Edoardo Lanciano et al., Insurance as a stabilizer: SME credit access during the COVID-19 crisis (2026)
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Article by Edoardo Lanciano, Daniele Previati and Ornella Ricci of the University of Roma Tre in The Geneva Papers on Risk and Insurance, received 28 November 2025 and accepted 17 April 2026. Using a sample of 1,998 Italian micro, small and medium-sized enterprises from a 2021 Bank of Italy survey, it estimates how insurance coverage affected the probability of obtaining credit after the COVID-19 shock. The authors find that higher insurance coverage positively and robustly affects access to credit, a result that holds after controls for endogeneity and multiple robustness checks. They argue that insurance acts as a stabilizer for small firms during macroeconomic shocks and so raises their creditworthiness with banks.
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The Geneva Papers on Risk and Insurance - Issues and Practice
https://doi.org/10.1057/s41288-026-00383-6
ORIGINAL ARTICLE
Insurance as a stabilizer: SME credit access during the
COVID-19 crisis
Edoardo Lanciano1 · Daniele Previati1 · Ornella Ricci1
Received: 28 November 2025 / Accepted: 17 April 2026
© The Author(s) 2026
Abstract
This paper investigates the role of insurance coverage in promoting access to credit
for micro, small, and medium-sized enterprises (MSMEs) in the aftermath of the
COVID-19 pandemic. The relationship between risk management strategies, credit
availability, and small firm dynamics is sparsely analyzed in the literature, particu-
larly given the characteristic data limitations of small businesses. Using a compre-
hensive sample of 1,998 Italian MSMEs, drawn from a 2021 Bank of Italy survey,
we estimate the effect of insurance coverage on the probability of obtaining credit.
We find that higher insurance coverage positively and robustly affects access to
credit. These results remain persistent after controlling for potential endogeneity
and undergoing multiple robustness checks. Our findings provide novel evidence
by demonstrating that insurance functions as a stabilizing mechanism for MSMEs,
safeguarding them against risks during macroeconomic shocks and thus increasing
their creditworthiness. This study enriches the literature on the credit channel of
risk transfer and provides timely insights into the relationship between firms and
financial intermediaries during periods of high economic uncertainty.
Keywords Insurance · Access to credit · Bank loan · Enterprises ·
Discouragement
JEL Classification G01 · G21 · G22 · G52 · E51 · M21
Edoardo Lanciano
edoardo.lanciano@uniroma3.it
1
University of Roma Tre, Via Silvio d’Amico 77, Rome 00145, Italy
E. Lanciano et al.
Introduction
Access to credit for small enterprises is a widely debated topic. Evidence from the
literature, usually based on survey data, generally highlights the overall difficulties
small businesses face in accessing credit compared to larger firms.
A company’s creditworthiness can depend on several factors. The borrowing
capacity of a firm, especially a small or medium-sized one, may be influenced, for
example, by its financial performance and the feasibility of strategic plans, its reli-
ability and market position, its past experience, and also by the level of insurance
coverage it possesses. Indeed, having insurance coverage makes the business safer
and protects it from unexpected events that may cause various types of damage (The
Geneva Association 2012). This protection is clearly a factor appreciated by banks
during the assessment of creditworthiness.
The relationship between insurance and access to credit becomes particularly rel-
evant during periods of economic shocks, such as the COVID-19 pandemic. In such
times, insurance coverage represents a crucial instrument for enhancing firms’ finan-
cial resilience by mitigating exposure to unexpected losses or disruptions in business
operations (The Geneva Association 2022). It helps preserve liquidity and maintain
solvency when revenues decline. This resultant financial stability is particularly valu-
able for supporting access to credit, as insured firms are often perceived by credit
providers as less risky and more capable of repaying their liabilities. Consequently,
adequate insurance coverage can improve creditworthiness and facilitate access to
credit, enabling firms to cover financial needs and sustain investments even during
critical periods.
The topic of the relationship between insurance coverage and enterprise access
to credit is relatively under-investigated, primarily due to the lack of data on the
insurance behavior of small enterprises, as well as on loan applications and their out-
comes. Although some evidence is available (Zou and Adams 2008; Guiso and Schi-
vardi 2010; Santoboni et al. 2012; Ricci and Santilli 2024), it remains limited and not
fully updated to recent economic conditions. The current context has changed signifi-
cantly, particularly due to major exogenous shocks like the COVID-19 pandemic and
the increased risks of natural disasters linked to climate change, making the topic of
insurance coverage and the availability of funding even more relevant today. Addi-
tionally, the propensity of firms to purchase insurance coverage is strongly shaped by
both national and international contextual factors, such as the structure of the insur-
ance market, the regulatory environment, the frequency of adverse events, and the
presence of public support schemes. These elements influence firms’ risk perception
and their incentives to adopt risk-mitigation tools.
This paper aims to verify the effect of owning specific types of insurance coverage
(property insurance, third-party liability insurance, business interruption insurance,
and life insurance for the owner) on enterprises’ access to credit. We utilize data from
the recent Bank of Italy survey (2021) on the level of financial literacy and digital
skills in micro, small, and medium-sized enterprises (MSMEs), which allows us to
investigate this relationship in a more contemporary scenario. By setting up regres-
sion models to estimate the relationships between our main variables of interest, we
find that insurance coverage is positively and highly significantly related to access
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
to credit. Our results are consistent across multiple robustness tests. Endogeneity is
addressed by employing an instrumental variable approach. This evidence demon-
strates how owning adequate insurance coverage is important for businesses not only
for protection against damages and risky situations but also for improving borrower
evaluation and increasing the probability of obtaining credit. These results are partic-
ularly important when considering the pandemic crisis period during which the data
was collected, a time when issues such as risk protection, insurance, and financing
needs assumed a crucial significance. Italy represents a particularly interesting case
in this scenario, considering that its productive system is largely based on small enter-
prises (MSMEs account for 99.9% of the total number of enterprises, with over 3.9
million units) (European Commission 2024). Furthermore, the COVID-19 pandemic
had a severe impact on the Italian economy and business health, further increasing
the relevance of issues related to insurance and credit availability. Moreover, the
Italian financial system is strongly bank-oriented, with limited alternative sources of
finance, making access to bank credit crucial for firms’ survival and growth.
This study aims to provide novel evidence on this relationship, which remains
overall poorly investigated, and thus enriches the existing related literature. The
paper proceeds as follows: Section 2 provides a review of the literature, Section 3
explains the data and methodology, Section 4 describes the results, Section 5 reports
robustness checks, and finally, Section 6 provides some discussion and conclusions.
