DOES THE COLLEGE WAGE PREMIUM DIFFER BY RACE?
SECTION I.  INTRODUCTION
            Recent
research in the economics literature has been devoted to the rising wage inequality
in the labor market. Among these many issues include the college wage premium,
which is defined as the difference of mean hourly earnings from an individual
graduating from high school versus that of a college graduate.  With the recent rise in college graduation
rates, researchers have sought to explain if this increase is being fueled by
similar changes in the college wage premium.  

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Many economists argue that in the early 1980’s the college wage premium started experiencing an upward trajectory in the United States. For instance, James (2012) provides evidence that the earnings of college graduates outweighed that of high school graduates by 30% between 1980 and 1990. However, the college wage premium began shrinking after 1990. Based on the wide variation in the wage gap between the two cohorts during the past three decades, youngsters are now faced with the decision of enrolling and completing college education once they graduate from high school.
In the general literature,
the wage differential is often attributed to the higher return to college
education. The literature conclude that the recent rise in earnings of college
graduates is the driving factor for the rise in college education. Recent
trends in the growth of technology has required positions that were previously
held by high school graduates to be filled or replace with college graduate
(skilled labor). Intuitively, the expansion of technology across various
sectors of the economy has necessitated the demand for skilled labor with
technical know-how which in turn causes the increase in college graduates. Thus,
an increase in the supply of college graduates would need to be met by a
greater increase in the demand for college graduates.
In this paper, we
empirically examine the wage gap between graduates from high school versus
those from college for both whites and non-whites using data provided by Integrated Public Use
Microdata Series (IPUMS) for 1980 and 2014 samples.
The motivation to undertake this research stems from our desire to investigate if the rise in college graduate and the rise in technology have different effect on the college premium across race.  Economists over decade have argued that nonwhites, especially Blacks and Latinos receive lower wages than their whites’ counterparts with similar characteristics. Specifically, the aim of this research is to determine if this differences in wages across race for workers with similar characteristics, holds for college wage premium. If the college wage premium differs by race, one can say that variation in the schools attended by either white or non-white is a source
The rest of the paper is
categorized into five themes. Section two discusses the theory and previous
literature which have a bearing on our topic. The next section describes the
source of data and the relevant variables controlled for in the paper. The
methodology and estimation strategy employed are presented in the fourth
section. Section five discusses our estimates and we conclude in the last
section.
SECTION II. LITERATURE REVIEW
     This section
explores the theoretical and empirical literature on college wage premium in
the United States. We first assess the human capital model which was initially
propounded by Becker (1964) and later improved on by Mincer (1974). The model
is a core econometric model in labor economics and it has become the standard
framework for specifying all wage models in most empirical papers. According to
Mincer (1974), education and experience are considered as the pivotal means by
which the individual increases his productivity and earnings in the labor
market.
Following Mincer’s (1974)
wage model specification, Card (1999) posits that two hypotheses must hold to
allow a meaningful assessment on the true return to education. First, he asserts
that in the standard human capital earning function, the education variable is
measured correctly when the number of years of completed education is used
rather than the individual’s level of education which is always measured as a
categorical variable. Secondly, he points out that a year increase in education
should affect earnings by the same magnitude, other things being equal. If the
two conditions above are satisfied, then one can estimate the true returns to
schooling with precision (Card, 1999). The previous discussions indicate that a
person who is well-educated or has more years of experience on a job is
expected to be efficient and is therefore rewarded financially. Proponents of
the human capital model, (Becker, 1964) and (Mincer, 1974) are of the view that
the rate of return at a person’s year of education is equivalent to his
discounted value of wages over time. It is therefore important for a person to
continue his/her education if and only if the internal rate of return outstrips
the marginal cost of schooling. Many empirical works in the literature have
confirmed how the accumulation of stock of knowledge and the increase in the
years of experience of a worker have resulted in an increased wage. For
instance, Day and Eric (2002) provide evidence that the annual earnings of a
high school graduate were about one-third lower than the earnings of a college
graduate.
Many studies also report on
how the college wage premium has been fluctuating over the years. To
empirically investigate the college wage premium within race, it is important
to discuss the reasons which have accounted for the fluctuations in the college
wage premium in recent times. According to David (2014), the college wage
premium has been increasing over the years due to the high usage of advanced
technologies in the operations of many companies. Advanced economies like the
United States and Japan deploy sophisticated machines in most sectors of the
economy. Because of this, most employers prefer to hire individuals who are
technically savvy enough to work in computer-based environment. The structure
