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on Economic Geography |
| By: | Anthony Frigon; David L. Rigby; |
| Abstract: | A growing body of research in economic geography, international business management and related fields focuses on geographies of knowledge sourcing. This work examines the organizational structure of innovation activities within the firm, the mechanisms by which knowledge is extracted from various external sources and the geography of these different activities. We augment this literature by exploring knowledge sourcing within multilocational firms operating in the US using a unique dataset matching patent records to firm-level ownership and geographical data. The results add value to existing research in three ways. First, the establishments of multilocational corporations are shown to produce different kinds of knowledge in different locations. Second, the patents generated within a firm’s establishments are linked to the knowledge stocks of the cities where they operate, supporting a vision of geographical knowledge sourcing. Third, the complexity of knowledge produced within the firm as a whole is positively related to the number of establishments in which multilocational firms undertake innovation activities. In sum these data suggest that multilocational firms distribute their innovation activities across locations in order to secure access to local pools of tacit knowledge. The complexity value of firms’ knowledge production is enhanced as a result of this spatial strategy. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2132&r= |
| By: | Hervás-oliver, José-luis; Parrilli, Mario Davide; Rodríguez-pose, Andrés; Sempere-ripoll, Francisca |
| Abstract: | European Union (EU) innovation policies have for long remained mostly research driven. The fundamental goal has been to achieve a rate of R&D investment of 3% of GDP. Small and medium-sized enterprise (SME) innovation, however, relies on a variety of internal sources —both R&D and non-R&D based— and external drivers, such as collaboration with other firms and research centres, and is profoundly influence by location and context. Given this multiplicity of innovation activities, this study argues that innovation policies fundamentally based on a place-blind increase of R&D investment may not deliver the best outcomes in regions where the capacity of SMEs is to benefit from R&D is limited. We posit that collaboration and regional specificities can play a greater role in determining SME innovation, beyond just R&D activities. Using data from the Regional Innovation Scoreboard (RIS), covering 220 regions across 22 European countries, we find that regions in Europe differ significantly in terms of SME innovation depending on their location. SMEs in more innovative regions benefit to a far greater extent from a combination of internal R&D, external collaboration of all sorts, and non-R&D inputs. SMEs in less innovative regions rely fundamentally on external sources and, particularly, on collaboration with other firms. Greater investment in public R&D does not always lead to improvements in regional SME innovation, regardless of context. Collaboration is a central innovation activity that can complement R&D, showing an even stronger effect on SME innovation than R&D. Hence, a more collaboration-based and place-sensitive policy is required to maximise SME innovation across the variety of European regional contexts. |
| Keywords: | regional innovation; SMEs; R&D; place-based; collaboration; EU regions |
| JEL: | O31 O32 L11 |
| Date: | 2021–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:112486&r= |
| By: | Moritz Kuhn (University of Bonn, Department of Economics); Iourii Manovskii (University of Pennsylvania, Department of Economics); Xincheng Qiu (University of Pennsylvania, Department of Economics) |
| Abstract: | Spatial differences in labor market performance are large and highly persistent. Using data from the United States, Germany, and the United Kingdom, we document striking similarities in spatial differences in unemployment, vacancies, job finding, and job filling within each country. This robust set of facts guides and disciplines the development of a theory of local labor market performance. We find that a spatial version of a Diamond-Mortensen-Pissarides model with endogenous separations and on-the-job search quantitatively accounts for all the documented empirical regularities. The model also quantitatively rationalizes why differences in job-separation rates have primary importance in inducing differences in unemployment across space while changes in the job-finding rate are the main driver in unemployment fluctuations over the business cycle. |
| Keywords: | Local Labor Markets, Unemployment, Vacancies, Search and Matching |
| JEL: | J63 J64 E24 E32 R13 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:ajk:ajkdps:122&r= |
| By: | David Card; Jesse Rothstein; Moises Yi |
| Abstract: | We use longitudinal data from the LEHD to study the causal effect of location on earnings. We specify a model for log earnings that includes worker effects and fixed effects for different commuting zones (CZs) fully interacted with industry, allowing us to capture potential impacts of local specialization. Building on recent work on firm-specific wage setting, we show that a simple additive model provides a good approximation to observed changes in log earnings when people move across CZ’s and/or industries, though it takes a couple of quarters for migrants to fully realize the gains of a move. We also show that the earnings premiums for different CZ-industry pairs are nearly separable in industry and CZ, with statistically significant but very small interaction effects. Consistent with recent research from France, Spain and Germany, we find that two thirds of the variation in observed wage premiums for working in different CZs is attributable to skill-based sorting. Using separately estimated models for high and low education workers, we find that the locational premiums for the two groups are very similar. The degree of assortative matching across CZs is much larger for college-educated workers, however, leading to a positive correlation between measured returns to skill and CZ average wages or CZ size that is almost entirely due to sorting on unobserved skills within the college workforce. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:21-32&r= |
