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RUTGERS UNIVERSITY

Masters Thesis

Effects of University Career Services on the Employment Outcomes of College Graduating

Students

Arinze Nwoye*

August 2016

Abstract

The choice of a job search method can influence the arrival rate of job offers and wage offer distribution. This paper explores the factors that affect the employment outcomes of first-time job seekers defined as college graduates. Specifically, it investigates the efficiency of University Career Services as a search method on the arrival of job offers and starting salaries. NACE 2015 student survey data for the U.S. is used for the study. The paper finds that ethnicity is a strong predictor for use of career services and using University Career Services does not have a statistically significant effect on receiving a job offer or on wage outcomes.

____________________________________ * I would like to thank my supervisor, Dr. Hilary Sigman, Dr. Ira Gang, Dr. Jennifer Hunt and Cedric Headley at Rutgers UCS for their time, patience and useful comments throughout the thesis process.

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Introduction: The early literature on the job search process assumes that the arrival rate of a job and the wage distribution are exogenous (e.g., McCall 1970; Mortensen 1970). The individual sets a reservation wage which determines if he accepts a job and is employed. However, the reality, this is more complicated. An individual can influence the arrival rate and quality of a job offer by his or her choice of search effort and search method used. Osborg (1993) likens it to fishing where the probability of catching a “big fish” differs depending on location. Given this analogy, it is possible that vacancies, quality of the match and the wage distributions may differ depending on the search method used. Moreover, the importance of the job search process is underscored by the fact that each search method has a cost and a benefit. It then becomes an exercise for the individual in choosing the most efficient search method to maximize his or her employment outcomes. This paper explores the job search process for a particular demographic: college educated first-job seekers and a particular search method: University Career Services (UCS). University Career Services (UCS) is an on-campus job center specifically for students and alumni of a particular university. It is concerned with providing career information, guidance, counselling, on-campus interviews and information sessions with employers and other job search skills. The institution is funded by the university to aid students in their transition to the labor force. And also provide efficient matching and employment outcomes. This paper is an attempt to investigate the effectiveness of University Career Services as a job search method. The study is of importance because a majority of universities in the U.S. have a career center that is funded by student tuition. In addition, the University Career Services is said to have professionals that provide services tailored to the student experience and as such compared to other job search methods within the job search literature should be the most efficient and accessible to the target demographic. Specifically, the paper empirically investigates: (i) The determinants of using university career services (ii) The effect of university career services on the probability of receiving an offer and (iii) The effect of university career services on wage outcomes. The study is performed using the 2015 U.S. student survey by the National Association of Colleges and Employers (NACE). Empirically, most papers use a two-part methodology: the first part is in line with theoretical models discussed earlier where the choice of a search method can be determined by cost and productivity considerations. These papers use probit models to estimate the determinants of search methods and an ordered probit for the number of search methods used (also known as extensive search effort, Holzer (1988)). The second part is to determine effects of search methods on employment outcomes. Outcomes defined by the probability of receiving an offer, the job offer rate (no of offers) and wages. Holzer (1988) uses such a two-part methodology. The first part determines the search method used (similar to a multinomial model with base comparison) and the search effort the number of search methods used (uses an ordered probit). The second part determines the effect of search methods on the number of job offers. He uses data from unemployed youth in the U.S. in the 1979 Youth Cohort of the National Longitudinal Survey. He finds that marital status and if

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the worker was laid off affect positively the number and type of search method used. In addition, the number of search methods used has a positive effect of the probability of receiving an offer. He also finds that no search methods have a negative effect on the arrival rate of offers. Also, informal search methods (friends, family and contacts) have the largest positive effect on the arrival rate of offers. Weber and Mahringer (2006), Boheim and Taylor (2002) also use similar empirical methods. Weber and Mahringer (2006) use data from Styria, the southern part of Austria. They find that age, education and longer unemployment durations have a positive effect on the number of search methods used. In addition, first-time workers put in less effort than unemployed workers. They do not find significant differences between search methods used and wage outcomes. They conclude that most of the variation in wages can be explained by only observable characteristics. However, they include job duration as an outcome and find that jobs gotten via public/state agencies have the shortest duration. Boheim and Taylor (2002) also use a similar methodology, using data for men in the British Household Panel Survey from 1996 – 1999. But notably, they augment their paper by including the premise that job search methods are not mutually exclusive and as such should not be analyzed as single variables. That is, one must analyze the search methods individually and also all combinations of search methods. They find that direct application to the employer increases the probability of being re-employed especially when used in combination with informal search methods and reply to job adverts. There is a lot of merit to using this approach because it is in line with Holzer (1988) theoretical work. In his paper, he states that search methods can also have complimentary and substitution effects on outcomes. Other papers such as Addison and Portugal (2001) and Gregg and Wadsworth (1996) study the impact of search methods on exiting unemployment. Addison and Portugal (2001) use a discrete time duration model using data on Portugal from the Portuguese National Institute of Statistics. They investigate all search methods but focus on Public Employment Agencies. They find that state employment agencies have low hit rates with short job duration. This paper uses the two-step methodology used by Holzer (1988) and Weber and Mahringer (2006). However, this paper focuses on first-job seekers identified as students leaving college and looking for their first job. In addition, this paper also uses novel data from the National Association of Colleges and Employers (NACE) student survey and focuses on University Career Services as a search method for college students. The results concern the difference on employment outcomes from including University Career Services as a search method within one’s overall search strategy versus not doing so. Due to the high correlation between search methods in the data it is hard disentangle the effects of one search method from the other or compare search methods directly. In addition, with the advent of the internet age, its penetration and ubiquitous use across all demographics (Kuhn and Skuterud, 2004) the process of distinguishing methods and embarking on this study is harder as more search methods become digitized and interdependent. For this reason, I chose to focus on one search method (UCS) that is highly used, widely available, has obvious costs (percentage of tuition) and benefits and is tailored to the demographic being studied. Moreover, University Career Services not only provides a platform to view vacancies but also has value-added activities such as counselling and networking activities that can potentially improve the employment outcome of the student.

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The paper finds that ethnicity is a strong predictor for who uses university career services. Furthermore, the effects of using university career services on employment outcomes - receiving a job offer and, the size of the wage offer - are small and positive but the results are not statistically significant. Theoretical Considerations: The classical theory regarding the job search process is from Mortensen (1986) where the offer rate and the wage distribution are constant and exogenously determined. The individual has a reservation wage that determines his or her base utility from which he decides to either accept the offer in this period or remain unemployed and keep searching. The first extension to the classical model was by Burdett (1980), in his model he posited that the individual chooses his or her reservation wage and optimal search effort to maximize current and expected future utility. In each period, the individual has to decide how much time for work and leisure. Search effort is a disutility in the current period because of the time it takes from leisure activity and the cost in terms of non-wage income. But it increases the probability of receiving an offer and an employment wage in the future. Holzer (1988) extends this model to one where the individual also chooses the search method. In the job search literature, the typical list if search methods include: Friends, family and contacts (informal search methods); direct contact with employers; newspapers and media; public or state agencies and other search methods. In Holzer’s model, each search method has its costs and productivity, so the individual has to choose the search method and then the amount of effort to put into the chosen method. The search method affects the offer rate and the worker decides to accept or reject the offer depending on the reservation wage. In addition, the cost and productivity of each method may vary depending on the skills of the individual, geographical location, time, etc. Holzer’s (1988) example is that an individual in a rural area with no contacts will incur higher cost using direct contact with employers and informal search methods than an individual with a number of employed friends who lives in the city. Other theoretical models, such as Berg and van der Klaauw (2006), have extended Holzer’s work further where the search method chosen can affect the wage distribution through endogenous search efforts that affect the offer rates. The theory states that an increase in search effort and an efficient job search method should increase the arrival rate of offers. In addition, an efficient job search method can also improve the wage offer distribution. This paper focuses on investigating the efficiency of UCS as a search method. The methodology used in this paper will follow the Berg and van der Klaauw (2006) extensions with the hypothesis that university career services as a search method can not only affect the offer rates of the individual but also affect the final wage outcome. In addition, the paper investigates the size and effect of UCS on employment outcomes and controls for endogeneity in the search method using an instrumental variable approach. Data: The paper uses cross sectional data from the 2015 NACE Student Survey. The survey was administered between February and April 2015 to 39,950 students of all class levels: bachelors, masters and doctoral graduating students. The student survey is focused on gathering

