The Impact of Government Consumption Cuts on Sovereign Yield Spreads, Lecture notes of Macroeconomics

Download The Impact of Government Consumption Cuts on Sovereign Yield Spreads and more Lecture notes Macroeconomics in PDF only on Docsity! Does austerity pay off?∗ Benjamin Born, Gernot J. Müller, Johannes Pfeifer August 2014 Abstract Austerity measures are frequently enacted when the sustainability of public finances is in doubt. Such doubts are reflected in high sovereign yield spreads and put further strain on government finances. Is austerity successful in restoring market confidence, bringing about a reduction in yield spreads? We employ a new panel data set which contains sovereign yield spreads for 26 emerging and advanced economies and estimate the effects of cuts of government consumption on yield spreads and economic activity. The conditions under which austerity takes place are crucial. During times of fiscal stress, spreads rise in response to the spending cuts, at least in the short-run. In contrast, austerity pays off, if conditions are more benign. Keywords: Fiscal Policy, austerity, sovereign risk, yield spreads, confidence, panel VAR, local projections JEL-Codes: E62 ∗Prepared for the SAFE Research Conference “Austerity and Growth: Concepts for Europe”, June 2014. Born: University of Mannheim and CESifo, born@uni-mannheim.de, Müller: University of Bonn and CEPR, gernot.mueller@uni-bonn.de, Pfeifer: University of Mannheim, pfeifer@uni-mannheim.de. We thank our discussants Nicola Fuchs-Schündeln, Alessandro Gioffré, Josef Hollmayr, and Klemens Hauzenberger. We also thank Kerstin Bernoth and seminar audiences at Bonn, Heidelberg, Naples, the Bundesbank/DFG/IMF Workshop “Credit frictions and default in macroeconomics” and the ifw/Bundesbank Workshop “Fiscal Policy and Macroeconomic Performance” for helpful discussions. Andreas Born, Diana Schüler, and Alexander Scheer have provided excellent research assistance. Gernot Müller also thanks the German Science Foundation (DFG) for financial support under the Priority Program 1578. The usual disclaimer applies. “Debt is a slow-moving variable that cannot—and in general should not— be brought down too quickly. But interest rates can change much more quickly than fiscal policy and debt.” (C. Reinhart and K. Rogoff 2013) “[F]inancial investors are schizophrenic about fiscal consolidation and growth. They react positively to news of fiscal consolidation, but then react negatively later, when consolidation leads to lower growth—which it often does.” (O. Blanchard 2011) 1 Introduction In the years following the global financial crisis, many European governments have been implementing sizeable austerity measures: spending cuts and tax increases in order reduce budget deficits. These measures were taken in order to confront mounting concerns about rising levels of public debt or outright solvency issues. In fact, in a number of euro area countries sovereign yield spreads relative to Germany started to take off by 2010, arguably leaving policy makers with no alternative course of action.1 Yet a further deterioration of financial market conditions, coupled with dismal growth performance in the following years, lead many observers to question the wisdom of austerity. In this paper, we ask whether austerity pays off, that is, whether it actually helps restoring market confidence in the sustainability of public finances as reflected by sovereign yield spreads. We focus on how financial markets respond to austerity measures or, more specifically, on the response of yield spreads and sidestep the issue of how such measures impact the actual health of government finances. In fact, while the response of fiscal indicators such as the level of sovereign debt is of first-order importance in this regard, it generally does not provide a sufficient statistic for assessing the sustainability of debt. For the willingness and the ability of governments to honor a given level of debt obligations depends on a number of country-specific, partly unobserved factors, such as the ability to raise taxes. The same level of debt may thus have very different implications for debt sustainability in different countries (Bi, 2012). Sovereign yield spreads, instead, provide more comprehensive picture, both because they reflect a broader assessment of market participants and because 1Historically, in addition to primary surpluses, output growth as well as negative real interest rates have contributed to the reduction of debt-to-GDP ratios (Hall and Sargent, 2011). Real interest rates in turn may have been depressed due to “financial repression” (Reinhart and Sbrancia, 2014). While it is unclear to what extent these factors will play an important role in stabilizing debt levels in the years to come, they are arguably no viable means in order to meet market pressures instantaneously. 