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What Causes Skin Tone Disparities in Human Capital?
Johnny HuynhPomona College
Mira HowardPomona College
Version: April 2014
AbstractThis paper estimates the e�ect of skin color among African Americans and Hispanics onhuman capital accumulation before entering the labor market. We track schooling outcomesfor respondents ages 14 to 21 in the NLSY97. Proxying skin color as a child using skin colormeasured as an adult, our specification identifies how skin color a�ects AFQT scores andcompleted schooling levels. We find strong evidence of a human capital di�erential betweenthe light- and medium-skinned students in both racial/ethnic groups. After controlling forhousehold characteristics and school quality, light-skinned African Americans and Hispanicsscore 0.18 standard deviation higher on the AFQT than their medium-skinned counterparts.This di�erential is not apparent between medium- and dark-skinned students. Our analysisfor schooling levels also indicate a persistent disparity in human capital, especially past age18. Our findings revisit the long-studied racial gap in human capital, and put into lightdisparities within racial and ethnic group.
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1 Introduction
Race is a significant predictor of economic wellbeing in the United States, making its
inclusion in empirical studies crucial. Race is so engrained in our social psyche that even the
least educated of our society understand its importance within the economic arena. However,
despite its obvious social significance, social science does not o�er an objective definition of
race, complicating its measurement. One proposed definition, found in Harris (2002), uses
three categories: genotype (ancestry and genetic background); phenotype (observable and
generally immutable characteristics); and culture (behavior). While this definition is by no
means universal, it does convey the important idea that race is not static. It is a function
of the individual, other’s perceptions and time.
Understanding this concept that the perceptions of race change over time, it becomes
apparent that the term "African American" may assume di�erent connotations now than it
has in prior decades. While we tend to think of African American as a definite category,
there are plenty of Americans that appear racially ambiguous. Their inclusion into the
African American category is entirely contingent upon whether they want to be societally
identified as such. A diminished racial sigma may increase the number of people who self
identify as African American, as would an increased sense cultural pride. If these people are,
on average, wealthier than those who have always self-identified as African American, then
their measures of wellbeing among African Americans will be artificially inflated.
Economists have studied race and economic well being, particularly in the labor market,
since Gunnar Mydral published his seminal An American Dilemma in 1944. Conventional
data on race use indicator variables. These data have been self-declared since 1960, when
the United States Census allowed respondents to self-declare race. But if race is an aggre-
gate of genotype, phenotype, and culture, then utilizing one-dimensional measures of race
ignores nuanced channels through which race a�ects economic wellbeing. We are beginning
2
to realize now the wide variation that exists, not just from race to race, but also within races
themselves.
The objective of this paper is to determine if, and to what extent, phenotypic race a�ects
human capital development. Specifically, we are curious as to what the e�ects of skin tone
on test scores are, and how they a�ect highest grade completed (HGC). We disaggregate
racial groups using skin tone data. The benefits of using skin tone are two-fold. First, skin
tone is exogenous and invariant to time. For the most part, individuals do not determine
their skin tone. Recall the case when black people self-identify as African American because
of an exogenous reduction in stigma. Using skin tone instead of race does not su�er from
this endogeneity because it is orthogonal to stigma. Since measurements of skin tone do
not change over time, they are also preferable for time trends analysis. Second, markets
discriminate based on observable traits. Skin tone data lend themselves to studying racial
discrimination because individuals more easily discern skin tone than genotypic history or
culture.
When we choose only to look at race through a linear lens, in which people are either
one race or another, we deprive ourselves of so much data. The reality is that people do
not come in shades of black, brown, or white. Rather they belong to a whole range of skin
tones, each of which communicates a di�erent genetic history, and a di�erent set of societal
perceptions. By allowing this range of skin tones to be explored, we grant ourselves access
to a greater depth of racial understanding than ever before.
While the concept of using skin tone is not entirely new, within the context of economic
research, it is a relative novelty. While we cannot be sure as to what our results will be, if
other studies of skin tone are to be any hint, we have good reason to suspect that lighter skin
tones will have better success with regards to human capital development for many of the
same reasons that we would suspect that whites have superior human capital development
than African Americans. However, this paper is the first of its kind to look specifically at
3
test scores, so the results may not necessarily follow the trends of previous studies (though
we have good reason to suspect they will).
This paper begins with a survey of the literature on racial and skin tone disparities
in human capital production in Section II. Literature on skin tone is explored both for
African Americans, and for Hispanics. Section III describes the data used in great depth,
allowing for a clear understanding of who is included in the data, and how their skin tone
is both defined, and determined. Section IV introduces the empirical methodology, setting
up and understanding of the results in section V. Section V provides an interpretation and
discussion of these results, explaining them and giving them context. Finally, section VI
concludes, providing a brief summary of all sections just mentioned, in addition to framing
the reasons why the results are meaningful, and any implications thereof.
