Growth Forecast Errors and Fiscal Multipliers

Contributor Notes

Author’s E-Mail Address: oblanchard@imf.org; dleigh@imf.org

This paper investigates the relation between growth forecast errors and planned fiscal consolidation during the crisis. We find that, in advanced economies, stronger planned fiscal consolidation has been associated with lower growth than expected, with the relation being particularly strong, both statistically and economically, early in the crisis. A natural interpretation is that fiscal multipliers were substantially higher than implicitly assumed by forecasters. The weaker relation in more recent years may reflect in part learning by forecasters and in part smaller multipliers than in the early years of the crisis.

Abstract

This paper investigates the relation between growth forecast errors and planned fiscal consolidation during the crisis. We find that, in advanced economies, stronger planned fiscal consolidation has been associated with lower growth than expected, with the relation being particularly strong, both statistically and economically, early in the crisis. A natural interpretation is that fiscal multipliers were substantially higher than implicitly assumed by forecasters. The weaker relation in more recent years may reflect in part learning by forecasters and in part smaller multipliers than in the early years of the crisis.

I. Introduction1

With many economies in fiscal consolidation mode, there has been an intense debate about the size of fiscal multipliers. At the same time, activity has disappointed in a number of economies undertaking fiscal consolidation. A natural question therefore is whether forecasters have underestimated fiscal multipliers, that is, the short-term effects of government spending cuts or tax hikes on economic activity.

In a box published in the October 2012 World Economic Outlook (WEO; IMF, 2012b), we focused on this issue by regressing the forecast error for real GDP growth on forecasts of fiscal consolidation. Under rational expectations, and assuming that forecasters used the correct model for forecasting, the coefficient on the fiscal consolidation forecast should be zero. If, on the other hand, forecasters underestimated fiscal multipliers, there should be a negative relation between fiscal consolidation forecasts and subsequent growth forecast errors. In other words, in the latter case, growth disappointments should be larger in economies that planned greater fiscal cutbacks. This is what we found.

In the box published in October, we focused primarily on forecasts made for European economies in early 2010. The reason was simple: A number of large multiyear fiscal consolidation plans were announced then, particularly in Europe, and conditions for larger-than-normal multipliers were ripe.

First, because of the binding zero lower bound on nominal interest rates, central banks could not cut interest rates to offset the negative short-term effects of a fiscal consolidation on economic activity. Christiano, Eichenbaum, and Rebelo (2011) have shown, using a dynamic stochastic general equilibrium (DSGE) model, that under such conditions, fiscal multipliers can exceed 3.2 Since episodes characterized by a binding zero lower bound (also referred to as “liquidity trap” episodes) have been rare, only a few empirical studies investigate fiscal multipliers under such conditions. Based on data for 27 economies during the 1930s—a period during which interest rates were at or near the zero lower bound—Almunia and others (2010) have concluded that fiscal multipliers were about 1.6.3

Second, lower output and lower income, together with a poorly functioning financial system, imply that consumption may have depended more on current than on future income, and that investment may have depended more on current than on future profits, with both effects leading to larger multipliers (Eggertsson and Krugman, 2012).4

Third, and consistent with some of the above mechanisms, a number of empirical studies have found that fiscal multipliers are likely to be larger when there is a great deal of slack in the economy. Based on U.S. data, Auerbach and Gorodnichenko (2012b) have found that fiscal multipliers associated with government spending can fluctuate from being near zero in normal times to about 2.5 during recessions.5 If fiscal multipliers were larger than normal and growth projections implicitly assumed multipliers more consistent with normal times, then growth forecast errors should be systematically correlated with fiscal consolidation forecasts.

Our October 2012 box generated many comments, criticisms, and suggestions. In this paper, we restate our methodology, revisit our results, examine their robustness, and consider a number of extensions.

Section II presents our estimation approach and reports our baseline results. Our forecast data come from the spring 2010 IMF World Economic Outlook (IMF, 2010c), which includes forecasts of growth and fiscal consolidation—measured by the change in the structural fiscal balance—for 26 European economies. We find that a 1 percentage point of GDP rise in the fiscal consolidation forecast for 2010-11 was associated with a real GDP loss during 2010-11 of about 1 percent, relative to forecast. Figure 1 illustrates this result using a scatter plot. A natural interpretation of this finding is that multipliers implicit in the forecasts were, on average, too low by about 1.

Figure 1.
Figure 1.

Europe: Growth Forecast Errors vs. Fiscal Consolidation Forecasts

Citation: IMF Working Papers 2013, 001; 10.5089/9781475576443.001.A001

Note: Figure plots forecast error for real GDP growth in 2010 and 2011 relative to forecasts made in the spring of 2010 on forecasts of fiscal consolidation for 2010 and 2011 made in spring of year 2010; and regression line.

In Section III, we investigate the robustness of the baseline result along three dimensions.

