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Funding volatility, securitization and the SMEs’ access to bank credit

Davide Castellani1 and Laura Viganò2

Abstract

One of the claimed benefits of the securitization is that it allows issuing banks to diversify the funding

sources and increase liquidity. We investigate the bank funding diversification effect of securitization

in the post global financial crisis period (2010-2015) in the euro area. This period is characterized by

an increased funding stress for banks due in particular to the sovereign debt crisis and, as a response,

series of non-conventional monetary policies of the ECB. We test two hypotheses. The first

hypothesis is that securitization increases the acceptance rate of SME bank loans in countries that

suffer from a temporary liquidity stress. And, second hypothesis, securitization allows banks to offer

less stringent loan terms and conditions for SMEs. There is some evidence that placed securitization

has contributed to reduce the negative association of change in net wholesale funding on loan terms

and conditions. The analysis over firm’s size hints that placed securitization has offered some

mitigation effect of funding stress on both loan request approval and loan terms and conditions that

somehow favours small and medium enterprises as compared to micro enterprises. On the other hand,

we find some evidence that micro enterprise lending has been mostly supported by retained

securitization, mostly used as collateral for the refinancing operations with the ECB, during funding

stress.

Keywords: securitization; funding risk; SME lending.

1 Corresponding author. Department of Management, Economics and Quantitative Methods, University of Bergamo, Via Dei

Caniana 2, 24127, Bergamo, Italy. E-mail: davide.castellani@unibg.it 2 Department of Management, Economics and Quantitative Methods, University of Bergamo, Via Dei Caniana 2, 24127, Bergamo,

Italy. E-mail: laura.vigano@unibg.it

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1. Introduction

In Europe, equity and debt investment in banks and other financial institutions have been

representing an interesting option until recently. The level of the risk-adjusted return of banks has

historically been attractive but the banking and financial crisis of the last decade, have transformed

funding of banks a high-risk activity. As a matter of fact, the balance sheets of financial institutions

are inherently more opaque than non-financial firms (Morgan, 2002). This adverse selection problem

relates to the difficulty of potential investors and funders to observe the risk and composition of

various banks’ assets. When systemic shocks occur, such as in the recent financial and sovereign

crises, investors and funders become less confident in their ability to distinguish between high-risk

and low-risk banks. Therefore, the exacerbation of asymmetric information problems can lead to a

disruption in the bank funding market (e.g. Stiglitz and Weiss, 1981).

In addition to potential asymmetric information problems, banks are usually highly leveraged

and this entails agency problems. As a consequence, the cost of bank’s debt and equity can be higher

as compared to nonfinancial firms; this can limit the capacity of cash-strapped banks to fund new

loans. This is a severe consequence for banks since, while investing in securities and other financial

products is not specific of banking, banks should have a comparative advantage in selecting and

screening loans because lending is the core activity of traditional banks. Therefore, when adverse

selection and agency problems threatens funding, banks should look for other possible sources of

liquidity: securitization, and loan sales in general, can help overcome the bottleneck (Carlstrom and

Samolyk, 1995).

Theory suggests that the pooling and tranching process of securitization transforms individual

risky and illiquid loans into low-risk and highly liquid securities by reducing information asymmetries

(DeMarzo, 2005). The implied risk ultimately depends on the quality of the pool of loans but, with

adequate pricing, the return could be appealing for investors. One of the claimed benefits of

securitization is that it allows the issuing bank to diversify the funding sources and increase liquidity

without worsening the leverage level and capital requirements (Zweig, 2002; Fabozzi, 2001; Altrock

and Rieso, 1999; Weiss, 1999; Schwarcz, 1997; Bhattacharya and Fabozzi, 1996). With severe

asymmetric information problems, the pecking order theory (Myers and Majluf, 1984) suggests that

banks would prefer to fund their loans through securitization, where cash-flows are generated from

an insulated and diversified pool of assets (Shyam-Sunder and Myers, 1999). As the investors receive

their payment from the segregated credit exposures, banks can obtain terms of external finance that

are not related to the issuer’s own credit rating but to the quality of the underlying assets.

Securitization tranches resemble secured debt in this respect (James, 1988; Benveniste and Berger,

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1987). Kara, Marques-Ibanez, and Ongena (2011) report the positive effects emphasized in the

literature during the pre-crisis period: improved financial stability, better credit risk management,

higher profitability, increase in loan supply. Maddaloni and Peyò (2011) also find that securitization

amplifies the effect of some monetary policy measures (e.g. low short-term interest rates).

From a theoretical liquidity management perspective, through securitization, banks can have

access to new investors and funders that can provide resilience during liquidity crises. In periods of

liquidity stress, the asset-backed market may be relatively more open than the unsecured debt market.

Furthermore, local shocks in the retail deposit market can be mitigated by accessing the asset-backed

market, as well as other non-localized sources of external finance. For instance, Almazan, Martín-

Oliver and Saurina (2015) find that, in the pre-crisis period, Spanish banks were more likely to use

securitization when they were faced with liquidity constraints, given their costlier alternative sources

of funding, and the restricted access to capital markets due to adverse selection. They also observed

the securitization was high in the pecking order of financing choices of small and medium-sized banks

and non-listed banks, which are likely to face more severe adverse selection problems.

Further benefits of securitization are that securitizing banks are more flexible and can better

manage changes in the market conditions associated with monetary policy movements (e.g., Kuttner,

2000; Estrella, 2002; Altunbas et al., 2009; Loutskina, 2011) or high costs of deposits (caused by

local economic shocks). For example, Altunbas et al. (2009) show that, although securitization

considerably reduces the importance of the bank lending channel in Europe, only banks that are

particularly active on the securitization market become insulated from monetary policy. Loutskina

(2011) finds similar results for US banks. When access to external funding is limited, US banks with

higher loan liquidity tend to experience higher growth of less-liquid loans.

Securitization was generally welcome by banks in the last decades but, despite its positive

claimed benefits, also negative effects emerged. Among them, securitization may weaken the

traditional links that banks have with the stabilized depositor clientele; this increases information

asymmetries and, eventually, lending risk. Besides, the reduction in cross-selling opportunities

normally occurring with a stable depositor clientele, may reduce banks profitability. More than this,

securitization is often considered as one of the major causes the recent financial crisis (Financial

Crisis Inquiry Commission, 2011) because of its supposed contribution to the banks’ excessive risk-

taking, due to a weakening of the lending standards (Keys et al. 2012; Bord and Santos, 2013).

Specifically, the negative view of securitization is associated with the expansion of the market of sub-

prime loans in the US. The trust in the securitization market collapsed when securitized loans started

to underperform despite the market price was implicitly reflecting high quality. Acharya et al. (2013)

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show that, when the “with recourse” clause applied, credit risk was not actually transferred to the

investors and, when the market collapsed, the losses were ultimately absorbed by the issuing banks.

As a consequence, the 2007-2009 crisis has been characterized by a freeze of the securitization market

and, more in general, the wholesale funding market.

With specific reference to Europe, another increasing strain in the wholesale funding market

was caused by the sovereign debt crisis in 2010-2012. The negative impact was mainly on the cross-

country wholesale market. For example, the borrowing of banks in the stressed countries from banks

in the non-stressed countries almost disappeared in the second half of 2011 because of an increasing

counterparty risk (Frutos et al., 2016). At the same time, US money market funds dramatically

reduced their exposure to banks in the peripheral countries, and they also reduced their purchases of

short-term funding instruments issued by core euro area banks (van Rixtel and Gasperini, 2013).

