# Does relationship lending matter in an emerging market?*

Naël Shehadeh,† Faicel Belaid,‡ Gilles Dufrénot,§ Christelle Lecourt¶

May 2023

## Abstract

Based on a unique database (data on 2529 bank-firm relationships of 403 firms from 2012 to 2018) provided by the Central Bank of Tunisia, this paper analyses the impact of the intensity and duration of bank-firm relationship on loan quality. By estimating a panel ordered probit model, the results show that the intensity of the lending relationship has a positive (negative) impact on high (medium or low) quality loans. In addition, the duration of the bank-firm relationship increases the probability of low quality loans. We also find that the impact of relationship lending on loan quality differ according to the level of profitability of the firm. Low and non-performing firms tend to have longer and closer bank relationship whereas it is the opposite for performing firms. Our results suggest that in a distorted market concentrated around a few banks, longer and closer banking relationships are mainly in favour of low and non-performing firms, reflecting adverse selection and strong moral hazard.

<footnote>* The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Central Bank of Tunisia. We are grateful for the helpful comments of Stefan Avdjiev</footnote>

<footnote>†Aix-Marseille University, CNRS, EHESS, Centrale Marseille, AMSE (nael.shehadeh@graduateinstitute.ch), Corresponding author</footnote>

<footnote>‡United Nations Conference on Trade and Development (Faicel.belaid@gmail.com)</footnote>

<footnote>§Aix-Marseille University, CNRS, EHESS, Centrale Marseille, AMSE (Gilles.Dufrenot@univ-amu.fr)</footnote>

<footnote>¶Aix-Marseille University, CNRS, EHESS, Centrale Marseille, AMSE (Christelle.Lecourt@univ-amu.fr)</footnote>
# 1 Introduction

The global financial crisis in summer 2007 has clearly revealed that excessive risk-taking by banks can lead to a financial system collapse. Since then, the problem of the soundness of the banking system has been at the forefront of academic and political concerns. Among the numerous intervention measures aimed to contain the financial crisis, several perspectives suggest that banks need to refocus on traditional activities (Bongini et al. (2009)). In particular, some studies confirm that banks with a greater propensity for relationship lending have been less deeply affected by the crisis (Bongini et al. (2015)).

In this context, relationship lending has become an area of increasing interest both for financial institutions and for firms. Relationship lending can be defined as a long-term implicit contract between a bank and its client (Boot (2000)). The development of long-term relationship, which takes the form of repeated interaction with the borrower over time, is a means for the bank of obtaining private information on the quality of the borrower, especially about his quality and creditworthiness, thus establishing close ties between the bank and the borrower (Elsas (2005)). From firms’ perspective, this relationship can mitigate hardships in the face of a credit constraint during a downturn and helps firms continue to grow (Beck et al. (2018)). The prevalent theories of relationship lending (for a review, see Boot (2000) and Ongena and Smith (2001)) suggest that relationship lending play a key role in reducing informational asymmetries, resolving agency problems and mitigating financial market imperfections. The robustness of the relationship lending should have benefits for banks by producing informational rents because the borrowers’ risk may be assessed more precisely and fees and commissions revenues tend to increase.

However, it is not clear if relationship lending would increase or reduce the loan quality. On the one hand, it may be argued that a closer relationship lending improves bank monitoring and screening and reduce the monitoring costs, which should decrease the borrower’s probability of default (Diamond (1984)) and therefore increase the quality of loan. On the other hand, a closer and longer relationship lending can also have a perverse incentive to boost the willingness to take more risk and activate opportunistic behaviours for lenders by using the private information to extracts rents, thus increasing the accumulation of non-performing loans or poor-quality loans (Boot, 2000; Rajan, 1992).

The aim of the paper is to investigate the impact of relationship lending on loan quality (rating) granted by the credit registry in Tunisia. The case of Tunisia is interesting for several reasons. First, developing countries are characterised by high degrees of asymmetric information, which should favour bank-firm relationship lending. Second, the Tunisian financial system is mainly based on banks despite the reforms undertaken to set up a market-based financial sector. Third, the Tunisian banking system is a distorted market that
leads to centralised market power dominated by some banks. The Tunisian bank's credit management is inefficient, with a Non Performing Loans (NPL) ratio of 14.5% in 2019¹ which is one of the highest level of NPLs ratio among the Arab Mediterranean Countries. The concentration of this market as well as financial system inadequacies should promote bank-firm relationship lending, but also opportunistic behaviours and risk-taking from lenders. Specifically, we try to answer the following questions: do the duration and the intensity in the bank-firm relationship lending increase the loan quality granted by banks in a developing country? Do financial characteristics of the firm positively or negatively moderate this link between banking relationship and loan quality? For that, we use an unique database (data on 2529 bank-firm relationships of 403 firms from 2012 to 2018) provided by the Central Bank of Tunisia. This matched bank-firm database allows to take into account private information held by banks and include internal ratings that enable a better evaluation of the borrower quality.

The contribution of this paper is twofold. First, we try to explain the impact of relationship lending on the loan quality granted by Tunisian banks. Although literature based on the link between the relationship banking and the probability of credit risk is particularly vast (Jiménez and Saurina, 2004; Menkhoff and Suwanaporn, 2007; Chang et al., 2014; Fiordelisi and Mare, 2014a; Belaid et al., 2017), very few empirical studies investigate the potential effects of the relationship lending on the loan portfolio quality granted by banks and even less so when we consider developing countries.<fnref n="2" /> The loan quality measure we propose provides a more general and appropriate risk measure including probability of non-performing loan as a particular case and allows to take into account the great dispersion in terms of quality of loans granted by the Tunisian banks. Second, using matched bank-firm database, we analyse the effects of financial and relational characteristics of banks and firms on the probability of loan repayment, taking into account the balance sheet indicators of firms and banks as determinants of loan quality granted by banks to firms. In particular we test whether the link found between the loan quality granted by bank and the bank-firm relationship lending is positively or negatively moderated by the financial characteristics of firms.

Regarding the methodology, our research hypotheses describing the link between the duration and the intensity of the relationship banking and the loan portfolio quality were tested using an ordered probit panel model with fixed effects in order to take into account the unobserved heterogeneity between firms and banks in our panel data. Our findings reveal that it is important

<footnote n="1">¹Source: ceicdata.</footnote>

<footnote n="2">²If some studies examine the impact of the bank-firm relationship lending on the credit risk in developing countries (Berg and Schrader, 2012; Geršl and Jakubík, 2011; Mori and Ngʻurah, 2020), except Belaid et al. (2017), there is no study to our knowledge that investigates the effect of intensity and duration of the relationship lending on the loan portfolio quality in a developing country, probably due to the lack of data.</footnote>
to take into account different levels of loan quality because our results differ according to the quality of the firms’ loans. Our results show that the intensity of the lending relationship has a positive (negative) impact on high (medium or low) quality loans. In addition, the duration of the bank-firm relationship increases the probability of low quality loans. We also find that the impact of relationship lending on loan quality differ according to the level of profitability of the firm: low and non-performing firms tend to have longer and closer bank relationship whereas it is the opposite for performing firms. Our results suggest that in a distorted market concentrated around a few banks, longer-term and closer banking relationships are predominantly in favour of non-performing firms, reflecting opportunistic behaviours and risk-taking by lenders.

The rest of the article is as follows: section 2 develops our hypotheses and a review of the relevant literature. Section 3 covers in detail the data and a description of the variables we will use. Section 4 presents our empirical strategy and our results, while section 5 concludes.

## 2 Theoretical background and related hypotheses

The theory of relationship lending is based on the idea that close ties between lenders and borrowers may be economically beneficial. Relationship lending is defined as a long-term implicit contract between a lender (generally a bank) and a borrower. The issue of relationships banking and loan quality/credit risk in the banking sector has been extensively studied from the theory of asymmetric information perspective. The decision-making for a bank concerning its credit risk management is subject to valuation problems because the managers/shareholders of the firm knows more than the bank on the quality of its project and on its own intentions of repayment of the credits granted. This asymmetry of information creates an adverse selection and moral hazard problems (Akerlof, 1970; Stiglitz and Weiss, 1981). Adverse selection is created prior to the transaction, when selection increases the possibility of loans made to unsafe borrowers, resulting in an undesirable outcome (Mori and Ng’urah (2020). On the other hand, moral hazard is an outcome that occurs after the transaction. The lender runs the risk that the borrower will engage in other activities that do not match the lender’s initial analysis, making it more difficult to repay the loan.

The model of relationship lending was therefore developed to tackle the transition between the lender and the borrower due to information asymmetry. As explained by Cotugno et al. (2013), relationship lending implies repeated interaction between the bank and the borrower over time, which will allow the bank to accumulate private information and establish close ties between the bank and the borrower. Such ties should lead to benefits from the lending institution such as intertemporal smoothing, increased credit availability, reduction
of costs of providing further loans, improvement of borrower's project payoffs
and more efficient decision in case of financial distress of the borrower.

If relationship lending allows to reduce asymmetric information for banks, it is also associated with costs like exogenous monitoring costs and switching costs. The information privilege of banks could "lock-in" borrowers and permit banks to extract monopoly rents. As a matter of fact, ex post rents extracted by the banks over some periods after the initiation of the loan contract could induce lock-in costs and bargaining power, leading to what is known the 'hold-up problem' (Sharpe (1990); Rajan (1992)). As explained by Rajan (1992), the bargaining power of the relationship lender can decrease entrepreneurial incentives to spend effort in some projects and conduct to opportunistic behaviours for the borrowers, which constitutes opportunity costs of having an informed lender. Some banks with long running relationship, extract informational advantage and can strategically terminate a relationship before default (Salvadè et al. (2021)).

