Journal of Cultural Economics 28: 89â108, 2004. © 2004 Kluwer ... museums from an economics perspective with the museum as a firm producing an unusual ...
Journal of Cultural Economics 28: 89–108, 2004. © 2004 Kluwer Academic Publishers. Printed in the Netherlands.
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Causality and Museum Subsidies DAVID MADDISON Institute 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 regarding what factors cause changes in the level of funding given to particular institutions. It is nevertheless regularly asserted by those affected that governments will reduce the level of subsidy going to those museums that raise revenues for themselves, especially if these revenues were raised through charging for admission. This paper explores the causal influences underlying changes in the level of government grants to museums. Statistically analysing data drawn from a panel of UK museums funded by central government, evidence is found that increases in non-grant income do indeed result in a statistically significant reduction in future government subsidies. It is however unclear whether these reductions are sufficient to offset entirely the financial benefits from charging or the pursuit of private benefactors. Despite the government’s avowed intention to widen access to museums, changes in 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 1998/1999 museums in the U.K. received £585 million from the public sector. Visiting a museum is a very common leisure pursuit surpassing attendance at many sporting events, 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 the extent to which they draw on the public purse, there are nevertheless relatively few economic analyses of museums. One of the first economists to discuss the economics of museums was Robbins (1971) who concerned himself with discussing the issue of whether visits to museums generated external benefits for the rest of society. The paper of Peacock and Godfrey (1974) set the stage for thinking about museums from an economics perspective with the museum as a firm producing an unusual type of product and with specialised labour and the exhibits themselves as the 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 contributions have focussed on the question of whether museums should or should not charge for admission. This involves questions relating to the distributional impacts of 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); and whether museums are congestible resources (Maddison and Foster, 2003). Despite these and a handful of other papers, a large number of interesting questions regarding the behaviour of museums could yet be posed. Many of these are identified 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 research activities Johnson and Thomas (1998) write: “We know very little, about different forms of funding. . . Does public funding, from whatever source, reduce or increase private funding through donations? If a museum raises its own income, e.g., by increasing admission charges or its ancillary activities, how does government react?” 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 that it gives to the U.K.’s national museums are past experience and an assessment of future needs. In particular the DCMS has told me that, exceptional circumstances aside, there is no link between the revenues generated by the museum and grant in aid allocations. Likewise the DCMS has stated that the receipt of funding from the Heritage Lottery Fund (or HLF) does not directly determine the allocation of grants.1 From informal conversations with those involved in the actual running of museums, however, a very different set of beliefs emerges. It seems to be almost universally supposed that efforts at raising revenue, particularly if they involve charging for admission, will not result in additional income for the museum. Instead it is believed that additional revenues will be wholly offset by reductions in government grants, donations from private benefactors and visitors. If this is the case then no lesser person than the outgoing director of the British Museum has 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 that are 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 additional revenues are wholly offset by reductions in government subsidies. It is regularly noted that museums possess many more exhibits than they can ever display. And although they could relieve pressure on the public purse by lending the exhibits to rich 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 from giving gifts if they believe that the only consequence of their generosity will be an equivalent reduction in government grants. This perhaps is why they often strive to link their donation to specific projects (and in so doing diminish the value of their gift). The causal determinants of museum subsidies also reveal something about the budgetary processes operating within government. For example, if the government
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uses incremental budgeting whereby the needs of the museum are assessed on the basis of the amount of money that the museum has spent, then increases in expenditure are likely to cause increases in government grants. If, on the other hand, the government uses activity-based budgeting then it is changes in the scale of the activities undertaken by a museum that are likely to cause changes in its grant. Lastly, if the government is using performance-based budgeting procedures then those variables that the government is meant to be targeting ought to alter the level of the grant. Insofar as one has a preference for particular types of budgetary processes being employed on the grounds of efficiency or the provision of incentives one would be interested in knowing what the causal determinants of museum grants had been over recent years. Of course if there were an explicit formula linking grants to the past realisation of particular variables the statistical analysis of the type presented in this paper would not be necessary. But if such a formula exists, it is not in the public domain. A further reason for being interested in museum subsidies and their causality is 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 offer yet another opportunity to test the theoretical proposition that under certain circumstances government subsidies might crowd out voluntary donations. Using data from the 1989 survey of U.S. museums Hughes and Luksetich (1999) analyse the interactions among major categories of museum funding. The results indicate a strong, positive stimulus of federal funding on private contributions, with some possible displacement of state and local government contributions. The decline and possible elimination of federal support of the arts in the United States is likely to have a major impact on museum finances.3 This short paper attempts to explore causality and museums subsidies from an empirical perspective using panel data comprising the U.K. central government grant to a number of national museums, information regarding non-grant income and various categories of expenditure, and visitor numbers. These panel data are then examined for evidence that changes in the level of non-grant income causally influences changes in grants. The next section describes the data used in the analysis along with the implications of various approaches to budgeting. The third section reports on the econometric analysis of the data, and the fourth section provides a discussion of the results. The final section concludes with some thoughts regarding the ways in which this analysis might be extended in the future. It also considers the consequences of the recent policy change whereby all nationally funded museums are now free to all visitors.
