PCA Impact on the Micro, Small & Medium Enterprises Credit Market in India


In April 2018, Business Insider India addressed the current state of the Indian credit market for Micro, Small & Medium Enterprises (MSME). The MSME credit market revolves around loans under ₹1 million or approximately 15,000 (2018) US dollars.

Recently, in November 2016, the demonetization plan in which the government of India declared that all the ₹500 and ₹1000 banknotes would become invalid and replaced with new banknotes in an effort to reduce counterfeit currencies circulating within the economy (India Today). In addition, in December 2017 the Reserve Bank of India (RBI) announced the Prompt Corrective Action (PCA) credit limit for Public Sector Banks (PSB). The PCA put 11 out of India’s total 21 PSB’s that had a 15% non-performing loan ratio on a watch list to establish and enforce a more conservative lending approach. The intention was to reinforce the balance sheets of these PSB’s due to their losses from defaults.

MSME’s are a large contributor to job creation and hence 40% of India’s GDP (Dhillon, D., 2018). Consequently, the PCA and demonetization plan distorted India’s GDP and MSME credit markets. According to the MSME Pulse Report, the PSB’s MSME credit market share was reduced and both Private Banks and Non-Banking Financial Companies (NBFC) gained a larger credit market share.

MSME Credit Market Share

Date: PSB’s Private Banks NBFC
December, 2015 61.5% 25.4% 7.9%
December, 2017 55.4% 28.5% 10.4%

Source: MSME Pulse Report

This increase in the MSME credit market shares for both private banks and NBFC’s resulted in higher interest rates for private banks and NBFC’s. This very link between a higher market share and the respective increase in interest rates due to the PCA framework can be explained through the neoclassical credit market theory and the influence of asymmetric information.

Neoclassical Credit Market Theory

The neoclassical credit market theory operates on two important principles: perfect information and competitive markets. Both lenders and borrowers have complete knowledge about the borrower’s investment opportunity and therefore the borrower’s repayment structure. The interest rate posed on the borrowers by lenders is equivalent to the opportunity cost of capital. If the project has positive returns, after accounting for the cost of loan, the borrower will choose to incur them. Given the nature of perfect information, the lenders would accept some uncertainty in production, but in this case the influence of asymmetric information is a more realistic assumption.

 Asymmetric Information

Banks in India face the problem of asymmetric information when deciding to approve a loan to an individual or MSME. Banks or creditors will gather information on prospective borrowers to determine their creditworthiness and reduce the bank’s credit risk. However, even with said due diligence it is difficult to determine exactly whether a borrower will default on the loan. For that reason, banks are forced to consider how the terms of each loan affect the entire pool of borrowers. As a result, the two problems that arise from asymmetric information are moral hazard and adverse selection.

Moral hazard can be divided into two types: ex-ante and ex-post moral hazard. Ex-ante moral hazard, in this case, is the risk that the borrower has mislead the bank regarding his or her collateral, financial standing and therefore creditworthiness. To combat the ex-ante moral hazard banks will adjust their interest rates according to the perceived risk of a borrower after screening the borrower. Riskier borrowers would receive higher interest rates to compensate for the creditors risk.

Ex-post moral hazard is the risk that the borrower does not use the money accordingly as agreed with the bank’s terms and chooses not to repay the bank. The ex-post moral hazard is more difficult to mitigate, but in theory banks would prefer to establish financial interlinkages or relationships with borrowers to ensure orderly conduct in future business. These interlinkages would incentivize a proper repayment structure for borrowers and establish a market segmentation in which the banks can continuously work with or loan to the same people.

 Adverse selection is the risk of banks unknowingly loaning to riskier individuals or MSME’s which can result in higher probabilities of default. Banks will therefore raise interest rates to compensate for the riskier borrowers. Less risky borrowers will therefore be crowded out from the market, because they will not incur a higher interest rate, since safer borrowers have smaller profits compared to riskier borrowers. This very crowd out of safe borrowers is inefficient. It can also become a cyclical problem, because as more safe borrowers are crowded out the more pressure there is on banks to raise interest rates.

According to Joseph E. Stiglitz, the “screening” method can mitigate adverse selection. In that sense, banks would need to gather more information about the lenders to determine the creditworthiness of the borrower. However, as stated before, a borrower can mask his or her risk.

PCA Framework

In order to understand the PCA framework it is important to understand how banks differ from one another. PSB’s operate differently than private banks and NBFC’s. Private banks and NBFC’s rely on their private shareholders and their customer base. For this reason, PSB’s receive funds from the RBI and are tightly regulated by them. In theory, private banks are more prone to a liquidity crisis. Consequently, private and NBFC’s offer higher interest rates than PSB’s, since PSB’s are tightly regulated and more conservative in their lending approach.

Nevertheless, as demonstrated by the Business Insider India Article, PCB’s have performed poorly and their non-performing loan ratios increased. For this reason, the RBI intended to reduce the risk of moral hazard and adverse selection by passing the PCA framework. This caused PCB’s to become more conservative when making loans. As a result, more borrowers turned to alternatives such as private banks and NBFC’s. To accommodate the new borrowers in the private bank’s and NBFC’s respective pools, both lenders had to increase their interest rates to compensate for adverse selection and moral hazard.


