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.     

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。     

耕地流转与规模经营改善了农业环境吗？ ——基于耕地利用行为对农业环境效率的影响检验

研究目的:基于2007—2016年26省农业生产数据,测算"面源污染"和"碳排放"两类非期望产出下的农业环境效率,构建"耕地流转—利用规模—利用方式与利用强度—农业环境效率"的传导路径并进行计量检验。研究方法:超效率SBM模型,中介效应模型。研究结果:(1)耕地流转并未显著提高农业环境效率,但耕地利用规模对农业环境效率的影响呈倒U型;(2)耕地流转有助于扩大耕地利用规模,进而在样本中对农业环境效率呈现负向影响。与此同时,耕地流转对农业环境效率也存在正向的直接效应,正负效应基本抵消;(3)耕地利用规模主要通过改变化肥和机械投入强度进而负向影响农业环境效率。研究结论:农业环境效率受耕地流转、利用规模、利用方式和强度等多重因素影响,呈现明显的传导路径,因此,为了提高农业环境效率,在积极推行土地流转和规模化经营政策时,要注意结合地区农业生产力和地理环境,避免盲目扩大经营规模,同时鼓励规模经营主体引进农业绿色环保技术,注重农业生产资料的合理利用。     

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.     

Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting

We present a simple quantile regression-based forecasting method that was applied in the probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data are log transformed and split into a long-term trend component and a remainder term. The key forecasting element is the quantile regression approach for the remainder term, which takes into account both weekly and annual seasonalities, such as their interactions. Temperature information is used only for stabilizing the forecast of the long-term trend component. Information on public holidays is ignored. However, the forecasting method still placed second in the open data track and fourth in the definite data track, which is remarkable given the simplicity of the model. The method also outperforms the Vanilla benchmark consistently.     

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.