Social Capital and Access to Banks Loans: Case of Tunisian SMES

Keywords

Social Capital • Small and Medium Enterprises • Entrepreneursh ip • Credit • Access to credit

Introduction

Small and medium-sized enterprises are an asset for development, representing both an engine for growth and a tool for redistribution. It is, therefore, true that encouraging the creation and activity of SMEs is thus simultaneously encouraging initiative, fostering the creation of values and strengthening social and economic ties between the various actors and agents of the economy [1]. Despite their importance, SMEs often suffer from limited access to finance which constrains their emergence and growth. Likewise, several studies [2- 4]. Bellettante B and Levratto N [5] show that difficulties in accessing finance constitute the first obstacle to the development of SMEs in Sub-Saharan Africa, although there are other problems including corruption, dilapidated and poor infrastructure, inadequate regulatory framework, and excessive taxation, etc.

Despite these facts, financial institutions have tended to view small and medium-sized businesses as an attractive business segment in recent years. We observe the design and adaptation of the products offered by financial institutions and which are based on market research to identify the needs of SMEs. Entrepreneurs, for their part, take more interest in working with financial institutions, in using banking services and in particular in accessing credit. This is mainly due to the characteristics of SME managers, its structure and the social capital enjoyed by SMEs through the membership of managers in networks, groups and associations. New work on social capital theory raises the importance of social capital for access to credit. The share capital is an essential element in the decision of the loan which can go so far as to reduce the real guarantee required by the financial institution. In the presence of share capital, the transaction costs linked to the credit contract and the risk of business default are significantly reduced [6]. Social capital is, therefore, seen as an excellent catalyst for access to credit when the conditions set by financial institutions are not met. This positive effect of social capital on access to credit requires reducing the degree of opacity of the SME in the eyes of the financial institution and establishing a lasting link between the SME and the latter.

Accordingly, social capital is mentioned by many authors as a solution to the credit constraint [7]. Social capital refers to the characteristics of social organizations such as networks, norms and social trust that facilitate coordination and cooperation in a common interest [7-9]. It refers to the connections between individuals, social networks and the norms of reciprocity and loyalty that result from themselves. The share capital is seen as a substitute for the real financial guarantees required by financial institutions.

As for the city of GAFSA, this sector proves to be unstructured. Indeed, private initiative in the economic development of this part of the region is hampered by the lack of start-up capital and the weakness of business capacity. The asymmetry of information that characterizes the market means that financial institutions do not have the necessary information to distinguish between good and bad borrower. This fact pushes financial institutions to face the problems of adverse selection and chance moral. However, unlike large companies, SMEs are less equipped to overcome the information problem and are therefore exposed to credit rationing.

The social networks of Gafsian entrepreneurs are characterized by a variety of relationships. Indeed, there is the increase in entrepreneurial resources such as motivation, ideas, information, the capital as well as the confidence allowing them to make up for their lack of resources. Although some networks may find themselves in the best conditions to represent their activities than others, the social portfolio of SME managers appears to be an asset, opening up entrepreneurs to many opportunities allowing them to develop their respective activities.

The multiplicity of social networks of entrepreneurs in this city leads us to question the role played by the social capital from which these entrepreneurs benefit in financing their activities. This study is limited to SMEs in the city of Gafsa. Its objective is to test, among various other variables, the effect of social capital in terms of lending to SMEs in this region. In other words, we want to analyse the capacity of the social capital of the SME to facilitate its access to credit. In addition to the documentary approach, the interview and a field survey for data collection, a statistical analysis based on the chi-square test and logistic regression was used to meet the aforementioned object.

Literature Review

In this section, we discuss the theoretical aspects of social capital, the financing of the SME sphere as well as the convincing links that may exist between the two.

Social capital: What are we talking about?

All the literature on social capital remains at least unanimous as to the pioneers of the concept. Baret et al. and Bidart emphasize that the concept of social capital referring to the methods of access and use of the resources contained in social networks is the prerogative of the three "pioneer" authors, namely Pierre Bourdieu, James Coleman and Robert Putnam. For Bourdieu Social Capital is understood as "the set of real and potential resources linked to the possession of a lasting network of more or less institutionalized relationships of mutual knowledge and recognition - that is, in other words, belonging to a group ”. In 1986, the author distinguished three other notions of capital, among others: (1) Cultural capital; (2) economic capital (3) symbolic capital.