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information about their plans after graduation. That is, it is concerned with finding out their job search methods, post-graduation destination, participation in experiential learning and their employment outcomes. The paper uses data on bachelor’s degree students. Graduate students may have several more years of job search skills based on their age and previous experiences in the labor market which could potentially bias the results. In addition, we cannot control for the potential bias when including graduate students because the survey does not contain data on the number of years a graduate student worked before enrolling at a university. But most importantly, focusing on such a homogenous population (undergraduate students) allows us to control to for some unobserved heterogeneity and explore in-depth the factors that affect the employment outcomes of first-job seekers. Of the 39,950 students surveyed, 9,184 were graduating bachelor’s degree students. In addition, since the purpose of the paper is to investigate the effects of UCS as a search method we decided to subset the respondents to only those who participated in the job search process, that is, those that reported either having a full-time or part-time job and those seeking employment. Of the students who responded and after dropping missing values (684 observations representing 17% of the total observations), 3,156 reported to have participated in the job search process as defined earlier. This number serves as the full sample. Table 2A and Table2B contain descriptive statistics for all the variables in the full sample used in the regression. The table of descriptive statistics is also broken by the variable of interest; university career services, for those who used (UCS) and those that did not (Non-UCS). In addition, since the paper is interested in investigating the effect of university career services on wage outcomes, there is a salary sample for students who accepted an offer of employment upon graduation and reported a starting salary. However, there are a lot of missing values in the salary variable. I have salary data for 28% of the of the full sample. As a result, there are 869 students in the salary sample. Table 2C and 2D contain descriptive statistics for the salary sample also broken down by use of university career services. In addition, the variable job-offer is one of the dependent variables when analyzing effect of University Career Services on employment outcomes. It is constructed as a binary choice variable taking the value 1 if the student has a full-time or part-time job (employed) and 0 if the student is still seeking employment (unemployed). From Table 1 below, there is a small difference between the number of students unemployed (1,648) versus employed (1,508). Of the students who used UCS, 48% are employed and 52% are unemployed. Comparing the offers of those who used UCS to those that did not in Table 1; although using UCS has a marginally better offer rate, there does not seem to be a significant difference between using UCS and receiving an offer. On the other hand, close to three quarters of the sample reports having used UCS in the past year (see number of observations in Table 2A of the appendix). Table 1 Descriptive Statistics: University Career Services (UCS) by Job Offer

Offer

Unemployed Employed

University Career Services Not Use 408 (54%) 354 (46%) Used 1,240 (52%) 1,154 (48%)

Total 1,648 1,508

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The variable Internship in Table 2A is a measure of experiential learning. Yes, Internship if the student was hired as an intern in either a private or public institution in a paid or unpaid capacity at any time during their undergraduate degree. Internships mostly take place during school breaks (summer or winter internships) and less as a part-time activity when school is in session (Fall and Spring internships). Yes, Co-op if the student participated in cooperative education where the students take a break from school to work full-time for credit and usually can be between a semester to an academic year. Yes, both if the participated in both activities while enrolled as a student. Furthermore, the Parents (Higher Education) variable in tables of descriptive statistics identifies first generation students. It takes the value yes if students’ parents have at least a bachelor’s degree and or no if they do not. In addition, the CGPA of the student is the final cumulative grade point average of the student by the time of graduation. The CGPA is in categorical form and is transformed to a continuous variable using mean value on each CGPA interval. Furthermore, from Table 2A, most of students (87%) graduating are between the ages of 20 to 24 (the base category for the variable age). In addition, there are 312 distinct schools in the data and 44 distinct majors. Due to small number of students per school for most of the schools I reclassify the school variable. The school variable was classified using the Carnegie classification for schools of higher education according to the level of degree conferred (associates, bachelors, masters and doctorate), size and research activity (see Table 2B). In addition, for ease in comparing majors, the majors were reclassified according to the NACE classification of majors by broad categories. Refer to Table 2B in the appendix for full list of reclassified schools and majors. Table 2C and Table 2D contain descriptive statistics for the salary sample. From the tables, there are no major differences between the two samples (full sample and salary sample). In addition, Salaries is a categorical variable and an outcome variable. Figure 1 in the appendix shows a bar graph of salaries in the salary sample for estimation. In addition, figure 2 is similar to figure 1 but here salary is segmented by the use of University Career Services (UCS). Overall, there does not seem to be major differences in salary between students that used UCS and does that did not. In the $30,00 - $35,000 salary range there are more students that did not use UCS than those that did. In addition, in the $50,000 - $55,000 range students that used UCS are noticeably more than those that did not. Methodology: The first step in the analysis is to investigate the determinants of University Career Services. This is to find out if there are any distinguishing characteristics for students who are more likely to use UCS as a job search method. To do this, I use a binomial probit regression of UCS on student characteristics. From Cameron and Trivedi (2005), assume that UCS is a latent variable 𝑢𝑐𝑠∗ such that:

𝑢𝑐𝑠𝑖∗ = 𝑋𝑖

′𝛽 + 𝜀𝑖 , 𝜀 ∼ 𝛮(0, 𝜎2) (1)

𝑢𝑐𝑠𝑖 = {1 𝑖𝑓 𝑢𝑐𝑠𝑖

∗ > 0 (2)

0 𝑖𝑓 𝑢𝑐𝑠𝑖∗ ≤ 0

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𝑝𝑖 = 𝑃𝑟𝑜𝑏[𝑢𝑐𝑠𝑖 = 1 | 𝑥𝑖] = 𝑃𝑟𝑜𝑏[𝑢𝑐𝑠𝑖∗ > 0 | 𝑥𝑖] = Φ(𝑥𝑖

′𝛽) (3)

here we treat the search method UCS as a latent variable that takes the value 1 if the student used UCS and 0 otherwise. 𝛽𝑖 is a vector of parameter estimates and 𝑥𝑖 is a vector of characteristics and Φ is the cumulative distribution function of the standard normal. 𝑝𝑖 is the probability that the students used UCS. The second step is to determine if using UCS as search method affects the probability of receiving an offer by graduation. Here another probit model is used regressing Job Offer (dependent variable) on UCS and other control variables. This regression also shows other interesting factors that affect the probability of receiving an offer by graduation.

𝑜𝑓𝑓𝑒𝑟𝑖∗ = 𝑋𝑖

′𝛽 + 𝛽𝑢𝑐𝑠𝑖 + 𝜀𝑖 , 𝜀 ∼ 𝛮(0, 𝜎2) (4) The model is also similar to equation (1). The Offer variable is binary except in this case the dependent variable is the probability of receiving an offer and UCS is a binary categorical variable for if the student used UCS or not. Finally, I also investigate the effect of UCS search method on starting salaries using an interval regression; regressing the categorical variable salary (dependent variable) on UCS and other control variables.