Literature review
The literature widely addresses the issue of access to credit for small and medium
enterprises (SMEs), often highlighting the difficulties they face in obtaining financing
(Muske et al. 2007; Van Hoang et al. 2018). The general consensus is that small firms
face greater constraints in accessing credit compared to larger counterparts (Berger
and Udell 1998; Galindo and Schiantarelli 2003; Beck and Demirguc-Kunt 2006;
Muske et al. 2007; Van Hoang et al. 2018). These difficulties are primarily rooted
in two factors. First, informational asymmetry between the firm and the financial
intermediary is more severe for smaller companies. Banks possess less information
on the risk associated with small firms, leading to adverse selection and moral hazard
(Berger and Udell 1998; Santoboni et al. 2012). This information deficit often causes
banks to restrict credit or demand higher collateral from SMEs. Second, SMEs are
generally perceived as more vulnerable and prone to failure due to a lack of diver-
sification, resulting in greater risk of default (Beck and Demirguc-Kunt 2006). The
creditworthiness of an enterprise is a multi-dimensional concept, influenced by vari-
ous factors, including the firm’s financial performance (e.g., profitability, liquidity,
financial structure), the solidity of its strategic plans, its market position and reliabil-
ity, and its past credit history (Beck and Demirguc-Kunt 2006).
A factor less commonly investigated in the assessment of creditworthiness is the
firm’s insurance coverage. Insurance serves a crucial function in the management of
business risk, protecting firms from various damages caused by unforeseen events
(Guiso and Schivardi 2010; Santoboni et al. 2012). Insurance coverage significantly
impacts a firm’s financial resilience and stability. This stability is directly relevant
E. Lanciano et al.
to credit access (Eling and Schmeiser 2010). When assessing credit applications,
banks aim to minimize the risk of default. An insured firm is inherently less risky
because the probability of default due to unforeseen events (e.g., natural disasters,
major equipment failure, or third-party claims) is transferred, at least partially, to the
insurer (Santoboni et al. 2012). Consequently, banks may perceive insurance as a
substitute for collateral or, more generally, as a factor that improves the firm’s credit
rating. Additionally, the level of insurance coverage may be perceived as a signal of
managerial quality, by representing a sophisticated risk management strategy (Smith
and Stulz 1985; Ono and Uesugi 2009). This improved perception can lead to better
access to credit in terms of both availability and cost.
Despite its theoretical relevance, the empirical evidence on the relationship
between insurance coverage and access to credit in enterprises is scarce, mainly
constrained by the aforementioned lack of comprehensive data. At the international
level, a few studies have explored this nexus. For example, Zou and Adams (2008),
using data from China, found that insurance positively affects the probability of small
firms obtaining bank loans. Within the Italian context, the existing literature is also
limited. Guiso and Schivardi (2010) found that Italian firms with higher insurance
coverage are less likely to be credit rationed and face lower interest rates. Similarly,
Santoboni et al. (2012) showed that a higher level of insurance is associated with bet-
ter financing conditions and greater availability of credit lines for firms.
More recently, especially following the COVID-19 pandemic, which has severely
tested the financial stability of businesses and caused severe damage to the econ-
omy, the topic of insurance for businesses has certainly acquired greater significance.
These mentioned aspects have emerged from a few contributions in recent years.
Ricci and Santilli (2024) and Nebolsina (2021) highlight how insurance preserves
the firm’s liquidity and solvency by covering potential unexpected losses or business
interruptions. This protective function ensures that the firm’s cash flows remain stable
and its assets are protected, even in the face of adverse events, thereby enhancing its
overall financial stability. In the following years (Bank of Italy 2024) has confirmed
this evidence, suggesting that insurance is a relevant element in credit assessment.
Generally, the existing, albeit limited, literature converges on the finding that firms
with more extensive insurance coverage enjoy better access to credit, both in terms
of cost/conditions and availability of financing.
The existing literature presents two main gaps that this study aims to address.
First, the available evidence, particularly in the Italian context, is somewhat dated.
The current economic landscape has been profoundly altered by major exogenous
shocks, such as the COVID-19 pandemic and the increased frequency of climate-
related natural disasters. These events have amplified business risk, making the role
of insurance in financial stability and credit access even more critical today. There-
fore, a more recent analysis is necessary to understand the relevance of this relation-
ship in the post-shock scenario. In this terms, this paper provides new evidence on the
role of insurance coverage for risk mitigation and in enhancing firms’ access to credit
under uncertainty and crisis conditions. Additionally, this paper provides evidence
on insurance coverages not only as an instrument for reducing probability of default,
but also as an observable signal of managerial quality. By voluntarily opting for a
comprehensive suite of insurance policies, a firm signals a sophisticated approach to
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
risk management and a commitment to financial stability (Smith and Stulz 1985). In
fact, the number of insurance coverages held can provide a tangible metric for banks
to screen for higher-quality borrowers who are actively protecting their operational
continuity. This signaling power is particularly relevant for small firms, which are
typically characterized by significant information opacity (Ono and Uesugi 2009).
Second, many existing studies use aggregate or limited measures of insurance. This
paper contributes to the literature by analyzing the effect of specific types of insur-
ance coverage (property, third-party liability, business interruption, and owner’s life
insurance) on credit access, leveraging detailed data from the Bank of Italy’s 2021
survey on MSMEs. Furthermore, this survey provides a relatively recent dataset that
gathers specific information on target groups such as micro and small businesses,
which are generally underrepresented in the broader literature, in a post-COVID
scenario, thereby capturing more recent trends regarding these businesses and their
decisions. By providing novel and updated empirical evidence on the positive link
between specific insurance types and credit access for Italian MSMEs, this study
seeks to enrich the existing literature and offer policy-relevant insights, especially
given Italy’s bank-oriented financial system and the critical role of small enterprises.
Data and methodology
In order to study this relationship we used data from Bank of Italy, specifically from
the 2021 survey on entrepreneurs’ financial competences and use of digital tools
in their business, focused on enterprises of micro, small and medium dimension
(MSMEs). The survey was conducted by interviewing the business owner (or one
of them in case of multiple owners) (Oecd 2020), and was delivered from March to
May 2021. From this survey, we have a sample of 1,998 Italian non-financial firms.
We provide some information on the sample composition in Table 1. Among the
1,998 companies available in the sample, 1,851 are micro-enterprises (92.64%), 76
are small enterprises, 3 are medium-sized enterprises, and 68 are unknown. All sur-
vey respondents claim to be business owners and to make financial decisions relating
to the business. We therefore assume that the businesses are founder-run.