2. Data Sources Until recently the U.K. government funded directly seventeen museums and galleries 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 central government 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) and Department for Culture, Media and Sport (2001).
the past varied enormously. Even within a single museum accounting procedures evolve over time. Some museums publish separately information regarding revenue arising from sponsorship and donations. In the accounts of other museums these are subsumed into miscellaneous sources of income. Many museums, particularly the ones that charge for admission to their core collections, separately identify admissions revenue. Other museums include along with visitor admissions revenues any donations taken at the door. Furthermore, those museums that do not charge for admission to their core collections nevertheless charge for admission to special exhibitions.5 In this case the revenues raised from admissions are smaller and tend to be placed along with other sources of revenue into miscellaneous sources of income. Some museums distinguish between income that is tied to a particular project or earmarked for a particular use such as funding the construction of a new gallery 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 interesting to know whether income from particular sources as well as particular sorts of expenditure are more or less likely to influence the level of grant. But combining detailed information from different museums would require standardised accounting procedures that the preceding discussion shows were, until recently, lacking. In
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Table II. Summary statistics Variable
Definition
GRANT
Central government grant-in-aid (£1995 millions) Annual non-grant income (£1995 millions) Annual operating costs (£1995 millions) Annual capital expenditure (£1995 millions) Annual spending on new acquisitions (£1995 millions) Visitors (millions)
NGINC OPCOST CAPEXP ACQUIS VISITS
Mean
Std. dev.
Min.
Max.
12.04
10.47
0.37
35.34
8.16
9.37
0.09
50
14.37
12.05
0.44
44.21
5.18
8.40
0
54.82
1.13 1.44
2.38 1.67
0 0.03
15.23 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 are expressed in 1995 prices using the GDP expenditure deflator.
the absence of standardised procedures the best that can be done is to determine the impact of broad categories of income and expenditure on grants. These are available in the condensed accounts for these museums included in the Department for Culture, Media and Sport (formerly the Department for National Heritage) annual reports. Combined, these reports provide data from the financial year 1989/1990 to 2000/2001 but due to, amongst other things, changes in accounting procedures, temporary museum closures and changes in the basis for counting visitor numbers many missing variables are encountered. The data are summarised in Tables I and II and on a per visitor basis in Table III. Note that all the financial variables are expressed in terms of 1995 prices using the implied GDP deflator. The data on grants include sums attributable both to capital spending and running 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 like the Imperial War Museum, and non-charging museums like the British Museum and even a number of those who, like the Victoria and Albert Museum, changed their status half way through. The diverse and changing status of museums with regards to charging makes it easier to identify the temporal relationship between government grants and other sources of income. Apart from information on grants and non-grant income I also take data on museum expenditures from the Department of Culture, Media and Sport annual reports. Museum expenditures can be argued to have an effect on future grant allocations for two quite different reasons. First it is clear that many government departments still make their budgetary decisions using incremental budgeting whereby 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
GRANT/ VISITS NGINC/ VISITS OPCOST/ VISITS CAPEXP/ VISITS ACQUIS/ VISITS
Central government grant-in-aid (£1995/visit) Annual non-grant income (£1995/visit) Annual operating costs (£1995/visit) Annual capital expenditure (£1995/visit) Annual spending on new acquisitions (£1995/visit)
Mean
Std. Dev.
