/Floating rate

mortgage with fixed installments/Do not know”; iii) portfolio diversification“ Which

of the following investment strategies do you think entails the greatest risk of losing your

capital? Investing in the shares of a single company (correct answer) / Investing in the

shares of more than one company/Do not know/No answer”.

Questions (i) and (iii) are similar to the ones devised for the US Health and Retire-

ment Study (HRS) (Lusardi and Mitchell, 2011a) while knowledge of mortgage repay-

ment mechanisms is not considered in the HRS. All these questions follow the principles

of simplicity, relevance, brevity and capacity to differentiate identified by Lusardi and

Mitchell (2011c). Questions (i) and (ii) are common to all three waves of SHIW used in

this paper (2006, 2008, 2010). In 2006, unfortunately, financial literacy questions were

asked to only half of the sample, i.e. to household heads born in even years.

We claim that the answers to the questions about inflation, mortgages and portfolio

diversification in the SHIW reflect a broader knowledge, namely financial literacy. To

corroborate this statement, we perform a factor analysis. This exercise aims at checking

how many latent factors are captured by correct answers to those questions, moving

from the presumption that all questions do not only measure knowledge of a specific

economic concept but rather reflect the same common latent factor. For each question

related to financial knowledge asked in 2010, we construct a binary indicator for answer-

ing correctly, and, then, we perform a factor analysis on these indicators. The analysis

unambiguously suggest that only one factor should be retained and we name this factor

financial literacy. We replicate the same exercise using the repeated cross-section from

2006 and 2010 and the two indicators available across all three waves (the estimated fac-

tor loadings for both exercises are shown in Table 14). Then we obtained factor scores

using the regression method and perform our analysis also using this indicator: Table 13

reports the results using the same indicator used in the baseline regression and the esti-

mated latent factor. In Table 13 we standardized both indicators of financial literacy to

have zero mean and unitary variance so that coefficients can be interpreted as the effect

of a one-standard deviation change in financial literacy of financial assets for both indi-

cators and the comparison is more straightforward: our results are confirmed when using

standardized factor scores to measure financial literacy. This exercise further supports

our definition of financial literacy as a broad concept, that embeds different aspects of

financial knowledge. Table 14 shows that answering correctly the question on inflation

is positively related to the latent factor and this is true also for the other indicators

(answering the loan or portfolio question correctly). Therefore, since all indicators turn

out to capture the same latent factor, when we measure financial literacy by using each

of these questions in turn we get similar results (see Table 3).

Table 13: Effect of Financial Literacy on Financial Assets. Financial literacy is eitherthe number of correct answers out of the three questions asked in 2010 or the estimatedfactor score; both indicators are standardized to have zero mean and unitary variancein sample. 2010 SHIW Data.

Financial literacy measured with standardized number of correct answers

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Financial literacy 2.193*** 8.131***(0.619) (2.928)

Client of PattiChiari 0.239*** 1.941***(0.036) (0.723)

F-test on the excluded instr. 43.849N. of observations 4865 4865 4865 4865

Financial literacy measured with standardized factor score

Financial literacy 2.196*** 7.953***(0.631) (2.864)

Client of PattiChiari 0.244*** 1.941***(0.037) (0.723)

F-test on the excluded instr. 43.273N. of observations 4865 4865 4865 4865

Notes: All models include the same vector of household controls: age, age squared, gender, marital status and years of educationof the household head; number of household components, household labour income, reasons for choosing the main bank and size ofthe municipality. All models include province fixed effects. Financial literacy is measured as the number of correct question out ofthose asked in 2010. In IV columns, financial literacy is instrumented with a dummy that takes the value 1 if the household’s mainbank belongs to the PattiChiari Consortium and 0 otherwise. Heteroskedasticity-robust standard errors clustered at the provincelevel in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 14: Factor loadings corresponding to the three financial literacy questions.Thedata are from SHIW.

Indicator Factor loading2010 2006-2010∗

Inflation question correct 0.4487 0.4116Loan question correct 0.3745 0.4116Portfolio question correct 0.4353 not availableN. of observations 4865 11812

Notes: ∗ repeated cross-section.

B Robustness and Sensitivity Analysis

In this section, we check the robustness our findings to several falsification checks. In

particular, we explore: i) the role of retest effects and the timing of the effect of bank

information policies and financial literacy; ii) the role of trust in institutions; iii) the

sensitivity of our results to sample selection and the functional form. Our results are

robust to all these checks.

