Methodology: Higher Education-Workforce Pipeline IPEDS CIP SOC crosswalk

Original Article

To Be Completed!

** If you are using Microsoft Internet Explorer or Google Chrom browser, you would not be able to read the formulas in this article. These formulas were written in MathML, a W3C standard, and can be viewed in FireFox.

This methodology is much improved over my previous methodology.

In essence, the number of graduates can potentially be hired for a program with CIP code C is:
$$\text{Potentially\_Hired}\left(C\right)=(G_c+\epsilon )\sum _{S_c}{O}_{s_c}\left[\frac{1}{\sum _{C_{S_c}}(G_{C_{S_c}}+\epsilon )}\right]$$
Where $G_x$ is the number of graduates for CIP x, $O_z$ is the number of job opening for a given SOC z, $S_x$ is the SOC S related to a CIP x, $C_z$ is the CIP C related to an SOC z, and $\epsilon$ is a very small number, which play important role when $G_x$ is zero.

Following are drafts and is to be ignored for Now!
Number of graduates for CIP c:

Number of job open for SOC s:
$O_s$
SOC related to a CIP c:
$S_c$
CIP related to an SOC s:
$C_s$
$G_{S_{C_s}}$
$\sum _{-1}^{\mathrm{+1}}$
$\sqrt{{(x_1-x_2)}^2+{(y_1-y_2)}^2}$

An exciting discovery ... Yeh !!

An exciting discovery!

I started this blog to provide my analysis to the Internet society and, especially, as my thanks to wonderful people who contribute contents freely to the Internet society. Every time I am on the Internet reading personal websites, blogs, or the Wikipedia, I appreciate their volunteer works. These individuals may not volunteer in any physical form, but their contribution to the human society is of no less. A while back, I thought about creating some kind of website that can recognize these wonderful people for their volunteer works. Unfortunately, I haven't have time to move forward on the project.

Now my discovery. The other day, I was surprised to find that one of my post is actually referenced by a book. I considered that a great honor and totally flattered. But I would like to attribute this to all the people who volunteered their time and made our life better and easier.

 

Evaluate Nebraska's Education-Workforce Pipeline - CIP SOC Crosswalk

The methodology employed by this article is a major improvement over the previous model in that suggested increase or decrease of award production will have no side effects on related fields!

With the P20 initiatives bobbling up all over the United States, the idea of education pipeline has extended from education to the workforce supply. The idea of linking higher education production to economic prosperity has found its way into legislatures.

The idea of aligning higher education production with the workforce demand isn't new. A crosswalk system that linking the field of study to appropriate occupation was last updated in March 2012 by National Center for Education Statistics (NCES) and the Bureau of Labor Statistics (BLS). The crosswalk, also known as CIP to SOC crosswalk, had been used in various workforce supply studies. However, because the complexity of the crosswalk, most of the studies limited their use to few focused fields of interest.

While the CIP to SOC crosswalk is a useful tool, limitations must be observed. The guiding principle behind the development summarized it well:

• “A CIP-SOC relationship must indicate a “direct” relationship, that is, programs in the CIP category are preparation directly for entry into and performance in jobs in the SOC category. The programs satisfy requirements for entry and/or prepare individuals to meet licensure or certification requirements to work in the occupation.”

An example can provide some clarity to the principle. In the nursing field, the registered nurse program (CIP 513801 Associate degree) is crosswalked to the registered nurse (RN) occupation (SOC 291141 Registered Nurse) and not the Licensed Practical Nurse (LPN) occupation (SOC 292061) even though the registered nurse can certainly worked in the LPN occupation. When interpret the result of this work, please keep this in mind, the result is based on 'appropriate' mapping and not the 'all possible mapping'. In the case of Registered Nurse, State of Nebraska can decide to produce more Registered Nurses to fill the LPN occupation. But that probably isn't the most efficient approach.

This article presents a general approach that is applied to all field of studies. The approach is applied to the state of Nebraska data set but can be easily applied to other states.

The data employed are the 2012-22 long term occupation projection data from Nebraska's Department of Labor and the 2013 degree awarded data from IPEDS' (Integrated Postsecondary Education Data System) completion survey.

While the methodology possessed some very desirable characteristics, like all researches, the data employed played vital role in the outcome. During the development, the job opening advertise data available from Nebraska Department of Labor's website were examined. The data for occupation like 'bus driver, School or Special Client' seems to be OK. The data for hot occupations like nurse or computer related positions post some challenges. For one, there were a lot of entries posted by staffing companies or recruiters and, by reading through some of the entries, some of them are simply phantom entries that mimic the real positions. For the nursing jobs, multi-leveled job listings are common. For example, a position can be advertized as RN/LPN, which begged the question of which category this position is counted under. With these observations, the result presented is based on Labor's projection data which can easily be replaced with more accurate data if such data is available.

The methodology employed by this article is different from that of our previous articles. This new approach eliminated couple of the mentioned limitations of previous methodology. Noticeably, when increase or reduce the number of graduates for a given field following the suggestion of this article would not interfere with the number suggested by this article for other fields. This is a major improvement over the previous model in which additional analysis is needed to make sure no side effect occurred in other related fields.

At the heart of this new methodology is the assumption of equal chance of employment, which means that all graduates that can be walked to an occupation, they all have the same chance of getting hired. If supported by real-life data, this factor can be modified to provide better result.

The top 10 education programs that could be targeted to produce more graduates are:

CIP	Title	Dgr Level	Job Open	UnFilled
490205	Truck and Bus Driver/Commercial Vehicle Operator and Instructor.	LessAssct	1,024	(953)
513999	Practical Nursing, Vocational Nursing and Nursing Assistants, Other.	LessAssct	468	(456)
513801	Registered Nursing/Registered Nurse.	Associate	702	(292)
520301	Accounting.	Bachelor	467	(156)
110101	Computer and Information Sciences, General.	Bachelor	259	(113)
520801	Finance, General.	Bachelor	300	(55)
460302	Electrician.	LessAssct	154	(96)
120401	Cosmetology/Cosmetologist, General.	LessAssct	126	(84)
110701	Computer Science.	Bachelor	121	(75)
480501	Machine Tool Technology/Machinist.	LessAssct	144	(65)
460303	Lineworker.	LessAssct	71	(56)

The top 10 eduction programs that could consider reducing graduates are:

CIP	Title	Dgr Level	Job Open	Over Supplied
540101	History, General.	Bachelor	22	163
513801	Registered Nursing/Registered Nurse.	Master	11	180
230101	English Language and Literature, General.	Bachelor	27	198
510912	Physician Assistant.	Master	43	245
450101	Social Sciences, General.	Bachelor	41	255
130301	Curriculum and Instruction.	Master	13	398
131202	Elementary Education and Teaching.	Bachelor	276	404
260101	Biology/Biological Sciences, General.	Bachelor	70	436
520201	Business Administration and Management, General.	Bachelor	368	588
520101	Business/Commerce, General.	Bachelor	386	618