Spillover Effects in Spatial Models * *As one might expect, the specification, estimation, and indexing of spatial spillovers are of prime importance in spatial modeling. Some references which relate to the material in this chapter are Easterly and Levine (1998), Fujita et al. (1999), Persson and Tabellini (2009), LeSage and Fischer (2008), LeSage and Pace (2009), Autant-Bernard and LeSage (2011), Kim et al. (2003), Abreu et al. (2005), and Halleck Vega and Elhorst (2015).

2017 ◽  
pp. 61-69
Author(s):  
Harry Kelejian ◽  
Gianfranco Piras
Keyword(s):  
Spatial Modeling ◽  
Spatial Models ◽  
Spillover Effects ◽  
Spatial Spillovers

scholarly journals Spatial matching on the urban labor market: estimates with unique micro data

2021 ◽  
Vol 55 (1) ◽  
Author(s):  
Marcin Wozniak
Keyword(s):  
Labor Market ◽  
Returns To Scale ◽  
Spatial Models ◽  
Spatial Relationship ◽  
Spillover Effects ◽  
Spatial Panel ◽  
Spatial Spillovers ◽  
Panel Models ◽  
Matching Process

AbstractIn the paper, we investigate spatial relationship on the labor market of Poznań agglomeration (Poland) with unique data on job vacancies. We have developed spatial panel models to assess the search and matching process with a particular focus on spatial spillovers. In general, spatial models may provide different findings than regular panel models regarding returns to scale in matching technology. Moreover, we have identified global spillover effects as well as other factors that impact the job-worker matching. We underline the role of data on job vacancies: the data retrieved from commercial job portals produced much more reliable estimates than underestimated registered data.

scholarly journals Spatial Modeling of Voter Choice: The Survey of Theoretical and Empirical Approach

2021 ◽  
Vol 17 (2) ◽  
pp. 127-164
Author(s):  
Lada Kuletskaya ◽  
Keyword(s):  
Research Methodology ◽  
Spatial Modeling ◽  
Mutual Influence ◽  
Empirical Work ◽  
Spatial Models ◽  
Point Of View ◽  
Factors Affecting ◽  
Directional Model ◽  
Electoral Choice ◽  
Voter Choice

As for today, political elections are the key form of people’s participation in the formation of the state in all democratic countries, which is why theoretical works in the field of spatial modeling of voter choice appeared relatively long ago and played a major role in the development of both further theoretical and empirical research in this area. In this survey we firstly give a brief overview of the history of the formation of spatial modeling in relation to election results and political preferences of individuals from the point of view of research methodology, based on the classical theoretical ‘proximity model’ and ‘directional model’, where rational individuals determine their political positions and compare them with the positions of candidates. Secondly, we explain the appearance of the studies of the mutual influence of voters living in neighboring territories on each other as one of the factors that determine the voters’ political positions and, accordingly, the final choice of a candidate. We also point out the authors’ different explanations of the reasons for the appearance of such mutual influence of voters and other factors affecting voters living in neighboring territories (also called as ‘contextual effects’) and emphasize the importance of taking them into account in the studies of electoral preferences. A separate chapter in this paper presents the systematization and description of the main empirical approaches to spatial modeling of electoral choice: at the beginning, we present the basic econometric spatial models (used by the authors regardless of the subject of the study), and then we describe the empirical work in the field of voter choice, depending on the hypotheses, focusing on the research methodology and the data used. In conclusion, we define the main directions for the research development and the vector of further practical work in this area. This paper will help researchers understand existing fundamental works, evaluate current approaches to the modeling of electoral choice, and improve theoretical or empirical spatial analysis

Does digital proximity between countries impact entrepreneurship?

Info ◽  
2015 ◽  
Vol 17 (5) ◽  
pp. 46-65 ◽  
Author(s):  
Maria Veronica Alderete
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Fixed Effects ◽  
Spatial Models ◽  
Negative Influence ◽  
Entrepreneurial Activity ◽  
Spatial Effects ◽  
Content Type ◽  
Spatial Spillovers ◽  
Information And Communication

Purpose – This paper aims to determine if there is a spatial dependence in the entrepreneurial activity among countries. The existence of a “digital proximity” could explain the spatial pattern of entrepreneurship. Design/methodology/approach – This question is empirically addressed by using a five-period, 2008-2012, panel data for 35 countries. A spatial fixed effects panel data model is estimated by using the total entrepreneurial activity published by the global entrepreneurship monitor as the dependent variable. Findings – A significant negative influence of the digital proximity on the entrepreneurial activity is observed. Mobile broadband (MB) direct effect is positive while the indirect effect (the spatial spillovers) is negative, leading to a negative total effect on the total entrepreneurial activity. This result is contrary to non-spatial models’ results. Besides, a higher MB penetration in a country would lead to a competitive advantage fostering its opportunities for entrepreneurship, but reducing those of its neighbours’. Originality/value – This paper examines the relationship between information and communication technology (ICT) and entrepreneurship, by introducing the spatial effects is the main contribution. This paper expands the scant literature on the ICT impact on entrepreneurship. Results obtained support policies towards enforcing innovation, education and reducing entry regulations for encouraging entrepreneurship. Meanwhile, MB policies could counteract the entrepreneurial policies’ results due to the spatial dependence.

scholarly journals Spatial Spillover Effects of Agricultural Transport Costs in Peru

2021 ◽  
Author(s):  
Pedro Herrera-Catalán ◽  
Coro Chasco ◽  
Máximo Torero
Keyword(s):  
Spatial Configuration ◽  
Spillover Effects ◽  
Equilibrium Analysis ◽  
New Economic Geography ◽  
Spatial Equilibrium ◽  
Transport Costs ◽  
Economic Activities ◽  
Flow Models ◽  
Spatial Spillovers

The role of agricultural transport costs in core-periphery structures has habitually been ignored in New Economic Geography (NEG) models. This is due to the convention of treating the agricultural good as the numéraire, thus implying that agricultural transportation costs are assumed to be zero in these models. For more than three decades, this has been the standard setting in spatial equilibrium analysis. The paper examines the effects of agricultural transport costs on the spatial organisation of regional structures in Peru. In doing so, the Krugman’s formulation of iceberg transport costs is modified to introduce the agricultural transport costs into the dynamic of the NEG models. We use exploratory spatial flow data analysis methods and non-spatial and spatial origin-destination flow models to explore how the regional spatial structure change when real transportation data for agricultural goods is included into the iceberg transport costs formulation. We show that agricultural transport costs generate flows that are systematically associated with flows to or from nearby regions generating thus the emergence of spatial spillovers across Peruvian regions. The results of the paper support the contention that NEG models have overshadowed the role of agricultural transport costs in determining the spatial configuration of economic activities.

Technological spillovers in space and firm productivity: Evidence from China’s electric apparatus industry

Urban Studies ◽  
2017 ◽  
Vol 55 (11) ◽  
pp. 2522-2541 ◽  
Author(s):  
Ming He ◽  
Yang Chen ◽  
Ron Schramm
Keyword(s):  
Service Sector ◽  
Market Competition ◽  
Spillover Effects ◽  
Spatial Distance ◽  
Firm Productivity ◽  
Border Effect ◽  
Long Distance ◽  
Technological Spillovers ◽  
Spatial Spillovers ◽  
Spatial Autoregressive

Using a spatial econometric model this article studies the determinants and spatial spillovers of firm productivity in China’s electric apparatus industry over the period of 1999–2007. We apply Kelejian’s FE-2SLS procedure to a higher-order spatial autoregressive model and estimate the spatial dependence of firm-level TFP within and across regional borders. The model demonstrates positive and significant intra-regional technological spillovers among firms. It also provides direct evidence that technological spillovers attenuate rapidly in spatial distance. We find that firm productivity benefits from own R&D and export activities, employment density, market competition and public expenditure. Further analyses show that the strength of spillover effects is affected by a broad range of factors, including the surface area of the region, administration type, border effect, transport and ICT infrastructure, FDI intensity, the financial sector, the utility service sector, and human capital. Factors that facilitate long-distance economic connections in general make inter-regional spillovers stronger but intra-regional spillovers weaker.

