Historical dataset of population level and growth rate for the Santiago, Chile metro area from 1950 to 2025.

Comprehensive socio-economic dataset for Chile including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

This study aims to identify the determinants associated to food insecurity in a group of households composed of schoolchildren and their mothers/caregivers, who lived in a low-to-medium community development index area of the city of Santiago, Chile with a high presence of migrant population. The non-probabilistic and convenience sample was made up of 646 people, who answered a series of surveys with the aim of characterizing this group in sociodemographic terms (sex, age, number of inhabitants in the household, place of food purchase, conditional or non-conditional state transfer program beneficiary status, persons in charge of purchasing food for the household, mothers/caregivers education level and basic knowledge of food and nutrition). To assess moderate-to-severe food insecurity and severe food insecurity, the Food Insecurity Experience Scale-FIES was applied between September and October 2021. Logistic regression analysis were used to carry out multivariate analyses, with the use of stepwise back-and-forward strategies for the selected variables and defining p < 0.05. These models were adjusted per number of inhabitants in the household. The results indicate that 25.4% of households presented moderate-to-severe food insecurity, and 6.4% severe food insecurity experience. The variables that presented significant odds of risk to food insecurity were being a migrant, low maternal education level, low performance on basic knowledge in nutrition and when the father was responsible for food purchases. Several public policies have been implemented in Chile during the most recent decades aimed at increasing access to healthier foods and the implementation of healthier food environments. Despite this, there are still social and economic health determinants that contribute to the risk of odds insecurity for the most vulnerable groups in the country, thus putting at risk the fulfillment of the human right to adequate food at risk.

Dataset Description

The dataset includes 1,000 records with information about loan applications, including variables related to the applicant's financial status, credit history, and loan details. The goal is to analyze patterns in credit risk or build models to predict loan defaults.

Columns:

• checking_balance: Customer's current account balance in deutschmarks, classified as < 0 DM (negative balance), 1 - 200 DM, > 200 DM, or unknown (unknown).
• months_loan_duration: Duration of the loan in months.
• credit_history: Credit history of the applicant.
• purpose: Purpose of the loan.
• amount: Loan amount.
• savings_balance: Savings account balance.
• employment_duration: Length of employment.
• percent_of_income: Percentage of income allocated to loan repayment.
• years_at_residence: Years at the current residence.
• age: Applicant's age.
• other_credit: Presence of other credit agreements.
• housing: Housing status (e.g., rent, own).
• existing_loans_count: Number of existing loans.
• job: Job type or classification.
• dependents: Number of dependents.
• phone: Availability of a telephone.
• default: Target variable indicating loan default ("yes" or "no").

Inspiration

This dataset can be used for: - Building predictive models for loan default. - Exploring relationships between financial variables and credit risk. - Enhancing your understanding of credit risk analysis.

License

This dataset is published under the CC BY-NC-SA 4.0 license: - Permitted: Educational, research, and personal use. - Restricted: Commercial use is not allowed. - Attribution: Credit to Universidad de Santiago de Chile is required. - Sharing: Derivative works must use the same license.

This dataset was originally provided by the Universidad de Santiago de Chile as part of the course "Machine Learning for Management". I am not the original creator of the data, and my role is solely to share this resource for educational and research purposes. All rights to the original data belong to the university and/or the original authors.

This dataset may not be used for commercial purposes or in contexts that violate the copyright or policies of the institution that created it. Users are responsible for complying with the terms of use specified in the accompanying license and should ensure they provide appropriate credit.

Additional Notes If you are a student or researcher interested in using this dataset, please make sure to give proper credit to the original source in your publications or projects.

Dataset accompanying the publication "Antecedents and consequences of trust as a multidimensional construct. Cross-country analysis in the luxury retail sector" (Management Letters/Cuadernos de Gestión 2022). The objective of this study is to understand the role of the multidimensional trust of a luxury brand as an antecedent of consumer satisfaction and a consequence of reputation and familiarity, considering -in turn- that reputation and familiarity can be a consequence of the consumer's cognitive and affective experiences. A cross-country analysis in the luxury retail sector was carried out. Association relationships between variables are tested by a model of structural equations. For this, a transnational analysis has been carried out in the luxury retail sector. A non-probabilistic sample was used in this study. 1058 people were interviewed, 608 consumers in Santiago (Chile) and 450 in Madrid (Spain). The key role played by the multidimensional trust of a luxury brand as an antecedent of satisfaction and consequence of reputation and familiarity is confirmed. When observing the reputation and familiarity of a luxury brand as a result of the cognitive and affective experiences of the consumer, differences between Chile and Spain have been discovered. Managers can not only use the brand's own characteristics to differentiate themselves from the competition, but they can also do so through the multi-dimensional trust of the luxury brand.

The study develops and validates a theoretical model to predict thermal mortality under natural conditions, based on measurements of mortality performed in the laboratory at multiple constant temperatures. The theoretical model first fits a thermal tolerance landscape, which describes how survival probability is affected by both temperature and exposure time, to the empirical measurements of mortality obtained in the laboratory under controlled conditions. Then, employing a numerical approximation to the analytical solution based on differential calculus, it combines this tolerance landscape with ambient temperature records in natural settings to predict the survival probability curve under these thermal conditions. These predictions were validated by contrasting predicted and observed mortality curves in 11 Drosophila species under three different warming rates, reported in the literature, which were virtually indistinguishable. Having validated the model, the study then examines how mortality should be affected by climate change in a natural population of Drosophila subobscura from Santiago, Chile, employing temperature records for this location during 1984 - 1991 and 2014 - 2018. The cumulative mortality predicted from temperature records closely resemble the periods of population collapse recorded for this population during the Austral summer and, according to the model, warming temperatures in the past 30 years may have advanced this period by almost a month. This methodology is highly general and can in principle be employed to predict temperature mortality in small ectotherms under any varying thermal regime.