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Using a dynamic spatial panel model applied to 377 US Metropolitan Statistical Areas (MSAs), estimated over the period 2011-2021, significant differences are found between large MSAs regarding the relationship between labour productivity and economic mass, as measured by GDP. The methodology adopted illustrates the state of the art for spatial econometric modeling as it is often needed in practice, allowing for multiple endogenous regressors and dynamic effects. The estimation method applies synthetic instruments designed to limit negative effects of instrument overabundance. [ABSTRACT FROM AUTHOR]

The paper applies synthetic instruments, initially developed for cross-sectional regression, to estimate dynamic spatial panel data models. These have two main advantages. First, instruments correlated with endogenous variables and yet independent of the errors are difficult to find. Not only are synthetic instruments normally exogenous, but they are usually strongly correlated with endogenous variables, and thus help to avoid the problem of weak instruments. Secondly, they help to reduce instrumental variables proliferation, which is a common result of standard methods of avoiding endogeneity bias. As demonstrated by Monte Carlo simulation, instrument proliferation causes bias in the Sargan–Hansen J test statistic, which is an important indicator of instrument validity and hence estimation consistency. It is also associated with a downward bias in parameter standard error estimates. The paper shows the results of applying synthetic instruments across a variety of different specifications and data generating processes, and it illustrates the method with real data leading to more reliable inference of causal impacts on the level of employment across London districts. [ABSTRACT FROM AUTHOR]

This paper highlights a number of important gaps in the UK evidence base on the employment impacts of immigration, namely: (1) the lack of research on the local impacts of immigration – existing studies only estimate the impact for the country as a whole; (2) the absence of long-term estimates – research has focused on relatively short time spans – there are no estimates of the impact over several decades, for example; (3) the tendency to ignore spatial dependence of employment which can bias the results and distort inference – there are no robust spatial econometric estimates we are aware of. We aim to address these shortcomings by creating a unique data set of linked Census geographies spanning five Censuses since 1971. These yield a large enough sample to estimate the local impacts of immigration using a novel spatial panel model which controls for endogenous selection effects arising from migrants being attracted to high-employment areas. We illustrate our approach with an application to London and find that no migrant group has a statistically significant long-term negative effect on employment. EU migrants, however, are found to have a significant positive impact, which may have important implications for the Brexit debate. Our approach opens up a new avenue of inquiry into subnational variations in the impacts of immigration on employment. [ABSTRACT FROM AUTHOR]

This paper estimates the possible job-shortfall across Italian and EU regions using a time–space dynamic panel data model with a spatial moving average random effects structure of the disturbances. The paper is a companion paper to an earlier prediction exercise regarding Brexit. The model includes spatial and temporal dependencies involving the endogenous variable, leading to estimates based on a new dynamic spatial generalized moments estimator proposed by Baltagi et al. (Reg Sci Urban Econ, 2018. 10.1016/j.regsciurbeco.2018.04.013). The predictions use modified interregional trade estimates, assuming a reduction in trade flows between Italian and EU regions due to Italexit, to simulate the impact on employment across Italian regions and the wider EU. Comparisons are made between Italexit and Brexit. [ABSTRACT FROM AUTHOR]

This paper seeks to predict the impact of future housing supply on the affordability of residential space in the United Kingdom, using quantitative model-based simulation methods. Our spatially disaggregated analysis focuses on the greater South East region, approximately within 1.5 hours commuting time from Central London. A dynamic spatial panel model is applied to account for observed temporal variations in property prices and housing affordability across districts. The dynamic structure of this model allows us to assess the scale and extent of knock-on effects of local supply shocks in one district on other districts in the region. These complex spatial effects have been largely ignored in local or regional housing market forecasting models to date. Applying this model, we are able to demonstrate that local house prices and affordability are not only determined by the underlying supply and demand conditions in the market in question, but also depend crucially on conditions in neighbouring housing markets whose properties can be considered close substitutes within a larger regional housing market. We also show that increasing housing supply in the most critical areas has little impact on (both local and regional) affordability, even if wages do not change in response to an increase in employment. [ABSTRACT FROM AUTHOR]

In this paper, we show that the economic crisis commencing in 2007 had different impacts across US Metropolitan Statistical Areas, and seek to understand why differences occurred. The hypothesis of interest is that differences in industrial structure are a cause of variations in response to the crisis. Our approach uses a state-of-the art dynamic spatial panel model to obtain counterfactual predictions of Metropolitan Statistical Area employment levels from 2008 to 2014. The counterfactual employment series are compared with actual employment paths in order to obtain Metropolitan Statistical Area-specific measures of crisis impact, which then are analysed with a view to testing the hypothesis that resilience to the crisis was dependent on Metropolitan Statistical Area industrial structure. [ABSTRACT FROM AUTHOR]