For Coleman J, social capital is defined by its function as follows: "It is not a single entity, but a variety of different entities which have two characteristics in common. They constitute all an aspect of the social structure, and they facilitate certain actions of individuals who are within the structure. Like other forms of capital, social capital is productive, making it possible to achieve certain goals that could not be achieved in its absence. “Thus, social capital appears as an alternative to the law and to the contract to constrain behaviour. Here, Coleman represents social capital as a notion inherent in social structures.

For Putnam R, social capital is more akin to a concept relating to characteristics of social organization such as networks, norms and trust, which facilitate coordination and cooperation for mutual benefit. Perret B notes that where Bourdieu and Coleman pose social capital as a resource enabling individuals to achieve their personal goals, Putnam conceives of social capital as a quantity characteristic of the state of a society.

This approach has been widely publicized but is now widely criticized for various reasons. According to Ponthieux, it fails to make an amalgam of very distinct phenomena (some referring to information practices such as sociability, associative participation, and others to perceptions such as norms and values). In addition, it covers in a single whole what social capital derives value (networks) and what it produces (reciprocity, trust).

Apart from the pioneers, other researchers, notably Thirion and Casteels, have outbid the concept of social capital qualifying it more as an economic good or asset but very particularly a non-market resource which is not tradable on the market. Like the pioneers, they realize that the difficult measure of this concept explains a flourishing of approaches and indicators used to understand the mechanisms of interaction between social capital and access to credit.

By reconciling the various opinions on social capital, we can retain that this concept designates the set of institutions, relationships of trust, norms, values and adequate conditions for the circulation of information which shape the quality of social interactions in a group for the benefit of all of its members to develop them for economically and / or socially.

Having discussed the definition of social capital in the preceding lines, let us now question the literature on the various possibilities of its measurement. Various authors and schools of thought suggest multiple ways of apprehending approximately the concept of social capital. By way of illustration, Granovetter [10] defines 4 dimensions of the strength of a bond: amount of time, emotional intensity, intimacy (mutual trust), and reciprocal services. However, due to the difficulties of measuring and collecting data, researchers are often required, in order to measure the strength of a link, to measure only some of these dimensions. The most often used are frequency of contact and emotional intensity. By testing three of these four dimensions, Marsden and Campbell have shown that the best indicator of bond strength is emotional intensity. Johannisson [11] adds other indicators such as regularity in the use of relationships, level of maturity, and degree of confidence and nature of past experiences.

With reference to authors like Dubini and Aldrich [12] Jenssen and Koenig [13] and O’Donnell [14], Basimine, J schematically represents the major dimensions of social capital as shown in the Figure 1.

Figure 1. The dimensions of social capital.

Financing SMEs

Access to credit for SMEs is one of the major concerns of SME managers. The development of Microfinance over the last ten years has helped to challenge the idea that it is difficult for banks to grant loans to SMEs because the latter do not have sufficient guarantees but also because the risks and the costs associated with these transactions are enormous. This success of microfinance institutions is explained by the mode of lending they have successfully implemented in particular group lending.

Indeed, structural constraints are most often put forward as the main determinants of SMEs' access to microcredit. The absence of collateral, the restriction of eligibility conditions for loans, the interest rate, the unsuitability of loans to the needs of SMEs are all factors linked to financial institutions and which severely limit access by the largest number of SMEs on credit.

Beyond these commonly cited structural constraints, various other obstacles exist and contribute to limiting SMEs' access to credit. In the Tunisian context, it seems that the characteristics of the activities, the cultural and socio-demographic factors, the inadequacy and the uneven distribution of financial services contribute strongly to the exclusion of SMEs from financial services. The characteristics of the activities sometimes require funding which the formal system does not take into account. When the loan is granted for certain activities, it is often insufficient to conduct a profitable activity and this compromise the repayment period.

The unequal distribution of financial services and their inadequacy to the needs of SMEs are also considered to be factors that exclude SMEs from financial services. Regions of very high poverty where services are needed remain less covered by the formal financial system. And when they exist, they are unsuited to the needs of SMEs given the conditions of eligibility [15].

