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Book part
Publication date: 30 May 2018

Badi H. Baltagi, Francesco Moscone and Rita Santos

The objective of this chapter is to introduce the reader to Spatial Health Econometrics (SHE). In both micro and macro health economics there are phenomena that are…

Abstract

The objective of this chapter is to introduce the reader to Spatial Health Econometrics (SHE). In both micro and macro health economics there are phenomena that are characterised by a strong spatial dimension, from hospitals engaging in local competitions in the delivery of health care services, to the regional concentration of health risk factors and needs. SHE allows health economists to incorporate these spatial effects using simple econometric models that take into account these spillover effects. This improves our understanding of issues such as hospital quality, efficiency and productivity and the sustainability of health expenditure of regional and national health care systems, to mention a few.

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Health Econometrics
Type: Book
ISBN: 978-1-78714-541-2

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Book part
Publication date: 1 December 2016

Roman Liesenfeld, Jean-François Richard and Jan Vogler

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian…

Abstract

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.

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Spatial Econometrics: Qualitative and Limited Dependent Variables
Type: Book
ISBN: 978-1-78560-986-2

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Article
Publication date: 6 July 2015

Michael Beenstock, Daniel Felsenstein and Ziv Rubin

The purpose of this paper is to examine the determinants of immigration from European Neighborhood (EN) and new member states to the EU core countries over the period…

Abstract

Purpose

The purpose of this paper is to examine the determinants of immigration from European Neighborhood (EN) and new member states to the EU core countries over the period 2000-2010. Apart from income differentials, unemployment rates and other standard variables hypothesized to determine immigration, the paper focusses attention on welfare-chasing as well as measures to enforce immigration policy. Using a variant of the gravity model, the paper investigates whether tests of these hypotheses are robust with respect to spatial misspecification.

Design/methodology/approach

The determinants of migration from the European Neighborhood and new member states to the EU core countries is estimated using a spatial variant of the gravity model. The methodology is used for both multilateral and spatial flows. Gravity model estimations are presented for immigration into the EU core destinations using standard, non-spatial econometrics, as well as spatial econometrics for single and double-spatial dynamics.

Findings

Immigration to EU core countries varies directly with the change in social spending per head in the destination. This result stands out in all the models, both OLS and spatial. Immigrants are attracted by economic inequality as measured by the Gini coefficient. However, in this case it is the level that matters rather than its change. No evidence is found that the threat of apprehension at the destination deters migrants from the European Neighborhood and other countries.

Research limitations/implications

The authors assume multilateralism is spatial. This means that everything else given, destinations are closer substitutes the nearer they are, and that immigration shocks are likely to be more correlated among origins the closer they are. This implicit assumption is restrictive because multilateralism is just spatial.

Social implications

While immigration to EU core countries varies directly with the change in (not level of) social spending per head. If a given country becomes more benevolent it attracts more immigration. The results suggest that if during 2000-2010 social spending per capita grew by 1 percent, the immigration rate increased by between 1 and 2 percentage points relative to the number of foreign-born in 2000. This is a large demographic effect.

Originality/value

Uniquely, this paper does not assume immigration flows are independent and stresses their spatial and multilateral nature. A series of new non-spatial and spatial (single and double-spatial lag) models are used to empirically test hypotheses about the determinants of immigration to the EU core countries.

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International Journal of Manpower, vol. 36 no. 4
Type: Research Article
ISSN: 0143-7720

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Article
Publication date: 2 August 2013

Shanaka Herath and Gunther Maier

This study aims to examine the impact of relative importance of local characteristics, distance from the city centre and unobservable spatial relation in explaining values…

Abstract

Purpose

This study aims to examine the impact of relative importance of local characteristics, distance from the city centre and unobservable spatial relation in explaining values of constant‐quality apartment units in Vienna.

Design/methodology/approach

Drawing on recent developments in spatial econometrics and spatial hedonic house price modelling, the rent gradient hypothesis is examined by means of hedonic regression and spatial hedonic regression. Spatial autocorrelation tests are applied in order to assess possible presence of spatial dependence. The authors borrow Florax et al.'s specification search strategy in order to choose the most appropriate spatial model specification.

Findings

This research shows that local characteristics – or particularities – proxied by district and distance from the city centre are important location variables with regard to the Viennese apartment market. The spatial analysis suggests that the apartment prices are spatially autocorrelated and the Viennese apartment market has a distance‐based neighbourhood structure. The main finding is, however, that residents are willing to bid more for constant‐quality apartment units that are close to the centre of the city.

Originality/value

Rent gradient hypothesis is usually tested within non‐spatial hedonic frameworks: this study estimates a spatial hedonic model additionally in order to allow for comparison of results. This is also the first article to apply recent developments in spatial econometrics to examine explicitly rent gradient theory in the context of the Viennese apartment market.

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Journal of European Real Estate Research, vol. 6 no. 2
Type: Research Article
ISSN: 1753-9269

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Article
Publication date: 18 October 2011

Malin Song, Shuhong Wang, Jie Wu and Li Yang

This article aims to discuss the binary matrix of spatial association which is suggested by Moran, and proposes a new method of the definition of the w matrix to obtain a…

Abstract

Purpose

This article aims to discuss the binary matrix of spatial association which is suggested by Moran, and proposes a new method of the definition of the w matrix to obtain a new space‐time correlation coefficient considering the correlation of both time and space.