B.1 The Role of Retest Effects and the Timing of the Effect of BankInformation Policies and Financial Literacy

The SHIW dataset has a rotating panel sub-sample. Households that have been in-

terviewed in previous surveys may have looked for the correct answer to that specific

question (retest effect). In addition, attrition in the panel sub-sample tends to be higher

among worse-off households (Biagi et al., 2009). The simultaneous presence of retest

effects and non-random attrition may upwardly bias our IV results. To test whether this

bias is present, we check whether the effect of financial literacy is different for house-

holds who participated to the survey for the second time. Hence, we interact both the

instrument and financial literacy variables with a dummy equal to 1 if the household was

interviewed before 2010. Results, reported in Table 15, do not provide evidence of retest

effects in the sample. Controlling for this channel does not alter the ITT and IV results:

the interaction between being PattiChiari client and being interviewed more than once

is not statistically significant. The estimated effect of financial literacy for respondents

interviewed more than once is not statistically different from the one estimated for the

rest of the sample.

A different, albeit related, concern refers to the timing of the effect of the treatment

and of the instrumental variable. We examine two issues. First, year 2010 may not be a

representative year to assess the role of financial literacy. Household wealth in 2010 may

be affected by the financial turmoil due to the Great Recession and, particularly, by the

fall in the value of stock prices observed in 2007-2008 (Bottazzi et al., 2013). However

whether and to which extent the effect of financial literacy on financial assets depend on

the recession in not straightforward (see Guiso and Viviano (2015), Bucher-Koenen and

Ziegelmeyer (2014)). Hence, financial literate respondents are more likely to be exposed

to the stock market, and, therefore, to experience financial losses in late 2000s but, at

the same time, they may be better endowed to cope with the fall in stock prices. Our

data cover three years - 2006 (pre-crisis), 2008 (the Great Recession, after Lehman’s de-

fault), and 2010 (the short recovery period that was followed, from mid-2011, by the debt

crisis). Two questions concerning financial literacy, about inflation and loan repayment

schemes, are common to all three waves.37 We restrict our analysis to these common

items and we interact year dummies (for 2008 and 2010) with both the instrumental and

the financial literacy variables. Results, reported in Table 16. The impact of financial

literacy (and of the PattiChiari dummy) on financial assets turns out not to be statis-

tically different in 2006, 2008 and 2010. The model allows for the the level of financial

assets being affected by the Great Recession because we include year specific province

specific fixed effects, but the impact of financial literacy on financial assets being not

substantially different before and after the crisis. We fail to detect heterogeneity in the

estimated effect of financial literacy on financial assets over this short time span: this

may signal that financial literacy have positive effect on long-term financial accumula-

tion, while it does not play a specific role in the presence of large shocks, such as the

financial crisis. This would be consistent with the recent findings by Guiso and Viviano

(2015), who show that financial literacy had a positive but economically small impact

on the way in which households responded to the financial crisis.

Second, we exploit the panel component of the sample to estimate the effect of being

a client of a PattiChiari bank from, respectively, 1 3 or 5 years (Table 17).38 We do

not detect statistically significant differences in the impact of information policies on

financial literacy (first stage) nor financial assets (intention-to-treat) according to the

length of the exposure of clients to the treatment (test on the null assumption of equal

treatment does not allow to reject it at standard levels of significance). However, the

37More details are provided in Appendix A.38It is worth noting that being a client of a PattiChiari bank is measured at the beginning of the year,

while financial literacy and assets are measured at the end of the year.

number of respondent who become or cease to be client of a PattiChiari bank is limited,

because the client relation with the bank is typically long lasting and because entry/exit

of banks from the PattiChiari Consortium is limited. Indeed, all banks who were part

of the Consortium in 2010 entered in 2003 (except the Bank of Chieti that entered in

2004). This may prevent us from precisely estimating the effect of the duration of the

treatment.

