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The purpose of this paper is to examine the relationships of technical training and the peer effects of technical training with farmers' pesticide use behaviors.

This study uses survey data from 300 peanut growers in Zoucheng County, Shandong, China, in 2016 and employs spatial econometric models to examine the relationships of technical training and the peer effects of technical training with farmers' pesticide use behaviors.

This paper reveals that important peer effects can be channeled through technical training and that these peer effects are sufficiently significant to encourage neighboring farmers to reduce the amount of pesticide use, to transform the structure of pesticide use, and to increase the usage amount of low-toxicity, low-residue pesticide use per hectare. The estimated parameters for the peer effects from technical training are significantly larger than those from technical training alone, which suggests that the technical training of neighboring farmers plays a greater role than technical training for farmers individually.

The research finds that technical training within smaller, localized, groups can induce previously unobservable spillover effects, and this provides a scientific, theoretical and empirical justification for agricultural technology extension that can lead to a rapid, effective transformation of applying new agricultural technologies in an environmentally sensitive and economically sustainable manner.

The objective of this chapter is to introduce the reader to Spatial Health Econometrics (SHE). In both micro and macro health economics there are phenomena that are characterised by a strong spatial dimension, from hospitals engaging in local competitions in the delivery of health care services, to the regional concentration of health risk factors and needs. SHE allows health economists to incorporate these spatial effects using simple econometric models that take into account these spillover effects. This improves our understanding of issues such as hospital quality, efficiency and productivity and the sustainability of health expenditure of regional and national health care systems, to mention a few.

Spatial autocorrelation in regression residuals is a major issue for the modeller because it disturbs parameter estimates and invalidates the reliability of conclusions drawn from models. The purpose of this paper is to develop an approach which generates new spatial predictors that can be mapped and qualitatively analysed while controlling for spatial autocorrelation among residuals.

This paper explores an alternate approach using a Fourier polynomial function based on geographical coordinates to construct an additional spatial predictor that allows to capture the latent spatial pattern hidden among residuals. An empirical validation based on hedonic modelling of sale prices variation using a large dataset of house transactions is provided.

Results show that the spatial autocorrelation problem is under control as shown by low Moran's I indexes. Moreover, this geo‐statistical approach provides coefficients on environmental amenities that are still highly significant by capturing only the remaining spatial autocorrelation.

The originality of this paper relies on the development of a new model that allows considering, simultaneously spatial and time dimension while measuring the marginal impact of environmental amenities on house prices avoiding competition with the weight matrix needed in most spatial econometric models.

This study aims to empirically analyze the factors influencing digital financial inclusion in China.

Using panel data from 31 provinces in China for the years 2011-2018, the study constructed spatial econometric models for regression analysis at the national and regional levels.

Economic development, government intervention, internet penetration and the development of the credit level significantly affected the development of digital financial inclusion in China. However, the specific influence of the various factors varied by province. Provinces with less-developed economies generally had weaker economic foundations and underdeveloped digital financial services, making it more difficult to fully achieve digital financial inclusion.

Relevant government policies should strengthen digital infrastructure and improve the organizational systems and services of digital finance to support the balanced development of digital financial services in China.

China’s e-commerce development has been at the global forefront for decades, which suggests digital financial inclusion is also well-placed for strong development in China. However, quantitative research on the digital financial inclusion index has remained insufficient in China and worldwide, with most research ignoring the status of different development levels in a different region. To address this gap in the literature, this study empirically researched the status, regional differences and causes associated with these differences that impact digital financial inclusion in China.

The purpose of this study is to examine the net effect of golf courses’ proximity on home sale prices in Kern County, California.

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.

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.

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.

The purpose of this paper is to employ provincial data to study the relationship between several crime typologies, namely murder, robbery, extortion and fraud and economic output in Italy.

The authors propose a spatial econometric approach where the spatial proximity is defined by a measure of physical distance between locations, in order to take into account possible spill-over effects.

The results of the spatial estimation suggest that criminal activities, namely murder and robbery, exhibit a negative impact on Italian gross domestic product while fraud and total crime do not affect economic output and that there are beneficial spill-overs from neighbouring provinces.

The study empirically shows that only violent crimes have a crowding-out effect on economic output.

The purpose of this paper is to examine the real estate sales in Turkey on a district basis to reveal the current state of real estate sales and any meaningful changes in the last period. The real estate market is important and is an indicator of the country’s general economic health, as real estate is seen as an investment.

