scholarly journals Inefficiencies in a healthcare system with a regulatory split of power: a spatial panel data analysis of avoidable hospitalisations in Austria

2019 ◽  
Vol 21 (1) ◽  
pp. 85-104
Author(s):  
Anna-Theresa Renner
Keyword(s):  
Panel Data ◽  
Bayesian Model Averaging ◽  
Panel Data Analysis ◽  
Model Averaging ◽  
Social Health Insurance ◽  
Population Characteristics ◽  
University Education ◽  
Spatial Error ◽  
Spatial Panel ◽  
Spatial Panel Data

Abstract Despite generous universal social health insurance with little formal restrictions of outpatient utilisation, Austria exhibits high rates of avoidable hospitalisations, which indicate the inefficient provision of primary healthcare and might be a consequence of the strict regulatory split between the Austrian inpatient and outpatient sector. This paper exploits the considerable regional variations in acute and chronic avoidable hospitalisations in Austria to investigate whether those inefficiencies in primary care are rather related to regional healthcare supply or to population characteristics. To explicitly account for inter-regional dependencies, spatial panel data methods are applied to a comprehensive administrative dataset of all hospitalisations from 2008 to 2013 in the 117 Austrian districts. The initial selection of relevant covariates is based on Bayesian model averaging. The results of the analysis show that supply-side variables, such as the number of general practitioners, are significantly associated with decreased chronic and acute avoidable hospitalisations, whereas characteristics of the regional population, such as the share of population with university education or long-term unemployed, are less relevant. Furthermore, the spatial error term indicates that there are significant spatial dependencies between unobserved characteristics, such as practice style or patients’ utilization behaviour. Not accounting for those would result in omitted variable bias.

scholarly journals Modeling Human Development Index of Bali with Spatial Panel Data Regression

2019 ◽  
Vol 4 (5) ◽  
pp. 132-137
Author(s):  
Mita Lasdiyanti ◽  
Eka N. Kencana ◽  
Putu Suciptawati
Keyword(s):  
Panel Data ◽  
Human Development ◽  
Human Development Index ◽  
Panel Data Analysis ◽  
Spatial Error ◽  
Development Index ◽  
Spatial Panel ◽  
Panel Data Regression ◽  
Data Regression ◽  
Spatial Panel Data

Human development index (HDI) is an index that represents the successfulness of human development in a region. For Bali, one of 34 provinces in Indonesia, the progress of HDI in the period 2010–2017 showed an increasing trend. In the year 2010, the Bali’s HDI is accounted for 70.10, gradually increase to 74.30 in the year 2017. However, in 2017 there are some regions with their HDIs are below of Bali’s HDI, namely Jembrana, Buleleng, Klungkung, Bangli, and Karangasem. The aim of this work is to model the HDI of 9 regencies of Bali so that the main determinant to increase the HDIs especially for the regencies with lower HDIs could be determined. The model consists of one dependent variable (HDI) with three indicators as the independent ones, there are (a) life expectancy, (b) education, and (b) standard of living. By applying spatial panel data analysis, five models were built i.e. CEM, FEM (individual), FEM (time), REM, and spatial error FEM to determine the effect of each indicator. The result shows the best model is spatial error FEM in which education has the biggest influence compare than the others.

scholarly journals A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns

2018 ◽  
Vol 10 (8) ◽  
pp. 2800 ◽  
Author(s):  
Rui Jin ◽  
Jianya Gong ◽  
Min Deng ◽  
Yiliang Wan ◽  
Xuexi Yang
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Interaction Model ◽  
Spillover Effects ◽  
Spatiotemporal Pattern ◽  
Regional Economic ◽  
Spatial Panel ◽  
Economic Agglomeration ◽  
Spatial Panel Data

Understanding regional economic agglomeration patterns is critical for sustainable economic development, urban planning and proper utilization of regional resources. Taking Guangdong Province of China as the study area, this paper introduces a comprehensive research framework for analyzing regional economic agglomeration patterns and understanding their spatiotemporal characteristics. First, convergence and autocorrelation methods are applied to understand the economic spatial patterns. Then, the intercity spatial interaction model (ISIM) is proposed to measure the strength of interplay among cities, and social network analysis (SNA) based on the ISIM is utilized, which is designed to reveal the network characteristics of economic agglomerations. Finally, we perform a spatial panel data analysis to comprehensively interpret the influences of regional economic agglomerations. The results indicate that from 2001 to 2016, the economy in Guangdong showed a double-core/peripheral pattern of convergence, with strengthened intercity interactions. The strength and external spillover effects of Guangzhou and Shenzhen enhanced, while Foshan and Dongguan had relatively strong absorptive abilities. Moreover, expanding regional communication and cooperation is key to enhancing vigorous economic agglomerations and regional network ties in Guangdong by spatial panel data analysis. Our results show that this is a suitable method of reflecting regional economic agglomeration process and its spatiotemporal pattern.

ECONOMIC FREEDOM AND INCOME LEVELS ACROSS U.S. STATES: A SPATIAL PANEL DATA ANALYSIS

2018 ◽  
Vol 37 (1) ◽  
pp. 40-49 ◽  
Author(s):  
Joshua C. Hall ◽  
Donald J. Lacombe ◽  
Timothy M. Shaughnessy
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Economic Freedom ◽  
Panel Data Analysis ◽  
Income Levels ◽  
Spatial Panel ◽  
Spatial Panel Data

scholarly journals Convergence of electricity consumption in Turkey: Spatial panel data analysis

2020 ◽  
Vol 67 (2) ◽  
pp. 241-256 ◽  
Author(s):  
Gülsüm Akarsu ◽  
Burcu Berke
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Empirical Studies ◽  
Electricity Consumption ◽  
Spatial Panel ◽  
Spatial Panel Data ◽  
Per Capita ◽  
Conditional Beta ◽  
Beta Convergence

The issue of convergence has been discussed in many theoretical and empirical studies. Because per capita electricity consumption is considered as an indication of economic development, this study aims to determine the presence of ?absolute and conditional beta (?) convergence? of per capita total electricity consumption across the provinces of Turkey between 1986 and 2013. This work is the first investigation of electricity consumption convergence in Turkey. Based on the annual balanced panel data and the spatial panel data model, our findings indicate absolute ? convergence of per capita electricity consumption across the provinces of Turkey. We conclude that regional policies are successful in reducing regional disparities in per capita electricity consumption among the provinces of Turkey. However, other indicators of economic development should be examined to determine the overall convergence.

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