1 that government consumption leaves economic activity unaffected in the absence of fiscal stress. On the other hand, in the presence of fiscal stress spending cuts raise spreads and depress economic activity considerably. Fiscal stress episodes thus tend to dominate the overall sample. We also find that spreads tend to decline in response to spending cuts in the medium to long run. Our results are based on exogenous variations in government consumption, while austerity is typically considered to be an endogenous response to, say, financial market developments. That said, note that identifying an exogenous variation in government consumption is key to isolate the impact of austerity on the variables of interest per se, rather than the joint effect of financial market developments cum austerity. Still, it is certainly possible that austerity measures impact the economy in different ways than a “regular” fiscal shock—perhaps because they are implemented under special circumstances. Conditioning the effects of spending cuts on the state of the economy is one strategy to address this concern. Alternative, and complementary, approaches to assess the effects of fiscal consolidation episodes include case studies, notably the classic analysis of Giavazzi and Pagano (1990). Yet another approach goes back to Alesina and Perotti (1995), recently applied by Alesina and Ardagna (2013). It identifies (large) fiscal adjustments as episodes during which the cyclically adjusted primary deficit falls relative to GDP by a certain amount. Finally, fiscal consolidations have also been identified on the basis of a narrative approach (Guajardo, Leigh, and Pescatori, 2011; Devries, Guajardo, Leigh, and Pescatori, 2011). Our analysis also provides new results on the effects of government spending cuts on sovereign yield spreads for a large panel of advanced and emerging economies. Related studies include numerous attempts to assess the effects of fiscal policy on interest rates. In particular, Ardagna (2009) finds that interest rates tend to decline in response to large fiscal consolidations. Laubach (2009) investigates how changes in the U.S. fiscal outlook affect interest rates. Finally, Akitoby and Stratmann (2008) use a similar measure for sovereign yield spreads as we use in the present paper. They focus on emerging market economies, however, and assess the contemporaneous impact of fiscal variables on spreads within a given year. The remainder of the paper is organized as follows. Section 2 details the construction of our data set. In this section, we also establish a number of basic facts regarding the time-series properties of sovereign yield spreads and their relationship to government consumption and output growth. In Section 3 we discuss our econometric specification and identification strategy. We present the main results of the paper and an extensive sensitivity analysis in 4 Section 4. Section 5 concludes. 2 Data Our analysis is based on a new data set. It contains quarterly data for government consumption, output, and sovereign yield spreads for 26 emerging and advanced economies. Our econometric strategy below requires the use of quarterly data. While data on yield spreads are available at higher frequency, data on macroeconomic aggregates are not. In fact, for a long time, time-series studies of the fiscal transmission mechanism have been limited to a small set of countries, because data for government consumption has not been available at (non-interpolated) quarterly frequency.3 In a recent contribution, Ilzetzki, Mendoza, and Végh (2013) have collected non-interpolated quarterly data for government consumption for 44 countries. We reconstruct quarterly data for government consumption along the lines of Ilzetzki, Mendoza, and Végh (2013) for the subset of countries for which we are also able to compute sovereign yield spreads. In the process, we also extend the sample to include more recent observations. Our earliest observations for which we have both spread and government consumption data is 1990Q1 for Denmark and Ecuador. Our sample runs up to 2013. Table 1 provides summary statistics for the government consumption-to-GDP ratio. Gov- ernment consumption is exhaustive government consumption and does not include transfer payments or government investment. As in Ilzetzki, Mendoza, and Végh (2013), depending on the availability of quarterly time series, it pertains to either the general or the central government. The ratio of government consumption-to-GDP varies both across time and across countries. In case of general government data, government consumption fluctuates around 20 percent of GDP. As a distinct contribution, we also construct a panel data set for sovereign yield spreads in order to measure the assessment of financial markets regarding the sustainability of public finances. Given observations on quarterly government consumption, we aim to construct measures of yield spreads for as many countries as possible. As stressed in the introduction, we construct yield spreads using yields for securities issued in common currency. To the extent that goods and financial markets are sufficiently integrated, we are thereby 3Some studies have resorted to annual data (e.g. Beetsma, Giuliodori, and Klaasen, 2006, 2008; Bénétrix and Lane, 2013). In this case identification assumptions tend to be more restrictive. However, Born and Müller (2012) consider both quarterly and annual data for four OECD countries. They find that the estimated effects of government spending shocks do hardly differ. 