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APPLICATION OF INPUT‐OUTPUT TECHNIQUES TO QUALITY OF URBAN LIFE INDICATORS
Author(s) -
Hirsch Werner Z.,
Sonenblum Sidney,
Dennis Jerry St.
Publication year - 1971
Publication title -
kyklos
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 58
eISSN - 1467-6435
pISSN - 0023-5962
DOI - 10.1111/j.1467-6435.1971.tb00617.x
Subject(s) - environmental economics , quality (philosophy) , stock (firearms) , economic indicator , performance indicator , econometrics , computer science , environmental resource management , economics , geography , philosophy , management , archaeology , epistemology , macroeconomics
SUMMARY The meaning of ‘Quality of Urban Life’ is examined and the concept is related to urban systems and to their key components as well as to indicators for monitoring those components. Five urban systems, composed of numerous elements, are identified as useful descriptions of the quality of urban life. They are the natural, spatial, economic, public, and socio‐cultural systems. Indicators are proposed for specific urban systems. Urban input‐output analysis is discussed and a ‘typical’ structure for such a model is presented. Three techniques are advanced by which indicators of urban life quality can be related to market transaction information of an urban input‐output framework—one technique links indicators to input‐output sector outputs; a second technique creates indicator (dummy) sectors and incorporates them into an input‐output matrix; and a third technique disaggregates especially household and local government sectors, so as to correspond with specific indicators. However, input‐output analyses have very limited use in measuring many urban quality of life indicators, such as those relating to the socio‐cultural environment and various distribution aspects of the economic environment. And even where the method is useful its shortcomings must be remembered. For example, the public environment indicators tend to measure input quality rather than output quality; linearity assumptions seriously weaken indicator measurements in the spatial and natural environments; many externalities are not included in the natural environment indicators, relative price assumptions weaken the economic environment indicator measurements; finally, difficulties arise because while most indicators involve stock concepts, input‐output analysis estimates mainly changes in indicators rather than indicator levels.