REGIONAL POPULATION–EMPLOYMENT DYNAMICS ACROSS DIFFERENT SECTORS OF THE ECONOMY*

ABSTRACT An important subset of the literature on agglomeration externalities hypothesizes that intrasectoral and intersectoral relations are endogenously determined in models of local and regional economic growth. Remarkably, structural adjustment models describing the spatio‐temporal dynamics of population and employment levels or growth traditionally do not include intersectoral economic dynamics. This paper argues and shows that allowing for economic linkages across sectors in these models adds considerable value, especially in forecasting. An econometric model of population–employment dynamics, in which sectoral variations in economic development are explicitly taken into account, is applied to a large urban planning policy proposal in the Netherlands. The empirical analyses suggest that population dynamics are largely exogenous, population changes drive employment in particular in the industry and retail sectors, and employment in all sectors depends strongly on intersectoral dynamics. Intersectoral dynamics appear as important drivers of regional sectoral employment changes; they are even more important than population changes, and their effect shows up clearly even within the Dutch institutional context where strict regulatory housing and planning restrictions are enforced.     

Bayesian Estimation of Regional Production for CGE Modeling

Abstract Computable general equilibrium (CGE) models are often criticized for using restrictive functional forms and relying on external sources for parameter values in their calibration. CGE modelers argue that in many instances reliable econometric estimates of important model parameters are unavailable because they must be estimated using small numbers of time‐series observations. To address these criticisms, this paper uses a Bayesian approach to estimate the parameters of a translog production function in a regional computable general equilibrium model. Using priors from more reliable national estimates, and parameter restrictions required by neoclassical production theory, estimation is done by Markov chain Monte Carlo simulation. A stylized regional CGE model is then used to contrast policy responses of a Cobb‐Douglas specification with those from the estimated translog equation.     

Spatial Autoregressive Models for Geographically Hierarchical Data Structures

This article discusses how standard spatial autoregressive models and their estimation can be extended to accommodate geographically hierarchical data structures. Whereas standard spatial econometric models normally operate at a single geographical scale, many geographical data sets are hierarchical in nature—for example, information about houses nested into data about the census tracts in which those houses are found. Here we outline four model specifications by combining different formulations of the spatial weight matrix W and of ways of modeling regional effects. These are (1) groupwise W and fixed regional effects; (2) groupwise W and random regional effects; (3) proximity‐based W and fixed regional effects; and (4) proximity‐based W and random regional effects. We discuss each of these model specifications and their associated estimation methods, giving particular attention to the fourth. We describe this as a hierarchical spatial autoregressive model. We view it as having the most potential to extend spatial econometrics to accommodate geographically hierarchical data structures and as offering the greatest coming together of spatial econometric and multilevel modeling approaches. Subsequently, we provide Bayesian Markov Chain Monte Carlo algorithms for implementing the model. We demonstrate its application using a two‐level land price data set where land parcels nest into districts in Beijing, China, finding significant spatial dependence at both the land parcel level and the district level.     

Stochastic Efficiency of Bayesian Markov Chain Monte Carlo in Spatial Econometric Models: An Empirical Comparison of Exact Sampling Methods

Spatial econometric specifications pose unique computational challenges to Bayesian analysis, making it difficult to estimate models efficiently. In the literature, the main focus has been on extending Bayesian analysis to increasingly complex spatial models. The stochastic efficiency of commonly used Markov Chain Monte Carlo (MCMC) samplers has received less attention by comparison. Specifically, Bayesian methods to analyze effective sample size and samplers that provide large effective size have not been thoroughly considered in the literature. Thus, we compare three MCMC techniques: the familiar Metropolis‐within‐Gibbs sampling, Slice‐within‐Gibbs sampling, and Hamiltonian Monte Carlo. The latter two methods, while common in other domains, are not as widely encountered in Bayesian spatial econometrics. We assess these methods across four different scenarios in which we estimate the spatial autoregressive parameter in a mixed regressive, spatial autoregressive specification (or, spatial lag model). We find that off‐the‐shelf implementations of the newer high‐yield simulation techniques require significant adaptation to be viable. We further find that the effective sizes are often significantly smaller than nominal sizes. In addition, we find that stopping simulation early may understate posterior credible interval widths when effective sample size is small. More broadly, we suggest that sample information and stopping rules deserve more attention in both applied and basic Bayesian spatial econometric research.     