B.2 The Role of Trust in Institutions

As discussed in Section 5, our identification strategy is based on the assumption that the

choice of the bank is not correlated with factors that may affect wealth. Georgarakos

and Inderst (2011) find that the level of trust in institutions and financial advisors may

affect individual investment decision. We check whether including proxies for trust as

additional controls alters our results. In the first panel of Table 18, we add a dummy

capturing whether the respondent trusts his/her main bank.39 The effect of this variable

is not significant and the key results remain comparable to the baseline specification. In

the second panel, we control for the length of the relationship between the client and the

bank: we expect a longer relationship to reflect higher trust in the bank. This variable

is correlated with level of financial assets (ITT and IV) but controlling for it does not

affect the impact of the main variables of interest. The two proxies for trust in banks

are not significantly different from zero in the first stage equation (FS columns in the

top panels of Table 18) at conventional levels of significance. This evidence suggests

that the effects of bank information policies (proxied by our binary indicator of being a

PattiChiari client) on financial literacy are not mediated by trust.

39Our measure of trust relies on the answer to the question “Do you trust your principal bank? Pleaseassign a score of 1 to 10, where 1 means “I don’t trust it at all” and 10 means “I trust it completely” andthe intermediate scores serve to graduate your response”. Respondents who trust their main bank arethose who choose a value above the median of the distribution of answers (8); results are confirmed ifwe define the we set this binary indicator equal to one if the answer is above 5, 0 otherwise.

B.3 Sensitivity Analysis: Sample Selection and Functional Form

Our estimation sample excludes the upper and lower 5% tails of the financial assets

distribution. However, in the bottom part of the distribution there are tied values: about

11% of the households do not have any financial asset. One may question whether our

results are driven by sample selection, i.e. by the fact that we focus on households with

positive financial assets. Panel A in Table 19 presents estimates of our baseline model on

a sample that includes households with zero assets. In Panel B, we use a Tobit model to

distinguish the effect of financial literacy on the probability of having any financial asset

from its effect on the amount of assets owned. In both cases, we contrast the results of the

instrumental variable approach with those obtained when we consider financial literacy

as an exogenous variable. Results broadly confirm the findings illustrated in Section 6.

When endogeneity is not addressed, the effect of financial literacy on financial assets

is underestimated. The magnitude of the instrumental variable estimates on average

assets is fairly similar to the one discussed in Section 6. Some interesting new findings

emerge: instrumental variable estimates from the Tobit model suggest that financial

literacy affects both the decision to hold financial assets as well as their amount, so that

the marginal effect of one additional correct answer to the financial literacy questions

is lower for those who hold financial assets (+7 thousand euros) than for the average

individual (+ nearly 10 thousand euros).40

40Notice, however, that this model has two drawbacks: first, it imposes restrictions on the effect offinancial literacy over the intensive and the extensive margins; second, it is well suited for continuousendogenous variables. For these reasons, we choose as baseline the results delivered by linear models.

Table 15: Robustness: Retest effect.

OLS ITT IVDependent variable: Fin. assets Fin. assets Fin. assets

Financial literacy 2.003*** 7.715***(0.703) (2.954)

Financial literacy at 2nd/3rd interviews 0.489 1.366(0.578) (3.061)

Client of PattiChiari at 1st Interview 1.831**(0.920)

Client of PattiChiari at 2nd/3rd Interviews 0.153(1.162)

N. of observations 4865 4865F-test on the excluded instr. 22.827

Notes: the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α + βflip +Xipγ+λp+εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financial literacy of thehousehold head, Xip is a vector of household controls (age, age squared, gender, marital status, years of education of the householdhead and number of household components, household labour income in 2010) and λp is a province fixed effect. Financial literacyis instrumented with a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiari Consortium and 0otherwise. Heteroskedasticity-robust standard errors clustered at the province level in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p <0.01.

Table 16: Robustness: Heterogeneity of the Effect of Financial Literacy on FinancialAssets across years, 2006-2010 SHIW Data (repeated cross-section). Financial literacyis the number of correct answers out of the two common across waves.

OLS ITT IVDependent variable: Fin. assets Fin. assets Fin. assets

Financial literacy 0.731 16.404***(0.734) (4.973)

Financial literacy · 1(year=2008) 0.602 -0.418(0.704) (3.693)

Financial literacy · 1(year=2010) 0.898 -0.372(0.848) (4.037)

Client of PattiChiari 2.621***(0.822)

Client of PattiChiari · 1(year=2008) -0.127(0.983)

Client of PattiChiari · 1(year=2010) -0.236(1.098)

N. of observations 11812 11812 11812

Descriptive statistics -Avg (se)- on this sampleFinancial Assets : 18.45 (21.35); Financial Literacy : 1.37 (0.74)