Min.
Max.
11.34
6.17
0.74
27.36
8.31
8.87
0.93
45.19
13.99
9.45
3.46
52.56
5.01
5.81
0
34.33
0.46
0.71
0
3.69
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 are expressed in 1995 prices using the GDP expenditure deflator.
mental budgeting of course is that it is wasteful. Inefficiencies become locked into future grant allocations once they have become established and activities that have ceased to be worthwhile continue to be funded. The second reason for believing that past expenditures might influence future grants is that capital expenditure resulting in new galleries or new acquisitions implies additional running costs, which have to be met by the state. The possibility that the DCMS employs activity-based budgeting was raised by a number of individuals working in museums. Hence we distinguish between capital expenditure, expenditure on new acquisitions and operating costs. The recent focus on performance-based budgeting suggests that government might also attempt to allocate museum grants on the basis of museums’ achievements with respect to quantitative targets. Current funding agreements between the DCMS and particular museums frequently mention increasing visitor numbers. Visitor numbers are therefore included in the analysis below.7 Figure 1 illustrates the dispersion of and trend in visitor numbers and the financial variables. The financial variables are also presented on a per visitor basis. In each case these graphs are calculated for a subset of museums over differing periods of time in order to avoid the problem of missing values.8 Of particular note is the gradual decline in museum grants against a backdrop of static visitor numbers and the large capital expenditures taking place in the British Museum and the Tate Modern. The outlying observations in spending on new acquisitions are due to major purchases by the National Gallery. Non-grant income per visitor is markedly higher in the Museum of London than in other museums whilst capital expenditure per visitor for most museums is trending upwards. Unfortunately there are serious questions regarding the accuracy of visitor numbers (see the discussion in Creigh-Tyte and Selwood, 1998). Edwards (1996) found
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Figure 1. Trends in financial variables and visitor numbers. 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 are expressed in 1995 prices using the GDP expenditure deflator.
Figure 1. (Continued).
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Figure 1. (Continued).
Figure 1. (Continued).
CAUSALITY AND MUSEUM SUBSIDIES
Figure 1. (Continued).
Figure 1. (Continued).
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Figure 1. (Continued).
Figure 1. (Continued).
CAUSALITY AND MUSEUM SUBSIDIES
Figure 1. (Continued).
Figure 1. (Continued).
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Figure 1. (Continued).
that the British Museum counted British Library readers, museum and library staff as well as immediate re-entries. This led to an overstatement of visitor numbers in the region of 35 to 40%. It is also plausible to assume that the figures are likely to be less accurate in the case of non-charging museums like the British Museum since there it is impossible to reconcile attendance numbers with any admission receipts. Nonetheless, these are the official figures reported in the DCMS annual reports and according to some the only quantifiable measure of output upon which the DCMS can base its award. The DCMS now insists that visitor surveys follow written guidelines so the accuracy (and comparability) of more recent figures may be 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 from the concept of causality as it is more normally understood. The idea of Granger causality rests on statistical precedence. Thus, if lagged values of one time series help to explain the variation in another then there exists Granger causality. This definition clearly falls foul of the post hoc ergo propter hoc fallacy. For a review of Granger causality tests conducted in other branches of economics, see Pierce and Haugh (1977). The statistical analysis undertaken in this paper involves running the following regression in which i refers to the museum, t refers to the time period, j refers to
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the number of lags and the Greek letters represent parameters to be estimated. Note the presence of a year specific intercept and a museum-specific disturbance term: GRANTit = αt +
βj × NGINCit−j +
j=1
+
δj × CAPEXit−j +
j=1
+
j=1
γj × OPCOSTit−j +
j=1
φj × ACQUISit−j +
j=1
ηj × VISITSit−j +
θj × GRANTit−j + ui + eit .