Cluster Dynamics: New Evidence and Projections for Computing Services in Great Britain*

Abstract. 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.     

Evolution of the Labor Market in a Regional City:The Changing Economic Performance of Emigrants from Mexico City*

Abstract. This paper draws on micro‐level data to fully inform the debate on decentralization and regional development. Using labor‐income trajectories of emigrants from Mexico City, the paper analyzes how the labor market in a regional city, Leon, evolves. Results from the econometric model suggest that migrants’ labor‐income trajectories differ between the large agglomeration and the regional city in an early stage of the evolution of the labor market, but converge in a later stage. Specifically, the slope of the earning function for recent migrants is steeper and statistically different from the slope for early migrants. The findings presented in this paper enrich the existing theory by providing microfoundations to a typically macroeconomic area of research and enable policy makers to better understand the processes underpinning the evolution of regional labor markets.     

THE SLX MODEL

We provide a comprehensive overview of the strengths and weaknesses of different spatial econometric model specifications in terms of spillover effects. Based on this overview, we advocate taking the SLX model as point of departure in case a well‐founded theory indicating which model is most appropriate is lacking. In contrast to other spatial econometric models, the SLX model also allows for the spatial weights matrix W to be parameterized and the application of standard econometric techniques to test for endogenous explanatory variables. This starkly contrasts commonly used spatial econometric specification strategies and is a complement to the critique of spatial econometrics raised in a special theme issue of the Journal of Regional Science (Volume 52, Issue 2). To illustrate the pitfalls of the standard spatial econometrics approach and the benefits of our proposed alternative approach in an empirical setting, the Baltagi and Li (2004) cigarette demand model is estimated.     

Forecasting Structural Change With a Regional Econometric Input-Output Model

The sophistication of regional economic models has been demonstrated in numerous ways, most recently in the form of linking several modeling systems or in the expansion in the number of equations that can be manipulated successfully to produce impact analyses or forecasts. In this paper, an alternative perspective is employed. What do regional macro-level forecasts indicate about the process of structural change? A new methodology is illustrated that enables analysts to make forecasts of detailed structural change in the interindustry relations in an economy. Using a regional econometric input-output model developed for the Chicago metropolitan region, derived input-output tables are extracted for the period 1975–2016. These tables are then analyzed to determine the forecasted direction of structural changes for the region. The innovation illustrated here is based on a model that exploits the general equilibrium spirit of computable general equilibrium models through the adjustment of input coefficients to clear markets.     

TECHNOLOGICAL CATCH‐UP AND GEOGRAPHIC PROXIMITY*

ABSTRACT This paper examines productivity catch‐up as a source of establishment productivity growth. We present evidence that, other things equal, establishments further behind the industry frontier experience faster rates of productivity growth. Geographic proximity to frontier firms makes catch‐up faster. Our econometric specification implies a long‐run relationship between productivity levels, where nonfrontier establishments lie a steady‐state distance behind the frontier such that their rate of productivity growth including catch‐up equals productivity growth at the frontier. We use our econometric estimates to quantify the implied contribution to productivity growth of catch‐up to both the national and regional productivity frontiers.     

Alternative Nested Logit Model Structures and the Special Case of Partial Degeneracy

Partially degenerate nested logit (NL) models have broad applicability in regional science. However, the literature on them is relatively sparse and confusion exists on some aspects of identification and related matters. This paper addresses a number of conceptual and econometric aspects of partially degenerate NL models. These include identification, scaling, invariance, and consistency with the utility-maximizing postulate that underlies discrete choiceanalysis. This is accomplished within the larger, encompassing framework of nondegenerate NL models of which the partially degenerate model is a special case.     

