Heterogeneous climate effects on human migration in Indonesia

We examine the effect of anomalous temperatures, rainfall levels, and monsoon timing on migration outcomes in Indonesia. Using panel data from the Indonesian Family Life Survey and high-resolution climate data, we assess whether intra- and inter-province moves are used as a response to climatic shocks. We evaluate the relative importance of temperature, rainfall, and monsoon timing for migration. Only temperature and monsoon timing have significant effects, and these do not operate in the direction commonly assumed. Estimated effects vary according to individuals’ gender, membership in a farm household, and location. We also analyze climate effects on sources of household income, which highlights the multi-phasic nature of household responses. Results undermine narratives of a uniform global migratory response to climate change and highlight the heterogeneous use of migration as a response to such changes. By extending previous research on environmentally induced migration in Indonesia, we also highlight the sensitivity of estimates to alternative climate and migration measures.     

Evaluating the causal economic impacts of transport investments: evidence from the Madrid–Barcelona high speed rail corridor

This paper evaluates economic impacts arising from the introduction of high-speed rail (HSR) between Madrid and Barcelona. Using difference-in-differences estimation we estimate an average treatment effect for provinces with stops on the HSR line of 2.4% for economic output, 3.3% for numbers of firms, and 1.1% for labour productivity. We complement our DID results with a synthetic control analysis for Lleida and Tarragona, two provinces that we argue were assigned HSR stations largely due to their incidental location. We find that both the number of firms and labour productivity are substantially higher in these provinces than in their synthetic counterparts.     

A new algorithm for clustering based on kernel density estimation

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.     

A robust automatic clustering algorithm for probability density functions with application to categorizing color images

This study develops a robust automatic algorithm for clustering probability density functions based on the previous research. Unlike other existing methods that often pre-determine the number of clusters, this method can self-organize data groups based on the original data structure. The proposed clustering method is also robust in regards to noise. Three examples of synthetic data and a real-world COREL dataset are utilized to illustrate the accurateness and effectiveness of the proposed approach.     

Job-Related Well-Being of Immigrants

Social Indicators Research - Considerable literature exists on the acculturation and adaptation of immigrants. For the most part, studies looked at their psychological and social adaptation to the...     

Sexual Victimization and Sex-Related Drinking Motives: How Protective is Emotion Regulation?

One in five college women experience sexual victimization (SV), and SV severity is associated with subsequent psychological distress, including sex-related distress. SV severity may also be associated with drinking motives to cope with sex-related distress and to enhance sex (sex-related drinking motives [SRDMs]), particularly if individuals suffer from emotion regulation (ER) difficulties. College women (N = 151) completed a survey assessment of ER, SV history, childhood sexual abuse (CSA), and SRDMs. Twelve regression models assessed six facets of ER as moderators between SV severity and SRDMs. Among women with no or low levels of prior SV severity, women with greater access to ER strategies were less likely to endorse drinking to cope SRDMs. At higher levels of SV severity, women at all levels of access to ER strategies were equally likely to endorse drinking to cope SRDMs, suggesting that access to ER strategies did not mitigate motivations to drink to cope with sex-related distress for these women. Women with severe SV histories may benefit from interventions that build on existing ER strengths or address other factors. However, greater access to ER strategies may serve as a protective factor against SRDMs when SV severity is low.     

Cognitive distortions predict future gambling involvement

Disordered gambling is best conceptualized as a continuum of severity. Previous research has demonstrated the utility of studying individuals at all points of this spectrum. The sequence of the development of gambling problems and change in gambling involvement along this continuum of severity is not well understood. The present study examined the interplay between cognitive distortions and gambling involvement in a population sample recruited in Alberta, Canada. Data from 1372 participants over 4 assessment waves (5 years) were used to generate a 2-factor latent structure using gambling fallacies and gambling involvement measurements. Structural equation modelling showed that cognitive distortions more strongly predicted future gambling involvement than the reverse relationship, using the comparative fit index (CFI) and the root mean square error of approximation (RMSEA) to assess the models. In addition, cognitive distortions declined over time, whereas gambling involvement remained stable. The results of the study suggest that focusing primarily on cognitive mechanisms in public health initiatives for gambling disorders may be a more effective strategy than focusing on behavioural solutions.