j=1
The purpose of including a time-dependent intercept is to ensure that the significance of particular explanatory variables cannot be due to their trending with autonomous changes in the level of museum grants. Note that this specification differs markedly from the one employed by Hughes and Luksetich (1997) who, because their data set was cross sectional, assumed that different sources of museum finance 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 relatively short time period would suggest the use of panel data estimation techniques known to be consistent as the number of panels tends to infinity (see for example Arrellano and Bond, 1991). In this case, however, the number of panels is not much greater than the number of time periods. This creates a dilemma because methods used to overcome the problems caused by a lagged dependent variable are themselves biased when the number of panels is small (see for example Kiviet, 1995).9 Hence it is unclear what estimator is most appropriate here. Furthermore, as the number of lags included increases the problem is exacerbated as both the number of time periods and the number of cross sectional units declines (due to the fragmented nature of the data set). Note that observations with missing values are simply deleted from the dataset. The procedure adopted to deal with these issues is as follows. First, given the paucity of the data I commence with the simplest possible model (j = 1) and test whether additional lags are statistically significant. The alternative approach of starting with a more general model (e.g., j = 3 or 4) is unlikely to identify the correct lag length given the problem of small sample bias. Secondly, I compare the results provided by different estimation techniques, namely Least Squares Dummy Variables (LSDV) with the Generalised Method of Moments (GMM) estimator that is more frequently employed in this context. If these two estimators yield similar results 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 of observations similar to the one in the current dataset, if both estimates differ the GMM 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 acquisitions and capital expenditures are significant causal influences affecting changes in the level of grants at the one-percent level of confidence. By contrast, operating costs and the lagged number of visitors do not contribute any explanatory power even at the ten-percent level of confidence. The second model extends the number of lags from j = 1 to j = 2 and as a consequence the number of observations used in the estimation routine is somewhat reduced.10 With one exception the implications of the model are similar. Tests of joint significance suggest that acquisitions and non-grant income continue to Granger cause grants (F2,88 = 3.82 and F2,88 = 6.47 respectively) but unlike in the model characterised by j = 1 capital spending does not now appear to Granger cause grants (F2,88 = 1.91). Operating costs and the number of visits remain unimportant (F2,88 = 1.12 and F2,88 = 0.78 respectively). None of the coefficients that are lagged twice are statistically significant at the five-percent level. Furthermore a test of group significance for the twice-lagged variables is inconclusive being significant 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 LSDV technique. It is clear that the parameter estimates and associated t-statistics are almost identical to those generated by the preferred GMM estimator. Finally, an alternative version of the model was estimated measuring the dependent variable in terms of logarithms. The results of the model were disappointing in terms of the overall fit and are not shown here. There may even be an a priori reason for preferring a linear functional form. If one suspects, as most people do, that government grants are intended to cover a funding shortfall then a £1 increase in non-grant income most likely causes a subsequent reduction of approximately £1 in government grant. In this case a linear functional form is appropriate. A semi-logarithmic functional form by contrast implies that an increase of £1 in nongrant income causes a proportional change in government grant and moreover a proportional change that is equal across museums receiving very different levels of grant, something which is hard to rationalise.12
4. Discussion The results of the analysis presented above support the view that increases in nongrant incomes cause a reduction in the future level of government grants. At the same time, however, the results indicate that the offsetting reduction in grants is less than that required to entirely make up for any increase in non-grant income. The equilibrium impact of an additional £1 of revenue from non-grant income serves to reduce grants by £0.30.13 This is significantly different from zero at the one-percent level of confidence (the t-statistic is 3.76). This can be compared with the finding from Hughes and Luksetich (1997) that private donations attract federal funding to history museums.14
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Table IV. A dynamic panel data model of Granger causality Method
GMM
GMM
LSDV
LSDV
Dependent variable
GRANTt
GRANTt
GRANTt
GRANTt
NGINCt −1
–0.106 (–4.21) 0.045
–0.094 (–3.54)
–0.097 (–3.80) 0.045
–0.094 (–3.38)
NGINCt −2 OPCOSTt −1
0.048 (0.82)
OPCOSTt −2 CAPEXt −1
0.095 (4.74)
CAPEXt −2 ACQUISt −1
0.181 (2.98)
ACQUISt −2 VISITSt −1
0.039 (0.12)
VISITSt −2 GRANTt −1
0.648 (8.48)
GRANTt −2 Number of observations F test for zero slopes Shapiro–Wilk test for normality
(1.19) –0.000 (–0.01) 0.090 (1.44) 0.044 (1.75) 0.018 (0.65) 0.163 (2.48) –0.111 (–1.56) 0.258 (0.64) 0.413 (1.05) 0.709 (6.87) –0.084 (–0.81)
0.069 (1.18)
0.087 (4.26)
0.153 (2.54)
0.042 (0.12)
