Does local government competition aggravate haze pollution? A new perspective of factor market distortion

Haze pollution has become a new threat to China's sustainable development, but it may be that local government behaviour can play an important role in the prevention and control of pollutants. A dynamic spatial autoregressive (SAR) model is used to study the relationship between local government competition and haze pollution. To further explore the indirect impact of factor market distortion on haze pollution and control potential endogeneity problems, a newly developed intermediary effect model that incorporates the characteristics of the generalized method of moments (GMM) is utilized to explore how factor market distortion indirectly affects haze pollution. The research results show that regional haze pollution in China is characterized by significant spatial correlation, and local government competition has a positive impact on haze pollution; that is, local government competition exacerbates haze pollution. In general, local government competition not only directly leads to an increase in haze pollution but also further intensifies it by distorting the local factor market, and the intermediary role of factor market distortion is approximately 7.04%. The results of the regional inspection found that competition among local governments in the eastern region did not lead to haze pollution, and distortion of the factor market did not exist as an intermediary effect. However, both direct and intermediary effects are significant in the central and western regions. Therefore, an official performance appraisal system that includes ecological constraints should be established to guide the benign transformation of local government competition, and an environmental management mechanism must be developed for joint prevention and control to reduce haze pollution. In addition, the free flow of factors and marketization are equally important.     

Co-movement in Stock Prices in Emerging Economies: The Case of the Caricom Region

The Grand Anse Declaration of 1989 recognised the need for financial integration within the emerging economies that comprise the CARICOM region, as a way of furthering the wider process of economic integration and, indeed, economic development in that region. Using co-movement as a measure of financial integration, this paper investigates the co-movement in stock prices among the Barbados, the Jamaica and the Trinidad and Tobago Stock Exchanges, the three major exchanges within the CARICOM region. It also examines how integrated these exchanges are with the New York Stock Exchange. The GARCH-Copula methodology and, to a lesser extent, estimated correlation coefficients, are used to attain this objective. There appears to be co-movement in stock prices and returns within the CARICOM stock markets and significant dependence structures between the returns of the three CARICOM stock markets. However, there is considerably less evidence of integration between the CARICOM markets and the New York Stock Exchange.     

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.     

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。     

Economic Competition,Policy Interdependence,and Labour Rights

Extant scholarship treats national policies concerning labour rights as a function of economic factors and yet neglects influences of policies among economically competing states. Relying on the policy interdependence theory, this study argues that labour rights policy in a state is dependent on its economic competitors’ labour policy decisions. It specifically maintains that the intensifying competition for foreign direct investment and exports as well as against imports channels negative externalities of deteriorating labour protection in competing states which drives expansive downward policy mimicking and leads to a global decline in labour rights – a race to the bottom. Utilising spatial econometric technique to analyse a new data on labour rights for the period 1994–2009, it finds that labour rights practices are interdependent among economic competitors and experience global deteriorations; whereas labour rights laws remain largely independent due to high policy and reputational costs of lowering them and show more fluctuations.     

Capital market integration in ASEAN: A non-stationary panel data analysis

Motivated by the establishment of ASEAN Economic Community (AEC) at the end of 2015, we examine saving-investment relationship in various subgroups of ASEAN to assess their capital market integration. The results from second generation panel unit-root and cointegration tests that account for cross-sectional dependence as well as estimates of long-run saving-retention rate provide some evidence of market integration in ASEAN. The analysis of short-run dynamics suggests that capital mobility in ASEAN during 1980–2014 appears similar to that in OECD countries during 1970–1999. More importantly, across different panel estimators and subgroups of membership, there is considerable heterogeneity among the member countries. The saving-investment association is very weak, thereby implying very high capital mobility, in more developed members such as Singapore, Malaysia, and Brunei; the association is very strong, implying very low capital mobility, for much less developed members such as Laos, Myanmar, and Cambodia. The results call for renewed effort to develop capital markets in less developed nations and integrate them with the rest of the membership in ASEAN. In this paper, we also address several major shortcomings of the original Feldstein-Horioka framework.     

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.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

The saving–investment correlation puzzle is still a puzzle

A number of simulation studies claim to have solved the Feldstein–Horioka puzzle by demonstrating that a high time-series correlation between saving and investment naturally arises from business cycle shocks. This paper uses panel data of saving and investment controlled for business cycle shocks to empirically test the significance of cyclical shocks — productivity, fiscal and the terms of trade shocks — in explaining a high saving–investment correlation. The estimation results reveal that conventional aggregate shocks only partially explain the high saving–investment correlation. Moreover, country differences in the size of the GNP and the non-traded sector do not significantly affect the saving–investment correlation. The saving–investment correlation puzzle remains a puzzle after all.     

Stochastic Technology, Risk Preferences, and Adoption of Site-Specific Technologies

A model of farmer decision making is developed to determine the extent to which uncertainties about soil fertility and weather affect the value of site-specific technologies (SSTs) using jointly estimated risk and technology parameters. Uncertainty can lead risk-averse farmers to apply more fertilizers and generate more pollution than in the certainty case. Ignoring uncertainty and risk aversion would overestimate the economic and environmental benefits of SSTs and underestimate the subsidy required to induce adoption. Accounting for uncertainties and risk preferences might explain the low observed adoption rates of SSTs. Improving the accuracy of SSTs would increase the incentives for adoption.