Regional effects of fiscal policy: Analysis with spatial vector autoregressive models

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Аннотация

This paper attempts to assess the impact of fiscal policy measures conducted in Russian Federation units on gross regional product. For this purpose, we use panel data for 80 Russian regions for 2005–2020. As a method for assessing the response of GRP to the shock of government expenditures, we propose to use a spatial vector autoregression model consisting of three equations for the following endogenous variables: GRP, consolidated budget expenditures, tax revenues. The model also includes a set of exogenous factors: oil prices, MIACR interest rate, expenditures of the Russian Pension Fund. Additionally, we account for the structure of the regional economy. The advantage of the model is the ability to simultaneously consider spatial effects using the contiguity-based matrix and evaluate the impulse response function, while the Cholesky decomposition is used for shock identification. Overall, we estimated 3 SpVAR specifications and considered shocks of government expenditures for 7 categories of regional budgets. The main result of the study is the peak and cumulative values of IRF for 2 and 3 years, which reflect the evolution of the GRP response to an exogenous shock of expenditures over time. For all specifications of the model, the greatest positive effect on GRP is observed for the shock of expenditures on the national economy and education. Depending on the specification, over 3 years after the shock of increasing expenditures by 1%, an expected increase in GRP varies from 0.053 to 0.1% and from 0.051 to 0.1%, respectively.

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Авторлар туралы

А. Demyanenko

National Research University Higher School of Economics

Хат алмасуға жауапты Автор.
Email: ademyanenko@hse.ru
Ресей, Moscow

Әдебиет тізімі

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2. Figure. Impulse response functions of GRP to a 1% increase in government spending shock in Model 1. Note. Order of Choletsky decomposition: GRP - expenditures - revenues. The dotted line indicates 95% confidence intervals.

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