causality and museum subsidies

Journal of Cultural Economics 28: 89–108, 2004.© 2004 Kluwer Academic Publishers. Printed in the Netherlands.

89

Causality and Museum Subsidies

DAVID MADDISONInstitute of Economics, University of Southern Denmark, Campusvej 55, DK-5230 Odense M,Denmark

Abstract. Although museums are major recipients of public money, very little is known regardingwhat factors cause changes in the level of funding given to particular institutions. It is neverthe-less regularly asserted by those affected that governments will reduce the level of subsidy going tothose museums that raise revenues for themselves, especially if these revenues were raised throughcharging for admission. This paper explores the causal influences underlying changes in the levelof government grants to museums. Statistically analysing data drawn from a panel of UK museumsfunded by central government, evidence is found that increases in non-grant income do indeed resultin a statistically significant reduction in future government subsidies. It is however unclear whetherthese reductions are sufficient to offset entirely the financial benefits from charging or the pursuit ofprivate benefactors. Despite the government’s avowed intention to widen access to museums, changesin visitor numbers do not appear to cause changes in government grants.

Key words: causality, crowding out, museums, subsidies

1. Introduction

Museums are major recipients of public money. In the financial year 1998/1999museums in the U.K. received £585 million from the public sector. Visiting amuseum is a very common leisure pursuit surpassing attendance at many sportingevents, and in the same year over 66 million visits to museums were recorded(Selwood, 2001).

Despite the popularity of visiting museums as a recreational activity and theextent to which they draw on the public purse, there are nevertheless relatively feweconomic analyses of museums. One of the first economists to discuss the eco-nomics of museums was Robbins (1971) who concerned himself with discussingthe issue of whether visits to museums generated external benefits for the rest ofsociety. The paper of Peacock and Godfrey (1974) set the stage for thinking aboutmuseums from an economics perspective with the museum as a firm producing anunusual type of product and with specialised labour and the exhibits themselves asthe inputs. Jackson (1988) presented a cost function for a museum whilst Martin(1995) estimated the total economic value of a museum. Luksetich and Partridge(1997) estimated the demand for museum services in the U.S. More recent contri-butions have focussed on the question of whether museums should or should notcharge for admission. This involves questions relating to the distributional impactsof charging (O’Hagan, 1998); the scale of the external benefits; whether the mar-

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ginal costs of admission to a museum are zero (Bailey and Falconer, 1998); andwhether museums are congestible resources (Maddison and Foster, 2003).

Despite these and a handful of other papers, a large number of interesting ques-tions regarding the behaviour of museums could yet be posed. Many of these areidentified in the overview of research undertaken by Johnson and Thomas (1998)and in the earlier work of Frey (1994). In particular, in their review of researchactivities Johnson and Thomas (1998) write: “We know very little, about differentforms of funding. . . Does public funding, from whatever source, reduce or in-crease private funding through donations? If a museum raises its own income, e.g.,by increasing admission charges or its ancillary activities, how does governmentreact?”

According to information received from the U.K.’s Department for Culture,Media and Sport (or DCMS) the main factors determining the size of the grants thatit gives to the U.K.’s national museums are past experience and an assessment offuture needs. In particular the DCMS has told me that, exceptional circumstancesaside, there is no link between the revenues generated by the museum and grantin aid allocations. Likewise the DCMS has stated that the receipt of funding fromthe Heritage Lottery Fund (or HLF) does not directly determine the allocation ofgrants.1

From informal conversations with those involved in the actual running of mu-seums, however, a very different set of beliefs emerges. It seems to be almostuniversally supposed that efforts at raising revenue, particularly if they involvecharging for admission, will not result in additional income for the museum. In-stead it is believed that additional revenues will be wholly offset by reductionsin government grants, donations from private benefactors and visitors. If this isthe case then no lesser person than the outgoing director of the British Museumhas suggested that there is an incentive for museums not to charge (Anderson,1998).2 The reluctance of museums to charge admission fees to see exhibits thatare congested has been argued to result in welfare losses (Maddison and Foster,2003).

