metadata: authors: 0: author: Nie,Ou
count: World
docna: 0: docna: Expansionary Fiscal Austerity: New
            International Evidence
docty: Policy Research Working Paper
subtopic: Public Sector Economics,Macro-Fiscal Policy,Economic Adjustment and Lending,Public Finance Decentralization and
            Poverty Reduction,Taxation & Subsidies,Economic Conditions and Volatility
teratopic: Macroeconomics and Economic Growth,Public Sector Development
abstracts: cdata!: The expansionary fiscal contraction
            (EFC) hypothesis states that fiscal austerity can increase
            output or consumption when a country is under heavy debt
            burdens because it sends positive signal about the
            country's solvency situation and long-term economic
            wellbeing. Empirical tests of this hypothesis have suffered
            from identification concerns due to data sources and
            empirical methodology. Using a sample of OECD countries
            between 1978 and 2014, this paper combines new IMF narrative
            data and the proxy structural Vector Auto-regression (SVAR)
            method to examine whether fiscal austerities can be
            expansionary when debt levels are high. Fiscal austerities
            are measured as 1) narrative fiscal shocks and 2) structural
            shocks from a proxy SVAR. Additionally, this paper uses a
            model-based approach to determine the cutoff debt level
            beyond which EFC is expected to be observed. This paper
            finds empirical evidence in support of the EFC hypothesis
            for OECD countries: results for output are driven by changes
            in tax rates and are robust to how one defines a high-debt
            regime and how one measures austerity.
repnme: repnme: Expansionary Fiscal Austerity: New
            International Evidence
display_title: Expansionary Fiscal Austerity: New
            International Evidence
listing_relative_url: /research/2020/07/32287322/expansionary-fiscal-austerity-new-international-evidence
topicv3: Macroeconomic Vulnerability and Debt
            m1326057 1104,Fiscal Consolidation m1326651 1116,Fiscal Policy m1327144 445,Taxation m1327615 1121,Tax Simplification m1504614 2527
publishtoextweb_dt: 2020-07-30T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Public Sector Development
origu: EFI-FCI-Financial Stab.&Integr. (EFNFS)
author_keys: 0: author_key: 1818961
model_extractions: Policy Research Working Paper 9344 Expansionary Fiscal Austerity New International Evidence _Owen Nie_ Finance, Competitiveness and Innovation Global Practice July 2020
Policy Research Working Paper 9344

# **Abstract**

The expansionary fiscal contraction (EFC) hypothesis states that fiscal austerity can increase output or consumption when a country is under heavy debt burdens because it sends positive signal about the country’s solvency situation and long-term economic wellbeing. Empirical tests of this hypothesis have suffered from identification concerns due to data sources and empirical methodology. Using a sample of OECD countries between 1978 and 2014, this paper combines new IMF narrative data and the proxy structural Vector Auto-regression (SVAR) method to examine whether fiscal austerities can be expansionary when debt levels are high. Fiscal austerities are measured as 1) narrative fiscal shocks and 2) structural shocks from a proxy SVAR. Additionally, this paper uses a model-based approach to determine the cutoff debt level beyond which EFC is expected to be observed. This paper finds empirical evidence in support of the EFC hypothesis for OECD countries: results for output are driven by changes in tax rates and are robust to how one defines a high-debt regime and how one measures austerity.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at onie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Expansionary Fiscal Austerity: New International Evidence<sup>_∗_</sup> _Keywords_ : Fiscal Policy, Fiscal Consolidation, Structural Vector Autoregression _JEL Classification Numbers_ : C5,E61,H63.

> _∗_ I am grateful to my doctoral advisors Kathryn Dominguez, Linda Tesar and Chris House from the University of Michigan for their guidance. I thank Shu Yu(EPGDR) and Vasileios Tsiropoulos(EMFMD) at the World Bank for providing internal peer review and Carlos Pinerua(EFNFT) for valuable comments. All errors are my own. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.

> _†_ Email correspondence: `onie@worldbank.org`
# **1 Introduction**

A country engages in fiscal consolidation programs, increasing taxes or cut government expenditures, when the national debt level is deemed high enough to influence the ability of the country to borrow in the future or to prevent the government of the country to undertake necessary public spending programs. If the need for fiscal consolidation arises when the economy is at relative stagnation or decline, policymakers usually worry that fiscal consolidation efforts present an unnecessary drag on aggregate demand. It is therefore important to understand the output or consumption costs of fiscal austerity to weigh it against the benefit of reducing the national debt stock. The expansionary fiscal contraction (EFC) hypothesis states that fiscal austerity can increase, or at least not decrease output or consumption when a country is under heavy debt burdens because it sends positive signal about the country’s solvency situation and long-term economic wellbeing. If this hypothesis is true, policy implications for fiscal austerity programs are clear: the cost of austerity relative to its benefit will be low if debt burden is high enough, and more austerity programs should be undertaken in this case. The question remains whether there is sufficient support for the EFC hypothesis in the data.