This theory has been helpful in gaining a general framework of the relation-
ship between lender and borrower, but ample empirical studies have also enabled
the research community in testing such theoretical frameworks. A great number
of studies find that banks engaging in lending relationships tend to have lower
credit risk (Lehmann and Neuberger, 2001; Harhoff and Körting, 1998). At the
same time, Berg and Schrader (2012) find that bank-firm relationships are im-
portant determinants for increasing access to finance in times of unpredictable
aggregate shocks. Some other papers find more contrasted results, showing that
borrowers can have a perverse incentive to activate opportunistic behaviours
and consequently increasing the probability of non-repayment of loans (Boot,
2000; Ferri and Messori, 2000; Fiordelisi and Mare, 2014b). For example, Ferri
and Messori (2000) show that relationship banking can have both beneficial and
detrimental effects for credit allocation depending on the geographical position
of the banks within a country. They point towards socioeconomic structural
differences as being responsible for sub-optimal allocation in the south of Italy.

The topic of relationship banking and lender/borrower relationships has been
extensively studied, both theoretically and empirically, but less so in the con-
text of matched lender-borrower data in transition economies and even less so in
distorted markets lead mainly by few banks (Menkhoff and Suwanaporn, 2007;
Berg and Schrader, 2012; Geršl and Jakubík, 2011; Belaid et al., 2017; Mori and
Ng’urah, 2020). A distorted market leads to centralised market power, which
affects decision making on loan reimbursements differently than in a competitive
market. Economic theory demonstrates that in a monopolist creditor market,
the creditor shares in the future surplus is generated by the firm through the fu-
ture rents the creditor is able to extract. This implies the creditor would be more
willing to offer credit than in a competitive market (Petersen and Rajan, 1995;
Rajan, 1992). In a competitive market, the bank’s only way to generate rent is
through an information monopoly (Sharpe (1990)). Empirically, Crawford et al.
(2018) study credit demand and loan default using the Italian credit-registry. It focuses in particular in a small business lines of credit, and finds that both asymmetric information and market power affect this localised market. They find that market concentration can mitigate (in part) the negative effects of asymmetric information, where banks with higher margins can moderate price increase on their loans in order not to worsen the quality of their pool of lenders. On the other hand, Fiordelisi and Mare (2014b) uses EU cooperative bank data to study bank competition and market stability. Their findings point towards a negative relationship between bank concentration and bank financial stability.

More formally, relationships lending is generally characterised by two ma-
jor measures, namely its duration and its intensity (Petersen and Rajan, 1994;
Degryse et al., 2009; Belaid et al., 2017).<fnref n="3" /> The duration or length of the bank-
borrower relationship has been used as an indicator of the solidness of the rela-
tionships lending and the degree of relationship intensity over time. According
to the hypothesis of the informational advantage acquired by the bank through
the long-term lending relationship (Diamond, 1984; Ongena and Smith, 2001),
the long-term relationship with the borrower allows the bank to acquire con-
tinuously internal and private information necessary for monitoring the credit-
worthiness of its clients and therefore for decreasing the credit risk. However,
if the duration of a bank-borrower relationship is associated with private in-
formation accumulation over time, the lock-in costs of a borrower may increase
with the length of the lending relationship. Therefore, the duration should in-
crease switching costs as well as the hold-up problem explained above. If the
lender-borrower relationship tends to increase over time, or if borrowers become
locked into specific banks with prohibitive switching costs, then the likelihood
of terminating a relationship should decline over time, which may increase the
credit risk in the long run. But the relation between accumulated information,
duration and lock-in costs for the borrower should not be monotonic. There
should be first a valuable information advantage that might be decreasing after
some time as well as the marginal value of additional information (Diamond
(1991)).

Some studies formally model the benefits of long-term lending relationship (Diamond, 1984; Berglöf and Thadden, 1994; Chemmanur and Fulghieri, 1994; Morales Acevedo, 2016; Ongena and Smith, 2001). Using matched loan-level data from Colombia, Morales Acevedo (2016) find evidence that duration of banking relationship is a good predictor of lowers loan delinquencies. In the same way, Ongena and Smith (2001) conclude that the probability of bank relationship termination increase in duration, which means that the value of the relationship decline over time. In particular, they do not find evidence on the fact that firms become locked into bank relationships. Based on the

<footnote n="3"><sup>3</sup>Two other measures can also be taken into account, namely the proximity to the lender that is related to the physical and personal proximity between the lender and the borrower and the scope of the relationship that refers to the number of other products and services the borrower is utilizing from a single bank (Degryse and Ongena (2001)).</footnote>
aforementioned theoretical developments and empirical studies, we posit and
test our first hypothesis:

H1: There is a positive relationship between the duration of the borrower-bank relationship and the quality of loan.

Another indicator variable used to identify relationship lending is the number of bank relationships. The impact of a closer banking relationship on borrowers’ creditworthiness has been subject of debate in the financial intermediation literature. According to the theory of financial intermediation, the intensity of relationship lending allows for unique access to valuable information and therefore should reduce both costly informational frictions and problems associated with renegotiation of loan contracts (Diamond (1984)). In the same way, a closer or an exclusive relationship lending should facilitate the screening and monitoring of borrowers and carry beneficial implications like the sector specialisation of lenders, which may lead to the risk of financial distress. On the other hand, a close banking relationship may have two specific drawbacks that are associated with two primary costs: the soft-budget constraint problem and the liquidity risk (Boot (2000)). The soft-budget constraint problem relates to the potential lack of firmness of the bank to enforce credit contracts when there is a relationship-banking proximity. A borrower close to default may ask the bank for additional credit to avoid default. While any lender would decide not to lend to this borrower, a bank that has already lent money to this borrower may finally decide to grant additional credit in the hope of recovering its previous loan. Borrowers who realise that they can renegotiate their contracts ex post in this way may have perverse incentives ex ante to activate opportunistic behaviours and increase the default probability. This should also lead the bank to refinance unprofitable projects and drop entrepreneurial incentives to avoid financial distress (Dewatripont and Maskin (1995)). The liquidity risk issue is that the single-banking relationship is not an optimal solution for the borrower. As a matter of fact, this latter will face a serious problem of adverse selection if the bank-relationship is likely to end, due to the fact that other lenders have less information and the removal of the credit is likely interpreted as a problem with the borrowing firm. If the problem of adverse selection is severe, it can be advantageous for firms to establish relationships with several banks. The issue of multiple lending can be serious, especially in economies where bankruptcy costs are high and banks rarely have liquidity problems (Detragiache et al. (2000)).

Existing empirical studies demonstrate that intensity of banking relation-
ship matters, either by focusing on the eventual increasing costs of borrowing in
switching banking relationships (Ioannidou and Ongena, 2010; Degryse and On-
gena, 2001) or the increase in risky loans for small firms with multiple banking
relationships (Ioannidou et al. (2015)). Foglia et al. (1998) show that the rela-
tionships of a firm with just one bank creates a climate of trust and consolidate
the relationship over time. On the contrary, a relationship with many lenders
reduces the monitoring incentive, contributes to the dilution of the information
and to the occurrence of asymmetric information (Uchida et al. (2012)). In a
theoretical model based on the relationship lending in the Czech Republic, Geršl
and Jakubík (2011) show that the most optimal combination for firms in order
to avoid the 'hold-up' problem of being captured by one relationship lender is to
have two large banks as main lenders. Based on the aforementioned theoretical
developments, we formulate and test the following hypothesis:

H2: There is a positive link between the intensity of borrower-bank relation-
ship and the quality of loan.

It seems likely that the link between bank-firm relationship (duration and
intensity) and the quality of loan will depend on firm's specific characterist-
ics. Concerning the intensity, the number of banking relationships can signal
the quality of the firm. A firm of good quality in terms of performance and
level of debt will have a higher capacity to obtain refinancing from other banks
but it will prefer to have a close relationship lending with a single bank so as
to have advantageous loan conditions. In the same way, a poor quality firm
which expects a deterioration of its performance should develop several banking
relationships in order to dilute information and avoid the control of the main
bank. Therefore, we expect that link between the intensity of the bank-firm
relationship and the loan quality is lower (i.e. higher) for good (i.e. low) quality
firms:

H3: *The link between the intensity of the bank-firm relationship and the loan quality is lower (i.e. higher) for good (i.e. low) quality firms.*

As far as duration in firm-bank relationships concerned, firms facing large in-
formation asymmetries with outside investors benefit most from long-term bank-
ing relationships, but they are also particularly susceptible to hold-up problems
and high switching costs. Ongena and Smith (2001) find evidence that there
are small, profitable and highly leveraged firms that have the shortest relation-
ships. Therefore, we expect that the link between the length of the bank-firm
relationship and loan quality is lower (i.e. higher) for good (i.e. low) quality
firms:

H4: *The link between the length of the bank-firm relationship and loan quality is lower (i.e. higher) for good (i.e. low) quality firms.*

3 Data and variables

3.1 Sample and data collection

We collected credit-file data from the ten largest banks in Tunisia that hold
93% of total banking assets. The data come from the Central Bank of Tunisia
and Thomson Reuters Eikon Central database. This database contains different sources of information: information reported by the banks which share information between credit institutions in order to assist their credit risk assessment and management; detailed and annual accounting information on a large sample of Tunisian firms (age of the firm, sector of activity, information on the annual outstanding amount, status of loan and duration of banking relationship); data on loans granted to these firms with information on their status (defaulted or non-defaulted loans) and at last information on the rating given by banks to total loans depending on the credit risk assessments realised by the lenders. We do not have information on collateral, interest rate or purpose of the loan. Loan quality information and firm-specific variables are collected from the Risk and Central Balance Sheet database held by the Central Bank of Tunisia.