𝑠𝑎𝑙𝑎𝑟𝑦𝑖∗ = 𝑥𝑖𝛽 + 𝛽𝑢𝑐𝑠𝑖 + 𝜀𝑖 (5)

𝑠𝑎𝑙𝑎𝑟𝑦𝑖 = 𝑗 𝑖𝑓 𝛼𝑗 ≤ 𝑠𝑎𝑙𝑎𝑟𝑦𝑖∗ < 𝛼𝑗+1 (6)

The interval regression model above is an ordered model where the thresholds 𝛼 are known. As discussed earlier, the survey data on salaries has a number of missing values with a total 844 reported salary ranges (observations). The interval regression is used because we have known thresholds. Salaries are also left and right censored. In addition, based on the theory that job methods have costs and benefits that are known then it may be the case that students may self-select into using university career services based on unobserved factors that could potentially bias the result. That is, the coefficient can potentially be biased and pick up these unobserved factors that can affect the probability of receiving an offer instead the true effect of using university career services. To address the endogeneity, I use an instrumental variable approach. I employ a two-staged least squares (2SLS) approach to instrument the UCS variable. The instrument used is the student per UCS staff ratio. The instrument is relevant because it speaks to the capacity that the UCS office can manage in terms of number of students that can be attended to on average. One would expect that a lower student to staff ratio would mean that more students get attended to and increases UCS use and vice-versa. Furthermore, student per UCS staff ratio can also be seen as a proxy for the resources that the university is willing to spend on UCS. An institution with more resources (such as a large UCS staff) will have a higher ability to attend to students, market more programs and attract more students which will increase usage.

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The data for student per UCS staff ratio is constructed by using reported full-time equivalent (FTE) undergraduate enrollment 2014-15 data from IPEDS (Integrated Postsecondary Education Data System) and dividing it by the mean staff size from the NACE 2015 career services survey. The variable is continuous. However, a shortcoming with the construction of the variable is that the enrollment data contains information for all the universities in the sample whereas the staff size data does not. Instead, it is the average staff size according to the Carnegie classification of schools in higher education. In addition, I did not control for individual school dummies because there are few students per school rather I used the Carnegie reclassified schools.1 I also use reclassified NACE majors in all the regressions. Finally, all regressions in this paper are conducted with robust standard errors. Results: Table 3, contains results from the first binomial probit regression investigating the determinants of University Career Services (UCS) for the full- sample and the salary-sample. For the full-sample, the only variables that are significant are in the ethnicity category where based on the marginal effects, African-Americans students have a 9.8% higher probability of using UCS (compared to the base: White-American students); Hispanic-American students and International students have a 5.4% and 11.6% higher probability respectively of using UCS compared to White-American students. These results show that ethnicity is a strong predictor for who decides to use UCS search method. In addition, these results are also highly statistically significant (at the 1% or 5% significance levels). Similarly, for the salary-sample, the ethnicity is a strong predictor for use of UCS. African-American is omitted because it predicts success perfectly, that is all the African-Americans in the salary-sample use UCS. Hispanic-American students and International students have a 12.5% and 14% higher probability respectively of using UCS compared to White-American students. The results are consistent with Holzer (1987, 1988) and Kuhn and Mansour (2011) where both papers conclude that black individuals use more formal search methods because of the lack of strong networks in the labor market. It is especially true with international students who have the highest probability of using UCS possibly because of all the subgroups they have the weakest networks in the labor market. In addition, In the salary-sample, students that participated in co-op programs also have 15.9% higher probability of using UCS compared to students without experiential learning. The results from Table 3 also suggest an inverse relation between CGPA and use of UCS in both samples. That is, the lower the student’s CGPA the more likely the student will use UCS. These students may realize that they may need more assistance in securing a job and therefore reach out to the university career services. However, the result is not statistically significant. Both samples also suggest that students with parents who have higher education are also more likely to use UCS. 2% and 0.2% for the full-sample and salary-sample respectively. The result is also not statistically significant.

1 Controlling for individual schools is in principle better because the UCS effect is then calculated from comparing

students at the same school. However, because there are few students per school, it can lead to imprecise estimates, or bias from using small samples

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Dummies for reclassified schools and majors were also included in the regression as control variables but not reported. In addition, the F-test for the joint significance of schools and majors in the regression are highly statistically significant. Table 4A, reports the results of the effects of UCS on Job Offer using a probit estimation. Where the dependent variable, job offer, is binary; =1 if the student has a job offer by the time of graduation and =0 if not. Model 1, is a regression of UCS on job offer to set a baseline for our key variable. From the coefficient in the marginal effects using UCS increases the probability of receiving an offer by 1.8% but the result is not statistically significant. Model 2 includes UCS and demographic characteristics; gender, age and ethnicity. The marginal effect of UCS on job offer is still positive (2.1%) but not statistically significant. In addition, gender is significant with males having a 9.3% higher probability of receiving an offer. Age is also significant. It would seem that the older the student the less likely he/she will receive an offer by graduation. Compared to 20 – 24-year-old students, students within the ages of 25-30 and over 30 are -13.3% and -10.6% less likely to receive an offer by graduation. Model 3 includes school characteristics; CGPA, majors and schools to the previous model. The marginal effect of UCS on job offer is still positive but smaller (0.9%) and not statistically significant. Furthermore, CGPA is statistically significant; a point increase in CGPA is associated with a 13.7% increase in the probability of receiving an offer. Compared to Liberal Arts and related majors, Business and related majors have a 27.8% higher probability of receiving an offer. Similarly, Computer Science and related majors, Engineering and Psychology majors have a 30.8%, 28.8% and 22.5% increased probability respectively of receiving an offer compared to liberal arts and related majors2. The results are also statistically significant. Dummies for school types are included in the regression but not reported. In addition, the effect of age on receiving an offer is negative and consistent with the previous model; being between the ages of 25-30 and over 30 is related with a -9.6% and -10.4% reduced probability of receiving an offer and is statistically significant. Furthermore, from the data, a higher proportion of Asian-Americans have majors associated with higher job offers, such as: Business & related, Computer Science & related and Engineering majors. Therefore, after controlling for school characteristics (CGPA, Majors and Schools), Asian-Americans also experience a significant and reduced probability of receiving an offer compared to White-Americans; from -3% in model 2 to -10.8% in model 3. Finally, Model 4 includes other relevant variables such as internship and parent’s education to make up the full model. The marginal effects of UCS on job offer is consistent with all previous models. It is small (0.4%) and not statistically significant. Students that participated in experiential learning have characteristics associated with an increase with probability of receiving an offer. Participating in an internship (Yes, internship), cooperative education (Yes, Co-op) and both programs (Yes, both) is associated with a 13.1%, 15.6% and 19.5% increase in probability respectively of receiving an offer. In addition, students that have parents with higher education have attributes that increase their probability of receiving an offer by 5.4% compared to first generation students or students whose parents’ highest education level is high school. This is possibly because parents who fall in this category also earn better and have higher quality jobs. This is turn is beneficial in terms of job contacts in the labor market and advice on efficient job search skills which can lead to a higher chance of receiving an offer. In

