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1 – 10 of 511A widely held belief before the 1990s – referred to as the oil-blessing hypothesis – was that oil discovery and production should promote economic growth and development and lead…
Abstract
Purpose
A widely held belief before the 1990s – referred to as the oil-blessing hypothesis – was that oil discovery and production should promote economic growth and development and lead to poverty reduction. However, the so-called ‘oil-curse’ hypothesis, postulated by Sachs and Warner in 1995, challenged this belief, thus provoking a heated debate on the theme. The oil-curse hypothesis has been traditionally tested by means of cross-sectional and panel-data models. The author goes beyond these traditional methods to test whether the presence of spatial effects can alter the hypothesis in oil-producing African countries. In particular, this paper aims to investigate the effects on economic growth of oil production, oil resources and oil revenues along with the quality of democratic institutions, investment and openness to trade.
Design/methodology/approach
A Durbin spatial model, a cross-sectional model and panel-data model are used.
Findings
First, the validity of the spatial Durbin model is vindicated. Second, consistently with the oil-curse hypothesis, oil production, resources, rent and revenues have a negative and generally significant effect on economic growth. This result is robust for across the panel data, spatial Durbin and spatial autoregressive models and for different measures of spatial proximity between countries. Third, the author finds that the extent to which the business environment is perceived as benign for investment has a positive and marginally effect on economic growth. Additionally, economic growth of a country is further stimulated by a spatial proximity of a neighbouring country if the neighbouring country has created strong institutions protecting investments. Fourth, openness to international trade has a positive and marginally significant effect on economic growth.
Originality/value
This paper examines theories and studies that have been done before. However, as the related literature on the growth–resource abundance nexus has rarely examined spatial effects, this study seeks to test jointly the spatial effect and the neighbouring effect on the oil curse hypothesis.
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Nyakundi Momanyi Michieka, Donald John Lacombe and Yiannis Ampatzidis
The purpose of this study is to examine the net effect of golf courses’ proximity on home sale prices in Kern County, California.
Abstract
Purpose
The purpose of this study is to examine the net effect of golf courses’ proximity on home sale prices in Kern County, California.
Design/methodology/approach
A spatial Durbin error model is used with sales price data for 1,693 homes sold in Kern County in the third quarter of 2018. This paper compares 90 different spatial econometric models using Bayesian techniques to produce posterior model probabilities which guided model selection and the number of neighbors to use.
Findings
The results show that significant spatial dependence exists in home values in Kern County. Point estimates indicate that homes abutting golf courses are valued at less than those which are not. This study also finds that the farther away from golf courses the average home is, the higher its value.
Originality/value
This study contributes to the existing literature in three dimensions. First, this paper analyzes whether proximity to golf courses impacts home values in Kern County where a study of this nature has not been conducted. Second, the analysis uses transaction data for 2018 which was a period when the sport’s popularity was fading and golf courses closing. Third, Bayesian model comparison techniques are used to select the appropriate model.
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Malan Huang, Minghui Hua, Jin Li and Yanqi Han
As an important engine of economic growth, the digital economy is bringing new opportunities for the promotion of entrepreneurship. However, key questions regarding the extent of…
Abstract
Purpose
As an important engine of economic growth, the digital economy is bringing new opportunities for the promotion of entrepreneurship. However, key questions regarding the extent of the effect of the digital economy on entrepreneurship remain unanswered. This study examines how the digital economy influences entrepreneurship in China using provincial data from 2011–2020, applying convergence tests and spatial econometric models.
Design/methodology/approach
Based on theoretical analysis and using macro provincial data covering the period of 2011–2020, we adopt a diversified empirical analytical method and apply a combination of the convergence trend test, spatial auto correlation test, and spatial Durbin model to test the research hypotheses.
Findings
First, there is spatial correlation between the digital economy and entrepreneurship. Second, the overall trend of China’s digital economy shows s convergence, with the whole country and the eastern region showing absolute β convergence and the whole country as well as the central and western regions showing β conditional convergence. Third, the digital economy can significantly promote entrepreneurship and has spatial spillover effects. Moreover, higher education has a negative moderating effect on the process of digital economy empowering entrepreneurship.
Research limitations/implications
Studying the spatially correlated impacts of the digital economy on entrepreneurship enhances our understanding of its contribution to economic growth. Policy-makers can use these findings to develop targeted digital infrastructure investments in lagging provinces, guide entrepreneurs to better grasp the opportunities of the digital economy, and provide support for innovation and entrepreneurship. The findings also could offer Chinese experience that can be used to guide developing countries in utilizing the digital economy to enable entrepreneurship.
