The Tax Spending Nexus: Evidence from a Panel of US State-Local Governments
AbstractWe re-examine the tax-spending nexus using, for the first time, a panel of fifty US state-local government units over the period 1963-97 and panel techniques that allow for cross-sectional dependence. We find that, unlike tax revenues, expenditures adjust to revert back to a long-term equilibrium relationship. The evidence on the short-term dynamics is also consistent with the tax-and-spend hypothesis at the one percent level of significance. One implication of this finding is that the size of the government at the state-local level is not determined by expenditure demand, but rather by resource supply. This is consistent with the fact that many US state and local governments operate under constitutional or legislative limitations that seek to constrain deficits.
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Bibliographic InfoPaper provided by College of Business, University of Texas at San Antonio in its series Working Papers with number 0045.
Length: 30 pages
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tax-spend; state and local government; public finance; and panel cointegration.;
Other versions of this item:
- Westerlund, Joakim & Mahdavi, Saeid & Firoozi, Fathali, 2011. "The tax-spending nexus: Evidence from a panel of US state-local governments," Economic Modelling, Elsevier, vol. 28(3), pages 885-890, May.
- Westerlund, Joakim & Mahdavi, Saeid, 2009. "The Tax-Spending Nexus: Evidence from a Panel of US State- Local Governments," Working Papers in Economics 378, University of Gothenburg, Department of Economics.
- H21 - Public Economics - - Taxation, Subsidies, and Revenue - - - Efficiency; Optimal Taxation
- H71 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Taxation, Subsidies, and Revenue
- H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
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