On March 14th, 2018, Urjit Patel, the Governer of the RBI, expressed that “the emerging risk to the financial sector is [the] increasing trends in frauds in commercial banks and financial institutions. During the last five financial years, frauds have increased substantially both in volume and value terms. During this period, […] the volume of frauds has increased by 19.6 per cent.” With the PCA in action, default rates may decrease in the future in the public sector, but the influx of borrowers turning to private bank’s and NBFC’s may find higher interest rates unmanageable. In that sense, some borrowers will be left behind due to the crowding out problem.


Work Cited

Dhillon, D. (2018, April 09). India’s small businesses are suffering from a shortage of credit options. Retrieved from https://www.businessinsider.in/indias-small-businesses-are-suffering-from-a-shortage-of-credit-options/articleshow/63683278.cms

MSME Pulse Report (2018, March)

Patel, G. (2018, March 14) Inaugural Lecture: Centre for Law & Economics, Centre for Banking & Financial Laws

Stiglitz, J. (1975). The Theory of “Screening,” Education, and the Distribution of Income. The American Economic Review, 65(3), 283-300. Retrieved from http://www.jstor.org/stable/1804834


Sticky Loan Rates in Uganda


On February 27th of 2018, the Daily Monitor an independent, Ugandan daily newspaper – reported that the Ugandan government recently dropped the Central Bank Rate(CBR) to 9 percent. However, the author also noted that commercial lending rates have remained sticky within Uganda, staying at an average of 20.28 percent (Adengo). The situation is particularly interesting to explore from a developmental economic perspective, considering many reasons exist outside of neoclassical credit models for such situations to become possible. In particular, it’s interesting to observe the effects of adverse selection and moral hazard to arise within markets with asymmetric information that can lead to such credit frictions.

Before examining possibilities as to why the commercial lending rate remains sticky in Uganda, and the impact for small lenders, it’s important to understand the context and assumptions under which we are working. Uganda, for the past year, has aggressively been cutting the rate at which the Central Bank lends money to commercial banks and lenders in an attempt to improve access to credit and therefore increase economic growth. Trading Economics – a website which hosts and tracks various economic statistics across countries – reports the lending rate for Uganda has been between 18 and 26 percent since 2006(Figure 1). It is only since February 2017 the rate has been progressive cut from 17 percent down to the 9 percent we see today.

Figure 2. Uganda Lending Rate. Source: https://tradingeconomics.com/uganda/lending-interest-rate-percent-wb-data.html

Neoclassical Model

Firstly, to understand how asymmetric information may introduce the credit frictions we observe in Uganda today, it’s important to understand how the market would work with perfect information. In a neoclassical model of credit, the borrower and lender both fully know all information about each other regarding project investment choice, ability to repay the loan, and the market share of borrowers. Moreover, markets are considered competitive in that lenders are unable to raise their interest rates as other lenders would continue to compete against them at the lower interest rate. In these markets, borrowers loan capital at this competitive rate and use it to invest in some entrepreneurial project which yields back profits for loan repayment and income.

In this model, the assumption is lenders charge the opportunity cost of capital – here lenders would lend out money at a rate of 9 percent per unit of capital K since lenders are only responsible for the cost owed to the Central Bank. Since all information is known, no cost is incurred for monitoring or determining borrower type. Ultimately, entrepreneurs pursuing loans should profit and contribute to overall social welfare since projects are efficient and project benefits outweigh project costs.

Asymmetric Information

However, the real world is not a market of perfect information. Borrowers know things about themselves, such as risk preference, lenders cannot observe. In particular, this lack of information causes frictions – costs – the lender must incorporate into the rate at what they’re willing to lend money. In particular, lenders are faced with issues of adverse selection and moral hazard. In particular, Ugandan lenders do not know whether they’re borrowers are “safe” or “risky”, thus determining their ability to repay a loan based on project outcomes. For example, a Ugandan banker is unable to determine if a borrower will invest in the volatile market of cryptocurrency with his loan or whether he will pursue a traditionally “safe” business venture, such as a mobile phone reseller. As noted in Development Economics, “adverse selection arises… when potential borrowers differ in… default probabilities… such [that]… lenders cannot distinguish safer from riskier borrowers” (Shaffner, 641).

In particular, if Uganda bankers are unable to perceive borrower type, then they must offer an interest rate which takes into account the share of borrowers in the market who are risky, whether collateral is required, and whether safe borrowers remain in the market at such interest rates. There is a possibility that safe borrowers cannot afford to enter business ventures at a 20.28 percent rate of interest and have exited the market many time periods ago. Because risky borrowers only remain in the market, lenders must adjust accordingly to ensure the expected return on their loans is able to cover the rate at which they borrowed money from the CBR. Moreover, if projects in Uganda have become riskier or more expensive – it now costs more to invest in a riskier project or riskier projects are less likely to pay off – we would see an increase in the interest rate over time. However, we would expect since February of 2017 these rates would have dropped, since the CBR dropped significantly, which is not the case.