A number of other phenomena are also consistent with the idea that additionalrevenues are wholly offset by reductions in government subsidies. It is regularlynoted that museums possess many more exhibits than they can ever display. Andalthough they could relieve pressure on the public purse by lending the exhibits torich overseas institutions and wealthy individuals they choose not to (Frey, 1994).This might be because they are not allowed to keep any part of the proceeds.Similarly, private benefactors and other public institutions may be deterred fromgiving gifts if they believe that the only consequence of their generosity will be anequivalent reduction in government grants. This perhaps is why they often strive tolink their donation to specific projects (and in so doing diminish the value of theirgift).

The causal determinants of museum subsidies also reveal something about thebudgetary processes operating within government. For example, if the government

CAUSALITY AND MUSEUM SUBSIDIES 91

uses incremental budgeting whereby the needs of the museum are assessed onthe basis of the amount of money that the museum has spent, then increases inexpenditure are likely to cause increases in government grants. If, on the otherhand, the government uses activity-based budgeting then it is changes in the scaleof the activities undertaken by a museum that are likely to cause changes in itsgrant. Lastly, if the government is using performance-based budgeting proceduresthen those variables that the government is meant to be targeting ought to alterthe level of the grant. Insofar as one has a preference for particular types of bud-getary processes being employed on the grounds of efficiency or the provision ofincentives one would be interested in knowing what the causal determinants ofmuseum grants had been over recent years. Of course if there were an explicitformula linking grants to the past realisation of particular variables the statisticalanalysis of the type presented in this paper would not be necessary. But if such aformula exists, it is not in the public domain.

A further reason for being interested in museum subsidies and their causalityis that museums are highly reliant on private donations as well as public grants.So although I do not attempt to do so because of data problems, museums of-fer yet another opportunity to test the theoretical proposition that under certaincircumstances government subsidies might crowd out voluntary donations. Usingdata from the 1989 survey of U.S. museums Hughes and Luksetich (1999) analysethe interactions among major categories of museum funding. The results indicatea strong, positive stimulus of federal funding on private contributions, with somepossible displacement of state and local government contributions. The decline andpossible elimination of federal support of the arts in the United States is likely tohave a major impact on museum finances.3

This short paper attempts to explore causality and museums subsidies froman empirical perspective using panel data comprising the U.K. central govern-ment grant to a number of national museums, information regarding non-grantincome and various categories of expenditure, and visitor numbers. These paneldata are then examined for evidence that changes in the level of non-grant incomecausally influences changes in grants. The next section describes the data used inthe analysis along with the implications of various approaches to budgeting. Thethird section reports on the econometric analysis of the data, and the fourth sectionprovides a discussion of the results. The final section concludes with some thoughtsregarding the ways in which this analysis might be extended in the future. It alsoconsiders the consequences of the recent policy change whereby all nationallyfunded museums are now free to all visitors.

2. Data Sources

Until recently the U.K. government funded directly seventeen museums and gal-leries as well as the Royal Armouries.4 A list of these institutions is given in Table I.The level of detail given in the published accounts of each of these museums has in

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Table I. List of museums and galleries funded by centralgovernment and included in the data set

The British Museum

The Geffrye Museum

The Horniman Museum and Gardens

The Imperial War Museum

The Museum of London

The Museum of Science and Industry in Manchester

The National Gallery

The National Maritime Museum

National Museums and Galleries on Merseyside

The National Museums of Science and Industry

The National Portrait Gallery

The Natural History Museum

The Royal Armouries

Sir John Soane’s Museum

The Tate Galleries

Tyne and Wear Museums

The Victoria and Albert Museum

The Wallace Collection

Source: Department for National Heritage (1997),Department for Culture, Media and Sport (1999) andDepartment for Culture, Media and Sport (2001).

the past varied enormously. Even within a single museum accounting proceduresevolve over time. Some museums publish separately information regarding revenuearising from sponsorship and donations. In the accounts of other museums theseare subsumed into miscellaneous sources of income. Many museums, particularlythe ones that charge for admission to their core collections, separately identify ad-missions revenue. Other museums include along with visitor admissions revenuesany donations taken at the door. Furthermore, those museums that do not chargefor admission to their core collections nevertheless charge for admission to specialexhibitions.5 In this case the revenues raised from admissions are smaller and tendto be placed along with other sources of revenue into miscellaneous sources ofincome. Some museums distinguish between income that is tied to a particularproject or earmarked for a particular use such as funding the construction of a newgallery and income that can be used for any purpose whilst others do not.6

For the purposes of running causality tests it would of course be very interest-ing to know whether income from particular sources as well as particular sorts ofexpenditure are more or less likely to influence the level of grant. But combiningdetailed information from different museums would require standardised account-ing procedures that the preceding discussion shows were, until recently, lacking. In


Table II. Summary statistics

Variable Definition Mean Std. dev. Min. Max.