5 Table 1: Basic properties of government consumption-to-output ratio Country first obs last obs min max mean std Belgium 1995.00 2013.25 0.24 0.25 0.25 0.00 Denmark 1991.00 2013.25 0.25 0.29 0.27 0.01 Finland 1990.00 2013.25 0.18 0.26 0.21 0.02 France 1986.00 2013.25 0.23 0.27 0.25 0.01 Greece 2000.00 2011.00 0.17 0.23 0.18 0.01 Hungary 1995.00 2013.25 0.21 0.28 0.23 0.02 Ireland 1997.00 2013.25 0.16 0.18 0.17 0.01 Italy 1991.00 2013.25 0.19 0.22 0.20 0.01 Netherlands 1988.00 2013.25 0.23 0.28 0.25 0.01 Poland 1995.00 2013.25 0.17 0.21 0.18 0.01 Portugal 1995.00 2013.25 0.19 0.22 0.20 0.01 Slovenia 1995.00 2013.25 0.17 0.22 0.19 0.01 Spain 1995.00 2013.25 0.16 0.22 0.18 0.02 Sweden 1993.00 2013.25 0.07 0.12 0.09 0.02 United Kingdom 1986.00 2013.25 0.21 0.28 0.23 0.02 Argentina 1993.00 2013.25 0.12 0.15 0.13 0.01 Chile 1989.00 2012.75 NaN NaN NaN NaN Colombia 2000.00 2013.25 0.15 0.17 0.16 0.00 Ecuador 2000.00 2013.25 0.10 0.13 0.12 0.01 El Salvador 1994.00 2013.25 0.06 0.09 0.07 0.01 Malaysia 2000.00 2013.00 NaN NaN NaN NaN Peru 1995.00 2013.25 NaN NaN NaN NaN South Africa 1993.00 2013.25 0.13 0.18 0.15 0.02 Thailand 1993.00 2013.25 0.07 0.11 0.09 0.01 Turkey 1998.00 2013.25 0.09 0.12 0.11 0.01 Uruguay 1988.00 2013.25 NaN NaN NaN NaN Notes: Government consumption is consumption of the general government except for Chile, El Salvador, Malaysia, Peru, and Sweden, where it refers to central government consumption. For Chile, Malaysia, Uruguay, and Peru, we do not have information about the level of quarterly non-interpolated government consumption. eliminating fluctuations in yields due to changes in real interest rates, inflation expectations and the risk premia associated with them. In addition to a default risk premium, yield spreads may still reflect liquidity premium and, if duration differs or drifts, a term premium (see e.g. Broner, Lorenzoni, and Schmunkler, 2013). However, we try to minimize the term premium by constructing the yield spread on the basis of yields for bonds with a comparable maturity and coupon.4 Moreover, any liquidity premium is likely to be small in 4We focus on long-term rates whenever possible. As they are closely linked to the average of expected future short-term rates, they are a more appropriate measure of governments’ refinancing costs than short term-term rates. Assessing the effects of austerity on the term structure is beyond the scope of the present 6 Italy 1990 1995 2000 2005 2010 0 1 2 3 4 5 6 7 8 9 Spread Yield Benchmark B1 B1+B2 B2 ECB Long Term Convergence Rates Figure 2: Construction of the Italian yield spread series. four countries, it displays the yields of foreign currency bonds jointly with those of the associated benchmark bonds. For three countries (Italy, Denmark, UK), we consider bonds denominated in US dollars, while for Greece we consider a bond issued in Deutsche mark.10 Note that yield spreads are typically small relative to the level of yields and vary considerably over time. For Italy and Greece, data on foreign currency bonds allow us to extend the sample to include observations prior to the introduction of the euro. In case of Denmark and the UK, they allow us to compute common-currency yield spreads, although those countries are not members of the euro zone. Figure 2 details the construction for the case of Italy. Until 1991, only one foreign bond is 10Italy: 10year $US bond issued on 08/02/1991 with coupon 8.75% (XS0030152895); benchmark bond: US 10 year Treasury note issued on 15/11/1990 with coupon 8.25% (US912827ZN50). Denmark: 10year $US zero coupon bond issued on 8/6/1986 (GB0042654961); benchmark bond: US 10 year Treasury note issued on 15/08/1998 with coupon 9.25% (ISIN: US912827WN87). UK: 10year $US bond issued on 12/9/1992 with coupon 7.25% (XS0041132845); benchmark bond: US 10 year Treasury note issued on 06/05/1992 with coupon 7.5% (US912827F496). Greece: 10year DEM bond issued on 11/13/1996 with coupon 6.75% (DE0001349355); benchmark bond: German 15 year bond issued on 04/10/1996 with coupon 6.25% (DE0001135010). 9 available. Starting in 1992, we obtain a second bond and compute the yield spread as the average over those of both bonds. When the first bond matures in 1997, we are left with one bond until 1999. From that point on, we use the long-term convergence bond yields provided by the ECB. Table 2: Basic properties of sovereign yield spreads Country first obs last obs min max mean std ρ(∆yt, st) ρ(∆gt, st) Belgium 1991.75 2013.25 0.04 2.53 0.45 0.44 -0.38 -0.13 Denmark 1988.50 2002.50 0.02 1.93 0.57 0.42 -0.17 -0.01 Finland 1992.25 2013.25 -0.04 0.80 0.27 0.18 -0.46 -0.28 France 1999.00 2013.25 0.02 1.35 0.27 0.32 -0.33 0.03 Greece 1992.25 2013.25 0.15 23.98 2.80 5.24 -0.59 -0.22 Hungary 1999.00 2013.25 0.10 6.05 1.79 1.66 -0.63 -0.05 Ireland 1991.75 2013.25 -0.04 7.92 1.04 1.79 -0.23 -0.43 Italy 1989.00 2013.25 -0.07 4.68 0.77 0.98 -0.41 -0.42 Netherlands 1999.00 2013.25 0.00 0.67 0.19 0.17 -0.63 -0.15 Poland 1994.75 2013.25 0.42 8.71 1.97 1.43 -0.01 -0.11 Portugal 1993.25 2013.25 0.00 11.39 1.27 2.62 -0.44 -0.41 Slovenia 2006.00 2013.25 -0.17 5.13 1.59 1.59 -0.42 -0.42 Spain 1992.50 2013.25 0.01 5.07 0.71 1.15 -0.61 -0.50 Sweden 1986.00 2009.50 -0.95 2.95 0.90 0.94 0.33 -0.09 United Kingdom 1992.75 2007.75 -0.03 0.64 0.29 0.17 -0.18 0.06 Argentina 1993.75 2013.25 2.04 70.78 15.80 18.74 -0.09 -0.21 Chile 1999.25 2013.25 0.55 3.43 1.45 0.58 -0.45 0.01 Colombia 1997.00 2013.25 1.12 10.66 3.65 2.19 -0.38 -0.19 Ecuador 1995.00 2013.25 5.02 47.64 12.58 9.08 -0.12 0.03 El Salvador 2002.25 2013.25 1.27 8.54 3.33 1.36 -0.47 0.04 Malaysia 1996.75 2013.25 0.46 10.55 1.84 1.45 -0.62 0.03 Peru 1997.00 2013.25 1.14 9.11 3.60 2.01 -0.36 -0.05 South Africa 1994.75 2013.25 0.70 6.52 2.28 1.24 -0.43 -0.34 Thailand 1997.25 2006.00 0.48 5.55 1.51 1.11 -0.55 0.15 Turkey 1996.25 2013.25 1.39 10.66 4.19 2.44 -0.31 -0.15 Uruguay 2001.25 2013.25 1.27 16.43 4.07 3.18 -0.36 -0.29 Notes: spreads st are average of daily observations per quarter, measured in percentage points. Table 2 provides information on the coverage of our spread sample and some basic descriptive statistics.11 Spreads st are measured in percentage points and vary considerably across 11Figure A.1 in the appendix displays the time series on a country-by-country basis. 