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Econometric Analysis of Factors Impacting Early­Career
Wages
Prepared by Rebecca Turlip and Thiago Fernandes
ECON­UA 266 | Econometrics
Professor Lucas Bernard
New York University | College of Arts and Science
December 21, 2015

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Introduction
Decades have passed since a student could work a hard summer job in order to pay off tuition.
The exorbitantly higher costs of higher education today has caused a shift in outlook towards
many colleges as being more of an ​investment opportunity​. For this reason, students are pressed
with the much more daunting task of choosing “the right school” with much more money at
stake. When choosing the right school, common factors considered are the size of the school,
tuition cost and whether the school is in state or out of state, proximity and campus life, and
whether the school is public or private. Since making money has become a larger priority for
students coming out of college, especially at a school like New York University where tuition
and fees are $71K, it’s important for students to know what factors about the school that they are
at that will actually earn them their starting salary. In this paper we will focus more on other, less
commonly considered variables, such as school ranking, professor salaries and population
density of the school’s municipality, and we will consider their effects on income after
graduation.
Data and Empirical Model
In the analysis is a collection of data from the top 150 U.S. colleges and universities, as ranked
by U.S. News & World Report in their “2015­2016 National Universities Ranking.” Information
on the methodology used to rank universities can be found on their website. From the selected
universities, several data points were selected: early career salary, out­of­state tuition, total
undergraduate enrollment, percentage of males (gender distribution), average SAT score, and the

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percentage of students pursuing a degree in a Science, Technology, Engineering, or Math
(STEM) field.
The information for the fields of tuition, enrollment, gender distribution, and SAT scores were
provided on the corresponding page for each university on the U.S. News and Forbes websites.
Average SAT was computed by taking the arithmetic average between each school’s reported
25th percentile score and 75th percentile score. Early starting salary and the percentage of STEM
majors was obtained from PayScale’s “2015­2016 College Salary Report.” PayScale defines
early career pay as “the median salary for an alumni with 0­5 years of experience.”
In addition to these metrics, the population density of each school’s residence city was chosen as
an independent variable in the regression analysis. Initially, the regression was to use the
distance from the university to the nearest metropolitan area, with the hypothesis being that
salary was inversely correlated to the distance from a metropolitan area, but due to a difficulty in
obtaining reliable data effectively, population density was chosen as a substitute. The
replacement metric mimics the distance metric by abiding by the positive correlation between
urbanisation levels and higher salaries, according to a 2014 study done by the United States
Department of Agriculture (USDA). Due to the lack of direct availability of population density
data in the 2014 Census Estimates, the values used in the analysis were derived by dividing the
provided population data by the land area data.
In addition to these chosen variables, others were chosen, but did not fit in the regression model
due to factors like collinearity and high heteroskedasticity. Despite not providing additional
information for the regression model, these data points still provided interesting insight into what
factors influence starting salaries. They will be further discussed in a later section.

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The data obtained through the aforementioned sources was analysed in STATA using regression
and correlation analyses. In addition, tests like the White test, imtest, and hettest were used to
check for heteroskedasticity, skewness, and kurtosis. The above combination of tests ensured
that the regression model was a good fit with the data by making sure the predicted estimations
matched closely with the actual results.
Below is the standard regression model, including an overview of the variables used in the
analysis:
tarting Salary x β (popDensity) β (tuition) β (undergradEnroll) β (pctMale)S = 0 + 1 + 2 + 3 + 4
β (avgSAT) β (pctSTEM) + 5 + 6
Variables and their expected signs:
Starting Salary (Dependent)​: Starting salary denotes the amount of income that college
graduates have earned from entry level jobs (0­5 years work experience). Usually, when
measuring earnings, the natural log is used. However, with this data set there were no major
outliers for median starting salaries that might have affected our results, i.e. if one of the median
salaries were $100,000 or more. Starting salary was used as the dependent variable in order to
see the different factors that went into influencing its size.
Population Density: ​This independent variable, the population density of the municipality of
each university in our list, was used in lieu of a proximity measure to the closest metropolitan
area. Population density is a good representation of access to opportunities and resources for
undergraduate students and employers alike. We hypothesized that in larger cities where there is
greater population density, the starting salaries will be higher, therefore resulting in a positive
correlation between the two variables.