of the educational system of a country determines the flow of skilled personnel
and given that the higher the education of the individual, the greater his
skills, the college wage premium will continue to rise (Krueger, 1993). On the
contrary, Murphy & Welch (1989) argue that one factor which drives the
return to education is the interplay of demand and supply of college graduates.
In this regard, if individuals observe that attaining higher level of education
induces greater returns of schooling, then many people will be motivated to
further their education. When this happens, the returns to schooling will be
reduced if the demand of college graduates is not strong to outweigh the
supply. Their study highlights that the college wage premium will increase when
the growth in the demand for college graduates exceeds the growth in the
supply.  
Murphy & Welch (1989)
further claims that the variation in the college wage premium is subject to
questions. This is because an increase in college premium can be due to an
increase in the wages of college graduates or a fall in the wages for high
school graduates. In analyzing whether the increase in the college premium in
the 1980’s was due to college wage increase or a decline in high school
graduate wages, the study uses a price deflator to overcome the problem. The
consumer price index (CPI) helps to change the nominal wages of both college
graduate and high school graduate into real wages which facilitates comparison
at the same level. The study concludes that between 1979 and 1986 real wages of
high school graduates declined by about 6.5 percentage points while that of
college graduates during the same period rose by 5.6 percentage points.
Eide (1997) analyzed the
changes in college wage premium for whites and non-whites by examining the
changes in the earnings by major and changes in the distribution of majors for
the each race. The study concludes that the differences in the majors account
for the changes in the college wage premium for white men and non-white men.
However, for white women and non-white women changes in the distribution of
majors does not affect the college wage premium.
SECTION III. DESCRIPTION OF DATA
To examine how the
college wage premium differs within race, we obtain data from the Integrated
Public Use Microdata Series (IPUMS). Briefly, there are nine different censuses
and surveys under IPUMS. Among these projects, we obtain data from the U.S
Census and American Community Survey. Data from IPUMS-USA consist of census
organized every ten years from 1790 to 2010 and the American Community Survey
which began in 2010. Unlike the National Longitudinal Survey (NLS) which has a
lot of missing observations, this problem is less pronounced in the IPUMS-USA
data. Again, our primary source of data has over a million observations, hence
considering high school and college graduates will not affect the sample size.
Since we are interested in analyzing how the college wage premium has been changing
over time, we employ data from 1980 and 2014. For each year, only males are
included in the sample. We argue that most of the time women quit their
education and cater for their children upon giving birth. As a result, one
cannot truly measure the potential experience of such individuals. The data
available does not provide any information on the time females reentered into
education after nursing their children. Therefore, to overcome the problem in
computing the potential experience for individuals, we eliminate all females
from the sample. There are 207650 observations in the 1980 data whereas the 2014
data includes 593774 observations. However, after cleaning the data, sample
sizes of 196692 and 378076 were obtained for the 1980 and 2014 sample
respectively.
Log of hourly wage: The main dependent variable is the log of hourly wage. Since there is no
specific hourly wage variable in the data set, respondents wage and salary
income are used to derive the hourly wage. As a result, the hourly wage is
constructed by taking the ratio of the wage and salary income of a respondent
to his total hours worked in the year. We use the logarithm of hourly wage
rather than the absolute wage of individuals to assess the percentage change
for a given independent variable of interest. 
Education: The level of education is the main independent variable of interest.During the survey, respondents were
asked the highest year of schooling or degree attained ever. We consider
respondents who have completed their twelve grade (high school graduates) and
those who have graduated from a four-year college. For the purpose of analyzing
the wage gap between individuals who have completed high school versus college
(college wage premium), one needs information from only these two cohorts.
Therefore, any educational level outside this category is deleted. High school
graduates are used as the reference category.
Other covariates of interest: We also control for potential experience and
its squared term, race, geographical location of individuals marital status and
family income.
Descriptive Statistics
The summary
statistics for both 1980 and 2014 are displayed in table1.0 and table 1.1 in
appendix “A” respectively. The mean of the log of hourly wage in the 1980 data
is $1.69 while that of 2014 is estimated as $2.72. Even though the average of
hourly wage has increased over the thirty-four-year period, the magnitude of
increase is not high. About 82.16% of individuals in the 1980 sample are high
school graduates whereas the remaining 17.84% constitute college graduates.
However, in the 2014 sample, the proportion of high school graduates is higher
than that of the 1980 sample. Our estimate shows that college graduates exceed
high school graduates by 29.56% in the 2014 sample. For both periods, the
sample is skewed towards the whites. For instance, the proportion of whites in
1980 and 2014 are 89.29% and 90.22% respectively.
SECTION IV.  METHODOLOGY
            For the purpose of identifying whether the
college wage premium differ by race, we use the Ordinary Least Squares
estimation strategy (OLS). Following Mincer’s (1974) general human capital
model, we specify our first model as follows:

  The
model specification also recognizes the possible influence of other variables
on the hourly earnings of an individual. Therefore, a generalized model to be used
in the study to incorporate other factors which influence the hourly wages of
an individual is given below:

In equation (1), loghourlywages denotes the natural logarithm of hourly wage for each individual. The ‘college’ variable represents the educational attainment of the individual. A dummy variable is created where ‘1’ is assigned if the individual has a college degree and zero if he has a high school degree. A positive sign is expected from the coefficient of the college variable. This is because college graduates are expected to receive higher wages than high school graduates. The variable ‘Exp’ measures the potential experience of each respondent (age-years of education-6). The quadratic term of experience measures the diminishing marginal effect on log hourly wages. It is argued that as a worker gains more experience in the labor market his earnings decline. A positive sign is expected for the experience variable as acquiring more years of experience increases the earnings of the individual. The variable “µ” captures the stochastic error term.
From equation (2), a dummy variable (whites) is set to measure the differences in wages for whites and nonwhites. Asians are excluded from the sample since a higher proportion of Asians attain more education than other race. Including them in the sample will lead to bias estimates. Metro is a categorical indicator to capture the differences in wages associated with his residential location. Generally, it is believed that living in cities (metro area) has a greater positive influence on wages than living in non-metro area.  Thus, a positive sign is expected for the coefficient of the metro variable in the analysis. A log transformation of a respondent’s family income (logfamilyincome) measures the influence of a unit increase in the family income on hourly wage. It is argued that individuals from a strong financially background has a greater earning potential on the market. Dummy variable for the marital status of individual at the time the survey was conducted is included in the specification model. The “married” variable accounts for the marital status of the individual where the non-married is used as the reference category. The stochastic error term  µ measures the influence of other variables on the hourly wage which are not captured in the model.
SECTION V.     EMPIRICAL RESULTS
MINCER’S HUMAN CAPITAL MODEL
The first two columns
in table 1.3 in “appendix A” contains estimates in the Mincer’s (model A) human
capital model for the 1980 and 2014 samples respectively. Empirical evidence
from the models confirms the significant effect of the human capital variables
on the log hourly earnings. It can be observed that acquiring a college
education has a significant and a positive influence on the log hourly wage for
the individual.  Also, college graduates
receive 41% and 70% more hourly wages than those with high school degree, other
things being equal. The experience and its’ squared term had the expected sign
and they were significant both years. Briefly, all the variables in Mincers’
equation were significant with the expected sign and this confirms the validity
of the human capital model in this paper.
Moreover, the third
column in table 1.3 shows the estimates for the 1980 sample. It can be observed
that, college graduates received 39% more hourly wages than high school
graduates, all things being equal. The results also indicate that a year
increase in experience in 1980 causes a rise in hourly wages by 9.5% holding
other factors constant. The squared term of experience has a negative influence
on the earnings of the individual. Therefore, as depicted in the table, a rise
in experience (experience2) for a longer period of time reduces the
hourly wages of the individual by 0.02%, all else equal. Whites receives 24%
more in hourly earnings than nonwhites (excluding Asian).
The last column in
table 1.3 presents the estimates for the 2014 sample. The differences in hourly
wages (college premium) between college and high school graduates rose to 60%
all else equal.  This results depicts a
rise in the college premium by 22% moving from 1980 to 2014. The experience
term had a lesser (7.3%) positive influence on wages in 2014 than in 1980
(9.5%). The square of the experience term also had lesser negative influence on
the log wages by 0.1% all things equal. The estimates from the race dummy
(whites and non-whites) reveals that earning of whites outweighs the nonwhites
by 26% holding other factors equal (a 2% rise since 1980). Though living in a
metro area contributes positively to the hourly wages of the individual, the
percentage rise in hourly wages of 2.6% is less than that of 1980 (12.2%) all
else equal. 
DIFFERENCE IN WAGE PREMIUM BY RACE
We examine the differences in the college
wage premium for both whites and non-whites for both sample periods. Table 1.4 in “appendix A” presents estimates of the log of
hourly wage for both race. In the table, the
first two columns show estimates for whites in 1980 and 2014 whereas estimates
for non-whites for these periods are displayed in the last two columns.
Evidence suggests that the college wage premium is higher for non-whites than
the whites for both periods. Non-whites college graduates receive 43.1% wage
higher than non-whites high school graduates in 1980, other things being equal.
Similarly, among the whites, individuals who graduate from college benefit from
a wage increase by 38.3% than individuals who graduate from high school in
1980. It is noteworthy to mention that from 1980 to 2014, the college wage
premium has increased, and the rise is more pronounced for the non-whites than
the whites. Our empirical results suggest that in 2014, non-white college
graduates received 65.8% wage higher than the non-white high school graduates.
Specifically, we find that the college wage premium has increased by 22.7%
among non-whites when the two sample periods are considered. On the other hand,
the college wage premium for the whites in 2014 is estimated as 59.1%. By
comparing this estimate to that of the 1980’s, we assert that over the 34-year
period, the college wage premium has increased by 20.8% for the whites. The
empirical evidence suggest that the college wage premium has increased for both
whites and non-whites over the last three decades. However, the college wage
premium within non-whites is higher than the whites for each period. It is
worth exploring to find the possible reasons which has accounted to the rise in
the earnings premium
ECONOMIC INTERPRETATION OF RESULTS
The rise in hourly
earnings of college graduates within each race over the years have several
economics connotations. Table 1.2 shows the increase in the supply of college
graduates for both whites and non-whites. For the whites, the number of college
graduates increased by 16.27% whereas that of the non-whites rose by 1.11%. In
theory, the interaction of demand and supply of college graduates suggests a
fall in the hourly wages of college graduates as supply increases. However, the
results from the difference in estimates for college wage premium as discussed
in the previous section showed otherwise. An explanation for this difference in
the wage premium deviating from theory could stem from a rise in demand for
college graduates driven largely by skilled biased technological change. An
increase in the supply of college graduates reduce return to college as job
competition increases. Thus, a rise in supply of college graduates from 1980 to
2014 is expected to reduce college return, unless the growth in supply is
offset by a greater rise in the demand for college graduates. Most industries
have adopted new technology as means of improving productivity and efficiency
to match growing trends in their sectors of operation. The United States being a technologically advanced nation requires
literate, technical and scientifically trained minds to develop ideas, manage
complex organization and situations. The demand for skilled labor
(college graduates) increases in the market following the improvement in
technology in most sectors of the economy. This leads to a rise in the price
(wages) of skilled labor. Thus, the rise in the supply of college graduate from
1980 to 2014 has been met by an increase in the demand for skill workers over
the years as technology improves. Conclusion drawn from discussion above points
to the fact that improvement in technology causes a rise in the college wage
premium by 22% from 1980 to 2014 all else equal.
We
also give insights on why the college wage premium for the non-whites has a
greater magnitude than the whites. First, a factor which could contribute to
the wider gap among the non-whites than the white is the differences in the
geographical locations between race. In the United States, a higher proportion
of low income earners are non-whites. For instance, (xxx finds evidence that
about xxx% of non-whites and axxx% of whites fall into low income group).
Because of the residential segregation or non-whites self-selecting themselves
into rural or less developed districts, most of them are compelled to send
their wards to schools of low standard. This affects individuals’ schooling
decisions in terms of their preparation towards college. Since the schools
attended by these individuals are of poor quality, there is little motivation
for them to continue college upon graduating from the high school. The economic
implication of many non-white high school graduates is being translated in the
job market. Theoretically, in the labor market, equilibrium is established when
the wage rate equals the marginal productivity of labor. At times, some
employers who have no prior knowledge on the experiences or how productive an
individual will be decide on productivity levels based on type of school
attended. The argument is that an individual from a higher quality school is an
indication of a higher productivity than the one from low quality school. Based
on this premise, lower wage rates are paid to non-whites who graduate from low
standard high schools.  However, the few
non-whites families who fall into a higher income category and can afford
