REGULATION STRATEGIES FOR THE PROVISION OF PATERNALISTIC GOODS

In this study, we analyze the regulation of markets for the provision of services whose costs are subsidized for paternalistic reasons. We model the choice of a benevolent regulator who wants to maximize consumer welfare in a setting where quality cannot be verified and the good provided is fully subsidized. The choice is thus made between two types of providers (profit maximizers and altruistic providers) and two frameworks (monopoly franchise and quality competition). Our analysis shows that in this environment the performance of mixed markets is always dominated by pure forms. Moreover, although making efficient providers compete for the market minimizes cost, the choice of quality competition with altruistic providers may be preferable from a welfare point of view whenever service quality is relevant and the productivity differential is not substantial.     

Financial openness and Chinese regional growth imbalance: New insight from spatial spillovers

This paper uses spatial panel methods and Chinese provincial data from 2003 to 2017 to study the spatial spillovers of financial openness on economic growth. The results show, first, a positive direct effect and an overall negative spatial spillover of financial openness on provincial growth. Second, there are two spatial spillover channels: a positive growth externality and a harmful resource competition among provinces. Third, we estimate the state dependence and dynamics of spatial spillover, and find that the negative spatial spillover is smaller in provinces with high levels of financial openness and in the long term; thus, the negative spatial spillover declined over time. These results are robust to the choice of SDM and GNS spatial econometrics methods and under different spatial weight matrices.     

Do housing prices promote total factor productivity? Evidence from spatial panel data models in explaining the mediating role of population density

The issue of what to promote in total factor productivity (TFP) in urban areas has been widely discussed in academia and housing prices and population density are confirmed to be two of the most essential driving factors. However, research into the interaction of housing prices and population density with TFP has been neglected, with no previous studies taking spatial factors into consideration, which may bias the results. From this perspective, using spatial panel data models and employing instrumental variables to solve the endogenous problem, this study examines the impact of housing prices on TFP through the mediating effect of population density for 283 Chinese cities during the period 2000–2013, and confirms that the mediating effect accounts for 18.70 % of the total effect. The results show the positive and significant association of housing prices with TFP and the inverted U-shape of population density. The underlying logic is that housing prices change population density by attracting people with high purchasing power and discouraging those unable to afford housing, whereas increased density helps to promote productivity since the settled inhabitants always have highly developed work skills and are well educated. The influencing mechanism of housing prices on TFP through population density is analyzed, namely the spillover effect. We find that the spillover effect exists in the eastern and central regions, as well as first, second, and third tier cities, while for western regions and fifth tier cities, population mobility and increased in housing prices slows their economic development. There is no evidence of any spillover effect in fourth tier cities. A discussion and suggested policy implications are also provided.     

Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment

The need and interest to consider cognitive and motivational biases has been recognized in different disciplines (e.g. economics, decision theory, risk analysis) and has recently reached environmental decision-making. Within this domain, the intrinsic presence of a spatial dimension of both alternatives and criteria calls for the use of maps throughout the decision-making process to properly represent the spatial distribution of the features under analysis. This makes spatial Multi Criteria Decision Analysis (MCDA) a particularly interesting domain to explore new dimensions of cognitive biases. This study proposes a behavioral experiment aimed at discovering to what extent the spatial visualization (i.e. maps) of criteria versus the non-spatial one (i.e. tables) can bias the weight elicitation phase of a spatial MCDA process. The experiment simulates a very common analysis in environmental and land use planning: land suitability analysis. Our findings show that there are significant consequences on how important we perceive a certain criterion to be, depending on whether it is represented as a map or as a table among a mix of maps and tables. Indeed, the map representation of the same criterion leads to higher weights attributed to that criterion compared to the table representation. Visualizing the same information as a map or as a table, although technically equivalent, is thus not psychologically equivalent for Decision Makers. The results of this experiment are expected to have implications for spatial decision-making processes, by generating better awareness on the impacts of map-mediated land suitability analysis.     

Forecasting using heterogeneous panels with cross-sectional dependence

In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries.     