We have selected only loans with values from 1000 to 10 million dinars in order to avoid any specific effect related to credit scale. Only firms for which we have the information of the date of the loans and for which annual data are available over the considered period are included in our sample. After cleaning the data, our sample consists of 2529 bank-firm lending relationships for 403 firms over the period 2012 to 2018 at the annual frequency.<fnref n="4" />

This dataset allows us to investigate relationship lending effects on loan quality. In order to address potential self-selection issues, we control additionally for lender financial conditions, and borrower financial conditions, in all our regressions.

## 3.2 Description of variables

### 3.2.1 Dependent variable

As explained in the introduction, the aim of this paper is to investigate how the bank-firm relationship (intensity and duration) influences the loan quality and not only the probability of loan default as in many existing studies (Jiménez and Saurina, 2004; Menkhoff and Suwanaporn, 2007; Chang et al., 2014; Fiordelisi and Mare, 2014a; Belaid et al., 2017). In this study, we choose not to focus solely on the non-performing loans but to consider loan quality ranging from secure loans to non-secured loans, the extreme being non-performing loans. This variable provides a more general and appropriate risk measure including the probability of non-performing loan as a particular case and allows to take into account the great dispersion in terms of quality and volume of loans granted by the Tunisian banks.

This variable is built on the information given by banks that classify the

<footnote n="4">⁴Throughout the study, bank-firm relationships will be referred to as loans, as they represent the aggregated loan exposures between firm j, bank i at time t.</footnote>
loans granted to firms into six classes, in accordance with the regulation on loans' classification set by the Central Bank of Tunisia (the circular n°91-24). The first class represents safe loans for which there is integral repayment. The second class includes loans for which the repayment seems to be ensured but they are granted by firms facing deteriorating financial situation. The third class includes loans for which repayment is uncertain with reimbursement delays between 90 and 180 days. The fourth one contains loans for which repayment is uncertain with reimbursement delays between 180 and 360 days. In the fifth class, the reimbursement delay for loan are of more than 360 days. At last, the last class contains loans presenting a reimbursement delay of more than 360 days and for which there is a legal proceedings initiated by banks. Our loan quality measure, which is to rank loans granted to firms from most to least performing loans, can better capture risk generated by the bank, thus providing a more general and appropriate risk measure including the probability of non-performing loan as a particular case. On the basis of this information, we build an ordinal variable *Loanquality* ranging from 1 (very bad loan) to 6 (safe loan).<fnref n="5" />

### 3.2.2 Independent variables

As we are concerned with relationship lending, our second central point is to define and measure the two indicator variables of the strength of bank-firm relationship, which are duration and intensity.<fnref n="6" /> To capture the length of the bank-firm relationship, we build the variable *Duration* as the logarithm value of the duration (measured in years) of the lending relationship between the bank *i* and the firm *j* calculated from the beginning of the relationship (which cannot exceed the creation date of the original database, 2010). In the same way, if the relationship lending goes beyond the sample period, it is truncated at a duration starting at 2010.

To capture the intensity of the lending relationship, we build the variable *Intensity* following Belaid et al. (2017) as the ratio between the total amount of loans granted by bank *i* to firm *j* and the number of lending banks to firm *j*. Compared to other studies measuring the intensity as the number of banks lending to each borrower (Ferri and Messori (2000), Chang et al. (2014), Fiordelisi and Mare (2014a)), this indicator ranging from 0 to 1 shows to what extent the relationship lending between the bank *i* and the firm *j* is a strong (in terms of amount of loans granted to the firm) and a close (in terms of number of banks

<footnote n="5">⁵This variable allows us to have a more general and appropriate risk measure including the probability of non-performing loan as a particular case and allows to take into account the great dispersion in terms of quality and volume of loans granted by the Tunisian banks. This is the reason why we choose to take all the six values of this variable instead of aggregating some of them in order to have the most complete information on the loan quality.</footnote>

<footnote n="6">⁶Some papers (Jiménez and Saurina (2004), Fiordelisi and Mare (2014a)) consider the geographical distance between the bank and the firm as another measure of relationship lending. Here we could not use such a proxy as we do not know the location of the firms.</footnote>
lending to the firm) one. A value of one indicates that the firm has exclusively all its credits granted by the same bank and a value near zero means that the amount of credit obtained by the firm *j* to the bank *i* is not significant compared to total loans granted by other banks.

One another aim of this paper is to investigate how the bank-firm relationship (intensity and duration) influences the loan quality depending on firm's specific characteristics To control for firm quality, we include a broad set of firm characteristics as independent variables in the model. We consider the size of the firm (TA) approximated by the logarithm of the total assets of the firm. As a measure of firm's performance, we use the return on assets of the firm (ROA). As a proxy of the financial risk of the risk, we adopt two alternatives measures: the leverage ratio (*Leverage*) as a simple proxy of the firms' risk of financial distress and the liquidity ratio (*Liquidity*) as a financial health indicator.

As lending behaviour may vary across banks, we control for bank variables that may have an impact on bank lending. We therefore include the logarithm of the total assets of the bank, the return on assets, the leverage ratio and the liquidity ratio of the bank.

Table 1 summarises the main characteristics of variables described above.

<table><caption>Table 1: Summary of the variables.</caption><thead><tr><th>Name of variables</th><th>Description</th></tr></thead><tbody><tr><td>Dependent variable</td><td></td></tr><tr><td>Loan quality</td><td>This value is ranging from 1 (very bad loan) to 6 (safe loan).</td></tr><tr><td>Independent variables</td><td></td></tr><tr><td>Duration</td><td>The number of years of the relationship lending between bank i and firm j (cannot go back before 12 years).</td></tr><tr><td>Intensity</td><td>Ratio between the total amount of loans granted by bank i to firm j and the number of lending banks to firm j.<br>A value of one indicates that the firm has exclusively all its credits granted by the same bank.</td></tr><tr><td>Leverage ratio</td><td>Total equity / total assets (for both firms and banks)</td></tr><tr><td>Liquidity ratio</td><td>Liquid assets/ total assets (for both firms and banks)</td></tr><tr><td>Return on Assets</td><td>Net income/ total assets (for both firms and banks)</td></tr><tr><td>Total assets</td><td>Log of total assets (for both firms and banks)</td></tr></tbody></table>

### 3.2.3 Descriptive analysis

Table 2 gives descriptive statistics on the distribution of the observations among the six categories of loan classes. Approximately 22% of the firms in the sample were distressed at the end of the period (category 1-4). However, the average quality of loans is relatively high, with an average of 5.171 (a value of 6 indicating a safe loan and a value of 1 a very bad loan). It means that there is a great dispersion in terms of quality of loans granted by the banks. While the majority of loans are repaid on time (classes 5 to 6 in the loan quality indicator), those who are in the classes 1 to 4 are considered in default.<fnref n="7" /> These defaulted

<footnote n="7">⁷A loan is considered to be in default when there is a delay in the reimbursement of the principal and/or interests of more than three months (Bonfim (2009), Louzis et al. (2012), Belaid et al. (2017)).</footnote>
loans corresponds to 403 unique firms, which roughly represents 22% of total loans which are in some type of default category. Table 2 illustrates this great dispersion in terms of quality of loans and we can see that a great majority of loans are considered as very good loans in terms of quality ((70% of loans are in class 6).

The mean value of the variable *Intensity* is 0.663, which means that on average Tunisian banks maintain a relatively close relationships with their borrowers. The distribution of the variable *Intensity* between the six classes of loan quality clearly reveal a positive link between the intensity of relationship lending and the loan quality. Added to that, the average of lending relationship duration is 6.786 years and if the result is not as clear as the intensity, there is a a positive link between the duration of relationship lending and the loan quality until the class 3. For loans in class 4 to 6, this effect is less clear-cut.