GRANT Central government grant-in-aid

(£1995 millions) 12.04 10.47 0.37 35.34

NGINC Annual non-grant income

(£1995 millions) 8.16 9.37 0.09 50

OPCOST Annual operating costs

(£1995 millions) 14.37 12.05 0.44 44.21

CAPEXP Annual capital expenditure

(£1995 millions) 5.18 8.40 0 54.82

ACQUIS Annual spending on new acquisitions

(£1995 millions) 1.13 2.38 0 15.23

VISITS Visitors (millions) 1.44 1.67 0.03 6.91

Source: Department for National Heritage (1997), Department for Culture, Media and Sport(1999) and Department for Culture, Media and Sport (2001). Note that all figures areexpressed in 1995 prices using the GDP expenditure deflator.

the absence of standardised procedures the best that can be done is to determine theimpact of broad categories of income and expenditure on grants. These are avail-able in the condensed accounts for these museums included in the Department forCulture, Media and Sport (formerly the Department for National Heritage) annualreports. Combined, these reports provide data from the financial year 1989/1990to 2000/2001 but due to, amongst other things, changes in accounting procedures,temporary museum closures and changes in the basis for counting visitor numbersmany missing variables are encountered. The data are summarised in Tables I andII and on a per visitor basis in Table III. Note that all the financial variables areexpressed in terms of 1995 prices using the implied GDP deflator.

The data on grants include sums attributable both to capital spending and run-ning costs. Non-grant income includes revenues from trading, admission fees,private donations, grants from other public bodies, and other sources of income.Over the period in question the data set includes both charging museums likethe Imperial War Museum, and non-charging museums like the British Museumand even a number of those who, like the Victoria and Albert Museum, changedtheir status half way through. The diverse and changing status of museums withregards to charging makes it easier to identify the temporal relationship betweengovernment grants and other sources of income.

Apart from information on grants and non-grant income I also take data onmuseum expenditures from the Department of Culture, Media and Sport annualreports. Museum expenditures can be argued to have an effect on future grantallocations for two quite different reasons. First it is clear that many govern-ment departments still make their budgetary decisions using incremental budgetingwhereby future budgets are based on past expenditures. The problem with incre-

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Table III. Summary statistics on a per visitor basis

Variable Definition Mean Std. Dev. Min. Max.

GRANT/ Central government grant-in-aid

VISITS (£1995/visit) 11.34 6.17 0.74 27.36

NGINC/ Annual non-grant income

VISITS (£1995/visit) 8.31 8.87 0.93 45.19

OPCOST/ Annual operating costs

VISITS (£1995/visit) 13.99 9.45 3.46 52.56

CAPEXP/ Annual capital expenditure

VISITS (£1995/visit) 5.01 5.81 0 34.33

ACQUIS/ Annual spending on new

VISITS acquisitions (£1995/visit) 0.46 0.71 0 3.69

Source: Department for National Heritage (1997), Department for Culture, Media andSport (1999) and Department for Culture, Media and Sport (2001). Note that all figuresare expressed in 1995 prices using the GDP expenditure deflator.

mental budgeting of course is that it is wasteful. Inefficiencies become locked intofuture grant allocations once they have become established and activities that haveceased to be worthwhile continue to be funded. The second reason for believingthat past expenditures might influence future grants is that capital expenditure re-sulting in new galleries or new acquisitions implies additional running costs, whichhave to be met by the state. The possibility that the DCMS employs activity-basedbudgeting was raised by a number of individuals working in museums. Hencewe distinguish between capital expenditure, expenditure on new acquisitions andoperating costs.