Thus, it seems that understanding the financial practices, both formal and informal, of entrepreneurs through the cultural framework that supports them is essential to the establishment of services adapted to the real needs of SMEs. Knowledge of informal practices would allow financial institutions to better understand the behaviour of economic agents in terms of trade or financial transactions in the environment in which they operate. Hence the ultimate need to understand how the credit market works.

Several empirical studies have looked at the positive contribution of social capital to development. Social capital generates positive externalities allowing reaching more pareto-optimal equilibria than those conferred by a purely rational logic: (1) knowledge of the behaviour of agents especially on markets (labour, goods and services, ect...) where the risk is effective. Indeed, on the labour market, the dissemination of information carried out by social capital has a favourable influence on demand; knowledge of the actors' environment. When an economic actor wants to enter a credit market effectively, he necessarily needs not only a sufficient quantity of information but also an adequate quality Figure 2.

Figure 2. Conceptual model of the study.

Across the world, access to credit is often described as one of the main obstacles faced by micro-entrepreneurs. Finding money to start or expand a business is often the biggest challenge facing many micro entrepreneurs. Many of them invest part, if not all, of their own savings. But, along the way, most of them have to consider other sources of funding including banks, suppliers, family, friends, etc. In the city of Bukavu, entrepreneurs working more in the industrial sector are still among those who suffer the most from these problems of access to credit [16].

Methodological Approach and Assumptions

This section first discusses data collection techniques, then measures of the various variables used and fundamental assumptions of the study, and finally, data processing techniques.

Why a sample by Quota?

In this research, we could not constitute a certain base of survey. The problem of statistical representativeness (according to the probabilistic or random method) therefore does not arise for the following reasons:

• The API industrial companies’ directory appeared on the following electronic site (www.tunisianindustry.nat) shows the absence (in the directory) of micro companies whose jobs vary between 2 and 9. This means that 'We cannot use this directory as an exhaustive sampling frame to select the units of the sample for our research.

• The heterogeneity of the target population, industrial companies and their leaders (entrepreneurs): this is a diverse population, whether in terms of the structure of companies (size, age, activities, technological models, etc.) or the profile of their leaders (level of education, ages, etc.); which prevents us from selecting a random sample requiring the homogeneity of the target population.

• Entrepreneurs do not have the same chance of being part of the sample, which prevents us from having a certain base of units (entrepreneurs) to interview. This difficulty is mainly due to their travel and professional commitments. For these reasons, we choose to represent some characteristics of the mother population (industrial enterprises) in the survey sample and we therefore follow the following steps:

♦ Ensure variables of size and age of the company as central rule to constitute the sample with a distribution more or less equivalent to that of the mother population. That is to say, take into account the predominance, on the one hand, of small businesses, that is 61% for small businesses (whose jobs vary between 10 and 49) and keep the same percentage of medium-sized businesses (whose jobs vary between 50 and 499) or 36% and, on the other hand, the large number of micro enterprises (whose jobs vary between 2 and 9), or at a rate of 20% of all samples respondents.

♦ Having a majority share of the companies that have been created since 2000: 32% of the companies surveyed while representing a category of companies that were born during the period from 1970 to 1989, having a majority share for the governorate of Gafsa with a percentage of 57.04%.

♦ Diversify the selected units; to do this, we took the activity sector as another variable without being able to represent the same percentages in the sample. In this context, a survey was conducted in 2019 on a sample of 50 companies from the governorates of Gafsa. For the purposes of our research, we chose the interview, that is to say direct discussion sessions with the contractors. The data collected has been processed. Only the first managers of the companies visited answered our questions. For the development of our interview, we conducted an exploratory pre-survey. Subsequently, survey questions were developed, validated and then pre-tested in order to verify the plausibility of the advanced research hypotheses. The initial version of some questions was pre-tested with fifteen people more or less familiar with the subject and the problem. This allowed the reformulation of the questions deemed ambiguous as well as the withdrawal of the questions deemed redundant or not very relevant to the verification of research hypotheses.

Measures of variables

In this phase, it is a question of determining the variables which intervene in the present study.

- The dependent variable discussed here is access to credit. It is a dichotomous variable which takes the value 1 if the SME has had access to credit and 0 if not.

- The independent variables are divided into two modules. The first module includes the socio-demographic variables, the characteristic variables of credit as well as the control variables and the second having significant dimensions of social capital after factor analysis.