<table><caption>Table 2: Loan distribution</caption><thead><tr><th>Loan Quality</th><th>Relationships</th><th>Nbr.</th><th>Unique Firms</th><th>Frequency (%)</th><th>Av. Intensity</th><th>Av. Duration</th></tr></thead><tbody><tr><td>1</td><td>121</td><td></td><td>18</td><td>4.78</td><td>0.14</td><td>6.29</td></tr><tr><td>2</td><td>195</td><td></td><td>30</td><td>7.7</td><td>0.47</td><td>6.98</td></tr><tr><td>3</td><td>126</td><td></td><td>21</td><td>5</td><td>0.69</td><td>7.67</td></tr><tr><td>4</td><td>118</td><td></td><td>20</td><td>4.67</td><td>0.68</td><td>6.14</td></tr><tr><td>5</td><td>166</td><td></td><td>30</td><td>6.56</td><td>0.75</td><td>6.86</td></tr><tr><td>6</td><td>1803</td><td></td><td>284</td><td>71.29</td><td>0.7</td><td>6.87</td></tr><tr><td>Total</td><td>2529</td><td></td><td>403</td><td>100</td><td>-</td><td>-</td></tr></tbody></table>

<table><caption>Table 3: Summary statistics</caption><thead><tr><th>Statistic</th><th>Mean</th><th>St. Dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Loan quality</td><td>5.171</td><td>1.528</td><td>1</td><td>6</td></tr><tr><td>TA_bank</td><td>8.683</td><td>0.574</td><td>7.392</td><td>9.389</td></tr><tr><td>TA_firm</td><td>21.681</td><td>1.478</td><td>18.486</td><td>24.987</td></tr><tr><td>Leverage_bank</td><td>7.991</td><td>2.400</td><td>2.971</td><td>15.070</td></tr><tr><td>Leverage_firm</td><td>22.884</td><td>19.982</td><td>-56.011</td><td>84.748</td></tr><tr><td>Liquidity_bank</td><td>77.133</td><td>6.337</td><td>57.658</td><td>95.428</td></tr><tr><td>Liquidity_firm</td><td>67.442</td><td>24.712</td><td>4.264</td><td>100.000</td></tr><tr><td>ROA_bank</td><td>0.966</td><td>0.996</td><td>-2.564</td><td>4.330</td></tr><tr><td>ROA_firm</td><td>0.043</td><td>0.134</td><td>-1.154</td><td>0.631</td></tr><tr><td>Intensity</td><td>0.663</td><td>0.382</td><td>0.002</td><td>1.000</td></tr><tr><td>Duration</td><td>6.786</td><td>3.652</td><td>1</td><td>12</td></tr></tbody></table>
# 4 Econometric methodology

A known approach to modelling NPls is to use logit or probit models (Bonfim (2009); Avdjiev and Serena (2020)). To answer our hypotheses we run our model following an ordered probit approach, with as dependant variable $LQ_{ijt}$ which represents quality of loan granted by bank i to firm j at time t (1 to 6, 6 being good quality loan), and our variable of interest is return on assets of banks. Define the index $κ = (ij)$ to represent an observation related to any pair (ij) of (bank,firm). We consider the following latent model:

$$Y_{\kappa t}^* = \beta_1 Duration_{\kappa t} + \beta_2 Intensity_{\kappa t} + X'_{mt} \beta_3 + \sigma_e \epsilon_{\kappa t}, \quad \epsilon_{\kappa t} \approx N(0, 1), \quad (1)$$

or

$$Y_{\kappa t}^* = Z'_{\kappa t} \Omega + \epsilon_{\kappa t}, \quad \kappa = (1, 1), \dots, (I, J), \quad t = 1, \dots, T. \quad (2)$$

The total sample consists of I banks, J firms and T years. Z is the vector of variables Duration, Intensity and X and $X_{mt}$ is a vector composed of the following variables:

$$(ROA_{m\tau}, TA_{m\tau}, liquidity_{m\tau}, leverage_{m\tau}, \lambda_i, \lambda_j), \quad m = b, f, \tau = t-1, t. \quad (3)$$

The indices b, f refer respectively to banks and firms. $\lambda_i$ and $\lambda_j$ are respectively bank and firm specific effects. The regressors in the vector $Z_{\kappa t}$ are assumed to be strictly exogenous and the panel is balanced. The latent model 2 allows to define a partition of the variable dependent variable (quality of a loan granted from bank i to firm j) as follows

$$Y_{\kappa t} = \begin{cases} 1, & \text{if } Y_{\kappa t}^* \in (-\infty, \gamma_1], \\ 2, & \text{if } Y_{\kappa t}^* \in (\gamma_1, \gamma_2], \\ \vdots & \\ 6, & \text{if } Y_{\kappa t}^* \in (\gamma_6, \infty]. \end{cases} \quad (4)$$

The coefficients $\gamma_1 < \gamma_2 < \dots < \gamma_6$ are unknown parameters. The probability of an outcome conditional on the exogenous regressors and fixed effects is :

$$Pr(Y_{\kappa t} = \nu | Z_{\kappa t}, \Omega) = \zeta(\gamma_{\nu+1} - Z'_{\kappa t} \Omega) - \zeta(\gamma_{\nu} - Z'_{\kappa t} \Omega), \quad (5)$$

where $\nu = 1, \dots, 6$. $\zeta(\cdot)$ is the standard Normal CDF $N(0, 1)$.

Our motivation for considering a fixed effect model, rather than random effect, is the following. The sample of banks and firms considered represent the bulk of the banking sector and private companies in Tunisia. So, our sample "coincides" with the population.

A challenging issue when dealing with unobserved heterogeneity in fixed effects panel data models is a bias caused by the so-called incidental parameter
problem. The maximum likelihood estimate of the residual variance is likely to be biased. Bias with the slope estimates on the explanatory variables in the latent model can also occur. To reduce the bias, we choose the following sufficient statistics for the fixed effect. Consider a given explanatory variable $x_{ijt}$ for a pair $(i, j)$ representing a bank and a firm. We add the following variables to $x_{ijt}$ in the latent regression:

$$w_{ijt}^1 = \bar{x}_i = \sum_j \sum_t x_{ijt} \text{ and } w_{ijt}^2 = \bar{x}_j = \sum_i \sum_t x_{ijt}.$$

This reduces substantially the number of parameters to estimate, since instead of $I \times J$ fixed effects, we now have only as many parameters as explanatory variables in the regression. The variable $w_{ijt}^1$ has the same values for a given bank $i$ for all firms and years. But the values differs across banks. Similarly, in the vector $w_{ijt}^2$, the values are similar for a given firm $j$ for all firms and years. The values change across firms. We can also proceed in a similar way for time fixed effects. Rather than including in the model as many dummies as years, we calculate the mean over banks and firms:

$$w_{ijt}^3 = \bar{x}_t = \sum_i \sum_j x_{ijt}.$$

To avoid considering too many variables in the model, we select $w_{ijt}^\eta$, $\eta = 2,3$ for our two variables of interest, i.e. duration and intensity of a loan.

In order to identify the scale of the parameter vector, we make the standard assumption of no constant in the latent mean equation and the variance of the error-term is set to 1. The model is estimated using maximum likelihood estimator.

Define an element of the vector $Z_{\kappa t}$ as $(z_{\kappa t}^l), l = 1, \dots, L$. We compute the marginal effects which measures the effect of a unit change in a variable $(z_{\kappa t}^l)$ on the probability of a given outcome $\nu, \nu = 1, 2, \dots, 6$. We denote $\hat{\omega}^l$ the estimated coefficients of $(z_{\kappa t}^l)$ in the latent regression.

We calculate the marginal effect of each observed set of $(z_{\kappa t}^l)$ and average the marginal effects across the sample:

$$AME(\nu) = \frac{1}{I \times J \times T} \sum_i \sum_j \sum_t ME(\nu, Z_{\kappa t}^l \hat{\Omega}), \quad \kappa = (i, j) \quad (6)$$

where

$$ME(\nu, Z_{\kappa t}^l \hat{\Omega}) = \frac{\partial P(Y_{\kappa t} = \nu)}{\partial z_{kt}^l} = -\frac{\hat{\omega}^l}{\sigma} \phi \left( \frac{\gamma_{\nu+1} - Z_{\kappa t}^l \hat{\Omega}}{\sigma} \right) - \phi \left( \frac{\gamma_{\nu} - Z_{\kappa t}^l \hat{\Omega}}{\sigma} \right)$$

where $\phi$ is the pdf of the error term $\epsilon_{\kappa t}$.
# 5 Results

The results of the baseline model assessing the impact of relationship lending on the probability of loan quality are presented in Table 4. Table 5 gives the results of the model integrating the interaction variables between firm's characteristics and the variable *Intensity* whereas Table 6 gives the result of the model integrating the interaction variables between firm's characteristics and the variable *Duration*. In order to interpret our results, we need to compute marginal effects of these three models that are reported in Tables 7, 8 and 9.