Notes: the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wipt = α + β1flipt +

β2flipt · 1(t = 2008) + β3flipt · 1(t = 2010) + Xiptγ + λpt + εipt where wipt is financial assets owned by household i, livingin province p, in year t; flipt is financial literacy of the household head measured as the number of correct answers over the twoquestions asked in 2006, 2008 and 2010; 1(·) is the indicator function, Xipt is a vector of household controls (as in the baselinespecification of Table 2, excluding motivations for choosing the bank), and λpt is a province-year fixed effect. Financial literacyand its interaction with year dummies are instrumented with a dummy that takes the value 1 if the household’s main bank belongsto the PattiChiari Consortium in year t, interacted with year dummies, and 0 otherwise. Heteroskedasticity-robust standard errorsclustered at the province level in parentheses.

Table 17: Robustness: Timing of the Effect of Being PattiChiari Client. Measure ofFinancial Literacy: Number of Correct Answer over the Three Questions (in 2010).SHIW panel individuals over 2006-2010.

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Financial literacy 2.274** 9.158(0.919) (8.000)

Client of PattiChiari in 2010 0.179*** 1.635(0.058) (1.516)

N. of observations 1662 1662 1662 1662F-test on the excluded instruments 9.376

Financial literacy 2.274** 7.997(0.919) (6.177)

Client of PattiChiari since 2008 0.215*** 1.720(0.060) (1.357)

N. of observations 1662 1662 1662 1662F-test on the excluded instruments 12.664

Financial literacy 2.274** 8.997(0.919) (6.646)

Client of PattiChiari since 2006 0.195*** 1.751(0.058) (1.292)

N. of observations 1662 1662 1662 1662F-test on the excluded instruments 11.124

Notes: Elaborations based on the 2010 wave data on financial assets and financial literacy and lagged values of the instrumentalvariable as indicated in the tables; elaborations restricted to the households in the panel sub-sample. Measure of financial literacy:number of correct answers over the three questions.All regressions include the controls in the baseline specification (Table 2).Errors robust to heteroskedasticity and clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 18: Effect of Financial Literacy on Financial Assets, Adding Additional Controlsto the Baseline Specification: Trust, Length of Relation with Bank or Number of BankAccounts Held by the Household

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Additional control: Trust

Financial literacy 2.230*** 8.258***(0.627) (3.058)

Client of PattiChiari 0.240*** 1.981***(0.036) (0.741)

High trust 0.150 0.049 0.373 -0.032(0.627) (0.044) (0.645) (0.746)

F-test on the excluded instruments 44.023N. of observations 4865 4865 4865 4865

Additional control: Length of Relation with Bank

Financial literacy 2.099*** 6.239**(0.656) (3.089)

Client of PattiChiari 0.223*** 1.393*(0.037) (0.713)

Length relation bank: more than 10 years 3.618*** 0.069 0.3620*** 3.191***(0.895) (0.044) (0.884) (0.947)

F-test on the excluded instruments 35.721

N. of observations 4757 4757 4757 4757

Additional control: Number of Bank Accounts Held by the Household

Financial literacy 2.195*** 8.123***(0.633) (2.997)

Client of PattiChiari 0.234*** 1.900***(0.035) (0.727)

Number of bank accounts 2.730*** 0.051 2.824*** 2.407***(0.750) (0.033) (0.744) (0.784)

F-test on the excluded instruments 43.479

N. of observations 4865 4865 4865 4865

Notes: the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α + βflip +Xipγ + λp + εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financial literacy ofthe household head, Xip is a vector of household controls (as in the baseline specification of Table 2 plus the additional controlsspecified in each panel), and λp is a province fixed effect. Financial literacy is instrumented with a dummy taking the value 1 ifthe household’s main bank belongs to the PattiChiari Consortium and 0 otherwise.Heteroskedasticity-robust standard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 19: Robustness: Sample Selection and Functional Form

Panel A: Baseline model including households with no financial asset

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Financial literacy 2.106*** 6.342**(0.539) (2.664)

Client of PattiChiari 0.238*** 1.509**(0.034) (0.629)

F-test of excluded instruments 49.278

N. of observations 5451

Panel B: Tobit regression model results

Coefficient of Average Partial Effects onFinancial Literacy E[Y ] E[Y |Y > 0] Prob[Y > 0]2.593∗∗∗ 2.005∗∗∗ 1.442∗∗∗ 0.038∗∗∗

