Being white is a full time job? Explaining skin tone gradients in income in Mexico

Figures & data

Table 1. Descriptive statistics of the continuous variables of interest.

Variable	Min	Max	Mean	Std Dev
Reported monthly income	640	60,000	6,369.780	5,794.757
Logged reported monthly income	6.461	11.002	8.487	0.724
Skin tone	1	11	6.634	1.433
Age	18	65	39.180	11.770

Source: INEGI (2018). N = 15,188, unweighted data

Table 2. Descriptive statistics of the categorical variables of interest, 2017.

Variable	Category	N	Pct
Education	Lower	3,404	22.412
	Middle	8,409	55.361
	Higher	3,375	22.221
Occupational status	Elementary	3,142	20.687
	Plant and machine operators	1,639	10.791
	Craft and related trades work	2,038	13.418
	Skilled agriculture and fishery	457	3.009
	Service work, shop and market sales	1,576	10.377
	Clerks	2,027	13.346
	Technicians and associate professionals	1,078	7.098
	Professionals	2,588	17.040
	Managers, senior officials and legislators	643	4.234
Region	Noroeste (North-West)	2,610	17.185
	Noreste (North-East)	1,226	8.072
	Occidente (West)	1,752	11.535
	Oriente (East)	2,654	17.474
	Centronorte (Central-North)	2,638	17.369
	Centrosur (Central-South)	1,133	7.460
	Suroeste (South-West)	1,283	8.447
	Sureste (South-East)	1,892	12.457
Gender	Man	8,690	57.216
	Woman	6,498	42.784

Source: INEGI (2018). N = 15,188, unweighted data.

Figure 1. The distribution of income in ENADIS 2017.

Note: N = 15,188; unweighted data.

Figure 2. Measuring skin tone in ENADIS 2017 and its distribution.

Note: N = 15,188; unweighted data.

Figure 3. The relationship between reported monthly income, skin tone, and the relevant mediating variables.

Table 3. Log of reported monthly income regressed on skin tone and relevant variables.

	Model 1: Controls	Model 2a: Education	Model 2b: Skills	Model 2c: Region	Model 3: Full
Intercept	8.292*** (0.074)	7.893*** (0.073)	8.033*** (0.062)	7.952*** (0.077)	7.581*** (0.067)
Skin tone	0.058*** (0.010)	0.024** (0.009)	0.028** (0.008)	0.048*** (0.009)	0.012 (0.008)
Education (Ref: Primary)
Secondary		0.354*** (0.028)			0.205*** (0.025)
Tertiary		1.110*** (0.035)			0.695*** (0.037)
Occup. (Ref: Elementary)
Operators			0.423*** (0.032)		0.310*** (0.030)
Craft and trade			0.276*** (0.047)		0.236*** (0.042)
Agriculture and fishery			−0.216*** (0.057)		−0.139** (0.053)
Service and sales			0.336*** (0.046)		0.247*** (0.048)
Clerks			0.197*** (0.047)		0.092* (0.044)
Technicians			0.735*** (0.048)		0.517*** (0.050)
Professionals			0.955*** (0.036)		0.565*** (0.038)
Managers			1.306*** (0.067)		0.837*** (0.064)
Region (Ref: Suroeste)
Sureste				0.332*** (0.045)	0.285*** (0.037)
Centrosur				0.423*** (0.066)	0.308*** (0.052)
Centronorte				0.453*** (0.043)	0.380*** (0.034)
Oriente				0.121* (0.047)	0.123** (0.039)
Occidente				0.497*** (0.052)	0.452*** (0.041)
Noreste				0.634*** (0.045)	0.499*** (0.038)
Noroeste				0.566*** (0.042)	0.474*** (0.034)
R^2	0.057	0.294	0.317	0.122	0.418

* p < 0.05; ** p < 0.01; *** p < 0.001. Entries represent regression parameters (with robust standard errors between brackets). Models are controlled for age and gender. N = 15,188. Population weights apply. Source: INEGI (2018).

Figure 4. The impact of education, occupational status and region on the regression coefficient of skin tone on reported monthly income.

Figure 5. The impact of education, occupational status and region on the regression coefficient of skin tone dichotomized on reported monthly income, reduced dataset.

Table 4. Log of equivalized HH income regressed on skin tone and relevant variables.

	Model 1: Controls	Model 2a: Education	Model 2b: Skills	Model 2c: Region	Model 3: Full
Intercept	7.657*** (0.066)	6.937*** (0.077)	7.096*** (0.088)	7.338*** (0.079)	6.443*** (0.094)
Skin tone	0.054*** (0.007)	0.031*** (0.006)	0.041*** (0.007)	0.041*** (0.007)	0.017** (0.006)
Education (Ref: Primary)
Secondary		0.536*** (0.037)			0.419*** (0.034)
Tertiary		1.221*** (0.054)			0.974*** (0.054)
Occ. status (Ref: Element)
Industrial & Skilled			0.640*** (0.070)		0.394*** (0.066)
Service and sales			0.591*** (0.063)		0.410*** (0.053)
Technicians and associate professionals			1.033*** (0.084)		0.630*** (0.073)
Professionals			1.339*** (0.098)		0.593*** (0.096)
Managers			0.508*** (0.073)		0.371*** (0.063)
Item nonresponse			0.380*** (0.063)		0.205*** (0.053)
Region (Ref: Suroeste)
Sureste				0.318*** (0.072)	0.313*** (0.063)
Centrosur				0.564*** (0.068)	0.461*** (0.059)
Centronorte				0.257*** (0.070)	0.278*** (0.060)
Oriente				0.054 (0.071)	0.102 (0.060)
Occidente				0.549*** (0.066)	0.567*** (0.057)
Noreste				0.713*** (0.065)	0.636*** (0.056)
Noroeste				0.628*** (0.066)	0.581** (0.057)
R^2	0.032	0.211	0.151	0.119	0.308

* p < 0.05; ** p < 0.01; *** p < 0.001. Entries represent regression parameters (with robust standard errors between brackets). Models are controlled for age and gender. N = 9,876. Population weights apply. Source: CONAPRED (2011).

INEGI. 2018. Encuesta Nacional Sobre Discriminacion 2017. ENADIS: Diseno Muestral. Mexico: Aguascalientes.

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CONAPRED. 2011. National Survey on Discrimination in Mexico: Enadis 2010. Overall Results. Mexico: Consejo Nacional Para Prevenir la Discriminación.

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