10 our sample. In a couple of countries the lowest realization of the spread is negative, that is, yields fall below those of the reference bond. For the advanced economies group12 we observe the highest spreads in Portugal (11) and Greece (24). For the emerging economies13 the highest values are reached in Ecuador (48) and Argentina (71). Measured relative to these values, most realizations of spreads in our sample are small. This is apparent from the empirical distribution function (CDF) which we plot in Figure 3 for the entire sample (solid line), but also for the set of advanced (dashed-dotted line) and emerging economies in isolation (dashed line). The total number of observations in our sample is 1497, of which 783 are for advanced economies. In each case, the mass of observations is very much concentrated on the left. For the full sample, for instance, about 50 percent of the observations for the spread are below 1 percentage point. Still, there are considerable differences across the two country groups: more than 99.8 percent of observations are below 10 percentage points in the sample of advanced economies. The corresponding number is only 93.3 percent in the sample of emerging market economies. An alternative and widely considered indicator of debt sustainability are credit default swap (CDS) spreads.14 CDS are insurance contracts that cover the repayment risk of an underlying bond. The CDS spread indicates the annual insurance premium to be paid by the buyer. Accordingly, a higher perceived default probability on the underlying bond implies, ceteris paribus, a higher CDS spread. While well-suited to capture market assessment of debt sustainability, CDS data are generally only available after 2003 when a liquid market developed (see Mengle, 2007). To check the quality of our constructed spread measure, we compare it to yields of 5year CDS spreads. We find a correlation of 0.95 (see also Figure A.1). Finally, in the last two columns of Table 2 we report the correlation of sovereign yield spreads with output growth and the growth of government consumption, respectively. It turns out that spreads are countercyclical in all countries, although sometimes the correlation is negligible. Instead, the correlation of spreads and government consumption growth varies across countries. It is negative in about two thirds of the countries, but is generally weak. Eventually, we seek to establish the co-movement of spreads and government consumption conditional on an exogenous variation in government consumption. In order to do so, we rely 12Denmark, Ireland, Greece, Spain, France, Italy, Portugal, Slovenia, United Kingdom, Belgium, Nether- lands, Finland, and Sweden 13Croatia, Hungary, Poland, Turkey, Argentina, Colombia, South Africa, Ecuador, Chile, El Salvador, Malaysia, Thailand, Uruguay, and Peru. 14For instance, in a recent study, Longstaff, Pan, Pedersen, and Singleton (2011) find an important role of global factors in accounting for CDS spread dynamics of individual countries. 11 we show that our key results are robust, once we adopt an identification scheme based on forecast errors and apply it to this subset of country observations. Another popular approach to identify fiscal shocks on a narrative basis. Following the work of Romer and Romer (2010) for the US, Devries, Guajardo, Leigh, and Pescatori (2011) have constructed a data set of fiscal shock for a large sample of OECD countries. However, these fiscal shocks are identified on a narrative basis with a view to being orthogonal to the business cycle. A large share of these shocks, however, reflects fiscal measures taken in order to reign in public debt or budget deficits. To the extent that sovereign yield spreads comove systematically with the latter, these “shocks” are not suited to investigate the effect of fiscal policy on sovereign yield spreads.17 Yet an alternative strand of the literature, following the lead of Alesina and Perotti (1995), identifies “fiscal adjustments” as episodes during which the cyclically adjusted primary deficit falls relative to GDP by a certain amount (see e.g. Alesina and Ardagna, 2010; IMF, 2010). In these studies, an episode of fiscal consolidation is assumed to take place over several years. Our focus, instead, is on the short run dynamics of spreads to cuts in government consumption. Finally, note that due to non-availability of appropriate tax data, we do not attempt to identify the effects of tax shocks. 3.2 Model specification Our results are based on two alternative model specifications. Traditionally, the Blanchard- Perotti identification has been employed within a VAR context. More recently, it has also been used in panel VAR models, see, e.g., Ilzetzki, Mendoza, and Végh (2013) and Born, Juessen, and Müller (2013). Below we will also report estimates based on such a model. In this case, the vector of endogenous variables includes three variables: the log of real government consumption, gi,t, the log of real GDP, yi,t, and sovereign yield spreads, si,t, measured in percentage points. In what follows, i denotes the country and t the time period. The VAR model is given by gi,t yi,t si,t  = µi +αit+ K∑ k=1 Ak  gi,t−k yi,t−k si,t−k  + νi,t , (3.1) 17Another strand of the literature employs sign restrictions to identify fiscal shocks, see Mountford and Uhlig (2009). This is not feasible in the context of our analysis, as there is no consensus on the sign of the responses to a fiscal shock as far as the variables in our sample are concerned. 