(0.621) (0.475) (0.341) (0.009)

Instrumental variable Tobit regression model results

Coefficient of Average Partial Effects onFinancial Literacy E[Y ] E[Y |Y > 0] Prob[Y > 0]13.094∗∗∗ 9.881∗∗∗ 7.056∗∗∗ 0.183∗∗∗

(3.425) (2.583) (1.845) (0.048)

N. of observations 5451

Notes: Models include the same vector of household controls (as in the baseline specification of Table 2) and province fixed effects.Financial literacy is instrumented with a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiariConsortium and 0 otherwise. Heteroskedasticity-robust standard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

C Additional Tables

Table 20: Robustness: Alternative measure of PattiChiari

OLS FS ITT IVDependent variable: Fin. assets Fin. literacy Fin. assets Fin. assets

Financial literacy 2.231*** 8.273***(0.630) (2.979)

Client of PattiChiari (main bank) 0.235*** 1.941***(0.035) (0.723)

N. of observations 4865 4865 4865 4865F-test on the excluded instr. 43.849

Financial literacy 2.231*** 8.886***(0.630) (2.811)

Client of at least one PattiChiari 0.258*** 2.289***(0.038) (0.742)

N. of observations 4865 4865 4865 4865F-test on the excluded instr. 46.655

Financial literacy 2.191*** 11.238***(0.582) (4.078)

Client of at least one 0.157*** 1.760***PattiChiari, imputed zero if missing (0.031) (0.613)

N. of observations 5756 5756 5756 5756F-test on the excluded instr. 26.128

Notes: Each column reports estimates of a separate regression. All models include the same vector of household controls: age,age squared, gender, marital status and years of education of the household head; number of household components, householdlabour income, reasons for choosing the main bank and size of the municipality. All models include province fixed effects. Financialliteracy is measured as the number of correct question out of those asked in 2010. In IV columns, financial literacy is instrumentedwith one of this indicators; i) a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiari Consortiumand 0 otherwise (top panel); ii) a dummy that takes the value 1 if at least one of the household’s bank account is held in aPattiChiari bank and 0 otherwise (middle panel); iii) a dummy that takes the value 1 if at all the bank accounts of the householdare held in a PattiChiari bank and 0 otherwise (bottom panel) Heteroskedasticity-robust standard errors clustered at the provincelevel in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 21: Robustness: Controlling for municipality fixed effect

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Financial literacy 2.331*** 12.051***(0.510) (4.275)

Client of PattiChiari 0.192*** 2.354***(0.037) (0.732)

N. of observations 4843 4843 4843 4843F-test on the excluded instr. 26.852

Notes: Notes: the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α +

βflip + Xipγ + λp + εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financialliteracy of the household head, Xip is a vector of household controls (age, age squared, gender, marital status, years of educationof the household head and number of household components, household labour income in 2010) and λp is a province fixed effect.Financial literacy is instrumented with a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiariConsortium and 0 otherwise. Heteroskedasticity-robust standard errors clustered at the province level in parentheses. ∗p <0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 22: Robustness: Not controlling for reasons for choosing the main bank

OLS FS ITT IVDependent variable: Fin. assets Fin. literacy Fin. assets Fin. assets

Financial literacy 2.293*** 8.802***(0.637) (3.243)

Client of PattiChiari 0.219*** 1.925**(0.034) (0.752)

N. of observations 4865 4865 4865 4865F-test on the excluded instr. 41.751

Notes: The table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α + βflip +Xipγ+λp+εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financial literacy of thehousehold head, Xip is a vector of household controls (age, age squared, gender, marital status, years of education of the householdhead and number of household components, household labour income in 2010) and λp is a province fixed effect. Financial literacyis instrumented with a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiari Consortium and 0otherwise. Heteroskedasticity-robust standard errors clustered at the province level in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p <0.01.