REGIONAL GROWTH IN THE SOVIET ECONOMY A MODEL AND ANALYSIS*

A multiregional econometric model of the Soviet economy is presented. It consists of 1,391 equations, with time-series information for 1965-1980. In conformity with the number of the country's republics, 15 input-output tables are incorporated. The discussion includes the methodological framework, the structure and equations, theoretical justification for the interaction between the econometric and input-output components, data and estimation, and forecasting results.     

Bayesian Model Averaging for Spatial Econometric Models

We extend the literature on Bayesian model comparison for ordinary least-squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of different matrices of explanatory variables. A Markov Chain Monte Carlo model composition methodology labeled MC ³ by Madigan and York is developed for two types of spatial econometric models that are frequently used in the literature. The methodology deals with cases where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. Estimates and inferences are produced by averaging over models using the posterior model probabilities as weights, a procedure known as Bayesian model averaging. We illustrate the methods using a spatial econometric model of origin–destination population migration flows between the 48 U.S. states and the District of Columbia during the 1990–2000 period.     

GRAVITY AND SPATIAL STRUCTURE: THE CASE OF INTERSTATE MIGRATION IN MEXICO*

ABSTRACT The estimation of gravity models of internal (aggregate) place‐to‐place migration is plagued with endogeneity (omitted‐variable) biases if the unobserved effects of spatial structure are not accounted for. To address this econometric problem, this paper presents a more general specification of the gravity model, which allows for (bilateral) parameter heterogeneity across individual migration paths—along with (unilateral) origin‐ and destination‐specific effects. The resultant “three‐way fixed‐effects” (3FE) model is applied for an analysis of interstate migration in Mexico based on cross‐sectional data. To overcome parameter‐dimensionality problems (due to limited or incomplete information), the 3FE model is estimated using the Generalized Maximum Entropy (GME) estimator. The empirical implications of this new modeling strategy are illustrated by contrasting the 3FE‐GME estimates with those for the traditional and two‐way fixed‐effects (2FE) models. The former are far more plausible and intuitively interpretable than their traditional and 2FE counterparts, with parameter estimates changing in expected directions. The (average) effect of the migrant stock is markedly smaller than usually estimated, providing a more realistic measure of network‐induced migration. Migration outflows from centrally located origins have significantly steeper distance decay. Path‐specific distance effects exhibit directional asymmetries and spatial similarities.     

TESTING THE SPATIAL SCALE AND THE DYNAMIC STRUCTURE IN REGIONAL MODELS (A CONTRIBUTION TO SPATIAL ECONOMETRIC SPECIFICATION ANALYSIS)*

This article addresses the problem of specification uncertainty in modeling spatial economic theories in stochastic form. It is ascertained that the traditional approach to spatial econometric modeling does not adequately deal with the type and extent of specification uncertainty commonly encountered in spatial economic analyses. Two alternative spatial econometric modeling procedures proposed in the literature are reviewed and shown to be suitable for analyzing systematically two sources of specification uncertainty, viz., the level of aggregation and the spatio-temporal dynamic structure in multiregional econometric models. The usefulness of one of these specification procedures is illustrated by the construction of a simple multiregional model for The Netherlands.     

Some approaches to election night forecasting in Australia

We outline the history of election night forecasting in Australia. We first identify the major structural features of the Australian voting system which determine how the problem must be approached: single‐member constituencies; preferential voting; the counting on election night, in polling booths, of first preference votes only: and the existence of a two‐party system. We note that the major statistical difficulty associated with election night forecasting in Australia is that progressive counts in a seat, far from being random samples of the votes cast, are non‐random and systematically biased samples. We finally outline the increasingly sophisticated computer models which have been used to correct for this bias, culminating in the ‘matched polling places’ model implemented by the Australian Electoral Commission at the 1990 election, which has effectively eliminated the problem of bias.     

