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on Economic Geography |
| By: | Marcel van den Berg; Charles van Marrewijk |
| Abstract: | Using micro-data for Dutch firms, we argue that the productivity spillovers from importing technology intensive products from Taiwan differ from importing unskilled- labor intensive products from Switzerland. We show that both the geographic component (what country is the import from) and the intensity component (what type of good is imported) is crucial for measuring and understanding these spillovers. We show that increasing distance and decreasing levels of development of the origin economy negatively affect the diffusion of efficiency gains embodied in imported goods. Similarly, these gains are larger for technology intensive goods and smaller for unskilled-labor intensive goods. This implies that the geographic- intensity markets are unique and cannot be lumped together. In addition, a diversified import portfolio (the extensive dimension) is always positively associated with firm-level productivity. |
| Keywords: | Firm heterogeneity, imports, productivity, geography, factor intensity |
| JEL: | D22 F14 F23 |
| Date: | 2013–09 |
| URL: | http://d.repec.org/n?u=RePEc:use:tkiwps:1312&r=geo |
| By: | Lehmann, Robert; Wohlrabe, Klaus |
| Abstract: | In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden- Württemberg) and Eastern Germany. We overcome the problem of a ’data-poor environment’ at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single– indicator, multi–indicator, pooled and factor forecasts in a pseudo real–time setting. Our results show that we can significantly increase forecast accuracy compared to an autoregressive benchmark model, both for short and long term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP. |
| Keywords: | regional forecasting; forecast combination; factor models; model confidence set; data–rich environment |
| JEL: | C32 C52 C53 E37 R11 |
| Date: | 2013–09–14 |
| URL: | http://d.repec.org/n?u=RePEc:lmu:muenec:17104&r=geo |
| By: | Manuel Portugal Ferreira (Instituto Politécnico de Leiria); Fernando Ribeiro Serra (Uninove – Universidade Nove de Julho); Benny Kramer Costa (Uninove – Universidade Nove de Julho); Emerson Maccari (Uninove – Universidade Nove de Julho); Hergos Couto (Uninove – Universidade Nove de Julho) |
| Abstract: | The ability to generate innovations and capture the rents from innovation are important for firms’ competitive advantage. Increasingly firms seek knowledge abundant locations, or industry clusters, to access novel knowledge and generate innovations through knowledge recombinations (Schumpeter, 1934). We examine how different types of clusters impact on the innovation output, the knowledge flows among the clustered firms and, ultimately, on who captures the rents from innovation. The type of cluster reflects the configuration of firms and the interactions among firms, individuals and agencies in the cluster and is likely to be a major driver of both the innovative output and of which firms will be more likely to capture the rents from innovation. Extant research has noted that the social and business networks binding firms in clusters are excellent vehicles for the flow of knowledge that eases innovations, but different types of clusters may lead to different outcomes. |
| Keywords: | clusters; types of clusters; innovation; appropriation of rents; innovation rents |
| JEL: | M0 M1 |
| Date: | 2013–09–29 |
| URL: | http://d.repec.org/n?u=RePEc:pil:wpaper:102&r=geo |
| By: | Courtemanche, Charles (Georgia State University); Soneji, Samir (Geisel School of Medicine at Dartmouth College); Tchernis, Rusty (Georgia State University) |
| Abstract: | We propose a Bayesian factor analysis model to rank the health of localities. Mortality and morbidity variables empirically contribute to the resulting rank, and population and spatial correlation are incorporated into a measure of uncertainty. We use county-level data from Texas and Wisconsin to compare our approach to conventional rankings that assign deterministic factor weights and ignore uncertainty. Greater discrepancies in rankings emerge for Texas than Wisconsin since the differences between the empirically-derived and deterministic weights are more substantial. Uncertainty is evident in both states but becomes especially large in Texas after incorporating noise from imputing its considerable missing data. |
| Keywords: | county, rank, health, factor analysis, Bayesian |
| JEL: | I14 C11 |
| Date: | 2013–09 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp7631&r=geo |
| By: | BIA Michela; VAN KERM Philippe |
| Abstract: | This note describes a Stata implementation of a space-filling location selection algorithm. It optimally selects a subset from an array of locations so that the spatial coverage of the array by the selected subset is optimized according to a geometric criterion. Such an algorithm is useful in site selection problems, but also in various non-parametric estimation procedures, e.g. to select (multivariate) knot locations in spline regression analysis. |
| Keywords: | spatial sampling; space-filling design; site selection; multivariate knot selection; point-swapping |
| Date: | 2013–09 |
| URL: | http://d.repec.org/n?u=RePEc:irs:cepswp:2013-17&r=geo |