2 For full list of related majors in regression tables please refer to Table 2B or Table 2D of descriptive statistics.

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addition, compared to Liberal Arts and related majors; Business and related majors, Computer Science and related Majors, Engineering and Psychology have a 27.6%, 30.7%, 27.7%, and 24.4% increased probability respectively of receiving an offer. Similar to the previous model, Asian-Americans have a -10.1% probability of receiving an offer compared to White-Americans. CGPA is also significant with a 1-point increase in CGPA increasing probability of receiving an offer by 11.6%. The negative relationship between the student’s age and the probability of receiving an offer still persists in this model. However, the effects are not statistically significant. Being male also has a higher probability of receiving an offer compared to female but it is not significant. Table 4B, are extensions to the full-model (Model 4, Table 4A). Table 4B contains models with interaction terms and an IV estimation. Model 5, contains the full model and an interaction of UCS with ethnicity. The results from Table 3 show that ethnicity is a strong predictor for who uses UCS. Model 5 will investigate if ethnicities that use UCS more have a higher probability of receiving an offer as a result. The interaction terms show that being an African American and using UCS is associated with a 3.9% higher probability of receiving an offer compared to being white. Similarly, using UCS and being Asian or Hispanic is associated with a 9.8% and 1.6% higher probability respectively of receiving an offer compared to White American. On the other hand, international students and multiracial students who use UCS experience a lower probability of receiving an offer (-16.6% and -17.7% respectively) compared to White Americans. However, interaction terms are not jointly significant. Figure 3 provides an alternative way to visualize the return to using UCS to each ethnic group in terms of probability of receiving an offer. From figure 3, African Americans, Asian Americans, Hispanic Americans and Native Americans all receive positive returns to using UCS (although the return is small in some cases) in terms of improved probability of receiving an offer. International and Multiracial students experience a negative return and White Americans experience virtually no return. Other results in Model 5 are consistent with previous models: A 1-point increase in CGPA is associated with a 10.7% increase in the probability of receiving an offer. Business and related majors, Computer Science and related majors, Engineering and Psychology have a 27.6%, 30.9%, 27.4% and 24.2% higher probability respectively of receiving an offer compared to Liber arts majors. Students who have parents with higher education are associated with a 5.4% higher probability of receiving an offer. Although not statistically significant, male have a positive higher probability of receiving an offer compared to females (2.7%) and the older the student the less likely to receive an offer. Model 6 is the full-model with an interaction of UCS with Parents (Higher Education). The model investigates if using UCS and having parents with higher education increases the probability of receiving an offer. The effect on the interaction term shows a negative relationship. That is, using UCS and having parents with higher education decreases your probability of receiving an offer (-2.6%) compared to first-generation students. Also suggests that first-generation students get more out of using UCS. Other results in Model 6 are similar to results in previous models except for minor changes in magnitude. Although not reported the interaction terms of UCS by Major are not jointly statistically significant. Figure 4 in the appendix provides a visualization of the return to UCS by major in terms of improved probability of receiving an offer.

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To control for endogeneity in the choice of UCS I use a two stage least squared approach with the student per UCS staff ratio as an instrument3. The instrument has to satisfy the relevance and exclusion restrictions. That is, the instrument (student per UCS staff ratio) has to be highly correlated with the endogenous variable (UCS) and has to be uncorrelated with the error term. The coefficient on the instrument from the first stage regression is negative and statistically significant (-0.0000294 (0.0000125)) confirming relevance; a higher student to staff ratio should negatively impact UCS usage. The F-statistic from the first stage is 3.50 indicating a weak instrument problem based on Stock et al. (JBES, 2002) threshold (F < 10). In addition, the student to UCS staff ratio should not directly affect the probability of receiving an offer. However, because we do not have complete information on the UCS staff size for all the universities in the sample most of the variation in the instrumental variable is coming from student enrollment data which may be highly correlated in with schools and in turn affect the quality of the instrument. Because the linear probability model is used in the IV estimation, I first provide a baseline model using OLS for comparison. The results in the OLS model are similar to Model 4, Table 4A except the magnitude on the significant variables are smaller. The coefficient on the UCS variable in the 2SLS is negative and not statistically significant (with high standard errors) which runs counter to the theory and previous models. As mentioned earlier, this might be due to the quality of the instrument in the IV estimation. On the other hand, the control variables are consistent in direction and magnitude with effects in the previous models. Finally, From Table 4A and 4B, the UCS variable across all models (except IV) has a positive effect on probability of receiving an offer. This is consistent with the theory and Holzer (1988) where all search methods have a positive effect on the offer rate. However, the result is not statistically significant and therefore we cannot say if UCS search method has any effect on the probability of receiving an offer by the time of graduation. Table 5A, shows results from the regression of UCS on starting salaries using an interval regression. The natural logarithm of salary is used in the regression. Model 1, is the regression of UCS on salaries to serve as a baseline. UCS has a positive effect on salaries but the result is not statistically significant. Model 2, includes demographic characteristics to the regression. In this model the effect of UCS on salary is negative and not statistically significant. Being male is significant with a 22% increase in salary compared to females. There also seems to be a negative relationship between the age and salary outcome but this is not statistically significant. Being Asian-American is also associated with a 28% increase in starting salary compared to White Americans. Model 3, includes school characteristics (CGPA, Major and Schools) to the model. The coefficient of the UCS on starting salaries in positive but not statistically significant. CGPA is significant with a 1-point increase leading to 19.2% increase in starting salary. Computer Science and related majors and Engineering also lead to much higher salaries compared to liberal arts and related majors and the result is statistically significant. This result is also consistent with an article by the Economist (Economist, “Is your degree worth it?”) which cites research done by PayScale on earnings that found that Engineering, Computer Science and Math majors get paid more on average than all other majors taking into account institution. Being male is still positive and statistically significant at 5.7%. After controlling for

3 The 2SLS estimation is done with a linear probability model (LPM). The LPM provides a more robust result that reduces

assumptions made by the probit model to ensure consistency of IV estimates.

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school characteristics, the effect of Asian-Americans on salary although positive is reduced; from 28% in model 2 to 13.9% in model 3. This might be because in the data a higher proportion of Asian-Americans have majors that are also associated with higher salary (Computer Science & related and Engineering) and controlling for these majors reduces the effect of being Asian-American. Model 4, represents the full-model. In this model, using UCS is associated with a 0.2% increase in starting salaries but the result is not statistically significant. In addition, students who participated in experiential learning experience an increase in starting salaries. Participating in internships, cooperative education and both programs is associated with a 10%, 11% and 17% increasing in starting salaries respectively. Also having a parent with higher education is associated with a 10.7% increase in salary. Coefficient on other variables are similar to the previous model except for age. Being older in this model is associated with an increase in starting salaries but the result is not statistically significant. Table 5B, contains extensions to the full-model in Table 5A. Specifically, it tries to deal with the problem of endogeneity in the UCS variable using an IV estimation and also investigates if there is a sample selection bias into belonging in the salary sample using a Heckman estimator. The IV estimation is done using a 2SLS on the linear salary model. Because the linear probability model is used in the IV estimation, I first provide a baseline salary model using OLS for comparison. The midpoints of the salary intervals are used in the OLS estimation. The OLS results are similar to the results in model 4, Table 5A, except for slight differences in magnitude. The effect of UCS on starting salary is still positive and not statistically significant at 0.1%. The first-stage regression of the 2SLS is the same as the first stage reported earlier for Table 4B with an F-statistic of 3.91. The effect of UCS on starting-salaries using the IV estimation is similar to previous models; positive but not statistically significant. Finally, I investigated for the presence of sample selection bias (into the salary-sample) with a Heckman sample selection model using a maximum likelihood estimator and identifying off the functional form in the first stage4. After estimation, a Wald test for sample selection bias is performed and I do not reject the null hypothesis that the correlation (between the error terms in first and second stage models) is zero5. Therefore, sample selection bias does not exist and the OLS model is preferred. From Table 5A and 5B, it would seem that the effect of UCS on starting salaries is small and positive but the result is not statistically significant. Therefore, the paper cannot assert that University Career Services has an effect on the wages of college graduating seniors. The results of UCS on employment outcomes are not statistically significant and therefore we cannot say that UCS has any significant effect on employment outcomes based on the empirical analysis. In addition, across all the regressions CGPA has a positive and significant effect on employment outcomes. Participating in experiential learning and having parents with higher education also has a positive and significant effect on employment outcomes. Computer Science and Engineering majors also have a positive and statistically significant effect on receiving an offer and starting-salaries. In addition, the effect of being Asian-American has a negative effect on receiving an offer and a positive effect on salaries. It is worth noting that the