Table 23: Robustness: Controlling financial assets in 2002

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Controlling for financial assets in 2002Financial literacy 1.491 5.199

(0.949) (8.168)Client of PattiChiari 0.184*** 0.957

(0.056) (1.582)Financial assets in 2002 0.021* 0.000 0.021* 0.019*

(0.011) (0.000) (0.012) (0.011)N. of observations 1027 1027 1027 1027F-test on the excluded instr. 10.679

BaselineFinancial literacy 1.605* 6.487

(0.961) (8.089)Client of PattiChiari 0.188*** 1.222

(0.056) (1.598)N. of observations 1027 1027 1027 1027F-test on the excluded instr. 11.194

Notes: the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α + βflip +Xipγ+λp+εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financial literacy of thehousehold head, Xip is a vector of household controls (age, age squared, gender, marital status, years of education of the householdhead and number of household components, household labour income in 2010) and λp is a province fixed effect. Financial literacyis instrumented with a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiari Consortium and 0otherwise. Heteroskedasticity-robust standard errors clustered at the province level in parentheses. ∗p < 0.1,∗∗ p < 0.05,∗∗∗ p <0.01.

Table 24: Effect of financial literacy on financial assets growth between 2002 and 2006or 2008 or 2010, allowing for heterogeneous effects across years. SHIW repeated cross-section 2006-2010. Financial literacy is the number of correct answers out of the twocommon across waves.

OLS ITT IVDependent variable: Fin. ass. growth Fin. ass. growth Fin. ass. growth

Financial literacy 0.018** 0.234***(0.009) (0.082)

Financial literacy · 1(year=2008) -0.001 -0.038*(0.008) (0.022)

Financial literacy · 1(year=2010) 0.003 -0.018(0.009) (0.022)

Client of PattiChiari 0.028**(0.013)

Client of PattiChiari · 1(year=2008) -0.007(0.014)

Client of PattiChiari · 1(year=2010) 0.008(0.020)

N. of observations 3181 3181 3181

Notes: Each column reports estimates of a separate regression. OLS, ITT and IV columns are regression where the dependent

variable is(yitp−yi2002p−E[yitp−yi2002p])

V ar[yitp−yi2002p], i.e. standardized growth in financial assets between 2002 and time t for household i

in province p. FS columns are regressions where the dependent variable is financial literacy. All models include the same vector ofhousehold controls: age, age squared, gender, marital status and years of education of the household head; number of householdcomponents, household labour income, reasons for choosing the main bank and size of the municipality. All models include andyear-specific province fixed effects. In IV columns, financial literacy is instrumented with a dummy that takes the value 1 if thehousehold’s main bank belongs to the PattiChiari Consortium and 0 otherwise and interactions of this indicator with year dummies.Heteroskedasticity-robust standard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 25: Robustness: Controlling for homeownership

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Financial literacy 2.131*** 6.975**(0.629) (2.975)

Client of PattiChiari 0.230*** 1.603**(0.036) (0.713)

Homeowner 4.906*** 0.070** 4.987*** 4.497***(0.764) (0.027) (0.783) (0.792)

N. of observations 4865 4865 4865 4865F-test on the excluded instr. 41.490

Notes: Observations: 4865. Notes: the table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the

model wip = α+ βflip +Xipγ+λp +εip where wip is financial assets owned by household i, living in province p, in year 2010; flipis financial literacy of the household head, Xip is a vector of household controls (age, age squared, gender, marital status, yearsof education of the household head and number of household components, household labour income in 2010) and λp is a provincefixed effect. Financial literacy is instrumented with a dummy that takes the value 1 if the household’s main bank belongs to thePattiChiari Consortium and 0 otherwise. Heteroskedasticity-robust standard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 26: Propensity Score Matching: Test of the Balancing Property on the commonsupport. SHIW 2010 data.

Mean t-testVariable Patti-Chiari non-Patti-Chiari % bias p-value

clients clients

Age 57.7 57.4 1.6 0.51Male 0.58 0.55 5.3 0.03Married 0.67 0.66 1.9 0.42N. hh. components 2.54 2.56 -2.1 0.39Years of education 10.4 10.3 2.9 0.23Household labour income 27.9 27.9 0.4 0.87Province∗ 43.7 43.3 1.4 0.59Municipality type∗ 1.43 1.42 0.5 0.81Reasons for choosing the main bank∗ 5.8 5.5 5.0 0.03Male · Married 0.46 0.45 2.8 0.24Small municipality · married 0.17 0.17 0.1 0.98Small municipality · n. hh. components 0.65 0.67 -1.4 0.56Large municipality · male 0.06 0.06 2.5 0.36Large municipality · age 6.3 6.03 1.8 0.47N. hh. components · hh. labour income 79.0 80.7 -2.5 0.34Married · hh. labour income 21.1 21.0 0.6 0.82

Joint test 0.103∗ For these variables we include in the pscore a list of K-1 binary indicators, where K is the number of

possible values of the variable.