ABSTRACT: This paper focuses on panel data models combining spatial dependence with a nested (hierarchical) structure. We use a generalized moments estimator to estimate the spatial autoregressive parameter and the variance components of the disturbance process. A spatial counterpart of the Cochrane‐Orcutt transformation leads to a feasible generalized least squares procedure to estimate the regression parameters. Monte Carlo simulations show that our estimators perform well in terms of root mean square error compared to the maximum likelihood estimator. The approach is applied to English house price data for districts nested within counties. [ABSTRACT FROM AUTHOR]

The response by regional and national economies to exogenous impulses has a well-established literature in both spatial econometrics and in mainstream econometrics and is of considerable importance given the post-2007 economic crisis, which is characterized by a period of severe global instability resulting from unprecedented economic shocks. This paper focuses on dynamic counterfactual predictions and impulse-response functions derived from appropriate econometric models. These provide insight regarding the question of whether responses to economic shocks are transitory or whether they have a permanent effect. Analysis shows that output shocks have had permanent effects on productivity so that economies have tended not to return to the pre-shock path but rather adjust to new levels. This suggests that the current recession will be embodied permanently within the memory of some of Europe's leading economies as a hysteretic effect. [ABSTRACT FROM AUTHOR]

This paper estimates individual wage equations to test two rival non-nested theories of economic agglomeration, namely New Economic Geography (NEG), as represented by the NEG wage equation and urban economic (UE) theory, in which wages relate to employment density. In the U.K. context, we find that for male respondents, there is no significant evidence that wage levels are an outcome of the mechanisms suggested by NEG or UE theory, but this is not the case for female respondents. We speculate on the reasons for the gender difference. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper examines the relationship between the level of public infrastructure and the level of productivity using panel data for the Spanish provinces over the period 1985-2004, a period that is particularly relevant due to the substantial changes occurring in the Spanish economy at that time. The underlying model used for the data analysis is based on the wage equation, which is one of a handful of simultaneous equations which when satisfied correspond to the short-run equilibrium of New Economic Geography (NEG) theory. This is estimated using various spatial panel models with either fixed or random effects to allow for individual heterogeneity. Using these models, we find consistent evidence that productivity depends directly on the public capital stock endowment of each province, but also there is evidence of negative spillover effects from changes in capital stock in neighboring provinces. [ABSTRACT FROM AUTHOR]

ABSTRACT Spatial econometrics has been criticized by some economists because some model specifications have been driven by data-analytic considerations rather than having a firm foundation in economic theory. In particular, this applies to the so-called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover, it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper, we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes. [ABSTRACT FROM AUTHOR]

ABSTRACT We analyze the resilience of U.K. regions to employment shocks. Two basic notions of resilience are distinguished. With engineering resilience, there is an underlying stable growth path to which a regional economy rebounds following a shock. With ecological resilience, shocks can permanently affect the growth path of the regional economy. Our data set consists of quarterly employment series for 12 U.K. regions (NUTS I) for the period 1971-2010. Using a seemingly unrelated regression (SUR) model specification, we test for the relevance of (engineering) resilience of U.K. regional employment to the four recessionary shocks in our sample. It turns out that U.K. regions do indeed differ in their resilience, but that these differences mainly concern the initial resistance to these shocks and not so much the recovery stage. The SUR model does not allow shocks to have permanent effects and it also does not take the possibility of time differentiated shock spillovers between the 12 regions into account. To this end, we also estimate a vector error-correction model (VECM) specification where employment shocks can have permanent effects and where also interregional employment linkages are included. We find that employment shocks typically have permanent effects when it concerns the own-region effects. Permanent effects can also be found for the impact on other regions but the interregional effects are typically only significant for nearby regions. [ABSTRACT FROM AUTHOR]

The paper by Cliff and Ord (1969) caused us to think more about the W matrix and the concept of spatial autoregression. This article reviews some applications of W, reinforcing the point that autoregression in time series is similar to, but not the same as, autoregression in spatial series. [ABSTRACT FROM AUTHOR]

