Johnes, Jill

Tsionas, Mike

English

University of Huddersfield Repository

University of Huddersfield RepositoryJohnes, Jill and Tsionas, MikeUniversity mergers in England: Effects on efficiencyOriginal CitationJohnes, Jill and Tsionas, Mike (2015) University mergers in England: Effects on efficiency. In: European Workshop on Efficiency and Productivity Analysis, 15-18 June 2015, Helsinki, Finland. (Unpublished) This version is available at http://eprints.hud.ac.uk/27825/The University Repository is a digital collection of the research output of theUniversity, available on Open Access. Copyright and Moral Rights for the itemson this site are retained by the individual author and/or other copyright owners.Users may access full items free of charge; copies of full text items generallycan be reproduced, displayed or performed and given to third parties in anyformat or medium for personal research or study, educational or not-for-profitpurposes without prior permission or charge, provided:• The authors, title and full bibliographic details is credited in any copy;• A hyperlink and/or URL is included for the original metadata page; and• The content is not changed in any way.For more information, including our policy and submission procedure, pleasecontact the Repository Team at: E.mailbox@hud.ac.uk.http://eprints.hud.ac.uk/UNIVERSITY MERGERS IN ENGLAND:EFFECTS ON EFFICIENCYEWEPA Helsinki 201517th June 2015Jill JohnesUniversity of HuddersfieldMike TsionasLancaster UniversityOutline of talk• Introduction• Background• Reasons for merger in higher education• Previous evidence on the consequences of mergers• Model• Results• ConclusionsIntroductionVarious forms of relationships can be observed in the English higher education (HE) sector:• shared purchasing and services• joint ventures and alliances• full mergerThis paper is concerned only with merger:• ‘Merger: two or more partners combining to create a single institution, which may retain the name and legal status of one of them or be an entirely new legal entity.’ (HEFCE 2012, p11)Introduction• The current economic climate puts pressure on publicly-funded sectors to deliver more for less – including English HE• Funding cuts can be absorbed by efficiency savings –possibly achieved by mergers (the efficiency theory)Introduction‘If institutional failure cannot be prevented …, then the Council will explore options such as mergers or takeovers led by other providers so that the institution in a new form becomes a going concern.’ (The Browne Report 2010 p46)‘Throughout the world concentration of research funding is the name of the game,... How can you possibly compete as a single institution?’ (Professor Sir Steve Smith, vice-chancellor of Exeter University, reported in The Guardian 16th October 2012)Sir Roderick Floud former president of Universities UK believes that the number of universities in the UK should be cut by “at least one-third if not one-half” (THE 19-25 June 2014)IntroductionSome questions:• Does the merger of 2 (or more) HEIs cause an increase in subsequent efficiency?• Do the efficiency effects of merger take time to reap?IntroductionSome problems:• Historically there are comparatively few mergers in English higher education• Merger activity and efficiency may themselves be endogenous• So conventional econometric techniques of analysis may not be appropriateIntroductionThis paper uses a Bayesian approach organised around the use of Markov chain Monte Carlo (MCMC) and proposes a method of analysis which• Assesses efficiency of  HEIs in England• Takes into account the endogeneity of merger activity and inefficiency• Quantifies the determinants of inefficiency and of merger activity• Identifies whether there have been efficiency gains following mergerBackground• There have been very few mergers in English HE, • These have varied in HEI composition• These have largely been HEI-motivated Background• This contrasts with the experience in Wales, for example:•“The Welsh government has stepped in to reduce the number of universities in Wales; maybe the English government will have to do the same.”