0.645 (8.40)
(1.14) –0.001 (–0.02) 0.090 (1.37) 0.044 (1.67) 0.018 (0.62) 0.163 (2.37) –0.111 (–1.49) 0.255 (0.61) 0.413 (1.00) 0.709 (6.58) –0.084 (–0.77)
125
108
142
125
F13,110 = 15.02
F18,88 = 10.11
F14,111 = 19.13
F19,89 = 11.87
4.14
3.18
5.99
5.65
Source: Own calculations. Note that the figures in parentheses are t-statistics. Year dummies are included but not shown.
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At the same time it seems that increases in a museum’s capital expenditures increase future museum grants. More specifically, it appears that the equilibrium impact 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 confidence with a t-statistic of 3.78.15 Similarly, the equilibrium impact of an additional £1 of expenditure on new acquisitions serves to increase future grants by £0.52. This is statistically different from zero at the one-percent level of confidence with a tstatistic of 2.97. Both of these findings are of course consistent with activity-based budgeting, with past capital expenditures and with an increased number of exhibits “necessitating” a greater level of support from the government to pay for higher running costs. In this sense it might now seem that, contrary to the assertions made in the introduction, the DCMS not only withholds grants from those museums that raise additional revenues on their own initiative but also that awards from the HLF do affect grant allocations. Bear in mind however that not all capital expenditure is funded by the HLF. The equilibrium impact of an additional £1 spent on running costs is not statistically different from zero at the ten-percent level of confidence with a t-statistic of 0.87. This suggests that those in charge of allocating the grants are alert to the incipient problems of incremental budgeting procedures and the risk of inefficiencies being accommodated by future budgets. The impact of additional visitors on museum grants is also statistically insignificant with a t-statistic of 0.10. On one hand this is perhaps unsurprising given widely expressed doubts regarding the accuracy of data on visitor numbers.16 But given the evident importance that the government attaches to widening access to museums and that fact that these are official figures coupled with the increasing use of performance-based budgeting, it is to some degree surprising. It invites the question by what means the government 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 such profound differences in the level of grant per visitor to each of the institutions listed in Table I above. For example, in the financial year 2000/2001 the average grant per visitor was £10.40 in 1995 prices. But this ranged from £23.91 per visitor for the Horniman Museum and Gardens to £0.74 per visitor for the Tyne and Wear museums.
5. Conclusions I have examined the causal influences determining differences in the level of central government funding provided to national museums. In doing so I have tested the hypothesis that increases in non-grant income are met by an equivalent reduction in subsequent levels of government grant. If this proposition were true others have argued that it would provide an explanation for a range of phenomena. Strong evidence is found suggesting that governments do indeed claw back a significant fraction of the additional revenues raised by museums. But notwithstanding certain
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limitations arising from the small sample size, one can equally reject the hypothesis that increases in non-grant income have a zero effect on museum revenues. At the same time there is evidence that those responsible for allocating the grant are using activity-based budgeting techniques rather than incremental budgeting practices or performance based ones. I have argued that the use of performance-based techniques might assist the Government in its objective of widening access. A large number of possible extensions suggest themselves. For one thing I have examined only a select subset of museums, namely all those in receipt of direct funding from central government. Do the same relationships hold in the case of funding provided by local government or other public bodies? Informal opinion has it that local governments claw back an even greater proportion of non-grant income. Do funding bodies distinguish between revenue streams from different sources such as those arising from admission charging or private donations? Is there a distinction between funds that can be used for any purpose and those funds that are earmarked for particular causes? One could also examine the causal relationship between other variables; for example, is it true that when a museum increases its admission charges private donations based on goodwill are reduced? Do increases in government grants cause an equivalent reduction in private donations? Although testing these hypotheses is impossible with the current dataset there are other data sets that might be used for this purpose. In particular the Digest of Museum Statistics enquires about the sources of museums’ incomes separately identifying public grants but somewhat bizarrely these data are closed until the year 2028! Other countries may well have equivalent or superior data sets. Following agreement with the Department for Culture, Media and Sport, all centrally funded museums have recently decided to dispense with charging and since 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 structural break at the very end of the data set or will the same patterns emerge if the data set were reanalysed at some point in the future? Likewise, will the improved reliability of figures on visitor numbers mean that the DCMS will in the future begin targeting that variable?