1° Age of the entrepreneur (age): this is a quantitative variable which results in the number of years already gone by since the birth of the entrepreneur. For this variable, older people are expected to have more access to credit than younger people. For Gaud, P. (2001), age-related difficulties generally lead older entrepreneurs to favour credit with low travel costs.

2° Gender of the entrepreneur (gender): this is a qualitative variable. It takes the value 0 if the entrepreneur is a man and 1 if it is a woman. Indeed, it has been observed that in terms of access to credit, women have a higher repayment rate than that of men. This is due to the fact that women have very little resistance to pressure from loan officers and the solidarity group, which is why they are always tempted to fulfil their obligations on time.

3° Level of study of the entrepreneur (Levstudy): It is a quantitative variable which captures the number of years of study of the entrepreneur. The more educated the entrepreneur, the greater the likelihood of accessing credit. In everyday life, an educated entrepreneur is more likely to meet people he knows at a financial institution who could facilitate access to credit. Soro A further notes that people with higher education tend to use their own funds than borrow. Hence it is expected that the more people are trained and graduated, the less they ask for credit.

4° Civil status of the entrepreneur (Civil Status): this is a qualitative variable which has been entered in a nominal manner. It takes the value 1 if the entrepreneur is single, 2 if he is married, 3 if he is divorced, finally 4 if he is widowed. Due to data processing, this variable was considered binary. It takes the value 0 if the entrepreneur is single, widowed or divorced and 1 if he is married. Empirical studies argue that a married person has a greater chance of accessing credit since their marital status allows them to judge the degree of responsibility and credibility in their engagements with lenders.

5° Size of the contractor's household (Household size): it is defined by the number of people taken care of by the contractor. Entrepreneurs with a large household size use credit more easily and access it easily through the repetitive effect of transactions. The work of Sahnert and Stein K supports this idea, asserting that the higher the number of dependents of the credit applicant, the greater the need for funds, the more he uses credit and has easy access to it.

6° Seniority of the entrepreneur in the activity (SME age): this variable is quantitative and refers to the number of years during which the entrepreneur carries out his activity. Generally, the more seniority the entrepreneurs have in an activity, the more they have access to credit. The seniority of the entrepreneurs creates a great mastery of its activity. The risk of bankruptcy becomes very minimal since we can predict certain events as well as solutions to deal with them [17].

7° Source of credit (source): this is a qualitative variable that tells us about the origin of the credit from which the entrepreneur benefits. It was entered nominally with 1 = friend, 2 = family, 3 = association, 4 = commission agent, 5 = bank / MFI. For the sake of brevity, this variable, in its capture, will take the value 1 if informal sources and 0 if formal sources. The source of credit has a positive effect on access to credit insofar as the source is informal and results from personal ties between the entrepreneur and his lender. The informal credit category includes credit obtained from a family member, friends and members of the association. The literature argues that the more informal credit is used, the higher the probability of accessing it given the degree of familiarity between the two agents [17].

8° Reason for requesting credit (reason): this is a qualitative variable that tells us about the need for which the entrepreneur requested the loan. The more the credit is allocated to profitable investments; the lender easily grants the credit because he knows in advance that the credit will have to be repaid thanks to the financed project.

9° Credit maturity (maturity): this is a quantitative variable and refers to the loan repayment time. The maturity of a loan facilitates access to credit for entrepreneurs since the time granted is important and allows him to finance his project and meet the monthly payments. Bellemare argues that this period must be acceptable, that is to say, converge towards the interests of these two parties because the more acceptable it is, the more entrepreneurs can make their activities profitable and repay the credit within the agreed time.

10° Social trust: (Soc trust): It refers to the trust that the entrepreneur places in the members of the association. Trust within a team increases the possibility of accessing new resources within a community. It is seen as a palliative measure for the opportunistic behaviour of certain members who want to take advantage of the effort made by the other members within the group. Mutual trust within a team alleviates the problem of information asymmetry through honest and open sharing of information, thereby facilitating access to credit.

11° Network links (Net links): this variable reflects the quality of relationship that the entrepreneur maintains with the members of his association and those of other groups. Social ties positively influence entrepreneurs' access to credit. Wamba H [6] argues that an entrepreneur who has successfully built strong relationships with other entrepreneurs, government officials and financial institutions has more opportunities to access credit than to not access it. In addition, such links are important insofar as they lead entrepreneurs to identify the resources and capacities of their partners and more particularly to access to credit.

