scholarly journals Peer effects and scholastic achievement: spatial models estimates using the student friendship network at the classroom level

2019 ◽  
Vol 23 (1) ◽  
pp. 5-24
Author(s):  
Isabel Pessoa de Arruda Raposo ◽  
Tatiane Almeida de Menezes ◽  
Ricardo Carvalho de Andrade Lima ◽  
Ricardo Zimmerle da Nóbrega
Keyword(s):  
Standard Deviation ◽  
Peer Effects ◽  
Fixed Effects ◽  
Spatial Models ◽  
Friendship Networks ◽  
Friendship Network ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
The Individual ◽  
Identification Strategy

This paper evaluates the peer effects on individual academic performance. The identification strategy considers the architecture of friendship networks within classrooms, in addition to group and individual fixed effects. Estimates of spatial autoregressive models show that an increase of one standard deviation (sd) in peers’ math grade improves by 6% sds the student’s grade. Furthermore, when we also consider the indirect friendship bonds, the aggregate peer impact raises to 45% sds of the individual math grade.

Testing Spatial Dependence in Spatial Models with Endogenous Weights Matrices

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Anil K. Bera ◽  
Osman Doğan ◽  
Süleyman Taşpınar
Keyword(s):  
Spatial Dependence ◽  
Spatial Models ◽  
Test Statistics ◽  
Simple Test ◽  
Robust Test ◽  
Finite Samples ◽  
Robust Tests ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
Empirical Illustration

Abstract In this study, we propose simple test statistics for identifying the source of spatial dependence in spatial autoregressive models with endogenous weights matrices. Elements of the weights matrices are modelled in such a way that endogenity arises when the unobserved factors that affect elements of the weights matrices are correlated with the unobserved factors in the outcome equation. The proposed test statistics are robust to the presence of endogeneity in the weights and can be used to detect spatial dependence in the dependent variable and/or the disturbance terms. The robust test statistics are easy to calculate as computationally simple estimations are needed for their calculations. Our Monte Carlo results indicate that these tests have good size and power properties in finite samples. We also provide an empirical illustration to demonstrate the usefulness of the robust tests in identifying the source of spatial dependence.

scholarly journals Contributions from Spatial Models to Non-Life Insurance Pricing: An Empirical Application to Water Damage Risk

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2476
Author(s):  
Maria Victoria Rivas-Lopez ◽  
Roman Minguez-Salido ◽  
Mariano Matilla Matilla Garcia ◽  
Alejandro Echeverria Echeverria Rey
Keyword(s):  
Life Insurance ◽  
Spatial Information ◽  
Spatial Models ◽  
Spatial Error ◽  
Insurance Pricing ◽  
Insurance Portfolio ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
Insurance Data ◽  
Damage Risk

This paper explores the application of spatial models to non-life insurance data focused on the multi-risk home insurance branch. In the pricing modelling and rating process, spatial information should be considered by actuaries and insurance managers because frequencies and claim sizes may vary by region and the premium should be different considering this rating variable. In addition, it is relevant to examine the spatial dependence due to the fact that the frequency of claims in neighbouring regions is often expected to be more closely related than those in regions far from each other. In this paper, a comparison between spatial models, such as spatial autoregressive models (SAR), the spatial error model (SEM), and the spatial Durbin model (SDM), and a non-spatial model has been developed. The data used for this analysis are for a home insurance portfolio located in Spain, from which we have selected peril of water coverage.

scholarly journals Analysis of the Relationship between Financial Development, Employment and Institutions on Economic Growth in WAEMU Countries

2020 ◽  
Vol 7 (12) ◽  
pp. 267-291
Author(s):  
Firmin Ayivodji ◽  
Rémy Hounsou ◽  
Emmanuel Tago
Keyword(s):  
Economic Growth ◽  
Financial Development ◽  
Fixed Effects ◽  
Employment Level ◽  
Pooled Mean Group ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
The One ◽  
The Relationship

This study analyzes the relationship between financial development and economic growth on the one hand and the link between employment level and economic growth on the other hand in the context of financial liberalization. Also the question of the role of the institutional factors in the facilitation of the credit granting in the eight (08) countries of the WAEMU is approached. In doing so, strategies based on conventional fixed effects methods, with correction of Driscoll-Kraay (1998), Pooled Mean Group (PMG) of Pesaran et al. (1995, 1999) and spatial autoregressive models (SAC) are used to estimate the different equations over the period 1990-2015. The results suggest that financial development is positively associated with economic growth in WAEMU countries while an improvement in the level of employment stifles economic development. The results show that there is a positive and significant correlation between quality of democratic institutions and economic growth whatever the indicators of financial development considered except the money supply. The study recommends a strengthening of the financial development with a possible greater regularity.

Individual and Social Network Predictors of Physical Bullying: A Longitudinal Study of Taiwanese Early Adolescents

2014 ◽  
Vol 29 (4) ◽  
pp. 701-716 ◽  
Author(s):  
Hsi-Sheng Wei ◽  
Wonjae Lee
Keyword(s):  
Social Network ◽  
Repeated Measures ◽  
Economic Status ◽  
Friendship Networks ◽  
Friendship Network ◽  
Individual Level ◽  
School Year ◽  
Bullying Experiences ◽  
The Individual ◽  
Physical Bullying

This study followed 125 7th-grade students in Taiwan for the entire school year and analyzed the individual and social network factors predicting their involvement in physical bullying over 5 waves of data. Using self-reports of bullying experiences, 20 classroom-level networks of bullying and friendship were constructed for 4 classrooms and 5 temporal points, from which 4 individual-level network measures were calculated. They included bully and victim centrality, popularity, and embeddedness in friendship networks. A series of mixed models for repeated measures were constructed to predict students’ bully and victim centrality in bullying network at time t + 1. Compared to girls, boys were more likely to be both the bullies and victims. Lower self-esteem and higher family economic status contributed to victim centrality. Having parents married and living together predicted lower bully centrality. Higher educational level of parents predicted lower victim and bully centrality. Regarding the social network factors, students’ bully centrality at t positively predicted their bully centrality at t + 1, whereas victim centrality predicted their subsequent victim centrality. Interaction effects between friendship network and bullying network were observed. Embeddedness in friendship network reduced victim centrality at t + 1 except for those students with low victim centrality at t. For those with high victim centrality at t, popularity increased their risk of physical victimization over time. Implications for research and practice are discussed.

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