This article considers some of the issues and difficulties relating to the use of spatial panel data regression in prediction, illustrated by the effects of mass immigration on wages and income levels in local authority areas of Great Britain. Motivated by contemporary urban economics theory, and using recent advances in spatial econometrics, the panel regression has wages dependent on employment density and the efficiency of the labor force. There are two types of spatial interaction, a spatial lag of wages and an autoregressive process for error components. The estimates suggest that increased employment densities will increase wage levels, but wages may fall if migrants are underqualified. This uncertainty highlights the fact that ex ante forecasting should be used with great caution as a basis for policy decisions. [ABSTRACT FROM AUTHOR]

The affordability of housing is a major policy issue that has increasingly become a concern for UK government as house prices have risen dramatically in recent years. This is partly because of the importance of affordability for the recruitment and retention of key workers, many of whom are on national pay scales and earning salaries that do not fully reflect the differences in prices that exist, in particular between London and the South East and the rest of Great Britain. Government policy is to increase the supply of housing in order to improve affordability in the greater South East. However, assuming that this expansion in housing supply is also to be accompanied by an expansion in employment, the outcome is that there will be both an increase in supply and in demand for housing, with the counter-intuitive result that, under one of the scenarios set out in this paper, in some areas affordability will worsen rather than improve. [ABSTRACT FROM AUTHOR]

This paper discusses estimation methods for models including an endogenous spatial lag, additional endogenous variables due to system feedback and an autoregressive or a moving average error process. It extends Kelejian and Prucha's, and Fingleton and Le Gallo's feasible generalized spatial two-stage least squares estimators and also considers HAC estimation in a spatial framework as suggested by Kelejian and Prucha. An empirical example using real estate data illustrating the different estimators is proposed. The finite sample properties of the estimators are finally investigated by means of Monte Carlo simulation. Resumen. Este artículo discute métodos de estimación para modelos incluyendo un intervalo espacial endógeno, variables endógenas adicionales debido a retroalimentación del sistema y un proceso autorregresivo o uno de error de media móvil. Amplia Kelejian y Prucha's, y los estimadores de mínimos cuadrados bietápicos espaciales generalizados factibles de Fingleton y Le Gallo y considera también la estimación HAC en un marco espacial tal y como sugieren Kelejian y Prucha. Proponemos un ejemplo empírico utilizando datos de bienes inmuebles ilustrando los diferentes estimadores. Las propiedades en muestras finitas de los estimadores se estudian finalmente mediante simulación de Monte Carlo. [ABSTRACT FROM AUTHOR]

The section introduces several articles within the issue of "Papers in Regional Science," including "A Comparison of Four Model Specifications for Describing Small Heterogeneous Space-Time Datasets: Sugar Cane Production in Puerto Rico," "Local Concentrations," and "Measuring the Impact of Large-Scale Transportation Projects on Land Price Using Spatial Statistical Models."

A multi-equation spatial econometric model is used to explain variations across EU regions in manufacturing productivity growth based on recent theoretical developments in urban economics and economic geography. The paper shows that temporal and spatial parameter homogeneity is an unrealistic assumption, contrary to what is typically assumed in the literature. Constraints are imposed on parameters across time periods and between core and peripheral regions of the EU, with the significant loss of fit providing overwhelming evidence of parameter heterogeneity, although the final model does highlight increasing returns to scale, which is a central feature of contemporary theory. [ABSTRACT FROM AUTHOR]

This article examines the distribution of residential property prices in 2001 across local areas in England using spatial econometric methods, showing that spatial variations in local income, income within commuting distance, the stock of residential properties and the quality of local schooling have significant effects. The residual spatial variation due to unknown factors is modelled by a proxy variable, but this does not rule out a significant spatial lag. The article argues that this represents endogenous interaction of property price levels between neighbouring areas, which is interpreted as the outcome of local market knowledge and preference, which produces greater price similarity between an area and its neighbours than one would anticipate from the levels of the exogenous price determinants. [ABSTRACT FROM AUTHOR]

The regional-growth literature emphasises the importance of positive network externalities as determinants of the long-run competitiveness of region, highlighting their role in reducing spatial transaction costs and facilitating collective learning and innovation. In this paper we contribute to this literature by presenting new evidence from a firm-based survey within three Turkish industrial centres about the causes of differentiated network activity. Using multivariate analysis, we show that locational rather than sectoral differences explain local-linkage intensities, but this is not the case for global links. Second, there is an important relationship between firm size and local-linkage and global-linkage densities; the density of local networks decreases with increasing firm size, whereas the density of global networks increases with firm size. Third, there is a positive relation between global-network density and firm productivity. We argue that the comparative and quantitative analysis we employ is a useful adjunct to case-study analysis, which could help avoid misconceptions and lead to a more realistic theory. [ABSTRACT FROM AUTHOR]