The geography of suppliers and retailers

In the last decades, supply chains have increasingly transcended national boundaries developing into global supply chains. Along with the many opportunities arising from international sourcing and the extended commercial presence over the world, the management of a globally dispersed supply chain is highly complex. A key issue to consider when dealing with the global supply chain design is the location of facilities, not only with respect to firms' owned facilities but also the supply and distribution side as factors that affect supply chain complexity and operational performance. This paper sets out a methodological framework to characterize the geographical configuration of a firm's suppliers and retailer networks. Quantitative indexes of network spatial concentration and relative proximity measures based on a nonparametric kernel density estimator are developed to identify both intra- and inter-firm patterns between the supply and point of sales' distributions. The method is first described by means of a series of theoretical-illustrative examples and exemplified by analyzing the geographical dispersion of four practical cases from the fashion-textile industry (i.e., Adidas, Benetton, C&A, and Puma). Subsequently, managerial implications and potential use of the metrics are discussed, showing how the proposed approach can support researchers and practitioners to improve supply chain location decisions and logistic integration, and evaluate changes in either the purchasing or distribution strategy.     

Relationship between city size and firm productivity – A new interpretation using the Chinese experience

This paper contributes to the literature on the relationship between city size and firm productivity by focusing on agglomeration, selection (market competition), and sorting (presence of firms with diverse productivity) effects using Chinese firm-level data for 1998–2013. Contrary to the existing literature, our parametric regression estimates from nearly two million firms show that when the selection effect is controlled, productivity advantage in big cities is reversed. This outcome is explained through a quantile regression showing the existence of left-sided sorting (inefficient firms sort themselves to larger cities) in big cities which is not captured in existing empirical literature. We further find that (1) left-sided sorting is stronger in exporters than non-exporters; (2) is also generated mainly in enterprises with high asset-liability ratios; and (3) selection has a positive effect on firm productivity, suggesting that market competition is key in an explanation of the rapid growth of big cities in China.     

Spatial and operational factors behind passenger yield of U.S. nonhub primary airports

Nonhub airports are an essential component in the National Plan of Integrated Airport Systems (NPIAS) of the United States in that they connect regional towns and small communities to the air transportation network. Understanding the interplay of operational and spatial factors in determining average passenger yield of nonhub airports provides airlines with valuable information for network planning and revenue management. This study examines factors contributing to the yield variation among nonhub airports in the U.S. Using ordinary least squares (OLS) based econometric models, this study captures the spatial dependence of passenger yield of nonhub airports, which tends to increase with a corresponding increase in distance to the nearest large hub airport. Nonhub airports surrounding large hub airports with higher passenger enplanements and higher average yields also have higher yields than other nonhub airports. In addition, this study finds the effect of Allegiant Airlines in lowering the average passenger yield of the nonhub airports served directly by the airline, which can be termed as ‘Allegiant Effect’. Findings of this study could provide valuable guidance for airlines to analyze network planning strategies and to identify future markets for growth and for policymakers when allocating resources to communities relying on these nonhub airports.     

Closeness matters. Spatial autocorrelation and relationship between socioeconomic indices and distance to departmental Colombian capitals

Socioeconomic indices that developing economies use to combat poverty may show a limited picture of all the variables related to the problem. This study analyzes spatial autocorrelation and clusters of three socioeconomic indices—living conditions, multidimensional poverty, and unsatisfied basic needs—in Colombia to explore the relation of the identified clusters with their physical distance from departmental capitals. Using a local index of spatial autocorrelation, it evaluates spatial patterns and the clustering of socioeconomic indices. Correlation analysis tests the relation between clusters and their distance from departmental capitals in three departments. The spatial patterns of indices in Colombia correspond to the model of economic development in the country and reveal the regions where socioeconomic characteristics form clusters of desirable/undesirable conditions and departments where the distance from main cities may be seen as a condition for a higher quality of life.     

Implementing efficient conservation portfolio design

Modern tools for cost-effective conservation reserve site planning require the planner to have information about spatial distributions of conservation costs and benefits. Climate change creates unprecedented uncertainty about future land values and species habitat ranges, such that conservation scientists cannot map costs and benefits with certainty anymore. This paper contributes to the literature on the economics of conservation in the face of climate change uncertainty. It advances a new method for using modern portfolio theory to choose lands to protect that yield total conservation returns with less uncertainty. It explores the implications for portfolio recommendations of variation in the correlations between ecological and land-value responses to climate change. It also tests the robustness of the method to shortcuts that might be taken to simplify analysis, identifying problems that arise if conservation costs are ignored in portfolio analysis and demonstrating when portfolio recommendations are sensitive to how ecological benefits are quantified.