For the purpose of conducting the analysis, we generated a proxy for insurance
coverage as the main independent variable of interest, and a proxy for access to credit
as the dependent variable. All variables utilized in this investigation were constructed
using survey questions. For the measure of insurance coverage, we considered the
number of insurance policies owned by the entrepreneur. The survey included four
questions asking entrepreneurs whether they currently owned, or had owned in the
last 24 months, the following specific insurance policies: property insurance (insur-
ing buildings, equipment, inventory, furniture, etc., against damage or theft), third-
party liability insurance (insuring against damages caused by the owner, employees,
or products to a third party), business interruption insurance (or business income/
profit losses insurance, which compensates a business for its lost income during
events causing a disruption to normal operations), and finally, life insurance for the
owner (insuring against the death of the owner). We treated each of these questions
as a dummy variable, where 1 represents an affirmative answer and 0 otherwise. We
E. Lanciano et al.
Table 1 Sample composition Freq Percent Cum
Gender
Men 1,427 71.42 71.42
Women 571 28.58 100.00
Total 1,998 100.00
Age
18-44 639 31.98 31.98
45-64 1,147 57.41 89.39
65+ 212 10.61 100.00
Total 1.998 100.00
Geographical location
North 956 47.85 47.85
Centre 436 21.82 69.67
South and islands 606 30.33 100.00
Total 1,998 100.00
Firm Age
0–5 607 30.38 30.38
5–10 651 32.58 62.96
10–20 253 12.66 75.62
This table reports some
20+ 487 24.38 100.00
descriptive statistics on sample
composition Total 1,998 100.00
thus derived an insurance coverage score (Insurance) with values ranging between
0 and 4. The dependent variable, Access to credit, is a proxy constructed by combin-
ing two survey questions. Respondents were first asked whether they had applied for
a new business loan since the start of the COVID-19 pandemic, and then whether
they had obtained the loan, at least partially. Both of these questions are treated as
dummy variables, thus the combined proxy is also considered as a dummy where 1
represents both the conditions satisfied (credit application made and at least partial
obtaining).This combined proxy therefore allows us to verify both whether an entre-
preneur sought financing following the onset of the pandemic shock and whether the
application was successful.
Among the independent variables, we also included a set of control variables that
may affect our main relationship of interest. A portion of these variables pertains to
individual characteristics of the entrepreneur, such as gender, age, education (gradu-
ated, high school, lower education), and the level of financial literacy. Financial lit-
eracy is generally considered a relevant positive driver for both insurance demand
(Ricci and Santilli 2024; Bongini et al. 2023) and access to credit (Calcagno et al.
2024; Lusardi and Mitchell 2014).1 Other control variables are related to business
characteristics, such as turnover (turnover level within a specific range, from “up
to 10,000 euros” to “up to 50 million euros”), number of employees, the age of the
1
The measure of financial literacy is composed by the three dimensions of knowledge, behaviour and atti-
tudes, according to the OECD/INFE toolkit for measuring financial literacy in MSMEs (OECD (2020)).
More details on the set questions used for building the measure of financial literacy are available on the
OECD/INFE survey instrument to measure the financial literacy of MSMEs (OECD (2020)) or upon
request.
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
firm, the geographical location of the firm (north, centre, south and islands) and the
industry (primary, secondary, other).
Additionally, we include also a measure of the level of digitalization of the busi-
ness, composed of questions related to the adoption of business practices based on
digitalization and of digital financial services, and an indicator of the impact of the
Covid pandemic in terms of reduction of turnover, profits, liquidity, employees and
increase of debts, that might have affected credit demand in those years. Finally, we
include an indicator of the financial stability of the enterprise. This is measured using
a question that asks respondents whether, since the start of the COVID-19 pandemic,
the enterprise had faced situations where cash inflows were insufficient to cover cash
outflows or to pay expected expenses. This final aspect could clearly affect credit
demand, especially during periods characterized by economic shocks.2
In order to verify the relationships between our variables, and considering the
dichotomous nature of the dependent variable (Access to credit), we employ a probit
regression model to analyze the effect of insurance coverage and the other indepen-
dent variables on the likelihood of accessing credit (Eq. 1):
∑ ∑ ∑
Pr(Access to credit=1)i = β0 + β1 Insurancei + δi Individual + δi Business + δi Industry + εi (1)
Due to the extension of the equation, the control variables were grouped into three
categories: Individual (gender, age, education, financial literacy), Business (digita-
lization, turnover, employees, firm age, Covid impact, stability, north, centre) and
Industry (primary, secondary).
We also define an alternative proxy for measuring credit access, denoted as Bank
loan. This is a dummy variable (where 1 indicates an affirmative answer) capturing
whether the enterprise currently uses or has utilized a loan from a bank within the last
24 months (or since business creation if the firm is younger than 24 months).
Access to credit remains our preferred measure because its construction requires
that financing was both requested and successfully received during the critical pan-
demic period, thus enabling a more accurate selection of firms actively seeking exter-
nal finance. Conversely, the Bank loan measure is less precise for our main analysis:
while it identifies firms with a current or recent banking relationship, it does not allow
us to distinguish firms that simply did not request a loan from those that were unsuc-
cessful or had no financing needs.
Nonetheless, we include Bank loan in our analysis because it offers a more general
indicator of Access to credit, spanning a broader two-year period and is not specific
to the demand conditions generated by the COVID-19 crisis. Using both measures
strengthens the overall robustness of our findings.
Furthermore, we have to address potential endogeneity concerns, as the results
may be subject to reverse causality, omitted variable bias, and measurement error.
The issue of reverse causality is particularly relevant in the relationship between
insurance and credit access. It is possible that firms acquired an insurance policy
simultaneously with, or as a direct requirement for, obtaining a bank loan, rather
2
More details on the survey questions used for building our variables are available on the OECD/INFE
survey instrument to measure the financial literacy of MSMEs (OECD (2020)) or upon request.
E. Lanciano et al.
than having stipulated the coverage beforehand to facilitate access to finance. Since
the survey instrument does not precisely capture the chronological sequence of these
decisions, this makes it challenging to establish a clear direction of causality. Fur-
thermore, omitted variable bias may arise due to unobservable characteristics that
simultaneously influence both insurance coverage and credit access. A key example
is the risk preference or prudence of the business owner, which is unobservable in
our data. An owner with a higher propensity for prudence may be more diligent in
implementing risk management practices (e.g., obtaining insurance) and in designing
robust investment projects, thereby making the firm inherently more creditworthy.
