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

Juan Torres Munguía ()
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Juan Torres Munguía: Faculty of Economic Sciences, Georg-August-Universität Göttingen, 37073 Göttingen, Germany

Social Sciences, 2024, vol. 13, issue 3, 1-21

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 (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2024
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