14 where µi and αi are vectors containing country-specific constants and time trends. The matrices Ak capture the effect of past realizations on the current vector of endogenous variables. νi,t is a vector of reduced form residuals. We estimate model (3.1) by OLS. Identification is based on mapping the reduced-form innovations νi,t into structural shocks: εi,t = Bνi,t , with εi,t iid∼ (0, I) . In the present context, identifying shocks to government consumption under the assumption that it is predetermined boils down to equating the first element in νi,t with a structural fiscal shock.18 Recently, local projections have been a popular tool to complement VAR analysis. As argued by Jordá (2005), local projections are more robust to model misspecification as they do not impose cross-equation restrictions as in sVAR models. Moreover, local projections prove highly flexible in accommodating a panel structure. Finally, and most importantly, they offer a very convenient way to account for state dependence—the focus of our analysis below. Earlier work by Auerbach and Gorodnichenko (2013a) has illustrated this in the context of fiscal policy. More specifically, they suggest a panel smooth transition autoregressive (STAR) model on which we rely below. Defining the vector Xi,t = [gi,t yi,t si,t]′, the response of a variable xi,t+h at horizon h to gi,t can be obtained by locally projecting xi,t+h on time t government spending and a set of control variables/regressors. That is, the following relation is estimated: xi,t+h =αi,h + βi,ht+ ηt,h + F (zi,t)ψA,hgi,t + [1 − F (zi,t)]ψB,hgi,t + F (zi,t) ΠA,h (L)Xi,t−1 + [1 − F (zi,t)] ΠB,h (L)Xi,t−1 + ui,t . (3.2) Here αi,h and βi,ht are a country-specific constant and a country-specific trend, respectively. ηt,h in turn captures time fixed effects, which we do not allow for in the VAR model above. ui,t is an error term with strictly positive variance. L denotes the lag operator. At each horizon, the response of the dependent variable to government spending is allowed to differ across regimes “A” and “B“, with the ψ-coefficients on the gi,t terms indexed accordingly. Similarly, Π∗,h(L) is a lag polynomial of coefficient matrices capturing the impact of control variables in each regime. We estimate (3.2) using OLS, assuming that government spending 18As a practical matter, we impose a lower-triangular structure on B, attaching no structural interpreta- tion to the other elements in εi,t. 15 is predetermined (see also Auerbach and Gorodnichenko, 2013b). Conceptually it is convenient to distinguish two polar regimes which give rise to possibly different dynamics after a fiscal impulse. These polar cases are characterized by F (zi,t) being equal to zero and one, respectively. It is quite unlikely, however, that actual economies operate in either of these regimes. Rather, they tend to be more or less close to one of the two. This is captured in the estimation, as the projection of the dependent variable at each horizon is a weighted average, whereby the fiscal shock as well as the control regressors are allowed to impact the dependent variable differently. The weights, in turn, are a function F (·) of an indicator variable zi,t which provides information of how close the economy is to one of the two regimes. By using this weighted average, all observations between the two polar help in identifying the two regimes. In our estimation below we use lagged yield spreads zi,t = si,t−1 as an indicator variable in order to measure how closely an economy operates to a regime of “fiscal stress”. Using the lagged value of the spread assures that the indicator is orthogonal to our identified government spending shocks. We weigh regressors on the basis of the country-group specific empirical CDF (see Figure 3 above). Formally, we have F (zi,t) = 1 N N∑ j=1 1zj<zi,t (3.3) where 1 denotes an indicator function and j indexes all country-time observations in the respective country group. As an alternative to the empirical CDF, one may assume a specific parametric function in order to map the indicator variable into specific weights.19 Using the empirical CDF, however, has two advantages. First, there are no degrees of freedom in specifying the transition function. Second, the polar cases are now given by states of the world that were actually obtained in-sample. 4 Results We now turn to our estimates of the effects of government consumption cuts. Our main focus is the dynamic response of sovereign spreads to such cuts. Still, as argued above, because the adjustment of output is likely to be a key determinant of spreads, we also 19Auerbach and Gorodnichenko (2012) use a logistic cumulative density function F (zi,t) = exp(−γzi,t) 1+exp(−γzi,t) as their transition function so that Prob(z < z̄) = F (z̄). The free parameter γ was chosen using extraneous evidence that the US is in recession 20% of the time. 16 Government spending 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 GDP 0 1 2 3 4 5 6 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 Spread (basis points) 0 1 2 3 4 5 6 −40 −20 0 20 40 60 80 (a) Linear model Government spending 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 No Stress Stress GDP 0 1 2 3 4 5 6 −2.5 −2 −1.5 −1 −0.5 0 0.5 1 Spread (basis points) 0 1 2 3 4 5 6 −200 −100 0 100 200 300 (b) Conditional with country group-specific CDFs Figure 5: Dynamic response to a government spending shock derived from local projections (two lags). All standard errors are clustered robust 90% standard errors. time and countries. In particular, earlier studies show that fiscal policy may affect the economy differently in “bad times” (Bertola and Drazen, 1993; Perotti, 1999). And in- deed, recent evidence established