metadata: authors: 0: author: Ilzetzki, Ethan
count: World
docna: 0: docna: Fiscal policy and debt dynamics in
            developing countries
docty: Policy Research Working Paper
subtopic: Macro-Fiscal Policy,Consumption,Economic Theory & Research,Fiscal & Monetary Policy,Economic Adjustment and Lending,Industrial Economics,Public Sector Economics,Economic Growth,Public Finance Decentralization and
            Poverty Reduction
teratopic: Law and Development,Private Sector Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: Using a new tax database for 28
            countries and a variety of econometric methods, this paper
            contributes to the debate on the effects of fiscal policy on
            economic activity in a number of ways. The analysis finds
            that tax cuts have a stimulative effect on economic growth
            in developing countries. Lowering the personal income tax
            rate by 1 percentage point, or cutting revenues by 1 percent
            of gross domestic product increases gross domestic product
            by 0.3-0.4 percent on impact and 0.8 percent in the long
            run. The author finds that cuts in personal income taxes are
            more effective in stimulating growth than cuts in corporate
            or valued added tax rates. The author incorporates debt
            dynamics into a fiscal vector autoregression model for a
            number of developing countries. Existing estimates of the
            effects of fiscal policy on growth use linear time-series
            methods, which may assess the effects of fiscal policy along
            a debt-path that is unsustainable. Incorporating the
            non-linear relationship between government expenditure,
            taxes, and debt alters estimates of the impact of fiscal
            policy on gross domestic product in several countries. In
            Brazil, for example, conventional time-series methods may
            overstate the effects of fiscal policy on gross domestic
            product, by ignoring the detrimental effects of debt accumulation.
repnme: repnme: Fiscal policy and debt dynamics in
            developing countries
display_title: Fiscal policy and debt dynamics in
            developing countries
listing_relative_url: /research/2011/05/14219586/fiscal-policy-debt-dynamics-developing-countries
topicv3: Economic Growth m1326241 471,Government Expenditure m1326649 1112,Fiscal Policy m1327144 445,Macroeconomic and Structural Policies
            m1327380 439
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Law and Development,Private Sector Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development
no_of_pages: 75
model_extractions: WPS5666 Policy Research Working Paper 5666 Fiscal Policy and Debt Dynamics in Developing Countries _Ethan Ilzetzki_ The World Bank Development Research Group Macroeconomics and Growth Team May 2011
Policy Research Working Paper 5666

# **Abstract**

Using a new tax database for 28 countries and a variety of econometric methods, this paper contributes to the debate on the effects of fiscal policy on economic activity in a number of ways. The analysis finds that tax cuts have a stimulative effect on economic growth in developing countries. Lowering the personal income tax rate by 1 percentage point, or cutting revenues by 1 gross domestic product of gross domestic product increases gross domestic product by 0.3–0.4 percent on impact and 0.8 percent in the long run. The author finds that cuts in personal income taxes are more effective in stimulating growth than cuts in corporate or valued added tax rates. The author incorporates debt dynamics into a fiscal vector autoregression model for a number of developing countries. Existing estimates of the effects of fiscal policy on growth use linear time-series methods, which may assess the effects of fiscal policy along a debtpath that is unsustainable. Incorporating the non-linear relationship between government expenditure, taxes, and debt alters estimates of the impact of fiscal policy on gross domestic product in several countries. In Brazil, for example, conventional time-series methods may overstate the effects of fiscal policy on gross domestic product, by ignoring the detrimental effects of debt accumulation.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at e.ilzetzki@lse.ac.uk.

_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
Fiscal Policy and Debt Dynamics in Developing Countries Ethan Ilzetzki* JEL Codes: E62, F41, H63

*London School of Economics and the Centre for Economic Performance.
<!-- Start of picture text --> 1 Introduction<br>In recent years, the debate on the use of fiscal policy as a countercyclical<br>stabilization tool has been central to the agenda of macroeconomic policy-<br>makers to the extent not seen for a generation or more. Several governments<br>responded rapidly and aggressively to the most significant financial crisis<br>in recent memory. One of the first acts of the newly-elected Obama ad-<br>ministration in February 2009 was to pass a $787 billion stimulus package<br>through the U.S. Congress. More recently, in December 2010, President<br>Obama proposed a bill, receiving bipartisan support, prolonging both the<br>Bush administration’s tax cuts and his own administration’s extension of<br>unemployment-benefit duration. A major justification of these measures was<br>as additional stimulus to economic activity. Similar policies have been pur-<br>sued in the U.K., with significant increases in government spending and cuts<br>in the VAT rate during 2009-10. Even in the developing world, governments<br>made similar efforts, with the Chilean government increasing its expenditures<br>by close to 3 percent of Chile’s gross domestic product, for example.<br>More recently, the debate has swung from stimulus to austerity. Concerns<br>about the solvency of a number of European governments, most prominently<br>in Greece and Ireland, have reversed the policy trend. Governments through-<br>out Europe have been legislating significant increases in taxation and cuts<br>in public expenditure. Most recently, the Irish government, facing a loss in<br>market confidence for its sovereign debt, took measures to increase public<br>savings by more than 3 percent of GDP. Concern of similar debt selloffs led<br>the incoming Parliament in the UK to take decisive measures to cut pub-<br>lic expenditures. In the U.S., Congressional Republicans have vowed to cut<br>discretionary government spending by $100 billion.<br>How effective are packages of fiscal stimulus in fueling economic recovery?<br>2<br><!-- End of picture text -->
<!-- Start of picture text --> How costly are austerity measures in deepening recessions? To what extent<br>does a country’s degree of indebtedness affect the impact of stimulus and<br>austerity? What are the merits of using tax policy relative to expenditure<br>measures in using fiscal policy for stabilization- or debt-reduction purposes?<br>These important policy questions have stimulated a significant revival<br>of the empirical study of the effects of fiscal policy on economic activity.<br>This literature has focused primarily on the United States or other OECD<br>countries.<br>In a recent contribution, Izetzki, Mendoza and Végh (2010) (IMV) as-<br>sembled high-frequency data on government purchases for a large sample<br>of countries. IMV then estimate the effects of government expenditure on<br>output using structural vector autoregressive (SVAR) methods. We extend<br>their evidence along two dimensions. First, we present a new dataset on tax<br>revenues, tax rates, and tax elasticities for a sample of 28 countries, most<br>of which are less-developed. This new dataset allows us to jointly estimate<br>the effects of taxes and government purchases on output using panel time-<br>series econometric methods. Second, using methods introduced by Favero<br>and Giavazzi (2007), we explore the degree to which government indebted-<br>ness affects the impact of fiscal policy.<br>An outline of the paper, and its main findings, can be summarized as<br>follows. In Section 2, we outline the new dataset and discuss the methodology<br>used to calculate average marginal tax rates and tax elasticities. Section 3<br>is a preliminary exploration of the data, where we use panel data methods<br>(OLS and Arellano-Bond GMM estimates) to estimate the effects of tax<br>cuts on economic output. We find that a cut in personal income taxes<br>by 1 percentage point increases GDP by 0.16%. The effect is more<br>substantial in developing countries, where GDP increases by close to<br>0.4% in response to a similar tax cut. Results regarding corporate- and<br>3<br><!-- End of picture text -->
<!-- Start of picture text --> value-added-taxes are less clear.<br>Section 4 uses Structural Vector-Autoregressive (SVAR) methods, as in<br>Blanchard and Perotti (2002) to estimate the effects of fiscal policy in a<br>number of country groupings. We re-affirm the results in IMV: government<br>expenditure is more potent in expanding output in high-income<br>countries than in developing countries; in closed economies than<br>in open economies; and under fixed than under flexible exchange<br>rates. These results are robust to the inclusion of taxes as an additional<br>control. We find, however, that the tax multiplier is virtually zero<br>in most country-groupings. This is consistent with previous findings using<br>SVAR methods. The exception is developing countries where the tax<br>multiplier is 0.3 on impact and close to 0.8 in the long run.<br>Finally, in Section 5 we use methods developed by Favero and Giavazzi<br>(2007) (FG) to incorporate debt dynamics into SVAR estimates of the impact<br>of fiscal policy in a number of developing countries. In contrast to FG’s<br>findings for the United States, we find that debt dynamics significantly<br>dampen the effects of fiscal policy in Brazil and Estonia, and to a<br>lesser extent in South Africa. The main channel identified is that of fiscal<br>reversals. Increases in government expenditure or tax cuts lead to higher<br>debt levels. These lead to reversals—decreases in government expenditure and<br>increases in taxation—within several years. These policy reversals diminish<br>the long-run expansionary effect of the initial fiscal stimulus. In the case of<br>Estonia, we find moreover that higher debt has a direct negative impact on<br>output growth. However, the mitigating effects of debt on the impact of fiscal<br>policy is not uniform. We do not find that incorporating debt dynamics in<br>SVAR regressions for Chile and Malaysia affect estimates of the impact of<br>fiscal policy on output.<br>The final section, section 6 concludes.<br>4<br><!-- End of picture text -->
<!-- Start of picture text --> 2 The data<br>A major challenge in estimating the effects of fiscal policy in developing<br>countries using VAR methods is the availability of reliable data. This paper<br>continues the efforts made by IMV to catalogue quarterly fiscal data for<br>developing countries. While the focus in IMV is on the expenditure side<br>of the fiscal ledger, we assemble data on tax revenues in this project. We<br>also introduce new series (these at an annual frequency) of average marginal<br>tax rates for a sample of 28 countries. This extends work such as Barro<br>and Sahasakul (1983) to a broader set of countries, including a number of<br>developing countries. Finally, for the purpose of our SVAR analysis, we<br>estimate tax elasticities for the countries in our sample. To our knowledge,<br>this is the first attempt to assemble time series of average marginal tax rates<br>for a cross-section of developing countries. It is also the first to estimate the<br>output elasticities of tax revenues, using macroeconomic data, for a number<br>of developing countries.<br>We outline a few of the steps involved in obtaining the time series used in<br>the analysis. We obtained the entire distribution of corporate, personal, and<br>value added tax rates from the OECD tax database for OECD members, and<br>from PricewaterhouseCoopers’ “Individual Taxes Worldwide Summaries” for<br>non-OECD countries. As an example, Figure 1 compares the marginal per-<br>sonal income tax rate schedules for Peru in 1993 and in 1994: directly before<br>and directly after, respectively, a major tax reform intended at simplifying<br>the tax system. Due to historically high rates of inflation, tax brackets in<br>Peru are based on income in “tax units” rather than in local currency.<br>We then followed the OECD’s methodology, outlined in Giorno et al<br>(1995) and Van den Noord (2000), for calculating the average marginal tax<br>rate for each country and year. We calculated the earnings elasticity of<br>5<br><!-- End of picture text -->
the personal income tax by following the OECD’s assumption that income distribution is lognormal, and allowing for a distribution of income based on each country’s Gini coefficient (taken in most cases from the World Bank’s Poveal database) around average wage income (taken from national sources). We then located within this stylized income distribution the tax brackets as outlined in the tax code. The average marginal tax rate is the average across tax brackets of marginal tax rates weighted by total income in each bracket. As an example, Figure 2 illustrates the calculation of the average marginal income tax rate for Brazil in 2008. The figure shows the approximated cumulative distribution function (CDF) of earnings. At each wage rate along the X-axis, the CDF gives the percentage of total earnings in Brazil earned by workers with wages below the indicated wage. There were three income-tax brackets in Brazil in 2008: 0%, 15%, and 27.5%. Although 78% of workers in Brazil fell into the lowest tax bracket, their earnings comprised only 39% of total earnings. Similarly, 14% of the working population faced a 15% marginal tax rate; this group earned 25% of Brazilian income. Finally, the remaining 7% of the population, and 36% of national income, faced the highest marginal rate. The average marginal tax rate in Brazil in 2008 was calculated as the sum of the three marginal rates, each multiplied by the share of total wage income that faced that marginal rate. To assure the reader that our methodology is consistent with earlier estimates in the literature, Figure 3 compares the time series of the average marginal income tax rate calculated by Barro and Redlick (2009), to that obtained using our methodology. The two series are broadly similar, with <u>~ 1aez (2001) and others have argued</u> that the Pareto distribution better characterizes the actual income distribution in the United States than does the lognormal distribution. Lopez and Serven (2006), in contrast, cannot reject a lognormal distribution of income in a large sample of countries. To allow a direct comparison with the income elasticities of tax revenues estimated by the OECD, we follow their methodology and assume a lognormal income distribution.