It is possible Uganda is facing a compound issue where moral hazard also plays a role in the credit friction. Moral hazards occur when effort and project choice remain unobservable to the lender, causing a lender to be unable to force a borrower to exert effort to select a choice of project. In particular, Ugandan borrowers faced with both ex-ante moral hazards and post-ex moral hazards. That is, they face moral hazards prior to their choice of project and after the completion of their project. Firstly, a borrower may choose to “slack” after receiving their loan and find themselves unable to pay back the lender. This would lead to increased interest rates as it becomes more difficult for lenders to break even and the increasing interest rate can even lead to incentivizing pursuit of riskier projects and causing lenders to exit the market. However, the Ugandan Banker’s Association reports the number of commercial banks branches increase from 400 in 2010 to over 500 in 2012(“History of Banks”)

Finally, Ugandan borrowers may be facing ex-post moral hazard in their choice of repayment. That is, a borrower may be successful with their project, but choose not to repay their lender in an attempt to take more money home. This is particularly true of borrowers with little personal wealth or those undergoing markets with high-interest rates. According to the OECD report commissioned by the Austrian Ministry of Foreign Affairs and Development Co-Operation titled “Microfinance in Uganda”, it’s possible that “The overall repayment rate of commercial banks continues to be extremely weak and may be a low as 55%”(Carlton et. al). Additional studies, such as “Causes of high default rate on loans in commercial banks” by Robert Kinyera find similar repayment rates(Robert). This would indicate that borrowers are unable to cope with the high-interest rates and would rather default.


Overall, while the original author of the article, Jonathan Adengo, points to interest rates coming down over time, I find it hard to believe given our understanding of developmental credit markets. The situation intuitively appears, given the evidence, that loan default rates in Uganda are high and borrowers continue to be at risk of ex-post moral hazard. Without an intervention, such as a decrease in interest rates either by government subsidy or policy change, interest rates will continue to stick at a high 20.28 percent without incentive to budge. Still, Uganda provides a real-world, tangible case study of the presence of asymmetric information in credit markets and how moral hazard projects can arise and how they influence the dynamics of a consumer credit market.



Does Mobile Money Lending in Kenya Payoff?

The advent of mobile money brought thousands of Kenyans out of poverty. however, the impacts of mobile lending appear less clear.

by: Vicky Yu

Credit can help households cover the costs of unexpected negative health events, one-time school fees or natural fluctuations in income that occur in certain occupations such as farming. More significantly, entrepreneurs can use credit to invest in businesses, potentially bolstering their economic and social well-being.

Barriers to credit access exist. Particularly in developing nations, formal institutions such as banks lack information about the clientele, and thus, risk losing money to defaults. To remedy this, banks might impose significantly higher interest rates or collect collateral. Both solutions prevent lower-income households from borrowing because of unaffordable interest rates or the lack of appropriate collateral.

Informal credit markets counter some of these problems. Local lenders can form relationships with specific individuals and transact with them. The increased trust and accountability should ward against higher interest rates and increase lending, but informal markets fail to completely eliminate several kinds of asymmetric information, resulting in inefficient outcomes.

Adverse Selection

Adverse selection occurs when risky borrowers (those with a higher probability of defaulting) disguise their types from the lender. In the absence of information, lenders set an interest rate for all parties.

Moral Hazard

The borrower possesses incentives to take on riskier projects or default strategically (deliberately not paying back the loan. Ex-ante moral hazard occurs when the borrower chooses how to use the loan, e.g. what kind of project to invest in. Ex-post moral hazard occurs when the borrower chooses whether to pay back the loan. Monitoring can reduce these risks, however, the costs of monitoring may exceed the expected benefits.

In both instances, interest rates rise to compensate for the lender’s increased risk, driving out safer borrowers and resulting in unmet demand from borrowers.

Enter Microcredit


Microcredit describes small loans, typically intended to fund small enterprises. The prototypical microcredit strategy is characterized by:

  1. Group liability: Borrowers form groups of their own choosing. Any single person defaulting eliminates the ability of all members to borrow.
  2. Targeting
  3. Frequent repayments

The first condition, group liability, serves two important purposes. First, sorting can weed out risky borrowers more effectively since borrowers know more about each other than lenders. Secondly, borrowers can monitor each other for project choice and completion, employing social sanctions as a non-pecuniary penalty. These safeguards theoretically lower the cost of lending by decreasing the risk of default, increasing lending overall and subsequently, improving well-being. Notably, Gine and Karlan (2014) find that once groups are established, switching to individual liability fails to reduce repayment rates1. This suggests that groups may serve a screening purpose that, once satisfied, independently maintains the risk of defaulting.

In practice, a review of six microcredit experiments by Banerjee et al. (2015) demonstrates mixed impacts of microcredit on borrowing and minimal, inconsistent impacts on other downstream effects such as consumption2.

The impact of microcredit via mobile loan apps in Kenya appears to bolster some of those conclusions.

