Using Chilean data we present evidence about the relationship between job satisfaction, own wage, and reference group wage. We conducted a semi-nonparametric estimation of extended ordered probit models in order to identify the determinants of job satisfaction. Our main result indicates that a 10 % increase in the reference group wage would need to be compensated for by a 24.9 % increase in the own wage to give the same level of job satisfaction. This result shows the enormous importance of the reference group wage for job satisfaction.

No abstract

Our aim is to cast light on socioeconomic residential segregation effects on life satisfaction (LS). In order to test our hypothesis, we use survey data from Chile (Casen) for the years 2011 and 2013. We use the Duncan Index to measure segregation based on income at the municipality level for 324 municipalities. LS is obtained from the CASEN survey, which considers a question about self-reported well-being. Segregation’s impact upon LS is not clear at first glance. On one hand, there is evidence telling that segregation’s consequences are negative due to the spatial concentration of poverty and all the woes related to it. On the other hand, segregation would have positive effects because people may feel stress, unhappiness, and alienation when comparing themselves to better-off households. Additionally, there is previous evidence regarding the fact that people prefer to neighbor people of a similar socioeconomic background. Hence, an empirical test is needed. In order to implement it, we should deal with two problems, first, the survey limited statistical significance at the municipal level, hence we use the small area estimation (SAE) methodology to improve the estimations’ statistic properties, and second, the double causality between segregation and LS; to deal with the latter, we include lagged LS as a regressor. Our findings indicate that socioeconomic segregation has a positive effect on LS. This result is robust to different econometric specifications.

No abstract

Today the web generates a large amount of data, the same ones that come from social networks, online platforms, communities, cloud computing, etc., but one type of data has not been recognized for its relevance and that is data from Learning Management Systems like Moodle in the educational context. Considering this context, this research will apply some Artificial Intelligence methods and techniques such as the TSA methodology, Text mining, and Sentiment Analysis to assess the data about the opinion of the students, converting them into stable information structures that allow their reflection and analysis. The work carried out focuses on determining the level of user satisfaction, in this case, the students, of the virtual learning platforms. The results obtained show that applying Artificial Intelligence allows obtaining relevant information that helps to undertake improvement actions by authorities and managers in the educational context based on the opinion of the students, detecting important problems in online learning during these times of COVID-19 we are just past.