However, the drop in international and cross-country lending was partially compensated by an

increase in domestic interbank transactions. These shocks led to a fragmentation of the European

banking system. The relationship between sovereign and bank risk is a critical characteristic of the

European sovereign debt crisis (e.g. Acharya et al., 2014). Sizeable holdings of GIIPS3 sovereign

debt by European banks impaired their ability to borrow from the wholesale market. Increasing

potential losses from government bond holdings can indeed increase bank’s funding costs. Second,

an increase in sovereign risk decreases the eligibility of government bonds as collateral in the secure

wholesale funding market. Moreover, a change in the haircuts of government bonds can imply a

change also in the haircuts applied to other securities, further reducing access to the secured market

and increasing the cost of funding (Drechsler et al., 2016). As for what concerns the securitization

market, from mid-2011 to the third quarter of 2012, the share of retained securities increased sharply,

also because of the low securitization activity since the beginning of the financial crisis (AFME,

2012; see also chapter 4).

The sovereign debt crisis had a negative impact also on the retail funding of banks. The

peripheral countries went through an accelerating outflow of retail deposits while core countries

registered deposit inflows (e.g. de Haan and van den End, 2013; de Haan et al., 2015). This divergence

accelerated in the first half of 2012, with outflows in Greece, Portugal and Spain and inflows in

Germany and in the Netherlands (van Rixtel and Gasperini, 2013). Banks suffered a “quiet” banks

run or “bank walk” (Portes, 2012).

3 Greece, Italy, Ireland, Portugal and Spain.

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During the European financial and sovereign debt crises, interbank funding was mostly

substituted by central bank funding. The ECB policies mitigated the impact of wholesale funding

shocks on bank lending (ECB- Economic Bulletin 1/2016). In October 2008, after the collapse of

Lehman Brothers, the Eurosystem switched from the variable to a fixed-rate full allotment. The

monetary policy rates had an evident impact on the overnight market. During the sovereign crisis, the

two three-year LTROs (long term refinancing operations) in December 2011 and February 2012

attenuated the interbank market stress in, at least, the secured market. The stress in the unsecured

market (proxied as, for example, the spread between three-moth EURIBOR and the EONIA rate)

decreased only after the ECB started its Outright Monetary Transactions (OMT) program in

September 2012. In terms of impact, de Haan et al. (2015) argue that the central bank liquidity supply

mitigated the banks’ balance-sheet constraints and supported private credit growth. On the contrary,

Popov and van Horen (2015) do not find evidence that the LTROs from December 2011 arrested the

decline in syndicated lending. Other compensations are represented by external funds, as well as

recent capitalization processes, in softening European banks’ liquidity problems (ECB, 2016).

The global crisis revealed critical condition not only in the financial markets, but also in the

real economy. In fact, it was not only a financial but also a real crisis, showing sharp decrease in

competitiveness of companies all along the size range which implied a credit crunch affecting in

particular the small and medium enterprise sector. Therefore, lending was curtailed as a consequence

of both the bank liquidity strains and the limited capacity of companies to react to the crisis, with a

generalized increase in credit risk of new loans. It is however undeniable that banks, on their side,

became more conservative in order not to worsen the quality of loan portfolios already subject to

degradation and as a consequence of liquidity shortage.

This paper aims to analyse, in general, the relationship between bank funding and SME

lending. In particular, we investigate whether securitization in Europe has been supportive to the SME

lending activity during episodes of bank’s funding stress in the post global financial crisis period

(from March 2010 to March 2016). More in general, we study the role of securitization as a funding

diversification source. At the same time, in the analysed period, the borrowing costs for SMEs have

remained much higher than for larger enterprises (EIF, 2016).

We test two hypotheses. The first hypothesis is that, in case of a temporary liquidity stress,

securitization increases the probability that bank loan or credit line requests by SMEs are approved.

And, as second hypothesis, securitization allow banks to offer improved terms and conditions on

SME loans. The paper aims at contributing also to the discussion on the role of securitization in the

light of possible new policies and regulations.

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The remaining part of the paper is organized as follows: sections 2 and 3 review the literature

on the impact of funding risk and securitization on lending, with a focus on SMEs; section 4 discusses

the recent trends in the banks’ funding and securitization activity in Europe; section 5 presents the

data; section 6 illustrates the empirical approach; section 7 presented the results; and section 8

concludes.

2. Funding risk and lending

The financial crisis has renewed interest in studying banks’ liability structure, particularly in

relation to their lending during periods of liquidity stress. The theoretical and empirical debates are

focused on the distinction between retail and wholesale funding. Freixas and Holthausen (2005) study

interbank markets in an international context and find that in an economy with unsecured markets

peer monitoring plays a key role in lending decisions. This leads to an efficient outcome as money is

allocated to the most efficient projects. While wholesale market funding offers more flexibility for

banks, it also increases their vulnerability to market-wide liquidity shocks. Providers of short-term

wholesale funding may have little incentive to monitor banks and instead may simply withdraw their

funds at the first negative market signal regarding the client bank’s financial health, thus triggering

immediate funding tensions (e.g. Bologna, 2011; Huang and Ratnovski, 2011). When information

asymmetry about counterparty risk becomes indeed large, interbank market trade can break down and

banks may hoard liquidity to self-insure against liquidity shocks (Heider et al., 2015). It may be a

rational strategy for banks to resort to wholesale funding in the face of economic uncertainty and

volatile demand for loans (Dinger and Craig, 2014). However, the advantages of such funding

flexibility during normal times are overshadowed in a crisis by the prohibitively high adjustment costs

of short-term wholesale funding in particular.

Funding liquidity shocks can activate the so-called liquidity channel of financial transmission

through which funding liquidity shocks are propagated to bank lending and the real economy. This

was, for example, the case during the recent 2007-08 financial crisis (BCBS, 2011). An increasing

body of empirical studies suggests that banks that relied more heavily on deposit funding during the

financial crisis fared better than those more dependent on other sources. Retail deposits are in general

a more stable source of funding, often benefitting from deposit guarantee schemes (see Gatev and

Strahan (2006) for explicit government guarantees in the US). Berlin and Mester (1999) argue that

core deposits allows banks to offer borrowers insurance against credit shocks. Households’ savings

indeed tend to move into bank deposits during times of economic systemic shocks (Gatev and

Strahan, 2006; Pennacchi, 2006). So when market liquidity dries up, the supply of bank loans increase

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(because funding from retail deposits increase) whereas demand for bank loans increases (firms use

their credit lines and increase demand for loans) because they have less access to the equity and

capital markets.

In fact, deposit-funded banks continued to lend during the crisis relative to other banks,

showed better overall performance and were less risky. Ivashina and Scharfstein (2010) find that a

greater volatility of deposits and more frequent and greater draws on committed credit lines prompted

US banks to cut their syndicated lending by more than banks with more retail deposit financing.