4 Selection into the salary-sample is conditional on receiving an offer (and reporting a starting-salary). A drawback was

finding of a valid excluding instrument that explains the offer rate, but not what salary the offer provides. Thus the need to identify off the functional form. 5 The Wald test of independent equations. (H0: rho = 0): chi2(1) = 0.02 and Prob > chi2 = 0.8772.

13

effect of Asian-Americans on both employment outcomes reduces after controlling for school characteristics. As a higher proportion of Asian-Americans take majors that are also associated with a higher probability of employment and starting-salary. An empirical drawback is the quality of the instrument used in the IV estimation; student per UCS staff ratio. I obtained enrollment data from all the universities in the sample from IPEDS but the NACE survey does not have similar data on UCS staff for all the universities. This could be the cause of the weak instrument as most of the variation was coming from the enrollment information and not the UCS staff size. Obtaining more information on university career services for a large sample of universities such as: budget information, staff size and other resources can improve the quality of instruments used to control for endogeneity in the UCS variable. Furthermore, consistent with the theory it would have been ideal to have a variable for UCS search effort. That is, a variable that measures the amount of time spent using UCS facilities and services in the year before graduation. Finally, this empirical methodology delivers results for including or not including university career services in an individual’s search strategy. That is, it does not provide pure UCS effects controlling for all other search methods. An alternative methodology also used by Mahringer and Weber (2006) would be to collect data on the unique search method that lead to an offer for each individual (successful search methods) and identify pure UCS effects from a categorical variable of successful search methods. Another way to obtain pure UCS effects might also be using an experimental approach with full randomization (similar to a randomized control trial) used by Berg and van der Klaauw (2006) where there is a treated group (use UCS) and a control group (did not use UCS). However, such an experiment would be costly and would have problems with generalization to an entire population. Conclusions: The paper investigates the effect of University Career Services as a job search methods on employment outcomes of college graduating students. Specifically, it investigates the determinants of UCS and its efficiency in terms of offer rate and wage distribution. I use the NACE 2015 student survey dataset. The survey questions focus on post-graduation destination and activities while in school that lead to the destination. The paper uses two samples: the full-sample and the salary-sample to investigate the effect of UCS on job offer and salaries respectively. Both samples include only individuals that participated in the job-search process and from the descriptive statistics there are no distinctive differences between the samples. However, the salary-sample was considerably smaller than the full-sample. A probit regression is used to determine who uses university career services. The results show that ethnicity is a strong predictor of UCS. A probit regression is also used to determine the effect of UCS on job offers and interaction terms of UCS with ethnicity and parent’s education on the probability of receiving an offer. The results show that UCS has a positive effect on the probability of receiving an offer but not statistically significant. Similarly, the return to using UCS is positive for some ethnic groups and first-generation students but the results are also not statistically significant. I also address endogeneity in the UCS variable using an instrumental variable approach. An interval regression is also used to investigate the effect of UCS on the salaries. The results show a small but positive effect of UCS on salaries but the effect is also not significant. Therefore, the paper does not see an effect of using university career services on employment outcomes.

14

References: Addison, J.T. and P. Portugal (2001). “Job Search Methods and Outcomes”. IZA Discussion Paper

No. 349. A. C. Cameron and P. K. Trivedi (2005), "Microeconometrics: Methods and Applications," Cambridge University Press, New York. Blau, D.M. and P.K. Robins (1990). “Job Search Outcomes for the Employed and Unemployed”.

Journal of Political Economy, 98, pp. 637-655. Boheim, R. and Taylor, M. P. (2001). “Job search methods, intensity and success in Britain in the

1990s”. Working Paper 01-07, Institute for Social and Economic Research, University of Essex, Colchester. Gregg, P. and J. Wadsworth (1996). “How Effective Are State Employment Agencies? Jobcentre

Use and Job Matching in Britain. Oxford Bulletin of Economics and Statistics, 58, pp.43-67. Holzer, H.J. (1988). “Search Method Used by the Unemployed Youth”. Journal of Labor

Economics, 6, pp.1-20. Holzer, H. J. (1987). “Informal job search and black youth unemployment”, American Economic

Review, 77, 3, 446. "Is your degree worth it? It depends on what you study, not where" Economist. Economist, 14

March. 2015. Web. Accessed 4 September. 2016. http://www.economist.com/news/united-states/21646220-it-depends-what-you-study-not-where

Kuhn, P. and M. Skuterud (2004). “Internet Job Search and Unemployment Durations”, The

American Economic Review, 94, 1, 218. Mortensen, D. J. (1986).” Job search and labor market analysis”. In: Ashenfelter, O., Layard, R.

(Eds.), Handbook of Labor Economics. Vol. 2. North Holland, Amsterdam. Osberg, L. (1993).” Fishing in different pools: job-search strategies and job finding success in

Canada in the early 1980s”. Journal of Labor Economics 11, 348-386. Stock, James H.; Jonathan H. Wright; and Motohiro Yogo. (2002). “A Survey of Weak

Instruments and Weak Identification in Generalized Method of Moments.” Journal of Business and Economic Statistics, 20, 518-529.

Van den Berg, G. J., van der Klaauw, B., (2006). “Counseling and monitoring of unemployed

workers: Theory and evidence from a controlled social experiment.” International Economic Review Vol. 47, No. 3, August 2006.

Weber, A. and Mahringer, H. (2008). “Choice and success of job search methods”, Empirical

Economics, 35, 1, 153-178.