This confounding effect would lead to an upward bias in the estimated relationship.
Finally, as we utilize secondary data from a survey, the possibility of measurement
error in the self-reported answers–a common limitation in cross-sectional firm-level
data–must be acknowledged.
In order to overcome these limitations, we adopt an Instrumental Variable (IV)
approach by employing a two-stage regression analysis, following previous studies
(Semadeni et al. 2014).3 We consider the entrepreneurial experience of the respon-
dents’ parents (Parents’ experience) as the instrumental variable for the entrepreneurs’
insurance coverage. This variable is expected to capture whether the respondent may
have acquired business and financial skills from the family, including an under-
standing of the importance of owning insurance coverage. It is plausible that having
parents who were entrepreneurs themselves has a positive and significant effect on
the respondent’s propensity to adopt sound financial practices, such as purchasing
adequate insurance. The survey includes a specific question asking entrepreneurs
whether their parents currently own or have owned a business in the past. Parents’
experience is a dummy variable, which takes the value of 1 if at least one of the
entrepreneur’s parents was a business owner, and 0 otherwise. Crucially, this parental
characteristic is exogenous to the entrepreneur’s current decisions regarding credit
access, as it is a characteristic determined prior to the respondent’s own economic
and financial choices. To address potential violations of the exclusion restriction–spe-
cifically that parental experience might directly affect credit access through manage-
rial ability or risk preferences–we include a comprehensive set of controls. Beyond
standard demographics (gender, age, education) and firm age (a proxy for business
experience), we leverage a multidimensional measure of financial literacy. This goes
beyond basic numeracy to include indicators of managerial best practices (e.g., sys-
tematic expense tracking, provider benchmarking) and individual risk/planning atti-
tudes (e.g., pension planning and long-term goal setting). By explicitly controlling
for these traits, we isolate the path through which parental background affects credit
access: the propensity to adopt formal insurance tools.
As a robustness check, we consider a further alternative dependent variable, indi-
cating whether the entrepreneurs have been affected by discouragement in applying
for credit (Discouragement). This measure is designed to capture firms that needed
credit but did not apply. It includes entrepreneurs who did not apply for credit for
specific reasons, such as the complexity of the application procedure or the belief that
the loan would not be approved. This is a dummy variable, taking the value of 1 when
3
We use the ivprobit command in STATA.
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
Table 2 Access to credit, bank Freq Percent
loan and discouragement
Access to credit
0 947 47.40
1 1,051 52.60
Total 1,998 100.00
Bank loan
0 752 37.64
1 1,246 62.36
Total 1,998 100.00
This table shows the answers Discouragement
given by the respondents to the 0 1,835 91.84
questions on access to credit,
1 163 8.16
bank loan and discouragement
among the overall sample Total 1,998 100.00
Table 3 Insurance score Insurance Freq Percent Cum
0 484 24.22 24.22
1 469 23.47 47.70
This table shows the 2 557 27.88 75.58
distribution of the score 3 369 18.47 94.04
obtained in the proxy of
4 119 5.96 100.00
insurance coverage among the
overall sample Total 1,998 100.00
Table 4 Single insurance coverages
Insurance Freq Percent
Property insurance 469 37.29
Third-party liability insurance 1,364 68.77
Business interruption insurance 236 11.81
Life insurance for the owner 811 40.59
This table shows statistics on the different insurance coverages owned by the respondents among the
overall sample
the respondent is classified as discouraged, and 0 otherwise. Descriptive statistics
for our main variables of interest (Access to credit, Bank loan, Discouragement, and
Insurance) are reported in Tables 2, 3 and 4. The full text of the survey questions used
to construct our main variables is available in Appendix A.
Results
Tables 2 and 3 present the descriptive statistics for our main variables of interest.
Regarding the dependent variables (Access to credit and alternative proxies, Bank
loan and Discouragement), the analysis reveals that more than half of the sample
(52.60%, 1,051 firms) obtained credit since the beginning of the pandemic. Fur-
thermore, over 60% (1,246 respondents) currently use, or have used in the last 24
E. Lanciano et al.
months, a loan from a bank. It is noted that the 8.16% of the respondents (163 units)
are included in the Discouragement category (Table 2).
Considering the main independent variable (Insurance), the evidence suggests that
insurance coverage is not widely diffused among Italian Micro, Small, and Medium
Enterprises (MSMEs). Approximately 25% of the sample (24.22%) scores 0 on this
proxy, indicating the absence of the insurance types investigated in this survey. Con-
versely, only 24.43% of the firms possess at least three kinds of insurance coverage
(Table 3).
It is relevant to examine the specific distribution of insurance coverage across
the sample to identify the most and least common types. Table 4 indicates that the
most frequent coverage is the Third-Party Liability Insurance (68.77%, 1,364 respon-
dents), which provides protection against damages caused to a third party by the
owner, employees, or products. The least common is the Business Interruption Insur-
ance (or business income/profit losses insurance), which compensates lost income
due to events disrupting the normal course of business, held by only 11.81% of the
sample (236 respondents). Life Insurance for the Owner (40.59%) and Property
Insurance (37.29%) fall into an intermediate range, though they are owned by less
than half of the sample. This initial evidence suggests that the adoption of insurance
coverage among Italian enterprises appears to be generally low. Beyond the distribu-
tion of the insurance coverages among the sample, it is also interesting to analyse
clustering patterns and overlaps in the ownership of specific insurance policies. For
these purposes, since our insurance coverages are represented by dummy variables,
we first provide a tetrachoric correlation matrix for examining correlations among
the types of insurance policies (Table 5). Correlation coefficients are all positive and
moderately high. These findings suggest that the adoption of different insurance poli-
cies is not independent: firms that hold one type of coverage are more likely to hold
others as well. Insurance decisions therefore tend to move together. This pattern indi-
cates a widespread combined insurance decision-making process, consistent with a
broader and more structured risk-management strategy. Furthermore, we investigate
the clustering of insurance policies by showing the combinations in the ownership of
the insurance coverages considered. We report these findings in Table 6. The com-
binations are listed in order from the most to the least common in the sample. These
findings show that, apart from the largest group (24% of the sample), which has no
insurance at all, there are no dominant patterns in insurance coverage. The remaining
firms have a wide variety of insurance packages. This highly heterogeneous distri-
bution ensures that our estimates are not driven by specific recurring combinations.
