In the 15 years since Kobe’s Hanshin-Awaji Earthquake, awareness is growing that simply gathering information may not be enough for preparing systems, executing emergency responses, and making decisions rapidly and precisely. The question has become how - and whether - emergency response information can be used effectively and efficiently for rapid disaster response, recovery, and rebuilding. We analyzed emergency response decision making from the perspective of information processing, looking for the features organizations need to process information efficiently. We also propose how to continuously improve emergency response performance.
This paper compares Japanese banks in terms of liquidity creation (LC). For this intent, three groups of banks were created using K-means clustering on a panel data of 1,254 observation-years, spanning from 2002 to 2020. The results showed that although large banks hold more than half of total banks' assets, in 2009 they were overtaken by medium banks in terms of LC. The random-effect regression model shows that bank size and the proportion of lending activity are the only variables that affect simultaneously (and positively) all groups of banks. Furthermore, medium and small banks are more efficient at creating liquidity with the assets at hand and respond positively to expansionary monetary policy. Therefore, the paper argues that medium banks should be incentivized to increase the size of their assets as by doing so more liquidity would be flowing to the market thus spurring the economic growth.
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