Acknowledgements The author is grateful to Richard Hartman of the DCMS for comments on an earlier draft of this paper as well as to Matthew Sherman for ideas that emerged from his dissertation on the same topic. The author would also like to acknowledge the helpful comments of two anonymous referees. Any remaining errors are the sole responsibility of the author, and the views contained in this paper should not be attributed to anyone else but him.
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Notes
1. The advent of the National Lottery has created the opportunity for the large scale funding of capital projects involving museums and by the end of the year 2000 it has been estimated that the 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 are the residual claimants of any unspent funds. Rather it is because in the absence of a profit motive it is plausible to assume that museums seek to maximise some weighted combination of revenues and visitor numbers. These are closely linked to status, remuneration and employment. Hence revenue-neutral increases in admission charges unambiguously reduce the welfare of museum directors. 3. For recent surveys of the empirical evidence on the crowding out hypothesis in the broader context of the non-profit sector, see Andreoni (1993) and especially Brooks (2000). Most empirical models 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 consider whether causality also runs in the other direction, specifically whether government subsidies can also be crowded out. 4. A number of other museums including the National Coal Mining Museum recently started to receive funds from the DCMS. These are excluded from the analysis along with the British Library. 5. The justification for distinguishing between access to core collections and access to borrowed collections is difficult for a mere economist to understand on grounds of either cost or external benefits. 6. Selwood (2001) amply highlights the difficulties inherent in the collection of data from this sector. Recently laws have been introduced that have standardised the accounting procedures adopted by charitable organisations. 7. The most recent set of agreements between national museums and the government has been published 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 Wear museums as well as the Royal Armouries. The graphs displaying NGINC, OPCOST, CAPEX and ACQUIS are also restricted to the period 1992–2001 whilst the graphs displaying GRANT and VISITS run from 1990–2001. 9. Kiviet (1995) proposes an alternative estimator correcting the bias in the Least Squares Dummy Variable estimator. Unfortunately his estimator cannot be applied to unbalanced panels like this one. 10. Grant allocations are revised every other year. The implication of the model with j = 1 is that a change in the level of non-grant income has an effect whose impact declines geometrically over time. 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 discussed above. It might seem strange that a one period lag is sufficient to capture the dynamic relationship involved, especially given that funding decisions obviously have to be made in advance. The presence of informational lags also calls into question the assumption of Hughes and 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 approximate the true lag relationship in as parsimonious a fashion as possible. The approximation used here involving the use of a geometrically declining lag function has often been found sufficient to 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 the dynamic relationships in their widely-cited panel data study of public revenues and expenditure.
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12. Following the suggestion of one of the referees, the model was re-estimated after dropping the largest museums from the dataset. The results of the regressions are not very different and are not shown here. 13. These figures as well as those that follow are calculated from the model estimated using GMM in which the number of lags j = 1. 14. In order to identify the funding equations Hughes and Luksetich (1997) assumed that federal funding 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 capital expenditure on grants is positive and statistically significant at the five-percent level on a onesided t-test (t = 1.90). 16. Attempting to distinguish between those museums that charge (and presumably therefore have a better idea of the true number of visits) and those that do not did not alter the statistical insignificance 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 which performance-based budgeting procedures can be employed.
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