Prioritization of government expenditure on health in India: A fiscal space perspective

Unequal distribution of fiscal resources and lower prioritization of budget towards healthcare are the most important challenges in achieving universal health coverage in India. This study has examined relationships between government health expenditure and fiscal space (i.e. tax revenue, non-tax revenue, fiscal transfer, and borrowings) in twenty-one states of India for the period of 1980–2014. Our panel regression results imply that mobilization of tax revenue has a positive impact, while borrowings have a negative impact on the allocation of government expenditure on healthcare in the long-run. The panel quantile regression results show that states associated with the low and middle level of revenue growth have been mobilizing finance through central government transfer and borrowings in short-run. Further, the panel vector error correction models show that sum of the lagged coefficients of borrowings have a greater impact on health financing process as compared to other sources of fiscal space at short-run, and the speed of adjustment towards long-run equilibrium is relatively slower. The overall analysis concludes that less domestic revenue mobilization and higher dependency of borrowings for healthcare financing may create fiscal stress on state finances in the long-run, and thereby it could possibly reduce the prioritization of spending. Therefore, improvement in revenue growth and proper utilization of fiscal transfer would be appropriate policy implications from this study.     

Do special issues offer something special? An overview of research trends in Telecommunications Policy special issues

This study explores the value of special issues by analyzing the characteristics of special issues of Telecommunications Policy (TP). Specifically, this study examines the status of special issue publications, the characteristics of their articles compared with those of general issues, and the factors affecting article citations. For the analysis, all information on TP articles published from 1976 to 2018 was collected from the Scopus database. A comparative analysis as well as a negative binomial regression were conducted on papers published after 1994, when the first special issue was published. The results show that special issues are distinguished from general issues by various characteristics, such as multinational author collaboration, number of references, keywords, and funding. While general issue papers are more frequently cited in academia, special issue papers show better performance in terms of online captures and social media sharing. Moreover, the smaller number of variables affects the frequency of citations in the case of special issues, revealing that content and format are more influential than author factors.     

Does economic growth and energy consumption drive environmental degradation in China’s 31 provinces? New evidence from a spatial econometric perspective

The panel data analysis points to economic and social factors contributing to NO_x, PM_2.5, PM₁₀, SO₂, and VOCs in China’s 31 provinces. The spatial correlation analysis using Global and Local Moran’s I values indicates the existence of a significant and positive spatial autocorrelation with respect to environment, economy and energy, and the high spatial correlation is evident in the eastern region, covering the northern part of Yangtze River Delta, Huaihai Economic Zone, and the lower reaches of the Yellow River Economic Belt. The empirical estimation is performed through spatial lag and spatial Durbin models. All emitted air pollutants in 31 provinces have significant spatial dependence and strong spillover effects. There is an inverted U-shaped relationship between emitted air pollutants (NO_x, PM₁₀, VOCs, and PM_2.5) and per capita GDP, which follows the EKC hypothesis. The relationship between SO₂ and per capita GDP does not follow the EKC hypothesis. There is a positive relationship between pollutant emissions and coal consumption, which is consistent with current studies for various countries like Canada, Denmark, UK and US and regions like New York State. However, the effects of science and technology investment on air pollutants are mostly positive, which is not as policy expected.     

Do Indian States Mimic,Compete or Interact in Local Public Spending? A Spatial Econometric Analysis

This paper analyses spatial interaction in public spending decisions across 22 Indian states during the period 1980–1981 to 2014–2015. In particular, we estimate interactive hypotheses for different proximities of states using a spatial panel data approach. The empirical results support strong spatial interaction and yardstick competition in public spending. Interactive behavior among the states has been found to be consistent and conditional on per capita income, fiscal transfers, infrastructure, literacy and population density. Interaction arising from yardstick competition significantly affects public spending decisions. The present study realizes the need for a well‐developed and comprehensive network to strengthen the interdependence in public spending among the states for higher welfare gain.     

Corruption in space: A closer look at the world's subnations

Corruption levels differ not only between but also within countries. In this paper we analyze spatial interdependencies in corruption levels for a large sample of 1232 subnational regions from 81 countries. Based on a spatial autoregressive model, which controls for country-fixed effects and corrects for spatial autocorrelation in the error term, we find that a subnational region's corruption level is positively correlated with neighboring subnational regions' corruption levels. Extending the core model and allowing for heterogeneous spatial effects, we find that most spillovers among subnational regions occur within national borders. Moreover, in particular high income subnational regions and subnational regions with relative low corruption levels tend to spill in space. This is due to their high degree of connectivity in terms of economic, sociocultural and political exchange with other subnational regions. Our estimation results underline the importance to consider not only a subnational region's own characteristics, but also spatial interdependencies when implementing efficient anti-corruption policies at the local level.     

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

Monetary policy and food inflation in South Africa: A quantile regression analysis

Although optimal monetary policy stabilizes food inflation theoretically, empirical studies remain limited not only in the context of volumes and the estimation approaches, but are focused on selected advanced and emerging countries to the neglect of Africa where poverty and dominance of food in the consumption basket are more pronounced. We provide empirical evidence in the context of South Africa using quantile regressions. Rising food prices are destabilized even further by restrictive monetary policy; a finding that has ramifications for inflation targeting, especially given that a quarter of the country’s population is food poor.     

Industry risk transmission channels and the spillover effects of specific determinants in China’s stock market: A spatial econometrics approach

This paper uses a spatial econometrics approach to study the industry risks in China’s stock market. We comprehensively consider the real linkage and information risk transmission channels and analyze the risk spillovers of specific determinants. Our empirical results show the following: 1) The real linkage channel and information channel are both effective transmission channels for driving spillover effects, and the information channel is of the utmost importance. 2) The spillover effects of specific determinants exist and are persistent. The superposition of spillover effects may lead to extreme risk. 3) The transmission channels and spillover effects are asymmetric in different regimes.     

Why cryptocurrency markets are inefficient: The impact of liquidity and volatility

In this research, we study the multifractality, long-memory process, and efficiency hypothesis of six major cryptocurrencies (Bitcoin, Ethereum, Monero, Dash, Litecoin, and Ripple) using the time-rolling MF-DFA approach. For an in-depth analysis, this study uses the quantile regression approach to examine the determinants of efficient markets. The results show that all markets present evidence of long-memory property and multifractality. Furthermore, the inefficiency of cryptocurrency markets is time-varying, and Dash is the least inefficient market while Litecoin is the most inefficient. Finally, we find that higher liquidity improves but higher volatility weakens the efficiency of cryptocurrencies, depending on the quantiles. Therefore, we conclude that high liquidity with low volatility helps active traders to arbitrage away opportunities, resulting in market efficiency.     

城镇群空间结构与要素集聚

依据城镇化发展过程中要素空间集聚理论，展开区域经济发展的空间经济结构研究。通过城市引力、克鲁格曼指数计算，比较分析城镇群内部城市吸引力和城市产业分布的差异性，通过人口与生产空间分布集中度指标和不一致系数的测算，分析了山西省城镇群空间要素集聚效应。研究表明，山西省的城镇群规划与要素空间集聚趋势整体一致，城市间产业分工体系基本形成，城镇群的要素集聚效应明显，但人口与生产分布仍存在不一致性。研究认为，山西省城镇群发展应该继续明确各城市的产业分工和特色，引导劳动力和资本要素的合理流动，实现人口与生产空间分布的一致。