12° Honesty (Hont): Refers to what is honest and suitable. An honest entrepreneur is more likely to access credit. Dufy C emphasizes that an entrepreneur who speaks honestly with his banker reduces his vulnerability towards him by reducing the transaction costs related to the acquisition of information and the control [18]. In the presence of honesty, the risk of bad debt becomes minimal.

In view of the above, we announce our central hypothesis formulated as follows:

H1: Access to credit is influenced by socio-demographic variables, characteristic variables of credit and control variables.

3.3. Data processing techniques

We use 2 important techniques: the descriptive approach and logistic regression.

The descriptive analysis essentially consists in the construction of simple cross tables in the form of frequencies, finally to assess the importance of access to credit for SMEs according to their socio-demographic characteristics but also to test the relationship of dependence or independence. Between access to credit and the latter. This is done since it is necessary to check beforehand that the preachers can influence the variable to be predicted using statistical tests. Variables that are significant are then retained and included in the model [19].

Logistic regression is finally used. As the dependent variable "Access to credit" is dichotomous (takes the value Y = 1 if the SME has had access to credit and 0 if not), both theory and empirical studies plan to use either the logit or the probit model. . From the results of the residuals normality test (K = 4.62; S = -4.84, ie S ≠ 0 and K ≠ 3), the logit model was retained. Hence the model thus developed:

Credit Access = α + βAge + β2Gender + β3 Levstudy + β4Civilstatus + β5Householdsize + β6age SME + β7 Trust + β8 network links + β9 honesty + ε SPSS and STATA software were used to estimate the logistical model and conduct the Exploratory Factor Analysis, while the Excel spread sheet and SPSS 16.0 software were used to generate descriptive statistics (Table 1).

Source: our calculations under SPSS v20.

Presentation and interpretation of the results

Descriptive approach to qualitative variables and Khi deuxtest

In this section, the different relationships that have been established between qualitative variables and access to credit for entrepreneurs in the city of Gafsa are presented (Table 2).

Source: Our calculations Under SPSS v20.

This Table 3 shows that the average age of our respondents is 40 years and the dispersion around this average is 10.642. The minimum age of our respondents is 26 and the maximum age is 76. This indicates that our sample is made up of adults. In terms of household size, on average each household is made up of 6 people with a standard deviation of 3.001. We note a maximum of 23 people and a minimum of 2 people per household, which shows a strong dispersion around this average. As for seniority in activity, on average most of our respondents have 10 years of seniority in entrepreneurship with a maximum of 48 and a minimum of 1. This shows a strong dispersion around the average. Finally, as far as the credit repayment deadline is concerned, on average entrepreneurs obtain a deadline of 10 months with a standard deviation of 6.87. As the maximum is 40 months and a minimum of 1 month, this result indicates that there is a large dispersion around this average.

Presentation and interpretation of logistic regression results

In this section, the results of the logistic regression will be presented and interpreted. He the main thing is to present and interpret the results of this study, and then follow a short discussion of the results. The results from this table indicate that there is a relationship between social capital and access to credit for entrepreneurs in the city of Gafsa. The results obtained prove that a single dimension of social capital positively and significantly influences access to credit. This is social trust within the network (prob 0.017˂0.05), a single socio-demographic variable including sex (prob 0.046˂0.1 positively influencing access to credit and a single control variable called household size (prob 0.093˂0,1).The gender of the entrepreneur is significant and positively influences the model, knowing that for in this study, men have more access to credit than women. SMEs led by men are 1.60 times more likely to access credit than not to access it. This result is explained by the context of the city of Gafsa where men are heads of households and have several responsibilities to assume although in some households women often provide the second source of income with small entrepreneurial activities. These results contradict those of Saruzi. This is due to the fact that, in the town of Gafsa, most of the SMEs are managed by men although it is the woman who manages them. And by ensuring the management, it facilitates the repayment of the credit through better management of the SME. We understand that when funds are needed, it is the man who requests credit on behalf of his SME and the repayment efforts come from the woman's management effort. From the above, we understand that the gender of the entrepreneur plays in favour of access to credit through a joint management of men and women.