Cornett et al. (2011) report that US banks with stable funding sources (core deposits and more equity

capital) were better able to continue lending during the crisis. Goetz and Gozzi (2010) discover that

wholesale funding had a negative impact also on the lending activity of small US banks. The credit

crunch effect was stronger for bank-dependent firms, such as small enterprises. In the case of

mortgage lending, Dagher and Kazimov (2015) find that retail-funded banks increased their rejection

rates on jumbo loan applications (less liquid mortgage loans) less at the height of the liquidity crisis.

For the authors, this shows that wholesale funded banks cut their lending to preserve liquidity, thus,

contributing to disproportionally to the contraction of credit supply during the crisis.

In Europe, De Haan and van den End (2013) find that Dutch banks reacted to the liquidity

shock caused by the financial crisis by reducing lending, especially wholesale lending, hoarding

liquidity in the form of liquid bonds and central bank reserves, and making fire sales of securities,

especially stocks. Similarly, de Haan et al. (2015) describe that shocks in the securities and interbank

markets during the financial and sovereign debt crises forced European banks to adjust loan rates and

credit supply. Banks adjusted mainly their corporate lending, since, in general, the former has a

shorter maturity and a higher risk profile than the household lending. Several other studies also

focused on the impact of the sovereign debt crisis on the extensive and intensive margins of the

lending activity of European banks (e.g. Ivashina et al., 2012; Bofondi et al., 2013; Correa et al.,

2014; De Marco, 2014; Popov and van Horen, 2015; Adelino and Ferreira, 2016). In general, these

studies suggest that banks in peripheral countries and banks with a high holdings of peripheral

government bonds reduced lending in order to de-risk their balance sheet. It should be noted that in

peripheral countries also suffered a freeze in the interbank markets and a slow flight of retail deposits

that further prompted them to cut lending. The extraordinary liquidity injections of the Eurosystem

have though mitigated the funding liquidity needs of cash-stripped banks.

3. Securitization and SME lending

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There is a vast literature that stresses how securitization contribute to support the lending

supply of banks (e.g., Altunbas et al., 2009; Loutskina and Strahan, 2009; Mian and Sufi, 2009; Keys

et al., 2010; Demyanyk and Van Hemert, 2011; Loutskina, 2011; Shivdasani and Wang, 2011),

especially previous to the global financial crisis.

The increased funding capacity and the risk transfer benefits offered by the access to the

securitization market can have a positive impact on the intensive and extensive margins of lending,

and on the terms and conditions offered to the borrowers, in particular on the interest rate.

Securitization can sustain supply of loans to SMEs through different channels. First, through

securitization, banks can diversify their lending activity and credit risk by selling, for example,

residential mortgages and extending new loans to SMEs (e.g., Demsetz, 1999). The benefits from

portfolio diversification can also be passed on to the borrowers in terms of lower interest rates

(Nadauld and Weisbach, 2012). A lower cost of loans can in turn spur further demand from firms that

is accompanied by an increased inflows of liquidity generated by securitization. Moreover, banks

with a comparative advantage in SME lending may issue ABSs to fund their core lending activity

(e.g., Phillips, 1996; Affinito and Tagliaferri, 2010). Second, if regulatory risk transfer requirements

are met, securitization can alleviate regulatory capital constraints and allow banks to issue more loans

(e.g. Norden et al., 2014) at lower interest rates because of the risk-transfer mechanism. Finally, banks

can use asset-backed securities as collateral in wholesale repurchase agreements. This extra liquidity

can then be channelled to SME lending.

On the contrary, according to Baradwaj et al. (2013), securitization can also discourage SME

lending. SME loans are indeed usually more risky and less liquid than asset-backed loans to

households, such as mortgages. Baradwaj et al. (2013) describes how this may entail a potential

substitution effect whereby “…banks actually take advantage of flexible financing and alter their

lending strategy to shift away from one lending category (small business lending) to another (real-

estate loans).” (p. 3).

From the demand perspective, easier access to loans is not a generalized effect. Jiménez et al.

(2010) suggest that securitization can have a negligible influence on the supply of credit if firms have

access to multiple sources of funding. They find evidence from Spain during the securitization boom.

It follows that the impact on the access to bank credit can be better estimated by considering the

aggregate securitization activity of the banks that serve the specific SME.

In terms of the types of banks involved, several empirical studies argue that securitization can

have greater impact on the supply of credit of large banks as compared to small banks. Baradwaj,

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Dewally and Shao (2013), in fact, find that the securitization activity of a sample of U.S. banks has a

positive impact on their lending activity but the effect is confined to large banks. In a similar way,

Louskina (2011) in the U.S. and Carbo-Valverde et al. (2015) in Spain discover that firms borrowing

from small banks benefit from securitization to a smaller extent than firms borrowing from large

banks. These results can be explained by looking at the features of small banks. First, it seems that

small banks are more penalized in their access to the securitization market than large banks (e.g.

Carbo-Valverde et al., 2011) and can resort to securitization to fund their operations much less than

large banks. Furthermore, small banks with a competitive advantage in SME lending, may issue ABSs

for reasons not related to their lending specialization (Baradwaj, Dewally and Shao, 2013).

From the bank’s funding and liquidity perspective, studies that considered periods previous

to the global financial crisis demonstrate that securitization makes the supply of bank credit less

exposed to funding shocks (Loutskina and Strahan, 2009; Loutskina, 2011). Dagher and Kazimov

(2015) confirm that banks more based on wholesale funding curtailed their supply of credit

significantly more during the crisis while, before, rejection rates where not influenced by the type of

source. This means that, in normal conditions, securitization is not contributing to credit crunch but

that banking and financial crises affect the securitization-reliant banks’ ability to obtain funding on

the securitization market and negatively impact the loan supply, especially to SMEs. Irani (2012), for

example, finds that listed U.S. banks that rely more on securitization for funding were more reluctant

to provide lines of credit during the financial crisis. As for what concerns European countries, Carbo-

Valverde et al. (2015) find that the securitization activity of Spanish banks had a negative impact on

SME loans as compared to alternatively asset-backed securities, i.e. covered bonds. In Italy,

Bonaccorsi di Patti and Sette (2015) use data on individual bank-borrower relationships and find that

banks that were more exposed to the freeze in the securitization market tightened lending supply both

at the intensive and extensive margins. Bonner et al. (2016) document that an increase in the issuance

of ABSs by European banks is positively associated with the bank loan supply before the financial

crisis but not afterwards. They also find that retained securities even have a negative impact on banks’

credit supply after the onset of the crisis. However, the negative impact seems to be driven by

mortgage-backed securities.

Besides effects on the quantity of loans supply, securitization may affect lending standards

and conditions (Greenbaum and Thakor, 1987 and Gorton and Pennacchi, 1995). Some studies

explain the weakening of the lending standards and the offer of better terms and conditions by the

reduction in the cost of capital deriving from securitization (Nadauld and Wiesbach, 2012). However,

on this issue, Almazan, Martín-Oliver and Saurina (2015) did not find evidence of a link between

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securitization and regulatory capital arbitrage for Spanish banks before the crisis. With specific

reference to lending conditions, Shivdasani and Wang (2011) find that large US issuers of

collateralized debt obligations (CDO) offered more favourable lending conditions. On the quality of

loans, however, the same authors did not find a worsening of lending risk while Le et al. (2016) find

that lending risk was pushed by the securitization process only before the crisis. The distinction

between pre and post-crisis periods is relevant as the generalized low quality of bank portfolio

emerged during the financial crisis increased awareness on lending risk. More generally, the

relationship between the loans quality and amount, spreads, risk taking and securitization is more

ambiguous than what the common opinion expressed right after the start of the financial crisis. For

example, Kara, Marques-Ibanez, and Ongena (2011) report that the economic cycle conditions are

far more important than the securitization process in explaining looser loan conditions. Lending

conditions or credit rationing also depend on factors such as the organizational structure (Canalez and

Nanda, 2012) or the type of the type of bank-client relationship (Cennia et al., 2014).