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Tuition: ​The cost of tuition, in the context of our paper, includes the 2015 price of tuition and
fees for each of the universities in our list. Ideally, a higher tuition cost should have a positive
correlation with student salaries, as the graduating students will succumb to more immediate debt
otherwise. However, at first glance it is unclear whether the cost of tuition may actually be
correlated with starting salary.
Undergraduate Enrollment: ​A measurement of the total undergraduate population at each
college, undergraduate enrollment provides the opportunity to analyse how a change in the
student body size affects the salary outcome post­graduation. Because a large student body
results in a bigger output of graduates, it is expected that lower salaries come as a result of a
saturation of the market by graduates looking for jobs. This increase in supply, given the same
level of demand for workers, results in a lower salary. We estimate that there is a negative
correlation between school size and starting salary. Further, by measuring undergraduate
enrollment, we also isolate those students that enrolled into a graduate programme from the
analysis
Percentage of Male Students: ​With the belief of the gender wage gap ever so present, we
figured it would be an interesting analysis to see if that gap was present as soon as students left
college and joined the workforce. By measuring the percentage of males in the student body, we
can observe if, ceteris paribus, gender has any impact on the wage of a recent graduate. With the
expectation of the gender wage gap, we predict that there is a positive correlation between male
student percentage and starting salary.
SAT Scores: ​Though graduating students have likely not taken their SAT tests in a number of
years, SAT scores are a good indication of students’ academic abilities, as well as their abilities

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to analyze and solve problems, traits that are absolutely crucial to succeeding in the workforce.
Despite its issues as an evaluator of students’ performance, the SAT still has the advantage of
being a standardised measurement tool. Other metrics, such as Grade Point Average, are
determined via policies that are unique to each university, with some accepting +/­ grade
brackets, while others adhere strictly to letter grading. We also avoid the issue of GPA inflation
that’s ever present in colleges, especially among the higher tier schools. By using SAT, we
accept the tradeoff of older, potentially inaccurate data for a measurement tool that is
standardised across all schools in order to avoid externalities caused by different methods of
evaluating GPA. We hypothesise that there exists a positive correlation between SAT scores and
post­graduation salaries.
Percentage of Students in STEM Programmes: ​This stat was first seen on PayScale’s annual
salary analysis report. We hypothesised that since engineering schools predominantly have a
high rate of STEM students, that there would be a strong correlation between STEM percentage
and early­career salary. It was included as a test, and produced some interesting results.

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Results
ANOVA Analysis
Table 1: results from simple regression
tarting Salary 9820.056 0.09713(popDensity) 0.10695(tuition) 0.06081(undergradEnroll))S = + + +
119.4086(pctMale) 20.9181(avgSAT) 171.2699(pctSTEM) + + +
R² value, p­value, and coefficients
R­squared, also known as the coefficient of multiple correlation, is the proportion of total
variation of ​Starting Salary​ explained by the regression of ​Starting Salary ​on ​tuition​,
undergradEnroll ​and all of the other independent variables. Tab1e 1 reads an R­squared value of
0.8142. An R­squared value so close to one indicates how well the regression equation fits the
data. Another important factor in the above table is the consistent low p­values. All p­values
being below our significance level of .05, we were able to reject the null hypothesis. Ensuring an
R­squared value close to one and p­values close to zero was crucial for our empirical model to be

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accepted and have any meaning at all. The implications of the correlation coefficients are that
some of our factors are more correlated with post­graduate salaries than others. For example, the
regression showed ​to be a very significant variable, as expected, while some otherctSTEMp
correlation coefficients we found more surprising. Further discussion of such correlations will be
provided in the next section.
Correlation Analysis
Table 2: Correlation of the dependent variable and other independent variables
Taking a look at the correlation analysis reveals that the variables analysed had mild
relationships with one another. Population density’s correlation of nearly 0.2 with median
starting salary shows that its effect on salary is much smaller than anticipated in our hypothesis.
A more interesting datapoint about population density is its effect on increasing the price of
tuition. Although a correlation rate of 0.344 is small, it still represents an uphill hike in tuition
price as population density increases. Moving from a rural, uncrowded area, to an increasingly
urban environment is typically accompanied by increases in the price of land, due to the
increased competition for stakes of land. Supply of land for sale is already scarce in city areas.
Combined with steady increases in demand, the value of land rises. Cost of living in urban areas
is normally elevated​ ​as well, accompanying the increase in population density. Universities are
not exempt to to this pattern and, thus, must either absorb the additional costs or pass them on to

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their students. As evidenced by Table 2, an increase in one leads to a slight increase in tuition
cost, meaning schools in urban areas are passing on at least part of those additional costs to
students.
SAT scores are also moderately correlated to a few other regressors, primarily tuition cost. This
can be easily explained, especially in the case of tuition, that higher­ranked schools demand
more of a price premium. Those are also the schools that accept students with higher SAT scores.
The test scores also correlate with median salary quite strongly, indicating that the role of
academic performance, at least in high school, helps students get into higher­ranked schools,
where they are set to earn more money in the future.
The question of how the gender wage gap manifests itself was one of the more interesting things
to come out of this regression. It’s not typically in best practice to include in a regression
variables that are as tightly correlated as the percentage of males in a school and the percentage
of students studying STEM. However, in this case it reveals a lot about how they interact with
each other, as well as the dependent variable. Having a correlation coefficient of 0.762 and
0.631, respectively, STEM percentage and male percentage affect the increase of salaries
together substantially. The majority of schools that presented male­dominated percentages were
the schools that the most people did programmes related to STEM fields ­­ engineering schools.
Since engineering is already a male­dominated field with high salaries, this correlation is
explained. Though only a partial analysis of the gender wage gap,
Heteroskedasticity, Skew, and Kurtosis
According to the BP/CW test, the variables chosen represent a chi­squared value that is
statistically significant in line within 95% confidence level, due to the critical value of 4.01. In

