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Geography of Income and Education Inequalities in Mexico: Evidence from Small Area Estimation and Exploratory Spatial Analysis

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Abstract

This article examines the spatial distribution of income and education inequalities and their association in Mexico, focusing on the municipal level. We rely on a small area estimation methodology to construct measures of income inequality that are representative at the municipal level. We also construct variables accounting for education inequality. Based on these variables and on an exploratory spatial analysis, we emphasize a negative association between income and education inequalities, particularly salient among the poor and ethnically diverse municipalities from the southern states such as Oaxaca. Moreover, results from spatial econometrics analyses reveal the existence a U-inverted association between these two types of inequalities. Our results are discussed in relation to education returns, employment opportunities and migration.

Résumé

Cet article analyse la distribution spatiale des inégalités de revenu et d’éducation ainsi que leur association à l’échelle des municipalités du Mexique. Nous mobilisons les méthodes de small area estimation afin de construire des mesures d’inégalité de revenu représentatives à l’échelle municipale. Nous construisons également une mesure d’inégalité d’éducation. A partir de ces variables et d’une analyse spatiale exploratoire, nous mettons en évidence une association négative entre les inégalités de revenu et d’éducation, particulièrement marquée dans les municipalités pauvres et ethniquement fragmentées des états du sud. De plus, l’estimation de modèles d’économétrie spatiale révèle l’existence d’une association en U-inversé entre ces deux types d’inégalités. Nos résultats sont discutés en relation avec les questions de rendements d’éducation, d’opportunités d’emploi et de migration.

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Data availability

The data that support the findings of this study are available on request from the corresponding author.

Notes

  1. As argued by Gasparini (2003, pp. 53–54), the empirical literature “unambiguously suggests that Latin America is the region with the highest levels of inequality in the world, and that this has been true for as long as statistics have been kept”. Focusing more specifically on the Mexican case, Corbacho and Schwartz (2002) explain that income inequality in Mexico is significantly higher than the Latin American average.

  2. According to Barbary (2015), the indigenous populations are primarily located in the most remote areas (which impedes their access to productive resources) and have not benefited from migration dynamics to improve their living conditions.

  3. Data available at: http://dgeiawf.semarnat.gob.mx:8080/ibi_apps/WFServlet?IBIF_ex=D1_POBREZA00_27&IBIC_user=dgeia_mce&IBIC_pass=dgeia_mce&NOMBREENTIDAD=*&NOMBREANIO=*.

  4. As explained by Haslett and Jones (2008), in the successful applications of ELL method, the R-squared value of the welfare model tends to be about 0.50 or higher. Our examination of numerous SAE implementations reveals that most of them are based on a welfare model with an adjusted R-squared between 0.5 and 0.7 (e.g. Cuong et al. 2010; World Bank 2015). Moreover, Haslett and Jones (2008) also explain that the variance of the municipal-level component of the error term \({\eta }_{m}\) should be as small as possible. Many SAE applications are based on welfare models with a variance lower than 0.05.

  5. Our calculation of the Gini index is based on the following formula:

    $$G=1+\frac{1}{n}-\frac{2}{\stackrel{-}{y}n^{2}}{\sum }_{i=1}^{n}\left(n+1-i\right){y}_{i}$$

  6. The class of entropy indices is defined with the following formula:

    $$GE\left(\alpha \right)=\frac{1}{\alpha^{2}-\alpha }\left(\frac{1}{n}{\sum }_{i=1}^{n}{\left(\frac{{y}_{i}}{\stackrel{-}{y}}\right)}^{\alpha }-1\right)$$

    The parameter represents the weight given to distances between incomes in different parts of the income distribution. For lower (higher) values of, GE() is more sensitive to income changes in the lower (upper) tail of the distribution. Using the most common values for (0, 1 and 2), three main indices can be derived: the mean log deviation GE(0), the Theil index GE(1) and half the squared coefficient of variation GE(2).

  7. The Moran’s I statistic is a correlation coefficient measuring the overall spatial autocorrelation of a dataset.

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  1. GREThA (CNRS), University of Bordeaux, Avenue Léon Duguit, 33608, Pessac Cedex, France

    Matthieu Clément & Lucie Piaser

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Appendix

Appendix

See Tables 5, 6, 7, 8 and 9; Figs. 5, 6, 7, 8, 9, 10 and 11.

Fig. 5
figure 5

Source Authors’ calculations based on CONEVAL data

Income Gini index (CONEVAL estimates), 2015.

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Fig. 6
figure 6

Source Authors’ calculations

Income Gini index (own estimates), population-weighted quartiles, 2015.

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Fig. 7
figure 7

Source: Authors’ calculations

Income Gini index (CONEVAL estimates), population-weighted quartiles, 2015.

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Fig. 8
figure 8

Source Authors’ calculations

Mean per capital household income (monthly pesos), 2015.

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Fig. 9
figure 9

Source Author’s calculations

Index of ethno-linguistic fractionalization (NGV), 2015.

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Fig. 10
figure 10

Source Authors’ calculations based on CONEVAL data

Local spatial autocorrelation analysis (LISA) for the income Gini (CONEVAL estimates), 2015.

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Fig. 11
figure 11

Source Authors’ calculations

Municipalities with the highest levels of education inequality (top-11%).

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Table 5 Crosstab analysis between quartiles of income Gini (own estimates) and education Gini, 2015
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Table 6 Crosstab analysis between quartiles of income Gini (CONEVAL estimates) and education Gini, 2015
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Table 7 Crosstab analysis for local spatial autocorrelation between income Gini (own estimates) and education Gini, 2015
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Table 8 Crosstab analysis for local spatial autocorrelation between income Gini (CONEVAL estimates) and education Gini, 2015
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Table 9 Econometric estimates (SARAR models) of the relationship between income inequality (CONEVAL estimates) and education inequality
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Clément, M., Piaser, L. Geography of Income and Education Inequalities in Mexico: Evidence from Small Area Estimation and Exploratory Spatial Analysis. Eur J Dev Res 34, 703–732 (2022). https://doi.org/10.1057/s41287-021-00386-0

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