## 5.1 Direct and indirect effects of duration

Does the duration of the bank-firm relationship increase the likelihood that loans are of good quality? In Table 4, we present results obtained from the ordered probit model with core variables and marginal effects of this model are given in Table 7. We see that the coefficient of the variable *Duration* is positive for outcomes $\nu = 1, ..., 5$ and negative for outcome $\nu = 6$. This result suggests that the duration of the relationship between banks and firms increases the probability of poor quality loans, and decreases the probability of good quality loans. This result is consistent with agency theories (soft-budget constraint discussed above), where long relationships are opportunistically maintained by firms to delay or mitigate default risks (customer relationships matter more than the risks inherent in loans). In order to test whether this explanation holds for our sample, we add to the original model cross-effects of the variable *Duration* with variables describing firms' health, i.e. ROA, leverage, liquidity ratio and total assets (see Table 6). The underlying idea is the following: if the length of the customer relationships dominates, then firms with degraded liquidity/credit ratios, or firms taking on high leverage risks, should increase the risk of ex-ante adverse selection and increase the likelihood that the loans granted will be, ex post, non-performing.<fnref n="8" /> When we compare the results found in the original model 4 and results including interaction variables in Table 6, we find that in the former no threshold is significant, while at least one is in the latter. This shows that the first regression does *not* adequately discriminate behaviours across outcomes and that the mean effect captured by the negative sign of the duration variable certainly captures behaviours in one of the outcomes $\nu$. It means that the variables in the regression in Table 4 do not help to differentiate their effects on the different loan quality levels.<fnref n="9" />

In Table 9, we report marginal effects of the model including interaction

<footnote n="8">⁸In this case, we expect the marginal effects crossed with firm characteristics to be positive for the lowest values of $\nu$ (which correspond to outcomes with the lowest quality of loans) and negative for the highest values of $\nu$ (which corresponds to the highest quality of loans.)</footnote>

<footnote n="9">⁹The correct interpretations are therefore those of marginal effects corresponding to estimates where at least one threshold is statistically significant.</footnote>
variables between firm characteristics and the variable *Duration*. First, the coefficient of the variable *ROA* is the highest compared to the coefficients of the other variables describing firm characteristics. The marginal effect is negative for the outcomes 1, ..., 5 and positive (and high) for the outcome 6. This corresponds to what is expected, i.e. that an increase in the return-of-assets improves loan quality only for firms whose loans are already of good quality. Second, the variable *Duration*, taken alone, has no significant effect, regardless of the outcome. When we look at the marginal effects of the variables crossed with *Duration*, although they are significant and positive for all outcomes, the coefficients obtained are small. This means that duration does not significantly alter the direct effects of firm's characteristics on loan quality. It is therefore difficult to validate hypothesis H4 for our sample.<fnref n="10" />

The conclusion is that, for our sample, none of the hypotheses H1 and H4 seem to be validated. *Duration* is not a discriminant variable that directly influences loan quality. The indirect effects, via firm characteristics, are small. One explanation is that the variable *Duration* captures a learning effect. Firms take out loans with banks and the banks continue to do so as long as the firms have strong economic and financial performance. *Duration* thus appears to be a process of selecting the right firms over time. In this situation, *duration* has a positive effect on the probability of selecting firms with good loans. According to the agency theory, we would say that *duration* increases the incentive constraint on agents (in this case firms) who must continue to improve firm performance to continue to receive loans from banks. This behaviour is opposed to that of "customer relationships". In this case, there are no effects related to the incentive constraint. There may even be moral hazard or adverse selection effects, if loan offers are uncorrelated (or weakly correlated) with firms' performance. Other studies (Sohn and Choi (2011) find that banks can have a conflict of interest that comes with pre-existing lending relationships, and that bank quality does not necessarily convey the risk classes of its client firms.

## 5.2 Direct and indirect effects of intensity

For the same reasons of identification and omission bias as in the previous section, we interpret the marginal effects calculated from the regression with the cross-effects of the firms' characteristic variables with the intensity variable, i.e. Table 5.

<footnote n="10">¹⁰The introduction of these cross effects is however useful. Indeed, comparing Tables 4 and 6, we see how drastically the coefficients common to both regressions change. In the cross-effects regression, the coefficient on firms’ ROA is 3 times higher than in the regression where these effects are omitted. The coefficient on firms’ total assets is 2 times higher. The coefficient on duration itself changes sign and becomes positive and at least 5 times higher in absolute value. All this indicates the existence of omission biases in the original regression (Table 4) with a systematic underestimation of the effects.</footnote>
If we look at the marginal effects of the variable *Intensity* (Table 8), they are negatively significant for outcomes 1 to 5 and positively significant with a high coefficient for outcome 6. This result suggests that the intensity of the relationship lending positively impacts the loans of high quality and negatively impacts the loans of average or low quality. This result suggests a dilution effect and an adverse selection problem in the sense that firms that already have poor quality loans would have an incentive to multiply their loans with different banks. This result validates our hypothesis *H2* when we consider firms with high quality loans but not for firms with average or low quality loans. This result also shows that it is important to consider the different types of loans in terms of quality to estimate the impact of the relationship lending on the loan quality.

When we estimate the cross effects between the variable *Intensity* and firm's characteristic variables, we find an interesting result. Indeed, among all the firms' characteristic variables, the one that plays the most important role is the variable *ROA* as in previous section. For outcomes $v = 1, ..., 5$, the individual marginal effect of ROA is negative, while its cross effect with the intensity variable is positive (the total effect is positive). This result reveals that, for firms with initially poor or medium quality loans, improving their ROA decreases the probability of continuing to have low quality loans. But, when these firms increase their banking relationships, they are likely to continue to have degraded loans, despite the improvement in their financial indicator. This phenomenon can be interpreted as the fact that firms that increase their banking relationships have less incentive to improve their ROA as a signal to banks. For outcome $v = 6$, we see a positive sign for the marginal ROA effect (taken alone) and a negative sign with the cross-intensity effect. Therefore, for firms with good quality loans, increasing bank relationships also has a disincentive effect since it reduces the probability of continuing to have good quality loans. The link between the intensity of the bank-firm relationship and loan quality is lower (higher) for good (low) quality firms. Our results are in line with Ongena and Smith (2001) but not with those found by Belaid et al. (2017) who conclude that Tunisian firms which have an intense relation with banks are more likely to have loans of higher quality.<fnref n="11" />

To summarise our results, we find that the intensity of the lending relationship has a positive (negative) impact on high (medium or low) quality loans. In addition, the duration of the bank-firm relationship increases the probability of low quality loans. We also find that the impact of relationship lending (in the form of duration and intensity) on loan quality differ according to the level of profitability of the firm: the link between the intensity of the bank-firm relationship and loan quality is lower (higher) for high (low) performing firms. Second, our result reveals that the length of the relationship between

<footnote n="11">¹¹However, this paper does not take into account the significance level of thresholds in the order probit model as well as marginal effects in the different outcomes of the loan quality level.</footnote>
banks and firms increases the probability of poor quality loans, and decreases the probability of good quality loans. Our results suggest that in a distorted market concentrated around a few banks, longer and closer bank relationships are mainly in favour of non-performing firms, reflecting opportunistic behaviour and risk-taking by banks. The information privilege of banks could "lock-in" poorly performing firms and allow them to extract monopoly rents. From the non-performing firm's perspective, having a long-term relationship with a single bank may have as a consequence to reduce entrepreneurial incentives to spend effort in some projects and also lead to opportunistic behaviour. This moral hazard situation, where banks continue to have relationship lending with poor or non-performing firms, can be explained by several factors: the fact that the Tunisian banking sector is concentrated around a few large banks; the fact that Tunisian firms are highly dependent on loans (with low market capitalisation of non-financial institutions); and finally the fact that there are few alternatives for banks outside of lending to firms (Kaufmann (2013)).

<table><caption>Table 4: Regression results of the core variables</caption><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>t value</th><th>P-value</th></tr></thead><tbody><tr><td>ROA(banks)</td><td>-0.12</td><td>0.03</td><td>-3.93</td><td>0.00 ***</td></tr><tr><td>ROA(firms)</td><td>0.59</td><td>0.21</td><td>2.78</td><td>0.01 **</td></tr><tr><td>Total assets (banks)</td><td>0.39</td><td>0.06</td><td>6.23</td><td>0.00 ***</td></tr><tr><td>Total assets (firms)</td><td>0.04</td><td>0.02</td><td>2.03</td><td>0.04 *</td></tr><tr><td>Leverage (banks)</td><td>0.08</td><td>0.01</td><td>5.84</td><td>0.00 ***</td></tr><tr><td>Leverage (firms)</td><td>-0.01</td><td>0.00</td><td>-4.21</td><td>0.00 ***</td></tr><tr><td>Liquidity(banks)</td><td>-0.02</td><td>0.00</td><td>-5.00</td><td>0.00 ***</td></tr><tr><td>Liquidity(firms)</td><td>0.00</td><td>0.00</td><td>3.63</td><td>0.00 ***</td></tr><tr><td>Intensity</td><td>3.67</td><td>0.49</td><td>7.45</td><td>0.00 ***</td></tr><tr><td>Duration</td><td>-0.17</td><td>0.06</td><td>-2.98</td><td>0.00 **</td></tr></tbody></table><table><thead><tr><th></th><th>Threshold parameters</th><th>Coeff.</th><th>Std. Error</th><th>t value</th><th>P-value</th></tr></thead><tbody><tr><td>Threshold</td><td>(1⇒2)</td><td>1.63</td><td>7.88</td><td>0.21</td><td>0.84</td></tr><tr><td>Threshold</td><td>(2⇒3)</td><td>2.25</td><td>7.88</td><td>0.29</td><td>0.78</td></tr><tr><td>Threshold</td><td>(3⇒4)</td><td>2.50</td><td>7.88</td><td>0.32</td><td>0.75</td></tr><tr><td>Threshold</td><td>(4⇒5)</td><td>2.69</td><td>7.88</td><td>0.34</td><td>0.73</td></tr><tr><td>Threshold</td><td>(5⇒6)</td><td>2.92</td><td>7.88</td><td>0.37</td><td>0.71</td></tr></tbody></table>