Table 7 presents the estimation results of our main regression model, which ana-
lyzes the effect of insurance coverage on access to credit. It is important to note that
Table 5 Tetrachoric correlation matrix of insurance coverages
Property Third-party Business interr Life insurance
Property 1.0000
Third-party 0.7078 1.0000
Business interr 0.4872 0.4654 1.0000
Life insurance 0.4165 0.5235 0.4298 1.0000
This table reports the tetrachoric correlation matrix among the different types of insurance
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
Table 6 Combinations between insurance coverages
Property Third-party Business interr Life insurance Freq Percent
0 0 0 0 484 24.22
0 1 0 0 363 18.17
1 1 0 1 290 14.52
0 1 0 1 260 13.01
1 1 0 0 244 12.22
1 1 1 1 119 5.96
0 0 0 1 77 3.85
1 1 1 0 45 2.25
0 1 1 1 32 1.60
1 0 0 0 25 1.25
0 1 1 0 21 1.05
1 0 0 1 19 0.95
0 0 1 1 12 0.60
0 0 1 0 4 0.20
1 0 1 1 2 0.10
1 0 1 0 1 0.05
1,998 100.00
This table reports the distribution of combinations of insurance coverage (property insurance, third-
party liabilities, business interruption, and life insurance) across the sample, with corresponding
frequencies and percentages
Table 7 Insurance coverage and access to credit - baseline model
Variables (1) (2) (3) (4)
Access to credit Access to credit Access to credit Access to credit
Insurance 0.0649*** 0.0536*** 0.0517*** 0.053***
(0.0088) (0.0092) (0.0090) (0.0090)
Individual No Yes Yes Yes
Business No No Yes Yes
Industry No No No Yes
Observations 1,998 1,998 1,998 1,998
This table presents the average marginal effects of the relationships between the level of insurance
coverage and access to credit, with control variables progressively included. Robust standard errors in
parentheses. *, **, *** indicate statistical significance at the 10%, 5% and 1% levels, respectively
the table reports the Average Marginal Effects (AMEs) derived from a Probit model,
rather than the raw coefficients. Consistent with previous findings in the literature
(Bank of Italy 2024; Zou and Adams 2008; Guiso and Schivardi 2010), insurance
coverage is positively and highly significantly related to access to credit. To dem-
onstrate the robustness of this relationship and the consistency of the results, we
introduce our control variables progressively across four specifications. Column (1)
reports the baseline model with no control variables. Column (2) incorporates indi-
vidual entrepreneurial characteristics (gender, age, education, financial literacy). Col-
umn (3) further includes business characteristics (digitalization, turnover, employees,
firm age, Covid impact, stability, north, centre). Finally, Column (4) presents the
full model, also controlling for industry dummies. The estimated Average Marginal
Effect of the relationship between Insurance and Access to credit remains positive
E. Lanciano et al.
and highly significant across all specifications. This AME represents the average
change in the probability of obtaining credit when the insurance coverage proxy
increases by one unit on its 0–4 scale. Considering the results of the fully specified
model (Column 4), the AME is 0.053, indicating that, on average, each additional
insurance policy is associated with a 5.3 percentage point increase in the probability
of access to credit. This effect is not only statistically significant but also shows clear
economic relevance, since it represents a meaningful improvement for MSMEs in
credit accessibility.
In order to have a clearer understanding of the economic magnitude of the esti-
mated relationship, we show the adjusted predictions considering each level of the
Insurance variable. Specifically, these predictions represent the estimated average
probability of obtaining credit if all individuals in the sample have exactly 0, 1, 2, 3
or 4 insurance policies, while keeping their other actual characteristics (individual,
business, industry) unchanged. While the AME provides a summary of the average
unit change, the predicted probabilities offer a more detailed view of the economic
magnitude across the full range of the variable (from 0 to 4 policies). These values
are reported in Table 8. As shown in the analysis, when the number of policies is
set to zero, the predicted probability of credit access is 44%. This probability rises
progressively as more policies are held, reaching a maximum of 65% for individuals
with the full set of four insurance covers. Overall, the total change, moving from no
coverage (0 policies) to full coverage (4 policies), results in a 21 percentage points
increase in the likelihood of access to credit (from 0.44 to 0.65). This substantial gap
suggests that insurance coverages serve as a critical signal of creditworthiness or risk
mitigation in the lending process. These are clearly non-negligible numbers in terms
of potential relevance for policymakers, that underscore the importance of insurance
coverage for access to credit.
We report also the estimation results including the Average Marginal Effects
(AMEs) for all control variables. These coefficients are reported in Appendix B,
where we also show regression results considering Bank loan as alternative depen-
dent variable. It is crucial to interpret these effects especially in the context of our
main dependent variable, Access to credit, which is defined as a loan being requested
and accepted during the pandemic period. Thus, a significant effect on this variable
may reflect changes in the firm’s necessity (demand side) or its creditworthiness (sup-
ply side).
Table 8 Insurance coverage Insurance Adjusted prediction
and access to credit - adjusted
0 0.4411***
predictions
(0.0181)
1 0.4951***
This table presents the adjusted (0.0119)
predictions of access to credit 2 0.5492***
considering each level of the (0.0113)
Insurance variable. Robust 3 0.6024***
standard errors in parentheses. (0.0166)
*, **, *** indicate statistical
4 0.6536***
significance at the 10%, 5% and
1% levels, respectively (0.0235)
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
Several variables demonstrate a positive and significant relationship with access
to credit (Column 1). Specifically, financial literacy of the entrepreneur, the level of
digitalization, the number of employees, and the impact of the COVID-19 pandemic
are found to be significant. The positive association with the COVID-19 impact
likely reflects a heightened credit demand from firms due to liquidity needs during
that period.
This evidence suggests potential factors influencing both demand and supply:
firm size (employees), the entrepreneur’s financial competence (financial literacy),
and the adoption of digital practices and services (digitalization). Digitalization, for
instance, may render business activities more flexible and simplify relationships with
financial intermediaries, potentially easing both the request and the acceptance pro-
cesses (Zhang et al. 2023; Deng and Qian 2024).
Conversely, negative and significant effects are observed for firm age, potentially
indicating that younger, less established firms had a greater financing necessity and
thus a higher probability of requesting credit. A negative effect is also found for the
proxy of financial stability, which strongly suggests that financially stable enterprises
had a lower necessity to request external credit, thereby reducing the probability of
observing a joint request and acceptance. Finally, the negative effect observed for the
primary industry suggests that credit demand was likely higher in other sectors dur-
ing the studied period.
No significant results are reported for turnover. This lack of significance may
be explained by the flexible policies implemented for credit provision in the post-
COVID period, including public guarantees on loans, which allowed credit access
even to enterprises experiencing temporary financial difficulties, overriding typical
constraints related to revenue performance.
