ABSTRACTThe interplay between the Bayesian and Frequentist approaches: a general nesting spatial panel-data model. Spatial Economic Analysis. An econometric framework mixing the Frequentist and Bayesian approaches is proposed in order to estimate a general nesting spatial model. First, it avoids specific dependency structures between unobserved heterogeneity and regressors, which improves mixing properties of Markov chain Monte Carlo (MCMC) procedures in the presence of unobserved heterogeneity. Second, it allows model selection based on a strong statistical framework, characteristics that are not easily introduced using a Frequentist approach. We perform some simulation exercises, finding good performance of the properties of our approach, and apply the methodology to analyse the relation between productivity and public investment in the United States.

ABSTRACTRegional labour force participation across the European Union: a time–space recursive modelling approach with endogenous regressors. Spatial Economic Analysis. Although there is an abundant regional labour market literature taking a spatial perspective, only a few studies have explored extending the analysis of labour force participation with spatial effects. This paper revisits this important issue, proposing a time–space recursive modelling approach that builds on and appraises Fogli and Veldkamp’s methodology from 2011 and finding for the United States that participation rates vary with past values in nearby regions. Major shortcomings in their study are corrected for, including stationarity and the control for endogenous regressors other than the time and space–time-lagged dependent variable using system generalized method of moments (GMM). The paper also highlights interaction effects among explanatory variables for the first time in this context. Using a panel of 108 regions across the European Union over 1986–2010, the results for total, male and female participation rates throw a new light on the socio-economic relevance of different determinants. Importantly, characteristics in neighbouring regions play a significant role, and neglecting endogeneity is found to have serious consequences, underlining increased attention on the specification and estimation of spatial econometric models with endogenous regressors.

This paper develops a new methodology of spatial econometric modelling for regional public investment in local economies. The approach, based on the financial concept of net present value of cash flows, can be applied in the analysis of regional public investment with long-term financial inputs and outputs. With local public revenues related to spending in the model, one can observe economies of scale of investment with possible saturation effects and a marginal investment multiplier, explaining the extent to which public investment costs translate into public revenues. The main advantage of the proposed model is to behave counter-cyclically due to accumulation over the periods and to cover multi-period investments as well as postponed effects. This paper gives an example of public investment efficiency in Polish NUTS-5 municipalities regions.

This paper presents a gravity model of student migration flows to higher education institutions (HEIs) in Ireland. The analysis is performed on a novel dataset containing detailed information on a range of ‘push’ and ‘pull’ factors, allowing one to estimate the effects of a number of important school-level characteristics on these flows. This is achieved by estimating and comparing a fixed-effects Poisson model and two conditional fixed-effects negative binomial models and selecting the best model on the basis of the Akaike information criterion (AIC). The preferred negative binomial model accounts for over-dispersion in the student flow data and allows for estimation of the parameter coefficients of the HEI-invariant characteristics. The analysis suggests that while geography plays a very important role in explaining student flows, so too do a range of school-level characteristics. Furthermore, it is found that distance has a differential impact across HEIs and HEI types with important implications for policy-makers and HEI managers.

This paper describes an approach developed to measure regional economic resilience across Europe which is novel in three key dimensions. Firstly, it seeks to date regional downturns as opposed to assuming that all regional economies are affected by economic shocks at the same point in time; secondly, it measures the amplitude and duration of economic downturns and subsequent recoveries; and thirdly, as well as measuring recovery, it measures the resistance of regional economies to economic shocks. The paper applies this methodology to selected European countries to provide an analysis of differential regional responses to several economic shocks since the early 1990s. The paper then reflects upon the utility of this methodology for operationalizing regional economic resilience in cross-comparative studies.

The missing data problem has been widely addressed in the literature. The traditional methods for handling missing data may be not suited to spatial data, which can exhibit distinctive structures of dependence and/or heterogeneity. As a possible solution to the spatial missing data problem, this paper proposes an approach that combines the Bayesian Interpolation method [Benedetti, R. & Palma, D. (1994) Markov random field-based image subsampling method, Journal of Applied Statistics, 21(5), 495–509] with a multiple imputation procedure. The method is developed in a univariate and a multivariate framework, and its performance is evaluated through an empirical illustration based on data related to labour productivity in European regions.

This paper discusses competing-destinations formulation of the gravity model for the flows of patients from their residential areas to health supplier regions. This approach explicitly acknowledges the interdependence of the patient between a set of alternative health supplier regions. This competing-destinations-based approach may be implemented as a probabilistic demand function or conditional logit model, with a Poisson outcome. A Texas-based case study of residential areas and State Mental Hospitals (SMHs) is presented. The results of the estimation do not lend support to the presence of scale effects in SMHs due to the size of population. This result, combined with the negative effect of average length of stay and with the positive effect of the provision of forensic services on patient flows, highlights the problem of caseload growth in SMHs.

In this paper, we apply a hedonic housing price model to estimate the willingness to pay for less air pollution and noise in the city of Madrid. Using subjective data on the perception of air pollution and noise by the Madrid residents, we apply a quantile conditionally parametric model that allows one to quantify the heterogeneity of this willingness to pay values across quantiles of the conditional distributions of housing prices and their spatial heterogeneity across the whole study area. The results show that implicit prices for clean and quiet environment differ substantially across the housing markets, depending on the perceived intensity of pollution, accessibility to jobs and leisure, and some socioeconomic characteristics of the population. In particular, in some areas, households seem to make a trade-off between improvements in communication and some worsening in environmental conditions.

We address the question of measuring and testing industrial spatial concentration based on micro-geographic data with distance-based methods. We discuss the basic requirements for such measures and we propose four additional requirements. We also discuss the null assumptions classically used for testing aggregation of a particular sector and propose an alternative point of view. Our general index measure involves a cumulative and a non-cumulative version. This allows us to propose an alternative version of the Duranton–Overman index with a proper baseline as well as a cumulative version of this same index. We present simulations to evaluate the respective powers of this new approach and the classical ones.

This issue marks the start of our 10th year of publication. On this anniversary, as co-editors, we would like to thank everybody involved with the Journal, both currently and in the past, who have contributed to its success. In this editorial, we summarise and comment on papers published in issue 10.1. The first paper is entitled ‘Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroskedastic Disturbances’, by Harald Badinger and Peter Egger. Secondly, we have the paper by Liliana Garrido-da-Silva, Sofia B. S. D. Castro and Paulo B. Vasconcelos entitled ‘Discrete Dynamics for the Core-periphery Model’. The third paper ‘An Investigation of the Relation between Cooperation Intensity and the Innovative Success of German Regions’ is by Tom Broekel, Thomas Brenner and Matthias Buerger. Fourthly, we have the paper entitled ‘Intra-regional Wage Inequality in Portugal’ by João Pereira and Aurora Galego. The final paper is by Jesús Peiró-Palomino and Emili Tortosa-Ausina entitled ‘Social Capital, Investment and Economic Growth: Some Evidence for Spanish Provinces'.