Design/methodology/approach

From the perspective of the multi‐dimension of space and time, this article proposes a new computational method of a correlation coefficient considering both temporal and spatial factors, based on the analysis of the characteristics of Moran's Global Index and Moran's Local Index. The number of patents granted in mainland China's provinces and municipalities is taken as an example of multi‐dimensional analysis.

Findings

The results of quantitative analysis using this space‐time correlation coefficient show that the outcomes calculated by this new correlation coefficient are not only highly correlated with Moran's Index, but also have advantages in analyzing the trends of both spatial and temporal indicators simultaneously, which is verified by the illustration of the algorithm.

Research limitations/implications

Due to a scarcity of data in China, the algorithm is based on data for the last 20 years, which may not be long enough for this research. Although this does not reduce the value of the conclusions of this article, a closer look should be taken at the effectiveness of the new space‐time correlation coefficient in the future.

Practical implications

The results of space‐time correlation coefficient are highly correlated with Moran's Index. In addition, it can not only analyze the “flow” indicators in a certain period but also analyze the “stock” indicators to reflect both space and time changes. These may reflect superiority of space‐time correlation coefficient to Moran's Index.

Originality/value

This new correlation coefficient that considers both temporal and spatial factors and will provide a more scientific and effective tool for spatial econometric analysis in time and space changes of management on society and the economy.

Details

Management Decision, vol. 49 no. 9
Type: Research Article
ISSN: 0025-1747

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Book part
Publication date: 30 December 2004

James P. LeSage and R. Kelley Pace

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely…

Abstract

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.

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Spatial and Spatiotemporal Econometrics
Type: Book
ISBN: 978-0-76231-148-4

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Abstract

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Spatial Econometrics: Qualitative and Limited Dependent Variables
Type: Book
ISBN: 978-1-78560-986-2

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Book part
Publication date: 19 October 2020

Tiziano Arduini, Eleonora Patacchini and Edoardo Rainone

The authors generalize the standard linear-in-means model to allow for multiple types with between and within-type interactions. The authors provide a set of…

Abstract

The authors generalize the standard linear-in-means model to allow for multiple types with between and within-type interactions. The authors provide a set of identification conditions of peer effects and consider a two-stage least squares estimation approach. Large sample properties of the proposed estimators are derived. Their performance in finite samples is investigated using Monte Carlo simulations.

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Article
Publication date: 28 January 2020

Hui Wang and Meiqing Zhang

The large-scale construction of China’s transportation infrastructure has driven the flow of elements between regions, which has provided convenient conditions for the…

Abstract

Purpose

The large-scale construction of China’s transportation infrastructure has driven the flow of elements between regions, which has provided convenient conditions for the accumulation of advantageous resources.

Design/methodology/approach

Based on the panel data of 31 provinces in China in the past 2003-2017 years, this paper applies the spatial econometric model and partial differential method and empirically analyzes the spatial spillover effect of transportation infrastructure on employment in the service industry under four spatial weighting matrices.

Findings

The results show that for every 1 per cent increase in the level of transportation infrastructure, the employment density of the service industry in the region can be increased by 0.1274 per cent. It is worth noting that roads promote the employment of the service industry more than railways and inland waterways. However, inland waterways have not shown positive effects. The results on spatial spillover of transportation infrastructure indicate that railway has obvious promotion effect on the employment level of service industry in the surrounding area, while the highway has hindered the effect. The spatial spillover effect of inland waterway is not obvious.

Originality/value

The value of this paper is to consider the impact of China’s transportation infrastructure on employment in a particular industry, especially in the service industry. The research will help to provide empirical evidence for policymakers. The government needs to invest and build transportation infrastructure based on the stage and development potential of the employment development of the regional service industry.

Details

Kybernetes, vol. 49 no. 11
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 12 October 2021

Bart Niyibizi, B. Wade Brorsen and Eunchun Park

The purpose of this paper is to estimate crop yield densities considering time trends in the first three moments and spatially varying coefficients.

Abstract

Purpose

The purpose of this paper is to estimate crop yield densities considering time trends in the first three moments and spatially varying coefficients.

Design/methodology/approach

Yield density parameters are assumed to be spatially correlated, through a Gaussian spatial process. This study spatially smooth multiple parameters using Bayesian Kriging.

Findings

Assuming that county yields follow skew normal distributions, the location parameter increased faster in the eastern and northwestern counties of Iowa, while the scale increased faster in southern counties and the shape parameter increased more (implying less left skewness) in southwestern counties. Over time, the mean has increased sharply, while the variance and left skewness increased modestly.

Originality/value

Bayesian Kriging can smooth time-varying yield distributions, handle unbalanced panel data and provide estimates when data are missing. Most past models used a two-stage estimation procedure, while our procedure estimates parameters jointly.

Details

Agricultural Finance Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0002-1466

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