This paper seek to bring new empirical evidence to bear on the subject of expansionary austerity by producing a new model-based definition of a high-debt regime, leveraging a newly constructed cross-country narrative dataset and using a new econometric methodology. An empirical answer on this subject will depend crucially on how one defines when the debt level is high enough for austerities to have anti-Keynesian effects and on how one defines austerities themselves. In this paper, I introduce a new definition of a high-debt regime by calibrating a model of expansionary austerity for each country in my sample, and I use this definition along with the conventional cutoff debt levels. I consider two alternative definitions of austerity: fiscal consolidations constructed by the reading of narratives, a direct measure, and structural shocks from identified VARs, an indirect measure. For the former, I use a new source of cross-country narrative dataset constructed by the Fiscal Affairs department of the International Monetary Fund. For the later, I employ a relatively new econometric approach, the proxy structural VAR approach, which combines the attributes two leading empirical methodology in this literature.

1
**Related Literature** A large literature focuses attention on the macroeconomic effect of fiscal measures. One branch of this literature identifies the motivation of fiscal reforms from careful examination of narrative records, such as legislative documents, news articles and presidential speeches, and using exogenous fiscal changes thus obtained for the estimation of fiscal multipliers of output and consumption. Ramey and Shapiro (1998), Romer and Romer (2010) are leading examples studying fiscal changes in the United States. Researchers have also applied the narrative approach to the study of other countries: Devries et al. (2010) for fiscal consolidations in a sample of OECD countries, Cloyne (2013) for the UK and Hayo and Uhl (2014) for Germany. Most recently, Alesina et al. (2016) extends the Devries studies by focusing on a detailed composition of a multi-year fiscal plan. Alesina et al. (2017) extended the Devries sample to 2009-2013, a period where plenty of consolidation was taking place. With the increasing availability of micro-level data, cross-sectional studies of fiscal policy, such as Nakamura and Steinsson (2014), provides evidence on the size of the cross-sectional fiscal multiplier.

Another branch of the empirical study of fiscal policy, most notably Blanchard and Perotti (2002), focuses on the identification of structural shocks in Vector Autoregression(VAR). The estimates of the size of the fiscal multiplier differ substantially from narrative estimates. A number of works attempt to reconcile the difference from various perspectives: Ramey (2010) pointed out that VAR shocks are missing the timing of the news announcements; Mertens and Ravn (2013, 2014) developed the proxy structural VAR(PVAR) approach: a structural VAR with narrative shocks as instruments in The subfield of the literature on deficit-driven fiscal consolidations is rich with policy implications. Frequently-asked questions include: What is the output effects of fiscal consolidation? When is fiscal austerity programs more likely to succeed? Is there such a thing as “expansionary austerity”? Theoretically, Perotti (1999) and Sutherland (1997) provided theoretical channels through which fiscal austerity programs could be associated with increased consumption when debt burden is high. Alesina and Perotti (1996) provides early empirical evidence in this direction, and 2
recent advances include Blanchard and Leigh (2013), Guajardo, Leigh and Pescatori (2014) and House, Proebsting and Tesar (2015). In general, empirical studies using a sample prior to 2009 show mixed or supportive evidence of “expansionary austerity”, while data since 2009 points to the contrary.

This paper is organized as follows. Section 2 reviews a theoretical model of expansionary austerity and calibrate the model to deliver a definition of high-debt regime for each country in my sample. Section 3 introduces a new source of narrative data, checks the quality of the data in a VAR exercise and presents empirical results using the narrative series as a direct measure of austerity. Section 4 uses structural shocks recovered from a proxy structural VAR as an indirect measure of austerity and presents another set of empirical results under this alternative definition. Section 5 concludes.

# **2 Model-based definition of high-debt regime**

An empirical study of expansionary austerity has to start by defining when debts are ”too high”. The literature has so far used empirical debt thresholds advocated by policy institutions. In this section, I provide a way of defining high-debt regimes based on Sutherland(1997)’s theoretical model of expansionary austerity. I calibrate the model to provide a cutoff debt-to-GDP value for each country. The model is presented below and a cutoff value for each country derived.

The model is in continuous time and the environment features a finitely-lived representative consumer and a government. Let _Bt_ be the stock of per capita public debt, F deficit spending and r the interest rate paid on public debt. The evolution of government debt is governed by a Brownian motion, where _Wt_ is a standard Wiener process and _σ_ scales up the variance: 3
This process means the government routinely overspends, and hence it institutes periodic fiscal austerity program to satisfy the intertemporal budget constraint. Stabilization programs take the following form: when the stock of debt, _Bt_ is larger than some crisis level, U, the government impose a one time lump-sum tax of the size T to bring down the debt level to U-T. Symmetrically, when _Bt < L_ , a one time lump-sum transfer of the size T bring up the debt level to L+T.