2 Review of the Literature
Economists have long studied the e�ects of race on test scores and educational achieve-
ment. While the vast empirical literature has focused on the educational di�erences be-
tween African Americans and whites, there is a growing body of research on the educational
achievements of Hispanics as well. While household and school factors, such as socioeco-
nomic status and school quality, correlate with di�erentials in test scores between African
Americans, Hispanics, and whites, other factors, such as proficiency in the English language,
have been found to be apply to Hispanics or African Americans specifically.
Additionally, perhaps due to the relatively smaller body of literature on Hispanic edu-
cational achievement, most literature with regards to skin tone—not simply race—and its
e�ects on test scores and educational achievement focuses on the African American popula-
tion compared to the white population.
4
2.1 African Americans and Educational Achievement
On average, African American students underperform other racial and ethnic groups in the
United States (McWhorter 2000). While the black-white test score gap can be detected from
as early on as kindergarten, at that young age it can be, for the most part, accounted for
by basic child and family characteristics, such as socioeconomic status (Fryer and Levitt,
2004). However, by the end of third grade, not only does the black-white educational gap
substantially widen, but it also becomes no longer explicable by household characteristics
alone (Fryer and Levitt, 2006). This would suggest that some other factors play into this
black-white test score gap other than socioeconomics status, family, child, and household
characteristics.
While there is no single, empirically supported theory as to exactly what might be con-
tributing to the widening of the black-white test score gap over the course of the schooling
process, there do exist several established possible explanations. Perhaps the most estab-
lished of these explications is the "culture of poverty" theory. This theory suggests that
there exists a somewhat "anti-intellectualist" culture amongst African-Americans, and that
this philosophy has spawned a cultural disconnect from learning and education, in addition to
condoning single-mother families, and perpetuating the poverty cycle (Loury 1985). While
this theory has no strong empirical grounds, it is the oldest, and most broadly accepted
theory explaining the widening of the black-white test score gap over time. Hopefully, as re-
search on the black-white education gap continues developing in breadth and sophistication,
a more conclusive theory will be reached.
One way in which this sophistication is being developed is by the abandonment of the
conventional paradigm of singular and separate races. This is the area in which our paper
specifically strives to contribute. Essentially, what this means is understanding that there is
no one "black" race, and that within the whole group of persons who identify as black, there
is a spectrum of skin colors, ranging from darker to lighter. What previous race-education
5
research has failed to recognize is that gains in educational achievement since the Civil Rights
movement have been largely the result of gains in educational achievement of the lightest
skinned blacks, and that there are large gaps in equality between lighter skinned blacks and
their darker skinned counterparts. For instance, in one study (Loury, 2009), when divided
into groups of light, medium, and dark skin, by the time they reached 36, the darkest skinned
individuals were educated a statistically significant 0.88 years less than their light skinned
counterparts. This penalty was reduced to 0.8 for the medium skinned group. Another
study with statistically significant results (Hersch, 2006) divides skin-tone into four groups
and finds a -1.156 year di�erence in education levels between the darkest and the lightest
group, a -0.534 di�erence between the second darkest and the lightest group, and a -0.403
di�erence between the third darkest and the lightest group.
By accounting for skin-tone, researchers eliminate the risk of understating the educational
repercussions for some blacks, and overstating the educational gains for others (Loury, 2009).
Possible mechanisms by which di�erent skin-tones within a race might achieve di�erently in
terms of education include levels of perceived attractiveness—where one end of the skin-tone
spectrum is perceived as more attractive than the other—and di�erential treatment based on
skin-tone, in which one end of the skin-tone spectrum is treated as though they are smarter,
or better in general (Hersch, 2006).
2.2 Hispanics and Educational Achievement
While the literature on the e�ects of skin-tone variations for Hispanics is somewhat less
developed, there still exist a substantial number of studies that demonstrate statistically
significant di�erences between the lighter and darker ends of the skin-tone spectrum. Two
such studies, Telles and Murguia (1988) and Murguia and Telles (1996), found that the
lightest skinned—and consequently least indigenous looking—group of Hispanics achieved
1.5 more years of education than their darker skinned counterparts—note how similar this
6
number is to those of the studies done on African Americans. This di�erence in years of
education between the lightest and the darkest skinned Hispanics could not be accounted for
by other educational achievement and human capital correlates. As with African-Americans,
mechanisms such as attractiveness and di�erential treatment might be at play.
