A wealth of research exists exploring the benefits of implementing the Kuder Career Planning System® (KCPS) and the effects of career guidance in general. Experts have surveyed thousands of students to learn about how Kuder interventions have helped improve their ability to plan, set goals, foster motivation, and gain hope for the future (Trusty, 2012; Trusty, 2013; Grote, Trusty, & Chae, 2014). Because of this research, we’re able to accurately demonstrate why it’s important to take the Kuder assessments; identify interests, skills confidence, and work values; and let the results inform decision making.
Given this information, it seems obvious to me and my colleagues that the KCPS should be utilized as widely as possible. This raises the question: What reason might those tasked with making district or statewide decisions have for not yet adopting the system on behalf of their students? The answer is often related to the problem of scarcity. That is, resources – and by extension budgets – are limited. Due to the nagging problem of scarcity, legislators and administrators are forced to consider what economists refer to as opportunity costs when deciding how best to allocate their resources.
What is an opportunity cost?
Simply put, an opportunity cost is the benefit resulting from a choice that is forgone when an alternative choice is made. For example, if an employee with an hourly wage of $15 chooses to forego two hours of work so that she can go to the dentist’s office, her opportunity cost is $30. She chooses to accept this cost because, in the long-run, she understands that the benefit realized from deciding to visit the dentist will ultimately exceed that $30 value.
The difference when applying this idea to the KCPS is that, even though the micro-level (individual) benefits of investing in the system are very apparent, it’s difficult to quantify all potential benefits that will be realized at the macro-level (aggregately) over time. Regardless of the former, the latter will always be considered by those who make investment decisions of any type. In the context of a dental appointment, it’s far less complicated to conceptualize; we readily forego $30 dollars in wages because we know that it will help prevent costly problems like cavities or gum disease.
Demonstrating the big picture.
The challenge is finding ways to demonstrate that what’s seen at the micro level combines to produce quantifiable benefits at the macro level. A good starting point is to use micro-level findings to form hypotheses about what you might expect to see at the macro level. Prior research has linked usage of the KCPS to improvements in a variety of important career-related skills/qualities including self-awareness, opportunity awareness, development of educational and career planning, and increased motivation to improve classroom performance (Trusty, 2013).
Based on this knowledge, you could infer that there should be observable increases in academic metrics that might be associated with these increased student academic outcomes and workforce readiness. One hypothesis to this effect might be that increased KCPS usage improves high school completion as measured by graduation rates.
Testing a hypothesis.
While this hypothesis seems plausible, the actual task of identifying a statistical relationship between the KCPS and high school completion as measured by graduation rates is complex. A set of obstacles arise for researchers concerning the nature of the group that is sampled, the methods used to isolate the effect they hope to measure (that of system use), and the type of data that is available. Accurately making assumptions about entire populations requires adequate sample size and the proper method of selection.
Furthermore, demographic and socioeconomic variables related to high school completion must be observed so as to not mischaracterize the degree to which system usage affects completion relative to other factors.
When the necessary conditions are met, careful analysis can produce a “best-fit” model that allows us to estimate an approximate relationship between variables with a stipulated degree of certainty. This model is linear, and the relationship between variables is the slope of the line we estimate based on the observed data points – the ratio of the change in y to the change in x on a scatter plot.
In other words, we can plot the change in y (graduation rates) that occurs when x (KCPS usage) is increased by one unit. This estimate becomes the foundation for the projection of a large-scale economic benefit.
Expressing economic benefit.
This was the approach taken in the May 2017 study Kuder Navigator® and Texas Graduation Rates, in which modeling produced an estimate of an approximately 0.5% increase in a school’s graduation rate accompanying each year of Navigator use. This is the key finding for one specific reason: there are measurable returns to receiving a diploma, and to education in general. Existing research confirms that better educated populations are better paid populations. Additional benefits include multiplier effects like increased spending and tax revenues. Inversely, crime rates and transfer payments drop when the number of high school graduates increases. When we characterize the KCPS as being a tool for economic development, these are the types of improvements to which we’re referring.
Estimating returns to education.
So we’ve established that the positive relationship between education and income is widely accepted. The degree to which education affects income, however, is less definite. Depending on the time frame in question, the location, and the effect you’re trying to encompass, this measurement can vary widely. Frankly, there are so many factors involved that researchers are unable to find the exact fraction of income attributable to earning a high school diploma. The solution that we turn to is the expected value – a calculation based on average earnings and probability.
It’s in this phase of the process that my colleague Dr. Maureen Kilkenny – an expert on the quantitative analysis of regional economies, economic policy, and markets – has contributed substantially. Secondary literature has provided defensible estimates for the annual gains in terms of tax revenues and reduced social spending, but an annual expected gain in terms of income for the average high school graduate at the individual level requires more work.
To estimate this number in the Texas study, Dr. Kilkenny used data from the Current Population Survey and the Bureau of Labor Statistics to determine the likelihood that a high-school graduate or non-graduate between the ages of 18-24 is employed, and if so, the likelihood that they are either full- or part-time. Next, she found the expected annual earnings for graduates and non-graduates between the ages of 18-24 working either full or part-time. Once this data was collected, she used the initial probabilities to weight the earnings for each group (part-time or full-time earners) and summed them to establish expected earned income.
After this figure was established for both groups, the differential (the difference between the expected earned income of grads and non-grads) provided the estimate for the annual earnings boost that those with a diploma could expect to receive. In other words, the micro effect in terms of earnings. To estimate the macro effect, the differential was applied to every additional high school graduate to produce a total earnings increase.
This approach is just one of many that can be taken to estimate the large-scale economic gains resulting from use of the KCPS, as was our approach in the Texas study, which was conducted using historical data. Going forward, we hope to gather new data from those utilizing Kuder products and services to learn about the quantifiable benefits that come with better career planning. With new data, we can research test additional hypotheses and continue to further demonstrate the value of investing in career guidance and education.
Trusty, J. (2012). Kuder High School Graduate Follow-Up Study: Survey of Kuder Navigator Users who Graduated from 2010 to 2012. Retrieved from Kuder, Inc. https://www.kuder.com/research/outcome-studies/
Trusty, J. (2013). The Influence of Kuder Navigator: A Follow Up Survey of High School Students Through Graduates. Retrieved from Kuder, Inc. https://www.kuder.com/research/outcome-studies/
Grote, D., Trusty, J., & Chae, M. (2014). Career Coaching Influences on College & Career Readiness & Confidence A Focus on JAG Secondary Students. Retrieved from Kuder, Inc. https://www.kuder.com/research/outcome-studies/
McGrew, R., Kilkenney, M., & Gates, R. (2017) Kuder Navigator® and Texas Graduation Rates. Retrieved from Kuder, Inc. https://www.kuder.com/research/outcome-studies/
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