15

Appendix

Table 2A Descriptive Statistics: Full Sample

All Students UCS Non-UCS

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

CGPA 3.40 0.391 3.39 0.39 3.43 0.40

Internship:

Yes, both 0.03 0.18 0.04 0.18 0.03 0.18

Yes, co-op 0.03 0.16 0.03 0.16 0.03 0.16

Yes, internship 0.65 0.48 0.66 0.48 0.62 0.49

Parents (Higher Education)

0.63 0.48 0.64 0.48 0.62 0.49

Male 0.34 0.47 0.35 0.48 0.31 0.46

Age:

25 to 30 0.07 0.25 0.07 0.26 0.06 0.24

Over 30 0.06 0.23 0.05 0.23 0.05 0.22

Ethnicity:

African-American 0.05 0.21 0.05 0.22 0.03 0.17

Asian-American 0.07 0.25 0.07 0.26 0.06 0.24

Hispanic-American 0.1 0.30 0.10 0.30 0.09 0.28

International 0.03 0.16 0.03 0.17 0.01 0.12

Multiracial 0.03 0.17 0.03 0.17 0.03 0.16

Native American 0.0043 0.07 0.00 0.07 0.00 0.06

Number of Observations

3,156 2,394 762

16

Table 2B Descriptive Statistics: Full Sample

All Students UCS Non-UCS

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Schools (Carnegie Reclassification)

Baccalaureate Colleges 0.15 0.36 0.16 0.37 0.12 0.32

Doctoral Universities: Higher Research

0.18 0.39 0.17 0.38 0.21 0.41

Doctoral Universities: Highest Research

0.29 0.45 0.28 0.45 0.30 0.46

Doctoral Universities: Moderate Research

0.03 0.18 0.04 0.19 0.03 0.17

Master's Colleges & Universities: Large

0.19 0.39 0.19 0.40 0.19 0.39

Master's Colleges & Universities: Medium

0.11 0.31 0.11 0.31 0.11 0.31

Master's Colleges & Universities: Small

0.04 0.20 0.04 0.20 0.04 0.20

Majors (NACE Reclassification)

Biological and Biomedical Sciences.

0.04 0.04 0.03 0.18 0.04 0.20

Business, Management, Marketing, and Related Support Services.

0.28 0.28 0.31 0.46 0.20 0.40

Communication, Journalism and Related Programs.

0.07 0.07 0.07 0.26 0.07 0.26

Computer and Information Sciences and Support Services.

0.04 0.04 0.04 0.19 0.04 0.20

Education. 0.08 0.08 0.06 0.25 0.11 0.32 Engineering. 0.12 0.12 0.12 0.32 0.13 0.34 Family and Consumer Sciences/Human Sciences.

0.01 0.01 0.01 0.09 0.00 0.06

Health Professions and Related Programs.

0.06 0.06 0.06 0.23 0.08 0.28

Homeland Security, Law Enforcement, firefighting and Related Protective Services.

0.01 0.01 0.01 0.12 0.02 0.12

Languages, Literature and Linguistics.

0.02 0.02 0.02 0.14 0.01 0.12

Mathematics and Statistics.

0.02 0.02 0.02 0.13 0.02 0.15

17

Physical Sciences. 0.02 0.02 0.01 0.11 0.02 0.15 Psychology. 0.04 0.04 0.04 0.20 0.03 0.17 Social Sciences. 0.09 0.09 0.09 0.29 0.08 0.26 Visual and Performing Arts.

0.02 0.02 0.02 0.13 0.03 0.18

Other. 0.07 0.07 0.07 0.25 0.08 0.27

Number of Observations 3,156 2,394 762

18

Table 2C Descriptive Statistics: Salary Sample

All Students UCS Non-UCS

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

CGPA 3.45 0.381 3.45 0.374 3.47 0.39

Internship:

Yes, both 0.04 0.21 0.04 0.21 0.04 0.20

Yes, co-op 0.03 0.16 0.03 0.17 0.02 0.13

Yes, internship 0.74 0.44 0.74 0.44 0.73 0.44 Parents (Higher Education)

0.70 0.46 0.70 0.46 0.72 0.45

Male 0.40 0.49 0.40 0.49 0.39 0.49

Age:

25 to 30 0.04 0.20 0.04 0.20 0.03 0.17

Over 30 0.04 0.20 0.04 0.19 0.04 0.19

Ethnicity:

African-American 0.04 0.19 0.05 0.21 0.00 0.07

Asian-American 0.08 0.27 0.08 0.27 0.07 0.25

Hispanic-American 0.07 0.25 0.07 0.26 0.04 0.20

International 0.03 0.16 0.03 0.17 0.01 0.12

Multiracial 0.02 0.15 0.02 0.15 0.03 0.17

Native American 0.00 0.06 0.00 0.06 0.00 0.07

Number of Observations

869 663 206

19

Table 2D Descriptive Statistics: Salary Sample

All Students UCS Non-UCS

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Schools (Carnegie Reclassification)

Baccalaureate Colleges 0.14 0.35 0.14 0.34 0.12 0.32 Doctoral Universities: Higher Research

0.16 0.37 0.16 0.37 0.15 0.35

Doctoral Universities: Highest Research

0.40 0.49 0.38 0.48 0.45 0.50

Doctoral Universities: Moderate Research

0.04 0.19 0.04 0.20 0.02 0.15

Master's Colleges & Universities: Large

0.14 0.34 0.14 0.35 0.13 0.34

Master's Colleges & Universities: Medium

0.08 0.27 0.08 0.28 0.07 0.26

Master's Colleges & Universities: Small

0.04 0.20 0.04 0.20 0.05 0.22

Majors (NACE Reclassification)

Biological and Biomedical Sciences.

0.02 0.14 0.02 0.14 0.02 0.14

Business, Management, Marketing, and Related Support Services.

0.37 0.48 0.41 0.49 0.26 0.44

Communication, Journalism and Related Programs.

0.04 0.19 0.04 0.20 0.05 0.21

Computer and Information Sciences and Support Services.

0.06 0.24 0.06 0.23 0.08 0.27

Education. 0.03 0.18 0.02 0.15 0.07 0.25 Engineering. 0.21 0.40 0.19 0.39 0.25 0.43 Family and Consumer Sciences/Human Sciences.

0.01 0.10 0.01 0.10 0.01 0.10

Health Professions and Related Programs.

0.05 0.21 0.04 0.20 0.05 0.22

Homeland Security, Law Enforcement, firefighting and Related Protective Services.

0.01 0.08 0.01 0.08 0.01 0.10

Languages, Literature and Linguistics.

0.01 0.11 0.01 0.11 0.01 0.10

Mathematics and Statistics.

0.01 0.12 0.01 0.11 0.02 0.14

20

Physical Sciences. 0.02 0.14 0.02 0.12 0.03 0.18 Psychology. 0.03 0.18 0.04 0.18 0.02 0.15 Social Sciences. 0.08 0.27 0.08 0.27 0.04 0.20 Visual and Performing Arts.

0.02 0.13 0.01 0.11 0.03 0.17

Other. 0.03 0.18 0.03 0.17 0.05 0.21

Number of Observations 869 663 206

21

Figure 1

Figure 2

0%

2%

4%

6%

8%

10%

12%

14%

16%

% o

f St

ud

ents

Salary Distribution by UCS

UCS: Not Used UCS: Used

22

Table 3 Determinants of UCS, Probit Estimation

Full Sample Salary Sample

Variables Coeff Marginal

Effects Coeff

Marginal

Effects

CGPA: -0.074 (0.077)

-0.023 (0.023)

-0.040 (0.148)

-0.012 (0.045)

Internship:

Yes, both 0.164 0.050 0.202 0.062

(0.150) (0.043) (0.267) (0.079)

Yes, co-op 0.273 0.079 0.611 0.159

(0.166) (0.044) (0.366) (0.077)*

Yes, internship 0.095 0.029 0.113 0.036

(0.061) (0.019) (0.146) (0.048)

Parents (Higher Education) 0.067 (0.058)

0.020 (0.018)

0.006 (0.123)

0.002 (0.038)

Male 0.113 0.034 0.033 0.010

(0.060) (0.018) (0.113) (0.034)

Age:

25 to 30 0.178 0.051 0.293 0.080

(0.112) (0.030) (0.293) (0.071)