Recent contributions to the regional science literature have considered spatial effects in empirical growth specifications. In the case of spatial dependence, following theoretical arguments from new economic geography, and endogenous growth models, this phenomenon has been associated with the existence of externalities that cross regional borders. However, despite the general consensus that interactions or externalities are likely to be the major source of spatial dependence, they have been modelled in a rather ad hoc manner in most existing empirical studies. In contrast, we advocate basing the analysis on structural growth models which include externalities across economies, applying the appropriate spatial econometrics tools to test for their presence and estimate the magnitude of these externalities in the real world. [ABSTRACT FROM AUTHOR]

The article examines the performance of two competing non-nested models of regional wage variations in Great Britain, one motivated by the Solow-Swann neoclassical growth model which assumes constant returns to scale, the other by new economic geography theory, which assumes internal and external increasing returns. Both models also include controls for labour efficiency variations across regions. The empirical analysis, which is based on the bootstrap J test, shows that the neoclassical model does not reject the new economic geography specification, but the converse is not true and the model with a basis in new economic geography has significantly superior explanatory power. This adds support to the notion that in order to correctly understand differential regional economic development, we should move beyond neoclassical orthodoxy and that an increasing returns stance is more appropriate. However, the article also highlights some limitations of new economic geography theory. [ABSTRACT FROM AUTHOR]

This paper tests some of the main hypotheses about the importance of horizontal clusters for the growth of employment in small firms using data from Computing Services in Great Britain. In the main section of the paper, spatial econometric models are estimated controlling for supply- and demand-side conditions to isolate the effect of initial cluster intensity. The paper then projects cluster development using the fitted model, showing how clusters are likely to emerge and intensify. One aspect of the paper is the existence of a de-clustering mechanism due to congestion effects. [ABSTRACT FROM AUTHOR]

This paper provides new evidence that, controlling for other effects, the growth of employment in high-technology SMEs depends on the initial horizontal clustering. The paper focuses on employment change over the period 1991-2000 in computing services and research and development (R&D) industries analysed at the local and county level within Great Britain. A new measure is proposed to map clustering in each sector. In the main section of the paper, spatial econometric models are estimated controlling for supply- and demand-side conditions, human capital, the local economic environment and spatial externalities in order to isolate the effect of initial clustering level. The estimates support the hypothesis that clustering is a cause of employment growth, although there are important differences of geographical scale at which this effect operates for the two sectors considered. The paper raises some important issues regarding the implied dynamics associated with the evolution of clustering. [ABSTRACT FROM AUTHOR]

Describes a spatial econometric modeling approach based around theory developed in the field of urban economics. Features of the approach; Evolution of the thinking about externalities; Importance of proximity for innovation sharing; Focus of spatial econometric modeling.

Proposes a third way to analyze regional development located somewhere between new and old economic geography. Empirical evidence supporting the proposed model; Discussion on the model of regional economic growth; Implications of the model.

Introduces the concepts of spatial unit roots and spatial cointegration. Illustration of their implications for spatial regression using Monte-Carlo simulation; Spurious regression resulting from spatial unit roots; Similarity of spatial cointegration to its time-series counterpart.

Examines the evidence for convergence along neoclassical lines and from a point of view that accepts the critique of neoclassical theory. Description of the neoclassical theory; Key feature of the neoclassical theory; Nonlinear least squares and Maximum Likelihood convergence estimates; Convergence time estimates based on a heteroscedastic autonormal model.

Analyzes changes in the location of high-technology manufacturing employment in Great Britain to identify localization and delocalization of the potential for growth. Use of shift-share analysis; Lower high-technology manufacturing employment growth of Greater London; High employment growth in Wales.

Neoclassical theory assumes diminishing returns to capital and spatially constant exogenously-determined technological progress, although it is questionable whether these are realistic assumptions for modeling manufacturing productivity growth variations across European Union (E.U.) regions. In contrast, the model developed in this paper assumes increasing returns and spatially varying technical progress, and is linked to endogenous growth theory and particularly to 'new economic geography' theory. Simulations, involving 178 E.U.regions, show that productivity levels and growth rates are higher in all E.U. regions when the financially assisted (Objective 1) regions have faster output growth. This also reduces inequalities in levels of technology. Allowing the core regions to grow faster has a similar effect of raising productivity growth rates across the E.U., although inequality increases. Thus, the simulations are seen as an attempt to develop a type of 'computable geographical equilibrium' model which, as suggested by Fujita, Krugman, and Venables (1999), is the way theoretical economic geography needs to evolve in order to become a predictive discipline. [ABSTRACT FROM AUTHOR]