Mobile Lending in Kenya

Mobile money first arrived in Kenya during 2007. A 2016 report by Tavneet Suri and William Jack, argues that mobile money likely helped reduce poverty for up to 194,000 (2%) households, women-lead households in particular3. These services gave Kenyans “the ability to safely store, send, and transact money” which likely created a “more efficient allocation of labor, savings, and risk.” The enhanced ability to transact money could also have positive implications for the credit market.

An article in The Standard4 details findings from a new survey by Financial Sector Deepening Kenya (FSD- Kenya) analyzing the state of the digital credit market in Kenya. The survey estimates that about a 35% of Kenyan phone owners are mobile lenders, with approximately 35% having tried multiple applications.

The take-up rate of around a third is marginally higher than the typical rates from Banerjee et al (2015) which range from 17-31% with a few exceptions. This indicates some previously unmet demand, but it’s unclear whether these are new borrowers or if digital borrowing crowds out existing lenders.

Access to multiple sources of credit also differs from theoretical models of informal markets. The availability of multiple lenders might increase defaults because the threat of permanently losing access to credit becomes less credible. In turn, increased competition may pressure lenders to lower interest rates, lowering the incentive for default and increasing access.

Another major distinction from models and past empirical studies is the use of private data (e.g. GPS information, social media, call logs, etc…) in lieu of a more formal, “tedious” application process.  This presents a significant contrast to standard informal markets and models of microcredit because it essentially gives the lender perfect information about potential borrowers. Access to data serves as a better source of information on the borrowers than trust or applications and it likely reduces the cost of monitoring for lenders. This could potentially eliminate asymmetric information and the inefficient outcomes (high interest rates) that arise from it.

In reality, The Standard reports that around “half of borrowers had an outstanding loan” and institutions charge “crazy interests” (e.g. 138% compounded annually by popular lender, M-Shwari), leading to cycles of debt and indebtedness. By contrast, the interest rates in Banerjee et al (2015) ranged from 12-27% APR with one outlier at 110%2. What explains the high interest rates and defaults?

Use of loans

Survey respondents report their reasons for borrowing as follows:

Loan Use

Only 37% of credit goes to business; the majority is intended for non-income generating activities. Thus, the mobile credit system appears unable to mitigate ex-ante moral hazard. This could potentially drive up interest rates in the absence of restrictions on loan use.

Of those who own businesses, 59% borrow for business purposes. One of the theoretical foundations for credit assumes the presence of efficient projects which generate more revenue than they cost. The Standard reports that some who default on loans do so because of “poor business performance” or a loss in the source of income. The experiments reviewed by Banerjee et al. (2015) suggest that even using funds for business may fail to result in significant profit increases2. Thus, microcredit, in the absence of profitable business investment options, may fail to generate enough income to cover the costs of borrowing.


Males make up a larger proportion of digital borrowers. Jack and Suri’s analysis of Kenyan households showed poverty reduction driven by female-headed households3. The Standard also notes that men are “more frequent culprits” of delayed payments. Other studies on microcredit do not identify a similar “female” effect, however, it remains a possible contributing factor.


Finally, The Standard discusses the lack of regulation in the burgeoning digital market. Perfect competition and perfect information naturally drive down interest rates even in the absence of regulation. However, barriers to entry may cause imperfect competition, creating a need for regulation. Furthermore, the lenders may still lack data on other loans the borrower might hold. 14% of individuals reported in the survey held multiple loans. Riskier borrowers could lie or fail to disclose this information, creating a type of adverse selection where lenders misidentify lender types.

Overall, while the evidence in Kenya refutes the predictions of theoretical microcredit models, it supports other empirical work which also casts doubts on the efficacy of microcredit in improving well-being.


  1. Giné, X., & Karlan, D. S. (2014). Group versus individual liability: Short and long term evidence from Philippine microcredit lending groups. Journal of development Economics, 107, 65-83
  2. Banerjee, A., Karlan, D., & Zinman, J. (2015). Six randomized evaluations of microcredit: Introduction and further steps. American Economic Journal: Applied Economics, 7(1), 1-21
  3. Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288-1292.
  4. Alushula, P., & Omondi, D. (2018, April 01). Loan Apps: How mobile loan platforms have lured Kenyans into debt trap. The Standard. Retrieved from https://www.standardmedia.co.ke/

When There Is Not Enough Credit to Go Around: The Challenges of Accessing Microcredit in Myanmar

As financial regulations lax and with the entrance of more nongovernmental organizations into Myanmar, microfinance and the availability of microloans have made it much easier for citizens of Myanmar to gain access to credit that they would not otherwise have access to. However, the demand for credit is far greater than the current supply. Currently, over 2.8 million clients have access to microloans in Myanmar. As that number continues to grow, credit constraints, the lack of credit availability, and the lack of financial literacy has made it difficult for people who need credit the most to access it. (The World Bank)

An article from the Myanmar Times published on March 1, 2017, Agricultural Sector and SMEs to Receive Private Bank Loans, talks about recent policy changes implemented to help farmers and small business owners. With the intervention of the Myanmar Private Sector Development Committee (PSDC), the committee has passed into law that private banks in Myanmar must grant a minimum percentage of all their commercial loans to people in the agricultural or SMEs (Micro, small, and medium-sized enterprises) sectors (Htwe.)