Consumers are finitely lived and discount future utility with a Poisson death rate, _θ_ , in addition to the interest rate. Hence, they maximize subject to the following flow budget constraint, where government deficit spending is assumed to be a transfer to the household which increases its stock of wealth: Here, y is fixed per-period income and _At_ household wealth in the form of bond holdings. _δ_ ( _t_ ) is an indicator that takes on the value of 1 when there is a stabilization tax, 0 during “normal times” and -1 when there is a stabilization transfer. Like usual deficit spending, austerity programs change the wealth of households. Define S, the expected discounted future tax liabilities from possible austerity programs, as follows: Clearly, _St_ is a function of current stock of debt, _Bt_ , since the possibility of future stabilization depends on how close _Bt_ is to the critical values. Rewrite S as the sum of expected future tax liabilities from t to ∆ _t_ and from ∆ _t_ to _∞_ , and let ∆ _t_ approaches _dt_ , assuming stabilization do not take place during this small time period, and differentiate S, I obtain: 4
Applying Ito’s lemma to the function _S_ ( _Bt_ ), another equation is obtained: Combining equation 6 and 7, we have a second order differential equation in _S_ ( _Bt_ ). Solution is obtained with two initial conditions given by government behaviors at the two critical values, U and L: Having characterized the solution for S, I return to the consumer’s problem. With quadratic utility, intertemporal consumption smoothing implies a consumption rule of the following: Changes in consumption depends on changes in asset position and expected future tax liability from possible stabilization programs. An increase in deficit spending, whether a spending hike or a tax break, increases household asset one for one as it is assumed to be a direct transfer. However, deficit spending increases total stock of debt and hence S, the expected future taxes from austerity. In other words, the conventional Keynesian effect of fiscal stimulus increases consumption through changing consumer’s permanent income, but the worry of impending stabilization taxes works to the opposite direction. The precise magnitude of the unconventional effect depends on how close the current debt level is to the critical levels and on the first derivative of the function S.

It should be clear that this model, with proper calibration, is capable of delivering a cutoff level of national debt level beyond which one may observe expansionary austerity. This cutoff level, _B_<sup>_∗_</sup> , is defined as: At _B_<sup>_∗_</sup> , the conventional and unconventional effect would exactly cancel out. When _B_<sup>_∗_</sup> _< B_ , the conventional effect dominates and hence it is impossible to identify the 5
<!-- Start of picture text --> Expansionary Austerity: an Illustration<br>4<br>0.8<br>x<br>oO<br>F 06<br>r=<br>‘<br>2 04<br>n<br>=<br><oO2 0.2<br>|<br>So<br>LL<br>no}<br>@ 02<br>[o}<br>oO<br>a<br>< 04<br>uw<br>m «6<br>n<br>0.8<br>“a 0.8 0.6 04 0.2 o 02 04 0.6 08 1<br>B: Public Debt<br><!-- End of picture text -->
the six parameters at annual frequency using data between 1981 - 2009. I set every parameter as a percentage of GDP, whenever applicable. The OECD advocates a prudential debt target of 70-90% for advanced economies, but only 50-70% for Eurozone countries since they do not control monetary policy. Hence, I set U = 0.6 for Eurozone countries and U = 0.8 otherwise. r is the sample average of annualized return on 10-year government bond. _θ_ is the inverse of the sample average of life expectancy at birth. _σ_ is the variance of government primary balance from IMF’s Historical Public Finance dataset.

T is calibrated with the narrative fiscal series in Devries et al. (2010). The dataset provided sizes of both tax and spending based austerity programs relative to GDP on an annual basis. I calculate T as the average of the size of austerity programs in the sample, not the average of annual size, since most such programs span through multiple years. In the model, T is the size of a one-time tax that constitutes the entire stabilization program, so its empirical counterpart should be an entire program. For instance, if a country reduced its budget deficit by 3% of GDP in each year between 1983 and 1986 as prescribed in a legislation passed in 1982 and by 4% of GDP in 1996 as prescribed in a legislation passed in 1995, then there are two austerity programs in the sample with respective sizes of 9% and 4%. Accordingly, T is set to 0.065, the average size of these programs. All parameter values used for each country are shown in Table 7 Appendix III.

Table 1 below displays the value of _B_<sup>_∗_</sup> for each country in the sample. A definition of a high-debt regime for country i at time t, _Dit_ , is available: _Dit_ = 1 if _Bi,t > B_<sup>_∗_</sup> , _Dit_ = 0 otherwise.

