Search results

Does the standard of living vary from region to region in Portugal and are spatial units in Portugal converging in income? We observe spatial error dependence between municipalities and estimate spatial econometric models to test convergence. For conditional convergence we conclude that primary sector employment, activity rate, and percentage of active population with higher education are important to distinguish the “steady state” of the regional economies, reflecting the labor market at regional level.

The contribution of the Indian Automobile industry in the economic growth of the country is significantly high. Besides catering to a large domestic market, the automobile industry in India has also captured market shares in many foreign countries successfully in the last few decades. Not only is it an important export-oriented industry of the nation but also the fourth largest exporter of automobiles in Asia. However, in the recent years (2018–2019), it has faced an unprecedented slump. This chapter captures this fact by calculating the growth of car selling for the four quarters of the period 2018–2019 across the Indian states. It primarily tries to find out whether the variation in income and tax levied on petrol and diesel has an impact on the variation in the car selling across the states for the abovementioned time period. It has been proven from our study that higher income of a state has a positive impact, whereas higher tax on petrol and diesel which varies across the states has a negative impact on car selling. Apart from this, this study then distinctively tries to find out whether there exists any neighborhood impact on growth rate of car selling and different tax rate on petrol and diesel on the basis of Moran's Index. It is witnessed that there exists a high level of spatial autocorrelation among the different states in case of growth of a car selling and tax imposition on diesel as well as on petrol. This fact necessitates some degree of regional orientation in formulating an effective policy to revive the automobile industry on the part of the Government.

Terrorism dates back to the uprising against the Roman Empire in first century BC and the term entered Western literature during the eighteenth century. Conceptually, it has emerged as a security problem in a national and international context. However, terrorism is not only a security problem but also has significance on political, social, cultural, psychological, and especially economical aspects. A weak economic structure of a country makes it easier for terrorist organizations to manipulate its society in a certain direction. Issues such as economic growth, foreign trade, employment, foreign investments, and public expenditures are first affected by terror incidents, and thereafter are known to have medium- and long-term effects. In general, it is observed that developed countries are less affected from terror incidents than developing countries. In this context, in this study, regional assessments will be made using the following indicators: the Global Terror Index (GTI), gross domestic product (GDP), export, foreign investments, and public expenditure. Regional and intercontinental assessments will be implemented using spatial econometric techniques. The GeoDa package program will assess the diversity in terrorism between continents. Our main hypothesis is that terrorism’s economic effect is more in developing countries and the Middle East than among other developed countries. The other aim of the study is to determine which terrorism is more effective and which economic indicator is more affected and gives the best result about effects of the terrorism on the countries and continents. This study predominantly tries to examine whether terror incidents are most influential on the economies in the Middle East region.

Purpose – The purpose of this study is to explore the concept of spatial modeling in adolescent and under-five children’s nutritional status.

Design/Methodology/Approach – The indicator used to identify spatial autocorrelation is the Local Indicator of Spatial Association (LISA). LISA is a method of exploratory analysis of spatial data capable of detecting spatial relationships at the local level and its effects globally. Aplication of stochastic modeling in spatial nutrition identification mapping can be categorized into two cases based on spatial autocorrelation and non-spatial autocorrelation.

Findings – This results of this study indicate that there is no spatial autocorrelation in the adolescent nutritional dataset. The thematic map for anemia showed that that the highest number of anemia in adolescents was in KutaAlam sub-districts (48 people). Sub-districts that were second most common were Meuraxa, Jaya Baru, and Baiturrahman sub-districts. The fewest cases were found in Lueng Bata sub-district (12 people). There were no sub-districts affected by neighboring areas, in the case of adolescents’ anemia in Banda Aceh. For the under-five nutritional data set, it shows that there are four factors that significantly affect spatial influence, which are malnutrition, chronic energy deficiency, woman of child-bearing age, proportion of family planning, percentage of households with PHBS and coverage of access to clean water.

Research Limitations/Implications – Anemia data were obtained with a school-based survey. Household survey would be better to implement in spatial analysis.

Practical Implications – The comparison of the dataset with the two methods provides a simple example to implement special autocorrelation in practice.

Social Implications – The results contribute to a much better comparison in many cases in the nutritional field.

Originality/Value – This is the initial nutritional status of adolescents in Banda Aceh.

This chapter proposes augmenting a simple income stock price model with spatial structures to evaluate the significance of real and financial linkages in instigating stock market contagion. The treatment is premised upon the clustering of excessive returns and volatilities during the Subprime crisis envisaged from our regime switching analysis over a long time span, and the presence of spatial autocorrelation in the baseline income stock model. With the channel factors manifested as spatial weights, this chapter explores specifications of explicit interrelated stock price returns and implicit spatial autocorrelation in the error term for the 3-year period from 2007 to 2009. Model validity is authenticated by way of model choice and spatial weight selection. The finding shows that spatial dependence in either specification is not too sizable indicating that contagion is not spreading fast in the sample period. Of the various factors considered, non-performing loans, market liquidity, and credit to deposit ratio turn out to be the most important transmission factors. Current account balance, net FDI flows, and size of GDP are among the least significant media. In sum, these suggest that financial linkages could play a more important role in facilitating shock transmission when compared to real linkages such as trade.