3 Data
The sample draws from the National Longitudinal Study of Youth 1997 (NLSY97). The
NLSY97 is a panel data set that annually follows a nationally representative sample born be-
tween 1981 and 1985. The sample includes detailed observations of household characteristics
with an emphasis on human capital and labor market outcomes. We use a subset of the data
from 1997 (the baseline) to 2011, and restrict the data to respondents who self-identified
as non-Hispanic African American1 or Hispanic (any race) men and women in 1997. The
remaining subsample consists of 2,335 African Americans and 1,901 Hispanics per year.
In 2008, the NLSY97 began collecting data about each respondent’s skin tone. Skin tone
was ranked from 0 (lightest) to 10 (darkest). Surveyors were asked to memorize an identical
scale shown in Figure 1. Skin tones were rated without using the scale during interviews.
The majority of skin tone data were collected in 2008. Those interviewed by phone in 2008
had their skin tone recorded in subsequent years.
Since our analysis investigates human capital accumulation from schooling, which occured
almost entirely before 2008, we cannot observe skin tone during childhood and adolescence.
Given the panel structure of the NLSY97, however, we retroactively match skin tone data
collected in or after 2008 using a unique identifier. This allows us to recover information
about skin tone during human capital accumulation.
Although skin tone as an adult is correlated with skin tone as a child, we acknowledge
1We now refer to non-Hispanic African Americans as African Americans unless otherwise stated
7
that skin color is not entirely time-invariant. If the di�erence in skin tone as an adult and
as a child is random noise, the data are measured with classical error. In this case, our
estimates are simply attenuated. The majority of our empirical work, however, divides skin
color into percentile groups. This analysis only requires rank preservation. For instance, if a
child’s skin tone were in the darkest tercile relative to her child peers, then her skin tone as
an adult should be in the darkest terciles relative to her adult peers. The rank preservation
assumption is more innocuous than assuming perfect correlation between skin color as an
adult and child.
4 Empirical Strategy
The motivating question of our analysis is to understand the factors that contribute to
the human capital disparities within race and by skin tone. Given the intrinsic di�erence
between African Americans and Hispanics, we model the relationship between human capital
and skin tone separately for the two groups.
We pool men and women for two reasons. Since Hispanics comprise less than 25 percent
of the sample, we lose a lot of statistical power if we disaggregate by gender. Second, unlike
studies of wage or labor force participation, young men and women cannot opt-out of exams
or reporting their schooling levels. We argue that, in the United States, it is equally expected
that men and women attend school, but the same is not true for the labor maket. Since we
limit our scope to learning and schooling outcomes, we follow the large body of literature
that pools young men and women.
4.1 Cross-Sectional Analysis
To estimate the e�ect of skin tone on human capital, we begin with a reduced-form linear
regression of the following type.
8
yi = —
Õf(SkinTonei) + „1X1i + „2X2i + ‘i
where Yi is the AFQT score for individual i in 1999. X1i is a vector of individual and
school controls in 1999. These include age, highest grade completed, school type, and an
index for whether the student felt safe at school. X2i is a vector of controls collected at
baseline, including indicators for metropolitan statistical area, citizenship status, household
income-to-poverty ratio, educational attainment of the mother, height, weight, and an index
measuring general health.
In our first specification, SkinTonei is the ordered variable from 0 to 10. f is the identity
map. The coe�cient of interest, — denotes the average change in AFQT given a one unit
increase (darker) in skin tone. A negative value for — implies that darker-skinned individuals,
on average, have lower AFQT scores.
The second specficiation divides the sample into terciles of the skin tone distribution.2
f(SkinTonei) is a vector of two dummy variables indicating the darkest and lightest skin
tone terciles. — estimates the e�ect of having the darkest or lighest skin tone relative to the
median. Dividing skin tone into tercile dummies captures nonlinearities in how skin tone
a�ects AFQT.
4.2 Panel Analysis
We take advantage of the panel structure of the NLSY97 by investigating time trends.
Data for highest grade completed are collected annually, so we pool observations by individual
and time. Our identification relies on interactions between skin tone and age.
yit = —
ÕAgeit ú f(SkinTonei) + “Ageit + „Xi + ‘it
2We follow Kreisman and Rangel (2014) in using terciles. However, specifications using di�erent percentilegroups reveal similar results.
9
where Yit and Ageit are the highest grade completed and age, respectively, for individual i at
year t. Xi is a vector of controls collected at baseline, including indicators for metropolitan
statistical area, citizenship status, household income-to-poverty ratio, educational attain-
ment of the mother, height, weight, and an index measuring general health.
The panel specification divides skin tone into three terciles as in the cross-sectional anal-
ysis. We similarly omit medium skin tone, so estimates are relative to the median group.
