Identifying Gender-Specific Risk Factors for Income Poverty across Poverty Levels in Urban Mexico: A Model-Based Boosting Approach

Abstract

This paper aims to identify income-poverty risk factors in urban Mexican households. Special emphasis is paid to examine differences between female- and male-headed families. To this, a dataset with 45 theoretical factors at the individual/household, community, and regional levels, integrating information from nine sources, is created. To these data, additive quantile models are estimated via the boosting algorithm. From a gender standpoint, the following main contributions come from this paper. First, educational lag is particularly relevant for female-headed households. Second, there is a gendered life cycle in the income trajectory for poor households with a head having a medium level of education. Third, some households, traditionally disregarded, are found to be even poorer: those lacking social connectedness, without credit cards, with an extended composition, in which the female head spends a large part of her time on housework, and families headed by young women with a medium level of education. Finally, communities and regions where families have a lower income-to-poverty ratio are characterized as having an unequal income distribution, lower human development, lower levels of women’s economic participation, poor quality of services, and lower gender-based violence levels in the public sphere but higher gender-based violence levels in the family context.

Keywords

• income poverty
• gender
• additive quantile models
• boosting algorithm