by Corsetti, Meier, and Müller (2012a), Auerbach and Gorodnichenko (2013a) and Ilzetzki, Mendoza, and Végh (2013) suggests that the gov- ernment spending multiplier on output tends to be relatively low, if debt is high. This is particularly relevant, as austerity is often enacted in response to concerns about the sustainability of debt. However, as discussed above, public debt per se is an insufficient statistic to assess the sustainability of public finances, because fiscal capacity varies strongly with a number of country-specific factors. Instead, sovereign yield spreads provide more comprehensive information regarding the extent of “fiscal stress”, both because of the underlying—arguably broader—assessment of financial market participants and, not least, because of the immediate budgetary consequences. In what follows we therefore estimate the non-linear model (3.2) relying on the spreads as an indicator variable and their empirical CDF (3.3) as weighting function. We thus allow the effects of spending cuts cuts to differ depending on whether they are in a regime of fiscal stress, evidenced by high spread or not (“benign times”). Recall, however, from 19 Government spending 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 GDP 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 Confidence 0 1 2 3 4 5 6 −6 −4 −2 0 2 4 6 Government spending 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 No Stress Stress GDP 0 1 2 3 4 5 6 −2.5 −2 −1.5 −1 −0.5 0 0.5 1 Confidence 0 1 2 3 4 5 6 −10 −5 0 5 10 Figure 6: Confidence: dynamic response to a government spending shock. Left panel: linear model (LP). Right panel: responses conditional of fiscal stress (solid lines) and benign times (dashed lines). Confidence, based Ifo World Economic Survey, pertains to expectations regarding economic conditions in the next 6 months. our discussion in section 3 that we allow for a smooth transition across regimes, as we weigh observations according to the relative size of the spread during a given country-time observation. For the baseline specification, in order to account for fundamental heterogeneity across the set of advanced and emerging economies, we use the empirical CDF obtained for each country group in isolation. The second row of Figure 5 reports the results for the baseline specification, contrasting it to the results for the linear model (reproduced in the first row of Figure 5). Solid lines represent point estimates for the regime of fiscal stress, with shaded areas indicating 90 percent confidence bounds. Dashed lines represent the estimates for the other polar case, the response to austerity during benign times. Results are rather stark: the dynamic adjustment of the economy under fiscal stress resembles those obtained for the unconditional estimates very closely. Hence, fiscal stress episodes apparently dominate the overall sample. Only from a quantitative point of view, we find that the effects under fiscal stress differ from the baseline case: they are considerably magnified. The point estimate for the multiplier now reaches a value of about 0.7, while spreads rise to up to approximately 80 basis points in response to a cut of government consumption. The effects of austerity in benign times, on the other hand, differ considerably from those obtained from the unconditional estimates. We now find the output effect not significantly different from zero. Importantly, our estimates also suggests that spreads decline in response to cuts in government consumption, provided that the economy enjoys more benign times. In this case spreads come down by about 50 basis points. The adjustment of the economy to the fiscal shock thus differs considerably, depending on the two regimes. Earlier research on the consequences of fiscal consolidations has argued that its impact on “confidence” is crucial (see, for instance, the discussion in Perotti, 2013). 20 Government spending 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 Boom Recession GDP 0 1 2 3 4 5 6 −2 −1.5 −1 −0.5 0 0.5 1 Spread (basis points) 0 1 2 3 4 5 6 −150 −100 −50 0 50 100 150 200 Figure 7: Dynamic response to a government spending shock during booms and recessions. Notes: output gap used as indicator variable. Bachmann and Sims (2012) find that confidence responds strongly to fiscal shocks during periods of economic slack. To gain a better understanding of what mechanism may drive our results, we therefore estimate the dynamic response of confidence to a fiscal shock. For this purpose we rely on the Ifo World Economic Survey (WES), which surveys a number experts for all countries in our sample.22 Figure 6 displays the results. In the left panel we show results obtained for the linear model. As in Bachmann and Sims (2012) we find that confidence is fairly flat after a fiscal shock, although there is a marginally significant increase after about 5 quarters. Results do differ, however, once we condition on fiscal stress (right panel). In times of fiscal stress (solid lines) confidence is still unresponsive with respect to the spending cut. In benign times, in contrast, confidence tends to improve markedly after about a year after a spending cut. These findings are consistent with the notion that austerity is less harmful to economic activity whenever it is associated with an improvement of confidence. In our setup this coincides also with a decline in yield spreads. Times of fiscal stress are mostly likely times of low output growth, for reasons discussed above. Of course, the converse does not necessarily hold: a recession may not give rise to fiscal stress if public finances are in good shape. Still, to put our results into perspective, it is useful to assess to what extent the effects of austerity on spreads change with the state of the business cycle. For this purpose we compute, following Auerbach and Gorodnichenko (2013a), a measure of the output gap.23 We use it as an indicator variable and compute the empirical CDF as in the case of sovereign yield spreads (see Figure A.2 in the appendix). 