<!-- Start of picture text --> 6<br><!-- End of picture text -->
some differences (of the order of one to two percentage points) in the 1984-5 and 1993-2000 periods. Armed with average marginal income tax rates for each country in the sample, we were then able to estimate the (wage-) income elasticity of tax revenues. An increase in income that is distributed equally among current wage earners would increase tax revenues by precisely the average marginal tax rate. The wage elasticity of tax revenues is therefore the average marginal tax rate divided by the average tax rate (which is tax revenues deriving from labor income taxes divided by wage income).? The first two columns of Table 1 show our estimates of the earnings elasticity of tax revenues for the countries in our sample in column A for income taxes and in column B for social security taxes. The first two columns of Table 3 compare our estimates with the OECD’s, for overlapping countries; they are virtually identical. VAT rates are flat, while corporate income taxes are flat in most countries, with some exceptions (such as Brazil). A single rate therefore summarizes both the average and marginal rates for these taxes. The elasticity of these taxes with respect to their respective tax bases is therefore 1. Figure 4 shows all three average marginal tax rates for the countries in the sample.® We then followed Giorno et al (1995) directly in using the elasticities of tax revenues with respect to specific tax bases to obtain an overall outputelasticity of tax revenues. Table 1 outlines the calculation of the output elasticity of tax revenues in detail. The employment elasticity of wages is shown in column D; the output elasticity of employment is presented in column E. As shown in detail in Giorno et al (1995) and Van den Noord (2000), these two elacticities can be combined with the earnings elasticities to obtain the output elasticity of income taxes. As the GDP identity, using income <u>~ 2'Phis measure varies, of course,</u> from year to year. We average this measure across years for each country. 3In Brazil and the United States we show the sales tax rather than the VAT. 7
<!-- Start of picture text --> accounting, implies that total profits are equal to GDP excluding wage earn-<br>ings, we can use the same elasticities to estimate the output elasticity of the<br>corporate income tax. Again, we follow the formulae in Giorno et al (1995)<br>and Van den Noord (2000) in calculating this elasticity.<br>The output elasticities of personal income taxes, social security taxes,<br>corporate income taxes and sales/VAT taxes are then summarized in columns<br>F to I. Finally, the output elasticities of specific taxes are weighted by their<br>share of total tax revenue to give the output elasticity of tax revenues.<br>Table 2 shows our estimates of elasticities using annual data. Table 3<br>compares our estimates of the output elasticity of tax revenues—using annual<br>data-with those of the OECD, for the seven countries that appear in both<br>samples. As noted earlier, our estimates of the personal income tax elasticity<br>are virtually identical. Thus all differences in estimates of the final elasticities<br>are due to differences in estimates of macroeconomic elasticities, such as the<br>output elasticity of employment.<br>3. The effects of tax cuts: A panel estimate<br>We begin our analysis with a simple panel regression, which attempts to<br>determine the effects of tax changes on output. In doing so, we follow the<br>specification in Barro and Redlick (2009) who estimate the effects of a lagged<br>change in the tax rate on current output. They argue that tax changes at a<br>one-year lag are more likely to have an effect on output than contemporane-<br>ous changes and less likely to be affected by reverse causation.<br>These regressions are an illustration of the data available in the collected<br>dataset. The methodology used here does not fully control for the possibility<br>of reverse causation: tax rates, particularly average marginal ones, respond<br>endogenously to business cycle conditions. For this reason, we address iden-<br>8<br><!-- End of picture text -->
tification more seriously in the following section, using an SVAR approach.

An alternative tack would be to use a narrative of changes in tax policies in the spirit of Romer and Romer (2010). To date, however, a historical narrative of the changes in tax policy, together with the rationale for their change (endogenous/exogenous to economic conditions) is not available for a large sample of countries.‘ We estimate the following equation Ayn = @+ BAT rH + YZnt + Ents (1) where Ay,,; is the growth rate of (the change in log) real GDP, AT,4-1 is the lagged change in the tax rate in country n, and Z,,; is a vector of controls, some dated t and some t — 1. We include the lagged unemployment rate (UR(—1)); Barro and Redlick (2010) also argue for including an exogenous shock as a covariate. In their case they include the (squared) spread between long-maturity Baa-rated bonds and Treasury bonds of similar maturity as a measure of tightness of credit conditions. For our sample, including smaller countries, we found it more appropriate to consider an external shock. We include a trade shock variable, defined as the weighted average of the GDP growth of each country’s trading partners. Each country’s trade shock is then scaled by the country’s exports-to-GDP ratio, as a shock to trade might have a larger effect on countries more open to trade. Results are also robust to inclusion of the lagged growth of government expenditure, although unlike Barro and Redlick (2009), who include military expenditure only, inclusion of total government expenditure raises concerns of reverse causation. We estimate (1) in a panel of 28 countries, of which 19 are developing and 9 are high-income, based on World Bank classifications. Estimates reported <u>~ 4Tp addition to the US narrativbye</u> Romer and Romer (2010), Cloyne (2011) compiled a similar narrative for the UK.