We also limit our analysis to ages 14 to 21 because we see little change in schooling after
age 21. —, therefore, is a matrix of estimators of size two (the lightest and darkest terciles)
by eight (number of ages in the sample).
5 Results
Summary statistics for African Americans disaggregated by skin tone terciles are reported
in Table 1. Light skinned African Americans are better o� under every measure of health
and economic wellbeing. Dark skinned African Americans are the worst o�. While this
pattern of skin tone and wellbeing is consistent, the e�ects individually are not statistically
significant.
Table 2 reports summary statistics for Hispanics disaggregated by skin tone terciles. We
similarly notice that light skinned Hispanics have better health and economic wellbeing.
As with African Americans, di�erences in wellbeing by skin tone are rarely statistically
significant, except for household size.3 Light-skinned Hispanic households have, on average,
0.26 fewer people relative to medium-skinned Hispanics.
We notably see large disparities in AFQT scores and highest grade completed between
light- and medium-skinned African Americans and Hispanics. This human capital disparity is
3Dark-skinned Hispanics are slightly older than medium-skinned Hispanics. However, the di�erence is sosmall that we attribute statistical significance to pure chance.
10
not present between medium- and dark-skinned individuals. Figure 2 plots the kernel density
of AFQT scores for African Americans by skin tone terciles. The density for light-skinned
African Americans has a thicker right-hand tail, whereas the densities for medium- and
dark-skinned African Americans are very similar. The AFQT kernel density for Hispanics in
Figure 3 reveal the same result, though the AFQT di�erences between light- and medium-
skinned Hispanics are more robust. Figure 2 and 3 indicate that, prima facie, light-skinned
individuals possess more human capital than medium- and dark-skinned individuals. The
following analysis attempts to explain these disparities.
5.1 AFQT Scores
Tables 3 and 4 present statistics on the di�erences in AFQT score by aggregate skin tone
for African Americans and Hispanics, respectively. Through a series of six regressions, both
tables present the point-di�erence in AFQT scores in response to a unit increase in skin-tone.
Recall that a one-unit increase in skin tone represents a skin tone that is one shade darker.
Column (1) regresses AFQT score on skin tone. Column (2) controls for age and highest
grade attended, while column (3) iteratively controls for metropolitan statistical area and
citizenship status. Column (4) also controls for household socioeconomic status. Column
(5) adds health controls, and column (6) includes school quality.
All estimates in Table 3 are significant at the 99% level, and all the estimates in Table
4 are significant at the 99% level with the exception of column 6 which is significant at the
95% level.
From Table 3, we see that, with nothing else controlled for, African Americans score
1506 points less on the AFQT for every unit increase in skin tone. That is, the darkest
skinned African Americans, who have a skin tone of 10, score 1506 points less than the
second darkest skinned African Americans, who have a skin tone of 9. This value decreases
to 1178 for column (2), signifying that grade level accounts for 400 points of this disparity.
11
Controls in column (3) to column (7) do not significantly alter the AFQT skin tone
disparity. The results makes clear that the largest observable factor for African Americans
with regards to di�erences in AFQT scores by skin tone is highest grade completed.
Table 4 shows the corresponding results for Hispanics. For the uncontrolled regression,
column (1), Hispanics score 2358 points less on the AFQT for every unit increase in skin tone.
This result is 1000 points larger than the uncontrolled testing gap for African Americans.
As with African Americans, however, this value decreases to 1531 in column (2), signifying
that age and highest grade completed accounts for one-fourth of this disparity.
This value decreases in column (4) to 1499, which suggests that household socioeconomic
status has a minor e�ect on the Hispanics skin tone disparity. The second substantial change
is in column (6), which falls from 250 points, which can be attributed to school quality. The
final, controlled gap for Hispanics is over 100 points greater than the corresponding African
American value.
While there is one defining factor for African Americans—age and highest grade completed—
there are two major controls for Hispanics. The 800-point jump from (1) to (2) suggests age
and highest grade completed is a major contribution to the AFQT gap, as with African
Americans. However, the nearly 300-point jump from (5) to (6) suggest that school quality
also plays a major role in the point gap for Hispanics.
Tables 5 and 6 investigate di�erences in AFQT score by skin tone disaggregated into three
terciles—dark, medium and light. Using the medium skin tone as the reference, these models
perform the same six regressions as Tables 3 and 4 for African Americans and Hispanics,
respectively. For the light skinned category, regressions (1) and (4) are significant at the
99% level, while regressions (2), (3), (5), and (6) are significant at the 95% level in Table 5.
No estimates are significant for the dark skinned category.
Similarly, in Table 6, the light skinned regressions for (1), (2), (3), and (6) are significant
at the 99% level, while regressions (4), and (5) are significant at the 95% level. None of the
12
dark skinned regressions yield significant results.
