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Journal of Business Ethics

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01.12.2014

Vive la Différence: Social Banks and Reciprocity in the Credit Market

verfasst von: Simon Cornée, Ariane Szafarz

Erschienen in: Journal of Business Ethics | Ausgabe 3/2014

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Abstract

Social banks are financial intermediaries paying attention to non-economic (i.e., social, ethical, and environmental) criteria. To investigate the behavior of social banks on the credit market, this paper proposes both theory and empirics. Our theoretical model rationalizes the idea that reciprocity can generate better repayment performances. Based on a unique hand-collected dataset released by a French social bank, our empirical results are twofold. First, we show that the bank charges below-market interest rates for social projects. Second, regardless of their creditworthiness, motivated borrowers respond to advantageous credit terms by significantly lowering their probability of default. We interpret this outcome as the first evidence of reciprocity in the credit market.

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Own calculations based on the figures in GABV (2012).

We refer to the figures of the European Federation of Ethical and Alternative Banks (FEBEA) available on www.febea.org.

We henceforth use “social bank” to describe any bank claiming to pay attention to extra-financial criteria, regardless of their specific nature, be they social, ethical, or environmental. Arguably, a triple bottom line may be advocated (Global Report Initiative 2011) insofar as social banks often combine ethical and environmental concerns. Akin to other works on socially responsible lending (e.g., Gutiérrez-Nieto et al. 2011; Allet and Hudon 2003), we consider environmental concern as part of social concerns. Moreover, Norman and MacDonald (2004) state that the triple bottom-line rhetoric may be misleading and act as a smokescreen.

Becchetti et al. (2011) identify the following foundational principles of social banks: (1) awareness of non-economic consequences, (2) access to finance as a human right, (3) efficiency and probity, (4) fair redistribution of profits, (5) full transparency, (6) encouragement of active involvement of shareholders and savers in decision making, and (7) ethical inspiration in all activities.

Cooperative status affects not only the design of the institution's governance but also the capital structure of its balance sheet. Ferri et al. (2010) and Iannotta et al. (2007) show that financial cooperatives tend to be better capitalized than commercial retail banks. Plausibly, this set-up is stronger in social banks. Management can use the diffuse ownership structure to easily retain earnings within the bank (Périlleux et al. 2012). This strategy is in line with the investors’ commitment to forgo financial returns in exchange for the accomplishment of the bank’s social mission. In addition, the cooperative status helps aligning the managers’ behavior with the bank’s social mission (Kitson 1996). Becchetti and Huybrechts (2008) draw the same conclusion for fair trade organizations.

Each ABS shareholder must remain below the 3-percent voting-right threshold. Triodos Bank’s shares are held in trust by an ad-hoc foundation, whose board is appointed by depository receipt holders with limited voting rights.

We only consider the two key categories of stakeholders: investors (shareholders, savers) and borrowers, and disregard other categories such as the staff. Nevertheless, Cornée et al. (2012) show that employees of social banks exhibit higher social preferences than their counterparts working in mainstream banks.

Paradoxically, more evidence is available on microfinance institutions active in developing countries than on social banks active in developed countries. The existing evidence on microcredit activity is, however, not transposable to social banking because the microcredit lending methodology is specific. It is based on the supply of standardized small loans without collateral (Armendariz and Morduch, 2010). Microfinance institutions typically charge identical interest rates to most—if not all—borrowers, and simply tailor loan size to their borrowers’ perceived creditworthiness (Agier and Szafarz 2013a).

Actually, our model includes a homogenous group of opportunistic borrowers (zero cost of cheating) and a continuum of motivated borrowers characterized by their degree of motivation, defined by their cost of cheating.

See www.lanef.com.

In 2010, its deposits-to-assets ratio was 85.92 % and its loans-to-assets ratio was 40.12 %, which is quite low. However, the resources not directly used for loans are entrusted to Le Crédit Coopératif, a partner cooperative bank sharing La Nef’s social values. In 2010, this represented 35.76 % of the balance sheet (La Nef 2010).

The data were collected in November 2008. The sample period for loan granting stretches from January 1, 2001 to November 25, 2004. The November 2004–November 2008 period is used only as a feedback period.

Since September 2007, La Nef has operated four branches.

The Ile-de-France, Provence-Alpes-Côtes-d’Azur and Rhône-Alpes regions are overrepresented since they include the three largest French cities: Paris, Marseille and Lyon, respectively.

Most likely, our sample does not suffer from a selection bias. The missing loans were excluded by accident, not on purpose. Unfortunately, we had no access to information on the denied applications. This in turn limits the possibility of observing the bank's full selection process.

Due to data unavailability, some statistics have been obtained from reduced samples. Location and loan officers are known for 367 firms, age and firm status for 369, and turnover and staff for 55.