9
<!-- Start of picture text --> in Table 4 are from OLS estimates of (1) including fixed effects, and from an<br>Arellano-Bond GMM estimator. We include all three tax rate variables in<br>our regressions: the personal income tax rate (PITR), the corporate income<br>tax rate (CITR) and the VAT rate (or sales tax rate for the US and Brazil).<br>The first two columns of Table 4 give estimates for the entire sample. We<br>are particularly interested in estimates for developing countries—as their in-<br>clusion is novel to this dataset; we therefore show estimates for the developing<br>countries within the sample in the third and fourth columns.<br>We find a negative relation between increases in the average marginal<br>personal income tax rate and GDP growth in the following period. For<br>the entire sample, a one-percentage-point cut in the average marginal tax<br>rate increases GDP growth in the following period by 0.16%. The effect is<br>larger for developing countries, where a tax cut of similar magnitude increases<br>output by close to 0.4%. This result is statistically significant at the 95%<br>confidence level for all specifications, and the 99% confidence level for almost<br>all specifications. It is possible that the tax codes in developing countries are<br>more distortionary than those in high-income countries. This is a possible<br>explanation for the higher effect of personal income tax changes in developing<br>countries.<br>Results for the corporate income tax rate and the VAT rate are less clear.<br>The effect of changes in the corporate income tax rate are never statistically<br>significant. The effect of VAT increases are statistically significant at the<br>95% confidence level only in the OLS regression for the entire sample, and of<br>an unexpected sign. A potential explanation for this last result is that there<br>is very little time-series variability in the VAT, which gives the regression<br>less power to identify the effects of VAT changes. Moreover, VATs have<br>frequently been introduced, and phased in, in the context of broader tax<br>reforms or other economic reforms. Thus the positive coefficient on the VAT<br>10<br><!-- End of picture text -->
<!-- Start of picture text --> rate may reflect the positive impacts of these reforms as a whole, rather than<br>a reliable prediction of the effects of a cut in the VAT rate.<br>Other covariates generally have the expected signs and are statistically<br>significant. Higher lagged GDP growth predicts higher growth in the current<br>period and growth in trading-partners’ GDP has a positive effect on GDP.<br>Lagged unemployment, however, flips signs in the different specifications and<br>is not statistically significant in some cases.<br>In summary, we find preliminary evidence that cuts in personal income<br>taxes may have a stimulative effect on GDP growth. This effect is larger in de-<br>veloping countries. These results should be interpreted cautiously, however.<br>As mentioned, this is an illustrative regression that does not fully identify<br>changes in tax rates independently of changes in economic activity. Insofar<br>as tax policy is countercyclical in high-income countries, tax rates are likely<br>to increase during periods of high economic growth in this group of countries.<br>Thus estimates of the tax multiplier might be understated in the case of high<br>income countries. Insofar as tax policy is procyclical in developing countries,<br>and considering that the tax code tends to be less progressive in developing<br>countries, our estimates of the tax multiplier may be overstated in this group<br>of countries. On the other hand, the widespread prevalence of tax evasion<br>in developing countries might mean that actual tax rates are less relevant<br>for economic activity. In this case, our estimates of the tax multiplier reflect<br>the degree to which tax policy affects economic outcomes only after filtering<br>through tax evasion. The results of Table 4 are then biased downwards, if<br>interpreted as the economic effects of actual effective reductions in tax pol-<br>icy. To address these problems, we now turn to a more detailed study of the<br>effects of taxes and government expenditure on output using structural VAR<br>methods.<br>11<br><!-- End of picture text -->
<!-- Start of picture text --> 4 The effects of taxes and government<br>expenditures: An SVAR approach<br>4.1 Approach<br>A major difficulty in estimating the effects of government expenditure and<br>taxes on output at business cycle frequency is the identification of fiscal<br>shocks. While fiscal policy may affect economic activity, it also responds to<br>business cycle conditions. Disentangling this bicausal relation has been the<br>primary challenge for the empirical study of the macroeconomics of fiscal<br>policy, and the main source of methodological debate.<br>Two main approaches have been pursued in the estimation of the effects<br>of fiscal policy on GDP. First, Barro (1981) suggested that large military<br>buildups in the U.S. could be viewed as exogenous increases in government<br>expenditure that should have no other immediate impact on economic activ-<br>ity. Ramey and Shapiro (1998) and Ramey (2009) extended this methodol-<br>ogy, adding a narrative approach to the identification of fiscal shocks. They<br>document, through private forecasts and Business Week reporting, the time<br>at which the public began to anticipate increases in military expenditures.<br>Fisher and Peters (2009) use the stock prices of military contractors in a sim-<br>ilar attempt to detect when the private sector had internalized the news of an<br>impending government expenditure shock. Nakamura and Steinsson (2010)<br>use shocks to military expenditure as an instrument for military procure-<br>ment in U.S. states, while exploiting heterogeneity in the size of the military<br>industries across states. Shoag (2011) uses shocks to the value of pension<br>plans for state workers as an instrument for state-level public spending to<br>estimate the government-expenditure multiplier. In a novel effort to extend<br>this approach outside the OECD, Kraay (2010) uses disbursement lags of<br>12<br><!-- End of picture text -->
<!-- Start of picture text --> World Bank projects to isolate exogenous changes in public expenditures<br>from those endogenously responding to the state of the business cycle..<br>Romer and Romer (2010) also adopt a narrative approach and document<br>the time of legislative changes to the U.S. tax code in an attempt to identify<br>tax policy shocks and classify whether tax changes were anticipated and<br>whether they were explicitly aimed at addressing business cycle conditions.<br>The second approach is the Structural Vector Autoregression (SVAR)<br>methodology introduced by Blanchard and Perotti (2002). They identify fis-<br>cal shocks using information about fiscal institutions. On the expenditure<br>side, their identifying assumption is that discretionary government expendi-<br>ture requires at least one quarter to respond to business cycle conditions.<br>For taxes, they use the output elasticities of tax revenues, estimated from<br>information about tax codes, to differentiate between the endogenous reac-<br>tions of tax revenues to output fluctuations and unanticipated shocks to tax<br>revenues.<br>There has been much debate as to the relative merits of these two ap-<br>proaches. Perotti (2004) and Ramey (2009) have extensive comparisons of<br>the SVAR and narrative approaches to identifying government expenditure<br>shocks. Ilzetzki, Mendoza and Végh (2010) discuss extending the two ap-<br>proaches to countries outside the OECD. Favero and Giavazzi (2010) and<br>Perotti (2011) compare and attempt to reconcile the very different results<br>arising from Romer and Romer’s (2010) and the SVAR approaches to iden-<br>tifying tax policy shocks. Our study does not innovate on identification of<br>fiscal shocks; we rather take off the shelf the Blanchard and Perotti (2002)<br>methodology. But it is worth making two points on identification.<br>First, whatever the merits of the narrative and natural experiment ap-<br>proaches in general, when studying a broad cross-section of countries, these<br>methods are difficult to implement. While Barro (1981) and others have<br>13<br><!-- End of picture text -->
shown that military expenditure is the main source of volatility in U.S. government expenditure, this is not the case in all developing countries. In fact, for some developing countries, military expenditure is a negligible portion of budgetary expenditure. And in those developing countries whose expenditures are driven by large swings in military spending, it is hard to argue that these war buildups have little direct impact on GDP. Moreover, there is a growing literature arguing that either business cycle conditions or swings in natural resource prices are major drivers of public expenditure in developing countries’. This makes it difficult to find a general econometric instrument that drives public spending in developing countries, but which has no direct effect on economic activity. On the tax side, there is to date no comprehensive documentation of tax changes as in Romer and Romer (2010) for countries other than the U.S. and the U.K. Second, the SVAR approach may be more effective at identifying unanticipated fiscal shocks in developing countries than in high-income countries. while Ramey (2010) has presented evidence that SVAR shocks were forecasted by the private sector, this is less likely to be the case in developing countries. As IMV discuss, government expenditure is very volatile on a quarter-to-quarter basis in developing countries. Fiscal planning is poor and not credible. Quarterly fiscal data is subject to large revisions even a year or two after the fact. It is therefore unlikely that the public will have foreseen many of the fiscal shocks identified by an SVAR, and even less likely that their exact timing at a quarterly frequency will have been known in advance. Moreover, if the volatility of public expenditures is driven largely by political pressures to increase expenditures when resources are abundant, a VAR should be able to pick up on this relationship, as long as it is relatively sys- <u>~ 58ee for example KaminskReinharty,</u> and Végh (2004), Izetzki and Végh (2008) for stylized facts; Talvi and Végh (2005), Alesina, Campante, and Tabellini (2008) and Ilzetzki (forthcoming) for political economy theories of this phenomenon. 14
<!-- Start of picture text --> temic, and does not lead to a contemporaneous relationship between output<br>and public expenditures (due to implementation lags).<br>In addition, there are particular merits to the Blanchard and Perotti<br>(2002) approach to identification of shocks to tax policy in developing coun-<br>tries. As noted in Section 3, estimates of the fiscal multiplier from changes<br>in statutory rates may understate the economic effect of effective changes<br>in tax payments, when tax evasion is prevalent. As the Blanchard-Perotti<br>SVAR. backs out shocks to tax revenues, it captures effective changes in tax<br>payments, not only changes that are on the books. To illustrate the rele-<br>vance of this point, consider a government that improves the enforcement of<br>tax payments. However desirable this may be for fiscal management, this<br>improved enforcement also entails an effective increase in the tax burden.<br>Improved enforcement leads to a de-facto increase in effective tax rates, the<br>omission of which may understate the effects of tax policy. In contrast, back-<br>ing out shocks to tax policy from tax revenues captures changes in the actual<br>incidence of taxes.<br>We now detail the SVAR approach used in this study. We estimate the<br>following panel vector autoregression (PVAR):<br>K<br>AnY" = S— ChY nan + Bunge, (2)<br>k=1<br>where Y,,; is a vector of J macroeconomic variables. In this section J = 3<br>and this vector includes-from top to bottom—government expenditure gn+,<br>tax revenues T;,;, and GDP y,7. The J * J matrices C;, trace the k-order<br>autoregressive process of the macroeconomic system, while wu;4 is a J*1 vector<br>of structural shocks to the macroeconomic observables. Structural shocks are<br>assumed to be distributed N (0,1) and such that F [un+ (4) Ung (j)| =0 Vi F<br>j. The diagonal matrix B rescales these shocks to units of the macroeconomic<br>15<br><!-- End of picture text -->
<!-- Start of picture text --> variables, based on their respective variances. A,, allows for contemporaneous<br>relations amongst these variables.<br>The matrix A, is allowed to vary from country to country. We use the<br>tax revenue elasticities shown in Table 1 to obtain heterogeneous values of<br>A,, (2,3). However, in order to maximize the estimation’s sample size, we do<br>not allow heterogeneity in the other values of the A matrix.<br>A reduced-form version of (2), where both sides of this equation are mul-<br>tiplied by A>', can be estimated via OLS. This provides us with estimates<br>of A; *C; and of the error terms €,4 = A;*Bun4.<br>The estimated variance-covariance matrix of €,, gives J (J — 1) equations<br>for the estimation of the unknown parameters in A, and B:<br>E [Ent@,4] = 4,BB’ (A;1)’.<br>There are J (J — 1) unknowns in A,, and an additional J in B,,. J restrictions<br>are required on the elements of A, to estimate the remaining elements. To<br>summarize the structural restrictions, let<br>1 arg yg<br>A, = FAgy Aty1 "|1 .<br>The parameters of greatest interest are ag, and ayy, as they determine the<br>effects of government expenditure and taxes on output. We leave them un-<br>restricted. We use our tax elasticity estimates to determine a,-. Following<br>Blanchard and Perotti (2002) we also note that government purchases (as<br>opposed to transfers) have no automatically stabilizing components and set<br>dyg = 0. (In the PVAR estimation of this section, we use data on government<br>consumption as g/'.)<br>16<br><!-- End of picture text -->
<!-- Start of picture text --> Finally, Blanchard and Perotti (2002) and Favero and Giavazzi (2007)<br>show that results are not sensitive to assuming ar, = 0, agr = 0 or setting<br>both to zero. Estimates presented here set agr = 0, but any of the alternative<br>assumption leads to essentially identical results.<br>4.2 Results<br>Following IMV, we pool countries in several ways, based on country charac-<br>teristics. In Figures 5 and 6 we show results for high income and developing<br>countries, respectively. Figures 7 and 8 present results for open and closed<br>economies, defined as in IMV as countries with ratios of trade (imports plus<br>exports) exceeding, or below, 60% of GDP, respectively. Finally, using the<br>classification in Ilzetzki, Reinhart and Rogoff (2011), we divide the sample<br>into country-episodes where the exchange rate was fixed and those where it<br>was flexible in Figures 9 and 10, respectively. The upper panel of each of these<br>figures gives the cumulative multiplier to a shock to government consumption<br>at horizons ranging from one to twenty quarters. The lower panel gives the<br>cumulative multiplier in response to a shock to taxes. Point estimates are<br>presented in a solid line and the boundaries of the 90% confidence interval,<br>estimated using Monte Carlo simulations with bootstrapped standard errors<br>and 500 repetitions, are shown in dotted lines. Reported regressions include<br>4 lags.<br>A number of results emerge from Figures 5 and 6. First, in high-income<br>countries the response of output to a $1 increase in government consumption<br>is estimated at approximately $0.8. The response is statistically different<br>from zero, but not from one, at every forecast horizon. The long run multi-<br>plier slightly exceeds 1, but error bands are wide at longer forecast horizons.<br>In developing countries, the response of output to increases in government<br>17<br><!-- End of picture text -->
<!-- Start of picture text --> consumption is more muted. On impact, output increases by merely 0.2 cents<br>for every dollar spent by the government, implying that more than 80% of<br>government expenditure is crowded out by other components of GDP (con-<br>sumption, investment or net exports). This estimate is statistically different<br>from zero, but is lower by a statistically significant margin from our estimate<br>of the fiscal multiplier in high-income countries. In the long run, govern-<br>ment consumption appears to be almost fully crowded out, with the long run<br>multiplier for developing countries estimated at 0.15. This estimate is not<br>different from zero by a statistically significant margin, but can be said to be<br>lower than 1 with 99% confidence.<br>Long-run estimates of the government expenditure multiplier are similar<br>to those found in IMV, but the impact multipliers in both these groups of<br>countries are larger in this study. There are different reasons for these diver-<br>gent results in the case of high-income countries and in the case of develop-<br>ing countries. In high-income countries, the current sample is significantly<br>smaller than that used in IMV, with only 6 of the 19 countries from IMV in-<br>cluded in this sample. A bivariate regression with government consumption<br>and GDP as in IMV yields similar results as those reported here, when only<br>including the six countries that are in both studies.<br>While the sample of developing countries is not perfectly overlapping with<br>that of IMV (only 17 of the 20 countries in IMV are included here), this is<br>not the main cause for the difference in the estimates of fiscal multipliers.<br>The exclusion of tax revenues from the regression in IMV may have under-<br>stated the short-run impact of government consumption on GDP (and lead<br>to estimation of a negative impact multiplier). In fact, as shown in Figure<br>11, taxes are found to rise in response to government consumption increases,<br>which may bias results of estimations that exclude taxes.<br>Turning to the effects of taxes on output, Figure 5 re-affirms results seen<br>18<br><!-- End of picture text -->
<!-- Start of picture text --> elsewhere in SVAR estimates of taxes on output for industrialized countries.<br>As in Perotti (2004), the effects are small and not statistically significant.<br>See also Caldara (2011) on the range of tax multipliers that are plausible<br>given reasonable assumptions on tax elasticities; these estimates are clus-<br>tered around zero as well. However, Favero and Giavazzi (2010) and Perotti<br>(2011) show that once narrative information from Romer and Romer (2010)<br>is incorporated into SVAR estimations, tax multipliers may be larger than<br>in standard SVAR estimates.<br>In contrast to high-income countries, we find that tax cuts in developing<br>countries do have the ability to stimulate output. A tax cut that decreases<br>revenues by $1 increases output by approximately $0.3, a multiplier that is<br>statistically different from zero. In the long run, the tax multiplier increases<br>to exceed 0.8, although error bands are wide around this estimate.<br>Figures 7-10 reaffirm additional results from IMV. Long-run government<br>consumption multipliers are large, statistically different from zero, and esti-<br>mated to exceed one in closed economies and when exchange rates are fixed.<br>In contrast, multipliers are essentially zero in open economies or those oper-<br>ating under flexible exchange rates.<br>In all these country groupings, tax multipliers appear to exceed zero only<br>slightly on impact, but to be zero or negative in the long run. One exception<br>is closed economies, where the long-run tax multiplier appears to exceed<br>one (although it is not statistically different from zero). Figure 12 shows,<br>however, that a simple interpretation of this result as implying that tax policy<br>is effective in stimulating output in closed economies is slightly misleading.<br>Government consumption decreases significantly in response to tax increases<br>in this group of countries. As the government consumption multiplier is<br>large in this group of economies, the estimated decrease in output in the<br>long run conflates the effects of government spending cuts with tax increases.<br>19<br><!-- End of picture text -->
<!-- Start of picture text --> Once these are accounted for, the multiplier is close to zero in this group of<br>countries as well.<br>In conclusion, in a Panel SVAR. analysis of the effects of government<br>consumption and taxes on output in several country groupings, we find results<br>on government expenditure that are similar to those in IMV: the multiplier<br>is substantially larger in high income countries than in developing countries,<br>in closed economies than in open economies, and under fixed- than under<br>flexible-exchange arrangements. We also re-affirm results from earlier SVAR<br>studies on the macroeconomic effects of taxes: the tax multiplier is close to<br>zero in high-income countries. We also report a new finding: tax policy is<br>more effective at stimulating short-run output in developing countries. The<br>impact multiplier there is approximately 0.3 and statistically different from<br>zero. The tax multiplier in the long run is estimated at 0.8.<br>5 Fiscal policy and debt dynamics<br>In the previous section we estimated fiscal multipliers for a broad range of<br>countries and found very heterogeneous responses to fiscal shocks. But in<br>some cases, the fiscal multiplier was found to be sizable. Active counter-<br>cyclical fiscal policy would seem to have some benefit in terms of stimulated<br>output in these cases. However, as recent experiences in Europe and many<br>past experiences in developing countries have made all too clear, active fiscal<br>management may have large costs as well. Increases in government expen-<br>ditures and tax cuts imply higher deficits, which in turn imply higher debt.<br>Will markets tolerate the higher debt levels caused by fiscal stimulus? Will<br>governments be forced to implement sharp and sudden fiscal reversals, as<br>appears to currently be the case in several European countries?<br>Recent studies have attempted to address these questions. Reinhart and<br>20<br><!-- End of picture text -->
Rogoff (2010), for example, show in a panel of high-income and developingcountries that high levels of sovereign debt lead to lower GDP growth in subsequent years. A recent debate has emerged, also, on the effectiveness of fiscal policy when debt levels are high, or conversely whether fiscal contractions may at times be expansionary, as claimed, for example by Alesina and Ardagna (2010). The IMF (2010) critiqued this result and concluded that fiscal contractions, while perhaps necessary at times, come at a short-run cost.

