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We are interested in modeling the impact of spatial and interindustry dependence on firm-level innovation of Chinese firms The existence of network ties between cities imply that changes taking place in one city could influence innovation by firms in nearby cities (local spatial spillovers), or set in motion a series of spatial diffusion and feedback impacts across multiple cities (global spatial spillovers). We use the term local spatial spillovers to reflect a scenario where only immediately neighboring cities are impacted, whereas the term global spatial spillovers represent a situation where impacts fall on neighboring cities, as well as higher order neighbors (neighbors to the neighboring cities, neighbors to the neighbors of the neighbors, and so on). Global spatial spillovers also involve feedback impacts from neighboring cities, and imply the existence of a wider diffusion of impacts over space (higher order neighbors).

Similarly, the existence of national interindustry input-output ties implies that changes occurring in one industry could influence innovation by firms operating in directly related industries (local interindustry spillovers), or set in motion a series of in interindustry diffusion and feedback impacts across multiple industries (global interindustry spillovers).

Typical linear models of firm-level innovation based on knowledge production functions would rely on city- and industry-specific fixed effects to allow for differences in the level of innovation by firms located in different cities and operating in different industries. This approach however ignores the fact that, spatial dependence between cities and interindustry dependence arising from input-output relationships, may imply interaction, not simply heterogeneity across cities and industries.

We construct a Bayesian hierarchical model that allows for both city- and industry-level interaction (global spillovers) and subsumes other innovation scenarios such as: (1) heterogeneity that implies level differences (fixed effects) and (2) contextual effects that imply local spillovers as special cases.

This chapter analyses some internal territorial and economic conflicts in Spain among its autonomous communities. The Basque country has a very favourable tax system from 1878, which historically is stipulated in the Spanish constitution as a special case. This generates an asymmetry with respect to the other 18 Spanish communities including Catalonia, which would like to have a fiscal regime similar to that of the Basque country. After the Spanish state has built the fiscal balances for all autonomous communities, the Catalans argue that Spain steals them and they demand independence for Catalonia, which would affect the political and economic stability of the European Union. Specifically, this chapter attempts to describe a way to resolve territorial conflicts that have been exacerbated by the results of the fiscal balances in a context of fiscal decentralisation, since capital stock balances are not considered in the fiscal balances or in the inter-regional balance of payments. In this chapter, a production function approach, where the public capital production factor is separated into internal and imported capital stock, is used to calculate how the capital stock of the transportation infrastructure actually used can affect the labour productivity in each province or region. This study takes into account the direct effects of the capital stock of the road transport infrastructure of a region and the indirect effects that it receives from the use of infrastructures in other regions. Both types of public capital have been calculated by a network analysis, which allows us to calculate the stock of public capital effectively used in commercial activities, across 47 Spanish provinces during the period 1980–2007. The author estimates the spillover effects using spatial panel data techniques including spatial auto-correlation models with auto-regressive disturbances. In terms of labour productivity, the results indicate that the stock of imported capital is highly significant in all estimates while internal capital is not significant for all Spanish provinces, which classifies the Spanish provinces into users and used. This indicates that capital stock balances should be considered in some way into the inter-regional compensation fund to balance local fiscal balances, minimising some conflicts among regions.

The objective of this chapter is to introduce the reader to Spatial Health Econometrics (SHE). In both micro and macro health economics there are phenomena that are characterised by a strong spatial dimension, from hospitals engaging in local competitions in the delivery of health care services, to the regional concentration of health risk factors and needs. SHE allows health economists to incorporate these spatial effects using simple econometric models that take into account these spillover effects. This improves our understanding of issues such as hospital quality, efficiency and productivity and the sustainability of health expenditure of regional and national health care systems, to mention a few.

Within spatial econometrics a whole family of different spatial specifications has been developed, with associated estimators and tests. This lead to issues of model comparison and model choice, measuring the relative merits of alternative specifications and then using appropriate criteria to choose the “best” model or relative model probabilities. Bayesian theory provides a comprehensive and coherent framework for such model choice, including both nested and non-nested models within the choice set. The paper reviews the potential application of this Bayesian theory to spatial econometric models, examining the conditions and assumptions under which application is possible. Problems of prior distributions are outlined, and Bayes factors and marginal likelihoods are derived for a particular subset of spatial econometric specifications. These are then applied to two well-known spatial data-sets to illustrate the methods. Future possibilities, and comparisons with other approaches to both Bayesian and non-Bayesian model choice are discussed.

This chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.

To address the safety concerns generated by truck crashes occurred in big cities, this paper analyzes the zip code tabulation area (ZCTA)-based truck crash frequency across four temporal intervals – morning (6:00–10:00), mid-day (10:00–15:00), afternoon (15:00–19:00), and night (19:00–6:00) in New York City in 2010. A multivariate conditional autoregressive count model is used to recognize both spatial and temporal dependences. The results prove the presence of spatial and temporal dependencies for truck crashes that occurred in neighboring areas. Built environment attributes such as various types of business establishment density and traffic volume for different types of vehicles, which are important factors to consider for crashes occurred in an urban setting, are also examined in the study.

This chapter examines the effects that airports have had on economic development in cities from 1950 to 2010. It uses a novel dataset consisting of previously unexploited data on the origins and history of the aviation system in the United States. Applying the method of synthetic controls to a set of medium and small airports, I examine both the overall impacts and the heterogeneity within the outcomes of various airports. Then, I use regression analysis to determine key factors differentiating successful airports from less successful ones, as it pertains particularly to population and employment growth. I find that, first, on average, cities have benefited from airports over this period. Airports, overall, provided a causal contribution of 0.2– 0.6% per year on population and employment growth over the time period. Second, I show that city-level factors contributing to airport success include: (1) closer proximity to a major research university, (2) a capital city location, and (3) climate factors, particularly higher January mean temperatures and/or hours of sunshine. City size is a consideration as well; cities in larger metropolitan areas, with larger shares of employment in nontradables in the 1950s, were also better positioned to reap the benefits that airports provided on city growth. Significant differences were not found across regions, airport governance structures, or other factors.

Urbanisation, environmental sustainability and property markets are intertwined. Consequently, studies on any of these three topics need to take the other two topics into consideration. By critically reviewing 33 hedonic pricing studies in 16 key journals in the urban studies and environmental policies areas, we summarise quantitative evidence on the price of environmental externalities resulting from China's urbanisation process. We find that Chinese residents are willing to pay more for the access to green space and waterbody as well as the treatment of urban pollution. The cost and benefit of these amenities and disamenities have already been capitalised in house prices. The central and local government in China can leverage market force to encourage, support and facilitate sustainable urban development and environmental protection, instead of directly intervening in the property market by using public resources. Meanwhile, the estimated hedonic price of Urban Green, Urban Blue and Urban Grey helps policymakers to understand the cost and benefit of their urban development decisions. Our review of the papers on Urban Green, Urban Blue and Urban Grey suggests that there have been promising and encouraging development in studies on all three topics in the last decade. The quality and quantity of hedonic price research has been improving notably. However, it is also clear that there is virtually no empirical evidence from the second- or third-tier cities, particularly, regarding Urban Green and Urban Blue investigations. The small number of existing hedonic studies is far from sufficient to draw reliable conclusions about the costs of environmental externality for cities that have not been studied. What works in first-tier cities may not hold elsewhere in China due to the large geographical variation in natural endowment, economic development status and local customs. There are many pieces that are missing from this big picture. More hedonic price studies are needed.