•“…experience suggests that universities [in England] will not make such radical changes for themselves…”•Sir Roderick Floud, THE 19-25 June 2014BackgroundBoxall and Woodgates (2014)Reasons for merger in higher education1. Efficiency theory •A merger will occur if the merging HEIs believe they can be run more efficiently and effectively together than separately• Economies of scale• Economies of scope•Efficiency theory is the main underlying cause of merger activity in GB (Rowley 1997) •Prediction: merger leads to lower inefficiencyReasons for merger in higher education2. Strategy motive• A merger will occur for reasons of survival and/or growth for at least one of the participants (Pritchard 1993; Rowley 1997; Harman and Meek 2002; Harman and Harman 2003)•Prediction: inefficiency leads to mergerReasons for merger in higher education2. Strategy motive• A merger will occur to enhance reputation (Skodvin1999; Engwall 2007; Harman and Harman 2008; Tirronenen and Nokkala 2009; Aula and Tienari2011)• A merger will occur to improve international competitiveness (Mok 2005; Tirronenen and Nokkala 2009)• Prediction: merger leads to lower inefficiencyPrevious evidence• Statistical analyses:• China (Hu & Liang 2008; Mao 2009): efficiency, outcomes and productivity improved in year following merger; but did not in the second year• England (Johnes 2014): - the typical merged HEI is significantly more efficient than either pre-merger or non-merging HEIs- the effects can vary by the types of HEI participating in the merger; there are both winners and losersPrevious evidenceStatistical analyses: some caveats•Previous statistical analyses fail to take into account • the complex relationship between inefficiency and merger• that other underlying characteristics might cause merging institutions to perform differently from non-merging onesPrevious evidenceStatistical analyses: some caveats•Any measurement of efficiency typically • does not incorporate any loss caused by the merger in learning experience on the part of students or staff• does not incorporate any social costs arising from reduction in diversity between HEIs in the sectorModel• Suppose: universities use k inputs (݇ = ͳ,… , ܭሻ to produce l outputs (݈ = ͳ,… , ܮሻ• inputs and outputs are denoted by X and Yrespectively• subscript it represents university i in time period t ሺ� =ͳ,… ,ܰ; ݐ = ͳ,… , �ሻ. Model• Inefficiency is estimated using a standard translogoutput distance function (ODF): D �ܻ௧ , �ܺ௧ = ͳ ⇒ ݕଵ,�௧ = �  ݕ௠,�௧ , ݔ௦,�௧ + ݒ�௧ − ݑ�௧• ݒ�௧~���ܰሺͲ, �௩ଶሻ represents the error • ݑ�௧~��� +ܰሺͲ, �௨ଶሻ is the one-sided component, independently distributed and independent of the regressors• lower case letters indicate logs, and  ݕ௠ = ݕ௠ −ݕଵ, ݉ = ʹ,… ,ܯModelTendency to merge• �ܹ௧∗ = ��௧′ ࢽ + �ଵ log ݑ�௧ + �ଶ log ݑ�,௧−ଵ + � �ܹ,௧−ଵ∗ +���,௧−ଵ + ��௧ , ��௧~iidܰ Ͳ,ͳ• ��௧ = � ሺ �ܹ௧∗ ≥ Ͳ) is an observed merging indicator which is 1 if a merger took place and zero otherwise• ��௧ is a vector of covariates• Tendency to merge also depends on current and past inefficiency and is also possibly persistent (autoregressive)ModelInefficiencylog ݑ�௧= �଴ + �ଵ log ݑ�,௧−ଵ + ��௧′ ࢾ + �ଶ �ܹ,௧−ଵ∗ + �ଷ��,௧−ଵ + ��௧ ,��௧~iidܰ Ͳ, ��ଶ• The dependence of technical inefficiency on �ܹ௧∗ (latent merging indicator) and ��௧ (actual merging indicator) helps to distinguish between “latent” and “actual” effects of mergers• Allowing for persistent inefficiency implies that there may be adjustment costs and inertia in decreasing inefficiency which could be present even after a possible mergerModel• At time period t-1 HEIs i and j merge to become a new HEI (n) • Inefficiency improvement is calculated as: ∆ݑ௡௧ = ݑ௡௧ − ݑ௡,௧−ଵ• Probabilities of such events are difficult to compute using the classical approach; Bayesian approach MCMC methods simplify the task• We estimate the ODF in an unrestricted manner and examine the probability that improvements in inefficiency have occurred  • The required probability is � Δݑ௡௧ > Ͳ Data marginally on the parameters to account for parameter-related uncertainty• MCMC: probabilities can be computed easily for all n and t• These are probabilities of efficiency improvement after merger, assuming that mergers and inefficiency are endogenousOutput distance functionX and Y variablesPGINPUT Total number of FTE PG studentsUGINPUT Total number of FTE first degree and other UGsSTAFF Number of FTE academic staffACSERV Expenditure on centralised academic services (in £000s)ADMIN Expenditure administration and central services (in £000s)PGOUTPUT Number of higher degrees plus other PG