Investigating applications of university productivity measurement models using Australian data

ABSTRACT

The paper contributes to the growing and often controversial study of higher education productivity measurement. The paper clarifies core productivity ideas, and considers alternative models for productivity assessment. Results from these models are explored using data from Australian higher education. Findings reveal implications for the technique and the practice of higher education productivity measurement. The paper provides evidence that productivity change estimates in higher education are not robust to the choice of estimation method. The research then demonstrates how the practice of higher education productivity measurement may benefit from approaches different to conventional econometric means of estimating frontier efficiency. Focusing on the drivers of productivity change and on the value weighting of data elements in models may provide more nuanced and actionable information for stakeholders and decision-makers. The paper concludes by urging more work to advance this field.

KEYWORDS:

• Productivity
• measurement
• higher education
• Törnqvist index
• data envelopment analysis

Acknowledgements

The authors are grateful for feedback from two anonymous reviewers.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Source data are available through the Australian Government Department of Education and Training (DOET) at DOET (2017a), DOET (2017b), and DOET(2017c).

2 Rental value or capital service $K_s$ is calculated by: $K_{S}e=K_R\times \left(d+r\right)$, where $K_R$ is the replacement cost of a unit of capital, $r$ is the real rate of return, and $d$ is the depreciation rate (Sullivan et al. 2012).

3 The Batchelor Institute of Indigenous Tertiary Education and the University of Notre Dame were excluded in the MJH study.

4 Only the Batchelor Institute of Indigenous Tertiary Education is excluded for lack of complete data over the period.

5 Moradi-Motlagh, Jubb, and Houghton (2016) use DEA to announce a sector-wide productivity increase of 15.2%.