2017
DOI: 10.1111/pirs.12221
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The effect of state and local taxes on economic growth: A spatial dynamic panel approach

Abstract: This study revisits the growth effects associated with subnational fiscal policy. This, to my knowledge, is the first attempt to address the potential endogeneity of fiscal policy control variables. More specifically, the analysis used in this study implements a general method of moments spatial dynamic panel data model estimation procedure to arrive at a more refined set of estimates for the growth effects attributed to state and local fiscal policy. In deriving the estimable equation, this study extends a fa… Show more

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Cited by 15 publications
(17 citation statements)
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“…() concluded there were spillover effects of state policy, suggesting cooperation was needed among states. Segura () estimated a spatial dynamic panel model and found evidence of spatial spillovers that reduced the estimated effects of a state's own fiscal policy.…”
Section: Recent Trends In the Literaturementioning
confidence: 99%
“…() concluded there were spillover effects of state policy, suggesting cooperation was needed among states. Segura () estimated a spatial dynamic panel model and found evidence of spatial spillovers that reduced the estimated effects of a state's own fiscal policy.…”
Section: Recent Trends In the Literaturementioning
confidence: 99%
“…To show that our spatial diffusion processes meet stationarity and/or stability conditions, we report estimated parameter values of λ, φ, and Ω.. These parameter estimates reported underneath of Tables , , , , and all satisfy stationarity conditions as in Segura (), i.e., Ωφ0 and 1+Ωφ<λ. These stationarity conditions are stricter than those suggested in Yu, de Jong, and Lee () and Lee and Yu (). Our parameter estimates do meet all stationarity conditions as well as satisfaction of characteristic roots falling within the unit circle as discussed in Elhorst () on pages 97–99.…”
Section: Empirical Results and Discussionmentioning
confidence: 85%
“…We estimate a dynamic spatial Durbin model as described in Elhorst (), Debarsy, Ertur, and LeSage (), and Segura () to investigate the short‐ and long‐run growth effects of productive government spending on state real per capita income growth. Letting git represent the growth rate in real per capita income growth in state i at time t and gjt as a notation for growth rates in neighboring states j at time t , the dynamic spatial Durbin model can be written as: git=λgi,t1+ϕj=1Nωitrue(dijtrue)gi,t1+Ωj=1Nωitrue(dijtrue)gjt+Xitβ+j=1Nωitrue(dijtrue)Xjtγ+δt+φi+ɛit where λ is defined as the autoregressive time‐dependence parameter.…”
Section: Methodology and Datamentioning
confidence: 99%
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