Note: *,**,***: statistical significance at resp. 10%, 5%, 1%.
<table><caption>Table 5: Regression results with interaction variables between Intensity and firm's characteristics</caption><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>t value</th><th>P-value</th><th></th></tr></thead><tbody><tr><td>ROA</td><td>2.43</td><td>0.48</td><td>5.05</td><td>0.00</td><td>***</td></tr><tr><td>Total assets</td><td>0.01</td><td>0.04</td><td>0.38</td><td>0.71</td><td></td></tr><tr><td>Leverage</td><td>-0.02</td><td>0.00</td><td>-5.17</td><td>0.00</td><td>***</td></tr><tr><td>Liquidity</td><td>0.01</td><td>0.00</td><td>5.69</td><td>0.00</td><td>***</td></tr><tr><td>Intensity</td><td>2.72</td><td>1.20</td><td>2.26</td><td>0.02</td><td>*</td></tr><tr><td>Duration</td><td>-0.06</td><td>0.04</td><td>-1.56</td><td>0.12</td><td></td></tr><tr><td>CPI</td><td>-0.75</td><td>0.49</td><td>-1.52</td><td>0.13</td><td></td></tr><tr><td>GDP</td><td>0.14</td><td>0.08</td><td>1.78</td><td>0.07</td><td>.</td></tr><tr><td>ROA × intensity</td><td>-2.59</td><td>0.60</td><td>-4.31</td><td>0.00</td><td>***</td></tr><tr><td>Leverage × intensity</td><td>0.01</td><td>0.00</td><td>3.62</td><td>0.00</td><td>***</td></tr><tr><td>Liquidity × intensity</td><td>-0.01</td><td>0.00</td><td>-4.76</td><td>0.00</td><td>***</td></tr><tr><td>Total assets × intensity</td><td>0.07</td><td>0.05</td><td>1.36</td><td>0.17</td><td></td></tr><tr><th>Threshold</th><th>Parameters</th><th>Std. error</th><th>t value</th><th>P-value</th><th></th></tr><tr><td>Estimate</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Threshold</td><td>(1⇒2)</td><td>3.45</td><td>1.67</td><td>2.06</td><td>0.04</td></tr><tr><td>Threshold</td><td>(2⇒3)</td><td>4.08</td><td>1.67</td><td>2.44</td><td>0.01</td></tr><tr><td>Threshold</td><td>(3⇒4)</td><td>4.33</td><td>1.67</td><td>2.59</td><td>0.01 *</td></tr><tr><td>Threshold</td><td>(4⇒5)</td><td>4.52</td><td>1.68</td><td>2.70</td><td>0.01 *</td></tr><tr><td>Threshold</td><td>(5⇒6)</td><td>4.74</td><td>1.68</td><td>2.83</td><td>0.00 **</td></tr></tbody></table>

Note: *,**,***: statistical significance at resp. 10%, 5%, 1%.
<table><caption>Table 6: Regression results with interaction variables between Duration and firm's characteristics</caption><thead><tr><th></th><th>Estimate</th><th>Std. Error</th><th>t-value</th><th>P-value</th><th></th></tr></thead><tbody><tr><td>ROA</td><td>1.70</td><td>0.62</td><td>2.75</td><td>0.01</td><td>**</td></tr><tr><td>Total assets</td><td>0.10</td><td>0.05</td><td>2.02</td><td>0.04</td><td>*</td></tr><tr><td>Leverage</td><td>0.00</td><td>0.00</td><td>0.18</td><td>0.86</td><td></td></tr><tr><td>Liquidity</td><td>0.01</td><td>0.00</td><td>3.91</td><td>0.00</td><td>***</td></tr><tr><td>Intensity</td><td>0.85</td><td>0.07</td><td>12.47</td><td>2.20E-16</td><td>***</td></tr><tr><td>Duration</td><td>0.93</td><td>0.61</td><td>1.52</td><td>0.13</td><td></td></tr><tr><td>CPI</td><td>-1.16</td><td>0.47</td><td>-2.49</td><td>0.01</td><td>*</td></tr><tr><td>GDP</td><td>0.17</td><td>0.08</td><td>2.26</td><td>0.02</td><td>*</td></tr><tr><td>ROA×Duration</td><td>-0.50</td><td>0.30</td><td>-1.63</td><td>0.10</td><td></td></tr><tr><td>Leverage ×Duration</td><td>0.00</td><td>0.00</td><td>-1.90</td><td>0.06</td><td>.</td></tr><tr><td>Liquidity ×Duration</td><td>0.00</td><td>0.00</td><td>-2.65</td><td>0.01</td><td>**</td></tr><tr><td>Total assets ×Duration</td><td>-0.03</td><td>0.03</td><td>-1.16</td><td>0.25</td><td></td></tr><tr><td colspan="6">Threshold estimate</td></tr><tr><td></td><td>Parameters</td><td>Std error</td><td>t value</td><td>P-value</td><td></td></tr><tr><td>Threshold</td><td>(1⇒2)</td><td>14.81</td><td>4.43</td><td>3.34</td><td>0.00 ***</td></tr><tr><td>Threshold</td><td>(2⇒3)</td><td>15.41</td><td>4.43</td><td>3.48</td><td>0.00 ***</td></tr><tr><td>Threshold</td><td>(3⇒4)</td><td>15.65</td><td>4.43</td><td>3.53</td><td>0.00 ***</td></tr><tr><td>Threshold</td><td>(4⇒5)</td><td>15.83</td><td>4.43</td><td>3.57</td><td>0.00 ***</td></tr><tr><td>Threshold</td><td>(5⇒6)</td><td>16.05</td><td>4.43</td><td>3.62</td><td>0.00 ***</td></tr></tbody></table>

Note: *,**,***: statistical significance at resp. 10%, 5%, 1%.
# 6 Conclusion

In this paper, we analyse the impact of the intensity and duration of bank-firm relationship on loan quality in an emerging market characterised by market concentration. For that we use data collected from the credit registry of the Central Bank of Tunisia consisting of 2529 bank-firms lending relationships for 403 Tunisian firms over the period 2012-2018. Using a panel ordered probit model, we estimate non-performing loans on proxies of relationship lending interacting with firm balance sheets.

Our findings reveal that it is important to take into account different levels of loan quality (and not just a binary variable for the credit risk as in the prevailing literature) because our results differ according to the quality of the firms’ loans. First, we find that the intensity of the relationship lending has a positive impact on high quality loans and a negative impact on medium and low quality loans. In addition, the duration of the bank-firm relationship increases the probability of low quality loans. We also find that the impact of relationship lending on loan quality differ according to the level of profitability of the firm. When intersecting intensity and duration of a banking relationship with firm balance sheet indicators, we find that the impact of relationship lending on loan quality differs according to the level of profitability of the firm: low and non-performing firms tend to have longer and closer bank relationships, while the opposite is true for performing firms. These results suggest that the consideration of cross-effects between relationship lending and firm’s characteristics is also important.

Our results show that relationship lending is not always relevant in the Tunisian banking sector which has difficulties to mitigate agency problems and knows soft-budget constraint and lock-in problems. Perverse and opportunist effects, in the form of strong moral hazard, are persistent for firms at different level of profitability.

Our findings have important policy implications. This moral hazard situation, where banks continue to lend to low-performing firms, will be detrimental to the real economic situation in the country. The Tunisian banking sector is far too concentrated around few big banks, leading to distorted lending behaviours and prevalence of poorly performing firms. Important efforts to introduce competition in this market would be highly beneficial for the stability of the domestic banking sector.
References

G. A. Akerlof. The Market for "Lemons": Quality Uncertainty and the Market Mechanism. *The Quarterly Journal of Economics*, 84 (3):488, 8 1970. ISSN 00335533. doi: 10.2307/1879431. URL https://academic.oup.com/qje/article-lookup/doi/10.2307/1879431.

S. Avdjiev and J. M. Serena. Regulatory capital, market capital and risk taking in international bank lending. (912), 2020. URL https://www.bis.org/publ/work912.htm.

T. Beck, H. Degryse, R. De Haas, and N. van Horen. When arm's length is too far: Relationship banking over the credit cycle. *Journal of Financial Economics*, 127(1):174–196, 1 2018. ISSN 0304405X. doi: 10.1016/j.jfineco.2017.11.007. URL https://linkinghub.elsevier.com/retrieve/pii/S0304405X17302957.

F. Belaid, R. Boussaada, and H. Belguith. Bank-firm relationship and credit risk: An analysis on Tunisian firms. *Research in International Business and Finance*, 42(April):532–543, 2017. ISSN 02755319. doi: 10.1016/j.ribaf.2017.04.011.

G. Berg and J. Schrader. Access to credit, natural disasters, and relationship lending. *Journal of Financial Intermediation*, 21(4):549–568, 2012. ISSN 10429573. doi: 10.1016/j.jfi.2012.05.003. URL http://dx.doi.org/10.1016/j.jfi.2012.05.003.

E. Berglöf and E.-L. v. Thadden. Short-Term Versus Long-Term Interests: Capital Structure with Multiple Investors. *The Quarterly Journal of Economics*, 109(4):1055–1084, 1994.

D. Bonfim. Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics. *Journal of Banking and Finance*, 33 (2):281–299, 2 2009. ISSN 03784266. doi: 10.1016/j.jbankfin.2008.08.006.

P. Bongini, M. L. D. Battista, and L. Nieri. Relationship banking: an old solution for the present crisis? *BANCARIA*, 5:2–20, 2009. URL https://ideas.repec.org/a/ban/bancar/v5y2009mmayp2-20.html https://ideas.repec.org/a/ban/bancar/v5y2009mmayp2-20.html.

P. Bongini, M. L. Di Battista, and L. Nieri. Relationship Lending Through the Cycle: What Can We Learn from Three Decades of Research? *SSRN Electronic Journal*, 12 2015. doi: 10.2139/SSRN.2925893. URL https://papers.ssrn.com/abstract=2925893.