PERSISTENCE OF REGIONAL UNEMPLOYMENT: APPLICATION OF A SPATIAL FILTERING APPROACH TO LOCAL LABOR MARKETS IN GERMANY*

ABSTRACT The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analyzing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region‐specific information (e.g., the endowment of natural resources, or the size of the “home market”) that is usually incorporated in the fixed effects coefficients. The advantages of our proposed procedure are that the spatial filter, by incorporating region‐specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time‐stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive coefficients estimated for unemployment data for German NUTS‐3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates.     

The regional impact of the channel tunnel: Qualitative and quantitative analysis

The paper addresses the question of how different types of regions mill be affected by the development of a major new transport infrastructure such as the Channel Tunnel. It summarizes a study on the regional impacts of the Channel Tunnel throughout the European Union (EU). The research design combined qualitative methods of futures exploration employing 13 regional case studies with quantitative forecasting techniques using the MEPLAN transport and economic model. The paper presents the methodology and major results of the two approaches with respect to the impacts of the Channel Tunnel on transport flows and regional development in Europe and draws conclusions for transport and regional policy of the EU. The paper demonstrates that the removal of a bottleneck like the Channel Tunnel does not necessarily induce economic gains in all adjacent regions. Much more important for regional economic development than the reduction of transport costs are the image to be a region well integrated into the European high‐speed transport network and an active political response of the region to take advantage of opportunities like the Channel Tunnel.     

Model Uncertainty in Matrix Exponential Spatial Growth Regression Models

This article considers the most important aspects of model uncertainty for spatial regression models, namely, the appropriate spatial weight matrix to be employed and the appropriate explanatory variables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models used in the regional growth literature, and develop a simple Bayesian model‐averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on previous work by reducing the computational costs through the use of Bayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial Durbin matrix exponential model has theoretical and computational advantages over the spatial autoregressive specification due to the ease of inversion, differentiation, and integration of the matrix exponential. In particular, the matrix exponential has a simple matrix determinant that vanishes for the case of a spatial weight matrix with a trace of zero. This allows for a larger domain of spatial growth regression models to be analyzed with this approach, including models based on different classes of spatial weight matrices. The working of the approach is illustrated for the case of 32 potential determinants and three classes of spatial weight matrices (contiguity‐based, k‐nearest neighbor, and distance‐based spatial weight matrices), using a data set of income per capita growth for 273 European regions.     

Aggregation of Regional Economic Time Series with Different Spatial Correlation Structures. 具有不同空间相关结构的区域经济时间序列集成

In this article, we compare the relative efficiency of different forecasting methods of space‐time series when variables are spatially and temporally correlated. We consider two cases: (1) univariate forecasting (i.e., a space‐time series aggregated into a single time series) and (2) the more general instance of multivariate forecasting (i.e., a space‐time series aggregated into a coarser spatial partition). We extend the results in the literature by including the consideration of larger datasets and the treatment of edge effects and of negative spatial correlation. We first introduce a statistical framework based on the space‐time autoregressive class of random field models, which constitutes the basis of our simulation study, and we present the various alternative forecasting methods considered in the simulation. We then present the results of a Monte Carlo study related to univariate forecasting. In order to allow a comparison with the findings of Giacomini and Granger (2004), we consider the same forecasting strategies and the same combinations of the parameter values used there, but with a larger parametric set. Finally, we extend our analysis to the case of multivariate forecasting. The outcomes obtained provide operational suggestions about how to choose between alternative forecasting methods in empirical circumstances.     

SUPPLY-SIDE MULTIREGIONAL INPUT-OUTPUT MODELS*

ABSTRACT This paper introduces a new class of supply-side multiregional input-output (MRIO) models and provides the necessary and sufficient conditions on the regional trade matrices that ensure that a generalized supply-side model will be convergent. The paper also introduces a new version of the row coefficient model, conceived as the “mirror image” of the Chenery-Moses demand-side column coefficient MRIO model. Given that the conventional supply-side input-output model has been shown to perform equally as well as the demand side model in forecasting exercises, the supply-side MRIO model is expected to be of value for policy and planning purposes. Moreover, this model is of potential theoretical value for a broad synthesis of demand-side and supply-side MRIO models.