Originality/value
This paper expands and enriches the analytical focus on digital economy-empowered entrepreneurship and complements the current theoretical research on the moderating effect of the digital economy in empowering entrepreneurship.
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This study aims to explore the spatial impact of an increase in the minimum wage on the labor productivity of star-rated hotels in China.
Abstract
Purpose
This study aims to explore the spatial impact of an increase in the minimum wage on the labor productivity of star-rated hotels in China.
Design/methodology/approach
The impact is analyzed by using the dynamic spatial Durbin model.
Findings
The authors find a U-shaped link between the increase in minimum wage and labor productivity of star-rated hotels. The long-term impact of a minimum wage increase has a greater influence on labor productivity than its short-term effects. While there is no notable spatial spillover impact observed in the sample of 31 provinces in China, the authors do identify a spatial spillover effect of the minimum wage rises on the labor productivity of star-rated hotels in the central area. Furthermore, they observe heterogeneity across China. The eastern and western regions exhibit a U-shaped relationship, whereas the central region exhibits an inverted U-shaped relationship.
Practical implications
The findings of this study allow government agencies to get a more comprehensive comprehension of the actual consequences of minimum wage hikes on the tourism and hospitality sector, thereby establishing a solid basis for them to develop appropriate policies. Moreover, it offers a variety of suggestions aimed at enhancing the quality and efficiency of hotel management.
Originality/value
Research on the effects of minimum wage standards is scant in the hospitality industry. Based on human capital investment theory, this study examines the effect of the minimum wage standard hikes on labor productivity of star-rated hotels from the spatial perspective, filling the existing research gap.
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Qingyan Jiang, Cuihong Yang, Jie Wu and Yan Xia
Known as the major capital providers in Belt and Road countries and the largest carbon emitter in the world, what role China's outward direct investment (ODI) plays in carbon…
Abstract
Purpose
Known as the major capital providers in Belt and Road countries and the largest carbon emitter in the world, what role China's outward direct investment (ODI) plays in carbon neutralization has become a matter of concern. This study aims to measure the impact of China's ODI on the carbon emissions of Belt and Road countries.
Design/methodology/approach
Based on an econometric model and an inter-regional input–output model, a new model measuring the carbon emission effects of ODI is developed.
Findings
The empirical results show that (1) in general, China's ODI generates an emission-reduction effect in Belt and Road countries; (2) The relationship between the emission-reduction effect and income level of host countries shows an approximate inverted U-shaped trend; and (3) China's ODI generates stronger emission-reduction effects on capital-intensive industries.
Originality/value
This study quantitatively measures the scale of carbon emission-increase and reduction effect, which is relatively lacking in previous studies. This study explores the heterogeneity from the perspectives of regions, countries and industries. The authors have compiled an inter-regional input–output table for the Belt and Road countries for 2014 to provide a broad basis for the study of related issues.
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R. Kelley Pace, James P. LeSage and Shuang Zhu
Most spatial econometrics work focuses on spatial dependence in the regressand or disturbances. However, Lesage and Pace (2009) as well as Pace and LeSage2009 showed that the bias…
Abstract
Most spatial econometrics work focuses on spatial dependence in the regressand or disturbances. However, Lesage and Pace (2009) as well as Pace and LeSage2009 showed that the bias in β from applying OLS to a regressand generated from a spatial autoregressive process was exacerbated by spatial dependence in the regressor. Also, the marginal likelihood function or restricted maximum likelihood (REML) function includes a determinant term involving the regressors. Therefore, high dependence in the regressor may affect the likelihood through this term. In addition, Bowden and Turkington (1984) showed that regressor temporal autocorrelation had a non-monotonic effect on instrumental variable estimators.
We provide empirical evidence that many common economic variables used as regressors (e.g., income, race, and employment) exhibit high levels of spatial dependence. Based on this observation, we conduct a Monte Carlo study of maximum likelihood (ML), REML and two instrumental variable specifications for spatial autoregressive (SAR) and spatial Durbin models (SDM) in the presence of spatially correlated regressors.
Findings indicate that as spatial dependence in the regressor rises, REML outperforms ML and that performance of the instrumental variable methods suffer. The combination of correlated regressors and the SDM specification provides a challenging environment for instrumental variable techniques.
We also examine estimates of marginal effects and show that these behave better than estimates of the underlying model parameters used to construct marginal effects estimates. Suggestions for improving design of Monte Carlo experiments are provided.