As a powerful method of spatial analysis and evaluation, geographic information systems have been used to examine real estate data in both spatial and temporal ways. In this study, 14 years of sales data covering the years 2004 to 2017 obtained from government agencies on a district basis were evaluated using spatiotemporal methods. Several maps were produced using Getis-Ord Gi* and local Moran’s I indices, which showed the spatiotemporal change of sales and sales rates.

When looking at the maps, provinces such as Istanbul, Ankara, Izmir, Antalya and their surrounding districts have buoyant real estate markets compared to the other side of the country. Real estate sales are more stagnant in the eastern and northern parts of the country. In addition, the authors found that the growth rate of annual average real estate sales was approximately seven times higher than the annual average population growth.

This spatiotemporal study, which presents 14 years of performance data of the real estate market and, by extension, the economic situation, also highlights the regions that stand out for investment planning throughout the country. The results of spatiotemporal analysis also present a new way of real estate market visualization using maps with well-designed categorizations.

By using a wide range of macro and micro factors, this paper aims to provide a new assessment of the recent literature on inbound tourism demand models.

This study examines the determinants and spatial effects of inbound tourism using Hausman–Taylor and spatial econometric models.

Several important factors were identified, including local economic growth, openness to the outside world, regional size, geographic distance, foreign direct investment, level of innovation and average annual temperature. In addition, the study found strong cross-city competition effects on tourism resource endowment and hotel infrastructure.

Inbound tourism is a crucial link in achieving high-quality economic development. However, previous studies have mainly focused on the analysis of single influencing factors, ignoring the spatial spillover effects of factors.

From the intra- and inter-regional dimensions, this paper investigates the linkage between industrial agglomeration and inventory performance, and further demonstrates the moderating role of firm size and enterprise status in the supply chain on this linkage.

Using a large panel dataset of Chinese manufacturers in the Yangtze River Delta for the period from 2008 to 2013, this study employs the method of spatial econometric analysis via a spatial Durbin model (SDM) to examine the effects of industrial agglomeration on inventory performance. Meanwhile, the moderation model is applied to examine the moderating role of two firm-level heterogeneity factors.

At its core, this research demonstrates that industrial agglomeration is associated with the positive change of inventory performance in the adjacent regions, whereas that in the host region as well as in general does not significantly increase. Additionally, both firm size and enterprise status in the supply chain can positively moderate these effects, except for the moderating role of firm size on the positive spillovers.

In view of firm heterogeneity, managers should take special care when matching their abilities of inventory management with the agglomeration effects. Firms with a high level of inventory management are suited to stay in an industrial cluster, while others would be better in the adjacent regions to enhance inventory performance.

This paper is the first to systematically analyze the effects of industrial agglomeration on inventory performance within and across clusters, and confirm that these effects are contingent upon firm size and enterprise status in the supply chain. It adds to the existing literature by highlighting the spatial spillovers from industrial clusters and enriching the antecedents of inventory leanness.

The purpose of this paper is to present an algorithm of real estate mass appraisal in which the impact of attributes (real estate features) is estimated by inequality restricted least squares (IRLS) model.

This paper presents the algorithm of real estate mass appraisal, which was also presented in the form of an econometric model. Vital problem related to econometric models of mass appraisal is multicollinearity. In this paper, a priori knowledge about parameters is used by imposing restrictions in the form of inequalities. IRLS model is therefore used to limit negative consequences of multicollinearity. In ordinary least squares (OLS) models, estimator variances might be inflated by multicollinearity, which could lead to wrong signs of estimates. In IRLS models, estimators efficiency is higher (estimator variances are lower), which could result in better appraisals.

The final effect of the analysis is a vector of the impact of real estate attributes on their value in the mass appraisal algorithm. After making expert corrections, the algorithm was used to evaluate 318 properties from the test set. Valuation errors were also discussed.

Restrictions in the form of inequalities were imposed on the parameters of the econometric model, ensuring the non-negativity and monotonicity of real estate attribute impact. In case of real estate, variables are usually correlated. OLS estimators are then inflated and inefficient. Imposing restrictions in form of inequalities could improve results because IRLS estimators are more efficient. In the case of results inconsistent with theoretical assumptions, the real estate mass appraisal algorithm enables having the obtained results adjusted by an expert. This can be important for low quality databases, which is often the case in underdeveloped real estate markets. Another reason for expert correction may be the low efficiency of a given real estate market.