Moving to our alternative proxy for access to credit (Bank loan), shown in the
same table (Appendix B), in Column 2, the relationship between insurance coverage
and access to credit remains positive and highly significant. It is also economically
significant, with a marginal effect around 9 percentage points. Results are confirmed
also for main control variables, such as financial literacy, digitalization, Covid impact
and financial stability. Obtaining similar results across different specifications pro-
vides strong credibility to the relationship analyzed, demonstrating that the primary
evidence is not dependent exclusively on the precise definition or measurement of
the dependent variable.
Table 9 presents the estimation results of our Instrumental Variable (IV) probit
model to address the endogeneity issues previously discussed. The first-stage regres-
sion (Column 1) regresses our endogenous variable, Insurance, on the selected
instrumental variable, Parents’ experience. We find a positive and highly significant
relationship between the instrumental variable and insurance coverage, satisfying
the relevance criterion. The table reports the F-statistic for the excluded instrument
(29.66) and the Cragg–Donald Wald F-statistic (30.17). Both values are well above
the conventional threshold of 10, and they exceed the critical values established by
Stock and Yogo (2002), allowing us to firmly reject the null hypothesis of weak
instruments, thus confirming the validity of the instrumental variable in terms of
relevance. In the second stage, we re-estimate the structural model using the fitted
value of insurance coverage obtained from the first stage, considering two distinct
E. Lanciano et al.
Table 9 Insurance coverage and access to credit – Addressing endogeneity concerns (IV)
Variables (1) (2) (3)
1st stage: Insurance 2nd stage: Access to credit 2nd stage: Bank loan
Parents’ experience 0.2870***
(0.0527)
Insurance 0.1301* 0.1874**
(0.0761) (0.0746)
F-statistic 1st stage 29.66
Cragg-Donald Wald F statistic 30.17
Observations 1,998 1,998 1,998
Controls Yes Yes Yes
This table presents the results of the regressions employed using the IV approach. The instrument
variables used is Parents’ experience. The average marginal effects are reported. Coefficients of control
variables are available upon request. Robust standard errors in parentheses. *, **, *** indicate statistical
significance at the 10%, 5% and 1% levels, respectively
dependent variables: Access to credit (Column 2) and Bank loan (Column 3). The
estimated coefficients for Insurance remain positive and statistically significant when
considering both dependent variables. The consistency of these findings across two
different measures of credit access strengthens the reliability of our results. The evi-
dence from the IV analysis provides strong support for the hypothesis of a causal
relationship running from enterprises’ insurance coverage to their credit outcomes.
By successfully mitigating endogeneity concerns through the use of a valid instru-
ment, we are able to confirm the fundamental positive association suggested by the
previous estimations.
Robustness checks
First, while recognizing that Probit models are particularly suitable for a dichoto-
mous dependent variable, they inherently rely on specific distributional assumptions
and involve more complex estimation procedures. As a primary robustness check,
we replace our baseline estimation with a Linear Probability Model (LPM). Further-
more, to verify the findings obtained through the Probit model addressing endogene-
ity, we employ a Two-Stage Least Squares (2SLS) regression, which replaces the
Probit framework entirely. We find that the results are substantially consistent across
these alternative specifications.4 This consistency confirms the robustness of the pri-
mary findings, demonstrating that the observed relationship is not dependent on the
specific non-linear functional form assumed by the Probit model nor the specific
estimation technique used to address endogeneity.
Second, we consider the possibility that the individual types of insurance poli-
cies may have a differential impact on credit access. Table 10 presents the results of
four separate regressions, analyzing the relationship between each specific insurance
coverage type and the probability of accessing credit. In each of the four models,
the relationship between the individual insurance coverage and the dependent vari-
4
Results are available from the authors upon request.
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
Table 10 Insurance coverage Variables (1) (2) (3) (4)
and access to credit – focus on
Access to Access to Access to Access to
different insurance policies
credit credit credit credit
Property 0.0638***
insurance (0.0222)
Third-party 0.114***
liability (0.0233)
insurance
Business 0.0660**
interrupt. (0.0326)
insurance
Life insur- 0.113***
ance for the (0.0216)
owner
Male − 0.0117 −0.0129 −0.0125 −0.0137
(0.0235) (0.0235) (0.0236) (0.0235)
Age −0.000324 −0.000298 −0.000289 −0.000242
(0.000375) (0.000370) (0.000373) (0.000372)
Graduated −0.0414 −0.0405 −0.0414 −0.0303
(0.0360) (0.0359) (0.0360) (0.0360)
High school 0.00828 0.0115 0.00805 0.0150
(0.0326) (0.0324) (0.0326) (0.0325)
Financial 0.120*** 0.108*** 0.124*** 0.113***
literacy (0.0202) (0.0203) (0.0201) (0.0201)
Digitaliza- 0.128*** 0.125*** 0.134*** 0.120**
tion (0.0486) (0.0484) (0.0485) (0.0483)
Turnover 0.00101 0.000915 0.000956 0.000955
(0.000640) (0.000641) (0.000638) (0.000628)
Employees 0.00188** 0.00183** 0.00194** 0.00173**
(0.000782) (0.000788) (0.000787) (0.000784)
Firm age −0.0008** −0.0007** −0.00085** −0.00086***
(0.00030) (0.00034) (0.00034) (0.00033)
Covid 0.187*** 0.189*** 0.194*** 0.200***
impact (0.0333) (0.0332) (0.0332) (0.0331)
Stability −0.161*** −0.161*** −0.159*** −0.158***
This table presents the (0.0231) (0.0229) (0.0231) (0.0230)
regression results of the North −0.00813 −0.0131 −0.000803 −0.00993
robustness test consisting
(0.0251) (0.0251) (0.0249) (0.0249)
of re-estimating the model
considering each form of Centre 0.0566* 0.0493* 0.0628** 0.0583**
insurance individually. The (0.0299) (0.0298) (0.0299) (0.0296)
average marginal effects are Primary −0.0780* −0.0738* −0.0769* −0.0880**
reported. Robust standard (0.0408) (0.0408) (0.0411) (0.0408)
errors in parentheses. *, **, *** Secondary 0.0464 0.0596 0.0451 0.0434
indicate statistical significance
(0.0406) (0.0404) (0.0407) (0.0401)
at the 10%, 5% and 1% levels,
respectively Observations 1,998 1,998 1,998 1,998
E. Lanciano et al.
Table 11 Insurance coverage and discouragement – focus on discouragement
Variables (1) (2) (3) (4)
Discouragement Discouragement Discouragement Discouragement
Insurance −0.0247*** −0.0184*** −0.0146*** −0.0143***
(0.00548) (0.00548) (0.00519) (0.00517)
Individual No Yes Yes Yes
Business No No Yes Yes
Industry No No No Yes
Observations 1,998 1,998 1,998 1,998
This table presents the average marginal effects of the relationships between the level of insurance
coverage and discouragement in apply for credit, with control variables progressively included. Robust
standard errors in parentheses. *, **, *** indicate statistical significance at the 10%, 5% and 1% levels,
respectively
able (Access to credit) is found to be positive and highly significant. The effects and
significance levels of the control variables included in these models are also highly
similar to those observed in the previous aggregated specifications. This evidence
confirms that the positive link between risk mitigation and credit outcomes is robust
and does not rely solely on the overall level of insurance coverage, but is maintained
when considering specific policies individually.