This study is mainly concerned with the contribution of securitization to the bank’s funding

diversification and how the diversification-induced advantage supports SME lending. Given the

previous discussion, in our paper, we use the aggregate securitization at the country-level. As for

what concerns this study, controlling for bank size is less of interest because we consider the

aggregate data of the banking system in each country.

4. Trends in securitazion and funding

The European ABS market is heterogeneous: lending criteria are different in different

countries and banking institutions; and default law is specific to national jurisdiction in the euro area.

The latter introduce uncertainness and inefficiencies in the ABS market that could create arbitrage

opportunities. The ABS market peaked in Europe before the crisis, with a total of € 819 billion in

new ABS issuance in 2008. By 2015, total new issuance was only € 214 billion (SIFMA, 2016).

Demand for these assets dropped after 2008 because of the deterioration in the rating of the underlying

collateral, leading to a major fall in the price of ABS products. Regulation in the post-crisis contest

has been relatively unfavourable to these types of instruments, with heightened capital requirements

now necessary for the issuance of new asset-backed securities. Moreover, insurance funds,

traditionally large buyers of asset-backed securities, were also negatively affected by the introduction

of more restrictive regulation that limits their ABS purchases (Altomonte and Bussoli, 2014). Finally,

the freeze in European inter-bank lending reduced demand for these assets as collaterals for

repurchase (repo) agreements. In particular, after the start of the financial crisis in 2007-2008, the

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ECB progressively tightened the rating and structural requirements for ABS it would accept as repo

collateral, compared to covered bonds, until 2013, where a level playing field was introduced

(Altomonte and Bussoli, 2014). Hence, after the 2008, the amount of eligible ABS declined by 38

percent while covered bonds increased by 14 percent; until in mid-2012 covered bonds overtook ABS

as delivered repo collateral for the first time since 2007.

The composition of ABS market remarkably changed over time in Europe. Figure 1 reports

the monthly data from January 2007 to December 2015 and distinguishes between placed and retained

securitization. While overall issuances decreased, European banks continued to securitize assets also

after the crisis. It clearly emerges, however, that the placed securitization was predominant before the

financial crisis while its weight dramatically decreased after 2008. In fact, in 2006, almost all new

issuances were placed with investors, while in 2009 banks retained more than 90% of newly issued

ABS. Instead of selling them in the market, banks used ABS as collateral for central bank funding.

Peaks in securitization over the period have been characterized by large amounts of retained ABS,

boosted by LTROs of 2011-2012 and the TLTRO launched by the European Central Bank (ECB) in

2014.

[FIGURE 1 HERE]

It is therefore important to distinguish retained and placed ABS when analysing the impact of

securitization on credit supply. Strategies followed by the different banks in different countries also

depended on their internal situation. Table 2 explores the trends in ABS from 2010 to 2016 and

distinguishes between those countries in Europe which undergone rather severe problems in the

banking systems due to the sovereign crisis, and the remaining European countries. Although the first

group, which includes Greece, Italy, Ireland, Portugal and Spain (GIIPS), is heterogeneous and not

in all countries the crisis has been equally severe, loan-to-deposit shortfall has been remarkably higher

in GIIPS than in non-GIIPS countries (Table 3). Liquidity shortage, and flight of deposits, following

the sovereign debt crisis in GIIPS, explains why, in the six years under observation, securitization

was high in these countries, and in some periods even higher than in the non GIIPS countries. More

important, almost all securitization issuances were retained, especially in the most critical sovereign

debt period.

[FIGURE 2 HERE]

[FIGURE 3 HERE]

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5. Data

In this study we combine microdata on SMEs with macro data, i.e. at country-level, on the

banking and financial system. Data on the enterprises are collected in the frame of the Survey on the

Access to Finance of Enterprises (SAFE) of the European Commission and the European Central

Bank4. The surveys are conducted at the end of two different semesters (semi-annual frequency):

October-March and April-September. Data consist of qualitative information on the access to finance

for firms in European countries, and mainly in the euro area. Only some countries are included in all

data collection waves. In order to have a balanced panel over countries, first, we consider only data

from euro countries that have been included in all waves. We finally select those countries for which

securitization data are available. The final set of countries is: Belgium, Germany, Spain, France,

Greece, Ireland, Italy, the Netherlands, and Portugal. The first wave of the survey was held in June-

July 2009. In particular, our data refers to SAFE from wave 3 to wave 14 (From March 2010 to March

2016). The SAFE sample is stratified by country, firm size class, and economic activity. The number

of firms in each of these strata is adjusted to increase the accuracy of the survey across activities and

size classes5. As regards the stratification by firm size class, the sample was constructed to offer

comparable precision for micro (1-9 employees), small (10-49 employees) and medium-sized (50-

249 employees) enterprises, taking into account total employment in these size classes. In addition, a

sample of large enterprises (250 or more employees) was included. We though exclude large

enterprises from our sample as we are interested only in SMEs. In SAFE, the statistical stratification

is based on economic activities at one-digit level of the European NACE classification and the

enterprises are divided into four major economic sectors: industry, construction, trade and other

services. The interviewee in each company is a top-level executive (general manager, financial

director or chief accountant).

The final sample is made up of a total of 87,511 firm-level observations. Considering only

those firms that applied for a loan or a credit line, the total number of observations reduces to 28,726.

However, due to the presence of missing values the number of valid observations shrink according to

the type of information that is employed in the empirical analysis.

Enterprises in SAFE are anonymous. Moreover, for enterprises that requested or used bank

financial services SAFE does not provide any question about identity of the financial service

providers. In order to circumvent this problem, we use country-level data of the banking system.

4 https://www.ecb.europa.eu/stats/money/surveys/sme/html/index.en.html 5 For example, the proportion of small firms selected for the sample was higher than their economic weight. The results were than

adjusted using the appropriate weights.

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Information on securitization activity come from the AFME /esf Securitisation Data Reports. These

reports offer quarterly data (January-March, April-June, July-September, October-December) on

securitisation issuance in Europe, both by country of collateral and by type of collateral. Aggregate

data on the banking system in each country are retrieved from the ECB Statistical Data Warehouse.

We also use Eurostat database to obtain data on the performance of the economic system.

6. Empirical approach and variables

In order to investigate the two research questions about the role of securitization in supporting

SME bank lending, we develop and estimate two econometric models. The issue raises the traditional

identification challenge of separating between demand and supply effect. This is because little doubt

exists that the crisis had affected not only the overall market-wide liquidity but also the returns on

investment, thereby affecting both the supply and demand for loans. The use of individual loan-level

data allows us to formulate an empirical specification that address the typical challenges. The decline

of new lending could have reflected a drop in demand as firms scaled back expansion plans during

recession.