The recent focus on performance-based budgeting suggests that governmentmight also attempt to allocate museum grants on the basis of museums’ achieve-ments with respect to quantitative targets. Current funding agreements betweenthe DCMS and particular museums frequently mention increasing visitor numbers.Visitor numbers are therefore included in the analysis below.7 Figure 1 illustratesthe dispersion of and trend in visitor numbers and the financial variables. Thefinancial variables are also presented on a per visitor basis. In each case thesegraphs are calculated for a subset of museums over differing periods of time inorder to avoid the problem of missing values.8 Of particular note is the gradualdecline in museum grants against a backdrop of static visitor numbers and the largecapital expenditures taking place in the British Museum and the Tate Modern. Theoutlying observations in spending on new acquisitions are due to major purchasesby the National Gallery. Non-grant income per visitor is markedly higher in theMuseum of London than in other museums whilst capital expenditure per visitorfor most museums is trending upwards.

Unfortunately there are serious questions regarding the accuracy of visitor num-bers (see the discussion in Creigh-Tyte and Selwood, 1998). Edwards (1996) found


Figure 1. Trends in financial variables and visitor numbers. Source: Department for NationalHeritage (1997), Department for Culture, Media and Sport (1999) and Department for Culture,Media and Sport (2001). Note that all figures are expressed in 1995 prices using the GDPexpenditure deflator.

Figure 1. (Continued).

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that the British Museum counted British Library readers, museum and library staffas well as immediate re-entries. This led to an overstatement of visitor numbers inthe region of 35 to 40%. It is also plausible to assume that the figures are likelyto be less accurate in the case of non-charging museums like the British Museumsince there it is impossible to reconcile attendance numbers with any admissionreceipts. Nonetheless, these are the official figures reported in the DCMS annualreports and according to some the only quantifiable measure of output upon whichthe DCMS can base its award. The DCMS now insists that visitor surveys followwritten guidelines so the accuracy (and comparability) of more recent figures maybe substantially improved.

3. Econometric Model and Results

The econometric model described below seeks evidence of Granger causality(Granger, 1969). It is important to understand that this differs somewhat fromthe concept of causality as it is more normally understood. The idea of Grangercausality rests on statistical precedence. Thus, if lagged values of one time serieshelp to explain the variation in another then there exists Granger causality. Thisdefinition clearly falls foul of the post hoc ergo propter hoc fallacy. For a review ofGranger causality tests conducted in other branches of economics, see Pierce andHaugh (1977).

The statistical analysis undertaken in this paper involves running the followingregression in which i refers to the museum, t refers to the time period, j refers to


the number of lags and the Greek letters represent parameters to be estimated. Notethe presence of a year specific intercept and a museum-specific disturbance term:

GRANTit = αt +∑

j=1

βj × NGINCit−j +∑

j=1

γj × OPCOSTit−j +

+∑

j=1

δj × CAPEXit−j +∑

j=1

φj × ACQUISit−j +

+∑

j=1

ηj × VISITSit−j +∑

j=1

θj × GRANTit−j + ui + eit .

The purpose of including a time-dependent intercept is to ensure that the signif-icance of particular explanatory variables cannot be due to their trending withautonomous changes in the level of museum grants. Note that this specificationdiffers markedly from the one employed by Hughes and Luksetich (1997) who, be-cause their data set was cross sectional, assumed that different sources of museumfinance were simultaneously determined.

A number of issues arise concerning the econometric estimation of the model.First of all the presence of lagged dependent variables combined with the relativelyshort time period would suggest the use of panel data estimation techniques knownto be consistent as the number of panels tends to infinity (see for example Arrellanoand Bond, 1991). In this case, however, the number of panels is not much greaterthan the number of time periods. This creates a dilemma because methods usedto overcome the problems caused by a lagged dependent variable are themselvesbiased when the number of panels is small (see for example Kiviet, 1995).9 Henceit is unclear what estimator is most appropriate here. Furthermore, as the numberof lags included increases the problem is exacerbated as both the number of timeperiods and the number of cross sectional units declines (due to the fragmented na-ture of the data set). Note that observations with missing values are simply deletedfrom the dataset.

The procedure adopted to deal with these issues is as follows. First, given thepaucity of the data I commence with the simplest possible model (j = 1) andtest whether additional lags are statistically significant. The alternative approachof starting with a more general model (e.g., j = 3 or 4) is unlikely to identify thecorrect lag length given the problem of small sample bias. Secondly, I compare theresults provided by different estimation techniques, namely Least Squares DummyVariables (LSDV) with the Generalised Method of Moments (GMM) estimator thatis more frequently employed in this context. If these two estimators yield similarresults at least one is not confronted with the problem of choosing between them(although both might be biased). Using a Monte Carlo simulation Judson and Owen(1999) find that, for a dynamic unbalanced panel data model with the number ofobservations similar to the one in the current dataset, if both estimates differ theGMM estimator is preferable to the LSDV estimator as well as to other techniques.

