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.

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墨西哥贫困的决定因素：分位数回归分析

大多数关于贫困决定因素的研究都没有认为，这些决定因素中的每一个的相对重要性都可以根据每组贫困人口所遭受的贫困程度而变化。就墨西哥而言，尽管不同贫困群体之间的贫困程度差异很大，但迄今为止进行的研究并未考虑采用这种方法。研究这些差异对于设计更好的消除贫困政策非常重要，该政策应考虑到解释贫困的每个变量如何影响每个遭受贫困的人。本文研究了墨西哥家庭贫困的决定因素。使用2018年墨西哥全国家庭收支调查（ENIGH）的数据，该研究估计了一个概率模型和一个分位数回归模型，以检验贫困的决定因素在整个贫困范围内变化的程度。概率模型的结果表明，拥有一个以上成员，一个女户主或讲母语的家庭更可能贫穷。在分位数回归中获得的结果表明，与简单的普通最小二乘模型的结果存在显着差异，特别是对于赤贫家庭，但对于模型中使用的一些解释变量所分析的其他收入类别，也是如此。如果极端贫困和极度贫困的家庭在南部地区，或者户主说母语或老年人，则受影响最大。