# **3 Testing the EFC hypothesis with new narrative data**

In this section, I test the EFC hypothesis empirically by using a new source of crosscountry quarterly narrative data of tax-based consolidations as a direct measure of fiscal austerity. The narrative data is briefly introduced and a quality check of the 7
Table 1: Critical debt level _B_<sup>_∗_</sup> , % of GDP

|Country|AUS|AUT|BEL|CAN|DNK|FIN|FRA|DEU||
|---|---|---|---|---|---|---|---|---|---|
|_B_<sup>_∗_</sup>|78.9|58.9|56.1|75.9|76.7|54.1|59.5|58.1||
|Country|IRE|ITA|JPN|NLD|PRT|ESP|SWE|GBR|USA|
|_B_<sup>_∗_</sup>|56.4|54|79.1|57.1|58.3|57.4|77.2|78.8|79.2|

The calculation of these thresholds are based on prudential debt targets advocated by the OECD and meant as part of a simulation exercise. The numbers should not be interpreted as policy suggestions.

data with panel PVAR is performed before the main empirical analysis. Main empirical results are presented with two alternative definitions of high-debt regime, a model-based from the last section and a conventional one.

# **3.1 Overview of the narrative data**

Cross-country empirical studies of the effects of fiscal policy have suffered from a lack of data: quarterly narrative series exist for a number of advanced economies, such as Romer and Romer(2010) for the United States and Cloyne (2013) for the United Kingdom. Devries et al. (2010) constructed cross-country narrative series at annual frequency, but such data were not available at quarterly frequency until recently. In this section, I use a newly-constructed IMF quarterly panel dataset of narrative tax series to study expansionary austerity.

The narrative dataset is compiled by the fiscal affairs department at the IMF and it is described in Dabla-Norris and Lima(2018). The dataset contains detailed information on the expected revenue impact, motivation, announcement and implementation dates of nearly 2,500 tax measures of fiscal consolidation across 10 OECD countries between 1978 and 2014. The authors rely on contemporaneous primary sources including budget documents, reports from the Ministry of Finance and tax authorities, technical reports and notes produced during the legislative procedure, and discussions on tax reforms during parliamentary debates. When necessary, these are complemented by information from secondary sources such as IMF staff reports, OECD Economic Surveys, Stability and Growth Pact documents, and news articles 8
on tax reform from national newspapers or from the International Bureau of Fiscal Documentation (IBFD).

# **3.2 Quality check of the narrative data**

Before proceeding to the main empirical results, a quality check of the narrative data is conducted by extending results in Mertens and Ravn(2013) to a cross-country setting. Critics of the narrative approach in the literature are often skeptical of the quality of these narrative data because the construction of such data relies on the reading of a large amount of narrative sources by a few authors to determine motivations and intentions behind a large number of policy changes. Therefore, the produced dataset are subject to errors and depend heavily upon subjective judgment calls made by the authors. Hence, a quality check is necessary before using the series to study the main research question of this paper.

The quality check takes the form of examining the responses of macroeconomic and fiscal variables to change in personal income tax(PIT) rate and corporate income tax(CIT) rate for a panel of OECD countries. Mertens and Ravn(2013) documents such responses for the United States in a PVAR setting after constructing narrative series for PIT and CIT changes, respectively. The narrative dataset in Dabla-Norris and Lima(2018) provides granular information on tax-based fiscal consolidations, most importantly on tax rate or tax base changes for a panel of OECD countries. It can be seen as an extension of Mertens and Ravn(2013)’s work on narrative data to more countries. Hence, a natural quality check is to extend the results for the responses to PIT and CIT shocks to these OECD countries.

Since we do not have a long enough time-series to carry out PVAR for each country, we aim to extend the PVAR methodology to a panel setting, pooling our estimates of structural parameters(elasticities) while estimating reduced-form VARs for each country. The procedure is divided into four steps and described below: The first step estimates country-specific reduced-form VARs: Consider the following 9
model for _Xi,t_ for each country i, Equation 12 is estimated for each country so as to obtain country-specific reduced form VAR coefficients _A_ 0 _,i_ and _Ap,i_ . Lag orders p is determined by standard lag-order selection criterion. We obtain country-specific reduced-form shocks, _ui,t_ , in this step.