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Turning to the results, it appears that non-grant income, expenditures on acqui-sitions and capital expenditures are significant causal influences affecting changesin the level of grants at the one-percent level of confidence. By contrast, operatingcosts and the lagged number of visitors do not contribute any explanatory powereven at the ten-percent level of confidence.

The second model extends the number of lags from j = 1 to j = 2 and as aconsequence the number of observations used in the estimation routine is somewhatreduced.10 With one exception the implications of the model are similar. Testsof joint significance suggest that acquisitions and non-grant income continue toGranger cause grants (F2,88 = 3.82 and F2,88 = 6.47 respectively) but unlike inthe model characterised by j = 1 capital spending does not now appear to Grangercause grants (F2,88 = 1.91). Operating costs and the number of visits remain unim-portant (F2,88 = 1.12 and F2,88 = 0.78 respectively). None of the coefficients thatare lagged twice are statistically significant at the five-percent level. Furthermorea test of group significance for the twice-lagged variables is inconclusive beingsignificant at the five percent level but not at the one-percent level (F6,88 = 2.69).11

The last two columns of Table IV re-estimate the linear model using the LSDVtechnique. It is clear that the parameter estimates and associated t-statistics arealmost identical to those generated by the preferred GMM estimator. Finally, analternative version of the model was estimated measuring the dependent variablein terms of logarithms. The results of the model were disappointing in terms ofthe overall fit and are not shown here. There may even be an a priori reasonfor preferring a linear functional form. If one suspects, as most people do, thatgovernment grants are intended to cover a funding shortfall then a £1 increasein non-grant income most likely causes a subsequent reduction of approximately£1 in government grant. In this case a linear functional form is appropriate. Asemi-logarithmic functional form by contrast implies that an increase of £1 in non-grant income causes a proportional change in government grant and moreover aproportional change that is equal across museums receiving very different levels ofgrant, something which is hard to rationalise.12

4. Discussion

The results of the analysis presented above support the view that increases in non-grant incomes cause a reduction in the future level of government grants. At thesame time, however, the results indicate that the offsetting reduction in grants is lessthan that required to entirely make up for any increase in non-grant income. Theequilibrium impact of an additional £1 of revenue from non-grant income serves toreduce grants by £0.30.13 This is significantly different from zero at the one-percentlevel of confidence (the t-statistic is 3.76). This can be compared with the findingfrom Hughes and Luksetich (1997) that private donations attract federal funding tohistory museums.14


Table IV. A dynamic panel data model of Granger causality

Method GMM GMM LSDV LSDV

Dependent GRANTt GRANTt GRANTt GRANTt

variable

NGINCt−1 –0.106 –0.094 –0.097 –0.094

(–4.21) (–3.54) (–3.80) (–3.38)

NGINCt−2 0.045 0.045

(1.19) (1.14)

OPCOSTt−1 0.048 –0.000 0.069 –0.001

(0.82) (–0.01) (1.18) (–0.02)

OPCOSTt−2 0.090 0.090

(1.44) (1.37)

CAPEXt−1 0.095 0.044 0.087 0.044

(4.74) (1.75) (4.26) (1.67)

CAPEXt−2 0.018 0.018

(0.65) (0.62)

ACQUISt−1 0.181 0.163 0.153 0.163

(2.98) (2.48) (2.54) (2.37)

ACQUISt−2 –0.111 –0.111

(–1.56) (–1.49)

VISITSt−1 0.039 0.258 0.042 0.255

(0.12) (0.64) (0.12) (0.61)

VISITSt−2 0.413 0.413

(1.05) (1.00)

GRANTt−1 0.648 0.709 0.645 0.709

(8.48) (6.87) (8.40) (6.58)

GRANTt−2 –0.084 –0.084

(–0.81) (–0.77)

Number of

observations 125 108 142 125

F test for zero

slopes F13,110 = 15.02 F18,88 = 10.11 F14,111 = 19.13 F19,89 = 11.87

Shapiro–Wilk

test for

normality 4.14 3.18 5.99 5.65

Source: Own calculations. Note that the figures in parentheses are t-statistics. Year dummies areincluded but not shown.