In an important contribution, Favero and Giavazzi (2007) show that not only may current SVAR estimates be biased due to the omission of debt as a control variable, but also that its inclusion in a VAR analysis is non-trivial. After all, the debt stock relates to the endogenous VAR variables-government expenditure, taxes, GDP, interest rates, and inflation—in a highly non-linear way. So while it may be problematic to omit debt from the regression, it is also inappropriate to include debt as an exogenous control variable.

To fix ideas, consider augmenting (2) with a variable d;, measuring the debt-to-GDP ratio, as suggested by Favero and Giavazzi (2007): K AY; = S- (CeYi-k + Dedi_x| + Bur,

<!-- Start of picture text --> (3)<br>* 1<br>Y;.<br>4<br><!-- End of picture text --> k=1 where the estimation is for an individual country, and where D,; are J * 1 vectors representing the feedback from debt to endogenous variables in Y;. As mentioned, d; is itself an endogenous variable that relates to current and past values of the endogenous variables. Favero and Giavazzi (2007) describe the following debt-accumulation identity: us ~~(1+~~ <u>Ap;)</u> ~~1+2= Ay~~ ‘ ~~excep—~~ <u>Yt</u> ~~exp—T;~~ 21
where 7; is the nominal interest rate and p; the price level. The first term indicates that past debt incurs interest, but its ratio to GDP depreciates due to GDP growth and inflation. The second term is the primary deficit as a percentage of GDP. (The variables g;, T;, and y; are all in natural logarithms.) In adapting this approach to developing countries, one must remember that a large proportion of emerging-market debt is denominated in foreign currency. We therefore adapt the debt accumulation identity (4) to the case of developing countries: = ~~(1+ 7%) [6(1+ As;) +1 -— 4]~~ dha + ~~eXp 9: — exp Te~~ (5) (1 + Ap) (1 + Ay) EXP Yt where s; is the nominal exchange rate and 6 is the share of debt denominated in foreign currency, assumed here to be constant.° The additional term indicates that debt denominated in foreign currency may accumulate also due to exchange rate depreciation. As outlined in Favero and Giavazzi (2007), we can now estimate (3) by OLS and estimate the structural shocks u; using the same identification as in Section 4. However, in charting impulse responses, we track debt accumulation according to (5) and allow for debt feedback to the endogenous variables through D,. We augment our VAR to include the additional variables in (5), so that now Y; = (92, Ti, ys, Ape, tt, As)’. 9, T;, and y; are (log) government primary expenditure, tax revenues, and GDP, respectively; Ap; and As; are the inflation rate and the rate of depreciation of the nominal exchange rate. 7; is the nominal interest rate faced by the government. As in Favero and Giavazzi <u>~ 6Tp reality, in many emerging market</u> countries the currency denomination of debt has changed significantly over time. However, the currency composition of foreign debt is unlikely to be directly related to the endogenous variables in this study in the short run, but rather determined by foreign appetite for domestic currency debt and technocratic decisions within the Finance Ministry.

22
<!-- Start of picture text --> (2007) we use instead the ratio of interest payments to the debt stock, which<br>is the appropriate interest rate for (5) to hold as an identity.<br>5.1 Debt feedback: A conceptual framework<br>There are at least three ways how debt feedback might affect the impact<br>of fiscal policy. First, governments may respond to high levels of debt by<br>increasing tax rates or lowering government expenditures. In other words<br>D; (1) < 0 or D;, (2) > 0 for some k. There is some evidence that govern-<br>ments do respond in such a stabilizing way. Bohn (1998) shows that fiscal<br>surpluses in the U.S. have responded to the debt-to-GDP ratio. Mendoza and<br>Ostry (2008) show that developing countries respond in a similar stabilizing<br>manner. If this is a correct characterization of government behavior, a stan-<br>dard SVAR estimate using (2) might understate the degree of future fiscal<br>consolidation caused by current fiscal stimulus. It might therefore overstate<br>the long-run effects of fiscal stimulus on output, as it fails to recognize that<br>fiscal stimulus is followed by fiscal consolidation.<br>Second, higher debts may imply higher borrowing rates 7;. In other words<br>D,,(5) > 0 for some k. If this is the case, a standard SVAR estimate would<br>again overstate the effects of fiscal policy on output. It would fail to recognize<br>that current fiscal expansions increase the debt-to-GDP ratio, which may<br>cause higher interest rates. Higher interest rates may in turn crowd out<br>output.<br>Finally, high levels of debt may have direct impacts on output not (or<br>only partially) accounted for by the factors above. This is suggested by the<br>empirical results of Reinhart and Rogoff (2010), for example. One possible<br>theoretical channel for these results was suggested by Drazen and Helpman<br>(1990), who outline a theory where current fiscal stimulus creates expecta-<br>23<br><!-- End of picture text -->
tions of-or uncertainty about—future fiscal or monetary policy. These expectations may have an immediate effect on output or prices. As the VAR cannot identify expectations, these effects will be estimated as a direct effect of debt on output. 5.2. Estimation and results Favero and Giavazzi (2007) estimate the system (3) and (4), but found only minor changes in the macroeconomic effects of shocks to government expenditure and tax revenues. Here we estimate (3) and (5) using data from five developing countries and contrast our results with Favero and Giavazzi’s (2007). The five countries—Brazil, Chile, Estonia, Malaysia and South Africa— were selected based on data availability and consistency. The estimation of the system (3) and (5) is only valid if data on the endogenous VAR variables in Y; are consistent with time series for sovereign debt d; in equation (5). As time series for sovereign debt are usually compiled using different methods and sources from high-frequency fiscal and macroeconomic data, (5) does not obtain consistent estimates for most developing countries. The selected sample of countries were the only five in our sample of developing countries for which data were consistent.’ These countries represent a variety of regions (Asia, Africa, Eastern Europe, and Latin America), degrees of openness to trade and exchange rate regimes.