In Table 5, the uncontrolled regression (1), shows a 4599 di�erence between the AFQT
scores of the lightest skinned group and the medium skinned group. This means that the
lightest skinned group scored 4599 points higher on the AFQT than their medium skinned
counterparts. While insignificant, it also shows a 1306 di�erence between the darkest skinned
group and the medium skinned group, meaning that the darkest skinned group scored 1306
less than the medium skinned group. Regression (2) decreases the light skinned groups point
margin to 3287, signifying that age and schooling level—as we found in Table 3—account
for a large portion of the skin tone AFQT point disparity. Regression (3) has results almost
identical to (2), suggesting that geography and citizenship status do not a�ect the gap.
However, in regressions (4), (5), and (6) the lighter skinned groups scores hover around
4250. The dark skinned group’s scores remain insignificantly di�erent from zero.
These results suggest nonlinearity in AFQT score by skin tone as they di�er so much
from the aggregated skin tones of Table 3. Also unlike Table 3, there does not seem to be
any specific control which explains away the discrepancies in AFQT scores amongst African
Americans. In fact, the highly controlled regression (6) has a value less than 100 points o�
than the uncontrolled regression (1).
Table 6 tells a di�erent story for Hispanics. The uncontrolled regression (1) shows a
6358 di�erence between the AFQT scores of the lightest skinned group and the medium
skinned group. While insignificant, it also shows a 889.8 point di�erence between the darkest
skinned group and the medium skinned group. Regression (2) decreases the light skinned
groups point margin to 5705. In regressions (4), (5), and (6) the lighter skinned groups
scores fluctuate around 4500. The dark skinned group’s scores remain insignificant. These
results also suggest nonlinearity in AFQT score by skin tone as they di�er so much from
the aggregated skin tones of Table 4. However, unlike the results for African Americans, the
test score gap between the lightest and the medium skinned tone is explained in Hispanics
13
by two controls: highest grade completed and household socioeconomic status.
5.2 Highest Grade Completed
Tables 7 and 8 present the e�ect of skin tone on highest grade completed over time. The
pooled OLS model is given in section 4.2, and the coe�cients of interest are the interactions
between age and skin tone terciles.
Table 7 reports the skin tone disparity in schooling for African Americans. With no
controls, there is a small, statistically insignificant gap in schooling between light- and
medium-skinned students before age 18, which ranges from 0.07 to 0.16 year of schooling.
After controlling for age, geography, citizenship status, household socioeconomic status and
health, the disparity in highest grade completed completely vanishes.
We show, however, past age 18, a large schooling disparity emerges between light- and
medium-skinned African Americans. This disparity cannot be attributed to geography, citi-
zenship status, household socioeconomic status or health. At age 19, light-skinned African
Americans have 0.28 year of schooling more than medium-skinned African Americans. This
gap is significant at the 99% confidence level, and persists over time.
There is no appreciable di�erence in schooling between medium- and dark-skinned African
Americans, especially after controlling for individual and household factors.
We report the skin tone disparity in schooling for Hispanics in Table 8. Unlike African
Americans, our model detects a small gap in schooling between light-, medium-, and dark-
skinned Hispanics before age 18. However, the gap is significant at the 95% confidence level
only after age 16.
While this gap does not vanish after controlling for age, geography, citizenship status,
household socioeconomic status and health, the largest e�ect is realized after age 18. At age
19, light-skinned Hispanics have 0.23 year of schooling more than medium-skinned Hispanics,
and medium-skinned Hispanics have 0.20 year of schooling more than dark-skinned Hispanics.
14
Contrary to findings for African Americans and the AFQT scores, there is no evidence of
non-linearity past age 18. The schooling di�erence between the lightest and medium skin
tone is equal to the di�erence between the medium and darkest skin tone.
While at age 19, light-skinned African Americans have 0.22 year of schooling more than
dark-skinned African Americans, light-skinned Hispanics have 0.48 year of schooling more
than dark-skinned Hispanics.
6 Conclusion
This paper presented evidence on the relative e�ect of skin tone on human capital ac-
cumulation among young African Americans and Hispanics. The analysis focuses on two
measures of pre-market human capital: AFQT score, which has been a central tenet of the
racial human capital literature since Neal and Johnson (1996), and highest grade completed,
a commonly studied measure of human capital.
The results indicate that a large skin tone human capital disparity exists, and is both
economically and statistically significant. We show that this disparity, however, is nonlinear.