The second step pools estimates of structural parameters of the system: let _ut_ = [ _u_ 1 _,t, u_ 2 _,t, ..._ ]<sup>_′_</sup> be a vector of stacked country-specific reduced-form shocks and _mt_ = [ _m_ 1 _,t, m_ 2 _,t, ..._ ]<sup>_′_</sup> be a vector of stacked narrative shocks. _εi,t_ are structural shocks. The following proxy structural VAR model is then estimated: Here, the structural errors satisfies _E_ [ _εt_ ] = 0 and _E_ [ _εtε_<sup>_′_</sup> _t_<sup>]=</sup><sup>_I_.</sup> Since _E_ [ _utu_<sup>_′_</sup> _t_<sup>]=</sup> _BE_ [ _εtε_<sup>_′_</sup> _t_<sup>]</sup><sup>_B′_=</sup><sup>_BB′_,and the sample analogue of</sup><sup>_E_[</sup><sup>_utu′_</sup> _t_<sup>] is a n-by-n covariance matrix,</sup> it provides<sup>_n_</sup><sup><u>(</u></sup><sup>_n_</sup> 2<sup><u>+1)</u></sup> independent identification restrictions. There are n parameters in the original system, so the system is under-identified.

Mertens and Ravn’s PVAR identification approach avoids directly imposing values of certain structural parameters. Instead, the PVAR approach uses narrative shocks to obtain additional covariance restrictions. Consider the following partitioning of _B_ and _εt_ : _B_ = [ _β_ 1 _β_ 2] _, β_ 1 = [ _β_ 11<sup>_′β_</sup> 21<sup>_′_]</sup><sup>_′, β_2=[</sup><sup>_β_</sup> 12<sup>_′β_</sup> 22<sup>_′_]</sup><sup>_′_,</sup><sup>_εt_=[</sup><sup>_ε_1</sup><sup>_tε_2</sup><sup>_t_]andΣ</sup><sup>_mu′_=[Σ</sup><sup>_mu′_1Σ</sup><sup>_mu′_2].</sup> Let _mt_ be an k-dimensional vector of narrative tax shocks, we have the following assumptions: Namely, the narrative tax shocks are positively correlated with the structural tax disturbances they seek to capture, but are uncorrelated with other structural disturbances. Combining 3 and 4 with 2, the identification assumptions implies the following restrictions on elements of _B_ : 10
These additional restrictions allows the structural VAR to be just-identified. I obtain common structural parameters(elasticities) _Bi_ in this step.

The third step obtains country-specific impulse response functions using the vector of country-specific reduced form parameters Ω<sup>_d_</sup> _i_<sup>= [</sup><sup>_A_0</sup><sup>_,i, A_1</sup><sup>_,i, ..., Ap,i_] and common struc-</sup> tural parameters in the matrix _B_ .

The last step obtains averaging IRFs and their bootstrap confidence intervals: the average impulse response functions is calculated as a simple arithmetic average of the country-specific impulse response functions. The confidence intervals of the average IRFs is computed by an amended wild bootstrap procedure designed specifically for our setting. The algorithm used is detailed in the computational appendix.

Figure 2 shows responses of selected variables to a 1 percentage point cut in average personal income tax rate(APITR). 95% bootstrap confidence intervals are shown along with the point estimates of the impulse responses. I find that APITR remains below the expected level prior to the shock during the first year. Afterwards, the APITR converges to its pre-shock level in the long-run. The tax-rate cut sets off an increase in personal income tax base in the short-run, but the increase moderates in the longer run. The tax-rate cut provides a short-run stimulus to output: a 1 percentage point decrease in APITR leads to a peak increase of output of around 1 percent which occurs three quarters after the cut. The confidence intervals indicate a significant increase in economic activity within a 6-quarter window after the tax cut. Government spending also increases following the cut in APITR. These results are broadly in line with results in Mertens and Ravn(2013), but differ slightly in magnitude and timing of the responses, since their study focuses on the United States and this paper extends the analysis to a panel of OECD countries.

11
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Table 2 presents results for equation 18 using model-based definition of high-debt regime. As expected, fiscal consolidation programs based on tax increases slow down output growth. However, when the economy is in high-debt regime, an additional expansionary effect of tax consolidations is present. This additional effect is driven entirely by changes in tax rate: the second column shows that a 1 percentage point increase in taxation by raising tax rates is associated with a 54 basis point decrease in output at all times, and a 75 basis point increase in output during “bad times”. Both of these effects are statistically significant. Importantly, the net effect of a tax-hike by raising tax rate during bad times is expansionary.

Table 2: Expansionary Austerity: Narrative shocks

||GDP Growth<br>Coef./SE|GDP Growth<br>Coef./SE|GDP Growth<br>Coef./SE|
|---|---|---|---|
|Tax Shock|-0.6023***<br>(0.1685)|||
|Tax Shock*Dummy|0.2586<br>(0.2892)|||
|Tax Rate Shock||-0.5403***<br>(0.1871)||
|Tax Rate Shock*Dummy||0.7564**<br>(0.3805)||
|Tax Base Shock|||-0.2630<br>(0.2174)|
|Tax Base Shock*Dummy|||-0.3782<br>(0.3592)|
|Sample Size|1215|1215|1215|

- ** ***

- _p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Standard errors in parentheses.

