000 03991nam a22005535i 4500
001 978-3-642-57698-0
003 DE-He213
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008 121227s2000 gw | s |||| 0|eng d
020 _a9783642576980
_9978-3-642-57698-0
024 7 _a10.1007/978-3-642-57698-0
_2doi
050 4 _aHB139-141
072 7 _aKCH
_2bicssc
072 7 _aBUS021000
_2bisacsh
072 7 _aKCH
_2thema
082 0 4 _a330.015195
100 1 _aKeilbach, Max C.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aSpatial Knowledge Spillovers and the Dynamics of Agglomeration and Regional Growth
_h[electronic resource] /
_cby Max C. Keilbach.
250 _a1st ed. 2000.
264 1 _aHeidelberg :
_bPhysica-Verlag HD :
_bImprint: Physica,
_c2000.
300 _aX, 194 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aContributions to Economics,
_x1431-1933
505 0 _a1 Introduction and Motivation -- 2 Why and How Does Economic Activity Grow? An Overview of the Literature -- 3 Why and How Does Economic Activity Concentrate in Space? Another Overview of the Literature -- 4 Spatial Knowledge Spillovers and the Dynamics of Agglomeration and Regional Growth -- 5 Marshallian Externalities, Spatial Self-Organization and Regional Growth - an Agent Based Approach -- 6 Spatial Processes in the Economy - an Empirical Investigation -- 7 Summary and Conclusion -- A Generalization of the Model Developed in Chapter 4 -- A. 1 Illustration of the Allocation Dynamics for a Region with an Arbitrary Number of Firms -- A.2 Proof that Firms Employ Identical Factor Ratios or Identical Factor Shares -- B Mathematical Appendix -- B.l Proof that the Bias of an OLS Estimation in the Presence of Spatial Autocorrelation is Biased -- B.2 Derivation of the Log-Likelihood Function of Model (6.2) -- C Data -- List of Symbols -- List of Figures -- List of Tables -- References.
520 _aWhen considering the dynamics of regional growth rates, one usually observes growth convergence on spatial aggregates but non-convergence or even divergence within smaller regions of different type. This book suggests various approaches to investigate this puzzle. A formal model, merging approaches from growth theory and new economic geography, shows that spatial knowledge spillovers might be the driving force behind this behavior. To analyze an arbitrary number of regions, the model is implemented on a locally recursive simulation tool - cellular automata. Convergence regressions from different runs of the automaton confirm previous findings. Finally, the existence of spatial knowledge spillovers is tested. Regressions give strong evidence for spatial knowledge spillovers. All the relevant literature and spatial econometric methods are surveyed. Data is reproduced in the appendix.
650 0 _aEconometrics.
650 0 _aEconomic growth.
650 0 _aRegional economics.
650 0 _aSpatial economics.
650 1 4 _aEconometrics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/W29010
650 2 4 _aEconomic Growth.
_0https://scigraph.springernature.com/ontologies/product-market-codes/W44000
650 2 4 _aRegional/Spatial Science.
_0https://scigraph.springernature.com/ontologies/product-market-codes/W49000
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783790813210
776 0 8 _iPrinted edition:
_z9783642576997
830 0 _aContributions to Economics,
_x1431-1933
856 4 0 _uhttps://s443-doi-org.br.lsproxy.net/10.1007/978-3-642-57698-0
912 _aZDB-2-SBE
912 _aZDB-2-SXEF
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