Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study.

Background: Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis.

Aim: This study aimed to evaluate the predictive value of ML algorithms for the restriction of life-space mobility (LSM) among elderly people and to identify the most important risk factors for that prediction model.

Do workers negative self-select when they commute? Evidence for the Chilean case of long-distance commuting.

Long-distance commuting (LDC) as a strategy of labor factor mobility has become relevant in recent decades, mainly in those economies characterized by a significant relative weight of extractive activities. The phenomenon is key to understanding the current structure and dynamics of these labor markets, although little is known about self-selection in LDC. This document addresses this knowledge gap by analyzing the case of Chile using functional areas.

Fertility and Economic Development: Quantile Regression Evidence on the Inverse J-shaped Pattern.

This paper analyses empirically the relationship between economic development and fertility. Through a new sample selection and quantile regression, it investigates whether there is an inverse J-shaped pattern between these two variables, and, if so, whether it depends on development and fertility levels. Our results confirm that the inverse J-shaped pattern exists, but only when a certain level of economic development is attained.