Blanchard O. and R. Perotti, 2002, “An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output.” QJE, November 1329–68.
Carriere-Swalow, Y., A. David and D. Leigh, 2018, “The Macroeconomic Effects of Fiscal Consolidation in Emerging Economies: Evidence from Latin America.” IMF Working Paper WP/18/142 (Washington: International Monetary Fund).
Céspedes, L. F., J. Fornero y J. Galí, 2012, “Non-Ricardian Aspects of Fiscal Policy in Chile.” En Fiscal Policy and Macroeconomic Performance, editado por L.F. Céspedes y J. Galí, serie Banca Central, Análisis y Políticas Económicas, Vol. 17, Banco Central
David, A., 2017, “Fiscal Policy Effectiveness in a Small Open Economy: Estimates of Tax and Spending Multipliers in Paraguay.” IMF Working Paper WP/17/63 (Washington: International Monetary Fund).
Estevao, M. and I. Samake, 2013, “The Economic Effects of Fiscal Consolidation with Debt Feedback.” IMF Working Paper WP/13/136 (Washington: International Monetary Fund).
Fornero, J., J. Guerra-Salas, and C. Pérez, 2019, “Multiplicadores fiscales en Chile.” Revista de Economia Chilena, Central Bank of Chile, 22: 58–80.
IMF Western Hemisphere Department Regional Economic Outlook, 2018, Chapter 4. “Fiscal Multipliers: How Will Consolidation Affect Latin America and the Caribbean?” (Washington: International Monetary Fund).
International Monetary Fund (IMF), 2018, “Poverty, inequality and redistribution in the Dominican Republic,” in Dominican Republic 2018 Article IV Consulta tion—Selected Issues, IMF Country Report No. 18/294 (Washington).
López-Vera, A., A. Pinchao-Rosero, and N. Rodríguez-Niñoa, 2018, “Non-Linear Fiscal Multipliers for Public Expenditure and Tax Revenue in Colombia.” Ensayos de política económica vol.36 no. 85. Banco de la República de Colombia.
Matheson, T. and Pereira, J., 2016, “Fiscal Multipliers for Brazil” IMF Working Paper WP/16/79 (Washington: International Monetary Fund).
Ministry of Economy and Finance of Peru, 2015, “Multiannual Macroeconomic Framework,” 2016–18. Ministry of Finance of Peru, April.
Ouliaris, S. A. Pagan, and J. Restrepo, 2018, Quantitative Macroeconomic Modeling with Structural Vector Autoregressions – An EViews Implementation. https://www.eviews.com/StructVAR/structvar.html
Ramey, V. S. Zubairi, 2018, “Government Spending Multipliers in Good Times and in Bad: Evidence from US Historical Data.” JPE 126, No. 2 (April 2018): 850–901.
Vtyurina, S. and Z. Leal, 2016, “Fiscal Multipliers and Institutions in Peru: Getting the Largest Bang for the Sol” IMF WP/16/144.
World Bank, 2018, “Fiscal Adjustment in Latin America and the Caribbean: Short-run pain, long-run gain?” Semiannual Report Office of the Regional Chief Economist April.
I thank comments by Jaime Guajardo and members of the country teams: Pelin Berkmen, Julia Faltermeier, Pedro Rodriguez, Tobias Roy, and Christian Saborowski (WHD); Jiro Honda, Babacar Sarr, and Hiroaki Miyamoto (FAD), as well as excellent research assistance by Genevieve Lindow.
Where the Es are the coefficients obtained estimating the reduced-form.
This elasticity is assumed to be 1 or slightly larger. A reas onable level for LAC could be between 1 and 2, since economic growth is associated with higher taxation, including through the effects of formalization. For the sake of pragmatism and comparability, the BP estimate of 2 was used for all countries.
In some cases, one of these coefficients, c1and/or c2, was picked from the respective instrumental variable estimation if it was statistically significant.
To get the dollar for dollar response of tax revenues to a spending shock
All responses throughout the paper are dollar for dollar. For instance, a shock to government spending of one dollar generates 1.29 dollars of GDP reached in 15 quarters (Figure 1), i.e., the vertical axis corresponds to dollars in all impulse responses and all shocks are one-dollar shocks. Also, all figures show one-standard-deviation confidence intervals.
The Dickey-Fuller test results point to the existence of stochastic trends and cointegration in Paraguay.
Whenever the variables have a unit root and, at the same time, they are not cointegrated, I estimate the system in first differences (no deterministic trends). Otherwise, the relation among trending variables in levels could be spurious. If the variables are cointegrated, I estimate vector error correction models instead, given that a SVAR in first differences would not be correctly specified as above said. It is worth adding that, not here but in the literature, cointegrated systems are alternatively estimated in levels.
Akaike, Schwarz, Hannan-Quinn are commonly used criteria to select the number of lags. Each one penalizes differently the use of lags. However, what really matters is to have the (minimum) number of lags that produce white noise residuals, and that is what I did here. Note that using quarterly data it is commo n to have 4 lags.
Restrepo and Rincón (2006), using a different s ample, find a peak spending multiplier of 1.9 for Chile, which stabilizes at 1.35, but it is only significant in the first two-to-three quarters. The tax multiplier is smaller at 0.4.
Previous estimates of fiscal multipliers for Colombia can be found in Restrepo and Rincón (2006), although for a different definition of government.
These results are compatible with the multipliers of Lopez-Vera and others (2018) after two years: Spending shock 0.64 (boom) and 0.99 (recession); tax shock -0.71 (boom) and -1.12 (recession).
The revenue data excludes that related to Petro Caribe’s debt, which was forgiven in 2015.