scholarly journals Space oddity: The mission for spatial integration

2017 ◽  
Vol 74 (11) ◽  
pp. 1698-1716 ◽  
Author(s):  
Aaron M. Berger ◽  
Daniel R. Goethel ◽  
Patrick D. Lynch ◽  
Terrance Quinn ◽  
Sophie Mormede ◽  
...  
Keyword(s):  
Data Collection ◽  
Spatial Modeling ◽  
Fishery Management ◽  
Stock Assessment ◽  
Spatial Models ◽  
Fine Scale ◽  
Spatial Analyses ◽  
Spatial Integration ◽  
Ecological Processes ◽  
Management Advice

Fishery management decisions are commonly guided by stock assessment models that aggregate outputs across the spatial domain of the species. With refined understanding of spatial population structures, scientists have begun to address how spatiotemporal mismatches among the scale of ecological processes, data collection programs, and stock assessment methods (or assumptions) influence the reliability and, ultimately, appropriateness of regional fishery management (e.g., assigning regional quotas). Development and evaluation of spatial modeling techniques to improve fisheries assessment and management have increased rapidly in recent years. We overview the historical context of spatial models in fisheries science, highlight recent advances in spatial modeling, and discuss how spatial models have been incorporated into the management process. Despite limited examples where spatial assessment models are used as the basis for management advice, continued investment in fine-scale data collection and associated spatial analyses will improve integration of spatial dynamics and ecosystem-level interactions in stock assessment. In the near future, spatiotemporal fisheries management advice will increasingly rely on fine-scale outputs from spatial analyses.

scholarly journals Land Withdrawal Vs. Regional Development: Does Withdrawal of Agricultural Land Lead to Increase in Entrepreneurial Activity and Generate Positive Spatial Spillovers? (Slovak Republic)

2018 ◽  
Vol 10 (4) ◽  
pp. 590-613 ◽  
Author(s):  
Katarína Melichová ◽  
Ina Melišková ◽  
Lucia Palšová
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Slovak Republic ◽  
Spillover Effects ◽  
Sustainable Land Use ◽  
Entrepreneurial Activity ◽  
Foreign Direct Investments ◽  
Spatial Spillovers ◽  
Soil Sealing ◽  
Established Firms

Abstract In an increasingly urbanized world, the scarcity of space is a growing problem along with land consumption and soil sealing. To achieve sustainable development and sustainable land use, society has to resolve conflicts between residential, industrial, transport, commercial and green areas while creating a balance between social, economic and ecological targets. However, coordination of sustainable land use is a challenge for policymakers. The paper examines whether the withdrawal of land from the agricultural land fund leads to development, measured both by the increase in domestic entrepreneurial activity, as well as by the increase in foreign direct investments. The results are based on the analysis of panel data on the amount of land withdrawal, newly established firms and inward flow of FDI covering 41 administrative districts of Slovak Republic over 9 years (6 years in case of the FDI, due to the availability of data). Additionally, the spatial Durbin panel model was used to examine, whether land withdrawal and its non-agricultural use generate positive spillover effects on surrounding regions in terms of increased entrepreneurial activity and flow of FDI.

scholarly journals The Health Potential of Neighborhoods: A Population-Wide Study in The Netherlands

2020 ◽  
Author(s):  
Louise H. Dekker ◽  
Richard H Rijnks ◽  
Jochen O. Mierau
Keyword(s):  
Health Disparities ◽  
The Netherlands ◽  
Population Health ◽  
Policy Design ◽  
Spillover Effect ◽  
Population Data ◽  
Spillover Effects ◽  
Neighborhood Density ◽  
Spatial Spillovers ◽  
Socioeconomic Characteristics