A. W. Boot. Relationship Banking: What Do We Know? *Journal of Financial Intermediation*, 9(1):7–25, 2000. ISSN 10429573. doi: 10.1006/jfin.2000.0282.
C. Chang, G. Liao, X. Yu, and Z. Ni. Information from Relationship Lending: Evidence from Loan Defaults in China. *Journal of Money, Credit and Banking*, 46(6):1225-1257, 9 2014. ISSN 0022879. doi: 10.1111/jmcb.12139. URL http://doi.wiley.com/10.1111/jmcb.12139.

T. J. Chemmanur and P. Fulghieri. Reputation, Renegotiation, and the Choice between Bank Loans and Publicly Traded Debt. *The Review of Financial Studies*, 7(3):475-506, 1994.

M. Cotugno, S. Monferrà, and G. Sampagnaro. Relationship lending, hierarchical distance and credit tightening: Evidence from the financial crisis. *Journal of Banking and Finance*, 37(5):1372-1385, 5 2013. ISSN 03784266. doi: 10.1016/j.jbankfin.2012.07.026.

G. S. Crawford, N. Pavanini, and F. Schivardi. Asymmetric Information and Imperfect Competition in Lending Markets. *American Economic Review*, 108 (7):1659-1701, 7 2018. ISSN 0002-8282. doi: 10.1257/aer.20150487. URL https://pubs.aeaweb.org/doi/10.1257/aer.20150487.

H. Degryse and S. Ongena. Bank Relationships and Firm Profitability. *Financial Management*, 30(1):9, 2001. ISSN 00463892. doi: 10.2307/3666389.

H. Degryse, P. de Goeij, and P. Kappert. Industry Characteristics On Small Firms’ Capital Structure: Evidence From Dutch Panel Data. *CentER Discussion Paper*, 2009.

E. Detragiache, P. Garella, and L. Guiso. Multiple versus single banking relationships: Theory and evidence. *Journal of Finance*, 55(3):1133-1161, 2000. ISSN 00221082. doi: 10.1111/0022-1082.00243.

M. Dewatripont and E. Maskin. Credit and efficiency in centralized and decentralized economies. *Review of Economic Studies*, 62(4):541-555, 1995. ISSN 1467937X. doi: 10.2307/2298076.

D. W. Diamond. Monitoring and Reputation: The Choice between Bank Loans and Directly Placed Debt. *Journal of Political Economy*, 99(4):689-721, 1991.

D. W. Diamond. Financial intermediation and delegated monitoring. *Review of Economic Studies*, 51(3):393-414, 1984. ISSN 1467937X. doi: 10.2307/2297430.

R. Elsas. Empirical determinants of relationship lending. *Journal of Financial Intermediation*, 14(1):32-57, 1 2005. ISSN 10429573. doi: 10.1016/j.jfi.2003.11.004.

G. Ferri and M. Messori. Bank-firm relationships and allocative efficiency in Northeastern and Central Italy and in the South. *Journal of Banking and Finance*, 24(6):1067-1095, 6 2000. ISSN 03784266. doi: 10.1016/S0378-4266(99)00118-1.
F. Fiordelisi and D. S. Mare. Competition and financial stability in European cooperative banks, 7 2014a. ISSN 02615606.

F. Fiordelisi and D. S. Mare. Competition and financial stability in European cooperative banks. *Journal of International Money and Finance*, 45:1–16, 2014b. ISSN 02615606. doi: 10.1016/j.jimonfin.2014.02.008. URL http://dx.doi.org/10.1016/j.jimonfin.2014.02.008.

A. Foglia, S. Laviola, and P. Marullo Reedtz. Multiple banking relationships and the fragility of corporate borrowers. *Journal of Banking and Finance*, 22(10-11):1441–1456, 10 1998. ISSN 03784266. doi: 10.1016/S0378-4266(98)00058-2.

A. Geršl and P. Jakubík. Relationship lending in emerging markets: Evidence from the Czech Republic. *Comparative Economic Studies*, 53(4):575–596, 2011. ISSN 08887233. doi: 10.1057/ces.2011.12.

Harhoff and Körting. Lending relationships in Germany Empirical evidence from survey data. *Journal of Banking & Finance*, 22:1317–1353, 1998. doi: 0378-4266/98/$. URL papers3://publication/uuid/716C305E-54CA-4DB6-9765-54D3E64BF037.

V. Ioannidou and S. Ongena. "Time for a Change": Loan Conditions and Bank Behavior when Firms Switch Banks. *The Journal of Finance*, 65(5):1847–1877, 10 2010. ISSN 00221082. doi: 10.1111/j.1540-6261.2010.01596.x. URL http://doi.wiley.com/10.1111/j.1540-6261.2010.01596.x.

V. Ioannidou, S. Ongena, and J. L. Peydró. Monetary policy, risk-taking, and pricing: Evidence from a quasi-natural experiment. *Review of Finance*, 19 (1):95–144, 2015. ISSN 1573692X. doi: 10.1093/rof/rfu035.

G. Jiménez and J. Saurina. Collateral, type of lender and relationship banking as determinants of credit risk. *Journal of Banking and Finance*, 28(9):2191–2212, 2004. ISSN 03784266. doi: 10.1016/j.jbankfin.2003.09.002.

S. Kaufmann. Monetary policy transmission in the Tunisian banking sector-Comments. In *10th Annual NBP - SNB Joint Seminar*, 2013.

E. Lehmann and D. Neuberger. Do lending relationships matter? *Journal of Economic Behavior & Organization*, 45(4):339–359, 2001. ISSN 01672681. doi: 10.1016/s0167-2681(01)00151-2.

D. P. Louzis, A. T. Vouldis, and V. L. Metaxas. Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. *Journal of Banking and Finance*, 36(4):1012–1027, 4 2012. ISSN 03784266. doi: 10.1016/j.jbankfin.2011.10.012.

L. Menkhoff and C. Suwanaporn. On the rationale of bank lending in pre-crisis Thailand. *Applied Economics*, 39(9):1077–1089, 5 2007. ISSN 00036846. doi: 10.1080/00036840500447815.
P. Morales Acevedo. Firms' Strategic Choice of Loan Delinquencies. *Sveriges Riksbank Working Paper Series*, 2016.

N. Mori and G. Ng'urah. The Influence of Relationship Lending on SMEs Loan Repayment Performance. *Emerging Economy Studies*, 6(2):166-178, 2020.
ISSN 2394-9015. doi: 10.1177/2394901520977461.

S. Ongena and D. C. Smith. The duration of bank relationships. *Journal of Financial Economics*, 61(3):449-475, 9 2001. ISSN 0304405X. doi: 10.1016/S0304-405X(01)00069-1.

M. A. Petersen and R. G. Rajan. The Benefits of Lending Relationships: Evidence from Small Business Data. *The Journal of Finance*, 49(1):3-37, 3 1994. ISSN 00221082. doi: 10.1111/j.1540-6261.1994.tb04418.x. URL http://doi.wiley.com/10.1111/j.1540-6261.1994.tb04418.x.

M. A. Petersen and R. G. Rajan. The Effect of Credit Market Competition on Lending Relationships. *The Quarterly Journal of Economics*, 110(2):407-443, 5 1995. ISSN 0033-5533. doi: 10.2307/2118445. URL https://academic.oup.com/qje/article-lookup/doi/10.2307/2118445.

R. G. Rajan. Insiders and Outsiders: The Choice between Informed and Arm's- Length Debt. *The Journal of Finance*, 47(4):1367, 9 1992. ISSN 00221082. doi: 10.2307/2328944.

F. Salvadè, M. Troege, and N. Taillet. Passing the Parcel ? Bank-
ing Relationships at the Onset of Financial Distress. SSRN Elec-
tronic Journal, 10 2021. doi: 10.2139/SSRN.3936411. URL
https://papers.ssrn.com/abstract=3936411.

S. A. Sharpe. Asymmetric Information, Bank Lending, and Implicit Contracts: A Stylized Model of Customer Relationships. *The Journal of Finance*, 45(4): 1069-1087, 1990. ISSN 15406261. doi: 10.1111/j.1540-6261.1990.tb02427.x.

W. Sohn and H. Choi. Banks' lending decisions after loan acquisitions: Do banks favour pre-existing relationships? *Applied Economics*, 43(9):1099-1112, 2011. ISSN 00036846. doi: 10.1080/00036840802600202.

J. E. Stiglitz and A. Weiss. Credit Rationing in Markets with Rationing Credit Information Imperfect. *The American Economic Review*, 71(3):393-410, 1981.
ISSN 0002-8282. doi: 10.2307/1802787.