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White’s test, the chi­squared value represents a critical value slightly above that allowed in the
95% confidence level range. However, the 0.6% difference is negligible in this case, so we can
reject the null hypothesis for both tests.
Additional Variables
Rank: ​Rank and admissions rates
were initially up for consideration
in being used in the regression
model, but were removed because
of their high correlation to SAT
scores, which we believed told
much more of the salary story than
rank and admissions rates alone. It
logically makes sense that the
three would correlate, as
high­ranking schools are typically applied to by more people and, thus, must be more selective,
making the overall accepted GPA higher. Including only one of these variables in the regression
was enough for all three to count implicitly.
Geographic Data is Mostly Irrelevant: ​Prior to running the regression on the final set of
variables found in the above data, more geography­focused data was included alongside
population density. Specifically, the analysis accounted for the GINI coefficient (a measure of
inequality in the area), the American Development Index (an aggregate coefficient giving a
higher score to more developed and better­off areas), and median household income. All of these

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metrics were measured on a state­by­state level. Despite an initial hypothesis that all three
variables would affect the median starting salary for recent graduates, none of them had much
correlation with the dependent variable without introducing p­values high above the 0.05
threshold. A possible blanket explanation for all of these values failing to produce results is that
many graduates don’t stay in the state they went to college. Out­of­state attendance isn’t reported
by every college, but for the major private institutions that reported it, around 75­80% of
students were from out­of­state. The assumption that these workers stay in the state where they
were going to school breaks down in this case. In addition, it’s also possible that, because GINI
and ADI are measures of inequality and development, the salary earned by graduates is above
what can be captured by those indices.
Future Analysis Variables: ​The variables we took into consideration in our regressor may not
necessarily be optimised for determining an early­career salary. A few substitutions were made
in order to accommodate for a lack of specific data that we wanted to analyse. The distance of a
college from a metropolitan area would have provided a variable that may have better
represented the opportunities available nearby, especially in terms of gaining work experience
during school in the form of internships and co­ops. Cities represent work opportunities not
necessarily available on a secluded campus, so there’s a strong possibility this would have
helped develop a stronger regression model. Further, having data from colleges’ career
development center about how professionally involved students are in college would have been
great for analysing how work experience before officially entering the workforce affects salaries.
Conclusion

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Predicted post­graduation salary is usually a heavy consideration for high schoolers aiming to get
into a good college. For those already in university, future salary is a source of constant worry
about whether the right major and school school were chosen, as well as if they did well enough
academically. The above analysis was done in an effort to quantify different factors that affect
how such a crucial number comes to be. Through the analysed parameters, we determined that
the main drivers in determining early­career salary are academic performance, as measured by
students’ SAT scores, and having completed a STEM programme. It can also be said with
confidence that the effect that the percentage of males in a university has on that university’s
graduates’ median salaries is directly tied to those students’ engagement in a STEM­related field,
as evidenced by the high correlation between the two factors.
Interestingly, through a long­run
analysis done on the same model
using PayScale’s provided
mid­career salaries, it’s revealed
that factors like gender ratio and school size are irrelevant in the long run, as shown by the 0.457
and 0.949 p­values for each parameter, respectively. In that analysis, tuition’s close correlation
with academic performance, the strongest among the independent variables, also indicate that it
can be disregarded.
The constant in this is that, despite the length of career, a STEM programme is guaranteed to
generate a strong salary early on and in the future. If we extend academic performance in high
school, as indicated by the SAT, to future performance in college and in the workforce, we see

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that, if paired with a STEM education, we’re likely to observe a consistently high salary through
the duration of the subject’s career.

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Works Cited
"Best Universites and Colleges by Salary Potential." ​Best Universities and Colleges​. 2015 PayScale, Inc., n.d.
Web. 12 Dec. 2015.
Kusmin, Lorin. "Rural America at a Glance: 2013 Edition." ​SSRN Electronic Journal SSRN Journal​ (n.d.): n.
pag. USDA. Web.
"National Universities Rankings." ​National University Rankings​. U.S. News & World Report LP, n.d. Web. 12
Dec. 2015.
"Population Estimates, July 1, 2014, (V2014)." ​QuickFacts​. U.S. Department of Commerce, n.d. Web. 12 Dec.
2015.