The current Agricultural Minister of Myanmar, Myint Hlaing, has stated, “The agricultural sector is the backbone of Myanmar’s economy as the entire agricultural sector contributes 30% of its current GDP. In addition, 61% of the country’s labor force is working in the agricultural sector” (Centre for Agriculture and Bioscience International.) Since agriculture is such a large part of the Myanmar economy, it is understood that additional funding and capital is required for the industry and economy to develop.

As of now, microloans are only made by state-owned banks. Local private banks rarely lend to local borrowers because of the lack of profitability and high risks of lending. Many of the private citizens who require microloans do not have the collateral nor credit history to justify receiving a loan, and laws set by the government cap the amount of interest that private banks can charge on these private loans (13%.)

(The World Bank)

The state-run banks currently charge an 8.5% interest rate and an 8% interest rate to SMEs and farmers, respectively. Should a borrower go to a private bank, they would be charged a 13% interest rate. Currently, it is unfeasible for private banks to
match the interest rate of the state-run bank, as they pay 8% interest rates on banking deposits (Htwe.) The main issue here, is deciding upon interest rate that would satisfy the state-run bank, the privately-owned bank, and the borrower.

With the passing of the 2016 Monetary Law, banks are no longer required to collect collateral when deciding who to give loans out to, but that just makes the vetting process more difficult. Despite the law, many private banks still require collateral as they cannot thoroughly vet borrowers, and many banking relationships in Myanmar are built on trust and reputation (Htwe.)
U Thein Myint, a deputy general manager at one of Myanmar’s privately-owned banks argues that, “If people fail to pay back their loans, the banks will encounter difficulties in paying deposits from its customers. This is detrimental to the financial system and the national economy. Therefore, for people seeking bank loans, they need to provide strong guarantee.” Until there is a proven high chance that commercial banks will be paid back, loans provided for the agricultural and SMEs sectors will remain low (Htwe.)

A retired vice president of the Myanmar Central Bank, U Than Lwin, hopes that the government can work out an arrangement with private banks so that money can be lent to people who need it the most. A proposed idea would be for the government to implement a system so that they can partially guarantee loan repayment, which would make the lending process for banks much easier. Another idea would be to mitigate risk by lending to a larger group of people, by spreading the amount of risk that people would take on when taking out a loan.

Some of the issues described in the article written by Chan Mya Htwe regarding microcredit are also issues seen in countries struggling to meet the demand for microcredit by their citizens. This is amongst one of the many challenges encountered for governments or NGOs implementing a microcredit and microfinance program in a developing country (Schaffner.) In a country where access to finance is difficult and people are spread out across rural areas, there is adverse selection on both sides for both the borrower and lender. Lending caps and inconsistent lending practices make it hard for borrowers to access loans. This usually results in a loan from multiple financial institutions or a loan shark (Schaffner.) The inconsistent income that depends on the planting and growing season along with the lack of good jobs makes it difficult in certain cases, for people to pay back their loans. With the new law instituted by the government preventing banks from collecting collateral on loans, the end result is an inefficient outcome where the borrower does not get the money they need for their everyday life and the lender just makes loans elsewhere where the financial institution knows they will be paid back.

Private financial institutions need a new way to thoroughly vet prospective borrowers if they cannot collect collateral beforehand (Htwe.) There is a possibility of lending to large groups and spreading out risk through group liability, but in every borrowing and lending situation, I feel that the lender assumes a lot more risk than the borrower.

The first microloan programs were first instituted in Myanmar in the mid-1990s (Soe.) As new laws are passed and as regulations become more lax, there have been an increase in NGOs in the country, making small loans to farmers and small business owners. The exchange rate, interest rate caps, along with high denominations in its currency discourage more NGOs coming in (Soe.) As the program continues to grow, I hope that microloans and microfinance can reach people in areas that still are not developed, or destroyed by the ongoing Civil War. I think that the Myanmar government needs to do more for its citizens, rather than rely on outside humanitarian organizations to provide a basic lifestyle for people who need it the most. This is a difficult problem that I feel would not be solved anytime soon, as lack of financial literacy in its citizens, lack of access to large amounts of credit, and lack of willing lending institutions keeps people stuck in the cycle of poverty.


Schaffner, Julie. Development Economics. N.p.: Wiley, 2014. Print.

Ray, Debraj. Development Economics. N.p.: Princeton UP, 1998. Print.

C. (n.d.). CABI and China boost agricultural development in Myanmar. Retrieved May 09, 2017, from http://www.cabi.org/membership/news/cabi-and-china-boost-agricultural-development-in-myanmar/

Tun, T., Kennedy, A., & Nischan, U. (2015). • Promoting Agricultural Growth in Myanmar: A Review of Policies and an Assessment of Knowledge Gaps (No. 230983). Michigan State University, Department of Agricultural, Food, and Resource Economics.

