Are observed and liquidity needs are private information. In both cases

Are observed and liquidity needs are private information. In both cases, they obtain that bank runs do not occur in equilibrium. In this study we suppose that depositors do not observe the decisions of all depositors preceding them, but only a subset. Do people rely on partial information provided by a sample? [1] test a contagion model which focuses on the banking panics in a New York bank in 1854 and 1857 based on the connections between Irish immigrants. They find that although there were also other factors at work, the most important one determining whether an individual panicked was his county of origin in order Vorapaxar Ireland. Almost identical persons, only differing in the county of origin, behaved differently during the panics, and the opinions of others with the same background and their choices had the most influence on the decisions of their peers. The origin had its effect through the fact that inmigrants from the same county tended to live in the same neighborhood and observed each other. [3] show that in a bank run episode that took place in India in 2001 a depositor’s probability of withdrawing was increasing in the share of other people in her neighborhood or among her introducers who had done so earlier (in India, to open an account banks may require that the person be introduced by someone who already has an account in the bank). Clearly, a depositor’s social connections or neighborhood comprise only a reduced subset of all depositors, so only a sample jir.2012.0140 of previous decisions affected if a depositor chose to withdraw or not. [5] find in an experiment that observing that some (but not all) previous depositor has (not) withdrawn increases (decreases) significantly the probability of withdrawal. As already mentioned, the law of small numbers means that agents expect small samples to exhibit large-sample statistical properties, so people tend to ABT-737 manufacturer overweigh information that is available. The main cause behind this phenomenon is that people are too inattentive to the sample size, so they tend to wcs.1183 draw overstretched inferences based on small samples (for examples see section 2 in [10]). Evidence of people falling prey to the law of small numbers and consequently to overinference abounds when the sample and the population outcome is generated by an exogenous device (e.g. flipping a coin, guessing the urn from which a ball was drawn), but there is less evidence when the observed sample is the outcome of other individuals’ decision. However, the findings in [1] and [3] suggest that depositor behavior can be rationalized by the law of small numbers. More precisely, individuals observing a low number of withdrawals among their peers in their sample may believe that overall the number of withdrawals will be low, so there is no need to run the bank. By the same token, observing many withdrawals in a sample may cause individuals to think that there is a bank run underway. We do not claim that this is the only or the best explanation for their behavior, but it seems a reasonable one. This suggests that a threshold rule may reflect the law of small numbers when studying depositor behavior. In a bank-run setup, [4] explore experimentally the coordination problem among depositors and find that simple cutoff rules explain fairly well the behavior of the participants. The arguments in [1] suggest that correlation of observed behavior is important as people in the same neighborhood had highly overlapping information and they acted also in a very similar way.Are observed and liquidity needs are private information. In both cases, they obtain that bank runs do not occur in equilibrium. In this study we suppose that depositors do not observe the decisions of all depositors preceding them, but only a subset. Do people rely on partial information provided by a sample? [1] test a contagion model which focuses on the banking panics in a New York bank in 1854 and 1857 based on the connections between Irish immigrants. They find that although there were also other factors at work, the most important one determining whether an individual panicked was his county of origin in Ireland. Almost identical persons, only differing in the county of origin, behaved differently during the panics, and the opinions of others with the same background and their choices had the most influence on the decisions of their peers. The origin had its effect through the fact that inmigrants from the same county tended to live in the same neighborhood and observed each other. [3] show that in a bank run episode that took place in India in 2001 a depositor’s probability of withdrawing was increasing in the share of other people in her neighborhood or among her introducers who had done so earlier (in India, to open an account banks may require that the person be introduced by someone who already has an account in the bank). Clearly, a depositor’s social connections or neighborhood comprise only a reduced subset of all depositors, so only a sample jir.2012.0140 of previous decisions affected if a depositor chose to withdraw or not. [5] find in an experiment that observing that some (but not all) previous depositor has (not) withdrawn increases (decreases) significantly the probability of withdrawal. As already mentioned, the law of small numbers means that agents expect small samples to exhibit large-sample statistical properties, so people tend to overweigh information that is available. The main cause behind this phenomenon is that people are too inattentive to the sample size, so they tend to wcs.1183 draw overstretched inferences based on small samples (for examples see section 2 in [10]). Evidence of people falling prey to the law of small numbers and consequently to overinference abounds when the sample and the population outcome is generated by an exogenous device (e.g. flipping a coin, guessing the urn from which a ball was drawn), but there is less evidence when the observed sample is the outcome of other individuals’ decision. However, the findings in [1] and [3] suggest that depositor behavior can be rationalized by the law of small numbers. More precisely, individuals observing a low number of withdrawals among their peers in their sample may believe that overall the number of withdrawals will be low, so there is no need to run the bank. By the same token, observing many withdrawals in a sample may cause individuals to think that there is a bank run underway. We do not claim that this is the only or the best explanation for their behavior, but it seems a reasonable one. This suggests that a threshold rule may reflect the law of small numbers when studying depositor behavior. In a bank-run setup, [4] explore experimentally the coordination problem among depositors and find that simple cutoff rules explain fairly well the behavior of the participants. The arguments in [1] suggest that correlation of observed behavior is important as people in the same neighborhood had highly overlapping information and they acted also in a very similar way.