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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.

The paper investigates the prevalence of extreme poverty in a panel of 39 sub-Saharan African (SSA) countries over the period 2000–2018 while accounting for spillover effects.

The study adopts the recently developed spatial dependence-consistent, bias-corrected quasi-maximum likelihood (QML) estimators and the linear dynamic panel regression to control for the potential endogeneity in poverty and corruption spillovers.

The spatial model shows. consistently across all the specifications, that there is a substantial spillover effect of corruption and poverty across the region. Additionally, the study also found that investment in health and education is a significant determinant of poverty in the region. However, the effectiveness of these policy variables to reduce poverty declines in the face of corruption spillovers. More importantly, the empirical analysis shows that poverty does not only exhibit spatial spillovers but also has a persistent effect over time. The results, therefore, suggest that to reduce poverty in the region, sub-Saharan African governments must adopt spatially differentiated policies and programmes by working together to reduce unemployment and corruption in the region, and not the widely adopted spatially mute designs currently in place. The research and policy implications are discussed.

The study accounts for spatial dependency and spillover effects in the analysis of poverty and corruption in SSA

This paper aims to investigate spillover effects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial dependency yields inefficient, biased and inconsistent estimates in cross country panels. Although there are a number of studies aiming to estimate the output elasticity of public capital stock, many of those fail to reach a consensus on refining the elasticity estimates. We argue that accounting for spillover effects of the public capital stock on the production efficiency and incorporating spatial dependences are crucial. For this purpose, we employ a spatial autoregressive stochastic frontier model based on a number of specifications of the spatial dependency structure. Using the data of 21 OECD countries from 1960 to 2001, we estimate a spatial autoregressive stochastic frontier model and derive the mean indirect marginal effects of public capital stock, which are interpreted as spillover effects. We found that spillover effects can be an important factor explaining variations in technical inefficiency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches.

A burgeoning body of evidence points out the importance of spatial proximity in influencing firm efficiency besides internal characteristics. Nevertheless, the family status of the firm has been traditionally overlooked in that debate. Therefore, this study aims to investigate productivity spillovers stemming from the geographical closeness to innovators and family firms.

Using secondary data on Italian technology-intensive manufacturing firms, the paper exploits spatial econometric models to estimate productivity spillovers across firms.

As regards the presence of spatial dependence, this study reveals that a firm's level of efficiency and productivity is influenced by that of nearby firms. Specifically, three main results emerge. First, spatial proximity to innovators is beneficial for the productivity of neighbouring firms. Second, closeness to family firms is a source of negative externalities for spatially proximate firms. However, and this is the third result, the adverse effect vanishes when the nearby family firms are also innovators.

As the study relies on cross-sectional data, future research should explore productivity spillovers in a longitudinal setting. Additionally, the channels through which productivity spillovers occur should be measured.

The study highlights the importance of co-location for public policy initiatives to strengthen the competitiveness of firms and, indirectly, that of localities and regions. Moreover, the findings show the crucial role of innovation in mitigating the productivity gap between family and non-family firms.

Notwithstanding the advent of the digital era, spatial proximity and localized social relationships are still a relevant factor affecting firms' performance.

By exploring the role of family firms in influencing the advantages of geographical proximity, this study contributes to the growing efforts to explore family enterprises across spatial settings.

This study is the first that aims to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

With network structure analysis, this study finds that they contain different information content from the perspective of graphical display, node strength and correlation. Developed and emerging countries all play major roles in trade connection, while only developed countries play major roles in financial linkage. Second, by applying the multidimensional SAR model, only the spatial autocorrelation coefficients for trade and financial linkages are significant during the full sample period, which is in sharp contrast to published studies using the SAR model with a single matrix. Third, the spillover channels that play major roles in various periods are different. Only trade channel plays a role during crisis periods and it is the most important. Fourth, the spatial correlation among countries greatly amplifies the shock’s impacts on one market. And spatial effect for developed countries is larger than those for emerging countries, while the mean spatial effect of a unit shock in the USA on emerging countries is slightly greater than that on developed countries.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

By analyzing the mechanisms by which rural infrastructure resilience (RIR) impacted population loss in Longxi County, this study proposes measures to improve RIR, which provides a practical reference for realizing China's rural revitalization strategy, besides providing ideas for alleviating population loss in similar regions around the world.

This study considered 213 administrative villages in Longxi County in the Longzhong loess hilly region as the evaluation unit. Based on the construction of a multidimensional RIR evaluation system, the spatial spillover effect of RIR on population loss was determined using the spatial Durbin model (SDM).

The average resilience of each subsystem of rural infrastructure in Longxi County was low, and there were large differences in the spatial distribution. The mean RIR index value was 0.2258, with obvious spatial directivity and agglomeration characteristics. The population loss index of Longxi County had a value of 0.1759, with 26.29 of villages having a high loss level. The population loss was relatively serious and was correlated with the spatial distribution of RIR. The villages with larger RIR index values had lower population loss. The RIR had a significant spatial spillover effect on population loss. Productive infrastructure resilience and living infrastructure resilience (LIR) had negative spillover effects on population loss, and social service infrastructure resilience (SSIR) had a positive spillover effect on population loss.

By analyzing the mechanisms by which RIR impacted on population loss in Longxi County, this study proposes measures to improve RIR, which provides a practical reference for realizing China's rural revitalization strategy, besides providing ideas for alleviating population loss in similar regions around the world.

This study aims to identify extensive and intensive margins in exports and imports and examine whether incoming foreign direct investments (FDI) benefit local firms in Indonesia through the export and import channels.

Using Heckman’s two-step selection model to consider the potential of bias of self-selection in export–import participation, this study uses the firm-level data from 2008 to 2015 collected from Statistik Industri and proximate both export and import spillovers.

The authors found that internal factors are critical for a firm to be an exporter, signaling self-selection in exports and imports. Spillover effects from FDI (spatial properties) support export but lower import propensity and intensity.

This study implies that improving human capital (absorptive capacity) is needed to accelerate export intensity and policies supporting FDI inflows in complementary sectors (noncompeting industries) can increase export propensity and intensity and reduce imports.

This study contributes to the literature in several ways. First, the proposed export spillovers model that accounts for impacts through a demonstration channel is applied to the import channel. Moreover, this study extends the model developed by Franco and Sasidharan (2010) and Yasin et al. (2022) by incorporating spatial spillover effects at the provincial level. Subsequently, the authors test whether a firm’s technological intensity determines export–import propensity and intensity. This can indicate whether specific sectors are more likely to participate in international activities based on their use of technology.

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.

By using a wide range of macro and micro factors, this paper aims to provide a new assessment of the recent literature on inbound tourism demand models.

This study examines the determinants and spatial effects of inbound tourism using Hausman–Taylor and spatial econometric models.

Several important factors were identified, including local economic growth, openness to the outside world, regional size, geographic distance, foreign direct investment, level of innovation and average annual temperature. In addition, the study found strong cross-city competition effects on tourism resource endowment and hotel infrastructure.

Inbound tourism is a crucial link in achieving high-quality economic development. However, previous studies have mainly focused on the analysis of single influencing factors, ignoring the spatial spillover effects of factors.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.