That is, the human capital disparity is entirely explained by the disparity between light- and
medium-skinned students. Light-skinned African Americans and Hispanics score 0.18 stan-
dard deviation higher on the AFQT than their medium-skinned counterparts, whereas this
gap is nonexistant between medium- and dark-skinned students. Similarly, among African
Americans, a similar nonlinear disparity exists for highest grade completed past age 18. At
age 19, light-skinned African Americans have 0.28 year of schooling more than medium-
skinned African Americans, and the e�ect persists as they age. Evidence for Hispanics,
however, reveal a linear schooling disparity. Light-skinned Hispanics have 0.23 year of school-
ing more than medium-skinned Hispanics, and the gap is identical between medium- and
dark-skinned Hispanics.
15
While we would have thought to hold household socioeconomic status as accountable for
the AFQT by skin tone, we find instead that age and highest grade attended account for 400
of the 1500 points, while socioeconomic status only accounts for less than 100 points. This
means that much of the disparity we estimate between skin tones could be largely contingent
on the fact that—for whatever reason—darker skin tones are more often held back in school,
and this is what accounts for the di�erence in test scores.
School Quality also explains 300 of the 2000 point disparity for Hispanics. We believe
this is because language is an issue for new immigrants, and school quality is perhaps one
of the best ways to allow for the language gap between skin tones to be closed. African
Americans are, for the most part, native English speakers, so this could explain why school
quality does not play as large a role in their population.
Our findings revisit the long-studied racial gap in human capital. While between-race
disparities in human capital have been well studied, we show that racial nuances also exists.
Within-race disparities are equally significant but less explored. Given that human capital is
an important predictor of economic well being, and input for economic development, studying
the causes of the skin tone disparities in human capital ought to an important agenda for
economists and policymakers.
16
References
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grade. American Law and Economics Review, 8(2), 249–281.
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plexity of lived race. American Sociological Review, 614–627.
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skin tone e�ects. Economics of Education Review, 28(4), 428–433.
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17
Neal, Derek A, & Johnson, William R. 1995. The role of pre-market factors in black-white
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18
Table 1: Summary Characteristics of African Americans by Skin Tone !!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!
!
Light Medium Dark
Variable Mean p(=Medium) Mean Mean p(=Medium) Household size 4.567 0.554 4.625 4.585 0.704 (1.771) (1.858) (1.659)
Poverty ratio 189.9 0.960 190.5 171.6 0.150 (186.5) (218.4) (153.5)
Mother HGC < HS 0.246 0.433 0.227 0.200 0.297 (0.431) (0.419) (0.400)
Age 13.97 0.537 14.04 13.95 0.336 (1.425) (1.370) (1.407)
Health index 2.009 0.919 2.015 2.038 0.680 (0.970) (0.976) (0.977)
AFQT 32002 0.002*** 27403 26096 0.405 (25317) (23454) (23051)
Highest grade completed 13.36 0.040** 13.16 12.78 0.472 (4.355) (5.338) (2.647)
N 1358 470 261
Notes: All variables except AFQT and HGC were measured in 1997. AFQT was administered in 1999 and HGC was measured in 2011. Standard deviations are reported in parentheses. Mean differences (from medium skin tone) different from zero are reported at the 99% (***), 95% (**) and 90% (*) significance level.
19
Table 2: Summary Characteristics of Hispanics by Skin Tone !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!
Light Medium Dark
Variable Mean p(=Medium) Mean Mean p(=Medium) Household size 4.888 0.015** 5.148 5.018 0.311 (1.616) (1.789) (1.817)
Poverty ratio 184.19 0.278 170.3 160.1 0.492 (165.2) (142.1) (183.8)
Mother HGC < HS 0.149 0.104 0.112 0.132 0.456 (0.356) (0.317) (0.339)
Age 14.94 0.342 13.85 14.00 0.067* (1.421) (1.373) (1.367)
Health index 2.052 0.688 2.075 2.027 0.491 (0.909) (0.925) (0.965)
AFQT 38462 0.001*** 32103 31213 0.689 (26337) (24509) (24635)
Highest grade completed 13.23 0.009*** 13.02 12.47 0.115 (2.589) (5.038) (2.685)
N 716 400 329
Notes: All variables except AFQT and HGC were measured in 1997. AFQT was administered in 1999 and HGC was measured in 2011. Standard deviations are reported in parentheses. Mean differences (from medium skin tone) different from zero are reported at the 99% (***), 95% (**) and 90% (*) significance level.
