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on Economic Geography |
| By: | Corinne Autant-Bernard (Univ Lyon, UJM Saint-Etienne, GATE UMR 5824, F-42023 Saint-Etienne, France); James P. LeSage (Fields Endowed Chair in Urban and Regional Economics, McCoy College of Business Administration, Texas State University, San Marcos, Texas 78666) |
| Abstract: | Past literature has used conventional spatial autoregressive panel data models to relate patent production output to knowledge production inputs. However, research conducted on regional innovation systems points to regional disparities in both regions ability to turn their knowledge inputs into innovation and to access external knowledge. Applying a heterogeneous coefficients spatial autoregressive panel model, we estimate region-specific knowledge production functions for 94 NUTS3 regions in France using a panel covering 21 years from 1988 to 2008 and 4 high-technology industries. A great deal of regional heterogeneity in the knowledge production function relationship exists across regions, providing new insights regarding spatial spillin and spillout effects between regions. |
| Keywords: | knowledge production, spatial econometrics, region-specific parameters |
| JEL: | C21 O31 O52 R12 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:gat:wpaper:1814&r=geo |
| By: | J. Vernon Henderson; Sebastian Kriticos; Dzhamilya Nigmatulina |
| Abstract: | At the heart of urban economics are agglomeration economies, which drive the existence and extent of cities and are also central to structural transformation and the urbanization process. This paper evaluates the use of different measures of economic density in assessing urban agglomeration effects, by examining how well they explain household income differences across cities and neighborhoods in six African countries. We examine simple scale and density measures and more nuanced ones which capture in second moments the extent of clustering within cities. The evidence suggests that more nuanced measures attempting to capture within-city differences in the extent of clustering do no better than a simple density measure in explaining income differences across cities, at least for the current degree of accuracy in measuring clustering. However, simple city scale measures such as total population are inferior to density measures and to some degree misleading. We find large household income premiums from being in bigger and particularly denser cities over rural areas in Africa, indicating that migration pull forces remain very strong in the structural transformation process. Moreover, the marginal effects of increases in urban density on household income are very large, with density elasticities of 0.6. In addition to strong city level density effects, we find strong neighborhood effects. For household incomes, both overall city density and density of the own neighborhood matter. |
| Keywords: | cities, economic density, Africa |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1569&r=geo |
| By: | Henderson, J. Vernon; Storeygard, Adam; Deichmann, Uwe |
| Abstract: | This paper documents strong but differentiated links between climate and urbanization in large panels of districts and cities in Sub-Saharan Africa, which has dried substantially in the past fifty years. The key dimension of heterogeneity is whether cities are likely to have manufacturing for export outside their regions, as opposed to being exclusively market towns providing local services to agricultural hinterlands. In regions where cities are likely to be manufacturing centers (25% of our sample), drier conditions increase urbanization and total urban incomes. There, urban migration provides an "escape" from negative agricultural moisture shocks. However, in the remaining market towns (75% of our sample), cities just service agriculture. Reduced farm incomes from negative shocks reduce demand for urban services and derived demand for urban labor. There, drying has little impact on urbanization or total urban incomes. Lack of structural transformation in Africa inhibits a better response to climate change. |
| Keywords: | Africa; Urbanization; Climate Change |
| JEL: | O10 O55 Q54 R12 |
| Date: | 2017–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:67654&r=geo |
| By: | Giuseppe Arbia (Catholic University of the Sacred Heart, Rome, Department of Statistical Science); Anna Gloria Billé (Free University of Bozen-Bolzano, Faculty of Economics and Management) |
| Abstract: | Modeling individual choices is one of the main aim in microeconometrics. Discrete choice models have been widely used to describe economic agents' utility functions, and most of them play a paramount role in applied health economics. On the other hand, spatial econometrics collects a series of econometric tools which are particularly useful when we deal with spatially-distributed data sets. It has been demonstrated that accounting for spatial dependence can avoid inconsistency problems of the commonly used estimators. However, the complex structure of spatial dependence in most of the nonlinear models still precludes a large diffusion of these spatial techniques. The purpose of this paper is then twofold. The former is to review the main methodological problems and their different solutions in spatial discrete choice modeling as they have appeared in the econometric literature. The latter is to review their applications to health issues, especially in the last few years, by highlighting at least two main reasons why spatial discrete neighboring effects should be considered and then suggesting possible future lines of the development of this emerging field. |
| Keywords: | Discrete Choice Modeling, Health Economics, Spatial Econometrics |
| JEL: | C31 C35 C51 I10 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:bzn:wpaper:bemps54&r=geo |
| By: | Anna Gloria Billé (Free University of Bolzano‐Bozen, Faculty of Economics, Italy); Leopoldo Catania (Aarhus University, Department of Economics and Business Economics and CREATES, Denmark) |
| Abstract: | We propose a new spatio-temporal model with time-varying spatial weighting matrices. We allow for a general parameterization of the spatial matrix, such as: (i) a function of the inverse distances among pairs of units to the power of an unknown time-varying distance decay parameter, and (ii) a negative exponential function of the time-varying parameter as in (i). The filtering procedure of the time-varying parameters is performed using the information in the score of the conditional distribution of the observables. An extensive Monte Carlo simulation study to investigate the finite sample properties of the ML estimator is reported. We analyze the association between eight European countries' perceived risk, suggesting that the economically strong countries have their perceived risk increased due to their spatial connection with the economically weaker countries, and we investigates the evolution of the spatial connection between the house prices in different areas of the UK, identifying periods when the usually adopted sparse weighting matrix is not sufficient to describe the underlying spatial process. |
| Keywords: | Dynamic spatial autoregressive models, Time-varying weighting matrices, Distance decay functions |
| JEL: | C33 C61 C58 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:bzn:wpaper:bemps55&r=geo |