On the road to prosperity? The economic geography of China's national expressway network

Over the past two decades, China has embarked on an ambitious program of expressway network expansion. By facilitating market integration, this program aims both to promote efficiency at the national level and to contribute to the catch-up of lagging inland regions with prosperous Eastern ones. This paper evaluates the aggregate and spatial economic impacts of China's newly constructed National Expressway Network, focussing, in particular, on its short-run impacts. To achieve this aim, the authors adopt a counterfactual approach based on the estimation and simulation of a structural "new economic geography" model. Overall, they find that aggregate Chinese real income was approximately 6 percent higher than it would have been in 2007 had the expressway network not been built. Although there is considerable heterogeneity in the results, the authors do not find evidence of a significant reduction in disparities across prefectural level regions or of a reduction in urban-rural disparities. If anything, the expressway network appears to have reinforced existing patterns of spatial inequality, although, over time, these will likely be reduced by enhanced migration

A multidimensional spatial lag panel data model with spatial moving average nested random effects errors

This paper focuses on a three-dimensional model that combines two different types of spatial interaction effects, i.e. endogenous interaction effects via a spatial lag on the dependent variable and interaction effects among the disturbances via a spatial moving average (SMA) nested random effects errors. A three-stage procedure is proposed to estimate the parameters. In a first stage, the spatial lag panel data model is estimated using an instrumental variable (IV) estimator. In a second stage, a generalized moments (GM) approach is developed to estimate the SMA parameter and the variance components of the disturbance process using IV residuals from the first stage. In a third stage, to purge the equation of the specific structure of the disturbances a Cochrane–Orcutt-type transformation is applied combined with the IV principle. This leads to the GM spatial IV estimator and the regression parameter estimates. Monte Carlo simulations show that our estimators are not very different in terms of root mean square error from those produced by maximum likelihood. The approach is applied to European Union regional employment data for regions nested within countries

US Metropolitan Area Resilience: Insights from dynamic spatial panel estimation

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. </jats:p

Pleiotropic functions of the tumor- and metastasis-suppressing Matrix Metalloproteinase-8 in mammary cancer in MMTV-PyMT transgenic mice

Matrix metalloproteinase-8 (MMP-8; neutrophil collagenase) is an important regulator of innate immunity which has onco-suppressive actions in numerous tumor types

A time-space dynamic panel data model with spatial moving average errors

This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic panel data model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi, Fingleton and Pirotte (2014) and Fingleton (2008). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run e¤ects and evaluate the predictive effficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001-2012, with the last two years reserved for prediction

Multilevel Modelling with Spatial Effects

In multilevel modelling, interest in modeling the nested structure of hierarchical data has been accompanied by increasing attention to different forms of spatial interactions across different levels of the hierarchy. Neglecting such interactions is likely to create problems of inference, which typically assumes independence. In this paper we review approaches to multilevel modelling with spatial effects, and attempt to connect the two literatures, discussing the advantages and limitations of various approaches

Estimating the local employment impacts of immigration : a dynamic spatial panel model

This paper highlights a number of important gaps in the UK evidence base on the employment impacts of immigration, namely: (i) the lack of research on the local impacts of immigration - existing studies only estimate the impact for the country as a whole; (ii) the absence of long term estimates –research has focussed on relatively short time spans – there are no estimates of the impact over several decades, for example; (iii) 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 dataset of linked Census geographies spanning 5 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 are found to have a significant positive impact. Our approach opens up a new avenue of inquiry into sub-national variations in the impacts of immigration on employment

Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short- or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneouscoefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years)