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Table 3: Differences in AFQT Score by Aggregate Skin Tone of African Americans
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(1) (2) (3) (4) (5) (6)
Skin tone -1506*** -1178*** -1239*** -1160*** -1121*** -1108*** (328.0) (301.4) (302.1) (208.4) (312.7) (331.8)
Controls/Fixed effects
Age/HGC X X X X X MSA/Citizen X X X X
Household SES X X X
Health X
School quality X
R2 0.014 0.235 0.241 0.336 0.342 0.338 N 1473 1400 1389 1160 1127 968 2,057 Notes: The dependent variable is the AFQT score measured in 1999. AFQT for African Americans has mean 28,637 and standard deviation 24,116. Estimates come from an OLS with an ordered variable denoting skin tone (0 being the lightest; 10 being the darkest). The Age/HGC controls are dummies for age and highest grade completed in 1999. MSA/Citizen controls are dummies for metropolitan statistical areas and whether the individual has US citizenship. Household SES controls include mother’s educational attainment and ratio of household income to the poverty level. Health controls are height, weight and a standardized index for general health. School quality controls include dummies for public/private/parochial school and whether the student felt safe at school. Standard errors are given in parentheses. Statistical significance from zero at the 99% (***), 95% (**) and 90% (*) level are reported.
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Table 4: Differences in AFQT Score by Aggregate Skin Tone of Hispanics
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(1) (2) (3) (4) (5) (6)
Skin tone -2358*** -1531*** -1595*** -1449*** -1527*** -1263** (574.2) (558.7) (560.6) (556.1) (567.7) (598.3)
Controls/Fixed effects
Age/HGC X X X X X MSA/Citizen X X X X
Household SES X X X
Health X
School quality X
R2 0.016 0.157 0.168 0.300 0.306 0.300 N 1060 1022 1012 853 824 725 2,057 Notes: The dependent variable is the AFQT score measured in 1999. AFQT for Hispanics has mean 35,093 and standard deviation 25,666. Estimates come from an OLS with an ordered variable denoting skin tone (0 being the lightest; 10 being the darkest). The Age/HGC controls are dummies for age and highest grade completed in 1999. MSA/Citizen controls are dummies for metropolitan statistical areas and whether the individual has US citizenship. Household SES controls include mother’s educational attainment and ratio of household income to the poverty level. Health controls are height, weight and a standardized index for general health. School quality controls include dummies for public/private/parochial school and whether the student felt safe at school. Standard errors are given in parentheses. Statistical significance from zero at the 99% (***), 95% (**) and 90% (*) level are reported.
!
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Table 5: Differences in AFQT Score by Disaggregated Skin Tone of African Americans
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(1) (2) (3) (4) (5) (6)
Black, light 4599*** 3287** 3293** 4294*** 4114** 4496** (1471) (1350) (1353) (1395) (1420) (1541) Black, dark -1306 -1210 -1495 -203.8 -102.5 526.7 (1566) (1434) (1438) (1485) (1516) (1618)
Controls/Fixed effects
Age/HGC X X X X X MSA/Citizen X X X X
Household SES X X X
Health X
School quality X
R2 0.011 0.233 0.237 0.335 0.341 0.337 N 1473 1400 1389 1160 1127 968 2,057 Notes: The dependent variable is the AFQT score measured in 1999. AFQT for African Americans has mean 28,637 and standard deviation 24,116. Estimates come from an OLS with dummies indicating lightest tercile of skin tone and darkest tercile of skin tone. The Age/HGC controls are dummies for age and highest grade completed in 1999. MSA/Citizen controls are dummies for metropolitan statistical areas and whether the individual has US citizenship. Household SES controls include mother’s educational attainment and ratio of household income to the poverty level. Health controls are height, weight and a standardized index for general health. School quality controls include dummies for public/private/parochial school and whether the student felt safe at school. Standard errors are given in parentheses. Statistical significance from zero at the 99% (***), 95% (**) and 90% (*) level are reported.
!
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Table 6: Differences in AFQT Score by Disaggregated Skin Tone of Hispanics
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(1) (2) (3) (4) (5) (6)
Hispanic, light 6358*** 5705*** 5711*** 4475** 4525** 4832*** (1882) (1808) (1815) (1832) (1886) (1961) Hispanic, dark -889.8 2022 1628 344.9 122.1 502.2 (2223) (2158) (2184) (2194) (2242) (2346)
Controls/Fixed effects
Age/HGC X X X X X MSA/Citizen X X X X
Household SES X X X
Health X
School quality X
R2 0.018 0.159 0.171 0.300 0.307 0.300 N 1060 1022 1012 853 824 725 2,057 Notes: The dependent variable is the AFQT score measured in 1999. AFQT for Hispanics has mean 35,093 and standard deviation 25,666. Estimates come from an OLS with dummies indicating lightest tercile of skin tone and darkest tercile of skin tone. The Age/HGC controls are dummies for age and highest grade completed in 1999. MSA/Citizen controls are dummies for metropolitan statistical areas and whether the individual has US citizenship. Household SES controls include mother’s educational attainment and ratio of household income to the poverty level. Health controls are height, weight and a standardized index for general health. School quality controls include dummies for public/private/parochial school and whether the student felt safe at school. Standard errors are given in parentheses. Statistical significance from zero at the 99% (***), 95% (**) and 90% (*) level are reported.