| By: | Richard Bluhm (Leibniz University Hannover); Christian (Lessmann); Paul (Schaudt) |
| Abstract: | We study the link between subnational capital cities and urban development using a global data set of hundreds of first-order administrative and capital city reforms from 1987 until 2018. We show that gaining subnational capital status has a sizable effect on city growth in the medium run. We provide new evidence that the effect of these reforms depends on locational fundamentals, such as market access, and that the effect is greater in countries where urbanization and industrialization occurred later. Consistent with both an influx of public investments and a private response of individuals and firms, we document that urban built-up, population, foreign aid, infrastructure, and foreign direct investment in several sectors increase once cities become subnational capitals. |
| Keywords: | capital cities, administrative reforms, economic geography, urban primacy |
| JEL: | H10 R11 R12 O1 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:ajr:sodwps:2021-11&r= |
| By: | Florian Bonnet; Aurélie Sotura |
| Abstract: | This paper proposes homogeneous annual series on the income distribution of French metropolitan départements for the period 1960-69 and 1986-2018. We rely on unpublished and newly digitised archives of the French Ministry of Finance. They consist of fiscal tabulations that are a summary of households’ income tax declarations. Based on these raw sources, we interpolate the whole income distribution of French metropolitan départements after 1986. Before 1986, we need more assumptions as only households liable to French income tax filed income tax declarations at that time. We propose a methodology to estimate the number and average income of non-taxable households before 1986 that also allows us to reconstruct the income distribution of French metropolitan départements for the period 1960-69. |
| Keywords: | Intraregional Inequalities, Income Distribution, Economic Geography, Economic History |
| JEL: | D30 N34 N94 R12 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:832&r= |
| By: | Daniel Meierrieks; David Stadelmann |
| Abstract: | We investigate the effect of rising temperatures on economic development, using sub-national data for approximately 1,500 sub-national regions in 81 countries from the 1950s to the 2010s. Accounting for region- and time-fixed factors by means of a two-way fixed effects panel approach, we find no evidence that rising temperatures are adversely related to regional growth measured as changes in regional per capita gross domestic product (GDP). In addition to a panel setting, we also consider the long-run analogue of the panel model, exploring the relationship between regional temperature and growth over longer time periods. Applying this long-difference approach, we find evidence of a statistically significant negative association between temperature and regional economic activity. This suggests that intensification effects matter, meaning that the adverse relationship between temperature increases and growth may compound and materialize only in the longer run. What is more, we find that these adverse long-run effects of regional warming matter only to regions located in countries with relatively unfavorable economic and institutional conditions, that is, in countries with high levels of poverty, a lack of democracy, and a weak rule of law. This strongly points to the role of sound (country-specific) economic and institutional conditions in reducing vulnerability to higher temperatures. In line with this interpretation, we find no evidence for an adverse long-run relationship between temperature and growth for regions located in richer and democratic countries or those with an established rule of law. |
| Keywords: | regional temperature; regional economic growth; sub-national data; long- difference approach |
| JEL: | Q54 Q56 R11 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:cra:wpaper:2021-36&r= |
| By: | Zhu, Di; Liu, Yu; Yao, Xin; Fischer, Manfred M. |
| Abstract: | Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non- euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution - commonly known as filters or kernels - in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI. |
| Date: | 2021–10–19 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus046:8360&r= |
| By: | Kyle Butts |
| Abstract: | This paper formalizes a common approach for estimating effects of treatment at a specific location using geocoded microdata. This estimator compares units immediately next to treatment (an inner-ring) to units just slightly further away (an outer-ring). I introduce intuitive assumptions needed to identify the average treatment effect among the affected units and illustrates pitfalls that occur when these assumptions fail. Since one of these assumptions requires knowledge of exactly how far treatment effects are experienced, I propose a new method that relaxes this assumption and allows for nonparametric estimation using partitioning-based least squares developed in Cattaneo et. al. (2019). Since treatment effects typically decay/change over distance, this estimator improves analysis by estimating a treatment effect curve as a function of distance from treatment. This is contrast to the traditional method which, at best, identifies the average effect of treatment. To illustrate the advantages of this method, I show that Linden and Rockoff (2008) under estimate the effects of increased crime risk on home values closest to the treatment and overestimate how far the effects extend by selecting a treatment ring that is too wide. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.10192&r= |