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At the same time it seems that increases in a museum’s capital expendituresincrease future museum grants. More specifically, it appears that the equilibriumimpact of an additional £1 of expenditure serves to increase future grants by £0.27.This is also statistically different from zero at the one-percent level of confidencewith a t-statistic of 3.78.15 Similarly, the equilibrium impact of an additional £1of expenditure on new acquisitions serves to increase future grants by £0.52. Thisis statistically different from zero at the one-percent level of confidence with a t-statistic of 2.97. Both of these findings are of course consistent with activity-basedbudgeting, with past capital expenditures and with an increased number of exhibits“necessitating” a greater level of support from the government to pay for higherrunning costs. In this sense it might now seem that, contrary to the assertions madein the introduction, the DCMS not only withholds grants from those museums thatraise additional revenues on their own initiative but also that awards from the HLFdo affect grant allocations. Bear in mind however that not all capital expenditure isfunded by the HLF.

The equilibrium impact of an additional £1 spent on running costs is not statis-tically different from zero at the ten-percent level of confidence with a t-statisticof 0.87. This suggests that those in charge of allocating the grants are alert tothe incipient problems of incremental budgeting procedures and the risk of ineffi-ciencies being accommodated by future budgets. The impact of additional visitorson museum grants is also statistically insignificant with a t-statistic of 0.10. Onone hand this is perhaps unsurprising given widely expressed doubts regarding theaccuracy of data on visitor numbers.16 But given the evident importance that thegovernment attaches to widening access to museums and that fact that these areofficial figures coupled with the increasing use of performance-based budgeting,it is to some degree surprising. It invites the question by what means the govern-ment intends to pursue its policy of widening access other than by a(n) (expensive)policy of free admission.17 This point is especially important since there are suchprofound differences in the level of grant per visitor to each of the institutions listedin Table I above. For example, in the financial year 2000/2001 the average grantper visitor was £10.40 in 1995 prices. But this ranged from £23.91 per visitor forthe Horniman Museum and Gardens to £0.74 per visitor for the Tyne and Wearmuseums.

5. Conclusions

I have examined the causal influences determining differences in the level of centralgovernment funding provided to national museums. In doing so I have tested thehypothesis that increases in non-grant income are met by an equivalent reductionin subsequent levels of government grant. If this proposition were true others haveargued that it would provide an explanation for a range of phenomena. Strongevidence is found suggesting that governments do indeed claw back a significantfraction of the additional revenues raised by museums. But notwithstanding certain


limitations arising from the small sample size, one can equally reject the hypothesisthat increases in non-grant income have a zero effect on museum revenues. Atthe same time there is evidence that those responsible for allocating the grant areusing activity-based budgeting techniques rather than incremental budgeting prac-tices or performance based ones. I have argued that the use of performance-basedtechniques might assist the Government in its objective of widening access.

A large number of possible extensions suggest themselves. For one thing I haveexamined only a select subset of museums, namely all those in receipt of directfunding from central government. Do the same relationships hold in the case offunding provided by local government or other public bodies? Informal opinionhas it that local governments claw back an even greater proportion of non-grantincome. Do funding bodies distinguish between revenue streams from differentsources such as those arising from admission charging or private donations? Isthere a distinction between funds that can be used for any purpose and thosefunds that are earmarked for particular causes? One could also examine the causalrelationship between other variables; for example, is it true that when a museum in-creases its admission charges private donations based on goodwill are reduced? Doincreases in government grants cause an equivalent reduction in private donations?

Although testing these hypotheses is impossible with the current dataset thereare other data sets that might be used for this purpose. In particular the Digestof Museum Statistics enquires about the sources of museums’ incomes separatelyidentifying public grants but somewhat bizarrely these data are closed until the year2028! Other countries may well have equivalent or superior data sets.

Following agreement with the Department for Culture, Media and Sport, allcentrally funded museums have recently decided to dispense with charging andsince the beginning of 2002 have permitted free admission to their core collections.It will be interesting to discover what implications this has had. Is there a structuralbreak at the very end of the data set or will the same patterns emerge if the data setwere reanalysed at some point in the future? Likewise, will the improved reliabilityof figures on visitor numbers mean that the DCMS will in the future begin targetingthat variable?