Abstract Background: While differences in population health across neighborhoods with different socioeconomic characteristics are well documented, health disparities across neighborhoods with similar socioeconomic characteristics are less well understood. Studying the determinants of variation of health among neighborhoods with similar socio-economic characteristics is pivotal for gaining insight into where health potential lies. We aimed to estimate population health inequalities, both within and between neighborhoods with similar socio-economic status, and assessed the association of neighborhood characteristics and socio-economic spillover effects from adjacent neighborhoods. Methods: Based on whole-population data from the Netherlands we determined the percentage of inhabitants with good/very good self-assessed health (SAH) as well as the percentage of inhabitants with at least one chronic disease (CD) in 11,504 neighborhoods. Neighborhoods were classified by quintiles of a composite NSES score. Spatial models were estimated by including the spatially weighted NSES of adjacent neighborhoods. Results: Substantial population health disparities in SAH and CD both within and between neighborhoods NSES quintiles were observed, with the largest SAH variance in the lowest NSES group. These differences were partially explained by neighborhood density and the percentage of inhabitants ≥65 years old. Neighborhoods adjacent to higher SES neighborhoods showed a higher SAH and a lower prevalence of CD, adjusted for other explanatory variables. Policy simulations indicate how modest changes in NSES among groups of neighborhoods with similar socio-economic characteristics can contribute to population health, partially due to spatial spillovers. Conclusion: Population health differs substantially among neighborhoods with similar socioeconomic characteristics, which can partially be explained by a spatial socio-economic spillover effect. This provides interesting leads to policy design aimed at improving population health outcomes of deprived neighborhoods focusing on health potential.

scholarly journals Origin Sample Prediction and Spatial Modeling of Antimicrobial Resistance in Metagenomic Sequencing Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Maya Zhelyazkova ◽  
Roumyana Yordanova ◽  
Iliyan Mihaylov ◽  
Stefan Kirov ◽  
Stefan Tsonev ◽  
...  
Keyword(s):  
Relative Risk ◽  
Random Forest ◽  
Antimicrobial Resistance ◽  
Spatial Modeling ◽  
Geographical Origin ◽  
Spatial Models ◽  
Gradient Boosting ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Convolution Model

The steady elaboration of the Metagenomic and Metadesign of Subways and Urban Biomes (MetaSUB) international consortium project raises important new questions about the origin, variation, and antimicrobial resistance of the collected samples. CAMDA (Critical Assessment of Massive Data Analysis, http://camda.info/) forum organizes annual challenges where different bioinformatics and statistical approaches are tested on samples collected around the world for bacterial classification and prediction of geographical origin. This work proposes a method which not only predicts the locations of unknown samples, but also estimates the relative risk of antimicrobial resistance through spatial modeling. We introduce a new component in the standard analysis as we apply a Bayesian spatial convolution model which accounts for spatial structure of the data as defined by the longitude and latitude of the samples and assess the relative risk of antimicrobial resistance taxa across regions which is relevant to public health. We can then use the estimated relative risk as a new measure for antimicrobial resistance. We also compare the performance of several machine learning methods, such as Gradient Boosting Machine, Random Forest, and Neural Network to predict the geographical origin of the mystery samples. All three methods show consistent results with some superiority of Random Forest classifier. In our future work we can consider a broader class of spatial models and incorporate covariates related to the environment and climate profiles of the samples to achieve more reliable estimation of the relative risk related to antimicrobial resistance.

scholarly journals Spatial Spillover Effects of Agricultural Transport Costs in Peru

Land ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 58
Author(s):  
Pedro Herrera-Catalán ◽  
Coro Chasco ◽  
Máximo Torero
Keyword(s):  
Spatial Configuration ◽  
Spillover Effects ◽  
Equilibrium Analysis ◽  
Spatial Equilibrium ◽  
Transport Costs ◽  
Economic Activities ◽  
Flow Models ◽  
Spatial Spillovers ◽  
Structure Changes

The role of agricultural transport costs in core-periphery structures has habitually been ignored in New Economic Geography (NEG) models. This is due to the convention of treating the agricultural good as the numéraire, thus implying that agricultural transportation costs are assumed to be zero in these models. For more than three decades, this has been the standard setting in spatial equilibrium analysis. This paper examines the effects of agricultural transport costs on the spatial organisation of regional structures in Peru. In doing so, Krugman’s formulation of iceberg transport costs is modified to introduce agricultural transport costs into the dynamic of the NEG models. We use exploratory spatial flow data analysis methods and non-spatial and spatial origin-destination flow models to explore how the regional spatial structure changes when real transportation data for agricultural goods are included into the iceberg transport costs formulation. We show that agricultural transport costs generate flows that are systematically associated with flows to or from nearby regions generating thus the emergence of spatial spillovers across Peruvian regions. The results of the paper support the contention that NEG models have overshadowed the role of agricultural transport costs in determining the spatial configuration of economic activities.

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