Htwe, C. M. (2017, March 01). Agricultural sector and SMEs to receive private bank loans. Retrieved May 09, 2017, from http://www.mmtimes.com/index.php/business/25141-agricultural-sector-and-smes-to-receive-private-bank-loans.html

Soe, H. K. (2016, September 06). Can microfinance still make a difference? Retrieved May 09, 2017, from http://frontiermyanmar.net/en/can-microfinance-still-make-a-difference

A Fresh Look at the Tontine Loan Scheme

Women in the West Senegal region of Medine are exploring the use of Tontines to replace traditional loans


On April 30th, 2017, AfricaNews published an article about the recent resurgence of an old type of loan, the tontine, entitled “Traditional micro-credit scheme helps Senegalese women do business.”  The article explains that women’s access to credit has long been severely restricted in Africa.  Prohibitively high interest rates, low literacy rates, and cultural barriers all contribute to Senegalese women’s lack of willingness to sign formal loan contracts.  Tontines are being explored as an alternative option to formal loans from banks.

In the Médina area of Grand-Mbao, a neighborhood within Senegal’s capital, Dakar, women use the tontine to great benefit.  There are 250 women in the tontine.  They each contribute about three euros to the calabash daily, either in cash or via mobile banking transfers.  Every day, following a specific predetermined order, one woman gets the lottery payout of nearly 760 euros.  The members of the tontine describe it as having a family atmosphere in which the members support one another.  In order to ensure members continually pay each day, a Sanctions Regime is created to enforce the rules, promote transparency, and instill confidence in the process.  There are late fees for missed payments – which are less than a euro.  If a member is continuously late, their place in line for the payout will be pushed back.  If they continue to miss payments, they may be unable to collect their lottery payout until their late payments have been rectified with the Sanctions Regime.

Additionally, women must use the money productively.  If they are found to have squandered the payout on food or some other consumable item, they will lose face in their community.  In Médina, social standing within the village is paramount.  One woman goes so far as to say that not only will the individual have to pay it back, but even their grandchildren will likely be affected by those actions.  The women are supposed to use the payout for investments that they could not otherwise afford, such as construction, the purchase of durables, or investments in business.  One specific woman, Mame Ngone Cisse, stated that the tontine enabled her to purchase chicks to save her poultry farming business.


This article proposes a type of loan similar to the group lending model we have discussed in class.  We can compare this informal loan to a more formal loan through some of the theories covered in class, including Moral Hazards, Adverse Selection, and Time Preferences.  The tontine has both advantages and disadvantages as compared to a formal bank loan that we will discuss in this post.

Regarding Ex-Post Moral Hazard, the tontines use both monetary and social fines to incentivize members of the group to keep up their part of the contract.  Hypothetically, a woman who collects her payout could stop paying into the tontine if she no longer wants to participate.  The monetary fine of one euro each time a payment is late and the social fine of a loss of social standing within the local community strongly incentivize the members to continue to make their payments.  The fact that these women’s families generally live in the same town for years adds to the social incentive because, as mentioned in the article, an individual’s actions can affect their family’s social standing for generations.  As compared to banks, the only enforcement against Ex-Post Moral Hazard would be a loss of credit and seizure of collateral – if collateral was part of the contract.  As we have seen in class, areas with improved access to credit tend to also increase the community members’ access to informal credit regardless of if those seeking informal loans have access for formal loans or not.  Thus, locals frequently have workarounds to loss of credit, but in the case of tontines, there is no workaround as you would be stealing from your neighbors.  The community atmosphere of the tontine also naturally protects against Ex-Ante Moral Hazard, as the women can easily tell what the winner uses the payout for.  If the woman uses the payout for consumables, she will lose face in the community.

The Sanctions Regime has better information than a bank has; therefore, they are less susceptible to adverse selection.  Due to the fact that tontines consist only of members within the community, the Sanctions Regime has nearly perfect information regarding an individual’s past credit record and their character.  The members of the community are able to select individuals to take part in the tontine only if they are in good standing within the community, are considered financially responsible, and are believed to be trustworthy.  Access to this sort of intimate information would be highly unlikely for the banks, so they are at a real disadvantage as compared to the tontines.

The expectation of time inconsistent preferences makes the tontine an interesting loaning format.  Generally speaking, we know that people tend to have a classic hyperbolic discount rate – meaning that they are more patient with payouts in the future rather than in the present.  An individual adhering to time inconsistent preferences would be less willing to wait a month now for extra money as opposed to waiting an additional month atop an already six-month waiting period for extra money.  Due to the fact that, in a tontine with 250 people, each member gets paid roughly every eight months, there is an extended waiting period imposed upon them.  My main concern is that the relatively minimal monetary fine on late payments wouldn’t be enough to incentivize payments to always be on time.  One could only surmise that the social standing loss must be significantly severe to the point that the members would do all they can to avoid missing payments.  Aside from the social loss, the additional punishment of pushing the date of collection back would be a poor deterrent based on time inconsistent preferences.  An average person would likely think “I already have to wait eight months for this payout – what’s another few days?”  Though, I would say the punishment of restricting a member’s ability to collect the payout when they miss many payments is a strong refutation of that line of thinking.