5.2.1 Consistency of debt series Figure 13 shows the evolution of the debt-to-GDP ratio for the five countries studied, together with the evolution of debt-to-GDP implied by the highfrequency macroeconomic time series. Solid lines in Figure 13 represent the <u>~ TWe included countries whose simulated</u> debt series deviated from actual debt by 2 percentage points of GDP or less, on average.

24
left-hand side of (5) and the dotted lines its right-hand side. A number of factors complicate this calculation. First, time series for sovereign debt were available only at annual frequency. We interpolate these series using piecewise quadratic polynomials. Second, a significant proportion of revenues for most developing countries come from non-tax sources. Ignoring this factor, the right hand side of (5) exceeds its left hand side. The dotted lines in Figure 13 therefore contain non-tax revenues as well as tax revenues.® Third, time series on )—the share of debt that is denominated in foreign currency— are not available for most countries in the sample. However, we were able to obtain one or two observations on the shares of foreign-denominated debt for each country. When only one observation was available, we used the single observation and treated 6 as constant. When more than one observation was available, we created a series of debt shares 6, based on a linear interpolation between the observations. The values of 6 used are summarized in Table 5. In all five cases (perhaps with the exception of South Africa), the synthetic debt series closely match the realized debt series, indicating the validity of debt accumulation equation (5) and the consistency of other macroeconomic time series with debt series. 5.2.2 Results We run a VAR(1) in order to conserve degrees of freedom in the short time series used in this section. (This is in fact the lag order selected by the Schwartz information criterion.) Figures 14 to 18 give the impulse responses of the seven endogenous variables (including debt) to shocks to government expenditure and tax revenues. The dashed lines show responses ignoring debt feedback with 90% confidence intervals indicated in dotted lines. The <u>~ 8In the cases of Brazil and South</u> Africa, we use the average of net non-tax revenues over the entire time period, due to data limmitations.

