Abstract
In 2008, euro area governments instituted fiscal stimulus to counteract the shock of the Global Financial Crisis. In 2010, they changed tack and pursued consolidation. East Asia also implemented stimulus in response to its 1997–98 financial crisis but, unlike Europe, continued fiscal expansion until growth recovered. The baseline difference between the average growth rate of the east Asian countries and the European Periphery/GIIPS prior to their respective crises was 4.21 percentage points. This difference widens to 7.18 percentage points following the European pivot to austerity in region-specific crisis event time. Panel regressions confirm the statistical significance of this 2.97 percentage point increase in the difference-in-difference estimate suggesting that more gradual fiscal consolidation in the GIIPS might have promoted stronger recovery.
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Notes
In contrast to previous episodes, disciplined fiscal policy leading up to this crisis gave emerging-market countries room to pursue countercyclical fiscal policies during the crisis, and this made a substantial difference (Blanchard, 2013). The evidence on initial conditions in our paper suggests that east Asian countries consistently ran fiscal surpluses in the run-up to the crisis and had much lower debt-to-GDP ratios on the eve of their crisis in comparison to the GIIPS countries, giving them greater fiscal room to pursue countercyclical policies during the crisis.
The original euro area refers to member countries that adopted the euro before physical notes and coins were first introduced in January 2002.
The crisis year for east Asia is 1998; 2008 for the Global Financial Crisis. The five calendar time periods for the east Asian crisis are therefore 1994–97 (precrisis), 1998 (crisis), 1997–2002 (overall performance), 1999–2000 (postcrisis I), and 2001–02 (postcrisis II). For the Global Financial Crisis the time periods are 2004–07 (precrisis), 2008 (crisis), 2007–12 (overall performance), 2009–10 (postcrisis I), and 2011–12 (postcrisis II).
The Appendix provides a detailed derivation showing the correspondence between the Blanchard and Leigh (2014) specification and our baseline specification.
In a related paper, Fatás and Mihov (2003) find that the volatility of output caused by discretionary fiscal policy lowers economic growth by more than 0.8 percentage points for every percentage-point increase in volatility.
The finding is consistent with evidence that countries with less flexible exchange rate regimes tend to face greater macroeconomic and financial vulnerabilities (Ghosh, Ostry, and Qureshi, 2014). Similarly, large currency depreciations increase growth shortly after a crisis (Forbes and Klein, 2013).
Corsetti, Meier, and Müller (2012) demonstrate that the effects of government spending vary with the economic environment and find output and consumption multipliers to be unusually high during times of financial crisis.
The equation estimated in Blanchard and Leigh (2014) is: Forecast Error of Δy i,t:t+1=α+βForecast of ΔF i,t:t+1+ɛ i,t:t+1 where under rational expectations, and assuming that the correct model has been used for forecasting, the coefficient on the forecast of fiscal consolidation should be zero.
In Blanchard and Leigh (2014), the corresponding notation is of the form where ΔY i, t: t+1 denotes cumulative (year-over-year) growth of real GDP (Y) in economy i—that is, (Y i, t+1/Y i, t−1−1)—and the associated forecast error is ΔY i,t: t+1−f{ΔY i,t: t+1|Ω t }, where f denotes the forecast conditional on Ω t , the information set available early in year t.
In Blanchard and Leigh (2014) the corresponding notation is as follows: ΔF i,t:t+1 denotes the change in the general government structural fiscal balance in percent of potential GDP, a widely used measure of the discretionary change in fiscal policy for which we have forecasts. Positive values of ΔF i,t:t+1 indicate fiscal consolidation, whereas negative values indicate discretionary fiscal stimulus. The associated forecast is “Forecast of ΔF i,t:t+1|t ” defined as f{F t+1, i −F t−1,i |Ω t }.
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Additional information
*Anusha Chari is an Associate Professor of Economics at the University of North Carolina at Chapel Hill and a research fellow at the NBER. Peter Blair Henry is William R. Berkley Professor of Economics and Finance and Dean at New York University’s Leonard N. Stern School of Business. The authors thank Pierre-Olivier Gourinchas, Ayhan Kose, Morris Goldstein, two anonymous referees, and participants at the IMF’s Fourteenth Jacques Polak Annual Research Conference for helpful comments and suggestions. Allison Cay Parker provided stellar editorial assistance.