In summary, the use of tontines in the Médina area of Senegal are a unique approach to solving the credit access issue many women in Africa face today.  By circumventing the high interest rates, large initial down payments, and cultural barriers, these women have employed an intelligent solution to their financial woes.  As we know, many people in developing nations are frequently unable to save significant sums of money to purchase durables for their entrepreneurial aspirations due to budget constraints.  These Senegalese women have created a way to provide themselves with a large sum of money at least once a year at relatively minor costs to their daily income.  We saw how the theories of Moral Hazard, Adverse Selection, and Time Inconsistent Preferences are affected under the system of tontines.  Overall, tontines are an interesting temporary solution employed by the people of Médina who face credit access problems.


  1. http://www.africanews.com/2017/04/30/traditional-micro-credit-scheme-helps-senegalese-women-do-business/

Finance runs afoul

An inquiry into why financial companies might not be following the rules.

A recent article by published the Times of India titled “Finance companies not following rules: Self-help groups,” reported on the ongoing protest of women in Nashik. These women from a number of self-help groups (SHGs) were out protesting to demand an inquiry into multiple micro finance companies in the district. The protesters claimed that these financial companies were not following the rules agreed upon between the financial companies and the SGHs. These rules mostly dealt with contact between financial companies and SGH members. Two stated violations were contacting SGH members outside of agreed upon hours and harassing SGH members to repay debts of other SGH members. Other allegations included the charging of exorbitant fees, forced recovery (also known as seizing property), and that these companies have been forging groups to increase their business. The protesters are demanding that the collector, the government, and the Reserve Bank of India review the performance of these financial companies. An employee of a micro finance company did respond for the article and denied the allegations saying the protesters were all made aware of the terms and refuse to pay it back. They further made the statement that while some collection agents do play foul they are easily identified and reprimanded.

The protestors make troubling claims that suggest behavior different than what the theory of micro credit predicts. The claim that companies are setting up their own groups to expand their business goes against the main strength of micro credit theory; which is that by lending only to groups, the groups that arise are positively assorted matches. Micro credit uses these positive assorted matches to reduce the risk of the lender by shifting it onto the borrower. In micro credit if one member of the group fails to repay all the members lose access to credit. This risk is what causes the positive assertive matching because risky people who need the extra income of their peers to cover their loan if their project fails, and safe borrowers want to match with safe borrowers to make sure they don’t have risk losing access since they wouldn’t be able to cover another members’ share of the loan if they failed.

If the claims made by the protestors are true then the companies are ignoring the benefits this positive assertive matching provides, and assuming that they are rational there must be something the model is missing. One solution could be that there are strong laws in favor of property seizure in the region. If a contract is binding and legally strong enough to allow for easy seizure of property maybe the risk of failure is not a deterrent. By expanding their business and including the amount they must be repaid they don’t have to fear if their loan fails as long as the signer has the required wealth. Based on the protestors testimony we can assume that the seizure of property is not outside of the finance companies reach, since they are protesting forced recovery. The extra cost of seizure or its ease could also help explain the allegations that these firms are charging exorbitant prices. If it is difficult to seize property the extra price could cover the cost. The flip side of the coin is that if seizure is easy maybe it is worth increasing the price as failure of a loan can be recouped through property seizure.

Another possibility is that these financial companies are not truly following the microfinance model and are instead using a model closer to the neoclassical model. This model differs from the micro credit model because it doesn’t utilize the benefits of positive assertive matching and instead has individuals responsible for themselves. This decreases the chance that the lender will get repaid because the borrower doesn’t have a group to fall back on to repay his share. Because of the decrease in chance that the lender gets repaid they charge higher prices than they would under a grouped positively assorted micro finance system. This could help explain why the prices were viewed as exorbitant. The question that must then be answered is why would a company using this model bother with forming groups instead of individual contracts. One reason would be that by creating groups of shared liabilities they are able to solve the problem of ex ante moral hazard. Ex ante moral hazard is that the borrower might choose a risky project that has a higher chance of failure and thus not being able to repay the lender. By forming groups, they can rely on a sort of social pressure to not put the group at risk or to force the group to repay the failure. With the ability to force recovery the company now doesn’t have to worry about ex post moral hazard, the risk that the borrower doesn’t repay, because even if they don’t repay the company can seize the borrower’s assets.

A third possibility is that the financial company employee is telling the truth. This is the easiest possibility to explain in economic theory. If the employee is telling the truth then the current model of micro financial theory is working as intended, and the problem lies with the protestors trying to shirk from their obligations. If there are existing cultural standards that would make the recovery of loans impossible the borrower would be much better off. They would have the support of the government and not have to repay the money that they borrowed.

The current situation shows a desire for credit in the area, otherwise people would not be protesting the financial institutions and instead just ignore them. We can also assume that access to credit is under supplied if there are claims about exorbitant fees because any company charging exorbitant fees would be undercut and removed from the market in a perfect equilibrium. In conclusion, we can see that the entire story is not being told in this article. If it were we would know why the claims made by the protesters seem to contradict the micro credit theory, why the company might be using the neo-liberal credit model, or the incentives provided to the protesters for shirking from their repayment.