Table 27: Association and Causal Effect of Financial Literacy on Financial Assets withInstruments that Use Information on“Bank-Switchers”Between Two years (i.e. BetweenSurvey Waves) or Between Four Years (Between Two Survey Waves).

HHs observed in 2006-2008 or in 2008-2010Descriptive statistics -Avg (se)- on this sample

Financial Assets: 22.9 (29.9); Financial Literacy : 1.46 (0.68)

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Financial literacy∗ 2.009** 18.673**(1.008) (8.538)

Switchs to Client of PattiChiari -0.108 -0.989(0.067) (2.317)

Stays Client of Non-PattiChiari -0.133*** -3.660**(0.048) (1.448)

Switchs to Client of Non-PattiChiari -0.183*** -1.980(0.048) (1.884)

N. of observations 3196 3196 3196 3196

Hansen J p-value 0.4269

HHs observed in 2006 and 2010Descriptive statistics -Avg (se)- on this sample

Financial Assets : 21.9 (23.8); Financial Literacy: 1.43 (0.68)

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Financial literacy∗ 1.245 14.081*(1.091) (7.529)

Switchs to Client of PattiChiari -0.009 0.405(0.088) (2.991)

Stays Client of Non-PattiChiari -0.197*** -3.072**(0.064) (1.299)

Switchs to Client of Non-PattiChiari -0.087 0.638(0.071) (2.759)

N. of observations 1560 1560 1560 1560

Hansen J p-value 0.7535

Notes: ∗ We consider the two questions common across waves. The baseline analysis on year 2010 considers three questions. Seeappendix A for more details.

The table reports OLS, first-stage, intention-to-treat, and instrumental variable results of the model wip = α+ βflip +Xipγ+λp +εip where wip is financial assets owned by household i, living in province p, in year 2010; flip is financial literacy of the householdhead, Xip is a vector of household controls (as in the baseline specification of Table 2, excluding motivations for choosing the bank),and λp is a province fixed effect. Financial literacy is instrumented with three dummies identifying whether household switchedfrom being a PattiChiari to a non-PattiChiari bank (or vice-versa) or remained client of non-PattiChiari banks between 2008 and2010 (in the top panel) or between 2006 and 2010 (in the bottom one). Heteroskedasticity-robust standard errors clustered at theprovince level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.

Table 28: Effect of Financial Literacy on Financial Assets by Size of the Main Bank (Measured as the Level of Total Assetsin 2010 or the Number of Branches in 2010). Financial literacy is the Number of Correct Answers out of the Three QuestionsAsked in 2010. 2010 SHIW Data.

OLS FS ITT IV OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets Fin. assets Fin. lit. Fin. assets Fin. assets

Excluding top 10 largest banksfor nr. branches for total assets

Share of PattiChiari clients: 0.67 Share of PattiChiari clients: 0.66Avg. financial assets (sd): 18.26 (21.40) Avg. financial assets (sd): 18.40 (21.32)Avg. financial literacy (sd): 1.86 (1.00) Avg. financial literacy (sd): 1.86 (1.00)

Financial Literacy 2.268*** 7.710** 2.288*** 9.369**(0.733) (3.401) (0.765) (3.977)

Client of PattiChiari 0.276*** 2.127** 0.253*** 2.371**(0.048) (0.934) (0.050) (0.990)

N 3,084 3,084 3,084 3,084 3,002 3,002 3,002 3,002

F-stat excluded instrum. 33.47 25.87

Notes: Each column reports estimates of a separate regression. OLS, ITT and IV columns are regression where the dependent variable is financial assest. FS columns are regressionswhere the dependent variable is financial literacy. All models include the same vector of household controls: age, age squared, gender, marital status and years of education of thehousehold head; number of household components, household labour income, reasons for choosing the main bank and size of the municipality. All models include province fixedeffects. In IV columns, financial literacy is instrumented with a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiari Consortium and 0 otherwise.Heteroskedasticity-robust standard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.+ Medians are computed on the sample of 70 banks listed in Table 9. The median of the number of bank branches is 307; the median of total assets is 9328.5.

Table 29: Effect of Financial Literacy on Financial Assets by Size of the Main Bank (Measured as the Level of Total Assetsin 2010 or the Number of Branches in 2010). Financial literacy is the Number of Correct Answers out of the Three QuestionsAsked in 2010. 2010 SHIW Data.