Over 30 0.046 0.014 -0.049 -0.016

(0.122) (0.036) (0.311) (0.099)

Ethnicity:

African-American 0.353 0.098 - -

(0.143)* (0.034)**

Asian-American 0.158 0.047 0.221 0.065

(0.107) (0.031) (0.190) (0.052)

Hispanic-American 0.181 0.054 0.470 0.125

(0.094) (0.026)* (0.220)* (0.048)**

International 0.432 0.116 0.546 0.140

(0.186)* (0.041)** (0.356) (0.070)*

Multiracial 0.127 0.039 -0.099 -0.033

(0.158) (0.046) (0.312) (0.106)

Native-American 0.134 0.041 -0.226 -0.078

(0.407) (0.117) (0.885) (0.321)

Constant 0.814 (0.453)

0.732

(0.824)

Log Likelihood -1,531.93 -404.76

Pseudo-R2 0.04 0.07

Number of observations 3,156 869

* p<0.05; ** p<0.01

NOTE: Dependent Variable is binary: 1 if UCS method is used and 0 otherwise. Standard errors are in parentheses. **, *,

indicate significance at the 1% and 5% level. Marginal Effects are estimated at the mean, for categorical variables it is in

reference to the base level. Reclassified Majors and Schools are included in the regression but only significant results are

reported. The F-test show that schools are jointly statistically significant: chi2( 7) = 22.94 and Prob > chi2 = 0.0017.

Majors are also jointly statistically significant: chi2( 16) = 75.64 and Prob > chi2 = 0.0000

23

Table 4A Effects of UCS Search Method on Job Offer, Probit Estimation

Model 1 Model 2 Model 3 Model 4

Variables Coeff Marginal

Effects Coeff

Marginal

Effects Coeff

Marginal

Effects Coeff

Marginal

Effects

University Career

Services (UCS)

0.047 0.018 0.053 0.021 0.022 0.009 0.011 0.004

(0.052) (0.021) (0.055) (0.022) (0.058) (0.023) (0.058) (0.023)

Male 0.233 0.093 0.081 0.032 0.068 0.027

(0.049)** (0.020)** (0.056) (0.022) (0.056) (0.022)

Age:

25 to 30 -0.342 -0.133 -0.245 -0.096 -0.151 -0.060

(0.094)** (0.035)** (0.102)* (0.039)* (0.104) (0.041)

Over 30 -0.269 -0.106 -0.264 -0.104 -0.146 -0.058

(0.105)* (0.040)** (0.115)* (0.044)* (0.117) (0.046)

Ethnicity:

African American -0.006 -0.002 0.039 0.015 0.044 0.018

(0.114) (0.045) (0.126) (0.050) (0.128) (0.051)

Asian American -0.076 -0.030 -0.257 -0.101 -0.258 -0.101

(0.093) (0.037) (0.098)** (0.037)** (0.099)** (0.038)**

Hispanic

American -0.142

(0.081) -0.056 (0.032)

-0.097 (0.087)

-0.039 (0.034)

-0.054 (0.089)

-0.021 (0.036)

International 0.005 0.002 -0.132 -0.053 -0.137 -0.054

(0.145) (0.058) (0.152) (0.060) (0.155) (0.061)

Multiracial 0.034 0.013 0.047 0.019 0.034 0.014

(0.145) (0.058) (0.151) (0.060) (0.152) (0.061)

Native American -0.015 -0.006 0.012 0.005 -0.025 -0.010

(0.360) (0.143) (0.369) (0.147) (0.367) (0.146)

CGPA 0.345 0.137 0.284 0.113

(0.068)** (0.027)** (0.069)** (0.028)**

24

Majors:

Business & related 0.720 0.278 0.714 0.276

(0.205)** (0.072)** (0.206)** (0.073)**

Computer Sci. &

related

0.796 0.308 0.795 0.307

(0.233)** (0.084)** (0.234)** (0.084)**

Engineering 0.745 0.288 0.717 0.277

(0.212)** (0.075)** (0.213)** (0.076)**

Psychology 0.583 0.225 0.633 0.244

(0.236)* (0.086)** (0.237)** (0.086)**

Parents (Higher

Education)

0.135 0.054

(0.054)* (0.022)*

Internship:

Yes, both 0.495 0.195

(0.139)** (0.054)**

Yes, Co-op 0.397 0.156

(0.149)** (0.059)**

Yes, Internship 0.334 0.131

(0.059)** (0.023)**

Constant -0.126 -1.716 -2.081

(0.051)* (0.412)** (0.432)**

Likelihood -1,990.64 -1,813.48 -1,773.44

Pseudo-R2 0.01 0.06 0.07

Number of

observations 3,156 3,156 3,156

* p<0.05; ** p<0.01

NOTE: Dependent Variable is binary: 1 if the student received a Job offer by graduation and 0 otherwise. Standard errors are in parentheses. **, *, indicate significance at the 1% and

5% level. Marginal Effects are estimated at the mean, for categorical variables it is in reference to the base level. Reclassified Majors and Schools are included in the regression but

only significant results are included in the table.

25

Table 4B Effects of UCS Search Method on Job Offer: Probit, OLS & IV

Estimation

Model 5 Model 6 OLS IV

Variables Coeff. Effects Coeff. Effects Coeff. 2SLS

University Career

Services 0.004 (0.021)

0.002 (0.066)

0.043 (0.096)

0.003 (0.021)

0.004 (0.021)

-0.372 (0.502)

CGPA 0.268 0.107 0.269 0.107 0.107 0.097

(0.068)** (0.027)** (0.068)** (0.027)*

* (0.025)**

(0.030)**

Male 0.068 0.027 0.070 0.028 0.025 0.037

(0.055) (0.022) (0.055) (0.022) (0.021) (0.027)

Age:

25 to 30 -0.148 -0.059 -0.148 -0.059 -0.057 -0.034

(0.104) (0.041) (0.103) (0.040) (0.038) (0.050)

Over 30 -0.149 -0.059 -0.147 -0.058 -0.057 -0.042

(0.116) (0.045) (0.115) (0.045) (0.043) (0.046)

Ethnicity:

African-American

0.027 0.043 0.114 0.045 0.016 0.047

(0.298) (0.050) (0.124) (0.049) (0.048) (0.066)

Asian-American -0.469 -0.100 -0.243 -0.095 -0.094 -0.079

(0.208)* (0.038)** (0.098)* (0.038)* (0.036)* (0.045)

Hispanic-American -0.091 -0.023 -0.059 -0.023 -0.019 -0.001

(0.183) (0.035) (0.088) (0.035) (0.033) (0.042)

International 0.289 -0.022 -0.103 -0.041 -0.052 -0.013

(0.446) (0.065) (0.154) (0.061) (0.059) (0.080)

Multiracial 0.426 0.025 0.051 0.020 0.015 0.024

(0.332) (0.060) (0.151) (0.060) (0.057) (0.058)

Native-American -0.370 -0.012 -0.021 -0.008 -0.009 0.005

(0.826) (0.148) (0.369) (0.147) (0.137) (0.149)

Majors:

Business & related 0.714 0.276 0.714 0.276 0.262 0.282

(0.207)** (0.073)** (0.206)** (0.073)*

* (0.069)**

(0.081)**

Computer Sci. &

related

0.800 0.309 0.788 0.305 0.291 0.261

(0.235)** (0.085)** (0.234)** (0.084)*

* (0.080)**

(0.096)**

Engineering 0.710 0.274 0.716 0.277 0.263 0.237

(0.214)** (0.076)** (0.213)** (0.076)*

* (0.072)**

(0.086)**

Psychology 0.626 0.242 0.628 0.242 0.230 0.238

(0.238)** (0.087)** (0.237)** (0.086)*

* (0.083)**

(0.090)**

Parents (Higher

Education) 0.136

(0.054)* 0.054

(0.021)* 0.187 (0.104)