Note: All regressions include a constant and a full set of country and year dummies. Standard errors are cluster-robust.

Table 3 presents the same set of empirical results using an alternative definition of high-debt regime: _Dit_ switches to 1 if the country’s debt to GDP ratio is above the 80 14
percentile of its historical empirical distribution. This is a definition commonly used in the literature, such as Perotti(1999), and it can be thought of as a Reinhart and Rogoff(2008) type debt trigger. Again, when the economy is in high-debt regime, an additional expansionary effect is present. The additional effect of tax consolidations is driven entirely by changes in tax rate: the second column shows that a 1 percentage point increase in taxation by raising tax rates is associated with a 63 basis point decrease in output at all times, and a 57 basis point increase in output during “bad times”. Both of these effects are statistically significant. Although the net effect of a tax-hike by raising tax rate during bad times is not expansionary under this alternative definition of high-debt regime, it is hardly contractionary in terms of output losses.

Table 3: Expansionary Austerity: Narrative shocks, alternative regime

||GDP Growth<br>Coef./SE|GDP Growth<br>Coef./SE|GDP Growth<br>Coef./SE|
|---|---|---|---|
|Tax Shock|-0.7321***<br>(0.1972)|||
|Tax Shock*Dummy|0.4207*<br>(0.2740)|||
|Tax Rate Shock||-0.6310***<br>(0.2240)||
|Tax Rate Shock*Dummy||0.5742**<br>(0.3230)||
|Tax Base Shock|||-0.6051**<br>(0.3176)|
|Tax Base Shock*Dummy|||0.2930<br>(0.3806)|
|Sample Size|1215|1215|1215|

* ** *** _p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Standard errors in parentheses.

Note: All regressions include a constant and a full set of country and year dummies. Standard errors are cluster-robust.

In sum, using quarterly narrative data on fiscal consolidations, I find that austerity programs which increase tax rates during bad times do not necessarily cause output losses, and can be potentially expansionary. This finding is robust to how one defines the critical debt level beyond which an anti-Keynesian effect is expected to kick in.

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# **4 Testing the EFC hypothesis with VAR structural shocks**

In this section, I test the EFC hypothesis empirically by using structural shocks estimated from an identified VAR as an indirect measure of fiscal austerity. Proxy structural VAR, a relatively new empirical methodology in this literature, is employed to estimate the structural shocks. I discuss data and the PVAR identifiation scheme before presenting main empirical results with two alternative definitions of high-debt regime.

## **4.1 data**

My sample consists of annual data for 17 OECD countries between 1978 and 2009<sup>1</sup> . These countries are: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, Portugal, Spain, Sweden, United Kingdom and United States. Quarterly data would have increased the precision of my estimates and allowed for the estimation of a separate structural VAR for each country, but spending and tax revenue data are not available at quarterly frequency for a large fraction of the country-years I considered.

_Yt_ and _Tt_ are _Gross Domestic Product_ and _Tax Revenue_ from OECD statistics. _Gt_ is _Government Expenditure_ from the recently constructed IMF Historical Public Finance Dataset described in Mauro et al.(2015), the most comprehensive source of fiscal flows and stocks to date, where the debt-to-GDP ratios are also taken from. All three variables in the SVAR are in logs, per capita, real and in dollars. The narrative fiscal series are from Devries et al(2014), where the authors document the exact sources, methodology and construction of these discretionary changes in taxes and spending, drawing extensively on policy documents and reports to identify the motivation of such changes. Guajardo, Leigh and Pescatori(2014) use this dataset to study expansionary fiscal contractions.

> 1Final regressions are for the years 1981-2009, since I estimated the VARs with two lags and variables are

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# **4.2 Structural VAR specification**