22Respondents are asked to classify their expectations for the next six month using a grid ranging from 1 (deterioration) to 9 (improvement). 5 indicates that expectations are “satisfactory”. 23First, we compute a five-quarter moving average of the first difference of log output. The resulting series is then filtered using an Hodrick-Prescott filter with smoothing parameter λ = 10,000. 21 Government spending 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 GDP 0 1 2 3 4 5 6 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 Spread (basis points) 0 1 2 3 4 5 6 −40 −20 0 20 40 60 Government spending 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 No Stress Stress GDP 0 1 2 3 4 5 6 −2 −1.5 −1 −0.5 0 0.5 1 Spread (basis points) 0 1 2 3 4 5 6 −150 −100 −50 0 50 100 150 Figure 10: Dynamic response to a government spending shock derived from local projec- tions (two lags). Contemporaneous spread included in control vector. replace the level of government consumption with the period-t forecast error of the growth rate of government spending.25 Figure 9 displays the results, obtained for the sample for which government spending forecasts are available. In the first row we contrast results based on forecast errors (solid lines) and those obtained under our baseline approach (dashed lines). It turns out that explicitly accounting for anticipation does not alter results very much (see also Beetsma and Giuliodori, 2011; Corsetti, Meier, and Müller, 2012b; Born, Juessen, and Müller, 2013). In the second row we show that the main result of our analysis, the differential effect of spending cuts during times of fiscal stress and benign times, also obtains, once identification is based on forecast errors. Our results are based on the identification assumption that government spending is prede- termined relative to output and yield spreads. This assumption is plausible to the extent that decision lags prevent an immediate discretionary policy response to either the cycle or fiscal stress. Still, in the light of our finding that spending cuts raise spreads, one may worry that we actually pick up reverse causation: spending falls as spreads rise—despite the fact that a within-quarter adjustment of spending is unlikely due to institutional constraints. We thus consider an alternative specification, where we include the contemporaneous value 25We use growth rates rather than levels, because the base year used by the OECD changes several times during our sample period. 24 of the spread in the control vector. At the same time we impose the restriction that spreads do not contemporaneously respond to government spending shocks. Figure 10 displays the results, both for the linear model (top row) and the smooth transition model (bottom row). We find that results are qualitatively unchanged relative to those obtained for the baseline specification. In a second set of experiments we explore the robustness of our results with respect to changes in the composition of the sample. First, we consider the full sample, but, in contrast to the baseline specification, use a common empirical CDF as a weighting function. Figure A.3 in the appendix shows the results which are quite similar to those obtained for the baseline specification. Next, we exclude the Great Recession from our sample, that is, we consider only observations up to the second quarter of 2007. Figure A.5 in the appendix shows the results based on the LP approach, distinguishing unconditional estimates from those obtained once we condition on fiscal stress and the business cycle. Contrasting the results with those for the full sample, we conclude that results are not driven by the Great Recession. We also assess the robustness of our results regarding the role of fiscal stress through a number of sample splits, obtaining results for a sample which includes only euro area countries, for a sample of euro area periphery countries hit hardest by the crisis (Greece, Ireland, Italy, Portugal, Slovenia, Spain), and for a sample of the remaining euro area countries. Results, shown in Figure A.6, tend to be qualitatively similar to those obtained for the full sample—notably in terms of the differential impact of fiscal stress. The same holds for sub-samples comprising advanced and emerging economies only, see Figure A.7. As a caveat, however, we note that there are sizeable differences in some instances, reflecting perhaps also a strong decline in sample size. 5 Conclusion Does austerity restore market confidence, bringing about a reduction in sovereign yield spreads? In pursuing this question, this paper makes two distinct contributions. First, we set up a new data set which contains data on sovereign yield spreads for 26 emerging and advanced economies. We assemble quarterly observations from 1990 to 2013, not only for spreads, but also for government consumption and output. A first look at the data allows us to establish a number of basic facts. First, while there is a large variation in yield spreads, both across time and countries, yield spreads are moderate for the largest 25 part of our sample. Second, yield spreads are strongly