Finally, Table 11 displays the regression results examining the relationship between
Insurance and Discouragement. Consistently with previous results, when consider-
ing Discouragement as the dependent variable, the relationship between insurance
coverage and the outcome is negative and highly significant, with an economic mag-
nitude of around 1.4%. We confirm this finding by progressively introducing our
control variables across multiple specifications, noting that the results remain consis-
tent throughout the analysis. This latest evidence is congruent with previous findings
in the literature, demonstrating that insurance coverage is associated with a lower
likelihood of being discouraged in apply for credit and with generally positive credit
outcomes. Additionally, by focusing on the Discouragement variable, we isolate the
demand-side effect: we find that broader insurance coverage significantly reduces the
probability of a firm being discouraged. This complements our main model, which is
estimated only on firms that applied for credit and thus captures the supply-side effect
(the lender’s approval).
Discussion and conclusions
This study investigates the relationship between insurance coverage and credit out-
comes among a sample of Italian MSMEs, a topic generally under-explored in the
finance literature. Our findings provide compelling new evidence regarding the
crucial role of risk mitigation strategies in facilitating credit access. The evidence
robustly demonstrates that owning insurance coverages positively and significantly
affects the probability of accessing credit for small enterprises.
Furthermore, firms with higher levels of insurance coverage appear to be signifi-
cantly less discouraged from applying for financing. We assume these results to be
highly reliable and robust, considering the comprehensive set of control variables
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
included, the use of an Instrumental Variable (IV) approach to mitigate endogeneity
concerns, and the successful confirmation of the findings across multiple robustness
checks (alternative dependent variables, LPM, and the disaggregation of the insur-
ance proxy). These results underscore the importance of insurance for small busi-
nesses, not only as a tool for financial protection and resilience against unexpected
shocks but also as a powerful signaling mechanism to financial service providers.
Insurance coverage, by protecting assets and cash flows from sudden and signifi-
cant losses, signals greater solidity and stability to banks evaluating borrower credit-
worthiness. This mechanism is particularly relevant when considering the economic
uncertainty prevalent during the period of our survey.
The findings of this study yield several relevant policy and practical implications.
Policymakers and financial institutions should formally recognize the role of insur-
ance coverage as a complementary tool to traditional collateral for improving credit
access for small enterprises. Encouraging wider insurance adoption among small
and micro enterprises could simultaneously enhance their financial resilience and
strengthen their credibility in the eyes of lenders. Public interventions such as pre-
mium subsidies, targeted awareness campaigns, or the development of integrated
financial products combining credit and insurance could effectively help reduce credit
constraints for smaller firms. Overall, fostering broader insurance coverage within
the small business sector can contribute to greater financial stability and a more inclu-
sive credit market. Another factor that certainly affects the demand for insurance by
businesses is the level of financial and insurance literacy among entrepreneurs (Ricci
and Santilli 2024; Bongini et al. 2023; Tennyson 2011; Lin et al. 2019). These results
also show how important is to improve policies that pursue these goals, in order to
develop an environment where there is greater knowledge and awareness of financial
products and risk assessment.
The topic is also relevant in the light of the recent developments in the regulatory
framework. Risk mitigation strategies based on insurance are indeed consistent with
the principles set out in the EBA Guidelines on loan origination and monitoring (Eba
2020) and by the Basel Committee credit risk mitigation policies (Bank for Interna-
tional Settlements 2022) which stress the importance of robust risk-mitigation tools
for improving borrower assessment and credit allocation. In this context, adequate
insurance coverage represents a complementary risk-reduction mechanism that can
improve a firm’s risk profile and credit assessments. Our evidence therefore aligns
with these supervisory expectations, suggesting that broader insurance adoption
among small firms could not only strengthen their financial stability but also improve
their credit risk assessments.
Despite the significance and robustness of our findings, this research is subject
to certain limitations, including limited information contained in the survey, the
restricted sample size, the study’s national scope, the reliance on a single year of
observation, and the subjective nature of the survey data. More specifically, the cross-
sectional nature of the dataset implies that causal interpretations should be treated
with caution. Even though we employ an IV approach, which is commonly used to
mitigate endogeneity concerns, reverse causality cannot be completely ruled out. In
addition, the dataset does not provide information on potentially relevant aspects,
such as the precise timing of insurance purchases and loan applications, the length of
E. Lanciano et al.
the relationship with the bank, previous loan performance, or industry credit condi-
tions. Due to data constraints, our insurance measures are based on the existence of
specific policy types rather than on granular details such as premium costs or cover-
age limits (the so-called intensive margin). While this prevents us from constructing
a weighted index based on the magnitude of risk transfer, our empirical results nev-
ertheless suggest that the mere presence of insurance serves as a powerful signal of
risk-management sophistication to lenders. We argue that, for a lender, the aggregate
signal of being widely insured is a primary driver of credit access, which our index
captures effectively.
The dataset also does not include granular data and information at the regional,
province or municipality-level. The only geographical information available is the
macro-area in which the firm is located. Finally, the pandemic context may have
amplified the importance of risk mitigation tools and financing needs; therefore, the
generalization of these findings to non-crisis periods should be interpreted carefully.
For future research, conducting similar studies with larger, multi-country samples
over a multi-year period would be highly beneficial. Moreover, new surveys could be
designed to collect more granular data on specific insurance types, on timing of insur-
ance purchase and loan application, the conditions under which credit was obtained,
and additional instrumental variables to further refine causal inference.
Notwithstanding these constraints, this study makes a novel contribution to the
existing academic literature on access to credit and the financial characteristics of
small enterprises. The robust findings are also relevant for entrepreneurial education
and for improving the understanding of lending practices and dynamics of financial
intermediaries.
