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Bank Regulation and Macroeconomic Fluctuations
Charles Goodhart,Boris Hofmann,Miguel Segoviano 서울대학교 경제연구소 2006 Seoul journal of economics Vol.19 No.1
Over the last two decades, macroeconomic cycles were frequently associated with boom-bust cycles in bank lending and asset prices, often followed by financial instability. In this paper we argue that (i) the new pattern of macroeconomic cycles is partly the result of banking-sector liberalization since the early/mid-1970s, which has increased the procyclicality of the financial system; (ii) the regulation of bank capital in the form of capital adequacy requirements is itself inherently pro cyclical and may therefore amplify business-cycle fluctuations; (iii) the new Basel II Accord may considerably accentuate the procyclicality of the regulatory system.
DETERMINATS OF JAPANESE HOUSEHOLD EXPENDITURE ON CONSUMER GOODS SPECIALITY RETAILERS: 1991 – 2014
Charles A. Ingene,Ikuo Takahashi 글로벌지식마케팅경영학회 2016 Global Marketing Conference Vol.2016 No.7
The full-fledged Japanese census of commerce was conducted in 2014 and its data were publisized recently. We have chased the census data since 1991 in order to explore the determinants of Japanese household expenditure on consumer goods specialty retailers. The purpose of this study is to add some new findings to our previous research. In this research we theoretically address, and empirically estimate, key factors that affect sales per household at three major lines of retail trade that include frequently purchased consumables (food and drink), less frequently bought non-durables (apparel, shoes and dry goods), and infrequently acquired durable goods (furniture). We examine Industrial Classifications 57-60: Dry Goods, Apparel and Apparel Accessory stores (largely clothing, shoe, linen and accessories); Food and Beverage stores (primarily grocery, liquor, and specialty food stores); and Furniture, Household Utensils, and Appliances. These three trade lines deal with the necessities and supplies of life; they consist of relatively small specialty retailers. In addition, they have been an important target of urban planning and retail distribution policy of cities in Japan. Our data, which is drawn from six successive Japanese retail trade censuses (1991, 1994, 1997, 2002, 2007, 2014) encompasses 790 cities in all 47 prefectures. It is notable that the Japanese babble economy ceased in 1991; since then it has experienced an extended period of low growth. Note also that data from the most recent census (2014) is not yet available. Our theoretical model argues that retail sales per household are determined by three fundamental factors: the Market Environment (which is beyond the control of retail managers), Intertype Competition (which is influenced, but not controlled, by managers in each line of trade), and the Marketing Mix in each line of trade (which is set by managers). The essence of our argument is that the Market Environment determines a base level of sales per household; Intertype Competition may raise or lower sales in our focal lines of trade; and, the Marketing Mix in each line of trade can augment sales by (a) doing an above average job of appealing to customers and (b) countering the negative impact of Intertype Competition. Based on our research framework, we conduct a three-stage, hierarchical multiple regression analysis in each line of trade. Within Market Environment we include nine variables in a first-stage regression model: average number of people per household, household growth rate, average home size in square meters, income per household, population ratio aged 65+, auto ownership per household, distance to the prefectural capital city, residential land prices, and daytime population ratio. We expect each of these independent variables (except for population ratio aged 65+) to increase retail sales per household – which is our dependent variable. For Intertype Competition we use General Merchandise Stores (largely department stores and supercenters) that, in Japan, directly compete with Apparel, Food, and Furniture stores. In the second-stage regression model we include GMS sales per household along with the above nine Market Environment variables. GMS is anticipated to lower sales per household in Food stores, but is expected to raise sales per household in Apparel and Furniture stores as a spillover effect. For the Marketing Mix we measure four variables: assortment (proxied as square meters of selling space per store), service (employees per square meter of selling space), access (number of stores per land surface area of the city), and advertising (newspaper subscribers per household). These variables are included in the third-stage regression model along with the aforementioned ten independent variables; each of them should increase retail sales per household in its line of trade (e.g., the marketing mix for Food stores should only affect food sales per household). Thus, in of our analysis we show the results of eighteen regressions (i.e. the six census years and three lines of trade) . Our empirical research makes five contributions. First, we incorporate five independent variables that rarely (if ever) appear in studies of sales per household: out-shopping (daytime population ratio), home size, population ratio aged 65+, distance from the prefectural capital city, and residential land price. Second, we show the impact of intertype competition on sales in specific lines of retail trade. Third, we investigate data from five censuses that span a sixteen year period; few previous studies have examined changes in retail structure over such a lengthy time span. Fourth, we examine consumer goods retailers – who are an important target of urban planning and retail distribution policies of Japanese cities. Fifth, Japan had three important characteristics during the time span we examine: it was the world’s second largest economy and it is a nation of gradually declining population. As such, it may be a harbinger of the future of retailing in other large, wealthy economies. Additionally, Japan has rarely been the focus of retail trade studies.
Charles Huggins,Richard D. Robinson,Heidi Knowles,Heidi Knowles,Rosalia Mbugua,Jessica Laureano-Phillips,Chet D. Schrader,Nestor R. Zenarosa,Hao Wang 대한응급의학회 2019 Clinical and Experimental Emergency Medicine Vol.6 No.2
Objective A common emergency department (ED) patient care outcome metric is 72-hour ED return visits (EDRVs). Risks predictive of EDRV vary in different studies. However, risk differences associated with related versus unrelated EDRV and subsequent EDRV disposition deviations (EDRVDD) are rarely addressed. We aim to compare the potential risk patterns predictive of related and unrelated EDRV and further determine those potential risks predictive of EDRVDD.Methods We conducted a large retrospective observational study from September 1, 2015 through June 30, 2016. ED Patient demographic characteristics and clinical metrics were compared among patients of 1) related; 2) unrelated; and 3) no EDRVs. EDRVDD was defined as obvious disposition differences between initial ED visit and return visits. A multivariate multinomial logistic regression was performed to determine the independent risks predictive of EDRV and EDRVDD after adjusting for all confounders. Results A total of 63,990 patients were enrolled; 4.65% were considered related EDRV, and 1.80% were unrelated. The top risks predictive of EDRV were homeless, patient left without being seen, eloped, or left against medical advice. The top risks predictive of EDRVDD were geriatric and whether patients had primary care physicians regardless as to whether patient returns were related or unrelated to their initial ED visits. Conclusion Over 6% of patients experienced ED return visits within 72 hours. Though risks predicting such revisits were multifactorial, similar risks were identified not only for ED return visits, but also for return ED visit disposition deviations.
A self-configuration middleware for smart spaces
Charles Gouin-Vallerand,Bessam Abdulrazak,Sylvain Giroux,Mounir Mokhtari 보안공학연구지원센터 2009 International Journal of Smart Home Vol.3 No.1
The self-configuration process can simplify the complexity and reduce the cost of the management and deployment of devices, applications and services in smart spaces. Mechanisms inspired from the Autonomic Computing and Pervasive Computing can be used to automate management of the heterogeneous space's component. In this paper, we present our work on Autonomic Pervasive Computing and mainly the self-configuration process. We also introduce our self-configuration middleware that is deployed at DOMUS Laboratory.