The relatively low loans-to-assets ratio (40.12 %) may derive from a scarcity of social projects that break even.

Loan-loss provisioning is governed by law. Therefore, we rule out the possibility that loans with different social ratings are treated differently by the bank.

Admittedly, this argument would be stronger if we had access to data on denied loans, which is unfortunately not the case. Instead, we rely here on the assumption that the loan selection is made within a pool of applications large enough to allow the bank to make unconstrained choices. Although this assumption is debatable, we see no realistic scenario that would make the observed zero correlation spurious.

These percentages are obtained from a sub-sample of 367 firms.

The overall evolutions of the FIN and SR variables are stable. This excludes the possibility for the shift in spreads being driven by a change in the composition of the clientele.

We have also estimated a model explaining the spread. The estimation results are similar to those in Table 5 (“Impact of Social Rating on Interest Rate” section), regarding signs, amplitudes, and levels of significance. However, explaining the spread rather than the interest rate is detrimental to the quality of fit.

The loading of SR in specification (4) is lower than in the previous specifications. Presumably, this is because, unlike FIN ratings, the SR ratings are determined somewhat subjectively by loan officers.

The loans are extended for periods varying from 1 to 20 years. This four-year convention, fixed by the bank, is thus somewhat arbitrary. Still, 87 % of defaults occur within the four years following credit granting.

Logit estimations (not reported) bring similar results.

Sensitivity analysis reveals that variations in this parameter have little effect on the estimates of the NBR _t’s.

Loans in default are non-performing loans at least 90 days in arrears. Actually, LLP can also be manipulated strategically. For instance, banks have incentives to use provisions to manage earnings and regulatory capital as well as to signal information about future prospects (Ahmed et al., 1999). Nevertheless, working with differential—rather than absolute—costs likely offsets any strategic biases.

We use specification (3) rather than specification (4) in order to carry out the analysis on the full sample.

For French banks, Gouteroux (2006) and Ory et al. (2006) obtain operating ratios of between 62.5 and 68.5 %. In this respect, La Nef undoubtedly represents an outlier.

The robustness checks are carried out on the reduced sample for which we have full information (367 firms).

Even though La Nef has several branches, it has a single nationwide credit committee. This committee is composed of two persons: a headquarters-based manager and the loan officer. Importantly, branch-based loan officers take active part in the committee’s decision making. They can communicate all the relevant soft information either by being on-site or by phone. Since the headquarters are located in the South-East branch, loan officers from that branch perhaps influence the credit conditions more than their colleagues from other branches.

In other social banks, the social assessment is carried out according to distinct procedures. For example, in Banca Etica (Italy), a thorough social audit is conducted by the so-called “social auditors or experts”, who are cooperative members trained by the bank.

The provisioning rate of a loan in default is equal to LLP/loan size.

To obtain this figure, we have combined two sources of information. First, Robert de Massy and Lhomme (2008), mention that on average 15.97 % of total staff in French banks are devoted to the screening of SME loan applicants. Second, from annual reports (Banque Populaire de l'Ouest 2010; Crédit Agricole Ille-et-Vilaine 2010; Crédit Mutuel Arkéa 2011) of regional branches of the three major French cooperative banks dealing with SMEs we estimate their numbers of SME loans per officer: 41.09, 35.25, and 33.68 for Banque Populaire de l’Ouest, Crédit Agricole Ille-et-Vilaine, and Crédit Mutuel Arkéa, respectively. Averaging these figures yields 36.67 loans granted per officer per year. This computation is somewhat heroic since the activity sector, the type of clientele, and the lending technology should be held constant.

We used the following instrumental variables: ENVIRONMENT (dummy variable taking value 1 if the borrowing firm works in the environmental sector, and zero otherwise), RURAL (dummy variable taking value 1 if the borrowing firm is located in a rural area, and zero otherwise), NONPROF (dummy variable taking value 1 if the borrowing firm is a not-for-profit organization, and zero otherwise), UNLIMITED (dummy variable taking value 1 if the borrowing firm is an unlimited company, and zero otherwise), CONSORTIUM (dummy variable taking value 1 if the borrowing firm belongs to a consortium, and zero otherwise), and the duplicates (STARTUP and RELATIONSHIP).

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Titel: Vive la Différence: Social Banks and Reciprocity in the Credit Market
verfasst von: Simon Cornée
Ariane Szafarz
Publikationsdatum: 01.12.2014
Verlag: Springer Netherlands
Erschienen in: Journal of Business Ethics / Ausgabe 3/2014
Print ISSN: 0167-4544
Elektronische ISSN: 1573-0697
DOI: https://doi.org/10.1007/s10551-013-1922-9

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