Eyeballing the bowl of ugali lying on the rough-edged wooden table for eight, the African farmer feels once again let down by those more powerful than herself: weather, God and government. Rewinding this year’s events, the rain was unmoved by the collective prayers and tantalized villagers with an elusive appearance. With pest killers selling at exorbitant prices on the black market, many farmers could only look on as pests clung stubbornly to their crops. For some, the destructive effects have multiplied like cancer cells over the past several months. What now looks like a poor harvest will lead to poor market trading, scarce cash, and hunger pangs.
The African farmer has seen it all, more hunger seasons than she can count. Yet her repertoire of options to beat hunger has not expanded throughout the years. She will reach out to relatives and friends for a bag of maize while contemplating her next move: getting a loan to feed her family of eight until the next harvest. Absent from the village, commercial lenders are immediately dismissed as a sustainable solution. The farmer focuses instead on the one familiar face who lives just a few huts away: the village moneylender. He has been lending for decades and his services, though costly, have helped many villagers navigate their way through rough times. As the warm ugali is being rationed out spoon by spoon among the household members, this farmer knows that filling the bowl in the weeks and months to come will once again be her own individualized struggle against the system.
This introduction succinctly summarizes this article’s topic: investigating by means of microeconomic theory why the formal finance channels are closed to African smallholder farmers and why the village moneylender is a significant alternative to turn to in hard times.
That African rural households are exposed to credit shortages is a well-known reality. What is perhaps paradoxical is the fact that African banks are believed to have the biggest cash reserves in the world (Andrianova, et al., 2011). Yet, according to a study published by the African Development Bank on four East African economies – Kenya, Tanzania, Uganda, and Ethiopia – the share of commercial banks’ loans to agriculture in each of these countries is exceedingly low.
In Tanzania for example, access to credit is mainly available to well-off individuals that reside in big cities and, unlike the rural population, are able to meet high collateral requirements (Salami, et al., 2010). Despite ongoing government initiatives to address the challenges of agricultural financing, the continent’s potential in agriculture remains largely untapped while many countries continue to import large quantities of food. Inevitably one may wonder why formal credit eludes smallholder farmers and how microeconomic theory can be used to explain this.
In an ideal scenario, loans are allocated on a competitive basis and the interest rate is dictated by a supply-and-demand mechanism. When formal lenders extend loans, they are concerned with the loan interest rate and the borrower’s level of riskiness (Stiglitz and Weiss, 1983). According to Besley (1994), the interest rate must be set high enough to prevent some individuals from seeking loans and low enough for borrowers to want to repay their loans. It follows that those individuals with the best projects are ready to pay the highest interest rates and in a perfectly functioning loan market they should be selected as borrowers. However, this is often not the case because credit markets do not operate efficiently.
Banks make money with “good” customers and low transaction costs. However, the prospects of bad debt are a common reality for all lenders and so are the high enforcement costs that often render loan servicing unprofitable (Besley, 1994). It comes as no surprise that lenders aim to be ahead of the game by filtering out those potential borrowers that are either unable (due to various shocks) or unwilling (due to weak legal enforcement) to repay. The rural financial markets raise additional challenges in that there are no smooth information flows between potential borrowers (farmers) and commercial lenders. Banks cannot obtain the information they need and farmers are generally unable to send out the right signals of themselves as trustworthy customers. These informational asymmetries, discussed in detail below, help explain why formal financing channels continue to elude African smallholder farmers.
Credit officers employed by formal banks often travel to rural areas and acquire relevant information with respect to the average quality of the borrower group in any given village. Yet, it is highly unlikely that they will obtain a detailed profile of each individual borrower. Unsurprisingly, the banks find it impossible to distinguish between the low-risk and high-risk potential borrowers in this scenario.
This type of asymmetric information leads to adverse selection. Stiglitz and Weiss (1983: 393) perceive the adverse selection problem as the “consequence of different borrowers having different probabilities of repaying their loan.” Because the expected return of the lender depends on loan repayment, it is in the bank’s interest to distinguish between borrowers of different risk profiles and choose customers who can repay. Stiglitz and Weiss (1983) further argue that the interest rate can be a screening device because those customers that are ready to pay higher interest rates may be high risk themselves. Thus, as the interest rate rises, the level of “average riskiness” of borrowers rises as well and so does the likelihood of losing profits from the perspective of the bank.
Applying this concept to the rural credit markets in Sub-Saharan Africa, one can argue that by the adverse selection mechanism, banks will lose the low-risk borrowers and retain the high-risk ones. Moreover the high-risk customers do not mind paying the higher interest rates as they know they will not repay the loans should their projects fail. As a result, formal financial services providers choose to forgo lending altogether due to what they believe to be a lack of both creditworthy borrowers and sound projects to fund.