Since my time series is short relative to the number of parameters, I consider the following panel vector auto-regression(PVAR) model for _Xi,t_ = [ _Ti,t, Gi,t, Yi,t_ ]<sup>_′_</sup> , after taking out country specific deterministic time trend and demeaning _Xi,t_ : This specification allows me to pool estimates of the lag coefficient matrices in the VAR and hence to reduce the total number of parameters estimated, while still having country-specific reduced form shocks.<sup>2</sup> Let _ui,t_ = [ _u_<sup>_T_</sup> _i,t_<sup>_, uG_</sup> _i,t_<sup>_, uY_</sup> _i,t_<sup>] be reduced-form shocks</sup> and _εi,t_ = [ _ε_<sup>_T_</sup> _i,t_<sup>_, εG_</sup> _i,t_<sup>_, εY_</sup> _i,t_<sup>] be structural shocks, the following system is estimated for each</sup> country i: Here the structural errors satisfies _E_ [ _εi,t_ ] = 0 and _E_ [ _εi,tε_<sup>_′_</sup> _i,t_<sup>] =</sup><sup>_I_.</sup> I discuss the PVAR identification strategy proposed by Mertens and Ravn(2014) for this system, which is based in part on assumptions from Blanchard and Perotti(2002). In the later, identification involves imposing certain values on some parameters in the system above. First and foremost, because of decision and recognition lags, it is presumed that discretionary responses of spending to cyclical movements of output is unlikely, at least at quarterly frequency. Hence, _γY_ = 0. Second, one remains agnostic about whether spending or tax decisions come first. This means one of _θG_ and _γT_ is set to zero and the other can be estimated. It turns out that the answer doesn’t depend much on which one is set to zero and I have _γT_ = 0. Finally, _θY_ , the elasticity of tax revenue with respect to output, is calibrated to OECD estimates(See Giorno et al.(1995) for details of the calibration procedure). For instance, _θY_ is set to 2.08 for the case of the United States. The following decomposition is used in the calibration of _θY_ :

> 2Panel VAR estimation is carried out with Abrigo and Love(2015)’s package in STATA.

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Let _B_ be the tax base and all variables are in log levels. _θY_ consists of a sum, across all tax types, of the elasticity of each tax with respect to its tax base times the elasticity of its tax base with respect to output, weighted by the share of the tax in total tax revenue: The response of tax to output in this setting encompasses stabilization policy, so long as the policy is pre-ordained in the tax code: for instance, an extension of the duration of unemployment insurance when the economy is in a downturn.

The system in 20 - 22 can be written in matrix form as: or more compactly, _Aut_ = _Dεt_ . Therefore: Since the sample analogue of _E_ [ _utu_<sup>_′_</sup> _t_<sup>] is a three-by-three covariance matrix, it provides</sup> six independent identification restrictions. There are nine parameters in the original system, so the structural VAR will be just-identified if one imposes these three additional restrictions above.

Mertens and Ravn(2014) provides identification restrictions by utilizing information from available narrative series of exogenous fiscal shocks. The original purpose of this approach was to reconcile the marked differences between estimates of the fiscal multiplier from the SVAR approach and the narrative approach. Let _mt_ be the 18
narrative tax shocks, and the assumptions are as follows: In words, the narrative tax shocks are positively correlated with the structural tax shocks, but uncorrelated with structural spending and output disturbances.

Hence, Thus, two independent identification restrictions are obtained: This means two out of the three parameter restrictions in the Blanchard and Perotti(2002) system can be replaced. In particular, the calibrations of _θY_ is considered problematic and is no longer imposed. _γY_ = 0 is still imposed. The PVAR system is just-identified with eight parameters and eight independent identification restrictions. The structural shocks, _εi,t_ = [ _ε_<sup>_T_</sup> _i,t_<sup>_, εG_</sup> _i,t_<sup>_, εY_</sup> _i,t_<sup>],isestimatedforeverycountryiand</sup> retrieved for use as an indirect measure of austerity.

As an illustration, I plot the structural fiscal shocks for Denmark in figure 4 in the appendix. Between 1983 and 1986, the country implemented a large-scale austerity program involving actions on both the expenditure side, such as limits on public sector wages and social payments, and the revenue side, such as hikes in social security contributions, taxation of higher-yielding pensions and an increase in the maximum tax rates. The program was announced in 1982 with the formation of a new coalition government. As can be seen, the estimated structural tax and spending shocks speak to what happened during the period relatively well.

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# **4.3 Empirical results**

This section presents a second set of main empirical results. As the structural shocks from the PVAR in the last section is uncorrelated with output, they are used as a measure of austerity. In section 3, the Dabla-Norris and Lima(2018) narrative dataset only covers changes in taxation. This measure of austerity covers both changes to taxation and government spending. The following set of panel regression is estimated: where ∆ _Yit_ is output or consumption growth. The first regression looks at the general relationship between output/consumption and the structural fiscal shocks, and the second regression interacts the shocks with regime dummies, _Dit_ , to explore the presence of anti-Keynesian effects of austerity programs. _Dit_ is a debt regime dummy that equals 1 in a state of high-debt and 0 otherwise.

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Table 4: Expansionary Austerity: Mertens-Ravn shocks

||Consumption Growth|Consumption Growth|
|---|---|---|
||Coef./SE|Coef./SE|
|Tax Shock|-0.6821***|-0.8019**|
||(0.1450)|(0.3997)|
|Spending Shock|0.2552***|0.3700**|
||(0.0796)|(0.1598)|
|Tax Shock*Dummy||0.7720<br>(0.9220)|
|Spending Shock*Dummy||-0.6880***<br>(0.2174)|
|Constant|1.0190|0.8143|
||(0.7701)|(0.9210)|
|Sample Size|116|116|
|_R_<sup>2</sup>|0.5224|0.5363|

- ** ***

- _p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Standard errors in parentheses.