This chapter estimates heterogeneous productivity growth and spatial spillovers through industrial linkages in the United States and China from 1981 to 2010. The authors employ a spatial Durbin stochastic frontier model and estimates with a spatial weight matrix based on inter-country input–output linkages to describe the spatial interdependencies in technology. The authors estimate productivity growth and spillovers at the industry level using the World KLEMS database. The spillovers of factor inputs and productivity growth are decomposed into domestic and international effects. Most of the spillover effects are found to be significant and the spillovers of productivity growth offered and received provide detailed information reflecting interdependence of the industries in the global value chain (GVC). The authors use this model to evaluate the impact of a US–Sino decoupling of trade links based on simulations of four scenarios of the reductions in bilateral intermediate trade. Their estimation results and their simulations are as mentioned based on date that ends in 2010, as this is the only KLEMS data available for these countries at this level of industrial disaggregation. As the GVC linkages between the United States and China have expanded since the end of their sample period their results can be viewed as informative in their own right for this period as well as possible lower bounds on the extent of the spillovers generated by an expanding GVC.

Customer experience is a relevant concept in marketing and tourism research since its correct understanding allows companies to achieve competitive advantage and service providers can reach several outcomes such as customer engagement, loyalty, and customer satisfaction. This chapter aims to analyze one of the main outcomes of the customer experience, the customer satisfaction through online reviews, and using spatial analysis as a tool to incorporate the contextual nature of the customer experience. Thus, our study considers online rating as a measure of customer satisfaction and tries to analyze the impact of actions under the control of the service provider (price and objective quality) and actions under the control of the customer (subjective quality) on customer satisfaction.

With the Spanish hotel industry as a study framework, an empirical study is developed to analyze, through geographically weighted regression techniques, the relationship between price, objective quality and subjective quality, and online ratings given by consumers with a sample of 1870 of geolocated hotels in Spain. The findings show how a premium price, depending on the geolocation, is an indicator for better customer experiences, and they also show that objective quality is the antecedent of customer experience whose positive effect on customer satisfaction is geographically more widespread. Results show contradictory effects of subjective quality, while in some areas subjective quality does not match the product fit of customers, in others it allows hotels to provide more satisfactory experiences.

Introduction: Despite its significance, the research on international migration with a specific focus on the European Union (EU) needs to be strengthened with comprehensive studies, for developing better immigration and integration policies. Considering the amplitude of migration flows in Europe and recent challenges brought by the Covid-19 pandemic crisis, the Brexit decision and humanitarian dimension of the migration phenomena (asylum seekers and refugees), the need for better immigration and integration policies within the host countries’ labor markets stands out as a major research direction, especially in case of immigrants looking for better working and living conditions. Aim: This chapter aims to design specific immigration clusters within the main EU-10 destination countries (including Spain, Italy, Belgium, Denmark, Finland, France, UK, Germany, Austria, and Sweden) (cluster analysis procedure); and to identify feasible ways and specific policies for immigrants’ labor market success (spatial analysis and macroeconometric models). Method: The methodological framework consists of two parts: (i) immigration clusters analysis, based on the interlinkages between several fundamental migration coordinates, namely, economic welfare at destination, employment opportunities for the foreign population, migrant integration policies and associated governmental efforts, educational background; and (ii) spatial analysis models, namely spatial lag–autoregressive and spatial error, and other three econometric procedures, respectively, the robust regression, Panel Corrected Standard Errors, and Arellano-Bond Dynamic Generalized Method of Moments. National data compiled for the 10 main EU receiving economies during 2000–2015, with a particular focus on Spain were used. Findings: The impact of the proposed research is reflected through a set of new specific tailored ways, policies and strategies that can be adopted and implemented by the policy-makers across Europe. Our empirical results show that, overall, EU-10 countries still fail to identify immigrants with high levels of education and skills acquired to enhance their potential for labor market integration. Policy-makers should always monitor the specific ways in which migration policies lead to concrete positive labor market outcomes for immigrants and that the tools used for implementing these policies are suitable in achieving predefined migration goals. Therefore, a particular focus should be on developing a new immigration system to select migrants for their skills and high level of human capital, by following the best practices examples of other receiving countries.

“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.

Scholarship has demonstrated important consequences of segregation on health and health care outcomes, yet the mechanisms behind this association remain poorly understood. Several recent studies have shown inequities in the distribution of a wide variety of health-related organizations across urban neighborhoods, which may account for some portion of this negative health association. Though, within this literature, relatively little attention has been given to the distribution of health care facilities in particular.

Here, I consider how segregation is related to the distribution of several auxiliary health care practitioners in a series of spatial regression models of zip codes across the United States using data from the 2010 US Census and County Business Patterns (CBP).

I find that both Black and Latino segregation is negatively related to the density of a number of auxiliary health care practitioners, including mental health providers, dentists, physical/occupational/speech therapists, chiropractors, optometrists, podiatrists, and miscellaneous health care practitioners. However, this association is reduced (in certain instances to non-significance) with the inclusion of socioeconomic indicators, chiefly the percent of college educated individuals and the unemployment rate of the zip code. This is association is reduced for both Black and Latino segregation, with a larger reduction in the size of the effects for Latino segregation.

This research suggests that segregation plays an important role in the distribution of health care facilities, but that policy and public health interventions should focus on the intersection between racial residential segregation and socioeconomic considerations.