Andres Nigenda
Mexico is a very unequal country. Historically, southern Mexico, the more indigenous and darker skinned part of the country, has lagged in many social indicators when compared to northern Mexico. These noticeable regional differences are also true at an individual level nationwide: skin tone seems to be a strong determinant in many social outcomes such as educational attainment.
This work makes a visual connection between skin color, social outcomes and regional differences in Mexico. It builds on the existing work by some academics and institutions interested in bringing more visibility to the issue of how skin color affects every day life in Mexico.
In september 1821, independence broke with de jure racist caste system. Almost 200 years later, the Mexican government has a lot of pending work in breaking the de facto racist and classist structures that reinforce phenotype driven gaps.
Two surveys were instrumental in exploring this subject: Mexico's National Institute of Statistics and Geography (INEGI) 2016 Module on Intergenerational Social Mobility (MMSI) and the 2015 Mid-Census Survey. The former survey is nationally representative for the population aged 25 to 64 and the latter is representative down to the locality level. The data for the Mid-Census Survey was restricted to population aged 25 to 64.
title_1 = 'Southern Mexico is darker skinned than the North...'
subtitle_1 = '67% of Mexicans think their skin tone is around the middle of the scale'
title_2 = 'and it has significantly less years of schooling'
subtitle_2 = 'In the South, the average Oaxacan has less than a middle school education'
compare_map_variables(mean_skintone, mean_schooling, 'val_m', 'state', 'states', title_1, subtitle_1, title_2, subtitle_2, 'Skin tone', 'Years')
Source: Module on Intergenerational Social Mobility (MMSI) 2016
All Northern states in Mexico have, on average, over 10 years of schooling and are relatively lighter-skinned; some, like Nuevo Leon, have an attainment equivalent to three years of college. Oaxaca, Chiapas and Guerrero, Mexico's poorest states, are among the darkest-skinned states and have around 8 years of schooling on average, which is almost equivalent to a middle school education.
title = 'Mexico is a mixed country where color and race do not follow a one-to-one relationship'
subtitle = '14% of the population self-identify as Indigenous, 3% as Black, 11% as White and 61% as Mixed, but many skin tones coexist within each racial category'
notes = 'Notes: The purple line represents total population and the x-axis is log-scaled'
y_title = 'Population'
facet_bars(df_race_skintone, 'race_', 'skintone_self', title, subtitle, notes, y_title)
Source: Module on Intergenerational Social Mobility (MMSI) 2016
Skin tone and race are intertwined realities that hold different associations with social outcomes. These associations are not linear. Although largely brown, Mexico is a diverse country with a continuum of skin tones. In a more equal society, we would expect different skin tones to be proportionally represented in different outcome metrics. We will show how this is not the case for Mexico.
title_1 = 'In Oaxaca, educational attainment is heterogenous...'
subtitle_1 = 'Less saturated municipalities have lower years of schooling'
oax_geojson = 'https://raw.githubusercontent.com/andresnigenda/capp-30239-w20-A01/master/nyu-2451-36995-geojson.json'
title_2 = 'but it seems to be lower in more indigenous regions'
subtitle_2 = 'More saturated municipalities have larger indigenous populations'
nl_geojson = 'https://raw.githubusercontent.com/andresnigenda/capp-30239-w20-A01/master/nyu-2451-36983-geojson.json'
compare_states(mean_schooling_oax, mean_indigenous_oax, 'val_m', 'MUN', title_1, subtitle_1, title_2, subtitle_2, 'Schooling (years)', 'Indigenous percentage (%)', oax_geojson, oax_geojson, continuous_scale, continuous_scale_2)
Source: Encuesta Intercensal 2015
If we focus on regional differences and race within one of Mexico's poorest states, Oaxaca, we can notice an inverse relationship between a municipality's educational attainment and its percentage of indigenous population. Most indigenous people self-identify as having darker skin tones. Although this work does not focus on outcomes by race, it is important to acknowledge these realities when talking about skin tone.
