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. [3] also evoke the relevance of correlated samples as depositors in the

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. [3] also evoke the relevance of Vercirnon chemical information correlated samples as depositors in the same neighborhood may observe to a large degree the same previous decisions. Moreover, several depositorsPLOS ONE | DOI:10.1371/journal.pone.0147268 April 1,4 /Correlated Observations, the Law of Small Numbers and Bank Runsmay have the same introducer that also may make information correlated. Such overlapping of information may be due to clustering, one of the key empirical regularities found in social networks. Clustering refers to the tendency of linked nodes to have common neighbor(s) (for more details see for CGP-57148BMedChemExpress Imatinib (Mesylate) instance [18] jir.2010.0097 or [19]). That is, a depositor observes what her neighbors do and at the same time those neighbors are likely to observe each other. [20] studies the effect of radio penetration on bank runs and banking distress during the Great Depression. He finds that a 10-percentage point increase in radio penetration in a county resulted in a 4.4 percentage point fall in deposits. In our view, radio makes information more correlated, therefore this finding also suggests that the higher the correlation in information, the more likely are massive withdrawals and bank runs in times of financial distress. Regarding learning about the share of previous choices in the population, [21] is a closely related paper. In his model, individuals choose in a one-shot game between binary actions: playing stock or bond. He assumes that an increase in the fraction of the individuals playing stock increases the payoff related to that choice. Payoffs also depend on the aggregate state of the world and on idiosyncratic shocks. He shows–among others–that if only a sample of previous actions is observed, then multiple outcomes may arise and herds may form. [21] studies only the case of random sampling, he does not deal with the issue of correlated samples. Note that this study is not about social learning in which people want to learn the quality of a product or in our case the bank. The model makes it clear in the next section, wcs.1183 that the bank is known to work properly, there is no uncertainty about the fundamentals. Thus, if subsequent depositors withdraw in masses, then it is not the consequence of inferring that the bank is bad. In this sense, we do not have a herding model or a global game in which depositors receive noisy private signals about the quality of the bank. Note that [22] studies herding in financial intermediation and she assumes that depositors observe some previous actions. Her focus is on the signal extraction problem of depositors about the bank’s assets and she assumes away bank runs resulting from coordination failure. Depositors use the information about previous withdrawals to infer the quality of the assets. If their belief about this quality is low, then they withdraw. Since more withdrawals suggest that previous depositors inferred that the assets are not performing well, more withdrawals are more likely to get a depositor to withdraw as well. The idea that observing more withdrawals may start a withdrawal wave is common in Gu’s and our paper. However, in our paper the fundamentals are good and we focus on the possibility of coordination failure. The study rather can be seen as a special coordination game with two types of players in which the players observe a set of previous decisions and decide sequentially. Despite the obvious differences, correlation has been found an important factor in social learning as well. [23] show that too much corre.. [3] also evoke the relevance of correlated samples as depositors in the same neighborhood may observe to a large degree the same previous decisions. Moreover, several depositorsPLOS ONE | DOI:10.1371/journal.pone.0147268 April 1,4 /Correlated Observations, the Law of Small Numbers and Bank Runsmay have the same introducer that also may make information correlated. Such overlapping of information may be due to clustering, one of the key empirical regularities found in social networks. Clustering refers to the tendency of linked nodes to have common neighbor(s) (for more details see for instance [18] jir.2010.0097 or [19]). That is, a depositor observes what her neighbors do and at the same time those neighbors are likely to observe each other. [20] studies the effect of radio penetration on bank runs and banking distress during the Great Depression. He finds that a 10-percentage point increase in radio penetration in a county resulted in a 4.4 percentage point fall in deposits. In our view, radio makes information more correlated, therefore this finding also suggests that the higher the correlation in information, the more likely are massive withdrawals and bank runs in times of financial distress. Regarding learning about the share of previous choices in the population, [21] is a closely related paper. In his model, individuals choose in a one-shot game between binary actions: playing stock or bond. He assumes that an increase in the fraction of the individuals playing stock increases the payoff related to that choice. Payoffs also depend on the aggregate state of the world and on idiosyncratic shocks. He shows–among others–that if only a sample of previous actions is observed, then multiple outcomes may arise and herds may form. [21] studies only the case of random sampling, he does not deal with the issue of correlated samples. Note that this study is not about social learning in which people want to learn the quality of a product or in our case the bank. The model makes it clear in the next section, wcs.1183 that the bank is known to work properly, there is no uncertainty about the fundamentals. Thus, if subsequent depositors withdraw in masses, then it is not the consequence of inferring that the bank is bad. In this sense, we do not have a herding model or a global game in which depositors receive noisy private signals about the quality of the bank. Note that [22] studies herding in financial intermediation and she assumes that depositors observe some previous actions. Her focus is on the signal extraction problem of depositors about the bank’s assets and she assumes away bank runs resulting from coordination failure. Depositors use the information about previous withdrawals to infer the quality of the assets. If their belief about this quality is low, then they withdraw. Since more withdrawals suggest that previous depositors inferred that the assets are not performing well, more withdrawals are more likely to get a depositor to withdraw as well. The idea that observing more withdrawals may start a withdrawal wave is common in Gu’s and our paper. However, in our paper the fundamentals are good and we focus on the possibility of coordination failure. The study rather can be seen as a special coordination game with two types of players in which the players observe a set of previous decisions and decide sequentially. Despite the obvious differences, correlation has been found an important factor in social learning as well. [23] show that too much corre.

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