The row of P2P landmines behind the “Trap”

The current underlying risks behind Chinese P2P programs.
By Ping Lu


As the Chinese economy progresses rapidly throughout the decades, its people has also became increasingly wealthy as more cash begins to flow into the public market. Many small businesses saw this as an opportunity to expand themselves, however lacked the funds to take such action. And as a result of this demand for funds, P2P(peer-to-peer) lending became vastly popular among small companies that needed urgent money, as well as people who are willing to take a risk to lend the money for a greater return.  Yet with the growing popularity, problems between borrowers and lender also begin to arise. From XinHua news, it was listed that in the year 2013, only 76 P2P platforms had credit issues, and yet only 2 years later in 2015, this number has risen to 896.

The article itself presents the problem that P2P is still mostly unregulated in China, and that platforms are abusing the P2P system for their own benefits, whereas in one case the platform owner scammed 900,000 investors into a poor investment. If the problem is not dealt with soon enough, more companies and private investors that invest their money into P2P would find that their opportunity cost in P2P is not worth the risk that they are taking, hence pulling their money out and resulting in the collapse of P2P platform due to the shortage of funds.

Building the Model

This cause and effect could be clearly explained by the lender and borrower model despite the different platforms P2P take places on. We could simplify it to a model that is composed of two lenders and two borrowers, whereas the platform itself exists as both the lender and borrower. The platform on one hand collects the money from multiple lenders that expects return, and on the other hand it lends the money to investors that they trust.

The problem with this platform is the ex-post moral hazards and the ex-ante moral hazards that the original lender faces, where ex-post moral hazard is the case where the P2P company receives the profits from the original lenders and reject to inform them about the outcome, and ex-ante moral hazard is the case where the platform fails to return profit from their investments. In both of these cases, despite the different outcomes, the original lender cannot observe the risks they face as they invest the money into the P2P platform. It is the platform that determines the actual investments.

Risks and Rewards

If we return to the news post, we could learn that the lack of regulation increases both of these two types of risks, as investment companies may make poor investment decisions that yields negative returns due to the lack of the background check for its borrowers, or simply because the platform tries to take all the profit and pretends to have failed the project, which in both of these cases the original borrower loses their investment.

However, despite many investors are aware of the risk, the opportunity cost of the P2P investment is too large to be ignored. In the year 2015 alone, P2P yields an average return of 13.29% (China Economic Watch, June 27), far exceeding the bank interest rates as well as other wealth management products. Both lenders and platforms saw this opportunity as China’s outstanding loans skyrocketed from $4.3b to $71b in only two years. But despite this rapid growth, the regulations still remain to be old and outdated, giving risky borrowers and platforms opportunities to take advantage of the lender since the punishment is minor in comparison to the profits.

Yet the lack of regulation isn’t completely negative in terms economy development. It is similar to a double edged sword, and in this case the borrower benefits the most from this system. The lack of regulation and background check enabled certain poor people to qualify for P2P loans, whereas in other financial institutions they would have been rejected. And in China, this situation is actually more common than we think (Banking On the Poor in China, March 10) as current microfinance services does not come close to meeting its demands. Government and bank regulations make it difficult for small businesses to get access to the loans they need, and the smaller the scale of the business, the harder it becomes due to the risks it poses. Therefore, these small companies could only seek investments from P2P platforms in order to continue their businesses. But because of the high risks, the platform charges borrowers extremely high interest rates as compensation in order to reduce ex-ante moral hazards, Hence P2P has high risks but also higher rewards.


Therefore, in order to both reduce moral hazards P2P faces and satisfy the need for microfinance services, new regulations have been imposed which puts different limits of lending on different people (Bloomberg News, Aug 24). This reduces ex-ante moral hazard because even in the worst case scenario, investment yields no return, the company would still be able to function rather than collapsing and losing all of its investors’ money, and that the lending amount would still help small businesses while having a decent payoff for the platform. And in terms of reducing ex-post moral hazards, monitoring and laws must be enforced to ensure the clarity of platforms such that their cost of taking the risk of defaulting is much higher than not taking such action.


Martin Chorzempa (June 27, 2016), P2P Series Part 1: Peering into China’s Growing Peer-to-Peer Lending Market, Retrieved May 2, 2017, from https://piie.com/blogs/china-economic-watch/p2p-series-part-1-peering-chinas-growing-peer-peer-lending-market

Brendan Rigby (March 10,2011), Banking On the Poor China, Retrieved May 2, 2017, from http://www.whydev.org/banking-on-the-poor-in-china/

Bloomberg News (August 24, 2016), China Imposes Caps On P2P Loans to Curb Shadow-Banking Risks, Retrieved May 2, 2017, from https://www.bloomberg.com/news/articles/2016-08-24/china-imposes-caps-on-p2p-lending-to-curb-shadow-banking-risk