In both models, we control for the sample-selection bias that stems from the fact that we can

observe only those firms that applied for a bank loan or a credit line. We then estimate in the first

stage a selection equation following the approach in traditional Heckman models. The explanatory

variables of the selection equation are qualitative characteristics of the firms: number of employees,

turnover, if the firm is part of a group, age, if the firm used alternative sources of finance (cash, trade

credit, other loans, securities, and capital), and trends in costs, profit and turnover. We also control

for country and time fixed effects.

As for the outcome equations, the first model is meant to study the determinants of the

probability (probit model) that the SME bank loan (or credit line) request is approved. The dependent

variable is a dummy that takes the value of 1 if the firm affirms that the loan/credit line request has

been approved, and 0 otherwise (rejected or self-rejected). The main explanatory variables of interest

proxy for the level of securitization activity and the funding liquidity stress. With regards to

securitization, we consider the ratio between the total placed asset-backed securities in t and the total

loan portfolio in t-1 (Placed securitization) and the ratio between total retained asset-backed securities

in t and loan portfolio in t-1 (Retained securitization). We include the retained securitization because

in the period analysed most of issuance has been retained by the originators. On the base of the review

of the empirical literature, we use three different proxies of the banks' funding time-variant

14

characteristics. The first funding variable is the difference between loans and deposits to households

and non-financial enterprises in t-1 divided by total assets in t-2 (Loan-to-deposit shortfall). This a

proxy of the funding gap in the traditional intermediation activity. The second funding variable is the

spread between the three-month Euribor and the Eonia rate in t-1 (Euribor-Eonia spread). This a

proxy of the counterparty risk perceived in the unsecured interbank funding market. An increasing

spread can imply a higher funding cost but also an increasing rollover risk. The last funding variable

is the change in the net difference between the sum of interbank deposits and debt securities issued,

and interbank loans, between in t-2 and t-1, divided by total assets in t-2 (Δ net wholesale borrowing).

More in general, the data available do not allow to separate core deposits from non-core deposits. In

our analysis, we improperly refer to deposits from households and non-financial intermediaries as

"retail" and to the other forms of funding as "wholesale". The base specification of “loan/credit line

request approved” model is as follows:

𝑃𝑟(𝑎𝑝𝑝𝑟𝑜𝑣𝑒𝑑 = 1)𝑖,𝑐,𝑡 = 𝛼𝑖,𝑐,𝑡 + 𝛽1𝑃𝑙𝑎𝑐𝑒𝑑 𝑠𝑒𝑐𝑢𝑟𝑖𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑐,𝑡−1 + 𝛽2𝑅𝑒𝑡𝑎𝑖𝑛𝑒𝑑 𝑠𝑒𝑐𝑢𝑟𝑖𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑐,𝑡−1

+𝛽3𝐿𝑜𝑎𝑛 − 𝑡𝑜 − 𝑑𝑒𝑝𝑜𝑠𝑖𝑡 𝑠ℎ𝑜𝑟𝑡𝑓𝑎𝑙𝑙𝑐,𝑡−1 + (𝛽4𝐸𝑢𝑟𝑖𝑏𝑜𝑟 − 𝐸𝑜𝑛𝑖𝑎 𝑠𝑝𝑟𝑒𝑎𝑑𝑐,𝑡−1)

+𝛽5∆ 𝑛𝑒𝑡 𝑤ℎ𝑜𝑙𝑠𝑎𝑙𝑒 𝑓𝑢𝑛𝑑𝑖𝑛𝑔𝑐,𝑡−1 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝑑𝑢𝑚𝑚𝑖𝑒𝑠 + (𝑊𝑎𝑣𝑒 𝑑𝑢𝑚𝑚𝑖𝑒𝑠) + 𝜀𝑖,𝑐,𝑡 (1)

where Controls are time-varying characteristics of firms and national banking systems; and 𝜀𝑖,𝑐,𝑡 are

i.i.d. errors. Firm’s characteristics are proxied by qualitative variables that include firm’s size in terms

of employees and turnover (micro, small or medium), firm’s economic sector (industry, construction,

trade, or other services), firm’s age and profit dynamics (increased, decreased or unchanged). Banking

system’s characteristics consist of a measure of capitalization, tier 1 ratio, a measure of loan portfolio

quality, non-performing loans to total assets, and a measure of profitability, cost-to-income ratio. We

also control for the business cycle by including the annualized rate of growth in the wave period of

the GDP per capita. All the specifications allow for country and time fixed effects, except for

specifications that include the Euribor-Eonia spread where we control only for country fixed-effect

because such variable does not vary across countries.

As we are interested in the funding effect of securitization, we introduce in the base

specification (1) interaction terms of the securitization variables with the funding variables. In

particular, in order to avoid multicollinearity problems, we estimate three specifications where both

placed and retained securitization variables are interacted with just one the funding variables. Where

we consider the interaction of the securitization variables with the Euribor-Eonia spread, we are

forced to exclude the main effects of securitization because of a further problem of multicollinarity.

15

The second model considers as dependent variable a proxy of the change in the terms and

conditions. The dependent variable is built though a multiple correspondence analysis where the

inputs are binary variables that take the value of 1 if the specific term or condition has improved, and

0 otherwise. In the SAFE, the questions about the improvement of terms and conditions are five and

can be classified as follows: interest rate; fees; loan size; maturity; collateral; and other terms. The

index of the improvement in terms and conditions was tested against a linear frequency index. The

correlation is greater than 95%. This suggests that terms and conditions tend to co-improve linearly.

The base specification of “Improvement of terms and conditions” model is as follows:

𝐼𝑛𝑑𝑒𝑥(𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 𝑇𝑠&𝐶𝑠)𝑖,𝑐,𝑡 = 𝛼𝑖,𝑐,𝑡 + 𝛽1𝑃𝑙𝑎𝑐𝑒𝑑 𝑠𝑒𝑐𝑢𝑟𝑖𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑐,𝑡−1

+𝛽2𝑅𝑒𝑡𝑎𝑖𝑛𝑒𝑑 𝑠𝑒𝑐𝑢𝑟𝑖𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑐,𝑡−1 + 𝛽3𝐿𝑜𝑎𝑛 − 𝑡𝑜 − 𝑑𝑒𝑝𝑜𝑠𝑖𝑡 𝑠ℎ𝑜𝑟𝑡𝑓𝑎𝑙𝑙𝑐,𝑡−1

+(𝛽4𝐸𝑢𝑟𝑖𝑏𝑜𝑟 − 𝐸𝑜𝑛𝑖𝑎 𝑠𝑝𝑟𝑒𝑎𝑑𝑐,𝑡−1) + 𝛽5∆ 𝑛𝑒𝑡 𝑤ℎ𝑜𝑙𝑠𝑎𝑙𝑒 𝑓𝑢𝑛𝑑𝑖𝑛𝑔𝑐,𝑡−1 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠

+𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝑑𝑢𝑚𝑚𝑖𝑒𝑠 + (𝑊𝑎𝑣𝑒 𝑑𝑢𝑚𝑚𝑖𝑒𝑠) + 𝜀𝑖,𝑐,𝑡 (2)

As in the approach for the “loan/credit line request approved”, we estimate three further specifications

of where we introduce in the base specification (2) interaction terms of the securitization variables

with the funding variables.