qualificationsUGOUTPUT Number of first degree and other UG degrees awardedRESEARCH Income received in funding council grants plus incomereceived in research grants and contracts (in £000s)Tendency to merge and inefficiency modelsZ variablesLSIZE Total number of students i.e. PGINPUT+UGINPUT (inlogarithmsLSIZESQ The square of LSIZEFIRST Proportion of first degree graduates achieving first classhonoursUPSEC Proportion of first degree graduates achieving uppersecond class honoursLOWSEC Proportion of first degree graduates achieving lowersecond class honoursTHIRD Proportion of first degree graduates achieving third classhonoursUNC Proportion of first degree graduates achievingunclassified degreeData• Higher Education Statistical Agency (HESA) data• Unbalanced panel of data from 1996/97 to 2008/09 with n = 1694 (the number of HEIs varies from 126 to 138 in each year) • All money units in 2008 valuesResults: Tendency to merge and inefficiencyPosterior means (and SDs); Marginal effects (and SDs)Posterior means Marginal effects�ܹ௧∗ log ݑ�௧ �ܹ௧∗ log ݑ�௧constant -0.2481 (0.0972) 0.0445 (0.0138) --- ---�ܹ,௧−ଵ∗ 0.1734 (0.0315) -0.0107 (0.00315) 0.072 (0.0212) 0.034 (0.0021)log ݑ�௧ 0.3115 (0.6781) --- 0.085 (0.071) ---log ݑ�,௧−ଵ 0.0971 (1.2234) 0.0126 (0.0031) 0.0401 (0.373) 0.0215 (0.0027)LSIZE 0.2341 (0.0732) 0.02415 (0.0116) 0.0151 (0.0022) 0.0341 (0.0071)LSIZESQ -0.0110 (0.0113) -0.0021 (0.0002) -0.0035 (0.0001) -0.0017 (0.0002)FIRST -0.0003 (0.0001) 3 10-5 (7 10-6) -0.0005 (0.0001) 1 10-5 (1 10-6)UPSEC -0.0002 (0.0001) 3 10-5 (2 10-6) -0.0004 (0.0001) 1 10-5 (3 10-6)LOWSEC 0.0002 (0.0001) 1 10-5 (2 10-6) 0.0001 (0.0001) 2 10-5 (2 10-6)THIRD 0.0001 (0.0002) 2 10-5 (4 10-6) 0.0004 (0.0001) 3 10-5 (1 10-6)UNC 0.0003 (0.0001) 2 10-5 (1 10-6) 0.0003 (0.0001) 2 10-5 (1 10-6)��,௧−ଵ -0.0138 (0.0012) -1 10-5 (1 10-6) -0.0212 (0.0013) -2 10-5 (1 10-6)Results: Technical efficiency by merger typeResults: New model compared to other modelsComparison of models: Bayes factors of new model against 3 alternativesEntiresampleNew model against:Conventional SFM�ଵ = �ଶ = � = �ଵ = �ଶ = �ଷ = Ͳ 61.332Probit SFM�ଵ = �ଶ = �ଷ = Ͳ 31.225Dynamic SFM� = �ଵ = �ଶ = � = �ଵ = �ଶ = �ଷ = Ͳ 11.344Results: New model compared to other modelsResults: Efficiency improvement01234567890.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1FrequencyProbability� Δݑ௡௧ > Ͳ Data where ∆ݑ௡௧ = ݑ௡௧ − ݑ௡,௧−ଵOf 25 mergers, 11 have probability of efficiency improvement less than  70%Results: Efficiency improvementWhat are the characteristics of a “successful” merger?• Geography (Skodvin 1999)?• An examination of the distance between merging HEIs reveals no particular patterns• Similar culture and mission (HEFCE 2010)?• Of the 11 mergers with probability of efficiency improvement < 70%, 6 are between HEIs of the same type• Grants from HEFCE’s Strategic Development Fund?• For example: the Manchester merger attracted a grant of £10 million plus a further £10 million in repayable grantsResults: Efficiency of merged HEIs over timeConclusions• Inefficiency is significantly, positively affected by tendency to merge and action of merging (in the previous time period) • Tendency to merge is not significantly affected by inefficiency• The new model taking into account endogeneity of merging and inefficiency performs better than 3 nested models which do not• Inefficiency and tendency to merge are positively, significantly related to the size of HEI; the relationship is non-linear Conclusions• Merging HEIs are typically more efficient than pre- and non-merging HEIs• Efficiency improvement is not experienced across all mergers: 11 of 25 mergers examined have probability < 0.70 that efficiency does not improve in time tcompared to t-1 (year of merger).• Mean efficiency peaks soon after merger, and plateaus at a value of 0.94 to 0.95; dispersion around the mean is wide particularly in the 3 to 5 periods after merger. Conclusions• Caveats: measurement of efficiency does not incorporate - loss imposed by the merger in terms of learning (and teaching) experience on the part of students (or staff)- possible social costs arising from reduction in diversity between HEIs in the sector caused by merging- regional economic effects of HEI closuresThank you!

University mergers in England: Effects on efficiency

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