First, we consider the sensitivity of the baseline results to outliers and to the choice of economies in the sample. Robustness checks indicate an unexpected output loss, relative to forecast, that is for the most part near 1 percent and typically above 0.7 percent, for each 1 percent of GDP fiscal consolidation. We obtain similar results when we extend the analysis to forecasts for all advanced economies. However, and not surprisingly given their different economic circumstances, we find no evidence of multipliers being over- or under-estimated for emerging market economies during that period.

Second, we reestimate our baseline specification while adding control variables, ranging from initial fiscal and current account balances to initial bank credit risk and household debt levels. These could plausibly have both affected the growth forecast error and been correlated with fiscal consolidation forecasts. Not controlling for such factors could influence the estimated relation between fiscal consolidation forecasts and growth forecast errors. We find, however, that our results are robust to the introduction of such controls.

Third, we look at the results for other time intervals since the start of the crisis, as well as the results for “normal times” (1997–2008). Looking within the crisis, we find evidence of more underestimation of fiscal multipliers earlier in the crisis (for the time intervals 2009–10 and 2010–11) than later in the crisis (2011–12 and 2012–13). Results for the earlier samples yield coefficients typically between 0.7 and 1.0. Results for the later samples yield coefficients typically between 0.3 and 0.5 and are less statistically significant. Interestingly, and again perhaps not surprisingly, we find no evidence of systematic forecast errors related to planned changes in fiscal policy during the precrisis decade (1997–2008).

Having discussed robustness, Section IV turns to three extensions of our baseline results.

First, we check whether the baseline results differ depending on whether the fiscal consolidation reflects changes in government spending or changes in revenue. The results suggest that fiscal multipliers were, on average, underestimated for both sides of the fiscal balance, with a slightly larger degree of underestimation associated with changes in government spending.

Second, we examine forecast errors for the unemployment rate and for the components of GDP. We find that forecasters significantly underestimated the increase in unemployment and the decline in private consumption and investment associated with fiscal consolidation.

Finally, we compare the baseline results obtained using IMF forecast errors with those obtained using the forecast errors of other forecasters, including the European Commission (EC), the Organization for Economic Cooperation and Development (OECD), and the Economist Intelligence Unit (EIU). Here, we find that the results hold for all the forecasters considered, with coefficients ranging from -1.1 to -0.4. The results are strongest, in terms of both economic and statistical significance, for forecasts published by the IMF and, to a slightly lesser extent, by the EC.

We conclude in Section V with a discussion of what our results do and do not imply for actual multipliers. We conclude that multipliers were substantially above 1 in the early years of the crisis. The lower coefficients in recent years may reflect in part learning by forecasters and in part smaller actual multipliers than in the early years of the crisis. We end with a number of caveats.

First, forecasters do not typically use explicit multipliers, but instead use models in which the actual multipliers depend on the type of fiscal adjustment and on other economic conditions. Thus, we can only guess what the assumed multipliers, and by implication the actual multipliers, have been during the crisis.

Second, our results only give average multipliers for groups of countries, and individual countries may well have larger or smaller multipliers than the average.

Third, our findings that short-term fiscal multipliers have been larger than expected do not have mechanical implications for the conduct of fiscal policy. Some commentators interpreted our earlier box as implying that fiscal consolidation should be avoided altogether. This does not follow from our analysis. The short-term effects of fiscal policy on economic activity are only one of the many factors that need to be considered in determining the appropriate pace of fiscal consolidation for any single economy.

II. Forecast Errors and Fiscal Consolidation Forecasts

In this section, we explain our estimation approach, describe the dataset, and report our baseline results.

A. Specification and Data

To investigate whether growth forecast errors have been systematically related to fiscal consolidation forecasts, our approach is simple: we regress the forecast error for real GDP growth in years t and t+1 on forecasts of fiscal consolidation for t and t+1 made early in year t. We focus on two-year intervals to allow for lagged effects of fiscal policy. 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. The equation estimated is therefore:

(1)ForecastErrorofΔYi,t:t+1=α+βForecastofΔFi,t:t+1+εi,t:t+1,

where ΔYi, t:t+1 denotes cumulative (year-over-year) growth of real GDP (Y) in economy i—that is, (Yi, t+1/Yi, t-1 - 1)—and the associated forecast error is ΔYi, t:t+1 – f{ΔYi, t:t+1 | Ωt}, where f denotes the forecast conditional on Ωt, the information set available early in year t. ΔFi, 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.6 Positive values of ΔFi, t:t+1 indicate fiscal consolidation, while negative values indicate discretionary fiscal stimulus. The associated forecast is “Forecast of ΔFi, t:t+1|t” defined as f { Ft+1, i – Ft-1, i | Ωt }. Under the null hypothesis that fiscal multipliers used for forecasting were accurate, the coefficient, β, should be zero.7 Our data come from the IMF’s WEO database. We have posted the underlying data and estimation codes required to replicate all the results reported in this paper on the IMF’s website.8

As explained above, we focus in our baseline on forecasts made for European economies in early 2010. Growth forecast errors thus measure the difference between actual cumulative real GDP (year-over-year) growth during 2010–11, based on the latest data, minus the forecast prepared for the April 2010 WEO (IMF, 2010c).9 The forecast of fiscal consolidation is the forecast of the change in the structural fiscal balance as a percent of potential GDP during 2010–11, as prepared for the April 2010 WEO. We use all available data for the European Union’s (EU’s) 27 member states, as well as for the remaining three European economies classified as “advanced” in the WEO database: Iceland, Norway, and Switzerland. WEO forecasts of the structural fiscal balance made in April 2010 are unavailable for Estonia, Latvia, Lithuania, and Luxembourg. Thus, based on data availability, our baseline sample consists of 26 economies (27 + 3 – 4).10 As we report below, filling the four missing observations with forecasts from the spring 2010 EC European Economic Forecast (EC, 2010) makes little difference to the results.

B. Results

Table 1 reports our baseline estimation results. We find a significant negative relation between fiscal consolidation forecasts made in 2010 and subsequent growth forecast errors. In the baseline specification, the estimate of β, the coefficient on the forecast of fiscal consolidation, is −1.095 (t-statistic = -4.294), implying that, for every additional percentage point of GDP of fiscal consolidation, GDP was about 1 percent lower than forecast.11 Figure 1 illustrates this result using a scatter plot. The coefficient is statistically significant at the 1 percent level, and the R2 is 0.496. The estimate of the constant term, 0.775 (t-statistic = 2.023) has no strong economic interpretation.12

Table 1.

Main Results

Equation: Forecast Error of ΔYi, t:t+1 = α + β Forecast of ΔFi, t:t+1|t + εi,t:t+1

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Note: Table reports point estimates and heteroskedasticity-robust standard errors in parentheses. ***, **, and * denote statistical significance at the 1,5, and 10 level, respectively. Robust regression down-weights observations with larger absolute residuals using iterative weighted least squares (Andersen, 2008).

III. Robustness

The results reported above suggest that economies with larger planned fiscal consolidations tended to have larger subsequent growth disappointments. In this section, we examine the robustness of this result along three main dimensions. First, we repeat the analysis for different groups of economies and examine the role of potentially influential outlier observations. Second, we reestimate the baseline equation (1) while adding control variables that could plausibly have both affected the growth forecast error and been correlated with fiscal consolidation forecasts. Not controlling for such factors could influence the estimated relation between fiscal consolidation forecasts and growth forecast errors. Finally, we consider how the results change for forecasts made in more normal times (1997–2008) and for other time intervals since the start of the crisis (2009–12).

A. Choice of Economies and Role of Outliers

First, we investigate the sensitivity of the baseline results to changes in the economies included in the sample. We start by seeing how the results change when we replace the missing WEO forecasts for four EU member states—Estonia, Latvia, Lithuania, and Luxembourg—with EC forecasts. As Table 1 reports, this makes little difference to the results. Next, we consider how the results change when we remove observations associated with the largest fiscal policy changes. While such policy changes are worth considering, it is natural to ask how important they are for the results. As Table 1 reports, when we remove the two largest policy changes (those for Germany and Greece), the estimate of β declines to −0.776 (t–statistic = -2.249) but remains statistically significant at the 5 percent level. Thus, concerns raised by some in reaction to an earlier version of this paper, that excluding the largest policy changes from the sample might render the results insignificant, seem exaggerated.13

We also investigate whether forecasts made for economies with IMF programs are driving the baseline results. As Table 1 reports, excluding from the sample the five economies that had IMF programs in 2010 or 2011—Greece, Iceland, Ireland, Portugal, and Romania—yields an estimate of β of −0.812 (t-statistic = -2.890), which is statistically significant at the 1 percent level and is not statistically distinguishable from our baseline estimate of −1.095. Similarly, excluding the four economies classified as “emerging” in the WEO database from the sample (Bulgaria, Hungary, Poland, and Romania) has little effect on the point estimate of β, which is -0.992 (t-statistic = -3.568) in this case.14

Second, we investigate more formally the sensitivity of the results to outliers by applying three accepted estimation strategies designed to resist the influence of potential outliers. In particular, we reestimate the baseline specification using robust regression, which down-weights observations with larger absolute residuals using iterative weighted least squares (Andersen, 2008).15 Since robust regression is more resistant to outliers than is ordinary least squares (OLS), this provides a check of whether outliers are unduly influencing the baseline OLS results. As Table 1 reports, the robust regression estimate of β is −1.279 (t-statistic = -6.989), which is similar to the baseline OLS estimate and statistically significant at the 1 percent level. Next, we apply a quantile regression approach, which minimizes the sum of the absolute residuals about the median, rather than the sum of the squares of the residuals about the mean as in OLS, making the estimates less affected by outliers.16 The quantile regression estimate of β is −1.088 (t-statistic = -4.533) and is statistically significant at the 1 percent level. Finally, we also investigate the role of outliers using Cook’s distance method, by discarding observations with Cook’s distance greater than 4/N, where N is the sample size, and obtain a β estimate of −0.921 (t-statistic = -4.244) that is, again, statistically significant at the 1 percent level. Overall, these three methods that resist the pull of outliers confirm the baseline OLS result of a negative relation between fiscal consolidation forecasts and growth forecast errors.

Third, we consider how the results change when we broaden the sample to include the entire group of economies classified as “advanced” in the WEO database. This wider group adds 10 economies to our baseline sample.17 For most of these additional economies, including Australia, Hong Kong SAR, Israel, Korea, New Zealand, Singapore, and Taiwan Province of China, the conditions for larger-than-normal multipliers discussed above, such as the liquidity trap, are less relevant, which leads us to expect a smaller absolute value of β for this sample. As Table 1 reports, the estimate of β declines to −0.538 (t-statistic = -1.322) for this group of economies and is no longer statistically significant. By contrast, when we narrow this broad sample to include only economies that were, arguably, in a liquidity trap during this period, the estimate of β rises in absolute value to −0.986 (t-statistic = -3.652).18

The reduced statistical significance of the OLS estimates for this broader sample is, however, primarily driven by influential outliers, as Table 1 reports. The robust regression, which down-weights influential outliers, yields an estimate of β of −0.955 (t-statistic = -4.751), which is close to the baseline sample estimate and is statistically significant at the 1 percent level. The stark difference between these robust regression results and the OLS results highlights the fact that the OLS results are heavily influenced by outliers in this broader sample. The procedure gives the two smallest weights to New Zealand and Singapore due to their large absolute residuals.19 Similarly, the quantile regression yields an estimate of β of −0.999 (t-statistic = -7.866), and the estimate based on excluding observations with Cook’s distance greater than N/4 yields an estimate of −0.746 (t-statistic = -2.674). Overall, once we adjust for the influence of outliers, the results for the broader group of all advanced economies are consistent with those obtained for the baseline European sample. Finally, we repeat the analysis for the group of 14 (non-European) emerging market economies for which WEO forecasts of the structural fiscal balance made in early 2010 are available.20 As Table 1 reports, our results provide no evidence that forecasters underestimated fiscal multipliers for this group of economies. The estimate of β is 0.007 (t-statistic = 0.016). Moreover, in this case, the lack of statistical significance is not merely driven by influential outliers—reestimating the relation for emerging market economies using the robust regression, the quantile regression, and excluding Cook’s distance outliers leads to the same conclusion. These results, admittedly based on a very small sample, are consistent with the notion that the conditions leading to larger-than-normal fiscal multipliers discussed above are currently less relevant for these economies.21

B. Controlling for Other Variables

Having established that the baseline results are not unduly influenced by outliers, we check if the results are robust to controlling for additional variables that could plausibly have triggered both planned fiscal consolidation and lower-than-expected growth. The omission of such variables could bias the analysis toward finding that fiscal multipliers were larger than assumed.

In the context of forecast evaluation, controlling for other variables that were in the information set of forecasters is warranted. The question is: based on the information they had available at the time forecasts were made, did forecasters underestimate the effect of fiscal consolidation on growth, or did they instead underestimate the effect of other variables on growth? It is worth emphasizing that, to answer this question, controlling for ex-post developments—those unknown at the time forecast were made—is not valid. For example, an ex-post rise in sovereign borrowing costs could be the result of lower-than-expected growth as well as the cause of lower growth (Cottarelli and Jaramillo, 2012; Romer, 2012). In this case, lower-than-expected growth caused by fiscal consolidation could trigger a rise in sovereign borrowing costs, and these higher borrowing costs could, in turn, further reduce growth. Even if controlling for such variables significantly changed the estimate of β, the coefficient would no longer have an economic interpretation.22

Relatedly, controlling for the forecast error of the change in fiscal policy does not, in our application, provide a way of estimating the causal effect of fiscal policy on growth. Over the two-year intervals that we consider, changes in fiscal policy are unlikely to be orthogonal to economic developments. Thus, the forecast error of fiscal consolidation over our two-year intervals cannot be interpreted as an identified fiscal shock and cannot yield estimates of actual fiscal multipliers. A large literature seeks to identify such exogenous shifts in government spending and revenues. Doing so has proven difficult and lies beyond the scope of our analysis.

We start by considering the role of sovereign debt problems. Are the baseline results picking up greater-than-expected effects of sovereign debt problems rather than the effects of fiscal consolidation? As Table 2 reports, the results are robust to controlling for the initial (end-2009) government-debt-to-GDP ratio, for the initial fiscal-balance-to-GDP ratio, and for the initial structural fiscal-balance-to-GDP ratio. To ensure that these variables were indeed in the forecasters’ information set, the source of the data is the same (from the April 2010 WEO—IMF, 2010c) as for the fiscal consolidation forecasts. However, since these (backward-looking) measures of the fiscal accounts do not necessarily fully capture perceived future sovereign debt problems, we also control for perceived sovereign default risk, as measured by the sovereign credit default swap (CDS) spread in the first quarter of 2010.23 The estimate of β is, again, largely unchanged.

Table 2.

Europe: Robustness to Additional Controls

Equation: Forecast Error of ΔYi,t:t+1 = α + β Forecast of ΔFi,t:t+1|t + εi,t:t+1

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Note: Table reports point estimates and heteroskedasticity-robust standard errors in parentheses. ***, **, and * denotes statistical significance at the 1,5, and 10 level, respectively. Constant term included in specification but estimate not reported. The additional controls appear in the specifications one at a time.

Next, we check if the baseline result is picking up greater-than-expected effects of financial sector stress rather than the unexpected effects of fiscal consolidation. As Table 2 reports, the relation holds when we control for the initial bank CDS spread.24 We obtain similar results when controlling for the occurrence of banking crises, based on a zero-one event dummy variable indicating a systemic banking crisis, as identified by Laeven and Valencia (2012). Finally, it is worth recalling that, as reported in Table 1, the baseline result is robust to excluding economies with severe financial stress—namely, those with IMF programs.

The baseline finding also holds up to controlling for the fiscal consolidation of trading partners. To the extent that fiscal consolidations were synchronized, fiscal consolidation by others may be driving the results. In particular, forecasters may have understated the crosscountry spillover effects of fiscal policy, which, as recent research indicates, can be large (Auerbach and Gorodnichenko, 2012c). However, when we control for trade-weighted fiscal consolidation of other countries (scaled by the share of exports in GDP), the results are virtually unchanged.25

To investigate the role of precrisis external imbalances that may have triggered both fiscal consolidation and larger-than-expected headwinds to growth, we control for the precrisis (2007) current-account-deficit-to-GDP ratio, again taken from the April 2010 WEO database (IMF, 2010c), and find similar results. We obtain similar results when controlling for the stock of precrisis (2007) net foreign liabilities in percent of GDP, based on the updated and extended version of dataset constructed by Lane and Milesi-Ferretti (2007).

Finally, we investigate the possible role of household debt overhang, which can have negative effects on economic activity (Mian, Rao, and Sufi, 2011; IMF, 2012c, and others). In particular, we reestimate the baseline equation while controlling for the precrisis (2007) level of the household debt-to-disposable-income ratio. As Table 2 reports, controlling for this variable does not materially influence the estimate of β.26

Actual versus Planned Fiscal Consolidation

We address next the possibility that, although the assumed multipliers were correct, countries with more ambitious consolidation programs may have implemented more fiscal consolidation than originally planned. The concern, here, is that the baseline result reflects the fact that actual fiscal consolidation was much larger than planned rather than actual multipliers being larger than expected. It is worth emphasizing that this issue would only lead to a biased estimate of β to the extent that the unexpected fiscal consolidation (the fiscal consolidation forecast error) was correlated with the initial fiscal consolidation forecast.

We investigate this possibility using a two-stage-least-squares approach: the first stage involves a regression of actual fiscal consolidation on the forecast of fiscal consolidation; and the second stage is a regression of the growth forecast error on the instrumented values of actual fiscal consolidation obtained in the first stage. As Table 3 reports, the first stage is strong, and the slope coefficient is 1.057 (t-statistic = 5.714). This coefficient close to 1 indicates that, on average, actual consolidation was neither smaller nor larger than expected.27 The second stage indicates that a 1 percent of GDP fiscal consolidation is associated with a −1.036 percentage point output forecast error (t-statistic = −4.518), which is, again, close to the baseline.

Table 3.

Europe: Two-stage Least Squares

First stage: ΔFi,t:t+1 = γ + δ Forecast of ΔFi,t:t+1|t + ηi,t:t+1

Second stage: Forecast Error of ΔYi,t:t+1=α+βΔF^i,t:t+1+εi,t:t+1

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Note: Table reports point estimates and heteroskedasticity-robust standard errors in parentheses. ***, **, and * denotes statistical significance at the 1,5, and 10 level, respectively. ^ denotes instrumented values.

Overall, these robustness checks suggest that the results for the baseline sample are robust to the inclusion of additional variables that could potentially bias the results toward finding that actual multipliers were larger than assumed multipliers. In particular, controlling for variables that measure other weaknesses of the economy that might be associated with fiscal consolidation do not materially affect the coefficient on the forecast of fiscal consolidation.28

C. Different Forecast Vintages

So far, our analysis has focused on forecasts made in early 2010, when a number of large fiscal consolidation plans were announced. But it is worth examining whether the relation also holds for forecasts made in other years. We start by examining forecasts made in all years since the start of the crisis (2009–12), both jointly and individually. This exercise has the advantage of raising the sample size to 105 observations, up from the 26 observations in our baseline sample. Then, we consider forecasts made in more normal times—the precrisis decade (1997–2008). For this precrisis sample, our expectation is that in these more normal times, the coefficient β should be close to zero.

First, we discuss the results obtained when considering the set of two-year intervals since the start of the crisis (2009–12) together in a panel. The equation estimated is similar to equation (1), except that it now includes a vector of time-fixed effects, λt:

(2)ForecastErrorofΔYi,t:t+1=α+λt+βForecastofΔFi,t:t+1|t+εi,t:t+1,

where t = 2009, 2010, 2011, and 2012. Based on the available data, the size of our European sample size is now 105 observations. Note, however, that for forecasts made in early 2011 and early 2012, the dependent variable is a forecast revision rather than a forecast error, since actual data for 2012 (included in the October 2012 WEO (IMF, 2012b), our reference) are not yet complete, and data for 2013 are not yet available. Results for these more recent forecasts should therefore be seen as preliminary. Given our use of two-year overlapping intervals, we correct the standard errors for serial correlation of type MA(1) using the Newey-West procedure.29

Table 4 reports the estimation results. For the panel of forecasts made during 2009–12, the estimate of β is −0.667 (t-statistic = -4.143), which is smaller than the baseline value obtained for forecasts made in early 2010, but is still strongly statistically significant. Figure 2 illustrates this 2009–12 panel result using a scatter plot.30

Table 4.

2009-12 Panel of Forecasts

Equation: Forecast Error of ΔYi,t:t+1 = α + λt + β Forecast of ΔFi,t:t+1|t + εi,t:t+1

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Note: Table reports point estimates and Newey-West standard errors in parentheses (correcting for heteroskedasticity and autocorrelation up to one year). ***, **, and * denotes statistical significance at the 1,5, and 10 level, respectively. Constant term and time-fixed effects included in all panel regressions, but estimates not reported.
Figure 2.
Figure 2.

Europe: 2009-12 Panel Growth Forecast Errors vs. Fiscal Consolidation Forecasts

Citation: IMF Working Papers 2013, 001; 10.5089/9781475576443.001.A001

Note: Figure plots forecast error for real GDP growth in years t and t+1 relative to forecasts made in the spring of year t on forecasts of fiscal consolidation for t and t+1 made in spring of year t, for years t = 2009, 2010, 2011, and 2012; and simple regression line for panel of observations without time effects.

Considering years individually, we find that the estimate of β is statistically significant for forecasts made in early 2009, 2010, and 2012, but not for forecasts made in early 2011. For the 2011 forecasts, the estimate of β is −0.467 (t-statistic = -1.038). Thus, the concern, raised by some in reaction to the earlier version of this analysis, that the relation weakens for forecasts made in 2011 is warranted.31 For 2012, however, the estimate of β is –0.357 (t-statistic = 2.429), which is statistically significant at the 5 percent level. This decline in the coefficient in 2011–12 to around -0.4 could reflect smaller multipliers or partial learning by forecasters regarding the effects of fiscal policy on economic activity. However, as explained above, results based on these more recent forecasts should be seen as preliminary. Once data for 2012–13 are complete, the estimation results for forecasts made in 2011–12 could be revisited.32

Table 4 also reports estimation results based on the 2009–12 panel for our two alternative samples: the sample of all advanced economies and the sample of emerging market economies. For the broader sample of all advanced economies, the estimate of β is –0.410 (t-statistic = -2.060), which is statistically significant at the 5 percent level. Figure 3 illustrates this 2009–12 result for advanced economies using a scatter plot, and suggests that the lower significance of this coefficient is again partly due to noise introduced by outliers. Also, as before, for the subset of advanced economies in a liquidity trap, the results are stronger: the 2009–12 panel estimate of β is –0.648 (t-statistic = -3.042) and is significant at the 1 percent level. For emerging market economies, we again find no significant relation: the estimate of β is –0.108 (t-statistic = -0.394).

Figure 3.
Figure 3.

All Advanced Economies: 2009-12 Panel Growth Forecast Errors vs. Fiscal Consolidation Forecasts

Citation: IMF Working Papers 2013, 001; 10.5089/9781475576443.001.A001

Note: Figure plots forecast error for real GDP growth in years t and t+1 relative to forecasts made in the spring of year t on forecasts of fiscal consolidation for t and t+1 made in spring of year t, for years t = 2009, 2010, 2011, and 2012; and simple regression line for panel of observations without time effects.

How special is the crisis period? To address this question, Table 4 also reports the results of estimating equation (3) for the set of two-year intervals during the precrisis decade (1997–2008). We find no evidence of fiscal multipliers being underestimated, on average, during these more normal times. The estimate of β is near zero, –0.077 (t-statistic = -0.470), for this period.

IV. Extensions

Having discussed the robustness of our baseline results on a number of dimensions, we turn to three extensions. First, we check whether the baseline results differ depending on whether fiscal consolidation reflects changes in government spending or changes in revenue. Second, we consider the relation between planned fiscal consolidation and the forecast errors for the components of aggregate spending and for the unemployment rate. Third, we investigate whether the baseline results also hold when we rely on the forecast errors of other forecasters, including the EC, the OECD, and the EIU.

A. Government Spending and Revenue

To investigate whether the baseline results are driven primarily by spending cuts or by revenue increases, we split our measure of fiscal consolidation—the change in the structural fiscal balance—into the change in government spending and revenue. In particular, we estimate a modified version of our baseline equation, separating between the change in spending and the change in revenue:33

(3)ForecastErrorofΔYi,t:t+1=α+δForecastofΔTi,t:t+1|t+γForecastofΔSi,t:t+1|t+εi,t:t+1

where ΔSi,t:t+1|t denotes the forecast of the change in structural spending in 2010–11 and ΔTi,t:t+1|t denotes the forecast of the change in structural revenue in 2010–11, both in percent of potential GDP. As before, the forecasts are taken from the April 2010 WEO (IMF, 2010c). IMF forecasts give forecasts of headline, not structural, spending. We construct forecasts for the change in structural spending based on the conventional assumption of a zero elasticity of government expenditure relative to the output gap (IMF, 2009a). Thus, we approximate the forecast for the change in the structural spending ratio to potential GDP by the forecast of the change in the headline spending ratio to potential GDP. The forecast for the change in structural revenue ratio to potential GDP is the sum of the forecast of the change in the structural fiscal balance and the forecast for the change in structural government spending: ΔTi,t:t+1|t = ΔFi,t:t+1|t + ΔSi,t:t+1|t.

As Table 5 reports, the baseline results hold for both government spending and revenue. The point estimate of the coefficient on the forecast of government spending (1.244, t-statistic = 4.989) is slightly larger in absolute value than the coefficient on the revenue forecast (–0.865, t-statistic = –3.822), but the difference is just short of being statistically insignificant (p-value of 0.102).34 We estimate equation (3) using overall government spending or primary government spending (excluding interest payments), obtaining similar results. Overall, we conclude that fiscal multipliers were, on average, underestimated for both sides of the fiscal balance, with a slightly larger degree of underestimation associated with changes in government spending.

Table 5.

Europe: Government Revenue and Spending

Equation estimated: Forecast Error of ΔYi,t:t+1 = α + δ Forecast of ΔTi,t:t+1|t + γ Forecast of ΔSi,t:t+1|t + εi,t:t+1

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Note: Table reports point estimates and heteroskedasticity-robust standard errors in parentheses. ***, **, and * denotes statistical significance at the 1,5, and 10 level, respectively. T denotes government revenue, and S denotes government spending. p-value is for test of null that δ + γ = 0.

B. Components of Aggregate Spending and Unemployment

To get a sense of the sources of the growth forecast errors, we reestimate the baseline specification for the components of real GDP. For example, to investigate the relation between planned fiscal consolidation and forecast errors for private consumption growth, we estimate the following modification of our baseline equation:

(4)ForecastErrorofΔCi,t:t+1=α+βForecastofΔFi,t:t+1|t+εi,t:t+1,

where Forecast Error of ΔCi,t:t+1 is the forecast error for real private consumption growth, instead of real GDP growth as in the baseline.

As Table 6 reports, when we decompose the effect on GDP in this way, we find that planned fiscal consolidation is associated with significantly lower-than-expected consumption and investment growth. The coefficient for investment growth (–2.681) is about three times larger than that for private consumption growth (–0.816), which is consistent with research showing that investment varies relatively strongly in response to overall economic conditions. For example, based on U.S. data, Romer and Romer (2010) find that, in response to a tax increase, GDP, investment and consumption all decline, but investment growth falls by about four times more than consumption growth does. Conventional models predict that fiscal consolidation is normally associated with lower interest rates, supporting investment. The fact that investment growth falls by more than expected in response to fiscal consolidation could reflect the lack of the conventional interest rate effect during this period. In contrast, the results for export and import growth are not statistically significant.

Table 6.

Europe: Unemployment and GDP Components

Equation: Forecast Error of ΔYi,t:t+1 = α + β Forecast of ΔFi,t:t+1|t + εi,t:t+1

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Note: Table reports point estimates and heteroskedasticity-robust standard errors in parentheses. ***, **, and * denotes statistical significance at the 1,5, and 10 level, respectively.