Table (1) reports the summary statistics of the variables employed in the outcome models

[TABLE 1 HERE]

7. Main results and robustness analysis

7.1 Loan request approval

The results of the “loan request approval” model are reported in Table (2). The estimates of the self-

selection equation (i.e. first stage of the econometric model) are consistent with the expectations6.

First, the greater the size of the firm, the higher the probability the firm applies for a bank loan.

Second, enterprises that are not part of a group and so are financially autonomous have a higher

probability to apply for a loan. Third, younger enterprises (i.e. less than two years old) are much more

bank-dependent than older enterprises (i.e. more than two years old). Finally, enterprises that have

other sources of finance, such as for example own cash, trade credit or capital inflows, enterprises

6 These estimates are not reported in the tables for the sake of space. They can however be provided upon request.

16

with positive trends in costs and profits, and enterprises with a decreasing turnover, are less probable

to apply for a bank loan.

With regards to the firm’s characteristics that affect the probability that the loan request is approved,

the results are also in this case in line with the expectations. Firm’s size, both in terms of number of

employees and total amount of turnover, as well as firm’s age have a positive impact on the

probability of loan acceptance. On the other hand, if the enterprise belongs to the construction sector

(a sector that was particularly affected by the global financial crisis) and shows a decreasing profit

trend, the probability that the loan request is approved decreases.

As for what concerns our main variables of interest, the results suggest that only the ratio of retained

asset-backed securities and the loan-to-deposit shortfall variable have a straightforward statistically

significant association with the probability of loan approval. On the one hand, retained securitization

is positively associated with the probability that the loan request is approved by the bank. In terms of

economic importance, a one-standard-deviation increase in the ratio of the retained asset-backed

securities is associated with a 3.18% increase in the probability of loan approval. During the period

considered in the analysis, banks used most of the retained securities as collateral in the refinancing

operations with the ECB. Our analysis put forward that these extra-funding has been channelled to

some extent to SME lending. On the other hand, the gap between deposits from families and non-

financial enterprises and loans to families and non-financial enterprises (as proxied by the loan-to-

deposit shortfall variable) has a negative relationship with the loan request approval. A one-standard-

deviation increase in the loan-to-deposit shortfall is associated with a 14.87% decrease in the

probability of loan approval. There is also some evidence (in column (4)) that our measure of stress

in the unsecured interbank funding market, i.e. Euribor-Eonia spread, has a negative and statistically

significant association with the loan request approval. This relationship is though not confirmed in

the base specification (column (2)).

Despite the statistical significance of at least one of the two variables of the securitization activity,

the estimates of the interactions between the two ratios of the issuance of asset-backed securities

(placed and retained) with our proxies of funding stress suggest that securitization, in general, has not

provided a funding buffer against shocks that would support SME lending. Whereas this finding is

consistent with the almost null effect of our measures of stress in wholesale funding (change in net

wholesale funding and Euribor-Eonia spread), it is however relevant for the loan-to-deposit variable.

In this latter case, the interaction effect is not only non-statically significant but also negative.

[TABLE 2 HERE]

17

7.2 Terms and conditions

Firm’s characteristics have an impact on the loan terms and conditions that is similar to the

impact on loan request approval, with few exceptions. Age, in particular, has negative relationship

with the improvement in terms and conditions. We can assume that younger firms are more in need

of long-term investments in buildings and equipment that older firms. It follows that young firms are

more likely to apply for asset-based longer-term loans that in turn entail a greater loan size and a

lower interest rate than short-term loans.

On the contrary, the results for our variables of interest show that, on average, funding shocks

play a greater role in driving bank’s setting of terms and conditions as compared to loan request

approval (results are showed in Table (3)). Loan-to-deposit shortfall, change in the net wholesale

funding and the Euribor-Eonia spread are all statistically significant and negatively related to our

proxy of improvement in loan terms and conditions. In order to have an understanding of the

economic impact, a one-standard-deviation increase in the funding proxies is associated with a change

in the dependent variable that is approximately between -18% and -47% of the sample mean

(considering the estimates of column (2)). The impact on loan terms and conditions can be discussed

from different perspectives. For example, an increase in the loan-to-deposit shortfall forces banks to

raise interest rates on deposits. During the financial crisis and the sovereign debt crisis, evidence from

some European countries confirm that banks raised interest rates on retail funding to retain depositors.

Similarly, an increase in wholesale funding and in the Euribor-Eonia spread imply an increase in the

average cost of funding. The increasing cost of funding is then passed on to the borrowers. Moreover,

a decrease of deposits or an increase in wholesale funding would reduce the stability of funding

sources. In order to avoid an increase in the exposure to the interest rate risk, banks may tend to offer

shorter term loans to the borrowers.

As compared to funding proxies, the securitization variables are not statistically significant.

However, the estimates of the interaction variables hint that countries where banks have been able to

place asset-backed securities in the market show a less negative impact of wholesale funding stress

on the terms and conditions of loans to SMEs. The economic impact of this mitigation effect though

seems more relevant for changes in the net wholesale funding than changes in the spread of the

unsecured interbank funding market. In general, it can be argued that when banks are able to place

asset-backed securities in the market they can obtain a source of funding that is on average of longer

term than more traditional wholesale funding. This argument is supported by the evidence that

mortgage-backed securities have still represented a great share of total placed asset-backed securities

18

in the analysed period. For example, residential mortgage-backed securities only have represented

about 45% of the total placed asset-backed securities between March 2010 and December 2015.

Moreover, placing asset-backed securities (secured funding) can also be more favourable when the

unsecured market is under stress. Apart from funding motivations, it is relevant also to consider the

risk transfer benefits if placed assets-backed securities are true-sale securitization. In a period of

deleveraging process, true sale-securitization can alleviate more the pressure on the bank’s capital

requirements when banks increase their liabilities by raising funds in the wholesale market.

[TABLE 3 HERE]

7.3 Firms’ size

In this section, we test the effect of securitization over firm’s size7. In particular, we

distinguish between micro enterprises and small and medium enterprises. estimate separate models

for micro and, medium and small enterprises. This analysis departs from the hypothesis that micro

enterprises are in general more bank-dependent, especially for longer term finance, than small and

medium enterprises. We conduct two separate tests for placed and retained securitization and results

are reported in Table (4) and Table (5), respectively. A dummy that indicates whether the firm is

micro is interacted with all the relevant terms. It follows that the estimates of the interactions terms

are to be considered as conditional effects.

As what concerns the effect of placed securitization, previous results suggest that it has no

statistical association with the loan request approval but a positive effect on loan terms and conditions

when banks raise funds from the wholesale market or the spread on the unsecured interbank market

increases. The analysis over firm’s size further suggests that there is some evidence that placed

securitization seems to be positively associated with loan request approval for small and medium

enterprises when Euribor-Eonia spread increases. A similar but more statistically significant

relationship is found when considering the loan terms and conditions. In this case, placed

securitization is associated with better terms and conditions for small and medium enterprises when

net wholesale borrowing and spread increase. These positive effects are less straightforward with

respect to micro enterprises. However, it seems that, in general, the negative relationship between our

7 We consider firm’s size in terms of employees.

19

funding measures and loan terms and conditions is less stringent for micro enterprises. Since these

are conditional effects they would though require further investigation.

