Benchmarking HBCU Efficiency: Beyond Retention

Benchmarking HBCU Efficiency: Beyond Retention

Jason Coupet (North Carolina State University, USA)
DOI: 10.4018/978-1-5225-0311-8.ch006
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Abstract

HBCUs have played a vital role in the US higher education sphere. As initiatives to increase student retention move forward, the reality of funding constraints means that examining efficiency and effectiveness at HBCUs remains a vital part of institutional growth. This chapter presents a two-stage Data Envelopment Analysis (DEA) methodology as a tool to benchmark the relative efficiency of HBCUs. DEA is a quantitative, non-parametric technique used to measure efficiency, and has had a robust history as a benchmarking tool due to its ability to identify top performing organizations as well as less efficient peers. Using Department of Education data, the most efficient and effective HBCUs are identified. Implications for the use of DEA as a benchmarking tool are discussed.
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Data Envelopment Analysis

DEA is commonly used for efficiency benchmarking in higher education, and has many advantages (Bougnol & Dula, 2006; Johnes, 2006). DEA can combine multiple inputs and outputs into a single efficiency score, and this allows for a performance measure that encapsulates all of the many inputs and outputs that play a role in what HBCUs do. Additionally, DEA envelops the efficiency of each organization with scores from its most efficient peers. This way, the top performing HBCUs can be identified, as well as the peer institutions that might use it as a benchmark.

Graphically, the fully efficient institutions form a piece-wise linear frontier (line ABCD in Figure 2) on which the most efficient institutions lie. On the example shown in Figure 1, HBCU 1 and HBCU 5 are efficient, using the fewest inputs, such as expenditures on faculty and financial aid) to produce the same output. HBCUs 2, 3, and 4 are inefficient. Less efficient HBCUs can be benchmarked by whichever of HBCU 1 or HBCU 5 that most closely matches that institution’s structure.

Figure 1.

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While either of the two peers can be considered efficient, it is plausible that one or neither are effective. For example, an institution might maximize resources in an attempt to graduate students, but see a poor number of students repaying loans at an acceptable rate. Less efficient HBCUs might look to emulate efficient peers, but maximize use of benchmarking tools by looking to emulate peers that are both efficient and effective.

Figure 2.

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Data And Procedure

To illustrate, this paper uses data from this study from two Department of Education databases: the Integrated Post-Secondary Education System (IPEDS) and Federal Student Aid Data Center (FSADC). Financial data and retention data from IPEDS will be used for the first stage DEA. The data used in this study is published in Table 2. The input/output functional form are modified from Ryan (2004). (Figure 3)

Figure 3.

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Essentially, the four major university expenditure categories are combined, in ratio form, with each HBCU’s retention rate to produce a single efficiency score1. A three-year sample of four year HBCUs is included, and DEA scores will be estimated for each year2. The final sample included 80 four year HBCUs.

Table 1.
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Inputs (2008-2010)Outputs (2009-2011)
Instructional ExpendituresRetention Rate
Academic Support Expenditures
Student Support Expenditures
Institutional Support Expenditures

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