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<!-- Start of picture text --> solid lines give the impulse responses with debt feedback. Each figure gives<br>responses for one of the five countries, and each is divided into three panels.<br>Panel (a) gives the response to shock to government expenditure and panel<br>(b) to a shock to tax revenues. Panel (c) shows the dynamics of debt in<br>response to both shocks.<br>We first observe that the baseline responses of GDP to government ex-<br>penditure (in the lower left-hand corner of panel (a) in each figure) differ<br>significantly across countries. While error bands are wide, given the short<br>sample for each country, the response of GDP to a government expenditure<br>shock appears positive in Brazil and South Africa, but zero or negative in<br>Chile, Estonia, and Malaysia. This corresponds exactly with the observa-<br>tion in section 4 that the multiplier on government expenditure is larger in<br>relatively closed economies than in relatively open ones. (Brazil and South<br>Africa are classified as closed and the remaining as open).<br>Turning to Figure 14a, the responses for Brazil highlight the importance<br>of incorporating debt dynamics into SVAR estimates of the effects of fiscal<br>policy. Not surprisingly, responses are similar on impact—with or without<br>debt feedback—as the effects of debt dynamics affect impulse responses only<br>at longer horizons. Looking at the response of GDP in the lower left-hand<br>panel, however, shows a significant difference within the first year follow-<br>ing the initial shock. While the response of output in the traditional VAR<br>converges gradually to zero, giving a cumulative increase in GDP of 4%,<br>the response including debt feedback becomes negative after approximately<br>a year, yielding a cumulative decrease in GDP exceeding 3%. After a year<br>it also departs from the 90% confidence interval of the traditional SVAR,<br>indicating that the traditional VAR would place a probability of less than<br>5% that the best unbiased estimate of the VAR incorporating debt dynamics<br>reflect the true response. We acknowledge, however, that the standard er-<br>26<br><!-- End of picture text -->
rors of the debt-dynamics impulse responses are large enough that we cannot reject the hypothesis that the responses are identical. The reason for this difference are immediately apparent from the other responses on the left hand side of Figure 14a. First, the response of government expenditure to its own shock converges to zero in the traditional SVAR, but here becomes negative after five quarters (and exits the 90% confidence bounds of the traditional SVAR estimate after seven quarters). As predicted, the government decreases expenditure in response to higher debt, so that the initial government expenditure stimulus is followed by a fiscal consolidation within two years. Similarly, tax revenues increase slightly for the entire forecast horizon of the debt-feedback responses, but decline after several quarters when debt feedback is ignored. This is an implied tax rate increase in the debt-feedback response: with declining output, higher tax revenues imply higher tax rates.® As pointed out by Favero and Giavazzi (2007) for the case of the United States, in Brazil too, the VAR with debt-dynamics predicts a counter-intuitive decline in interest rates following an increase in government expenditure. In both cases, this is potentially due to the fact that the interest rate used is the one implied from interest payments, rather than a market interest rate. Fiscal stimulus increases the debt stock immediately; but rising interest rates affect actual interest payments only with a lag. With the debt stock increasing and interest payments constant, the interest rate implied from interest payments is likely to go down in the short run. Figure 14b shows the responses of variables in Y; to a 1% of GDP shock to tax revenues. (The shock is an unanticipated increase in taxes.) The longrun dynamics are very different once debt-feedback is accounted for. While ~~9A~~ <u>alternative explanation is that</u> the higher inflation predicted by the impulses with debt feedback is generating higher tax revenues through tax bracket creep. 27
<!-- Start of picture text --> GDP declines on impact due to the increase in taxation in the traditional<br>SVAR-with the effects gradually dying out, GDP begins to rise in the debt-<br>feedback responses in less than a year. In fact, the long run effect of the tax<br>increase is positive. This is caused partially by the increase in government<br>expenditure that is observed in the upper left-hand panel of Figure 14b. The<br>response of GDP in the debt-dynamics regressions exits the 90% confidence<br>interval of the traditional VAR within a year.<br>We also see a difference in the effects of tax increases on GDP on impact:<br>GDP declines by 0.2% without- but only by 0.1% with- debt dynamics. This<br>gives some evidence of the direct channel suggested in Section 5.1, where<br>the contractionary effects of tax increases are diminished on impact. This<br>is perhaps because of expectations of government expenditure increases—and<br>therefore higher GDP-identified in the upper-left-hand and lower-right-hand<br>responses of Figure 14a. A shock to tax revenues implies a permanently lower<br>level of debt, which leaves room for increases in public expenditure in the<br>long run.<br>The importance of accounting for debt dynamics is further apparent in<br>Figure 14c, which gives the dynamics of the (cumulative change) in the debt-<br>to-GDP ratio following each of the two fiscal shocks. As is apparent in both<br>panels, the traditional SVAR implies permanent increases in the debt-to-<br>GDP ratio due to fiscal stimuli. Once we account for the actual historical<br>response of fiscal policy to changes in sovereign debt in the regressions incor-<br>porating debt dynamics, we find that fiscal stimulus was followed by fiscal<br>consolidation within 1-2 years of the initial loosening of fiscal policy.<br>In Brazil, we find evidence that fiscal stimulus is followed by consoli-<br>dation within a relatively short forecast horizon, while fiscal consolidation<br>leaves room for future fiscal expansion. As we noted, we find no evidence<br>of the interest rate crowding-out channel through which debt feedback was<br>28<br><!-- End of picture text -->
<!-- Start of picture text --> predicted to affect output, but some potential indication of a direct channel<br>from higher debt to lower GDP.<br>Further evidence of the direct channel can be found in the case of Estonia,<br>in Figure 16a. The traditional VAR finds a response of GDP to increases<br>in government expenditure that is not significantly different from zero. The<br>regressions with debt dynamics indicate, however, that this may overstate<br>the effects of government expenditure on output in Estonia. In fact, once<br>debt dynamics have been accounted for, GDP declines significantly in the<br>medium term in response to increases in government expenditure. In con-<br>trast, in Figure 16b, a tax increase causes a greater decline in GDP once debt<br>dynamics have been accounted for. This unintuitive result, can be explained<br>by a glance at the right-hand panel of Figure 16c. The increase in taxation<br>decreases the debt-to-GDP ratio only temporarily. Debt then increases in the<br>upcoming years. This is because government expenditure shows a persistent<br>increase following the increase in tax revenues, as can be seen in the upper-<br>left-hand panel of Figure 16a. In Estonia, the fiscal space freed by increases<br>in taxation is more-than-fully used to increase government expenditures in<br>subsequent years. These increases in government expenditures increase debt<br>levels, which have a deleterious effect on GDP.<br>In the remaining three countries, as in the Favero and Giavazzi’s (2007)<br>exploration of the United States, we find little difference between the regres-<br>sions with and without debt dynamics. Only in the case of South Africa do<br>debt dynamics appear to diminish the effects of fiscal policy, due to future<br>policy reversals, but this effect is not statistically significant.<br>29<br><!-- End of picture text -->
<!-- Start of picture text --> 6 Conclusion<br>This paper explores the effects of fiscal policy-government expenditure and<br>taxes—on output in a sample of 28 developing- and high-income countries. Its<br>main new findings, using OLS, GMM and SVAR estimates are that the effects<br>of taxes on output appear moderate in high-income countries, but significant<br>in developing countries. Specifically, we find that cuts in personal income tax<br>rates have a significant impact on economic growth in the following year in<br>developing countries. In contrast to findings in SVAR studies of high-income<br>countries, we find that tax policy appears more effective at stimulating output<br>than increases in government expenditures.<br>The paper also incorporates debt dynamics into a Structural VAR esti-<br>mation and concludes that in some cases—Brazil and Estonia in the current<br>study—incorporating debt dynamics into a fiscal SVAR can have a substan-<br>tial effect on estimates of the effects of taxes and government expenditure on<br>output.<br>30<br><!-- End of picture text -->
<!-- Start of picture text --> References<br>References<br>[1] Alesina, Alberto F. and Silvia Ardagna (2010). “Large Changes in Fiscal<br>Policy: Taxes Versus Spending,” NBER Chapters in Tax Policy and the<br>Economy, volume 24..<br>(2) Alesina, Alberto, Filipe Campante and Guido Tabellini. 2008. “Why<br>is Fiscal Policy Often Procyclical?” Journal of the European Economic<br>Association 6(5): 1006-1036.<br>[3] Barro, Robert J. (1981) “Output effects of government purchases”, Jour-<br>nal of Political Economy 89, 1086-1121.<br>[4] Barro, Robert J., and Charles Redlick (2010) “Macroeconomic Effects<br>from Government Purchases and Taxes”, mimeo: Harvard University.<br>[5] Barro, Robert J. and Chaipat Sahasakul (1983). “Measuring the Av-<br>erage Marginal Tax Rate from the Individual Income Tax,” Journal of<br>Business, 56, October, 419-452.<br>[6] Blanchard, Olivier and Roberto Perotti (2002). “An Empirical Charac-<br>terization Of The Dynamic Effects Of Changes In Government Spending<br>And Taxes On Output,” The Quarterly Journal of Economics, 117(4),<br>pages 1329-1368.<br>[7] Bohn, Henning (1998). “The Behavior Of U.S. Public Debt And<br>Deficits,” The Quarterly Journal of Economics, vol. 113(3), pages 949-<br>963.<br>31<br><!-- End of picture text -->
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[8] Caldara, Dario (2011). “The Analytics of SVARs: A Unified Framework<br>to Measure Fiscal Multipliers,” mimeo, Stockholm University.<br>[9] Cloyne, James (2011). “What are the effects of tax shocks in the United<br>Kingdom? New evidence from a narrative evaluation.” mimeo, Univer-<br>sity College London.<br>[10] Favero, Carlo and Francesco Giavazzi (2007) “Debt and the Effects of<br>Fiscal Policy,” NBER Working Papers 12822.<br>[11] Favero, Carlo and Francesco Giavazzi (2010). “Reconciling VAR-based<br>and Narrative Measures of the Tax-Multiplier,” CEPR Discussion Pa-<br>pers 7769.<br>[12] Giorno, Claude, Pete Richardson, Deborah Roseveare and Paul van den<br>Noord (1995), “Potential Output, Output Gaps and Structural Budget<br>Balances”, OECD Economic Studies, No. 24, pp. 167-209.<br>[13] Uzetzki, Ethan. “Rent-Seeking Distortions and Fiscal Procyclicality.”<br>Forthcoming in the Journal of Development Economics.<br>[14] Uzetzki, Ethan, Enrique G. Mendoza and Carlos A. Végh (2010). “How<br>Big (Small?) are Fiscal Multipliers?,” CEP Discussion Papers DP1016,<br>Centre for Economic Performance, LSE.<br>[15] Uzetzki, Ethan, Carmen Reinhart and Kenneth Rogoff (2011), “Ex-<br>change rate arrangements entering the 21st century: Which anchor will<br>hold?” (mimeo, LSE, University of Maryland and Harvard University).<br>[16] International Monetary Fund (2010) World Economic Outlook, October.<br>32<br><!-- End of picture text -->
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[17] Kaminsky, Graciella, Carmen Reinhart and Carlos A. Végh (2004).<br>“When It Rains It Pours: Procyclical Capital Flows and Macroeconomic<br>Policies.” in NBER Macroeconomics Annual, edited by Mark Gertler and<br>Kenneth Rogoff, Cambridge, MA: MIT Press.<br>[18] Kraay, Aart (2010) “How Large is the Government Spending Multi-<br>plier? Evidence from World Bank Lending,” World Bank Policy Re-<br>search Working Paper No. 5500.<br>[19] Lopez, Humberto and Luis Serven (2006). “A normal relationship ?<br>Poverty, growth, and inequality,” World Bank Policy Research Work-<br>ing Paper Series 3814.<br>[20] Mendoza, Enrique G. and Jonathan D. Ostry (2008). “International<br>Evidence on Fiscal Solvency: Is Fiscal Policy Responsible?,” Journal of<br>Monetary Economics 55(6).<br>[21] Nakamura, Emi and Jén Steinsson (2010) “Fiscal Stimulus in a Mone-<br>tary Union: Evidence from U.S. Regions,” Mimeo: Columbia University.<br>[22] Perotti, Roberto (2004) “Estimating the effects of fiscal policy in OECD<br>countries,” Working Papers 276, IGIER Bocconi University.<br>[23] Perotti Roberto (2011) “The Effects of Tax Shocks on Output: Not So<br>Large, But Not Small Either” NBER Working Papers 16786.<br>[24] Ramey, Valerie A., 2009. “Identifying Government Spending Shocks: It’s<br>All in the Timing,” NBER Working Papers 15464.<br>[25] Ramey, Valerie A. and Matthew D. Shapiro (1998). “Costly capital re-<br>allocation and the effects of government spending,” Carnegie-Rochester<br>Conference Series on Public Policy, 48(1).<br>33<br><!-- End of picture text -->
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[26] Reinhart, Carmen M., and Kenneth S. Rogoff (2010) “Growth in a Time<br>of Debt,” American Economic Review, 100(2).<br>[27] Romer, Christina D. and David H. Romer (2010). “The Macroeconomic<br>Effects of Tax Changes: Estimates Based on a New Measure of Fiscal<br>Shocks,” American Economic Review, 100(3).<br>[28] Saez, Emmanuel (2001). “Using Elasticities to Derive Optimal Income<br>Tax Rates,” Review of Economic Studies 68(1), pages 205-29.<br>[29] Talvi Ernesto and Carlos A. Vegh (2005). “Tax Base Variability and Pro-<br>cyclical Fiscal Policy in Developing Countries.” Journal of Development<br>Economics 78 (October).<br>[30] Van den Noord, Paul (2000). “The Size and Role of Automatic Fiscal<br>Stabilizers in the 1990s and Beyond” OECD Economics Department<br>Working Papers No. 230<br><!-- End of picture text --> <!-- Start of picture text --> 34<br><!-- End of picture text -->
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<!-- Start of picture text --> 8002<br>L002<br>, 9002<br>\ S00Z<br>v00z<br>' €002<br>wy 7?we coz g<br>oa) i Toor<br>& < oooz x<br>cD \ 666t<br>- = ‘ aa<br>ea ‘ s66t &<br>ox ‘ l66r<br>es \ 1<br>=> \ geet |<br>O oc \<br><c \<br>oTnN \ S66T<br>o © \ v66T<br>oe ‘<br>oS . €66T<br>£0<br>es H 766T<br>= 3 ' T66T<br>g ' O66T |<br>62:a<br>& ! 686T 3<br>Y<br><< } ce<br>oe : gs6er =<br>mm Oo ser 5<br>vo Y oor" L861 O<br>BbLL wo 9861 |<br>nN G86<br>\<br>v86T<br>}<br>= E861<br>7861<br>TS6T<br>xs oS MR KC KR KR RR EL VR RB<br>oO oO ioe) BS WO a) + mM N a jo)<br>m~ N N N N N N N N N N<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 4a: Tax Rates<br>Argentina Australia<br>40% pruners 60% rprrrrmreererecrccnneeercrnncneeeee<br>TO eei a -o--" 20% vchossntnetntnsenntnninntnetstnetnetineinetneigenesietnntnetneigeiaeietnntneeseiaenetntneenesee<br>TomMmNORWODOAANDNDNAAAAAADOWDODWOOOOFOMN THN ON0 OOWO AmMMNnRCAD WDAWDHAAAAAAAD AMNMNR A AMM &<br>NAAAAAADOWDODOADA0O000At DI ADI TAT TN NNN NNN SN0 NAADAAAAARAaA A oA DA A A TD TD AATD TNOOOGOODNN OOSNO<br>VAT © == PIT ——CIT — VAT © == PIT ——CIT<br>Belgium Brazil<br>50% : = _eo-oer ~ncce CY 0 A ee<br>30% “fever 20% ee<br>____—_— 10% ee.<br>AmMuMNnrCanDdnWddWddAAAAAAAHRAaA MNMNrR A A MW hy nor WODaoentiananwtnworkRWw<br>NAAAAAAAAA DDO OO NADA ADWDODAOGOOO0O000 OO<br>aA nA At nt Tt ti ti Tt iTAAATNDDONN ODSN NADAA TDW TD TA TNADWOO0O0O0NNN NNN0000SN OeOO<br>—— VAT © == PIT ——CIT — VAT © == PIT ——CIT<br>Bulgaria Canada<br>40% r oc cccuuesuecuaseatestesuesssestussssuassasssessesessusssasessessessissnassasuucseeesseussaesuueeseeeeeeeeseeseeeseneee 40% (De eose Ts eseeeecllles<br>20% pegs |p EEE EEE EE EEEEEEEEEEE RTT OO I oy. 20% wera<br>ORDNANAA DA TDWIADAHHDHDWAHMDOWOHATANTNOOOONN000O00MTNNNN00 NONWO00 KR00 OeOOOOOW AmMMNnRCADNAADAAAARAaA A WDAWADWHAAHAHARAAt DA ADA AAAMNMNRTD TD AAAAATD TNA AMMOOOOOONN OO&SNO<br>— VAT © © = PIT ——CIT —— VAT © © = PIT ——CIT<br>Chile Colombia<br> ””TMTM 40% a<br>10% soprveeneevneenreerenrovnseanernesneronronnsentovneeaernerenrnnreaneeesnerenroanrnaroanesnesannnnsnnet :<br>see HH HK tL Lee. 20% »<br>10% a a<br>0% pao oo oo oot 0% $y oo ooo<br>TomnoOR WD OTANI MA TNO KR OW TomnoOR WD OAAN MA TN O KR OO<br>DAA AAAARDRDOADADAAO0DO0O00 0 NDNAAAAAADADADOAGAOA0O0O00<br>NAAAAAAADDADOAGOA0O000AA TDI TAI TATA TAN NNN NNN NO0 NAAAAAADODODOGDO0O000At tI TAI TAT TN NNN N NNN00<br><!-- End of picture text -->
# Figure 4b: Tax Rates