better housing in developed districts have a positive influence on the
educational life of their children. Evidently, children brought up in these
homes are privileged because of their access to quality high schools within the
district they reside. We therefore argue that there is a higher possibility for
children from these wealthy homes to continue their college education. In
effect, the higher wages received by the college graduates has a stronger
effect in increasing the college wage gap among the non-whites. Since most of
the whites are in the high-income group, good housing is readily available to
them. Children from these homes are naturally enrolled into good schools which
reinforce their educational attainment. From this perspective, high school
graduates are paid wages which are higher than that which would have been
received if they had graduated from a low quality high school. For instance,
evidence from table 1.5 shows the mean of the log hourly wage for both whites
and non-white high school and college graduate for years under study.  We find that on average white high school
graduates have greater wages than their non-whites colleagues. One then expects
the extent of wage gap between college graduates and high school graduate among
the whites to be lesser than that of the non-whites. If the income distribution
between whites and non-whites is uniform at each period, college wage premium
will not differ much across race.
SECTION V. CONCLUSION
            This paper considers the
college wage premium for both whites and non-whites. We use data from IPUMS for
the 1980 and 2014 sample. Our results suggest that over the thirty-four-year period,
the college wage premium has increased across wage. For the whites, the college
wage premium increased from 38% in 1980 to 59% in 2014. In addition, that of
the non-whites increased from 43% to 66% between the two periods. Most
importantly, for both race, the rise in the wage premium is higher for the
non-whites than the whites. An explanation for the increase in the wage premium
across race is due to the technological advancement in most sectors of the
economy. The use of advanced technology in industries has resulted in an
increase in the demand for skilled personnel who have the requisite knowledge.
Moreover, we find the wage premium to be higher for the non-whites than the
whites within each period. The rise in the wage gap among the non-whites than
the whites over the period can be attributed to the differences in the
geographical location for both groups. Most whites reside in well-developed
districts than the non-whites. The effect is that children raised from good
family background have access to quality education at the expense of the
non-whites who are financially deprived. Because of this, the wages of white
high school graduates are higher than the non-whites. We therefore assert that,
it is possible for the college wage premium for the non-whites to be higher
than the whites.
POLICY IMPLICATION
Given the fact that high
school graduates receive lower wages than college graduates, the main policy
implication from this paper is to increase accessibility to college education.
This will reduce the disparities in wages between the different educational
groups. Also, a rise in the number of college graduates following the
improvement in accessibility will increase the supply of college graduates. If
the increase in the supply of college graduates more than offset the demand for
college graduates, the college wage premium will fall as the wages of college
graduates reduces.  In addition, policies
should be enacted to upgrade educational structures in segregated area to
bridge the earning gap in the college wage premium between whites and
non-whites.
            Previous studies which analyze how the level
of schooling affect earnings of an individual argue that education is
endogenous in the earnings function. Because of this problem, economists have
resorted to the use of instrumental variables to mitigate the biased estimates
of the true return to education. Our paper does not employ the instrumental
variable approach. The reason is that a good instrument is not available in the
IPMUS data set.s
References

   Day, J. C., &
Newburger, E. C., (2002). The Big Payoff: Educational Attainment And Synthetic
Estimates Of Work-Life Earnings. Special Studies. Current Population Reports.
Retrieved from https://files.eric.ed.gov/fulltext/ED467533.pdf

Goldin,
C., & Lawrence F. K., (2007).The Race Between Education And Technology:
The

Krueger,
A. B. (1993). How Computers Have Changed the Wage Structure: Evidence from
Microdata, 1984–1989. The Quarterly Journal of Economics, 108(1),
33-60. Retrieved from https://doi.org/10.2307/2118494
Mincer, J. (1975). Education, Experience, and the Distribution of Earnings and Employment: An Overview in Education, Income and Human Behavior (pp. 71-94) NBER. Retrieved from http://www.nber.org/chapters/c3693

APPENDIX

Table 1.2 Percentage difference in College and High Scholl graduate

Table 1.3 ESTIMATION OF RESULTS

Table 1.4 ESTIMATION OF RESULTS BY RACE

Table 1.5 Mean of the log hourly wages