H. Uchida, G. F. Udell, and N. Yamori. Loan officers and relationship lending to SMEs. *Journal of Financial Intermediation*, 21(1):97-122, 1 2012. ISSN 10429573. doi: 10.1016/j.jfi.2011.06.002.
# 7 Appendix: marginal effects

<table><caption>Table 7: Marginal effects of core variables</caption><thead><tr><th></th><th>Pr(Out=1)</th><th>Pr(Out=2)</th><th>Pr(Out=3)</th></tr></thead><tbody><tr><td>ROA (banks)</td><td>0,007 ***</td><td>0,013 ***</td><td>0,007 ***</td></tr><tr><td>ROA (firms)</td><td>-0,038 **</td><td>-0,064 **</td><td>-0,035 **</td></tr><tr><td>Total assets (banks)</td><td>-0,025 ***</td><td>-0,043 ***</td><td>-0,023 ***</td></tr><tr><td>Total assets (firms)</td><td>-0,002 *</td><td>-0,004 *</td><td>-0,002 *</td></tr><tr><td>Leverage (banks)</td><td>-0,005 ***</td><td>-0,009 ***</td><td>-0,005 ***</td></tr><tr><td>Leverage (firms)</td><td>0,000 ***</td><td>0,001 ***</td><td>0,000 ***</td></tr><tr><td>Liquid. ratio (banks)</td><td>0,001 ***</td><td>0,002 ***</td><td>0,001 ***</td></tr><tr><td>Liquid. ratio (firms)</td><td>0,000 ***</td><td>0,000 ***</td><td>0,000 ***</td></tr><tr><td>Intensity</td><td>-0,235 ***</td><td>-0,400 ***</td><td>-0,218 ***</td></tr><tr><td>Duration</td><td>0,011 **</td><td>0,019 **</td><td>0,010 **</td></tr></tbody><thead><tr><th></th><th>Pr(Out=4)</th><th>Pr(Out=5)</th><th>Pr(Out=6)</th></tr></thead><tbody><tr><td>ROA (banks)</td><td>0,005 ***</td><td>0,006 ***</td><td>-0,039 ***</td></tr><tr><td>ROA (firms)</td><td>-0,027 **</td><td>-0,030 **</td><td>0,194 **</td></tr><tr><td>Total assets (banks)</td><td>-0,018 ***</td><td>-0,020 ***</td><td>0,130 ***</td></tr><tr><td>Total assets (firms)</td><td>-0,002 *</td><td>-0,002 *</td><td>0,013 *</td></tr><tr><td>Leverage (banks)</td><td>-0,004 ***</td><td>-0,004 ***</td><td>0,027 ***</td></tr><tr><td>Leverage (firms)</td><td>0,000 ***</td><td>0,000 ***</td><td>-0,002 ***</td></tr><tr><td>Liquid. ratio (banks)</td><td>0,001 ***</td><td>0,001 ***</td><td>-0,007 ***</td></tr><tr><td>Liquid. ratio (firms)</td><td>0,000 ***</td><td>0,000 ***</td><td>0,001 ***</td></tr><tr><td>Intensity</td><td>-0,170 ***</td><td>-0,188 ***</td><td>1,211 ***</td></tr><tr><td>Duration</td><td>0,008 **</td><td>0,009 **</td><td>-0,057 **</td></tr></tbody></table>

Note : *,**,*** : statistical significance at resp. 10%, 5% and 1%.

Pr(out=i): probability of outcome i.
<table><caption>Table 8: Marginal effects: firms’ characteristics interacted with intensity</caption><thead><tr><th></th><th>Pr(Out=1)</th><th>Pr(Out=2)</th><th>Pr(Out=3)</th><th></th><th></th></tr></thead><tbody><tr><td>ROA</td><td>-0,163</td><td>***</td><td>***</td><td>-0,145</td><td>***</td></tr><tr><td>Total assets</td><td>-0,001</td><td>***</td><td>***</td><td>-0,001</td><td>***</td></tr><tr><td>Leverage</td><td>0,001</td><td>***</td><td>***</td><td>0,001</td><td>***</td></tr><tr><td>Liquid. ratio</td><td>-0,001</td><td>***</td><td>***</td><td>-0,001</td><td>***</td></tr><tr><td>Intensity</td><td>-0,182</td><td>*</td><td>*</td><td>-0,162</td><td>*</td></tr><tr><td>Duration</td><td>0,004</td><td></td><td></td><td>0,003</td><td></td></tr><tr><td>CPI</td><td>0,050</td><td></td><td></td><td>0,045</td><td></td></tr><tr><td>GDP</td><td>-0,009</td><td>*</td><td>*</td><td>-0,008</td><td>*</td></tr><tr><td>ROA × Intensity</td><td>0,173</td><td>***</td><td>***</td><td>0,155</td><td>***</td></tr><tr><td>Leverage × Intensity</td><td>-0,001</td><td>***</td><td>***</td><td>-0,001</td><td>***</td></tr><tr><td>Liquidity ratio × Intensity</td><td>0,001</td><td>***</td><td>***</td><td>0,001</td><td>***</td></tr><tr><td>Total assets × Intensity</td><td>-0,004</td><td></td><td></td><td>-0,004</td><td></td></tr><tr><td></td><th>Pr(Out=4)</th><th>Pr(Out=5)</th><th>Pr(Out=6)</th><th></th><th></th></tr><tr><td>ROA</td><td>-0,110</td><td>***</td><td>***</td><td>0,816</td><td>***</td></tr><tr><td>Total assets</td><td>-0,001</td><td>***</td><td>***</td><td>0,004</td><td>***</td></tr><tr><td>Leverage</td><td>0,001</td><td>***</td><td>***</td><td>-0,005</td><td>***</td></tr><tr><td>Liquid. ratio</td><td>-0,001</td><td>***</td><td>***</td><td>0,004</td><td>***</td></tr><tr><td>Intensity</td><td>-0,123</td><td>*</td><td>*</td><td>0,911</td><td>*</td></tr><tr><td>Duration</td><td>0,003</td><td></td><td></td><td>-0,019</td><td></td></tr><tr><td>CPI</td><td>0,034</td><td></td><td></td><td>-0,250</td><td></td></tr><tr><td>GDP</td><td>-0,006</td><td>*</td><td>*</td><td>0,047</td><td>*</td></tr><tr><td>ROA × Intensity</td><td>0,117</td><td>***</td><td>***</td><td>-0,867</td><td>***</td></tr><tr><td>Leverage × Intensity</td><td>-0,001</td><td>***</td><td>***</td><td>0,005</td><td>***</td></tr><tr><td>Liquid. ratio × Intensity</td><td>0,001</td><td>***</td><td>***</td><td>-0,005</td><td>***</td></tr><tr><td>Total assets × Intensity</td><td>-0,003</td><td></td><td></td><td>0,022</td><td></td></tr></tbody></table>

Note : *,**,*** : statistical significance at resp. 10%, 5% and 1%.

Pr(out=i): probability of outcome i.
<table><caption>Table 9: Marginal effects: firms’ characteristics interacted with duration</caption><thead><tr><th></th><th>Pr(Out=1)</th><th>Pr(Out=2)</th><th>Pr(Out=3)</th></tr></thead><tbody><tr><td>ROA</td><td>-0,127 **</td><td>-0,190 **</td><td>-0,098 **</td></tr><tr><td>Total Assets</td><td>-0,008 *</td><td>-0,011 *</td><td>-0,006 *</td></tr><tr><td>Leverage</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>Liquid. Ratio</td><td>-0,001 ***</td><td>-0,001 ***</td><td>-0,001 ***</td></tr><tr><td>Intensity</td><td>-0,063 ***</td><td>-0,095 ***</td><td>-0,049 ***</td></tr><tr><td>Duration</td><td>-0,069</td><td>-0,103</td><td>-0,053</td></tr><tr><td>Inflation</td><td>0,087 *</td><td>0,130 *</td><td>0,067 *</td></tr><tr><td>GDP</td><td>-0,013 *</td><td>-0,019 *</td><td>-0,010 *</td></tr><tr><td>ROA × Duration</td><td>0,037</td><td>0,055</td><td>0,029</td></tr><tr><td>Leverage × Duration</td><td>0,000 *</td><td>0,000 *</td><td>0,000 *</td></tr><tr><td>Liquid. Ratio × Duration</td><td>0,000 **</td><td>0,000 **</td><td>0,000 **</td></tr><tr><td>Total Asset × Duration</td><td>0,002</td><td>0,003</td><td>0,002</td></tr></tbody><thead><tr><th></th><th>Pr(Out=4)</th><th>Pr(Out=5)</th><th>Pr(Out=6)</th></tr></thead><tbody><tr><td>ROA</td><td>-0,075 **</td><td>-0,081 **</td><td>0,571 **</td></tr><tr><td>Tot. Assets</td><td>-0,004 *</td><td>-0,005 *</td><td>0,034 *</td></tr><tr><td>Leverage</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>Liquid. Ratio</td><td>-0,001 ***</td><td>-0,001 ***</td><td>0,004 ***</td></tr><tr><td>Intensity</td><td>-0,037 ***</td><td>-0,040 ***</td><td>0,284 ***</td></tr><tr><td>Duration</td><td>-0,041</td><td>-0,044</td><td>0,310</td></tr><tr><td>Inflation</td><td>0,051 *</td><td>0,055 *</td><td>-0,390 *</td></tr><tr><td>GDP</td><td>-0,008 *</td><td>-0,008 *</td><td>0,058 *</td></tr><tr><td>ROA × Duration</td><td>0,022</td><td>0,024</td><td>-0,166</td></tr><tr><td>Leverage × Duration</td><td>0,000 *</td><td>0,000 *,</td><td>-0,001 *</td></tr><tr><td>Liquid. Ratio × Duration</td><td>0,000 *</td><td>0,000 **</td><td>-0,001 **</td></tr><tr><td>Tot. Asset × Duration</td><td>0,001</td><td>0,001</td><td>-0,010</td></tr></tbody></table>

Note : *,**,*** : statistical significance at resp. 10%, 5% and 1%.

Pr(out=i): probability of outcome i.