Determinants of poverty in Mexico: A quantile regression analysis

Jorge Garza-Rodriguez*, Gustavo A. Ayala-Diaz, Gerardo G. Coronado-Saucedo, Eugenio G. Garza-Garza, Oscar Ovando-Martinez

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Most studies on the determinants of poverty do not consider that the relative importance of each of these determinants can vary depending on the degree of poverty suffered by each group of poor people. For Mexico’s case, the studies carried out so far do not contemplate this approach, even though there is wide variation in the degree of poverty among the different groups of the poor. Investigating these differences is important to design better policies for fighting poverty, which consider how each variable that explains poverty affects each group of people who suffer from poverty differently. This article examines the determinants of poverty for Mexican households. Using data from the Mexican National Household Income and Expenditure Survey (ENIGH) 2018, the study estimates a probit model and a quantile regression model to examine the extent to which the determinants of poverty vary across the poverty spectrum. The results from the probit model indicate that households with more than one member, having a female head, or speaker of an indigenous language are more likely to be poor. The results obtained in the quantile regressions indicate that there are significant differences with the results of the simple ordinary least squares model, especially for households in extreme poverty but also for the other income categories analyzed for several of the explanatory variables used in the models. Households in the categories extremely poor and deeply poor are most affected if they are in the southern region or if the household head speaks an indigenous language or is an elderly person. It is observed that achieving a higher educational level is an effective way to increase income across the poverty spectrum.

Original languageEnglish
Article number60
Issue number2
Publication statusPublished - Jun 2021

Bibliographical note

Publisher Copyright:
© 2021 by the author. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Development
  • Economics, Econometrics and Finance (miscellaneous)


Dive into the research topics of 'Determinants of poverty in Mexico: A quantile regression analysis'. Together they form a unique fingerprint.

Cite this