Income Differences on Mexico’s Northern Border: A Perspective on Formal and Informal Employment

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DOI: https://doi.org/10.17428/rfn.v27i53.90
Keywords:
informal employment ANOVA logit model regional analysis northern states

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Rogelio Varela Llamas

Abstract

Using information from the “Encuesta nacional de ocupación y empleo (ENOE)”, income differences between formal and informal workers in the northern border states of Mexico are analyzed. The ANOVA methodology suggests that the income of workers differs according to the type of employment (formal/informal), type of work, and interaction effect. The logit model shows that the likelihood of informal employment is associated with lower levels of education and the job search process.

Article Details

How to Cite
Varela Llamas, R. (2015). Income Differences on Mexico’s Northern Border: A Perspective on Formal and Informal Employment. Frontera Norte, 27(53), 177–203. https://doi.org/10.17428/rfn.v27i53.90
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Vol. 27 No. 53 (2015): enero-junio, 2015
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Papers

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Notas

According to Coneval, the ITLP shows the trend in the percentage of people who cannot purchase the basic food basket with their work income each quarter.

When reference to a factor is made, it is being spoken of as a numerical categorical variable with different levels of study that are not necessarily ordinal variables, but rather nominal ones.

The error graphs that compare formal and informal workers with the same level of schooling and that constitute a pairwise analysis are not incorporated for space reasons; nevertheless, they are at the disposition of readers for their analysis.

The Brown-Forsythe statistic for an ANOVA of a factor it is given by the following expression:

where p is the number of groups, nj the number of observations in the group j and N is the total of observations. The term corresponds to the mean of zij and is the total of the average of zij.

With this methodology, the discussion can center on the results of an ANOVA with covariates, highlighting the inter-subject contrasts.

The statistic LR = -2ln (λ) = -2ln (LCR /LSR) or likelihood ratio, allows carrying out joint evaluation of the regressors, LCR and LSR, the logarithm of the likelihood function of the model with restrictions and without restrictions respectively.