0.053 (0.021)*

0.050 (0.020)*

0.059 (0.024)*

Internship:

Yes, both

0.492 0.194 0.478 0.189 0.183 0.200

(0.138)** (0.054)** (0.138)** (0.054)*

* (0.050)** (0.058)**

26

Yes, co-op 0.355 0.140 0.354 0.139 0.147 0.178

(0.146)* (0.058)* (0.147)* (0.058)* (0.054)** (0.071)*

Yes, internship 0.329 0.129 0.331 0.130 0.125 0.135

(0.058)** (0.022)** (0.058)** (0.022)*

* (0.022)** (0.026)**

UCS × Ethnicity:

ucs afr.-american

0.106 0.039

(0.325) (0.119)

ucs asn.-

american 0.284 0.098

(0.233) (0.079)

ucs his-american 0.043 0.016

(0.204) (0.075)

ucs international -0.454 -0.166

(0.476) (0.171)

ucs multiracial -0.481 (0.372)

-0.174 (0.130)

ucs nat.-american 0.448 0.161

(0.921) (0.320)

UCS Parents Ed. -0.070 -0.026

(0.118) (0.553)

Constant -1.789 -1.841 -0.273 0.060

(0.411)** (0.414)** (0.157) (0.448)

Likelihood -1,829.96 -1,832.22

Pseudo-R2 0.07 0.07

F-statistic 3.50

R2 0.046

Number of obs. 3,156 3,156 3156 3,156

* p<0.05; ** p<0.01

NOTE: Dependent Variable is binary: 1 if the student received a Job offer by graduation and 0 otherwise. Standard errors

are in parentheses. **, *, indicate significance at the 1% and 5% level. Marginal Effects are estimated at the mean, for

categorical variables it is in reference to the base level. Reclassified Majors and Schools are included in the regression but

only significant results are included in the table. Test of joint significance of UCS Ethnicity interaction parameters show

that the interaction terms are not jointly significant: chi2( 6) = 5.74 and Prob > chi2 = 0.4524. The F-statistic and R2 for

the IV estimation are from the first-stage regression of the 2SLS. Although not reported the interaction terms of UCS

Major are not jointly statistically significant: chi2( 16) = 22.20 and Prob > chi2 = 0.1369 .Figure 4 below provides a

visualization of the return to UCS by major in terms of improved probability of receiving an offer.

27

Figure 3

Figure 4

28

Table 5A Effects of UCS Search Method on Salary, Interval Regression

Model 1 Model 2 Model 3 Model 4

Variables Coeff Coeff Coeff Coeff

University

Career Services

0.014 -0.002 0.006 0.002 (0.035) (0.034) (0.026) (0.026)

Male 0.221 0.057 0.061

(0.028)** (0.024)* (0.023)**

Age:

25 to 30 -0.122 -0.023 0.027

(0.071) (0.049) (0.051)

Over 30 -0.012 0.074 0.140

(0.075) (0.075) (0.075)

Ethnicity:

African

American -0.123 0.018 0.051

(0.079) (0.069) (0.069)

Asian American 0.280 0.139 0.149

(0.042)** (0.033)** (0.033)**

Hispanic

American

0.012 0.038 0.078 (0.053) (0.047) (0.047)

International 0.162 0.150 0.152

(0.101) (0.076)* (0.077)

Multiracial 0.033 0.024 0.013

(0.100) (0.082) (0.081)

Native American -0.300 -0.268 -0.266

(0.288) (0.189) (0.193)

CGPA 0.192 0.167

(0.031)** (0.032)**

Majors:

Computer Sci. &

related

0.738 0.699

(0.329)* (0.302)*

Engineering 0.706 0.651

(0.325)* (0.299)*

Parents (Higher

Education)

0.107 (0.025)**

Internship:

Yes, both 0.172

(0.065)**

Yes, Co-op 0.112

(0.055)*

Yes, Internship 0.100 (0.035)**

Constant 10.635 9.547 9.317

(0.032)** (0.351)** (0.332)**

Likelihood -2,190.13 -1,922.46 -1,886.49

Number of 869 869 869

29

observations

* p<0.05; ** p<0.01

NOTE: Dependent variable Salary is in intervals. Standard errors are in parentheses. **, *, indicate significance at the 1%

and 5% level. The natural logarithm of Salary is used and Salary data is top and bottom censored. Reclassified Majors and

Schools are included in the regression but only significant results are included in the table.

30

Table 5B Effects of UCS Search Method on Salary: OLS, IV & Heckman

Estimation

OLS IV Heckman

Variables Coeff 2SLS Coeff

University Career

Services

0.001 0.831 0.001

(0.034) (0.511) (0.033)

Male 0.067 0.055 0.067

(0.029)* (0.039) (0.028)*

Age:

25 to 30 0.072 0.017 0.072

(0.061) (0.093) (0.059)

Over 30 0.142 0.157 0.142

(0.099) (0.128) (0.097)

Ethnicity:

African American 0.081 -0.149 0.081

(0.083) (0.176) (0.081)

Asian American 0.160 0.108 0.160

(0.035)** (0.066) (0.034)**

Hispanic American 0.093 -0.006 0.093

(0.058) (0.093) (0.057)

International 0.144 0.027 0.144

(0.093) (0.140) (0.091)

Multiracial 0.018 0.046 0.018

(0.099) (0.116) (0.096)

Native American -0.381 -0.353 -0.381

(0.287) (0.511) (0.280)

CGPA 0.189 0.198 0.189

(0.041)** (0.052)** (0.040)**

Majors:

Computer Sci. &

related

0.928 1.216 0.929 (0.422)* (0.479)* (0.412)*

Engineering 0.912 1.159 0.913

(0.418)* (0.465)* (0.409)*

Math & Statistics 0.834 1.139 0.834

(0.428) (0.501)* (0.418)*

Parents (Higher

Education)

0.114 0.112 0.114 (0.032)** (0.042)** (0.032)**

Internship:

Yes, both 0.169 0.128 0.169

(0.083)* (0.103) (0.081)*

Yes, Co-op 0.142 -0.004 0.142

(0.068)* (0.135) (0.066)*

Yes, Internship 0.124 0.101 0.124

(0.046)** (0.058) (0.045)**

Constant 8.996 8.100 8.992

(0.453)** (0.750)** (0.442)**

rho 0.003

31

lambda 0.001

R2 0.40 0.10

F-statistic 3.91

Log-pseudolikelihood -1873.875

Number of

observations 869 869 3,169

* p<0.05; ** p<0.01

NOTE: Dependent variable is the natural logarithm of Salary and the midpoints of the salary intervals are used. Standard

errors are in parenthesis. **, *, indicate significance at the 1% and 5% level. The F-statistic and R2 for the IV estimation

are from the first-stage regression of the 2SLS. The Heckman coefficients reported are from the second stage of the

regression. Do not reject the null hypothesis of no sample selection bias; The Wald test of independent equations. (H0: rho

= 0): chi2(1) = 0.02 and Prob > chi2 = 0.8772. Reclassified Majors and Schools are included in the regression but only

significant results are included in the table.