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The impact of ‘central places’ on spatial spending patterns: evidence from Flemish local government cultural expenditures

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Abstract

Governments often see it as their responsibility to support cultural life and at times spend a significant amount of resources in the pursuit of this goal. The present article analyses whether and how municipalities influence each other in this decision to spend resources on the arts (using data on local government cultural spending in 304 Flemish municipalities in 2002). Following ‘central place theory’, the focal point of the analysis is the idea that––especially for cultural expenditures––large municipalities (and, specifically, ‘central places’) may affect their neighbours’ behaviour differently than small municipalities. The empirical analysis supports this idea. Indeed, we show that Flemish municipalities’ cultural spending is generally positively affected by that in neighbouring municipalities. This pattern is, however, significantly more complex for municipalities neighbouring the 13 largest Flemish cities.

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Notes

  1. Withers (1979) and Jenkins and Austen-Smith (1987) study the relationships between public cultural spending by different levels of government (arguing that, say, federal arts outlays in a given jurisdiction affect state and local expenditures in that same jurisdiction). We focus on horizontal rather than vertical interdependencies between governments.

  2. The most straightforward definition of a jurisdiction’s neighbourhood is geographical (e.g. jurisdictions sharing a border). Still, one might also define the ‘peer’ group in terms of socio-economic or political similarities across jurisdictions (see e.g. Baicker 2005; Van Parys and Verbeke 2007).

  3. The reason––taken from financial economics––is that a decrease in the ‘strike price’ of an option (e.g. through lower anticipated travel costs) to acquire a given underlying commodity (i.e. cultural activities) increases its value.

  4. The influx of consumers from other jurisdictions may lead to (or enhance) potential congestion problems. As this generates costs for consumers (such as longer lines to buy tickets or reduced consumption experience due to noise; cfr. Traub and Missong 2005), this is likely to strengthen the point made here: viz. pressure on governments in all concerned municipalities to increase cultural goods provision.

  5. Empirical evidence on the electoral cost of taxation mostly confirms the idea that voters engage in such relative performance assessments (e.g. Besley and Case 1995; Ågren 2005; Vermeir and Heyndels 2006; Bosch and Solé-Ollé 2007; see, however, Revelli 2002).

  6. This ‘option value’ of the arts has received considerable attention in the literature. Individuals, so the argument goes, might be prepared to pay “for the option to consume art at some unspecified future time, fully realizing that they may never choose to exercise that option” (Shanahan and Hendon 1979, p. 12; for empirical support see Throsby and Withers 1986; Morrison and West 1986; Bille Hansen 1997).

  7. Beside these specific grants and general-purpose grants, higher-level governments may also provide matching grants when municipalities organize cultural activities. Unfortunately, we lack data on the prevalence and importance of such matching grants.

  8. Data availability precludes inclusion of the remaining four municipalities. Note also that the 2002 data are the most recent figures available and that a change in the municipal accounting rules in 2000 prevents us from using comparable data for a longer period of time. This effectively constrains us to a cross-section analysis. One important drawback of this is that there is no temporal development, making it hard to speak of ‘reactions’ to other municipalities’ behaviour in our findings. Essentially, we assess the ‘equilibrium’ outcome of the interdependent process at a given point in time.

  9. The Flemish municipalities spent €132.6 million on local public libraries in 2002. The ‘strict’ definition, however, excludes this spending. The reason is that Flemish Regional legislation requires every municipality to have its own library. One could argue that this diverts cultural spending from the budget of the higher-level government to that of the Flemish municipalities and thus ‘biases’ local spending upwards. Nonetheless, re-estimating the model including library expenditures and subsidies (the latter amounted to €43 million in 2002) does not alter the inferences from the analysis (full results available upon request).

  10. We assume that spending decisions are commonly observable and employ the contemporaneous level of cultural spending in the neighbouring jurisdictions. This assumption––also made in Buettner (2001) and Allers and Elhorst (2005)––is plausible since council meetings in Flanders are open to the public (except when personnel issues are discussed).

  11. Given the small average distance between Flemish municipalities, we also experimented with a possible effect from second order neighbours (i.e. neighbours of neighbours; Heyndels and Vuchelen 1998). As such effects were never detected, this is not retained in the final version of the article. One might also use a distance-decaying weights matrix (with weights equal to the inverse of the distance between municipalities) to construct the neighbourhood variable. We return to the results of such a specification in footnote 21 (and are grateful to Stefan Van Parys for providing these data).

  12. This redefinition is also important to appropriately account for ‘space’ in the spatial econometrics model. In fact, Mutl (2006) has shown that size adjusted weighting matrices are crucial in settings with widely heterogeneous jurisdictions.

  13. This is the case for 13 Flemish cities: Aalst, Antwerpen, Brugge, Genk, Gent, Hasselt, Kortrijk, Leuven, Mechelen, Oostende, Roeselare, Sint-Niklaas and Turnhout (see Moesen 2001). Note that these 13 cities are also the only ones designated as either ‘large’ or ‘regional’ cities through an extensive cluster-analysis incorporating dozens of socio-economic indicators in five categories (i.e. use of land and buildings, income levels, economic activity and structure of working population, demographic structure and externalities) by Dessoy (1998).

  14. Since there are only 13 central places identified, assessing H2b is empirically difficult due to a lack of observations. Nonetheless, preliminary tests indicate that the reaction of central places to their (smaller) neighbours is always positive (though not necessarily statistically significant). This indicates absence of free riding behaviour, as expected under H2b. Nonetheless, it also suggests some interaction between central places and their neighbours (due to yardstick competition, reference point effects or acquired taste effects, cfr. H4). Note that we also tested for the reaction of central places to the cultural spending in other central places (rather than their immediate geographical neighbours). Though coefficient estimations are positive, as might be expected, they fail to reach statistical significance. Given the insignificance of these results, we have not retained this extension in the main analysis.

  15. One alternative means to test H3 is to interact NonCP-neighbours spending with a dummy variable equal to 1 for neighbours of central places (0 otherwise). This implies estimation of regression Eq. 1 with the following neighbourhood variables: ψ (CP-neighbours spending) + ζ (NonCP-neighbours spending) + ξ (NonCP-neighbours spending)*NoCP, where CP- and NonCP-neighbours spending are constructed as before, ξ, ζ and ψ are parameters to be estimated and NoCP (Neighbour of Central Place) is a dummy variable equal to 1 if the municipality shares a border with one of the 13 Flemish central places. H3 leads us to expect a negative effect on the interaction term (ξ), indicating that the behaviour of small neighbours is less influential on the behaviour of municipalities adjoining a central place than on those not bordering a central place. While this approach effectively introduces multiple spatial regimes into the model (cfr. Bordignon et al. 2003; Allers and Elhorst 2005), this alternative modelling strategy (inappropriately) treats municipalities that do not share a border with a central place as if they have a central place neighbour with no cultural spending. This is not the case using the approach in the main text. The results under both approaches are, however, qualitatively similar (see footnote 22).

  16. An anonymous referee pointed out that this variable may well be endogenous. However, the standard Hausman test of exogeneity does not support this and treating the variable as endogenous does not affect our results (available upon request).

  17. The data concerning a party’s ideological position were obtained from Rihoux (2001) and are based on a self-placement survey asking presidents and spokesmen of the parties in the municipalities to locate their party on an ideological scale between 0 (Left) and 10 (Right). The figures range from 2.6 (Agalev) to 6.0 (VLD) (the extreme-right-wing party Vlaams Blok––now Vlaams Belang––was not represented in any local government and is therefore not in the dataset).

  18. Preliminary tests show, however, that the basic findings of the analyses shown in columns (1) through (3) are also retrieved using ML estimation (even when we control for spatial error dependence). In fact, the coefficient estimates (as well as statistical significance) are very similar under both procedures. The results in Columns (4) and (6) would necessitate giving some municipalities in the sample values from two spatial weights matrices (which we were, unfortunately, unable to adequately implement using Matlab).

  19. In the analyses where central places are awarded a special status (reported in columns (4) through (6)), we expand the set of instruments with dummy variables indicating whether or not a given municipality borders one of more central places. This is necessary to increase the strength of the instruments in these estimations (though their exclusion does not affect the estimation results).

  20. In order to ensure that we are actually measuring spatial interdependence, we re-estimated the model using a weights matrix where neighbours are defined according to the alphabetical order of municipalities’ names (cfr. Case et al. 1993; Brown and Rork 2005; Geys 2006). Every municipality is awarded one ‘neighbour’ preceding and following it in the alphabetical ordering. Since this alphabetical ranking has nothing to do with the competitive forces between municipalities, the use of such a weighing scheme should not lead to significant estimates of the spatial parameter. In line with this prediction, the estimations indicate the absence of spatial interactions using these alphabetical ‘neighbours’ (results available upon request).

  21. Using a distance-decay weights matrix gives a relatively large weight to cultural expenditures of large cities in the neighbourhood variable of all municipalities (due to the sheer size of cultural spending by large cities). Free riding behaviour on large city cultural spending (cfr. H2a) should then lead to more negative (or lower positive) values of the neighbourhood parameter estimate when using a distance-decay weights matrix. Effectively, we even observe a negative spatial interaction coefficient (available upon request). This indicates that using a distance-based weights matrix (which implicitly gives higher weights to cities), the free riding effect dominates.

  22. The alternative proposed in footnote 15––i.e. inclusion of an interaction between NonCP-neighbours spending (i.e. the population-weighted average level of cultural spending in those of a municipality’s neighbours that are not central places) and a dummy variable equal to 1 for neighbours of central places (0 otherwise)––indicates that the interaction term has the expected negative sign, but is statistically insignificant at conventional levels. Hence, being situated next to a central place leads to a weaker reaction to small neighbours’ spending (though insignificantly so). Note also that including a dummy variable for neighbours of central places directly into the analysis––in order to check whether vicinity to a central place an sich affects policy decisions––gives insignificant results (such that this dummy is not retained in the final model; except in the instrument vector).

  23. The Brussels Capital Region is geographically surrounded by Flanders. Flemish municipalities bordering Brussels may thus be affected by cultural spending decisions in the Brussels Capital Region. Unfortunately, lack of data on cultural spending in the Brussels municipalities prevents a direct test of this possibility. As an indirect test, we re-estimated the model excluding municipalities that neighbour the Brussels Capital Region. The findings are quantitatively similar to those presented in the main text (available upon request). Also, including a dummy variable to assess whether neighbours of Brussels act significantly different from the remaining Flemish municipalities indicates that this is not the case.

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Acknowledgments

The authors are grateful to Erik Desmedt, Stefaan Swaels and Stefan Van Parys for providing data and to Jan Brueckner, Hilde Coffé, Michiel Evers, Marc Jegers, James LeSage, Jan Mutl, Robert Nuscheler, Federico Revelli, three anonymous referees and participants of the 14th ACEI Conference on Cultural Economics (Vienna, July 2006), 2nd IEB Workshop on Fiscal Federalism (Seville, June 2007), 1st World Spatial Econometrics Meeting (Cambridge, July 2007) and research seminars at ZEW and KULeuven for helpful comments. The usual caveat applies.

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Authors and Affiliations

  1. Department of Applied Economics, University Antwerp, Prinsstraat 13, 2000, Antwerpen, Belgium

    Kristien Werck

  2. Department of Economics, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussel, Belgium

    Bruno Heyndels

  3. Wissenschaftszentrum Berlin für Sozialforschung (WZB) “Market Processes and Governance”, Research Unit, Reichpietschufer 50, 10785, Berlin, Germany

    Benny Geys

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  1. Kristien Werck
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Correspondence to Benny Geys.

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K. Werck, B. Heyndels and B. Geys contributed equally to this article and are given in reverse alphabetical order.

Appendix A: Summary statistics

Appendix A: Summary statistics

Table A1 Summary statistics (N = 304)
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Werck, K., Heyndels, B. & Geys, B. The impact of ‘central places’ on spatial spending patterns: evidence from Flemish local government cultural expenditures. J Cult Econ 32, 35–58 (2008). https://doi.org/10.1007/s10824-007-9056-5

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