This is just an awesome paper by the duo – Rajkamal Iyer (of MIT) and Manju Puri (of Duke). Iyer is clearly an economist to watch out for. His work is quite innovative and looks at some really interesting stuff.
The paper looks at a bank run which took place because there was trouble in some other bank in Gujarat. The some other bank is a large cooperative bank which failed in 2001. The authors have collected micro data of each depositor and minute by minute activity of the depositors of the first bank. This helps them understand several reasons for the bank run.
The paper has so many interesting findings. And the way it has been explained is also just so simple. They find deposity insurance is not effective. Then they see which kind of depositors run? They find people who have loans as well as deposits are less likely to run.
We find that depositors with balances under the insurance threshold are indeed less likely to run than those who are above the insurance limit, suggesting deposit insurance matters. However, we also find that deposit insurance is only partially effective. Even within the insurance limit, depositors with larger balances are more likely to run. Given deposit insurance is only partially effective in preventing bank runs; an important question is what other factors affect depositors’ propensity to run?
We first examine whether bank-depositor relationships affects depositor behavior. We measure length of the relationship by the age of the account, and depth through additional ties of taking a loan from the bank. We find that longer the bank-depositor relationship, the lower the likelihood of a withdrawal during the crisis. Further, depositors that have a loan linkage are less likely to run. Interestingly, we find that even depositors that had availed of a loan in the past (but currently have no outstanding loan) are less likely to run. We conduct several robustness checks to address the concern that loan linkages might proxy for other omitted characteristics like wealth or education levels of depositors. Our results suggest that relationship with depositors can help banks reduce fragility and thus add more value than just giving the bank information about its clientele.
They further find that social networks play a crucial role. And social network is not facebook etc but neighbourhood, introducer to the bank etc. They find social networks play a crucial role.
The second dimension that we examine is social networks. We capture social networks of a depositor using a unique feature in India: a person wishing to open an account with a bank needs an introduction from someone who already has an account with the bank. We also measure social network using the neighborhood of the depositor. We employ a variety of methods, which include simple probit models, and models which allow us to use the minute by minute variation in our data through Cox proportional hazard models with time varying covariates. We also explore and employ methods from the rich epidemiology literature that spends considerable effort in examining how diseases spread to estimate transmission probability of an infectious disease. In all estimations we find that the same factors are important. Deposit insurance partially helps mitigate runs. Social networks matter – if other people in a depositor’s network run, the depositor is more likely to run. However, even in a network where other depositors are running, the length and depth of the bank-depositor relationship significantly mitigates the propensity of the depositor to run.
They even show that timings of runs matter. Depositors with neighbors as depositors come during morning. Those who came via introducers come in the afternoon. Why? Well one good get to know from neighbors first thing in the morning. Whereas introducers who usually tend to be colleagues at office etc you get to knwo when at work..
What lessons for policy?
Apart from the factors that affect depositor runs, from a policy point of view, an important question which affects the decision for regulatory intervention, is the long term effect of bank runs. If the bank survives the run and stays solvent, do depositors that run return back to the bank? We find that the effects of a solvent bank run are long lasting. Of the depositors that withdrew during the crisis, only in 10% of the cases does the account balance return to pre-crisis levels even after 6 months of the crisis. Further, we do not find that the aggregate level of deposits of the bank return to the pre-crisis levels in the short run. This suggests that there are real costs to the bank that can potentially influence their asset portfolio and loans. Even if depositor runs do not lead to bank failure, the loss in deposits could lead banks to cut down on loans, which could impose high costs on borrowers in the presence of information asymmetry.
Well not sure whether lessons apply universally as this was a very small bank. Would be really interesting to see if it applies to other large banks as well.
A must read paper..Thoroughly enjoyable..
June 18, 2012 at 1:06 pm |
very versatile paper indeed !! a thorough analysis of empirical evidences a great mix of econometric models to test , however I feel basing the example of a “gujarat based bank run” example is quite flawed . I think the highlight of 2008 crises were bank runs if one looks closely each country’s banks had their own risk factors , agreed that there were an umbrella of contagion risk but each bank had different degree and reasons for depositors run . Since the dummy variables and probit models are on the outcome of the said example I doubt the robustness of these models .
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July 5, 2012 at 12:48 pm |
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