The size of the entrepreneur's household positively and significantly influences access to credit. On average, each entrepreneur is responsible for 8 people. Such a workforce reveals a priori the burden that falls on the responsibility of the contractor. These are school fees, academic fees, health care, consumption, clothing, transport, housing; His monthly salary and entrepreneurial activity alone are not enough to cover the expenses listed above. These results support those of Sahnert and Stein, but at the same time contradict those of Bolder and Mweze. This is explained by the fact that, in the city of Gafsa, SMEs are born overnight, competition is becoming more and more and sales tend to vary downwards. To alleviate this problem at all, entrepreneurs make use of credit with a guarantee of reimbursement for the activity carried out by the entrepreneur. The credit obtained enables them to cover the needs of the funds while awaiting the sales receipts which will then have to cover the charges linked to the credit.

The confidence expressed through the results shows that entrepreneurs who are members of an association, group or business network trust each other, that they feel secure within their networks. Also, these results reveal that the degree of familiarity between them is important, this makes them easily trust in matters of loan and borrowing and it is the cause which makes that entrepreneurs always remain together to take advantage of the other advantages of belonging to an association. Entrepreneurs who have managed to inspire confidence in others are 2.77 times more likely to access credit than not to access it. These results coincide with those of Wamba and Niyonsaba.

In the city of Gafsa, financial transactions between agents are generally based on trust, although other variables can be taken into account. According to our interviews with entrepreneurs, they raised the idea that trust is a key element in their relationship with their business partners (bank, MFI, suppliers, customers, members of the association, etc.) through honesty in sharing some information. This mitigates the risk of moral hazard in terms of credit insofar as the entrepreneur in all his transactions inspires confidence in his partners.

Social capital facilitates entrepreneurs' access to credit through trust. This trust results from the organization of the group, respect for the values and standards established by the hierarchical authorities, honesty in commitments and the absence of information asymmetry.

Faced with the problems facing Gafsian entrepreneurs in general, the following recommendations can be made:

• The authorities of the country should provide legal and humanitarian assistance to these networks of entrepreneurs, support in the training of managers of SMEs Gafsens is necessary in order to consolidate the advantages linked to business networks in the provision of joint and several guarantees; which would strengthen their negotiating powers in the bank-client relationship.

• Entrepreneurs should use qualified staff and set up a transparent management system for the promotion of their SMEs. Also, entrepreneurs should also diversify their business networks, developed in them the spirit of responsibility to guarantee compliance with commitments, the taste for savings in order to meet the future needs of funds.

• Financial institutions should at their level reinforce financial services adapted to SMEs to facilitate their access to more resources as well as the development of entrepreneurial activity.

Conclusions

This research attempted to question the possible links between the components of social capital and access to credit by entrepreneurs in the City of Gafsa. More concretely, its aim was to test, among various other variables, the effect of social capital in terms of lending to SMEs in this entity. In addition to the documentary approach, the interview and a field survey for data collection, a statistical analysis based on the chi-square test and logistic regression was used to meet the aforementioned object.

The result of the logistic regression through the pseudo R2 of McFadden analogous to the R2 of the linear regression reveals that these variables explain our model at 24.11%. Also, through the Odds ratios, we realize that trust between entrepreneurs in the city of Gafsa increases 2.4355 times more likely to access credit than not to access it. Of even the sex of the entrepreneur increases 2.3013 times more likely to access than not to access, the size of the entrepreneur's household increases 1.1492 times more likely to access credit than not to access it.

From these results, we conclude that our initial hypotheses were verified but partially since, unlike the dimensions measuring social capital which were supposed to significantly influence access to credit for SMEs in the city of Gafsa, only one was found to be significant and contrary to socio-demographic and control variables.

Without claiming to have exhausted all the aspects linked to the concept of social capital, the present study constitutes an important contribution in the reflection on the institutional reforms useful for the creation of an environment favourable to the development of entrepreneurship in the city of Gafsa. However, we note that it has led to convincing and scientifically robust results. As limits, the investigations focused mainly on formally registered SMEs and on a reduced sample size. This limits the scope of the results and their generalization to all of Gafsa's SMEs. That being said, further research, based on current results, could deepen the social capital aspect in terms of its impact on the cost of borrowing, the profile of entrepreneurs who access credit, and the key sectors for investing the funds obtained, etc.

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