Determinants of Poverty in Mexico: A Quantile Regression Analysis

Abstract

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.

Identifying income heterogeneity determinants using the method of moments quantile regression

“…As discussed in ‘Introduction’ section, even though the traditional approach can simply explain main determinants of poverty incidence, it has limited to capturing specific income heterogeneity of households positioned below the poverty threshold (Kedir and Sookram 2011; Heshmati et al 2019; Peng et al 2019; Garza‐Rodriguez et al 2021). In addition, the selection of a poverty threshold, either a relative or an absolute threshold, may be subjective.…”

“…Moreover, we can interpret a coefficient as a marginal effect as it is linear regression. In this regard, quantile regression can be a good supplement of the traditional approach for policymakers and researchers, and several studies have recently employed quantile regression to address the limitation of the traditional approach (Kedir and Sookram 2011; Heshmati et al 2019; Peng et al 2019; Haan et al 2020; Garza‐Rodriguez et al 2021).…”

“…This study employs the STATA user command xtqreg published by Machado and Silva (2019b) and it present the estimates of pooled Ordinary Least Squares (OLS) and MM‐QR fixed effects (Kedir and Sookram 2011; Garza‐Rodriguez et al 2021). As a result of the Hausman test, this study adopted a fixed effects model (Koengkan et al 2022).…”

“…They usually focused on whether a household (or an individual) was poor and examined the causal effect of poverty incidence at the conditional mean. While they compared the main characteristics of poor households with others, they did not sufficiently explain the heterogeneity of poverty among poor households (Kedir and Sookram 2011; Heshmati et al 2019; Peng et al 2019; Garza‐Rodriguez et al 2021). Therefore, the above traditional approach has limited implications for policymakers who design more tailored anti‐poverty measures in response to poverty heterogeneity (Heshmati et al 2019; Peng et al 2019).…”

Analysis of Difference in Household Debt across Regions of Thailand

“…From the perspective of methodology, most of these previous studies utilized the linear regression model to investigate the determinants of household debt. However, due to the great diversity of behavior and characteristics, household debt may exhibit a pattern of heterogeneity [27]. Therefore, it necessary to further observe whether the determinants change depending on distribution.…”