Community College Outcomes Vary by Program and Age

<p>In this article, we delve into the Career and Technical Education programs offered through North Carolina&rsquo;s community colleges, examining employment and wage outcomes by program area and age.</p>

Author: Oleksandr Movchan

In a blog article last year, we demonstrated that finishing a Career and Technical Education (CTE) credential at North Carolina community colleges is related to higher wage outcomes. However, we also know that wages vary by both age and area of study. The community college system enrolls students from a diverse array of life circumstances, with many students enrolling not only as young adults, but also as mid- or even late-career workers from a range of occupational backgrounds looking to retool or enhance their skills in a particular area. In this article, we home in on employment and wage outcomes for particular program areas, and include a focus on the age of graduates to better understand age variation in employment and wage outcomes across different fields of study. Overall, we find considerable variation in employment and wage outcomes by field of study, and some counterintuitive findings by age.

In Figure 1, median wage outcomes are shown grouped by program area for CTE associate degree graduates, academic years 2010-2014, from all NC community colleges (graduates with multiple majors are excluded to facilitate comparison). Median wages at one year after graduation range widely from around $13,000 to $35,000, depending on area of study, with graduates in Health Sciences and Industrial Technologies seeing the highest wages, followed by Construction and Engineering Technologies.

Rates of finding employment within North Carolina also differ by program (Figure 2). Health Sciences and Industrial Technologies again have the best outcomes, with 79% and 76% of graduates finding employment within three months after graduation, respectively, followed by increasing employment in the subsequent months.

While we do see substantial variation in how quickly graduates of different CTE programs find employment in North Carolina, it’s important to note that these employment rates do not include self-employment. Nationally, several occupational areas are known for high levels of self-employment, such as construction, agricultural, and artistic fields. It is likely that the levels of employment for these and other areas of study would be higher than shown in Figure 2 if we could capture self-employment. 

These programs also differ by age composition. While some program areas have a high percentage of younger graduates (age 18-24), most areas skew toward a somewhat older demographic. Figure 3 shows the age breakdown of CTE program areas, with the percentage in the largest age group shown in bold. 

Older workers typically have more work experience and earn a higher wage, on average, than their younger colleagues. However, Figure 4 indicates that while wages do increase each year after graduation, overall, the oldest community college CTE graduates do not attain the highest wages. The highest median wages occurred among the 25-40 age group, rather than the oldest workers (age 41-64). The oldest graduates (41-64) were also the slowest to find employment in North Carolina, as shown in Figure 5, whereas the youngest graduates (18-24) were the quickest.

Continuing our analysis of age and wages, we return to the two program areas noted previously as showing the highest employment and post-graduation wage outcomes: Health Sciences and Industrial Technologies. These programs are quite similar in terms of age composition, but are reversed in their gender composition (Figure 6).

For both of these top-earning programs, the youngest graduates are the quickest to find employment in North Carolina, while the oldest graduates are the slowest (Figure 7). The female-dominated Health Sciences field of study shows even larger gaps between the oldest graduates and their younger peers than the male-dominated area of Industrial Technologies. Although other processes may be at play, such as industry-specific differences, this general pattern is also consistent with recent research showing that older women face a greater and more robust level of discrimination in hiring as compared to older men.

Wage patterns by age also indicate that the oldest workers do not receive a wage advantage. The graduates aged 25-40 earned the highest median wage in both Health Sciences and Industrial Technologies, higher than both the 18-24 and 41-64 age categories (Figure 8). In Health Sciences, the oldest graduates (41-64) received the lowest median wage, whereas in Industrial Technologies, the youngest graduates (18-24) had the lowest initial median wage earnings. It’s possible that more workers in the 25-40 age range are building upon an existing career, while more of the individuals in the 41+ age range are transitioning to a new career or approaching retirement. However, age discrimination may also play a role in suppressing wage and employment outcomes for the oldest group of graduates.

Overall, we find that employment and wage outcomes for NCCCS CTE Associate program graduates vary considerably by program area and age. However, the oldest graduates do not fare as well as their younger peers in terms of employment and wage outcomes. This is also consistent for the two programs with the highest employment and wage outcomes, Health Sciences and Industrial Technologies. Furthermore, the female-dominated field of Health Sciences shows a greater disparity in employment and wages between the oldest workers and younger workers as compared to the male-dominated field of Industrial Technologies, consistent with research showing more evidence of hiring discrimination against older women as compared to older men. Regardless of age, though, these areas of study lead to higher than the typical CTE graduate wages. Additionally, graduates in the 25-40 aged group who would typically be considered ‘non-traditional’ show the best employment and wage outcomes overall. 

This blog article was created by Sibyl Kleiner and Oleksandr Movchan of the Data Analytics and Research Team at LEAD.
 

Related Topics: