Do merger policies increase universities’ efficiency? Evidence from a fuzzy regression discontinuity design

ABSTRACT

This paper focuses on the effect of merger policies in Russia on universities’ efficiency. We consider one particular policy of non-voluntary mergers conducted by the Ministry of education based on universities’ performance indicators. In a first stage, efficiency scores of universities are estimated using a bootstrapped DEA non-parametric technique. The efficiency scores were evaluated for universities that were merged and for an appropriate control group formed through a propensity score matching approach before and after implemented policy. Then, a fuzzy regression discontinuity design is implemented in order to reveal the causal impact of mergers on efficiency level. We find positive, statistically significant effect of merger policy on universities’ efficiency. The results of the analysis suggest that merged universities experienced greater efficiency gains (smaller efficiency declines) after the merger was implemented.

KEYWORDS:

• Universities
• mergers
• fuzzy regression discontinuity design
• efficiency
• DEA

JEL CLASSIFICATION:

• I21
• I23
• I28

Acknowledgments

We are very grateful for fruitful discussion and valuable comments on the previous version of this paper at the following conferences and workshops: 7th International Workshop on Efficiency in Education, Health and other Public Services (Barcelona, 2019), 16^th European Workshop on Efficiency and Productivity Analysis (London, 2019), LEER Conference on Education Economics (Leuven, 2019). This paper is an output of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (HSE).

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ T-tests for comparing means were conducted at the 5% significance level

² T-tests for comparing means were conducted at the 5% significance level

³ According to the results of this test, the estimated log difference in heights at the cut-off point is equal to 0.263 with the standard error equal to 0.327.

Additional information

Funding

This work was supported by the Basic Research Program at the National Research University Higher School of Economics (HSE).