Does your cultural background affect your credit profile?

In many economies – both developed and developing – economic transactions tend to cluster by ethnic or social group. For example, in China, entrepreneurs from a single county own a majority of the country’s private hospitals; and the dry-cleaning business in American is dominated by Korean-Americans. From a macro perspective, countries that share ethnic or religious ties tend to trade more with one another (Guiso et al. 2009).

Economic clustering and cultural proximity

There are two explanations that readily present themselves for this clustering based on what we’ll call ‘cultural proximity’:

1. The shared values, beliefs, and means of communication that result from a shared background may engender greater trust and understanding. According to this view, like transacts with like because it facilitates the flow of ‘soft’ information that leads to a more efficient economic exchange.
2. Alternatively, individuals may prefer to interact with others similar to themselves for reasons of taste – or rather, distaste for those that are different. In this view, in-group transactions may be economically inefficient, as groups limit their trading options because they simply don’t like others.

The implications of cultural proximity

These two explanations have very different implications for how we should feel about transactions based on cultural closeness – if they’re the result of discrimination, we may wish to take more aggressive steps to break up networks than if they are based on improving trust and communication. The problem for researchers, though, is that both explanations yield a similar prediction – more exchange will take place between culturally similar individuals than across cultural boundaries.

Furthermore, rarely can we measure whether a transaction is more or less efficient (who’s to say whether buying a car from someone who looks like me leads to a purchase that better suits my needs), and even if we could measure efficiency, there’s a deeper problem that comes from the decidedly non-random matching of buyers and sellers.

To illustrate the problem, if there is a higher bar for transacting with those outside one’s circle, we may erroneously conclude that out-group transactions are generally more efficient, because we only observe them when a buyer comes across an ‘offer he can’t refuse’ from an out-group individual, an offer so enticing that he overcomes his personal repugnance.

The study

We overcome these challenges by studying the effects of cultural proximity in the setting of bank lending in India (Fisman et al. 2017). There is a clear measure of efficiency – if the borrower defaults, it indicates that the bank’s capital has been misallocated – and because the bank we study (and Indian banks in general) tends to rotate its loan officers around the country at high frequency, we may explore what happens to the quantity and quality of a branch’s loans when the religion or ethnic identity of the branch manager changes.

Testing in-group versus out-group transactions

The broader social fabric of modern India makes it a suitable testing ground for theories of in-group versus out-group transactions. The caste system provides a set of well-identified social cleavages across the several government-classified groups that are identified in our data. It is also a country with a recent history of religious conflict – especially between Hindus and Muslims – that could stoke the types of out-group animosity that could affect economic transactions.

The data

We use data from a large state-owned bank in India, which provided us with five years’ worth of detailed credit and personnel records. We used this data to match all borrowers and branch head officers to their religion and caste, providing a pairwise measure of the cultural ‘distance’ between lender and borrower.

We consider each of the country’s main religions – Hindu, Muslim, Christian, Sikh, Parsi, Buddhist – and “Others” as distinct groups, and among Hindus, also distinguish among the four government-classified caste groups (General Class, Scheduled Tribes, Scheduled Castes, and Other Backward Castes). In all, we have ten distinct ‘cultural’ groups.

Because officers are rotated every three years, we can account for the possibility that, for example, Hindus are more creditworthy than Muslims in general (or General Caste relative to Scheduled Caste borrowers). If that is what was driving the higher rate of credit access among Hindus, there is no reason to expect any sudden changes when the identity of the branch manager switches from Hindu to Muslim.

Because our data also include the information on whether the loan goes into default, we can also examine whether the default rate among Hindus is affected by whether the loan is issued by a Hindu or Muslim branch manager.

The findings

• Cultural proximity impacts the size of the loan. In looking at how the manager’s identity affected a branch’s loan portfolio, we found that there was a discontinuous jump in how much lending is made to, say, Muslims when a Hindu manager is replaced by a Muslim one, and a corresponding drop in the loans received by Muslims when the Muslim manager departs and is replaced by a Hindu one.
• Cultural proximity impacts the quality of the loan. We also found that the quality of the loan to Muslims also increased with the arrival of a Muslim branch manager.
• Cultural proximity impacts loan defaults. When assessing loan repayment, one can deduce that Muslim loan officers are better-equipped to assess the creditworthiness of Muslim borrowers, and make sure that the loan is repaid.

The relationship between cultural proximity and loan repayment came as a surprise to us. This is because prior to our analysis, we had believed that preferential treatment of loan applicants from one’s own group was driven primarily by personal distaste for other groups. But this would have led to more defaults from in-group borrowers, who got loans because of personal preferences rather than credit quality. The improvement in lending quality indicates that any such effect is more than counteracted by the beneficial effects of better information that comes from cultural proximity.

We were expecting to find that discrimination occurred on the basis of personal distaste, but it was surprising to see that, based on the evidence, its effect on default is outweighed by the beneficial effects of a shared background or culture.

Implications

• Our findings have the most direct implications for the setting we study – the role of cultural proximity in Indian banking specifically, and Indian commerce more generally. Within this context, we see our results in part as a salient and important counter-example to the prevailing view that the main effect of cultural proximity is (economically inefficient) favoritism.
• Taking the policy consequences to an extreme, one may be tempted to argue that the bank should encourage the matching of bank loan officers to areas where their caste or religion is prevalent to facilitate more efficient lending and that, more generally, within-group transactions in India should be promoted rather than discouraged.
  This take on encouraging within-group transactions as a means promoting economic efficiency is a static one, however. Instead, we might be able to break down misunderstandings and animosities between groups through greater interaction. This is in fact another intuition that many share for which the evidence is at best mixed – exposure to other groups may reduce animosity as we find they’re ‘just like us’, or aggravate it because we look for the negative features of the other group that made us dislike them in the first place.
• In-group preferences – whatever their efficiency consequences – will disadvantage minority groups in a society. Consider, for example, a Christian borrower in our data – he has only a 0.45% chance of encountering a Christian branch manager when he goes to apply for a loan. For ‘general caste’ Hindus, the probability is more than 53% (Fisman et al. 2017). The fact that Christians are less likely to get loans because their Hindu lenders have less information on them doesn’t make the discrimination any less real – it is still a means by which a privileged majority gets a further leg up on disadvantaged minorities.
• This sort of phenomenon can also help explain why we think that minorities are bad credit risks – if we look at the credit access and repayment rates of Christians in our data, we’d conclude they are riskier borrowers than Hindus, if we don’t account for the fact that Christians’ low rates of loan access and repayment result, in part, from the fact that they deal with out-group lenders, whereas Hindus interact with in-group ones.

Conclusion

The better policy may then be to consider interventions – potentially ones that take place long before beliefs and mores are fully formed – that break down informational barriers between groups, rather than organising society in a way that reinforces the existing ones, whatever the immediate payoff to economic efficiency might be.

References

Fisman, R, D Paravisini and V Vig (2017), “Cultural Proximity and Loan Outcomes”, American Economic Review 107(2): 457–492.

Guiso, L, P Sapienza and L Zingales (2009), “Cultural Biases in Economic Exchange?”, Quarterly Journal of Economics 124(3): 1095–1131.