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Mingyong Hong, Mengjie Tian and Ji Wang
By discussing the spatial spillover effect and regional heterogeneity of digital economy and green agricultural development level, this paper aims to provide countermeasures and…
Abstract
Purpose
By discussing the spatial spillover effect and regional heterogeneity of digital economy and green agricultural development level, this paper aims to provide countermeasures and suggestions for the better development of green agriculture in the contemporary era when digital economy is universally developed and at the same time provide development suggestions suitable for green agriculture's development characteristics and initial conditions for different regions.
Design/methodology/approach
This paper discusses the theoretical foundation of the digital economy and green agriculture development and utilizes panel data from 30 provinces in China from 2011 to 2018. By employing the Super-Efficiency Slack-based Measure and Malmquist-Luenberger (SBM-ML) model based on unexpected output to measure the total factor productivity of green agriculture and employing the spatial panel Durbin model to empirically test the spatiotemporal effects of the digital economy on green agriculture development from both temporal and spatial dimensions. Finally, the model is tested for robustness as well as heterogeneity.
Findings
The research findings are as follows: First, from the perspective of time effect, digital economy has a continuous driving effect on the development of green agriculture and with the passage of time, this effect becomes more and more prominent; second, from the perspective of spatial effect, digital economy has a significant positive impact on the development of local green agriculture, while digital economy has a significant negative impact on the development of surrounding green agriculture. Finally, the impact of digital economy on the development of green agriculture shows significant differences in different dimensions and regions.
Originality/value
As an important driver of economic growth, the digital economy has injected new impetus into agricultural and rural development. Along with the intensifying environmental pollution problems, how to influence the green development of agriculture through the digital economy is a proposition worthy of attention nowadays. This paper analyzes the relationship between the digital economy and agricultural green development in multiple dimensions by exploring the temporal and spatial spillover effects of the digital economy on agricultural green development, as well as the heterogeneity in different dimensions and in different regions and derives policy insights accordingly in order to improve relevant policies.
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Weilin Liu, Robin C. Sickles and Yao Zhao
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…
Abstract
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.
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Abstract
Purpose
Given the recent rapid economic development, the processes of industrialization and urbanization are accelerating. At the same time, the contradiction between environmental quality and economic development has become increasingly prominent and is likely to restrict the normal pace of China’s economic development and environmental protection. As such, the purpose of this paper is to incorporate the urbanization factor into an analytic framework to discuss the relationship among urbanization, economic development, and environmental pollution.
Design/methodology/approach
A panel data of 31 Chinese provinces from 2004 to 2015 is selected for this research. A spatial correlation test is first conducted on the environmental pollution status, then the spatial Durbin model is used to carry out spatial econometric testing of the relationship among the above three factors.
Findings
Interprovincial environmental pollution in China has significant positive spatial correlation, environmental pollution discharge in most provinces is significantly stable, discharge of environmental pollutants is transitioning from coastal to inland provinces, and urbanization and economic growth can both aggravate environmental pollution, but economic growth can relieve environmental pollution in neighboring provinces.
Originality/value
The relationship between economic growth, urbanization, and environmental quality has always been an important issue for sustainable development. As such, China’s urbanization leads to economic development, while rapid economic growth and environmental pollution are coordinated. This paper focuses on the specific relationship between them. To this end, local governments make concerted efforts to formulate sound environmental regulation policies based on local environmental conditions, where economic development is an effective means of alleviating the contradictory relationship between economic development and environmental protection.
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Nadia Ben Abdallah, Halim Dabbou and Mohamed Imen Gallali
This paper explores whether the Euro-area sovereign credit default swap market is prone to contagion effects. It investigates whether the sharp increase in sovereign CDS spread of…
Abstract
Purpose
This paper explores whether the Euro-area sovereign credit default swap market is prone to contagion effects. It investigates whether the sharp increase in sovereign CDS spread of a given country is due to a deterioration of the macroeconomic variables or some form of contagion.
Design/methodology/approach
For this purpose, the authors use an innovative approach, i.e. spatial econometrics. Although modeling spatial dependence is an attractive challenge, its application in the field of finance remains limited.
Findings
The empirical findings show strong evidence of spatial dependence highlighting the presence of pure contagion. Furthermore, evidence of wake-up call contagion-increased sensitivity of investors to fundamentals of neighboring countries and shift contagion-increased sensitivity to common factors are well recorded.
Originality/value
This study aims to study a crucial financial issue that gained increased research interest, i.e. financial contagion. A methodological contribution is made by extending the standard spatial Durbin model (SDM) to analyze and differentiate between several forms of contagion. The results can be used to understand how shocks are spreading through countries.
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