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Table 7: Differences in HGC by Skin Tone of African Americans over Time
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(1) (2)
Light Dark Light Dark
Age 14 0.162 -0.008 0.060 -0.052 (0.127) (0.132) (0.137) (0.140)
Age 15 0.104 -0.008 0.011 -0.059 (0.102) (0.107) (0.106) (0.111)
Age 16 0.084 -0.044 0.039 -0.046 (0.088) (0.093) (0.092) (0.096)
Age 17 0.069 -0.079 0.047 -0.092 (0.081) (0.084) (0.082) (0.086)
Age 18 0.130 -0.043 0.107 -0.027 (0.081) (0.084) (0.082) (0.083)
Age 19 0.278*** 0.021 0.268*** 0.051 (0.081) (0.085) (0.082) (0.087)
Age 20 0.252*** -0.053 0.265*** 0.001 (0.082) (0.085) (0.084) (0.087)
Age 21 0.276*** -0.108 0.267*** -0.050 (0.081) (0.085) (0.825) (0.087)
Controls/Fixed effects
Age X X MSA/Citizen X
Household SES X
Health X
R2 0.472 0.549 N 12183 9858
Notes: The dependent variable is the highest grade completed at that age. Estimates are from an OLS with dummies indicating lightest tercile of skin tone and darkest tercile of skin tone interacted with age dummies. The Age controls are dummies for age. MSA/Citizen controls are dummies for metropolitan statistical areas and whether the individual has US citizenship. Household SES controls include mother’s educational attainment and ratio of household income to the poverty level in 1997. Health controls are height, weight and a standardized index for general health. Standard errors are given in parentheses. Statistical significance from zero at the 99% (***), 95% (**) and 90% (*) level are reported.
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Table 8: Differences in HGC by Skin Tone of Hispanics over Time
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(1) (2)
Light Dark Light Dark
Age 14 0.075 -0.158 0.070 -0.123 (0.136) (0.168) (0.154) (0.189)
Age 15 0.109 -0.144 0.101 -0.076 (0.110) (0.136) (0.121) (0.147)
Age 16 0.154 -0.118 0.107 -0.061 (0.098) (0.117) (0.105) (0.125)
Age 17 0.188** -0.196* 0.124 -0.191* (0.089) (0.107) (0.096) (0.114)
Age 18 0.216** -0.228** 0.137 -0.204* (0.089) (0.108) (0.096) (0.115)
Age 19 0.254*** -0.228** 0.231** -0.202* (0.091) (0.108) (0.097) (0.116)
Age 20 0.224*** -0.242** 0.252*** -0.224* (0.091) (0.109) (0.096) (0.115)
Age 21 0.209** -0.298*** 0.209** -0.242** (0.091) (0.109) (0.096) (0.115)
Controls/Fixed effects
Age X X MSA/Citizen X
Household SES X
Health X
R2 0.470 0.503 N 9502 7655
Notes: The dependent variable is the highest grade completed at that age. Estimates are from an OLS with dummies indicating lightest tercile of skin tone and darkest tercile of skin tone interacted with age dummies. The Age controls are dummies for age. MSA/Citizen controls are dummies for metropolitan statistical areas and whether the individual has US citizenship. Household SES controls include mother’s educational attainment and ratio of household income to the poverty level in 1997. Health controls are height, weight and a standardized index for general health. Standard errors are given in parentheses. Statistical significance from zero at the 99% (***), 95% (**) and 90% (*) level are reported.
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Figure 1: Skin Color Scale Used by NLSY97 !
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Notes: Notify the New Immigrant Survey staff by emailing Jennifer Martin at [email protected] for usage and more information.
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Figure 2: Density of AFQT Scores by Skin Tone Terciles of African Americans !
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Notes: AFQT scores are measured in 1999. Kernel density estimates are generated using the Epanechnikov kernel, and bandwidths minimize the mean integrated squared error. !
00.
51.
01.
52.
02.
5D
ensi
ty
0 20 40 60 80 100AFQT (by 1000)
Lightest skin Medium skin Darkest skin
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28
Figure 3: Density of AFQT Scores by Skin Tone Terciles of Hispanics !!!!!
Notes: AFQT scores are measured in 1999. Kernel density estimates are generated using the Epanechnikov kernel, and bandwidths minimize the mean integrated squared error. !!
00.
51.
01.
52.
0D
ensi
ty (b
y e-
05)
0 20 40 60 80 100AFQT (by 1000)
Lightest skin Medium skin Darkest skin
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