Note: All regressions include a full set of country and year dummies. Standard errors are cluster-robust.

Table 4 and table 5 present results for equations 32 and 33 using the model-based definition of high-debt regime. In general, increasing taxes or decreasing government spending reduces output and consumption, as expected. When the economy is in the high-debt regime, an additional expansionary channel of fiscal austerity is present: increasing taxes or decreasing government spending actually boosts output/consumption via this channel. This additional anti-Kenyesian channel is statistically significant for the effect of spending on consumption and for the effect of taxation on output. In table 4, cutting government spending by 1 percentage point decreases consumption growth by 37 basis point on average, but boosts it by 69 basis point in “bad times”. Fiscal consolidation programs based on government spending cuts actually increases consumption when the economy is in the high-debt regime. In table 5, increasing taxes by 1 percentage point lowers output growth by 75 basis point, but boosts it by 72 basis point in “bad times”. Fiscal consolidation programs based on tax hikes actually do not entail output losses when the economy is in the 21
high-debt regime.

Table 5: Expansionary Austerity: Mertens-Ravn shocks

||Real GDP Growth|Real GDP Growth|
|---|---|---|
||Coef./SE|Coef./SE|
|Tax Shock|-0.5839***|-0.7488***|
||(0.1932)|(0.2473)|
|Spending Shock|0.1460|0.1595|
||(0.2512)|(0.1598)|
|Tax Shock*Dummy||0.7209***<br>(0.2984)|
|Spending Shock*Dummy||-0.2064<br>(0.4394)|
|Constant|0.8717***|0.8143|
||(0.2252)|(0.9210)|
|Sample Size|105|105|
|_R_<sup>2</sup>|0.8094|0.8149|

* _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01. Standard errors in parentheses. Note: All regressions include a full set of country and year dummies. Standard errors are cluster-robust.

Table 6 presents the same set of empirical results for consumption, using the alternative definition of high-debt regime. Under this definition, the additional antiKenyesian channel is statistically significant for the effect of taxation on consumption. Increasing taxes by 1 percent lowers consumption growth by 1 percent, but boosts it by 1 percent in “bad times”. Fiscal consolidation programs based on tax hikes actually do not entail consumption losses when the economy is in the high-debt regime.

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Table 6: Expansionary Austerity: Mertens-Ravn shocks, alternative regime

||Consumption Growth|Consumption Growth|
|---|---|---|
||Coef./SE|Coef./SE|
|Tax Shock|-0.6821***|-1.0382***|
||(0.1450)|(0.3084)|
|Spending Shock|0.2552|0.2167***|
||(0.0796)|(0.0558)|
|Tax Shock*Dummy||0.9925***<br>(0.3606)|
|Spending Shock*Dummy||0.2603|
|||(0.2310)|
|Constant|1.0190***|0.8143|
||(0.7701)|(0.9210)|
|Sample Size|105|116|
|_R_<sup>2</sup>|0.5224|0.5497|

- ** ***

- _p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Standard errors in parentheses.

Note: All regressions include a full set of country and year dummies. Standard errors are cluster-robust.

In sum, using structural shocks recovered from a PVAR, I find that fiscal austerity programs do not necessarily entail significant output or consumption losses when the economy is under heavy debt burdens. These findings are generally in line with results from narrative data.

# **5 Conclusion and Caveats**

In this paper, I provide some limited empirical evidence in support of the Expansionary Fiscal Contraction(EFC) hypothesis: when a country’s debt level is perilously high, tax-based austerity programs could be expansionary(increase output or consumption), or at least not contractionary. This paper contributes to the empirical literature of this topic by employing a model-based definition of high-debt regimes and providing two alternative definitions of austerity: one definition is based on a 23
new source of cross-country narrative fiscal data and the other is based on structural shocks estimated from a proxy SVAR.

These results should be interpreted with caution for a few reasons: first, the sample includes only a selected number of OECD countries for a sample period between 1978 and 2014 as my choice of sample is constrained by available sources of narrative fiscal data. Hence the results may not generalize to Emerging Markets and Developing Economies(EMDEs). Second, my empirical results for consumption is robust to alternative definitions of high-debt regime but not to alternative definitions of austerity. In my empirical results for GDP growth, coefficient estimates for government spending is not significant. Finally, austerity programs are often accompanied by other structural reforms. which this paper does not examine. In sum, empirical results in this paper should not be readily interpreted as suggesting that fiscal austerity is always the appropriate course of action whenever debt level is high.

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