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on Economic Geography |
| By: | Gabriel M. Ahlfeldt; Nicolai Wendland |
| Abstract: | Can the demise of the monocentric economy across cities during the 20th century be explained by decreasing transport costs to the city center or are other fundamental forces at work? Taking a hybrid perspec¬tive of classical bid-rent theory and a world where clustering of economic activity is driven by (knowledge) spillovers, Berlin, Germany, from 1890 to 1936 serves as a case in point. We assess the extent to which firms in an environment of decreasing transport costs and industrial transformation face a trade-off between distance to the CBD and land rents and how agglomeration economies come into play in shaping their location deci¬sions. Our results suggest that an observable flattening of the traditional distance to the CBD gradient may mask the emergence of significant agglomeration economies, especially within predominantly service-based inner city districts. |
| Keywords: | Transport Innovations, Land Values, Location Productivity, Agglomeration Economies, Economic History, Berlin. |
| JEL: | N7 N9 R33 O12 |
| Date: | 2010–08–24 |
| URL: | http://d.repec.org/n?u=RePEc:eei:rpaper:eeri_rp_2010_24&r=geo |
| By: | Michael Fritsch (School of Economics and Business Administration, Friedrich-Schiller-University Jena); Holger Graf (School of Economics and Business Administration, Friedrich-Schiller-University Jena) |
| Abstract: | We compare two leading regional innovation systems (RIS) in East Germany with two RIS in West Germany of about the same size and internal settlement structure. Our analyses show that differences in the performance between the regions cannot easily be related to the structural properties of the respective innovation networks because divergent general economic conditions in the two parts of the country as well as the integration of regions into their neighboring spatial environment play a rather dominant role. Overall, our analysis clearly shows that an analysis of RIS should account for the general economic conditions as well as for the position of a region in its spatial environment. Focusing just on the respective region is not enough. |
| Keywords: | Regional innovation systems, national innovation systems, innovator networks, gatekeeper, social network analysis |
| JEL: | O31 Z13 R11 |
| Date: | 2010–08–24 |
| URL: | http://d.repec.org/n?u=RePEc:jrp:jrpwrp:2010-054&r=geo |
| By: | Girardin , Eric (BOFIT); Kholodilin, Konstantin A. (BOFIT) |
| Abstract: | In this paper, we make multi-step forecasts of the annual growth rates of the real Gross Regional Product (GRP) for each of the 31 Chinese provinces simultaneously. Beside the usual panel data models, we use panel models that explicitly account for spatial dependence between the GRP growth rates. In addition, the possibility of spatial effects being different for different groups of provinces (Interior and Coast) is allowed for. We find that both pooling and accounting for spatial effects helps substantially improve the forecast performance compared to the benchmark models estimated for each of the provinces separately. It is also shown that the effect of accounting for spatial dependence is even more pronounced at longer forecasting horizons (the forecast accuracy gain as measured by the root mean squared forecast error is about 8% at the 1-year horizon and exceeds 25% at the 13- and 14-year horizon). |
| Keywords: | Chinese provinces; forecasting; dynamic panel model; spatial autocorrelation; group-specific spatial dependence |
| JEL: | C21 C23 C53 |
| Date: | 2010–08–23 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:bofitp:2010_015&r=geo |
| By: | Shuangzhe Liu (University of Canberra, Australia); Wolfgang Polasek (Institute for Advanced Studies (IHS), Austria; The Rimini Centre for Economic Analysis (RCEA), Italy); Richard Sellner (Institute for Advanced Studies (IHS), Austria) |
| Abstract: | Spatial autoregressive models come with a variety of estimators and it is interesting and useful to compare the estimators by location and covariance properties. In this paper, we first study the local sensitivity behavior of the main least squares estimator by using matrix derivatives. We then calculate the Taylor approximation of the least squares estimator in the SAR model up to the second order. Also, we compare the estimators of the spatial autoregression (SAR) model in terms of the covariance structure of the least squares estimators and we make efficiency comparisons using Kantorovich inequalities. Finally, we demonstrate our approach by an example for GDP and employment in 239 European NUTS2 regions. We find a quite good approximation behavior of the SAR estimator in the neighborhood of ρ = 0, i.e. a small spatial correlation. |
| Keywords: | Spatial autoregressive models, least-squares estimators, Taylor approximations, Kantorovich inequality |
| Date: | 2010–01 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:22_10&r=geo |
| By: | Hernández-Mireles, C. |
| Abstract: | In this article we put forward a model where aggregate sales are a function of the online search of potential consumers at many locations. We argue that a location may be influential because of its power to drive aggregate sales and this power may be dynamic and evolving in time. Second, the influential locations may produce spillover effects over their neighbors and hence we may observe clusters of influence. We apply Bayesian Variable Selection (BVS) techniques and we use Multivariate Conditional Autoregressive Models (MCAR) to identify influentials locations and their clustering. Our results indicate that the influential locations and their economic value (measured by search elasticities) vary over time. Moreover, we find significant geographical clusters of influential locations and the clusters composition varies during the life-cycle of the consoles. Finally, we find weak evidence that demographics explain the probability of a location to be influential. |
| Keywords: | diffusion;new products;variable selection;spatial modeling |
| Date: | 2010–05–31 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:1765019671&r=geo |
| By: | Patricia Beeson (University of Pittsburgh); Tara Watson (Williams College); Lara Shore-Sheppard (Williams College) |
| Abstract: | It has long been recognized that average wages vary strikingly across regions and urban areas, in part due to differences in local amenities and fiscal policies. However, analogous differences in wage dispersion remain relatively unexplored. We develop a model suggesting that, after accounting for individual characteristics, wage dispersion across income groups should reflect differences in the relative valuation of local amenities and fiscal policies. We empirically investigate whether there is a link between local taxes and expenditures and the degree of dispersion in the wage structure, and find evidence that such a relationship exists. |
| Keywords: | inequality, wage distributions, local expenditures, local taxes |
| JEL: | H7 J31 |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:wil:wileco:2010-06&r=geo |
| By: | Hashiguchi, Yoshihiro; Chen, Kuang-hui |
| Abstract: | We conducted a Feldstein-Horioka test for the degree of China's inter-provincial capital mobility each year from 1978 to 2007 using the spatial error model (SEM), a model of spatial econometrics considering spatial dependence, and a data set reflecting revision of historical national and provincial accounts after China's first economic census in 2004. We found that the likelihood ratio test rejected the null of no spatial error correlation, or the appropriateness of the standard OLS model (OLSM), for 17 out of 30 years and that the Akaike information criterion selected the SEM over the OLSM for 20 years. Our estimations demonstrate that the mobility was high until the late 80's, fell to a bottom in the mid-90's, recovered, peaked in the early 2000's, and has weakened recently, even though it has been argued that mobility has been low since 1978 reform, leaving the impression that it has consistently been low. |
| Keywords: | fiscal and financial reform; Feldstein-Horioka paradox; spatial econometrics |
| JEL: | P21 O16 C21 |
| Date: | 2010–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:24595&r=geo |