Appendix A: Variables questions
Topic Number Question Answer
Insurance
Property insurance QP4_16 Please can you tell me whether you currently use or 1=Yes,
have used this type of financial product or service 0=Otherwise
for your business in the last 24 months (or since
business creation if the business existed for less
than 24 months)?
Third-party liability QP4_17 Please can you tell me whether you currently use or 1=Yes,
insurance have used this type of financial product or service 0=Otherwise
for your business in the last 24 months (or since
business creation if the business existed for less
than 24 months)?
Business interrup- QP4_18 Please can you tell me whether you currently use or 1=Yes,
tion insurance (or have used this type of financial product or service 0=Otherwise
business income for your business in the last 24 months (or since
insurance, or profit business creation if the business existed for less
losses insurance) than 24 months)?
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
Topic Number Question Answer
Life insurance for QP4_19 Please can you tell me whether you currently use or 1=Yes,
the owner have used this type of financial product or service 0=Otherwise
for your business in the last 24 months (or since
business creation if the business existed for less
than 24 months)?
Access to credit
Application QX9 Since the start of the COVID-19 pandemic, have 1=Yes,
you applied for a new loan for the business? 0=Otherwise
Outcome QX10 What was the outcome of the application? 1.The loan
has been fully
obtained, 2.The
loan has been
only partially
obtained
Bank loan
Business loan from QP4_2 Please can you tell me whether you currently use or 1=Yes,
a bank have used this type of financial product or service 0=Otherwise
for your business in the last 24 months (or since
business creation if the business existed for less
than 24 months)?
Discouragement
Application QX9 Since the start of the COVID-19 pandemic, have 1=Yes,
you applied for a new loan for the business? 0=Otherwise
Discouragement QX11 Why didn’t you apply for a loan? 1.Application
reasons procedure was
too complex,
2.Did not think
the loan would
be approved
Parents’ experience
(IV)
Parents’ entrepre- QD7 Could you tell me whether any of your parents 1=Yes – at least
neurial experience currently own a business, or owned a business in one of my par-
the past? ents is or was a
business owner,
0=Otherwise
Appendix B: Insurance coverage and access to credit – alternative
measures
Variables (1) (2)
Access to credit Bank loan
Insurance 0.0531*** 0.0902***
(0.00903) (0.00857)
Male − 0.0145 − 0.00924
(0.0234) (0.0228)
Age − 0.000305 0.000204
(0.000370) (0.000344)
Graduated − 0.0339 − 0.0503
E. Lanciano et al.
Variables (1) (2)
Access to credit Bank loan
(0.0357) (0.0345)
High school 0.0122 0.0179
(0.0322) (0.0312)
Financial literacy 0.103*** 0.0761***
(0.0203) (0.0194)
Digitalization 0.113** 0.118**
(0.0484) (0.0467)
Turnover 0.000881 0.000928
(0.000634) (0.000574)
Employees 0.00170** 0.000600
(0.000780) (0.000660)
Firm age − 0.000800** − 0.000150
(0.000300) (0.000310)
Covid impact 0.189*** 0.115***
(0.0330) (0.0325)
Stability − 0.162*** − 0.119***
(0.0229) (0.0225)
North − 0.0216 0.0211
(0.0251) (0.0240)
Centre 0.0491* 0.0341
(0.0297) (0.0285)
Primary − 0.0873** 0.0869**
(0.0408) (0.0400)
Secondary 0.0510 0.0643
(0.0399) (0.0397)
Observations 1,998 1,998
This table presents the average marginal effects of the relationships between the level of insurance
coverage, access to credit and bank loan with all control variables included. Robust standard errors in
parentheses. *, **, *** indicate statistical significance at the 10%, 5% and 1% levels, respectively
Funding Open access funding provided by Università degli Studi Roma Tre within the CRUI-CARE
Agreement. The research activities reported in this paper are supported by the MUR, Rome Technopole,
PNRR grant M4-C2-Inv. 1.5 CUPF83B22000040006.
Data availability The data that support the findings of this study are available on the Bank of Italy website,
precisely on the webpage of the 2021 survey on financial competence and use of digital tools in MSMEs.
Declarations
Conflict of interest No potential Conflict of interest are reported by the authors.
Ethical approval and consent to participate This study was conducted in accordance with the ethical
guidelines of COPE. All authors agreed with the content and gave explicit consent to submit.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative
Insurance as a stabilizer: SME credit access during the COVID-19 crisis
Commons licence, and indicate if changes were made. The images or other third party material in this
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ses/by/4.0/.
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Edoardo Lanciano is a Post-Doc in Financial Markets and Institutions and Teaching Assistant at the Uni-
versity of Roma Tre, Italy. He gained a PhD in Banking and Finance at the University of Rome Tor Ver-
gata, Italy. His research interests and publications are mainly focused on the topics of financial literacy,
financial education, and financial decision-making. He has published articles in well-known economics
and finance journals such as The European Journal of Finance, the Journal of Economics and Business and
the Journal of Behavioral and Experimental Finance, and with international publishers such as Springer
Palgrave Macmillan.
Daniele Previati is full professor in Financial Markets and Institutions at the Department of Business Stud-
ies of the University of Roma Tre, fellow of SDA Business School, Bocconi University, Milan, past Presi-
dent (2019-2021) of the Italian Association of University Teachers in Economics of Financial Markets and
Intermediaries and Corporate Finance. Nowadays is President of Assonebb, a nonprofit association that
supports the knowledge dissemination and culture development in the fields of banking and finance. His
teaching activities are focused on bank management, strategy and organization in the financial services
industry, fintech, alternative finance and crowdfunding. His main research interests relate to various per-
spectives on bank management: human resources management, intellectual capital, organizational change,
stakeholder management, risk management, communication, alternative finance, sustainability, financial
literacy. He has published widely, in academic and professional journals and books. He also acted as a
consultant for banks and Italian Central Bank on organization design and human resources management.
Ornella Ricci is full professor in Financial Markets and Institutions at the Department of Business Studies
of Roma Tre University, Academic Fellow at the Essex Finance Centre (Essex, U.K.) and at the Centre
for Responsible Banking & Finance (St Andrews, U.K.). Her research interests lie primarily in banking
supervision, risk management, financial literacy and sustainable finance. She has published in primary
finance journals, including the Journal of Financial and Quantitative Analysis, Review of Finance, Journal
of Financial Intermediation, Journal of Corporate Finance, and Journal of Banking and Finance. She is
currently an Associate Editor of the European Journal of Finance and the International Journal of Finance
& Economics.
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