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Fiscal pressures, institutional context, and constituents: a dynamic model of states’ arts agency appropriations

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Abstract

This report focuses on state government appropriations to state arts agencies (SAA), a primary figure in arts and cultural policy in the United States. A dynamic panel-data estimator can identify the fiscal, institutional, and demographic determinants on SAA appropriations. Agency budgets are particularly sensitive to past appropriations, past state revenues and NEA grants, some demographic variables, party control of state government, and state budgeting rules. Federal funds attract, rather than crowd out, state appropriations. While the influence of some demographic variables may be shifting over time, income growth continues to explain much of SAA appropriations.

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Notes

  1. Dollar figures throughout this article are given in 2000 US$, unless otherwise noted.

  2. Merrifield’s (2000) model of spending might also be used here. He models SPEND = f(marginal utility of public office, marginal perceived benefits of spending, marginal perceived costs of spending, income, tastes, decision-making constraints). Such an approach can lead to comparable empirical tests.

  3. Specifically, partition T it into (T 1it T 2it ) with state fixed-effects T 1it  = T 1is , for all t ≠ s. Let the corresponding vector of parameters for T 1it vary over time at a constant rate. Estimating (2) with state fixed-effects T 1it reveals parameters γ 1, which correspond to state-specific rates of change.

  4. Additional variables were tested in this model, but their role was found to be minimal or not enough years were available. They were excluded for the sake of parsimony. These variables include public school expenditures, percent Hispanic, additional age categories, gross state product, gross state product from federal sources, and additional variables describing state budgeting rules.

  5. The variable ArtsIncome is estimated from the Bureau of Economic Analysis (BEA) annual state estimates for personal income (http://www.bea.gov/regional/spi/) for 1969–2000. Two SIC categories are summed: “amusement and recreation services” (835), and “museums, botanical, zoological gardens” (865). Values for 2001 and 2002 are imputed based on a GLS regression using state fixed-effects, the BEA’s estimates for similar NAICS categories, and a time trend. Details available upon request. The log of real ArtsIncome is used.

  6. This alternate ArtsIncome variable derives from the NCCS’s Core Files containing financial information on reporting public charities since annual reporting began in 1989. The log of total real contributions, gifts, and grants reported for “Arts, Culture, and Humanities” charities in each state is used. Unfortunately, NCCS does not report non-governmental contributions separately.

  7. Since the Origin Year and BudgetBal variables are time-invariant, they must enter the model already first-differenced. Thus, their estimated coefficients must be interpreted differently than the others. For a dependent variable that is the first-difference in log SAA appropriations, the coefficient for Origin Year implies a 0.26% higher growth rate for an SAA founded in 1975 rather than 1965.

  8. The observed contemporaneous elasticity (0.13) is particularly interesting given the matching requirements of NEA grants (Lowell 2004). The share of SAA budgets from NEA grants dipped below 50% decades ago. A modest flypaper effect (Hines and Thaler 1995) appears, as the median effect of an additional NEA dollar on SAA appropriations is to boost it by $0.39 in 2004.

  9. Adding fixed effects for SAA locations (state, culture, economic development, and independent) to Model 1, treating each variable as exogenous without taking first differences, yields effects not statistically significantly different from zero when tested individually and jointly.

  10. Of the 33 studies, 12 report both WTP and mean sample incomes. The log of the average WTP (median when reported, otherwise mean) was regressed upon the log of the mean sample income, all in 2002 US$. Since studies typically report valuation estimates for multiple goods, samples, or methods, a random effects regression is employed to allow for study-specific error terms. The regression with N = 42 yields a R2 = 0.37, income elasticity of 0.737, and a robust standard error of 0.14.

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  1. School of Public Policy, Georgia Institute of Technology, Atlanta, GA, 30332-0345, USA

    Douglas S. Noonan

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Noonan, D.S. Fiscal pressures, institutional context, and constituents: a dynamic model of states’ arts agency appropriations. J Cult Econ 31, 293–310 (2007). https://doi.org/10.1007/s10824-007-9052-9

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