Reexamining the Influence of Conditional Cash Transfers on Migration From a Gendered Lens

Demography. 2019 Oct;56(5):1573-1605. doi: 10.1007/s13524-019-00815-0.

Abstract

Past research on the influence of conditional cash transfers-widespread antipoverty programs-on migration has tended to focus on beneficiaries as a homogenous unit. Drawing on feminist critiques of the contemporary international antipoverty agenda, this article views both conditional cash transfer programs and migration patterns from a gender-sensitive lens. Conditional cash transfers rely on a gendered division of labor in which the informal work of women is particularly called upon in order to fulfill program requirements. This work contends that conditional cash transfers emphasize gender responsibilities for women as mothers and caretakers, which mark their belonging in the domestic sphere and limit the likelihood of their migration while making no such demands on beneficiary men or nonbeneficiaries. Using logistic and multinomial logistic regression models and data from the Mexican Family Life Survey, the analysis finds evidence supporting the hypothesis that conditional cash transfer participation disproportionately limits migration for beneficiary women. This study broadly argues that the impact of such antipoverty programs is more gendered than previously thought and emphasizes the importance of examining previously studied outcomes in ways that consider the specific subject locations of recipients in order to better understand both the logics underlying development policy and the process of migration itself.

Keywords: Antipoverty; Conditional cash transfers; Gender; Migration; Women’s empowerment.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Emigration and Immigration / statistics & numerical data*
  • Female
  • Gender Identity*
  • Humans
  • Logistic Models
  • Mexico
  • Models, Theoretical
  • Poverty / statistics & numerical data*
  • Public Assistance / statistics & numerical data*
  • Socioeconomic Factors