In contrast to placed securitization, retained securitization is associated with a reduction of

the positive impact of wholesale funding on loan approval for small and medium enterprises. On the

other hand, whereas the change in net wholesale funding seems to have a reduced association with

loan approval for micro enterprises it though seems that the interaction of change in the net wholesale

funding with retained securitization is associated with a higher probability of approval for micro

enterprises. As for terms and conditions, the interaction of retained securitization with loan-to-deposit

shortfall is associated with worse terms and conditions for small and medium enterprises but there is

some evidence that such effect might be positive for micro enterprises. Moreover, there seems to be

some mitigation effect of retained securitization on terms and conditions for micro enterprises in case

of an increase in the net wholesale funding and negative effect for small and medium enterprises in

case of an increase in the interbank funding spread.

Even though these results are not conclusive, we can argue that, in general, there is some

evidence that placed securitization has contributed to mitigate funding stress in particular in favour

of small and medium enterprises. On the other hand, retained securitization, mostly used as collateral

for refinancing at the ECB, including non-conventional longer term operations such as the LTROs

and the TLTROs, has reduced to some extent the funding stress that has affected lending to the micro

enterprises.

[TABLE 4 HERE]

[TABLE 5 HERE]

8. Conclusion

This paper investigates the role of securitization as a funding diversification strategies for

banks. We analyse a period characterized by an increased funding risk associated in particular to the

sovereign debt crisis. We follow a micro-level approach by looking at the effect of securitization on

the loan and credit line request approval and the loan terms and conditions for SMEs. The data is

from nine euro-area countries. Our main identification strategy in the econometric analysis consists

in interacting our funding measures (Loan-to-deposit shortfall, change in net wholesale funding, and

Euribor-Eonia spread) with two different measures of securitization. The two measures are the total

20

issuance of placed and retained asset-backed securities in terms of total portfolio of loans to

households and enterprises. Different from the literature that studies securitization before the onset

of the financial crisis, we consider also retained securitization because it has represented a major

share of total issuance in the analysed period, especially for sovereign-stressed countries.

The results suggest that there is a positive direct effect of only the retained securitization and

only on the probability of loan request approval. There is, however, some evidence that placed

securitization reduces the negative association of change in net wholesale funding on loan terms and

conditions. We interpret this positive effect as potential longer term and lower cost benefits (as well

as risk transfer benefits) of placed securitization as compared to wholesale funding. These benefits

are then to some extent passed on to customers in terms of less stringent loan terms and conditions.

The analysis over firm’s size hints that placed securitization offers some mitigation effect of funding

stress on loan request approval and loan terms and conditions that somehow favours small and

medium enterprises as compared to micro enterprises. On the other hand, there is also some evidence

that micro enterprise lending is mostly supported by retained securitization during funding stress.

From a policy perspective, this paper contributes to debate about the positive role of

securitization not only as a risk transfer mechanism but also as a funding diversification strategy.

Future research can focus, for example, on considering alternative identification strategies

that can further reduces potential problems of endogeneity and that a more able to separate the funding

diversification effect from other effects of securitization.

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Tables and Figures

Figure 1 – Trends in the European primary securitization market between 2007 and 2015

Figure 2 – Trends in the primary securitization market: GIIPS vs. non-GIIPS

27

Figure 3 – Trends in retail and wholesale funding: loan-to-deposit shortfall and Euribor-Eonia

spread

Table 1 – Summary statistics

28

Table 2 – Securitization and loan request approval

Variable Obs Mean Std. Dev. Min Max

Placed securitization 28,726 0.0014 0.0014 0 0.0144

Retained securitization 28,726 0.0069 0.0089 0 0.0820

Loan-to-deposit shortfall 28,726 0.0046 0.0656 -0.2018 0.2024

Δ net whole funding 28,726 -0.0039 0.018 -0.0648 0.1355

Spread Euribor-Eonia 28,726 0.2567 0.1823 0.0943 0.6895

Tier 1 28,726 12.2853 2.0304 7.9557 23.1843

Non-performing loans 28,726 6.2214 4.927 1.6148 37.9169

Cost-to-income 28,726 63.7889 12.1568 32.1686 412.2138

Employees:

1-9 25,766 0.3123 0.4635 0 1

10-49 25,766 0.3616 0.4805 0 1

50-249 25,766 0.3260 0.4688 0 1

Turnover:

Up to € 2 Mln 28,332 0.4134 0.4925 0 1

€ 2 Mln - up to € 10 Mln 28,332 0.2781 0.4481 0 1

€ 10 Mln - up to € 50 Mln 28,332 0.2082 0.4060 0 1

More than 50 Mln 28,332 0.1003 0.3005 0 1

Economic sector:

Industry 25,766 0.2989 0.4578 0 1

Construction 25,766 0.1061 0.3080 0 1

Trade 25,766 0.2748 0.4464 0 1

Other services 25,766 0.3202 0.4666 0 1

Age:

More than 10 years 28,098 0.8166 0.3870 0 1

5 years - up to 10 years 28,098 0.1214 0.3266 0 1

2 years - up to 5 years 28,098 0.0498 0.2175 0 1

Less than 2 years 28,098 0.0122 0.1098 0 1

Profit:

Increased 28,307 0.2697 0.4438 0 1

Unchanged 28,307 0.2671 0.4424 0 1

Decreased 28,307 0.4633 0.4987 0 1

29

Table 3 – Securitization and loan terms and conditions

Dep. Var.

(1) (2) (3) (4) (5)

Placed securitization (t-1) -1.416 1.659 2.043 -0.690

(10.689) (10.188) (11.822) (10.991)

Retained securitization (t-1) 3.339*** 3.273*** 3.348*** 4.054***

(1.282) (1.216) (1.287) (1.408)

Loan-to-deposit shorfall (t-1) -2.131*** -1.627*** -2.218*** -1.564*** -1.995***

(0.676) (0.593) (0.712) (0.601) (0.708)

Δ net wholesale funding (t-1) 0.885 0.223 0.916 0.606 0.711

(0.796) (0.760) (0.798) (0.782) (1.029)

Spread Euribor-Eonia (t-1) -0.132 -0.210**

(0.092) (0.102)

-126.7457

(154.901)

-1.366

(9.929)

40.147

(29.779)

4.299

(2.769)

496.470

(442.524)

-42.199

(47.223)

Growth rate of GDP per capita YES YES YES YES YES

Other bank system controls YES YES YES YES YES

Firm controls YES YES YES YES YES

Time fixed effects YES NO YES NO YES

Country fixed effects YES YES YES YES YES

Observations 59,327 59,327 59,327 59,327 59,327

Retained securitization (t-1) x

Δ net wholesale funding (t-1)

Prob (loan/credit line request approved)

Note: std. errors are reported in parentheses. ***, **, and * indicate significance at the 1%,

5%, and 10% levels, respectively.

Placed securitization (t-1) x

Loan-to-deposit shorfall (t-1)

Retained securitization (t-1) x

Loan-to-deposit shorfall (t-1)

Placed securitization (t-1) x

Spread Euribor-Eonia (t-1)

Retained securitization (t-1) x

Spread Euribor-Eonia (t-1)

Placed securitization (t-1) x Δ

net wholesale funding (t-1)

30

Dep. Var.

