Neighbourhood Effects in Pesticide Use: Evidence from the Rural Philippines

This study investigates how pesticide use by neighbouring farmers affects a given farmer's pesticide use. Although it is common knowledge that pesticide use has spatial externalities, few empirical economic studies explicitly analyse this issue. Applying a spatial panel econometric model to plot‐level panel data for Bohol, Philippines, this study shows that pesticide use, especially for herbicides, is spatially correlated, although there is no statistically significant spatial correlation in unobserved shocks. This implies that farmers apply pesticides by referring to the behaviour of neighbouring farmers rather than responding directly to the intensity of their own infestation.     

水利建设市场主体信用评价指标体系改进——以施工单位为例

针对现行水利建设市场主体信用评价指标体系,以施工单位为例,在比较分析已有信用评价结果的基础上,采用问卷调查统计方法,得到市场主体对现有评价指标的认同度,并分析不同指标认同度差异的原因,进而提出删除门户网站及平台信息、固定资产占总资产比例及施工年产值等认同度低的指标,提高管理层素质、人员素质及管理制度等认同度高的指标的权重。     

京津冀雾霾污染与产业结构、城镇化水平的空间效应研究

京津冀地区是全国雾霾污染的重灾区。本文分析了京津冀地区13个城市的雾霾污染空间相关性，发现其具有显著的空间溢出效应，呈现出“高—高”型集聚和“低—低”型集聚的特征。基于京津冀地区13个城市2000—2012年的面板数据，使用三种类型空间权重矩阵构建空间杜宾模型发现：京津冀雾霾污染与产业结构、城镇化水平显著相关；产业结构与其呈“倒U型”曲线，目前产业结构和城镇化水平对自身及其邻近地区的雾霾污染具有明显的加重作用；城镇化水平对邻近地区雾霾污染具有双向效应。产业结构调整优化和城镇化水平质量的提高会缓解京津冀雾霾污染，实际人均GDP、对外贸易依存度、FDI、人口密度等也会对京津冀地区及其周边地区的雾霾污染产生影响。     

区域耕地生态价值补偿量化研究——以新疆为例

[目的]在耕地保护形势日趋严峻的情形下,开展耕地生态价值补偿量化研究对了解耕地外部性价值的大小及深化耕地生态价值的量化方法和思路具有重要意义。[方法]文章以新疆为例,从14个地州市的耕地生态服务价值出发,将生态超载指数作为耕地生态服务价值与生态足迹在各地州市转移的测度,同时综合考虑各地州市的经济发展状况,通过构建市域生态价值补偿量化模型分别测算各地州市耕地生态价值补偿量。[结果](1)2015年新疆耕地生态服务价值整体上有盈余输出,全疆共可获得生态补偿费103.31亿元;(2)2015年新疆南北疆耕地生态服务价值比为1:1.7,生态足迹总量比为1:2.2,生态超载指数北亏南盈,表明南北疆耕地生态足迹和耕地生态服务价值间呈"空间异位"格局;(3)新疆北疆乌鲁木齐市、克拉玛依市、吐鲁番市及哈密市共需支付耕地生态补偿费55.67亿元,北疆(乌鲁木齐市、克拉玛依市、吐鲁番市及哈密市外)和南疆地区分别可获得生态补偿费为96.59亿元和62.39亿元。[结论]该文可为新疆各地州市构建耕地生态价值补偿路径提供新思路,也可为以耕地生态价值量确定耕地保护指标和构建耕地生态补偿机制提供参考。     

Spatial statistical modelling of insurance risk: a spatial epidemiological approach to car insurance

Spatial models, such as the Besag, York and Mollie (BYM) model, have long been used in epidemiology and disease mapping. A common research question in these subjects is modelling the number of disease events per region; here the BYM models provides a holistic framework for both covariates and dependencies between regions. We use these tools to assess the relative insurance risk associated with the policyholders geographical location. A Bayesian modelling approach is presented and an elastic net is used to reduce the large number of possible geographic covariates. The final inference is performed using Integrated Nested Laplace Approximation. The model is applied to car insurance data from If P&C Insurance together with spatially referenced covariate data of high resolution, provided by Insightone. The entire analysis is performed using freely available R-packages. Including spatial dependence when modelling the number of claims significantly improves on the result obtained using ordinary generalised linear models. However, the support for adding a spatial component to the model for claims cost is weaker.     

State‐Led Financial Regulation and Representations of Spatial Fixity: The Example of the Spanish Real Estate Sector

This article offers a bottom‐up contribution to the fixity–motion literature. It aims to unravel the apparent contradiction of real estate spatial fixity in Spain, which is portrayed both as a barrier to accumulation and as a unique source of investment by different capitalist actors. Empirically, it describes the shifts in real estate ownership and changes in profit‐making strategies that have taken place across the real estate sector during the crisis years, and the role of the state in these shifts. The article asserts that the idea of spatial fixity representing a spatial barrier for accumulation does not necessarily apply in the Spanish case. It further claims that the tensions in capital circulation through real estate are not only to be found in the action of time, but in different state strategies pursued by various actors. The opposing representations of fixity are the result of state regulation of interest rates, taxation and risk weighting. The state also increasingly promotes land rents as a source of liquidity creation.     

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.     

Volatility dynamics under an endogenous Markov-switching framework: a cross-market approach

This study uses an endogenous Markov-switching framework to examine the interrelatedness of the volatility dynamics of the US and Korean markets. Previous literature assumes that the US market implied volatility index is exogenous to the Korean implied volatility index. Here, we allow for correlations between the US and Korean variables and suggest two types of endogeneity, namely endogeneity in the regressors and in the regime-switching probabilities. The estimation results show that both types of endogeneity are present in the US variables and that the parameter estimates are quite different when endogeneity is considered, indicating a serious endogeneity bias in the parameter estimates. The results of the endogeneity test for the regressors show that the effects of global shocks are often persistent and may last for as long as six periods. Sub-period analyses indicate that the degrees of endogeneity were especially strong during the global financial crisis.     

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.