<!-- Start of picture text --> Croatia Cyprus<br>30% © i oS errr<br>20% ocporvensermerscouerenreuntearerneenerenrenninnts oe 30% “pretnnnitnnnnnnnnninnninnnninnnnnnnnei nnn~ == = «=<br>20% a. sseecsnuuecsssensonnusssesecsonnasssessessnuss MQtonees<br>NORDADAADDAADADDAADDSWAHDOANMAtH OR LO NnoOoORDADAADOADADAADADDSWAHDOANMATMH OR<br>NADAee HDOODOO0OO0O0 0 0 0 DADAee HAHOOO0O GOGO OO Oo Oo<br>—— VAT — — Pll —_— (IT —— VAT — — PT = (IT<br>Czech Rep Dominican R.<br>40% 9 ors QQ cnnnnnnnnnggm<br>30% wfsecneennnnennnnniennneiitt oo scsessetesssensnsensnesnueesseeenseeenee<br>20% -fetnermnamrnnararnnanar a ee gy<br>NMoORWADHDOANMNtAHNHNAADAHDAHDHDODADADACAAADSee eeHAHA OOOO OOOORAO DOWO NORDAADADAADODADACAADAASDAee DADOANMtHA DODOGC GOOORFO DOSG<br>—— VAT — — Pll —_—|T —— VAT — — PIT —_((|T<br>Estonia France<br>20% * nnn 40% ee eye<br>30% Se ae<br>10% fev 20% 0<br>10% ae<br>0% i 0%<br>RSRKRERRSSSSSSESB HBB HRARARASSSS<br>FTAPAPAacegegeags Gs BQ ee<br>—S VAT — — PT — CIT — VAT 222 PIT ——CIT<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 4c: Tax Rates<br>Germany Hungary<br>60% 4s ssosssstssgenonnsssusseennnnnnssasesannnnssusesennnnsnsassennnnnnnuensenennnaneeeeeees i x<br>i 40%° “be eee QDene cone cnneenseesusesnensseseenssesesessasstnssaesteassestenseatsesossessteassusteascateeanseseeees<br>50% wprtnnnennnnninnnnnnnnnnnnnnimmsnnnnnsiinmm snnennnnnnnnnt i er ee f- - ~ eo —<br>om ee teint . 20% :<br>0% ro 0% oT<br>SRRESARRERSSSS s 288888<br>SASS 8 8B<br>SAA AAA AAR RAR RH HD HD DW FS F&F F& f& 8<br>Latvia Lithuania<br>ae e ew = = eeeme ae<br>20% pe oc cecesaesaseseseueueueseseseeeeeeeeeseseeeeseateeeeeeees<br>10% ssossnnesnannnnnannnnnnannanennnnnantannnnnnannunennannsensennanounsiunsnnannsnssannnninsenaennennsesnes<br>0% St ooo or 0% ooo ooo<br>norDAADAARAADADGAGAaADdwWoaooen&dthian+ytnwuor wa TomMNUORWDD OAAN MAT NO KR WO<br>(o>A ak ©)DHmk ©)TAD ©)TA ©)TTEE ©NNN<< NNNOe Oe OeONOe)Oe DDDAADARADNAAA oH TDI AAARHAAD A TN D OADADOGCODADANNN NNN D O0O000AACSAOe0<br>Malaysia Mexico<br>10% SS 10% Cp I I I i I I<br>0% | i 0% a |<br>ORDAAAGDGAAAGAQGGAAC0WDHWD DA AN M TM O KR OC norDADAADAD®AODGCCAGAGODOC AANM TM O KR OO<br>DNnNaAananneonoeandeaoseadbd oFo00 0 0 (o> mn ©) ak ©) ©) ©) © © Oe Oe Oe)<br>A DWH TDW TTA NNN NNN NOON OU A DWH TDA TD TAN NNN NNN ON Oe<br>Peru Poland<br>30% L a . 40% oe ss ssoussusnneseununesunnenesusnnenuusnoeennsnentunaseennsnniesunneeennnenesesesee<br>20% F Sea — 30% ~rvnsrnmnenennnennnnnennnnannnnneni os == =susssusenssnsssusesnsesssaseesnsensssnseusesseenseensenseeneenseenseaet= = = -<br>norDADAAADADGAGADdwWooaoaoctAnNnenmnwtnNnNwoO KR wW NnDn FTFDn BODH DOBA ODFO©@ Nn0B TFT&GF WOG&G GSBW<br>(o> ak ©) ms ©) a ©) a ©) EE © © <n Oe Oe Oe) non HDB HBO Hoe OF BOF OF OF Oo<br>A DH TAD TA TAN NNN NNN ON Oe aA DUNT TD NN NNN<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 4d: Tax Rates<br>Slovakia Slovenia<br>50% nnn , a<br>40% ' 30% green<br>30% wpe oo cssessnnnueestennnnuetseeesnnnnaeeeeecnnnnnseteeeennnnees Ee<br>20% i - 20% 00 ee SSS<br>eratEnna :<br>10% fovea 10%a<br>MyFnNnORWHAOAQN MTN WO KR OW TomnoOR WHA OAAN MA TN O KR OO<br>NAAAARARRARDIDODODOOAOA0DNDNAADHAAAAHWDODDODOODODOOAOAt ADI A TA TAT TNNNN NNN OeDOOO NAAAAAARADODODODOOFO0ODADAAt TDI TDI AADODOODOOOO0OTAT TN NNN N NNN0 AO0<br>South Africa Thailand<br>50% pn 40% "nnn<br>40% | i niindiiegiind ae 30% ss _.<br>fy ferent<br>10% (fornia 10% ore<br>nor woendodcdaeneonwstkswuMnwork wo a N ~m t+ w No) S ioe)<br>NAA A HA OOOO O OO C0 80 fo) fo) fo) fo) fo) fo) fo) jo)<br>NAA AHA DWOO0O0006UmUMDMUUCUMUCUCOUODUCUCO Oo Oo Oo Oo Oo Oo Oo Oo<br>AeA A AH A NNNN NNN NS N N N N N N N N<br>UK USA<br>60% "yn 60% "pn<br>50% oe sc seyasavavasasesusessasususssssens sees sesesauasasensusssesesapasisanisesesisineasnineuenneenenenenenseeneneenee 50% ~ sc cuesussssssessesesasesesasesasasestsssssnsususnsesesestaeseninensusnineeseiesusenenenenennteneeenen<br>40% 5 el ssssssssssnssnnnnnnnnnnnnsnsnsssusussssssseesonensnnnnnnnnnnnssssinnasnusseseenennnnnntnnnsnssessense® 40% veca |<br>30% Oe SS en CDS See eee 30% SL<br>10% ee 10% Oe<br>0% (I 0% Wo<br>AmMuMNnrCAanNDWdDdWDAAAHAAAAAAANAAAAAAAAAAee aAiMNMNrR AAA GOODGOODA i MWin hyODOot AmMNMNROAWHDWDdWDAAAAAAAAANAADAAAAAAAAAAaAd ea ae Aae AMNMNRAae AH A A AAANNANAMMGODGOGO ODO ANOO&OD<br>VAT ee PIT ——— CIT ——SalesTax ©@— PIT ——CIT<br><!-- End of picture text -->
# **Figure 5: Fiscal Multipliers in High-Income Countries**

<!-- Start of picture text --> High Income Countries: G multiplier<br>2<br>1.8<br>1.6<br>1.4<br>1.2<br>Long Run: 1.08<br>1 Impact: 0.77<br>0.8<br>0.6<br>0.4<br>0.2<br>Quarters<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>High Income Countries: Tax multiplier<br>1.2<br>1<br>0.8<br>0.6<br>0.4 Long Run: 0.28<br>Impact: 0.1<br>0.2<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>-0.2<br>-0.4<br>Quarters<br>-0.6<br><!-- End of picture text -->
# **Figure 6: Fiscal Multipliers in Developing Countries**

<!-- Start of picture text --> Developing Countries: G multiplier<br>0.8<br>0.6<br>0.4<br>0.2 Long Run: 0.15<br>Impact: 0.17<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>-0.2<br>-0.4<br>Quarters<br>-0.6<br>Developing Countries: Tax multiplier<br>2<br>1.6<br>1.2 Long Run: 0.83<br>0.8<br>Impact: 0.3<br>0.4<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters<br>-0.4<br><!-- End of picture text -->
# **Figure 7: Fiscal Multipliers in Open Economies**

<!-- Start of picture text --> Open Economies: G multiplier<br>1<br>0.8<br>0.6<br>0.4 Long Run: 0.08<br>0.2<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>-0.2<br>-0.4<br>-0.6 Impact: 0.03<br>Quarters<br>-0.8<br>Open Economies: Tax multiplier<br>0.4<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Long Run: -0.31<br>-0.4<br>Impact: 0.08<br>-0.8<br>Quarters<br>-1.2<br><!-- End of picture text -->
# **Figure 8: Fiscal Multipliers in Closed Economies**

<!-- Start of picture text --> Closed Economies: G multiplier<br>2.8<br>2.3<br>1.8 Long Run: 1.4<br>1.3<br>0.8<br>0.3<br>Impact: 0.39<br>Quarters<br>-0.2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>Closed Economies: Tax multiplier<br>2.4<br>2<br>1.6<br>Long Run: 1.28<br>1.2<br>0.8<br>0.4 Impact: 0.05<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>-0.4<br>Quarters<br>-0.8<br><!-- End of picture text -->
# **Figure 9: Fiscal Multipliers under Fixed Exchange Rates**

<!-- Start of picture text --> Fixed Exchange Rates: G multiplier<br>2.3<br>1.8<br>Long Run: 1.2<br>1.3<br>0.8<br>0.3<br>Impact: 0.02 Quarters<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>-0.2<br>Fixed Exchange Rates: Tax multiplier<br>0.8<br>0.4<br>Long Run: 0.03<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Impact: 0.1<br>-0.4<br>Quarters<br>-0.8<br><!-- End of picture text -->
# **Figure 10: Fiscal Multipliers under Flexible Exchange Rates**

<!-- Start of picture text --> Flexible Exchange Rates: G multiplier<br>1<br>0.5<br>0 Long Run: 0.55<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>-0.5 Impact: 0.32<br>-1<br>Quarters<br>-1.5<br>Flexible Exchange Rates: Tax multiplier<br>0.8<br>0.4<br>Long Run: -0.15<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>-0.4 Impact: 0.16<br>-0.8<br>Quarters<br>-1.2<br><!-- End of picture text -->
# **Figure 11: Impulse Responses in Developing Countries**

Response of G to a shock to G <!-- Start of picture text --> .05<br>.04<br>.03<br>.02<br>.01<br>.00<br>-.01<br>5 10 15 20 25 30<br>Response of Tax Revenues to a shock to G<br>.015<br>.010<br>.005<br>.000<br>-.005<br>-.010<br>5 10 15 20 25 30<br>Response of GDP to a shock to G<br>.006<br>.004<br>.002<br>.000<br>-.002<br>-.004<br>5 10 15 20 25 30<br><!-- End of picture text -->
# **Figure 12: Impulse Responses in Closed Economies**