(1) (2) (3) (4) (5)

Placed securitization (t-1) 1.416 0.486 1.883 7.432

(5.905) (5.522) (5.872) (6.308)

Retained securitization (t-1) 1.042 0.334 0.945 1.089

(0.684) (0.646) (0.704) (0.747)

Loan-to-deposit shorfall (t-1) -3.084*** -4.026*** -3.050*** -3.940*** -3.3406***

(0.455) (0.404) (0.479) (0.406) (0.487)

Δ net wholesale funding (t-1) -2.194*** -1.263*** -2.149*** -0.979** -3.410***

(0.467) (0.447) (0.470) (0 .460) (0.612)

Spread Euribor-Eonia (t-1) -0.273*** -0.297***

(0.051) (0.056)

-45.100

(68.615)

-4.418

(5.088)

32.846*

(17.913)

-1.307

(1.475)

799.234***

(289.928)

36.151

(26.902)

Growth rate of GDP per capita YES YES YES YES YES

Other bank system controls YES YES YES YES YES

Firm controls YES YES YES YES YES

Time fixed effects YES NO YES NO YES

Country fixed effects YES YES YES YES YES

Observations 60,589 60,589 60,589 60,589 60,589

Retained securitization (t-1) x

Δ net wholesale funding (t-1)

Improvement of terms and conditions (proxy)

Note: std. errors are reported in parentheses. ***, **, and * indicate significance at the

1%, 5%, and 10% levels, respectively.

Placed securitization (t-1) x

Loan-to-deposit shorfall (t-1)

Retained securitization (t-1) x

Loan-to-deposit shorfall (t-1)

Placed securitization (t-1) x

Spread Euribor-Eonia (t-1)

Retained securitization (t-1) x

Spread Euribor-Eonia (t-1)

Placed securitization (t-1) x Δ

net wholesale funding (t-1)

31

Table 4 – Placed securitization and loan request approval, and loan terms and conditions: micro vs.

small and medium enterprises

(1) (2) (3) (4) (5) (6)

Placed securitization (t-1) 9.147 14.201 1.657 3.305

(14.045) (12.489) (6.320) (6.685)

Placed securitization (t-1) x MICRO -21.880 -39.404*** -51.242** 1.928 8.900 -8.177

(18.328) (14.723) (22.892) (9.725) (10.060) (14.628)

Retained securitization (t-1) 3.335*** 3.561*** 2.831** 1.046 1.475** 0.050

(1.288) (1.323) (1.217) (0.684) (0.697) (0.650)

Loan-to-deposit shorfall (t-1) -2.369*** -2.163*** -1.483** -3.271*** -3.176*** -3.942***

(0.710) (0.675) (0.598) (0.472) (0.452) (0.408)

Loan-to-deposit shorfall (t-1) x MICRO 0.349 0.471***

(0.369) (0.202)

Δ net wholesale funding (t-1) 0.950 0.652 0.701 -2.205*** -3.760*** -0.860*

(0.798) (1.049) (0.788) (0.467) (0.609) (0.469)

Δ net wholesale funding (t-1) x MICRO -0.737 2.206***

(1.243) (0.832)

Spread Euribor-Eonia (t-1) -0.248** -0.463***

(0.110) (0.068)

Spread Euribor-Eonia (t-1) x MICRO 0.004 0.323***

(0.152) (0.095)

-24.224 -94.206

(181.426) (72.836)

-310.313 166.106

(235.959) (118.974)

56.058* 72.718**

(33.565) (32.070)

62.533 8.230

(76.559) (54.731)

690.671 1,092.129***

(520.463) (325.401)

-728.182 -860.462

(792.862) (559.839)

Growth rate of GDP per capita YES YES YES YES YES YES

Other bank system controls YES YES YES YES YES YES

Firm controls YES YES YES YES YES YES

Time fixed effects YES NO YES YES NO YES

Country fixed effects YES YES YES YES YES YES

Observations 59,327 59,327 59,327 60,589 60,589 60,589

Placed securitization (t-1) x Δ net

wholesale funding (t-1) x MICRO

Note: std. errors are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,

respectively.

Placed securitization (t-1) x Spread

Euribor-Eonia (t-1)

Placed securitization (t-1) x Spread

Euribor-Eonia (t-1) x MICRO

Placed securitization (t-1) x Δ net

wholesale funding (t-1)

Dep. Var.Prob (loan/credit line request

approved)

Improvement of terms and

conditions (proxy)

Placed securitization (t-1) x Loan-to-

deposit shorfall (t-1)

Placed securitization (t-1) x Loan-to-

deposit shorfall (t-1) x MICRO

32

Table 5 – Retained securitization and loan request approval, and loan terms and conditions: micro

vs. small and medium enterprises

(1) (2) (3) (4) (5) (6)

Placed securitization (t-1) -1.533 -3.206 1.889 1.072 3.330 2.054

(10.703) (10.834) (10.229) (5.903) (6.023) (5.522)

Retained securitization (t-1) 3.337** 3.941** 0.376 0.369

(1.547) (1.613) (0.786) (0.827)

Retained securitization (t-1) x MICRO 0.433 -0.638 6.259 2.407* 0.691 5.804**

(2.222) (2.349) (4.976) (1.268) (1.324) (2.964)

Loan-to-deposit shorfall (t-1) -2.069*** -1.985*** -1.621*** -3.077*** -3.291*** -3.856***

(0.707) (0.717) (0.598) (0.475) (0.490) (0.410)

Loan-to-deposit shorfall (t-1) x MICRO -0.105 0.266

(0.381) (0.206)

Δ net wholesale funding (t-1) 0.886 2.193** 0.280 -2.161*** -2.762*** -1.268***

(0.798) (1.025) (0.760) (0.469) (0.575) (0.447)

Δ net wholesale funding (t-1) x MICRO -2.870** 0.557

(1.324) (0.871)

Spread Euribor-Eonia (t-1) -0.1890* -0.251***

(0.107) (0.063)

Spread Euribor-Eonia (t-1) x MICRO 0.190 0.291***

(0.162) (0.099)

-9.389 -14.722**

(12.862) (5.929)

18.400 32.145***

(18.562) (9.787)

4.325 -14.940***

(3.353) (4.679)

-12.154 2.641

(11.916) (7.763)

-108.131** 18.207

(53.150) (30.122)

229.677*** 82.907*

(86.875) (48.636)

Growth rate of GDP per capita YES YES YES YES YES YES

Other bank system controls YES YES YES YES YES YES

Firm controls YES YES YES YES YES YES

Time fixed effects YES NO YES YES NO YES

Country fixed effects YES YES YES YES YES YES

Observations 59,327 59,327 59,327 60,589 60,589 60,589

Retained securitization (t-1) x Spread

Euribor-Eonia (t-1) x MICRO

Retained securitization (t-1) x Δ net

wholesale funding (t-1)

Retained securitization (t-1) x Δ net

wholesale funding (t-1) x MICRO

Note: std. errors are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,

respectively.

Dep. Var.Prob (loan/credit line request

approved)

Improvement of terms and

conditions (proxy)

Retained securitization (t-1) x Loan-to-

deposit shorfall (t-1)

Retained securitization (t-1) x Loan-to-

deposit shorfall (t-1) x MICRO

Retained securitization (t-1) x Spread

Euribor-Eonia (t-1)