ABSTRACTThis paper illustrates the importance of taking into account re-exports. It shows that not taking into account re-exports causes estimates from the gravity model to be biased. It also considers the implications of re-exports for two current policy events. It is shown that the United States actually ran a net trade surplus in the North American Free Trade Agreement (NAFTA). Second, it is argued there may be additional costs for the UK economy from Brexit that are not addressed in earlier studies. This paper derives bilateral trade flows corrected for re-exports for a large number of countries at a detailed product level, using the World Input–Output Database (WIOD). Through a constrained non-linear optimization procedure, complete origin–destination matrices of re-exports are determined. Trade is subsequently restored to its most likely origin and destination.

ABSTRACTUpdating or estimating regional input–output tables is a challenging task addressed with non-survey methods. These can be classified into two groups: location quotient (LQ) methods and constrained matrix-balancing methods. This paper focuses on the second group and, specifically, on the performance of the cross-entropy method (CE). The most important finding is that the RAS method slightly outperforms the CE method on average, but its efficiency varies greatly from country to country. On the contrary, the performance of the CE method is more stable over countries and time. More interestingly, a fair implementation of the CE method boosted by the Flegg location quotient (FLQ) method outperforming the competing CE approach in terms of accuracy.

ABSTRACTWhen an employer refuses to recruit a job applicant due to the applicant's place of residence, we speak of redlining in the labour market. There are two explanations for this practice by the employer. The first is the excessive distance between the applicant's place of residence and the workplace, justified by a logic of spatial mismatch. The second is based on the characteristics of the neighbourhood in line with a signal logic. We propose to measure the effects of these two mechanisms using a correspondence test conducted in the Paris region of France for two occupations: waiters and cooks. It appears that distance plays a significant role and reinforces the effect of a disreputable neighbourhood. The most deprived neighbourhoods combine these two types of drawbacks.

ABSTRACTThis paper evaluates the relationship between transit station proximity and new business creation in five US regions with varying levels of maturity in rail transit development and/or entrepreneurial ecosystems: Boston, San Jose, Austin, Cleveland and Philadelphia. It tests a variety of spatial econometric models to find the best specification and compares the results with the kinds of non-spatial models currently used in the literature. This provides a better understanding of the role of various forms of spatial dependence in the transit – new business creation relationship and shows that existing models may overstate the impact of transit on new business creation. In addition, the paper teases out differences between regions, rail modes and business types that can be usefully applied to a variety of urban contexts.

ABSTRACTThis paper develops a spatial vector autoregressive (SpVAR) model to investigate the transmission of sovereign, banking and corporate default risks among 11 Eurozone countries for the period January 2008–December 2013. The results show that a significant proportion of default risk variation is explained by foreign shocks. However, the cross-border sovereign–bank nexus is statistically significant, but economically moderate. Among the three sectors, shocks to the banking sector play the most critical role. On average, for the 11 countries, a foreign banking shock can explain 7%, 23% and 18% of the forecast error variance of changes in sovereign, banking and corporate credit default swap spreads respectively.

ABSTRACTThis paper estimates a spatial autoregressive (SAR) model of price dispersion using publicly available internet bookselling data. It uses a semiparametric adaptive estimator that does not require the usual Gaussian assumption of maximum likelihood (ML) estimators. The results suggest that both price competition and seller heterogeneity are key drivers of the observed price dispersion. The paper finds that sellers with large sales volume, newly established sellers and US mainland states-based sellers tend to price lower. The identified significant spatial interaction is evidence of spatial price competition. Controlling for everything else, a seller asks a lower price when large sellers charge relatively high prices, which is also evidence of price-based selling and undercutting.

ABSTRACTFunctional regression over irregular domains: variation in the shadow price of living space. Spatial Economic Analysis. Hedonic house price models need to account for spatial heterogeneity – the variation in the functional surface of shadow prices. In this context, the complexity of spatial domains raises issues for traditional spatial econometric methods. Specifically, discontinuities in the spatial surface need to be accounted for, including irregular boundaries, peninsulas and interior holes. Motivated by an application to housing markets, we develop a method for estimating the functional surface of a regression coefficient that varies over such an irregular spatial domain. Spatially varying coefficients for a specific regressor are estimated by a combination of three smoothing problems using splines based on finite element analysis. The effect of additional regressors is also allowed. We verify finite sample performance using a simulation study, and develop an application to the Aveiro–Ílhavo urban housing market in Portugal.

ABSTRACTRaising the bar (5). Spatial Economic Analysis. This editorial summarizes and comments on the papers published in this issue 12(1) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper examines the impact of the level of education on the decision to migrate and finds that it is approximately twice as large if both variables are modelled simultaneously. The second paper is one of the first papers to introduce a spatial component to models of international environmental agreements and to develop an exciting overlap with New Economic Geography. The third paper provides a tool, applied to Beijing, with which urban economic planners can investigate the role of variation and selection mechanisms in cluster development and identify possible paths of growth. The fourth paper contributes to the existing literature on retail geography by examining the role of consumption possibilities as an urban amenity. The fifth paper develops a Bayesian estimator of a linear regression model with spatial lags among the dependent variable, the explanatory variables and the disturbances. Finally, the sixth paper develops a semi-parametric generalized method of moments (GMM) estimator for a spatial autoregressive model with space-varying coefficients of the explanatory variables and a spatial autoregressive coefficient common to all units.

ABSTRACTHeteroskedasticity-robust semi-parametric GMM estimation of a spatial model with space-varying coefficients. Spatial Economic Analysis. The spatial model with space-varying coefficients proposed by Sun et al. in 2014 has proved to be useful in detecting the location effects of the impacts of covariates as well as spatial interaction in empirical analysis. However, Sun et al.’s estimator is inconsistent when heteroskedasticity is present – a circumstance that is more realistic in certain applications. In this study, we propose a kind of semi-parametric generalized method of moments (GMM) estimator that is not only heteroskedasticity robust but also takes a closed form written explicitly in terms of observed data. We derive the asymptotic distributions of our estimators. Moreover, the results of Monte Carlo experiments show that the proposed estimators perform well in finite samples.