Appendix
Appendix
Although regressing realized changes in real GDP growth on realized changes in the CAPB provides the most direct evidence for the question at hand (Do changes in the CAPB have an impact on growth?), we provide a detailed derivation below showing the correspondence of our benchmark specification with Blanchard and Leigh (2014). The Blanchard and Leigh specification is logically equivalent to our approach of including lagged terms for output growth and the change in the CAPB. The coefficient β3 in Equation (1) is the coefficient on lagged GDP growth and embeds a parsimonious forecast of current GDP growth under the assumption that the best guess of output growth for country i at time t is its lagged value at time t−1; that is, E(y i, t )=y i,t−1.
A parsimonious forecast model relating output growth to changes in the structural balance can be written as:
where 
is the forecast of real GDP growth for country i, 
is the forecast of the change in the CAPB conditional on the information set available early in period t. This implies that if we run the Blanchard and Leigh (2014) specification of the formFootnote 8:
where 
is the output growth forecast error,Footnote 9 under the null hypothesis that fiscal multipliers used for forecasting were accurate, the coefficient β1 on the fiscal forecast variable should be zero.Footnote 10
Plugging 
from Equation (A.1) into Equation (A.2), we get:
where u it =e it +ɛ it and is iid mean zero and uncorrelated with ΔCAPB i,t:t+1|t .
Rewriting:
where 
and 
.
This implies that:
Therefore, subtracting (A.5) from (A.4), we get:
Under the assumption that in small samples where it is hard to identify a unit root the best forecast of 
is γy
i,t
using quasi-differencing, we get:
under the parameter restrictions γ=β9 and β2=−β1γ. Using quasi-differencing and parameter restrictions, Equation (A.7) our benchmark specification encompasses the Blanchard and Leigh specification (equation (A.2)) albeit written in the form of y it and γy it−1.
Since we have a small sample, we do not want to assume a random walk in growth forecast. Instead we assume an AR(1) model where AR(1) parameter, γ, equal to unity would imply a random walk. To identify a unit root the parameter γ needs to be pinned down at frequency zero to give a value of γ=1 which is, however, a long-run phenomenon and hard to identify in small samples.
To see this in Equation (A.8), the parameter restrictions to identify a unit root would be α0=0, γ=1 where ɛ it is an iid shock with mean zero. In the absence of a theory to pin down the parameter γ on lagged output growth, we allow it to differ from one and leave it as a free parameter to be estimated and to let the data speak. Given model uncertainty we also include other conditioning variables in lagged form to the estimating equation (A.7).
Similarly, under rational expectations, the actual change in the CAPB should correspond to the forecasted change in the CAPB (similar to using realized earnings as a measure of expected earnings in asset pricing equations). Including the current and lagged values of ΔCAPB it therefore corresponds to “Forecast of ΔF i,t: t+1|t ” in the Blanchard and Leigh specification defined as f{F t+1, i −F t−1,i |Ω t } where f denotes the forecast conditional on Ω t , the information set available early in year t. Notice that in the absence of forecast data, (ΔCAPB i,t −ΔCAPB i,t−1) is numerically equivalent to f{F t , i −F t−2,i |Ω t−1 }, where ΔCAPB it =CAPB i,t −CAPB i,t−1 and ΔCAPB it−1=CAPB i,t−1−CAPB i,t−2. Under rational expectations, the actual change in the CAPB should correspond to the forecasted change in the CAPB. To reiterate, the dependent variable of interest in the Blanchard and Leigh specification is the forecast error in the year-over-year growth in real GDP, and the independent variable is the forecast of the change in the CAPB.
Corresponding to Blanchard and Leigh, in our benchmark specification, ΔCAPB i,t:t+1 denotes the change in the general government structural fiscal balance as a percent of potential GDP, a widely used measure of the discretionary change in fiscal policy for which we have forecasts. Positive values of ΔCAPB i,t:t+1 indicate fiscal consolidation, whereas negative values indicate discretionary fiscal stimulus.