Acknowledgements

The author is grateful to Richard Hartman of the DCMS for comments on an earlierdraft of this paper as well as to Matthew Sherman for ideas that emerged from hisdissertation on the same topic. The author would also like to acknowledge thehelpful comments of two anonymous referees. Any remaining errors are the soleresponsibility of the author, and the views contained in this paper should not beattributed to anyone else but him.

106 DAVID MADDISON

Notes

1. The advent of the National Lottery has created the opportunity for the large scale funding ofcapital projects involving museums and by the end of the year 2000 it has been estimated thatthe HLF had given £500 million to museums, and galleries (Selwood, 2001).

2. Obviously the hostility of museum directors to the concept of charging is not because they arethe residual claimants of any unspent funds. Rather it is because in the absence of a profit motiveit is plausible to assume that museums seek to maximise some weighted combination of revenuesand visitor numbers. These are closely linked to status, remuneration and employment. Hencerevenue-neutral increases in admission charges unambiguously reduce the welfare of museumdirectors.

3. For recent surveys of the empirical evidence on the crowding out hypothesis in the broader con-text of the non-profit sector, see Andreoni (1993) and especially Brooks (2000). Most empiricalmodels presented in the literature appear to consider causality running in one direction only,from government subsidies to voluntary donations. This is one of the few papers to considerwhether causality also runs in the other direction, specifically whether government subsidies canalso be crowded out.

4. A number of other museums including the National Coal Mining Museum recently started toreceive funds from the DCMS. These are excluded from the analysis along with the BritishLibrary.

5. The justification for distinguishing between access to core collections and access to borrowedcollections is difficult for a mere economist to understand on grounds of either cost or externalbenefits.

6. Selwood (2001) amply highlights the difficulties inherent in the collection of data from thissector. Recently laws have been introduced that have standardised the accounting proceduresadopted by charitable organisations.

7. The most recent set of agreements between national museums and the government has beenpublished by the DCMS on their website. Details of earlier agreements are unavailable.

8. Specifically these graphs exclude the Tate Galleries, the Geffrye, Horniman and Tyne and Wearmuseums as well as the Royal Armouries. The graphs displaying NGINC, OPCOST, CAPEXand ACQUIS are also restricted to the period 1992–2001 whilst the graphs displaying GRANTand VISITS run from 1990–2001.

9. Kiviet (1995) proposes an alternative estimator correcting the bias in the Least Squares DummyVariable estimator. Unfortunately his estimator cannot be applied to unbalanced panels like thisone.

10. Grant allocations are revised every other year. The implication of the model with j = 1 is that achange in the level of non-grant income has an effect whose impact declines geometrically overtime. In the model with j = 2 the lagged response is more complex.

11. I adopt the one-percent level of confidence here because of the risk of small sample bias dis-cussed above. It might seem strange that a one period lag is sufficient to capture the dynamicrelationship involved, especially given that funding decisions obviously have to be made inadvance. The presence of informational lags also calls into question the assumption of Hughesand Luksetich (1997) that funding decisions are made on the basis of same period information.One needs to remember however that the equation presented above merely seeks to approximatethe true lag relationship in as parsimonious a fashion as possible. The approximation used hereinvolving the use of a geometrically declining lag function has often been found sufficientto summarise the dynamic relationships present in other funding contexts. Holtz-Eakin et al.(1989), for example, also find that lags of one to two years are sufficient to summarise thedynamic relationships in their widely-cited panel data study of public revenues and expenditure.


12. Following the suggestion of one of the referees, the model was re-estimated after dropping thelargest museums from the dataset. The results of the regressions are not very different and arenot shown here.

13. These figures as well as those that follow are calculated from the model estimated using GMMin which the number of lags j = 1.

14. In order to identify the funding equations Hughes and Luksetich (1997) assumed that federalfunding was not influenced by the revenues raised by charging for admission.

15. Although capital expenditures are not significant in the model with j = 2, the impact of capitalexpenditure on grants is positive and statistically significant at the five-percent level on a one-sided t-test (t = 1.90).

16. Attempting to distinguish between those museums that charge (and presumably therefore havea better idea of the true number of visits) and those that do not did not alter the statisticalinsignificance of the variable describing the number of visits.

17. It has been suggested to the author that the statutory duties of museums limit the extent to whichperformance-based budgeting procedures can be employed.

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