title = 'Darker skin tones have consistently lower schooling than lighter ones'
subtitle = 'The lighter skinned someone is, the more likely they are to have a college education'
x_title = 'Skin tone'
y_title = 'Schooling (years)'
plot_ridge(df_mmsi, 'schooling_yrs', 'skintone_self', title, subtitle, x_title, y_title)
Source: Module on Intergenerational Social Mobility (MMSI) 2016
Darker skin tones have consistently less years of schooling than lighter skin tones across the whole distribution. In Mexico, skin tone seems to accord some the access to more education than others. However, these differences in access are not limited to education, they reach many other aspects of a Mexican's life.
title = "There is no clear pattern of upward educational mobility for darker skin tones"
subtitle = "Newer generations are more educated across the board, but are gaps being reduced?"
x_title = "Father's schooling (years)"
y_title = "Schooling (years)"
legend_title = "Skin tone"
notes = "Notes: The purple line divides the chart into upward and downward mobility triangles. The color represents the average skin tone for each bin"
plot_heatmap(df_mob, 'schooling_father_yrs', 'schooling_yrs', 'val_m', title, subtitle, x_title, y_title, legend_title, notes)
Source: Module on Intergenerational Social Mobility (MMSI) 2016
An overwhelming number of darker bins in the upper triangle would be a strong sign of equity-inducing upward mobility education wise. This does not seem to be happening in Mexico. If anything, the darkest bins appear to be in the downward mobility triangle, which is worrysome.
title = "Skin tone is also associated with differentiated household access to goods and services"
subtitle = "The purple bar shows how all skin tones have seen improved access since age 14, but these improvements have not equalized access"
access_var = 'access'
weights = 'wt'
access_chart(df_access, access_var, weights, title, subtitle)
Source: Module on Intergenerational Social Mobility (MMSI) 2016
If we look at a bundle of different goods and services, access among darker skin tones is, again, consistently trailing lighter skin tones. For instance, only 14% of the darkest skinned people had a car, 31% had a bank account and 55% had access to tubed water in their house. These rates are 38%, 45% and 77% respectively for the lightest skinned people in Mexico. When we look at the same indicators at age 14, we notice improvements for all skin tones, but these improvements barely closed the gaps between skin tones.
title = 'Lighter tones are overrepresented at higher levels of self-perceived socioeconomic level'
subtitle = 'Almost 20% of the highest socioeconomic level has light skin tones, their share in the total population is 12%'
x_title = 'Share of the level'
y_title = 'Socioeconomic (level)'
plot_stacked(df_mmsi, 'sociolevel', 'skintone_self', 'wt', title, subtitle, x_title, y_title)
Source: Module on Intergenerational Social Mobility (MMSI) 2016
As we previously saw, darker skin tones are consistently worse off than lighter skin tones in educational, access to goods and services and social mobility measures. When we look at self-perceived socioeconomic levels, which capture wealth, education and social "status" we notice a systematic overrepresentation of lighter skin tones at the highest level of socioeconomic status. Only .8% of the population that we are analyzing identify as being part of this level. If we were to look at income, these differences would probably be more dramatic.
title = "Less than half of the two darkest skin tones perceived improvements in their socioeconomic situation from age 14"
subtitle = "More than half of the rest of the groups perceived improvements in their socioeconomic situation when compared to their situation at age 14"
notes = "Notes: Changes in socioeconomic situation are self-perceived"
diverging_bars(df_diverging, title, subtitle, notes)
Source: Module on Intergenerational Social Mobility (MMSI) 2016
Perception does not hold a linear relationship with reality, but it does allow us to understand how people perceive their own life in the context of a society. In Mexico, perception around skin tone and social outcomes echoes uneven realities. More than 50% of the darkest skinned tones perceive that their socioeconomic situation has either worsened or not changed vis a vis their situation at age 14.
Final notes: