Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • 2 https://isni.org/isni/0000000404811396, International Monetary Fund

References

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Annex 1. Data for Shock Identification

Quarterly fiscal data used in shock identification for five shock-emitting (source) countries (France, Germany, Japan, United Kingdom, United States) are collected from respective national statistical bureaus, either directly or via Haver Analytics. The following sections describe the data, their limitations, and calculations underlying identified fiscal shocks.

Data Description

Quarterly real government spending and tax revenue data used in constructing fiscal shocks are all expressed in local currency units, seasonally adjusted, and annualized for the sample period, which covers the first quarter of 2000 through the second quarter of 2016. Government spending is calculated as the sum of quarterly general government consumption and general government gross fixed capital formation from the national accounts. For tax revenue, quarterly general government total tax income is used, except in the case of Japan. Annex Table 1.1 provides detail on data sources for each country.

Annex Table 1.1.

Sources for Quarterly Fiscal Data for Source Countries

article image
Source: IMF staff compilation.Note: For government spending, nominal levels are deflated using GDP deflator if real levels are not directly available from the source. For tax revenue (total revenue for Japan), real levels are calculated by deflating nominal levels using GDP deflator for each country, respectively. ARIMA = autoregressive integrated moving average; GFCF = gross fixed capital formation; SAAR = seasonally adjusted and annualized data; SWDA = seasonally and working days–adjusted data.

Quarterly nonfinancial accounts for general government database.

Data Limitations

For Japan, quarterly government total revenue is used instead of tax revenue because of data limitations. Government total revenue data are estimated using both monthly Treasury receipts data from Japan’s Ministry of Finance and annual general government revenue data from the IMF’s World Economic Outlook. Higher-frequency fiscal data cannot be used directly for our analysis owing to definitional differences. Treasury data cover receipts and payments of the private sector only, while official government budget data cover all receipts and payments (Ministry of Finance, Japan Statistical Yearbook). To reconcile this difference, we extrapolate quarterly data from the annual government revenue using information from Treasury receipts using the Denton proportional benchmarking method (Di Fonzo and Marini 2014). This method both preserves the seasonality observed from higher-frequency Treasury receipts data and matches the data published in the World Economic Outlook when converted to an annual basis.

Annex 2. Data for Spillover Analysis

The quarterly database of 55 recipient countries for the sample period (first quarter of 2000 to second quarter of 2016) includes series on real output, external demand, short-term interest rate, output gap, and exchange rate regime, collected from multiple data sources. The following sections explain how each data series is estimated. Annex Table 2.1 provides details on data sources for each series, and Annex Table 2.2 lists countries in our sample.

Annex Table 2.1.

Data Sources for Recipient Countries

article image
Source: IMF staff compilation.
Annex Table 2.2.

Recipient Countries in Sample

article image
Source: IMF staff compilation.Note: Shock-emitting (source) countries are shown in italics.

Data Description

  • Real GDP. Quarterly real output levels are rebased to 2010 prices, expressed in local currency units, seasonally adjusted, and annualized.
  • Bilateral goods exports/imports. Bilateral weights are calculated using the ratio of bilateral exports to imports of goods between 55 countries in the sample and 5 source countries (55 × 5 = 275 pairs). For each country pair, the average between reported values for both countries is taken.
  • External demand. This is calculated as a weighted sum of partner countries’ real growth based on bilateral export weights.
  • Short-term interest rate. The three-month London interbank offered rate (LIBOR) and three-month Treasury bill rate are used. For better country coverage and historical coverage, policy, deposit, and target rates are used where three-month LIBOR and Treasury bill data are not available.
  • Output gap. The quarterly output gap is first calculated as the gap between real output and potential output, estimated by the Hodrick-Prescott (HP) filter. Then, to reconcile any potential difference between our estimated output gap and the annual output gap numbers published in IMF’s World Economic Outlook (WEO), the Denton proportional benchmarking method is used. This method both preserves the seasonality observed from quarterly estimated output gap series and matches the WEO data when converted to an annual basis.

Before entering the regressions, variables with notable trends over the sample period are detrended using country-specific linear trends. In addition, outliers—that is, observations showing quarter-over-quarter GDP growth rates above 10 percent or below –10 percent in any given quarter—are removed (there are very few of these observations).

Exchange Rate Regime Classification

We construct a measure of bilateral exchange rate arrangement with respect to the US dollar to estimate spillovers for different exchange rate regimes.

For the Reinhart-Rogoff classification, the exchange rate regime is expressed as a time-varying index based on the annual coarse de facto classification from Ilzetzki, Reinhart, and Rogoff (2017a, 2017b), ranging from 1 (most rigid) to 6 (most flexible). For each period, if a country is assigned a value of 1 (de facto peg) or 2 (de facto crawling peg), it is deemed a “fixed regime.” The quarterly index is interpolated from annual data, assigning the same value for all four quarters within a year. For example, in 2015, this classification yields 7 “fixed”-rate countries out of the sample of 55 countries (Argentina, China, Costa Rica, India, Peru, Philippines, Vietnam).31

For the IMF classification, the pre-2008 (coarse) scheme consists of six categories, with 1 being most rigid and 6 being most flexible. Data for regime classification before 2008 are obtained from Carmen Reinhart’s website.32 The classification changed in 2008, and post-2008 data are obtained from the IMF’s website.33 Similarly to the Reinhart-Rogoff classification, a country is generally classified as having a “fixed” exchange rate with respect to the US dollar if it is assigned a value of 1 (de facto peg) or 2 (de facto crawling peg or crawling band that is narrower than or equal to +/–2 percent). Again, the quarterly index is interpolated from annual data. For example, in 2015, this classification yields two “fixed”-rate countries out of the sample of 55 countries (China, Vietnam), although there are more “fixed”-rate countries in earlier periods.

Annex 3. Blanchard and Perotti Methodology

This annex provides a brief overview of the SVAR shock identification methodology of Blanchard and Perotti (2002) as applied in this note.

VAR Specification

The identification of shocks under this methodology involves estimating the following VAR specification:

Yt=A(L,q)Yt1+Ut,(A.3.1)

in which Yt ≡ [Tt, Gt, Xt]' is a vector containing the values of quarterly taxes, spending, and GDP (all in logs of real, per capita terms), A(L, q) is a four-quarter distributed lag polynomial, and Ut ≡ [tt, gt, xt]' is the corresponding vector of reduced-form residuals. We can write

tt=a1xt+a2etg+ett,(A.3.2)
gt=b1xt+b2ett+etg,(A.3.3)
xt=c1tt+c2gt+etX,(A.3.4)

in which ett,etg,etX are the mutually uncorrelated structural shocks that we want to recover. For example, equation (A.3.2) says that unexpected movements in taxes can be due to a response to unexpected movements in GDP and a response to structural shocks to spending or taxes.

Identification

The identification follows three steps:

  • The effects of activity on taxes and government spending—captured by the coefficients a1 and b1—consist of two channels: (1) the automatic responses of these fiscal variables to activity under existing fiscal policy rules and (2) discretionary policy changes in response to unexpected shocks to activity. The key identifying assumption is that the second channel does not operate with the use of quarterly data because of decision lags (that is, it takes time for policymakers to realize a shock to GDP and make spending/tax decisions in response). In addition, there is no evidence of any automatic response of spending to activity, and thus b1 = 0. For taxes, the automatic response of tax revenues to activity can be calibrated using the empirically estimated elasticity of tax revenues with respect to output (or “tax elasticity”; see discussion later in the annex), pinning down the a1 coefficient.
  • With a1 and b1 pinned down, the cyclically adjusted reduced-form tax and spending residuals, t'ttta1 xt and g'tgtb1 xt = gt, can be constructed and can then be used as instruments to estimate c1 and c2 in a regression of xt on tt and gt since they are not correlated with etX.
  • The remaining parameters, a2 and b2, can be estimated under two alternative assumptions: (1) assuming a2 = 0 (taxes do not respond to spending) and estimating b2, or (2) assuming b2 = 0 (spending does not respond to taxes) and estimating a2. Both assumptions give similar results.

While the identified structural shocks are not very sensitive to the value of tax elasticity used, the domestic tax multiplier is. Blanchard and Perotti (2002) use data on institutional characteristics of the US tax system to estimate the elasticity at quarterly frequency, obtaining the number 2.08. Their estimate of the domestic tax multiplier after eight quarters is 0.72 or 1.32 depending on the VAR specification. Caldara and Kamps (2012) show that the size of the fiscal multiplier increases in the size of the elasticity, suggesting that careful calibration of this value is important to correctly estimate the size of the multiplier. Mertens and Ravn (2014) propose a new methodology—proxy SVAR, which integrates shocks identified from a narrative approach, such as, for example, those of Romer and Romer (2010), into the standard SVAR framework—that allows estimating the size of the elasticity rather than directly assuming it, and find that the underlying value of the elasticity is 3.13 rather than 2.08 for the United States. This higher elasticity value reconciles the size of the domestic multiplier typically obtained from SVARs with the estimates obtained using narrative shocks, the latter of which are typically higher.

To estimate the tax elasticities in the five source countries, we follow Mertens and Ravn (2014) and use information on other measures of tax shocks:

  • United States. We use the value of 3.13, which comes from Mertens and Ravn’s (2014) analysis based on Romer and Romer’s (2010) shocks and quarterly data.
  • United Kingdom. Cloyne (2013) estimates this elasticity for the United Kingdom using a new quarterly data set of narrative tax shocks and arrives at the value of 1.61, which we use in our analysis.
  • Germany, France, Japan. Elasticity estimates for these countries are not readily available from the literature; therefore, we estimate the elasticity values ourselves. Data on narrative shocks, which could be used in a proxy SVAR, for these countries are scarce. The only available narrative data set, that of DeVries and others (2011), has annual frequency and includes only fiscal consolidations, thus not fully capturing all possible tax shocks. Instead, we use forecast error shocks34 to complement the SVAR and recover the elasticity estimates. These shocks capture unanticipated tax changes based on OECD forecasts.35 The sample for each country is based on availability of forecast error shocks. The resulting values of elasticities vary depending on the exact VAR specification (trend, dummies), and we choose a specific value within the obtained range: 0.7 for Germany, 1.8 for France, and 1.3 for Japan.

Annex 4. Domestic Fiscal Multipliers

This annex discusses the results in regard to domestic multipliers and how they relate to the literature. We find that spending multipliers tend to be larger than tax multipliers in all source countries, except the United States. These results are broadly in line with the findings in the vast empirical literature on the size of domestic fiscal multipliers.

Since changes in a source country’s demand for recipient country exports is an important channel through which spillovers are propagated, a brief discussion of domestic (that is, source country) fiscal multipliers is warranted.

The “Baseline Results” section in the note text shows that government spending shocks have larger spillovers onto recipient country output than tax shocks. If trade is the main channel for international transmission of fiscal shocks, one would expect that domestic fiscal multipliers are also larger for government spending shocks. Indeed, we find this is almost universally the case. Annex Table 4.1 shows our estimated domestic fiscal multipliers for tax and expenditure shocks for our set of five source countries. Government spending multipliers tend to be slightly above 1 and are relatively tightly grouped between values of 1.12 (France) and 1.49 (United States). By comparison, tax multipliers are generally well below 1, with the notable exception of the United States, which is discussed later in this annex.

Annex Table 4.1.

Domestive Fiscal Multipliers

article image
Source: IMF staff estimates.Note: Table shows effect of one-dollar increase in spending/tax on real GDP level.

Peak impact or largest significant impact, Blanchard-Perotti methodology.

* p < 0.1.

The finding that government spending shocks have larger spillovers is consistent with traditional Keynesian theory. Consider two changes to fiscal policy: an increase in government spending and a cut in taxes, each with a budgetary cost of a dollar. The increase in government spending immediately contributes a dollar to aggregate demand, but the tax cut could contribute less than a dollar because it can be either spent or saved, since the marginal propensity to consume is typically less than 1. There is also considerable empirical evidence that suggests multipliers are larger for spending than for tax shocks: based on a survey of 41 studies, Mineshima, Poplawski-Ribeiro, and Weber (2014) show that first-year multipliers amount on average to 0.75 for government spending and 0.25 for government revenue in advanced economies.

The heterogeneity in domestic tax multipliers across the United States and Europe presents an apparent puzzle. One possible explanation for this result may rely on the differences between the tax systems in the United States and in Europe. The US tax system relies more on personal and corporate income taxes and less on consumption taxes relative to the European system. The literature using dynamic stochastic general equilibrium models (for example, Coenen, Straub, and Trabandt 2012; Kilponen and others 2015) tends to find that multipliers for personal and corporate income taxes are higher than those for consumption taxes, reflecting their more distortionary effects on labor supply and investment decisions. These findings suggest a higher tax multiplier in the United States, given its tax system structure.

The empirical literature on this topic also tends to find larger tax multipliers for the United States than for countries in Europe. For example, Romer and Romer (2010) find that the output response to a narrative-based tax shock peaks at –2.93 after 10 quarters in the United States. Similarly, Mertens and Ravn (2014) find a large multiplier (–2.5 after three quarters) from narrative-based personal income tax changes for the United States. By contrast, most estimates of tax multipliers for European countries lie below 1 (Kilponen and others 2015).

Annex 5. Robustness Tests

To ensure that our baseline results are not solely a function of our shock identification scheme, estimation approach, or various assumptions made during the analysis, in this annex we conduct numerous robustness checks. We find that our findings are robust to (1) estimation of spillovers in a panel VAR environment, which accounts for the endogenous response of exchange rates and monetary policy in recipient countries, (2) the use of alternative fiscal shocks based on both forecast error and narrative approaches, and (3) controlling for additional recipient country variables. These are explored in turn.

Spillover Estimates Using a Panel VAR

We conduct our spillover analysis in the context of a panel VAR (PVAR) to ensure that our results are not driven by the use of the local-projections method. The main goal here is to explicitly take into account the endogenous response of key macro variables when estimating spillovers to a fiscal shock. Consistent with this goal, we specify a six-variable PVAR, according to the following equation:

Yi,t=ci+Σp=01ApYi,tp+μi,t,(A.5.1)

in which ci is a vector of country-specific fixed effects, Ap is a reduced-form coefficient matrix, μi,t is a vector of shock terms, and Yi,t is a vector of six endogenous variables:

Y={ShockitGYi,t1;ShockitTYi,t1;effectiveexternaldemand;GDPgrowth;interestrate;REER},

in which REER is the real effective exchange rate.

With the exceptions of ShockitGYi,t1andShockitTYi,t1, which are identical to the government spending and tax shocks used in the baseline analysis (see “Fiscal Spillovers: Baseline Analysis”), each variable is in (detrended) quarter-over-quarter growth rates and relates to recipient country i’s domestic economy.36 The analysis is conducted for the same sample period as the baseline local-projections analysis.

The results from the PVAR analysis are closely aligned with the findings from the baseline local-projections model. Shown in Annex Figure 5.1, the spillover effects from a shock to government spending in source countries are larger than those for identically sized shocks to tax revenues. These results, shown by the orange lines in the figure—expressed in terms of the cumulative impulse-response functions from the PVAR following a 1 percent of source country GDP shock to government spending or tax revenues—are different from zero at the 5 percent level of statistical significance, according to simulations conducted using standard (Monte Carlo) resampling methods.

Annex Figure 5.1.
Annex Figure 5.1.

Effects of Spending and Tax Shocks on Recipient Countries’ Output: Comparison with Panel Vector Autoregression

(Percent)

Citation: Spillover Notes 2017, 002; 10.5089/9781484320303.062.A999

Source: IMF staff calculations.Note: Numbers on horizontal axes represent quarters; t = 0 is the quarter of the respective shocks. Solid blue lines denote the baseline response to respective shocks using local-projections method; dashed lines denote 90 percent confidence bands; and solid orange lines represent the response to respective shocks using panel vector autoregressions. Shocks are normalized to an average 1 percent of GDP across the source countries.

Robustness to Identification Using Forecast Errors

The second robustness check focuses on the identification of fiscal shocks as forecast errors in the growth rates of government spending or tax revenues. The method has been previously used in the literature (Auerbach and Gorodnichenko 2013; Ramey 2011) and identifies fiscal shocks by exploiting the difference between actual government purchases (tax revenues) and their forecast from the previous period. This approach captures only unanticipated changes in spending and revenues, as opposed to SVAR shocks, which are based on actual changes in fiscal variables and can be anticipated by agents if they have been announced earlier. The presence of such anticipated shocks in theory could bias the estimates, because the econometrician’s information set is different from the agents’ information set. Since forecast errors capture unexpected changes, this approach reduces the problem with fiscal foresight, as the econometrician’s and agents’ information sets are more aligned.

We rely on real-time OECD fiscal projections to construct the forecast error shocks. The data are at annual frequency, and the sample covers the period from 2000 to 2012 (after 2012, the forecasts data are not continuous). The forecast errors are constructed based on real-time information about expectations and actual data. The forecast error for each variable X = {G, T, Y} is constructed as

FEtX=XtXt|t1f,(A.5.2)

in which Xt is the growth rate of the variable from contemporaneous data release and Xt|t1f is the forecast made one period earlier. A positive forecast error therefore implies an expansionary spending and a contractionary tax shock. Following Auerbach and Gorodnichenko (2013), we regress the forecast errors of spending and taxes on the forecast errors of output to take into account any changes due to surprises in the business cycle and also on lagged macroeconomic variables growth (GDP, deflator, investment, government spending or tax revenues) to account for the part of the innovation that can be predicted from past observations. The forecast error shocks are then constructed as residuals from this regression, converted to levels using base year (2010) levels of expenditures or revenues, and substituted in the baseline regression equation instead of the SVAR shocks.

Spillover analysis using forecast error shocks confirms the baseline results—that spending shocks have larger spillovers than tax shocks—and provides a strong robustness check (Annex Figure 5.2). These shocks are constructed using a very different methodology, which relies on a different database, and they are estimated at a different frequency than the shocks used in our baseline specification. Obtaining similar spillovers using forecast error shocks is reassuring and suggests that problems related to fiscal foresight seem not to affect our main results. The size of the spillovers is somewhat larger than that of those obtained using structural shocks. In part this can be explained by a larger response of government spending and tax revenue to forecast error shocks than to structural shocks, especially for the United States (although in the former case, these impulse responses are imprecisely estimated because of the small sample).

Annex Figure 5.2.
Annex Figure 5.2.

Effects of Spending and Tax Shocks on Recipient Countries’ Output: Forecast Errors

(Percent)

Citation: Spillover Notes 2017, 002; 10.5089/9781484320303.062.A999

Source: IMF staff calculations.Note: Numbers on horizontal axes represent years; t = 0 is the year of the respective shocks. Solid lines denote the response to respective shocks, and dashed lines denote 90 percent confidence bands. Effects are estimated based on shocks derived from forecast errors. Shocks are normalized to an average 1 percent of GDP across the source countries.

Robustness to Identification with Narrative Approach

To further establish the robustness of our results, we consider spillovers from tax shocks given by the narrative shock of Romer and Romer (2010). Although some other studies construct narrative fiscal shocks (for example, DeVries and others 2011), the data set of Romer and Romer (2010) is the most suitable for our purposes, since it covers both expansion and consolidation episodes, making it most comparable to our baseline shock specification.37 To obtain spillover results using this type of shock, we simply replace each source country shock (sjt) from equation (2) with the narrative shock; this analysis is performed over the period from the first quarter of 1995 to the fourth quarter of 2007.38 A more comparable set of baseline results using our SVAR shocks is then constructed by restricting our baseline analysis to the same time period.

Analysis using narrative tax shocks for the United States shows similar spillovers onto partner countries. Despite their being derived from a very different identification scheme, the broad similarity between the estimated US tax shock spillovers from the narrative approach and those from our (time-sample-modified) baseline approach is notable. Results presented in Annex Figure 5.3 indicate that although spillovers identified by the narrative approach are somewhat smaller than those in our baseline, they are similar and fall comfortably within the confidence bands of our baseline estimates.

Annex Figure 5.3.
Annex Figure 5.3.

Effects of US Tax Shock on Recipient Countries’ Output: Comparison with US Narrative Tax Shock, 1995–2007

(Percent)

Citation: Spillover Notes 2017, 002; 10.5089/9781484320303.062.A999

Sources: Romer and Romer (2010); and IMF staff calculations. Note: Numbers on horizontal axes represent quarters; t = 0 is the quarter of the US tax shock. Solid blue line denotes the response to US tax shock using structural vector autoregression; dashed blue lines denote 90 percent confidence bands; and solid orange line represents the response to US narrative tax shock based on Romer and Romer (2010). Shocks are normalized to an average 1 percent of GDP across the source countries (note that this will represent a less than 1 percent of US GDP shock).

Robustness to Additional Control Variables

Baseline results are also robust to the inclusion of additional control variables. First, we use the short-term interest rate to control for the stance of recipient country monetary policy and the output gap and unemployment rate as measures of slack in recipients. Dynamic responses are presented in Annex Figures 5.45.6 and confirm that additional control variables do not materially change the baseline results. Controlling for domestic fiscal policies in the baseline specification is another important robustness check, however, estimating fiscal shocks for 55 recipient economies at quarterly frequency is infeasible, because quarterly fiscal data are unavailable for many countries. Since Eurostat provides fiscal data at quarterly frequency for European countries, we conduct a robustness check for this subsample in which we control for changes in primary balances (as a percent of GDP) to proxy for the stance of recipient country fiscal policy. Since this robustness check is conducted on a limited sample (European Union), we select Germany and France as source countries for this exercise, since shocks from these countries are most relevant for Europe. We find that the results of this robustness check are almost identical to those in the regression that omits the stance of recipient country fiscal policy (Annex Figure 5.7).

Annex Figure 5.4.
Annex Figure 5.4.

Dynamic Responses of Recipient Output to Fiscal Shocks, with Monetary Policy Controlled For

(Percent)

Citation: Spillover Notes 2017, 002; 10.5089/9781484320303.062.A999

Source: IMF staff estimates.Note: Numbers on horizontal axes represent quarters; t = 0 is the quarter of respective shocks. Solid blue lines denote the response to respective shocks, controlling for monetary policy; dashed blue lines denote 90 percent confidence bands; and solid orange lines represent the baseline response to respective shocks. Shocks are normalized to an average 1 percent of GDP across the source countries.
Annex Figure 5.5.
Annex Figure 5.5.

Dynamic Responses of Recipient Output to Fiscal Shocks, with Output Gap Controlled For

(Percent)

Citation: Spillover Notes 2017, 002; 10.5089/9781484320303.062.A999

Source: IMF staff estimates.Note: Numbers on horizontal axes represent quarters; t = 0 is the quarter of respective shocks. Solid blue lines denote the response to respective shocks, controlling for output gap; dashed blue lines denote 90 percent confidence bands; and solid orange lines represent the baseline response to respective shocks. Shocks are normalized to an average 1 percent of GDP across the source countries.
Annex Figure 5.6.
Annex Figure 5.6.

Dynamic Responses of Recipient Output to Fiscal Shocks, with Unemployment Rate Controlled For

(Percent)

Citation: Spillover Notes 2017, 002; 10.5089/9781484320303.062.A999

Source: IMF staff estimates.Note: Numbers on horizontal axes represent quarters; t = 0 is the quarter of respective shocks. Solid blue lines denote the response to respective shocks, controlling for unemployment rate; dashed blue lines denote 90 percent confidence bands; and solid orange lines represent the baseline response to respective shocks. Shocks are normalized to an average 1 percent of GDP across the source countries.
Annex Figure 5.7.
Annex Figure 5.7.

Dynamic Responses of Recipient Countries’ Output to France and Germany Fiscal Shocks, with Recipient Countries’ Fiscal Stance Controlled For

(Percent)

Citation: Spillover Notes 2017, 002; 10.5089/9781484320303.062.A999

Source: IMF staff estimates.Note: Numbers on horizontal axes represent quarters; t = 0 is the quarter of respective shocks. Solid blue lines denote the response to respective shocks, controlling for recipients’ primary balance estimated on a (time-varying) European Union sample; dashed blue lines denote 90 percent confidence bands; and solid orange lines represent the response to respective shocks from the baseline model estimated on a (time-varying) European Union sample. Shocks are normalized to an average 1 percent of GDP across the source countries.
Annex Table 6.1.

Comparison to Empirical Literature: Response of Recipient Country GDP

(Percent)

article image
Source: IMF staff calculations.Note: Table shows response to a shock normalized to 1 percent of recipient country GDP.*p < 0.1; **p < 0.05; ***p < 0.01.

Annex 6. Comparison of Spillover Estimates with Previous Literature

While we choose to present results in terms of source country GDP shocks for ease of interpretation, several previous studies on fiscal spillovers normalize shocks to recipient country GDP. Key among these studies are Auerbach and Gorodnichenko 2013 and Goujard 2017. Auerbach and Gorodnichenko (2013) estimate spillovers from shocks to government spending for a set of 30 OECD countries, using forecast error shocks constructed from the OECD’s Economic Outlook: Statistics and Projections database. Goujard (2017) considers spillovers on 34 recipient countries from both spending and tax shocks in 17 OECD countries using annual data for 1978–2011; shocks are taken from the narrative database of DeVries and others (2011) and pertain only to consolidation episodes.

Our estimates of fiscal spillovers are broadly similar to those obtained in these studies. Annex Table 6.1 compares the results from our baseline and alternative specifications—that is, panel VAR using structural shocks and local projections using forecast error shocks—to estimates reported in Auerbach and Gorodnichenko 2013 and Goujard 2017, focusing on separate estimates for spending and tax shocks. The comparison shows that

  • For government spending shocks, our average spillovers over the first three years are comparable to those in both studies and are statistically significant. The average first-year effects, which are not reported in comparable studies, are also statistically significant.
  • For tax revenue shocks, our estimates of spillovers are statistically significant over the first year, with more mixed results over the longer horizon. Meanwhile, Goujard (2017) finds no statistically significant effect from tax shocks.

All authors are members of the IMF’s Research Department. Superb research assistance was provided by Sung Eun Jung, also a member of the IMF’s Research Department. We thank Helge Berger, Jesper Linde, Gian Maria Milesi-Ferretti, and members of the IMF Spillover Task Force for insightful discussions, comments, and suggestions. This note should not be reported as representing the views of the IMF.

1

Forecast errors are constructed as the difference between actual and projected values of the relevant fiscal variable (spending or taxes). Shocks based on forecast errors are identified as residuals from a regression of the spending- or tax-revenue-based forecast errors on GDP forecast errors and lagged macroeconomic variables.

2

The narrative method, pioneered by Romer and Romer (2010), makes use of the narrative record, such as budget documents and speeches, to identify the size, timing, and principal motivation for fiscal actions. Romer and Romer’s data set also separates fiscal policy changes into those made for reasons related to prospective economic conditions and discretionary actions (for example, to reduce public debt), thereby allowing for a causal analysis of the impact of fiscal policy on output. A shortcoming of narrative-based shocks is that, for most countries other than the United States, mostly consolidation shocks have been identified so far in the literature, making them not directly comparable to the structural shocks used in our analysis.

3

As noted in Blanchard and Perotti’s original paper, possible implementation lags imply that structurally identified shocks could be subject to a fiscal foresight problem, in which the “shocks” may be anticipated by the private sector as a result of earlier announcement of policy changes (see, for example, Forni and Gambetti 2010; Ramey 2011; Leeper, Richter, and Walker 2012; Leeper, Walker, and Yang 2013; and Ben Zeev and Pappa 2015). However, there is some evidence that fiscal foresight may not present a critical issue, especially for assessing the impact of fiscal actions on relatively slow-moving variables such as activity (Perotti 2014), as opposed to forward-looking variables such as the exchange rate (Forni and Gambetti 2016; Auerbach and Gorodnichenko 2016).

4

In cases in which both tax and total revenues are available, shocks identified using the two revenue measures are very highly correlated. Consistent data on specific tax instruments (for example, corporate and personal income tax, consumption tax) are generally not available.

5

A dollar of government spending contributes directly to aggregate demand, whereas firms and/or households can spend or save a dollar of tax cuts. Thus, the spending multiplier would be larger than the tax multiplier if the marginal propensity to consume is less than 1. Mineshima, Poplawski-Ribeiro, and Weber (2014), based on a survey of 41 studies, document that first-year multipliers for government spending tend to be larger than those for tax revenues in advanced economies.

6

In a Mundell-Fleming-Dornbusch framework, a fiscal expansion puts upward pressure on interest rates, appreciates the nominal exchange rate, and increases domestic prices, which results in a real appreciation (see, for example, Fleming 1962; Mundell 1963; and Dornbusch 1976). Note, though, that other frameworks can deliver different exchange rate predictions (see, for example, Obstfeld and Rogoff 1995), in which case the expenditure-switching effect could go in the opposite direction.

7

This approach was selected, in part, because it is well suited to estimating nonlinear specifications—that is, spillovers under different states of the economy—which is an issue to which we return later in the note.

8

Bilateral trade data are available for trade in goods only.

9

Implicit in this weighting scheme is the assumption that the marginal propensity to import is the same for all source countries and for both spending and tax shocks. An alternative scheme that attempts to account explicitly for the differences in marginal propensity to import across source countries and types of shocks would be to have an additional scaling factor, Mjt/Gjt for government spending shocks and Mjt/Tjt for tax revenue shocks, in which Gjt and Tjt denote total government spending and tax revenues in country j, respectively. However, these ratios would serve only as very rough proxies for the marginal propensity to import of the public and private sector in any given country. At the same time, such a specification would introduce arbitrary asymmetries into the size of spending relative to tax shocks being transmitted abroad, confounding the comparison of spillover effects from different fiscal instruments. Thus, given that results are robust to alternative weighting schemes (more on this later in the note), we keep the simple scheme in equation (2) for ease of interpretation of the relative magnitude of spillovers from spending and tax shocks.

10

Estimated fiscal shocks are uncorrelated across countries.

11

This does not preclude spillovers through other channels, since our estimates capture the overall response of recipient country GDP. However, the use of a trade-driven weighting scheme may result in some bias of the estimates in situations in which other channels are not proportional to trade—for example, if a recipient country’s financial exposure to a source country differs markedly from its trade exposure, although these cases are likely limited.

12

Results shown in terms of source-country-GDP shocks would be unchanged under plausible alternative weighting schemes, as any alternative weighting schemes would also require us to recalculate the spillover coefficient estimated in equation (1), resulting in an equal-and-offsetting adjustment of this coefficient, since any transformation applied to the source shock would be constant across all recipient countries.

13

We use the term “spillovers” to refer to a recipient GDP’s response to the initial fiscal shock at the source, that is, a point estimate of the impulse-response function. Our approach does not estimate “multipliers” or “cumulative multipliers,” as the aggregation of shocks across source countries makes this infeasible.

14

The standard errors are clustered at the country level. The fiscal shock is generated outside of the local-projections model presented in equation (1), implying that confidence bands may be wider than presented in Figure 2 if uncertainty around the estimated shocks is also taken into account.

15

As long as any omitted variable not considered in this list is uncorrelated with the fiscal shock in the source country, then its omission will not affect our spillover estimates.

16

We use the narrative-based tax shocks for the United States as identified by Romer and Romer (2010) to conduct the robustness test, since these are the most comparable narrative shocks to our structural shocks (quarterly frequency, covering both expansion and consolidation episodes).

17

For simplicity, only spending and tax shocks are presented. These simple calculations are intended to allow for comparison to other studies in the literature and may be misleading for some countries in our sample—an example of this are spillovers from Japan to Asia, which may be overstated by these calculations, since Japan is a relatively closed economy and as such accounts for a relatively small share of Asia’s overall exports.

18

Note that 1 percent (the unit we pick for illustrative purposes) represents an abnormally large shock in historical terms. To put this in context, the largest US government spending shock in our sample is 0.5 percent, with the average (absolute) value at only 0.1 percent.

19

See, for example, Christiano, Eichenbaum, and Rebelo 2011; Eggertsson 2011; Woodford 2011; and Blanchard, Erceg, and Lindé 2017. This can be true for expansionary fiscal shocks as well as contractionary shocks. For example, if central banks aim for a more accommodative stance than feasible, a fiscal expansion may be fully accommodated, thereby increasing domestic multipliers and spillover effects. This rationale applies with a (rather unlikely) caveat. Should the actual and shadow policy rates in the source economy be at zero at times in which the economy is closing the output gap, nothing would prevent a policymaker from hiking rates to counteract fiscal expansions, preventing an amplification of the fiscal shock.