OLS FS ITT IV OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets Fin. assets Fin. lit. Fin. assets Fin. assets

Main bank: nr. branches ≤ median+ Main bank: nr. branches > median+

Share of PattiChiari clients: 0.55 Share of PattiChiari clients: 0.73Avg. financial assets (sd): 20.85 (20.38) Avg. financial assets (sd): 19.09 (22.09)Avg. financial literacy (sd): 2.07 (0.93) Avg. financial literacy (sd): 1.90 (0.99)

Financial Literacy 3.509*** 12.051 2.395*** 7.857**(1.294) (9.794) (0.701) (3.052)

Client of PattiChiari 0.292** 3.515 0.275*** 2.158**(0.116) (3.246) (0.043) (0.850)

F-stat excluded instrum. 6.35 40.89

N. of observations 471 471 471 471 4020 4020 4020 4020

Main bank: assets ≤ median+ Main bank: assets > median+

Share of PattiChiari clients: 0.65 Share of PattiChiari clients: 0.72Avg. financial assets (sd): 21.25 (23.16) Avg. financial assets (sd): 19.01 (22.09)Avg. financial literacy (sd): 2.00 (0.94) Avg. financial literacy (sd): 1.91 (0.99)

Financial Literacy -0.181 19.829 2.631*** 8.249***(1.485) (17.981) (0.708) (2.875)

Client of PattiChiari 0.246 4.882 0.273*** 2.253***(0.174) (3.437) (0.040) (0.785)

F-stat excluded instrum. 2.002 46.17

N. of observations 525 525 525 525 3966 3966 3966 3966

Notes: Each column reports estimates of a separate regression. OLS, ITT and IV columns are regression where the dependent variable is financial assest. FS columns are regressionswhere the dependent variable is financial literacy. All models include the same vector of household controls: age, age squared, gender, marital status and years of education of thehousehold head; number of household components, household labour income, reasons for choosing the main bank and size of the municipality. All models include province fixedeffects.. In IV columns, financial literacy is instrumented with a dummy that takes the value 1 if the household’s main bank belongs to the PattiChiari Consortium and 0 otherwise.Heteroskedasticity-robust standard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.+ Medians are computed on the sample of 70 banks listed in Table 9. The median of the number of bank branches is 307; the median of total assets is 9328.5.

Table 30: Robustness: Including Main Bank Assets and Number of Branches - Year2010

OLS FS ITT IVDependent variable: Fin. assets Fin. lit. Fin. assets Fin. assets

Controlling for costs

Financial literacy 2.344*** 7.549**(0.687) (3.277)

Client of PattiChiari 0.236*** 1.785**(0.042) (0.769)

Main bank: transfer (desk) -0.440 -0.013 -0.468 -0.367(0.630) (0.028) (0.639) (0.591)

Main bank: transfer (online) 0.109 0.022 -0.261 -0.429(0.894) (0.051) (0.912) (0.918)

Main bank: withdrawal (desk) 1.104 0.185 -0.035 -1.434(3.270) (0.175) (3.179) (3.208)

Main bank: withdrawal (ATM) 4.808 0.194 4.892* 3.425(2.970) (0.127) (2.914) (3.243)

N. of observations 4256 4256 4256 4256F-test on the excluded instr. 32.391

Controlling for bank characteristics

Financial literacy 2.301*** 8.614*(0.648) (4.626)

Client of PattiChiari 0.223*** 1.918*(0.043) (1.023)

Log(Assets) -Main Bank- 0.175 0.005 -0.114 -0.158(0.316) (0.015) (0.445) (0.440)

Log(Branch) -Main Bank- -0.540* -0.016 -0.294 -0.160N. of observations 4568 4568 4568 4568F-test on the excluded instr. 26.749

Notes: All models include the same vector of household controls: age, age squared, gender, marital status and years of educationof the household head; number of household components, household labour income, reasons for choosing the main bank and size ofthe municipality. All models include province fixed effects. In IV columns, financial literacy is instrumented with a dummy thattakes the value 1 if the household’s main bank belongs to the PattiChiari Consortium and 0 otherwise. Heteroskedasticity-robuststandard errors clustered at the province level in parentheses.∗p < 0.1,∗∗ p < 0.05,∗∗∗ p < 0.01.