Further investigation of the impact of adverse selection on the lending decisions of formal financial service providers would likely consider two scenarios. First, consider a situation where the banks are perfectly informed and they compete to attract farmers in a given village. They could charge each customer a different interest rate depending on their risk profiles (Bardhan and Udry, 1999). Low-risk farmers would be charged lower interest rates whereas high-risk farmers would be offered more expensive loans. Both types of farmers would get the needed credit and the lenders would be able to appropriately hedge their portfolio risks.
A second scenario is more reflective of reality, however, because it assumes that borrowers’ profiles can only be partially identified while relevant characteristics that drive their risk profiles often go unobserved. For example, farmers who own more land and diversify their crops may be considered low risk by the lender because they are believed to make better entrepreneurs with a smaller probability of default. By contrast, inexperienced farmers who rent small plots of land, have big families to feed and occasionally rely on help from others would usually come across as high-risk borrowers. Despite available information on their potential customers, formal lenders may be missing out on valuable facts that are not readily observed.
To stay safe and manage their risks effectively, formal lenders are, according to Ray (2014), compelled to apply a higher interest rate to the entire pool of borrowers (in this case, both low- and high-risk farmers). Stiglitz and Weiss (1983) argue that the lender itself can affect the riskiness of its borrowers’ pool through the interest rate it charges to screen potential borrowers (the adverse selection effect) as a result of asymmetric information. A higher interest rate will likely decrease the demand for loans from low-risk borrowers. Since these potential borrowers are committed to repaying their loans by all means, when the new interest rate goes up, they will be put off by suddenly unaffordable formal loans. As a result, they will drop out as potential borrowers even if they are running out of options. As the low-risk borrowers drop out, the lender’s income falls. The banks will be left with the high-risk borrowers who are more expensive to service and cause lenders to lose money. These borrowers are happy to stay; they will use the loans to carry out their risky ventures knowing that project failure will make repayment impossible (Bardhan and Udry, 1999). As a result, formal lenders are rather cautious and the high-risk borrowers rather careless in their actions.
One may argue that the introduction of collateral requirements would provide formal lenders with a solution to distinguishing between good and bad borrowers. This is not the case of African smallholder farmers for several reasons. First, this group of potential borrowers would be unable to secure collateral as a loan requirement because they cannot afford it. In addition, the farmers are often faced with disputed land titles, an unfavorable factor in a loan application. Furthermore, Stiglitz and Weiss (1983) caution against using collateral as a tool to reduce the risk of default or the demand for credit due to the adverse selection problem. Their model indicates that especially those well-off individuals who can afford collateral are likely to be the ones with high-risk projects; hence, collateral requirements would eventually lower the expected return of the bank.
In the absence of formal finance channels, the African smallholder farmers turn to the man next door, the village moneylender who has an intimate knowledge of his client’s land plots, productivity, and income levels; more important, this informal lender will know precisely the creditworthiness of the prospective borrower due to tightly knit social networks prevailing in the village. Once the reputation has been damaged by the failure of loan repayment, the damage is irreversible. Hence, the element of peer pressure acts as a strong deterrent of default among farmers as borrowers. Interesting to note here are the types of collateral the village moneylender is ready to accept. For example, a moneylender who regularly hires labor will accept labor as collateral should the borrower fail to repay (Ray, 2014). This collateral would be unthinkable for formal lenders whose ability to reach out to poor farmers continues to be a challenge.
While the informal moneylenders navigate the murky African rural credit markets with great skill, thanks to the insider information about their clientele, the commercial banks sit passively on vast cash reserves driven by fear of losses and insolvency. These unused resources could have bought an irrigation pump, a better variety of seeds or fertilizer or other capital to help raise crop yields and income. When channeled correctly, they can help eliminate the all-too-familiar pangs of hunger for millions of African farmer households.
Andrianova, Svetlana; Baltagi, Badi H.; Demetriades, Panicos O. and Fielding, David (2011) – Why do African Banks lend so little, Working Paper 11/19, March 2011, available online at: http://www.le.ac.uk/ec/research/RePEc/lec/leecon/dp11-19.pdf.
Bardhan, Pranab and Udry, Christopher (1999) Development Microeconomics, 1st ed., Oxford University Press.
Besley, Timothy (1994) How do market failures justify interventions in rural credit markets, The World Bank Research Observer, 9(1), pp. 27-47.
Ray, Debraj (2014) Development Economics, 23rd ed. Oxford University Press.
Salami, Adeleke; Kamara, Abdul B. and Brixiova, Zuzana (2010) – Smallholder Agriculture in East Africa: Trends, Constraints and Opportunities, African Development Bank Group Working Paper, No. 105, April 2010, available online at http://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/WORKING%20105%20%20PDF%20d.pdf?origin=publication_detail.
Stiglitz, J. and Weiss, A. (1983) Credit Rationing in Markets with Imperfect Information, The American Economic Review 71(3), pp. 393-410.