Since lower-than-expected output growth could be expected to reduce inflation pressure, we also look at the forecast error for the GDP deflator, finding evidence of a negative, but statistically insignificant, relation. When we repeat the exercise for the unemployment rate, we find a coefficient of 0.608, which is statistically and economically significant. Overall, we find that, for the baseline sample, forecasters significantly underestimated the increase in unemployment and the decline in domestic demand associated with fiscal consolidation.

C. Alternative Forecasts

Finally, we compare the baseline results obtained for IMF forecast errors with those obtained for the forecast errors of other forecasters, including the EC, the OECD, and the EIU. Data for EC forecasts of both the structural fiscal balance and real GDP are from the spring 2010 European Economic Forecast (EC, 2010). Data for OECD forecasts of the structural fiscal balance and real GDP are from the May 2010 Economic Outlook (OECD, 2010). Data for EIU forecasts of real GDP are from the April 2010 Country Forecast (EIU, 2010). Since the EIU does not publish forecasts of the structural fiscal balance, we take forecasts of fiscal consolidation from the April 2010 WEO (IMF, 2010c) for the EIU regressions. We estimate the regressions for our baseline sample, both for all the forecasts available from each forecast source and for a (smaller) subsample for which the economies included are the same in each regression. As Table 7 reports, we find that the baseline result of a negative relation between growth forecast errors and planned fiscal consolidation holds for all the forecasters considered, but that it is strongest in terms of both economic and statistical significance for IMF forecasts, and, to a slightly smaller extent, for EC forecasts.

Table 7.

Europe: Alternative Forecasters

Equation: Forecast Error of ΔYi,t:t+1 = α + β Forecast of ΔFi,t:t+1|t + εi,t:t+1

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Note: Table reports point estimates and heteroskedasticity-robust standard errors in parentheses. ***, **, and * denotes statistical significance at the 1,5, and 10 level, respectively.

V. Conclusions

What do our results imply about actual multipliers? Our results suggest that actual fiscal multipliers have been larger than forecasters assumed. But what did forecasters assume? Answering this question is not easy, since forecasters use models in which fiscal multipliers are implicit and depend on the composition of the fiscal adjustment and other economic conditions.35

We believe, however, that a reasonable case can be made that the multipliers used at the start of the crisis averaged about 0.5. A number of studies based on precrisis data for advanced economies indicate actual multipliers of roughly 0.5, and it is plausible that forecasters, on average, made assumptions consistent with this evidence. The October 2008 WEO chapter on fiscal policy presents multiplier estimates for 21 advanced economies during 1970–2007 averaging 0.5 within three years (IMF, 2008, p. 177). Similarly, the October 2010 WEO (IMF, 2010d) chapter on fiscal consolidation presents multiplier estimates for 15 advanced economies during 1979–2009 averaging 0.5 percent within two years.36 This evidence, and our finding of no gap, on average, between assumed and actual fiscal multipliers before the crisis, would imply that multipliers assumed prior to the crisis were around 0.5. Relatedly, the March 2009 IMF staff note prepared for the G-20 Ministerial Meeting reports IMF staff assumptions regarding fiscal multipliers based on estimates from various studies. In particular, it contains an assessment of the impact of the 2008–10 fiscal expansion on growth based on assumed multipliers of 0.3–0.5 for revenue and 0.3–1.8 for government spending (IMF, 2009b, p. 32).37

If we put this together, and use the range of coefficients reported in our tables, this suggests that actual multipliers were substantially above 1 early in the crisis. The smaller coefficient we find for forecasts made in 2011 and 2012 could reflect smaller actual multipliers or partial learning by forecasters regarding the effects of fiscal policy. A decline in actual multipliers, despite the still-constraining zero lower bound, could reflect an easing of credit constraints faced by firms and households, and less economic slack in a number of economies relative to 2009–10.

However, our results need to be interpreted with care. As suggested by both theoretical considerations and the evidence in this and other empirical papers, there is no single multiplier for all times and all countries. Multipliers can be higher or lower across time and across economies. In some cases, confidence effects may partly offset direct effects. As economies recover, and economies exit the liquidity trap, multipliers are likely to return to their precrisis levels. Nevertheless, it seems safe for the time being, when thinking about fiscal consolidation, to assume higher multipliers than before the crisis.

Finally, it is worth emphasizing that deciding on the appropriate stance of fiscal policy requires much more than an assessment regarding the size of short-term fiscal multipliers. Thus, our results should not be construed as arguing for any specific fiscal policy stance in any specific country. In particular, the results do not imply that fiscal consolidation is undesirable. Virtually all advanced economies face the challenge of fiscal adjustment in response to elevated government debt levels and future pressures on public finances from demographic change. The short-term effects of fiscal policy on economic activity are only one of the many factors that need to be considered in determining the appropriate pace of fiscal consolidation for any single country.

Growth Forecast Errors and Fiscal Multipliers
Author: Mr. Olivier J Blanchard and Mr. Daniel Leigh
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    Europe: Growth Forecast Errors vs. Fiscal Consolidation Forecasts

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