countercyclical. The correlation of yield spreads and current output growth is negative in almost all countries of our sample. Third, there is no systematic correlation pattern emerging for yield spreads and government consumption. As a second contribution, we assess how yield spreads react to austerity measures. If we do not condition on the state of the economy, we find that a cut of government consumption raises sovereign yield spreads in the short run. At the same time output declines considerably. A cut of government consumption by one percent of GDP raises spreads by 40 to 50 basis points and reduces economic activity by about 0.6 percentage points. It turns out that these effects are driven by episodes of fiscal stress. If we condition estimates on fiscal stress, captured by high yield spreads, we find that spending cuts have an even stronger effect on spreads: they increase by about 80 to 90 basis points. Similarly, the adverse output effects are also amplified in this case. Instead, if the economy enjoys more benign times, spending cuts pay off in that they bring about a sizeable reduction of yield spreads (about 50 basis points). In this case, output is hardly affected by the cut of government consumption. Moreover, for the linear model, we also find that spreads tend to decline in the long run once economic activity has fully recovered. In sum, the data reveal a very robust pattern: yield spreads tends to move negatively with output—both, unconditionally and conditional on fiscal shocks. Hence, to the extent that austerity impacts economic activity adversely, it likely fails to bring about a reduction in yield spreads. These findings are consistent with the view that financial markets are primarily concerned with output growth. Austerity may pay off in the long-term or if it is implemented during benign times. Under adverse fiscal conditions, instead, it may be beneficial to delay austerity measures. In this case, in order to reassure markets about the sustainability of public finances, one may rather enact policies directed towards boosting economic activity. While taken at face value, our results suggests that even expansionary fiscal policies may be beneficial in this regard, we caution against such conclusions, because of the possibly adverse long-term implications. 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Romer (2010): “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks,” American Economic Review, 100, 763–801. Sims, E. (2012): “News, non-invertibility and structural VARs,” Advances in Econometrics, 28, 81–135. A Appendix 31 −3 −2 −1 0 1 2 3 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Full Sample Advanced Emerging Figure A.2: Smoothed output gap: empirical distribution function (CDF). Notes: hor- izontal axis measures the smoothed output gap, computed as the z-scored deviation of the 5 quarter moving average of the output growth rate from its HP-filtered trend (λ = 10,000). Vertical axis measures fraction of observa- tions for which spread exceeds value on the horizontal axis. Solid line displays CDF for full sample (total number of observations: 1497), dashed-dotted line: advanced economies only (783), dashed line: emerging economies only (714). Government spending 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 No Stress Stress GDP 0 1 2 3 4 5 6 −2.5 −2 −1.5 −1 −0.5 0 0.5 1 Spread (basis points) 0 1 2 3 4 5 6 −300 −200 −100 0 100 200 300 Figure A.3: Dynamic response to a government spending shock: common CDF for both country groups. 34 2000 2005 2010 0.2 0.4 0.6 0.8 Belgium 199219941996199820002002 0.2 0.4 0.6 0.8 Denmark 1995200020052010 0.2 0.4 0.6 0.8 Finland 2000 2005 2010 0.2 0.4 0.6 0.8 France 2005 2010 0.2 0.4 0.6 0.8 Greece 2000 2005 2010 0.2 0.4 0.6 0.8 Hungary 2000 2005 2010 0.2 0.4 0.6 0.8 Ireland 1995200020052010 0.2 0.4 0.6 0.8 Italy 2000 2005 2010 0.2 0.4 0.6 0.8 Netherlands 2000 2005 2010 0.5 1 Poland 2000 2005 2010 0.5 1 Portugal 2008 2010 2012 0.2 0.4 0.6 0.8 Slovenia 2000 2005 2010 0.2 0.4 0.6 0.8 Spain 1995 2000 2005 0.2 0.4 0.6 0.8 Sweden 1995 2000 2005 0.2 0.4 0.6 0.8 United Kingdom 2000 2010 0.5 1 Argentina 2000 2005 2010 0.2 0.4 0.6 0.8 Chile 20020042006200820102012 0.2 0.4 0.6 0.8 Colombia 20020042006200820102012 0.2 0.4 0.6 0.8 Ecuador 20042006200820102012 0.2 0.4 0.6 0.8 El Salvador 20020042006200820102012 0.2 0.4 0.6 0.8 Malaysia 2000 2005 2010 0.2 0.4 0.6 0.8 Peru 2000 2010 0.2 0.4 0.6 0.8 South Africa 2000 2005 0.2 0.4 0.6 0.8 Thailand 2000 2005 2010 0.2 0.4 0.6 0.8 Turkey 20020042006200820102012 0.5 1 Uruguay Fiscal Stress Fiscal Stress Group−Specific Recession Figure A.4: Empirical CDF values for spreads and smoothed output gaps. 35 Government spending 0 1 2 3 4 5 6 −1.4 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 GDP 0 1 2 3 4 5 6 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 Spread (basis points) 0 1 2 3 4 5 6 −100 −50 0 50 100 (a) Unconditional Government spending 0 1 2 3 4 5 6 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 No Stress Stress GDP 0 1 2 3 4 5 6 −2 −1.5 −1 −0.5 0 0.5 1 Spread (basis points) 0 1 2 3 4 5 6 −200 −100 0 100 200 300 (b) Fiscal stress vs. tranquil times Government spending 0 1 2 3 4 5 6 −1.2 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 Boom Recession GDP 0 1 2 3 4 5 6 −3 −2 −1 0 1 2 Spread (basis points) 0 1 2 3 4 5 6 −200 −100 0 100 200 300 (c) Recession vs. boom Figure A.5: Dynamic response to a government spending shock derived from local projec- tions (two lags) when excluding the Great Recession. All standard errors are clustered robust 90% standard errors. 36