21

Credit market imperfections could also play a role in increasing multipliers when a country is in a liquidity trap (see Carrillo and Poilly 2013).

22

For example, in our post-2000 sample, about 26 percent of country-quarter observations fall under the definition of “effective lower bound,” three-quarters of which coincide with economic slack; similarly, about 55 percent of observations fall under the definition of “slack,” 35 percent of which coincide with the effective lower bound.

23

Results are robust to using alternative definitions of slack, including using the unemployment gap or smooth-transition probability as in Auerbach and Gorodnichenko 2013.

24

Our findings are consistent with the evidence for a sample of OECD countries in other studies in the literature (Auerbach and Gorodnichenko 2013; Goujard 2017). See Annex 6.

25

We use different distributions for advanced economies and emerging markets. The 25th percentile value for the cross-country distribution is about 0.57 percent for advanced economies and 3.0 percent for emerging markets. Results are robust to using alternative definitions of effective lower bound, such as using absolute thresholds (common to all countries) for the short-term interest rates.

26

The empirical analysis of the exchange rate response to fiscal shocks is beyond the scope of this note. Although the empirical literature has generally found mixed results, some recent work has found evidence of exchange rate appreciation following a fiscal expansion when the anticipation of the fiscal shock is properly taken into account. The exchange rate is a forward-looking variable that reacts on announcements about future spending, that is, before spending takes place. Two recent studies (Auerbach and Gorodnichenko 2016; Forni and Gambetti 2016) isolate the announcement component of fiscal shocks and show that the exchange rate appreciates following news about future fiscal expansions. Popescu and Shibata (2017) extend Forni and Gambetti’s (2016) work to a cross-country perspective to study the impact of US government spending shocks on external positions.

27

Qureshi and Tsangarides (2010) point to comparable effects in conventional and hard pegs (currency unions).

28

This mechanism is especially relevant in the short term, because real exchange rates will adjust over the medium term, mitigating the differences in spillovers between the two regimes.

29

For a discussion on contractionary devaluations, see Diaz Alejandro 1966 and Edwards 1987.

30

In 2015, for example, the Reinhart-Rogoff classification yields seven recipient countries with “fixed” exchange rates, while the IMF classification yields two “fixed” countries. However, the number of fixed-rate countries varies over time (and there tend to be more of these in earlier years).

31

The numbers of countries classified as having “fixed”-rate regimes can generally vary over time, since the exchange rate regime classification is time varying.

34

See Annex 5 for details on how the forecast error shocks are constructed.

35

One potential drawback of using these shocks is that they are available only at annual frequency, meaning that the elasticity should be recovered from a VAR specified on annual data and might not be a good measure for quarterly elasticity. Another potential problem is that forecast error shocks can capture only unanticipated changes in fiscal variables, while anticipated changes can play an important role as well. However, there is no quarterly measure of shocks available for these three countries, nor is there a measure of anticipated shocks, that we could use in the estimation.

36

Results from the PVAR are robust to several alternative specifications, including not detrending the data.

37

Narrative shock databases for government spending are much less common in the literature, which precludes a robustness check of spillovers from spending shocks based on narrative shocks.

38

The fourth quarter of 2007 is the last period for which data on these shocks are available.

Fiscal Spillovers: The Importance of Macroeconomic and Policy Conditions in Transmission
Author: Patrick Blagrave, Giang Ho, Ksenia Koloskova, and Mr. Esteban Vesperoni
  • View in gallery

    Effects of Spending and Tax Shocks on Recipient Countries’ Output: Comparison with Panel Vector Autoregression

    (Percent)

  • View in gallery

    Effects of Spending and Tax Shocks on Recipient Countries’ Output: Forecast Errors

    (Percent)

  • View in gallery

    Effects of US Tax Shock on Recipient Countries’ Output: Comparison with US Narrative Tax Shock, 1995–2007

    (Percent)

  • View in gallery

    Dynamic Responses of Recipient Output to Fiscal Shocks, with Monetary Policy Controlled For

    (Percent)

  • View in gallery

    Dynamic Responses of Recipient Output to Fiscal Shocks, with Output Gap Controlled For

    (Percent)

  • View in gallery

    Dynamic Responses of Recipient Output to Fiscal Shocks, with Unemployment Rate Controlled For

    (Percent)

  • View in gallery

    Dynamic Responses of Recipient Countries’ Output to France and Germany Fiscal Shocks, with Recipient Countries’ Fiscal Stance Controlled For

    (Percent)