Response of G to a shock to taxes <!-- Start of picture text --> .004<br>.002<br>.000<br>-.002<br>-.004<br>-.006<br>-.008<br>5 10 15 20 25 30<br><!-- End of picture text --> <!-- Start of picture text --> Response of Tax Revenues to a shock to taxes<br>.04<br>.03<br>.02<br>.01<br>.00<br>-.01<br>5 10 15 20 25 30<br>Response of GDP to a shock to taxes<br>.004<br>.002<br>.000<br>-.002<br>-.004<br>5 10 15 20 25 30<br><!-- End of picture text -->
°° ° Figure 13: Actual and Synthetic Debt Series <!-- Start of picture text --> 4 , \ r ey<br>// \\, \ NS // NYa<br>ry // \\ NM~ /ca ,\<br>- / WALTJ oN\ TM\Nene OL” N.S S<br>= y!iIIii “wo NS 7SA. N,<br>7 x<br>a \\\\\\\ \\<br>\<br>‘<br>‘ \<br>Ty,Ax.mA ///vA<br>+ w o s ~ SSN,\Na - _- a///<br>a | \\,NU -n. TM\Noe ..N . S SS. S NS //ui/ / // / y va“-a<br>4 NSN. ‘ \— N /a,//<br>ZY<br>VVa<br>“<br>I/ \<br>= = == “ e w ane fd/"/ti AN\\ NS \\ \ \\\\<br>\ N<br>N RNo<br><!-- End of picture text -->
|Series1<br>Series2<br>Series3<br>Series4<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Inflation**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1920 21 22 23 24<br>**Interest Rate**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Exchange Rate**<br>**nment expenditure, Brazil**|
|---|
|24<br>-0.006<br>-0.005<br>-0.004<br>-0.003<br>-0.002<br>-0.001<br>0<br>0.001<br>0.002<br>-0.015<br>-0.01<br>-0.005<br>0<br>0.005<br>0.01<br>0.015<br>0.02<br>-0.1<br>-0.08<br>-0.06<br>-0.04<br>-0.02<br>0<br>0.02<br>0.04<br>0.06<br>0.08<br>**gover**|
|1<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 <br>**Government Expenditure**<br>1 2 3 4 5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Tax Revenues**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**GDP**<br>**Figure 14a: Responses to a shock to**|
|-0.01<br>-0.005<br>0<br>0.005<br>0.01<br>0.015<br>0.02<br>0.025<br>0.03<br>-0.02<br>-0.015<br>-0.01<br>-0.005<br>0<br>0.005<br>0.01<br>-0.006<br>-0.004<br>-0.002<br>0<br>0.002<br>0.004<br>0.006<br>0.008<br>0.01|
|1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Inflation**<br> 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Interest Rate**<br> 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Exchange Rate**<br>**ax revenues, Brazil**|
|---|
|24<br>-0.004<br>-0.002<br>0<br>0.002<br>0.004<br>0.006<br>0.008<br>-0.02<br>-0.015<br>-0.01<br>-0.005<br>0<br>0.005<br>0.01<br>0.015<br>1 <br>-0.06<br>-0.04<br>-0.02<br>0<br>0.02<br>0.04<br>0.06<br>0.08<br>1 <br>**hock to t**|
|3 <br>**s**|
|1<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 2<br>**Government Expenditure**<br><br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Tax Revenues**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**GDP**<br>**Figure 14b: Responses to a**|
|-0.016<br>-0.014<br>-0.012<br>-0.01<br>-0.008<br>-0.006<br>-0.004<br>-0.002<br>0<br>0.002<br>0.004<br>0.006<br>-0.02<br>-0.01<br>0<br>0.01<br>0.02<br>0.03<br>0.04<br>1<br>-0.01<br>-0.008<br>-0.006<br>-0.004<br>-0.002<br>0<br>0.002<br>0.004|
<!-- Start of picture text --> 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9<br>Tax increase<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0.2% 0.0% -0.2% -0.4% -0.6% -0.8% -1.0% -1.2% -1.4%<br>Brazil 24<br>23<br>22<br>21<br>20<br>19<br>18<br>17<br>16<br>15<br>14<br>13<br>12<br>11<br>10<br>9<br>G increase<br>8<br>7<br>6<br>5<br>4<br>Figure 14c: Implied Changes in Debt-to-GDP ratio in BP (dotted) and FG (solid) VARs,<br>3<br>2<br>1<br>2.0% 1.5% 1.0% 0.5% 0.0% -0.5%<br><!-- End of picture text -->
<!-- Start of picture text --> Inflation<br>Interest Rate Exchange Rate<br>10 11 12 13 14 15 16 17 18 19 20 21 22 23 249 10 11 12 13 14 15 16 17 18 19 20 21 22 23 249 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9<br>8 8<br>8<br>7 7<br>7<br>6 6<br>6<br>5 5<br>5<br>4 4<br>3 3 4<br>2 2 3<br>1 1 2<br>1<br>0 0<br>0<br>0.0015 0.001 0.0005 -0.0005 -0.001 -0.0015 0.0004-0.002 0.0003 0.0002 0.0001 -0.0001 -0.0002 -0.0003 -0.0004 0.03 0.02 0.01 -0.01 -0.02 -0.03<br>24<br>23<br>22<br>21<br>20<br>19<br>18<br>GDP<br>17<br>16 Tax Revenues<br>15<br>Government Expenditure<br>14<br>13<br>12<br>11<br>10<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>Figure 15a: Responses to a shock to government expenditure, Chile 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9<br>7 8 8<br>7<br>6 7<br>6<br>5 6<br>5 5<br>4<br>4 4<br>3<br>3 3<br>2 2 2<br>1 1 1<br>0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 0 -0.002 -0.004 0.015 0.01 0.005 0 -0.005 -0.01 -0.015 -0.02 0.004 0.003 0.002 0.001 0 -0.001 -0.002 -0.003 -0.004 -0.005 -0.006<br><!-- End of picture text -->
|1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Inflation**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Interest Rate**<br> 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Exchange Rate**<br>**ax revenues, Chile**|
|---|
|23 24<br>-0.002<br>-0.001<br>0<br>0.001<br>0.002<br>0.003<br>0.004<br>-0.0008<br>-0.0006<br>-0.0004<br>-0.0002<br>0<br>0.0002<br>0.0004<br><br>-0.01<br>0<br>0.01<br>0.02<br>0.03<br>0.04<br>0.05<br>1 <br>**a shock to t**|
|2<br>3<br>4<br>5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 <br>**Government Expenditure**<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Tax Revenues**<br> 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**GDP**<br>**Figure 15b: Responses to**|
|1<br><br>1|
|-0.006<br>-0.005<br>-0.004<br>-0.003<br>-0.002<br>-0.001<br>0<br>0.001<br>0.002<br>0.003<br>0.004<br>0.005<br>-0.04<br>-0.02<br>0<br>0.02<br>0.04<br>0.06<br>0.08<br>1<br>-0.005<br>-0.004<br>-0.003<br>-0.002<br>-0.001<br>0<br>0.001<br>0.002<br>0.003<br>0.004<br>0.005|
<!-- Start of picture text --> 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9<br>Tax increase<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0.0% -0.1% -0.1% -0.2% -0.2% -0.3%<br>Chile 24<br>23<br>22<br>21<br>20<br>19<br>18<br>17<br>16<br>15<br>14<br>13<br>12<br>11<br>10<br>G increase 9<br>8<br>7<br>6<br>5<br>4<br>Figure 15c: Implied Changes in Debt-to-GDP ratio in BP (dotted) and FG (solid) VARs,<br>3<br>2<br>1<br>0.9% 0.8% 0.7% 0.6% 0.5% 0.4% 0.3% 0.2% 0.1% 0.0%<br><!-- End of picture text -->
|1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Inflation**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Interest Rate**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Exchange Rate**<br>**rnment expenditure, Estonia**|
|---|
|4<br>-0.007<br>-0.006<br>-0.005<br>-0.004<br>-0.003<br>-0.002<br>-0.001<br>0<br>0.001<br>0.002<br>-0.004<br>-0.002<br>0<br>0.002<br>0.004<br>0.006<br>0.008<br>-0.06<br>-0.04<br>-0.02<br>0<br>0.02<br>0.04<br>0.06<br>**gove**|
|3 2<br>**to**|
|1<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 2<br>**Government Expenditure**<br>1 2 3 4 5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Tax Revenues**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**GDP**<br>**Figure 16a: Responses to a shock**|
|-0.01<br>-0.005<br>0<br>0.005<br>0.01<br>0.015<br>0.02<br>0.025<br>0.03<br>-0.02<br>-0.015<br>-0.01<br>-0.005<br>0<br>0.005<br>0.01<br>0.015<br>0.02<br>-0.006<br>-0.005<br>-0.004<br>-0.003<br>-0.002<br>-0.001<br>0<br>0.001<br>0.002<br>0.003|
|1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Inflation**<br> 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Interest Rate**<br> 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Exchange Rate**<br>**ax revenues,Estonia**|
|---|
|3 24<br>-0.003<br>-0.002<br>-0.001<br>0<br>0.001<br>0.002<br>0.003<br>0.004<br>-0.006<br>-0.004<br>-0.002<br>0<br>0.002<br>0.004<br>0.006<br>0.008<br>1 <br>-0.04<br>-0.02<br>0<br>0.02<br>0.04<br>0.06<br>0.08<br>1 <br>**shock to t**|
|1<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 2<br>**Government Expenditure**<br>1 2 3<br>4 5 6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Tax Revenues**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**GDP**<br>**Figure 16b: Responses to a**|
|-0.005<br>0<br>0.005<br>0.01<br>0.015<br>0.02<br>0.025<br>-0.01<br>-0.005<br>0<br>0.005<br>0.01<br>0.015<br>0.02<br>0.025<br>-0.004<br>-0.003<br>-0.002<br>-0.001<br>0<br>0.001<br>0.002<br>0.003<br>0.004|
<!-- Start of picture text --> 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9<br>Tax increase<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0.3% 0.2% 0.2% 0.1% 0.1% 0.0% -0.1% -0.1% -0.2% -0.2%<br>24<br>Estonia<br>23<br>22<br>21<br>20<br>19<br>17 18<br>16<br>15<br>14<br>13<br>12<br>10 11<br>G increase 9<br>8<br>7<br>6<br>5<br>4<br>Figure 16c: Implied Changes in Debt-to-GDP ratio in BP (dotted) and FG (solid) VARs,<br>3<br>2<br>1<br>0.7% 0.6% 0.5% 0.4% 0.3% 0.2% 0.1% 0.0%<br><!-- End of picture text -->
<!-- Start of picture text --> Inflation<br>Interest Rate Exchange Rate<br>10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9 9 9<br>8 8 8<br>7<br>7 7<br>6<br>6 6<br>5<br>5 5<br>4<br>4 4<br>3<br>3 3<br>2<br>2 2<br>1<br>1 1<br>0<br>0 0<br>0.0015 0.001 0.0005 -0.0005 -0.001 0.004-0.0015 0.003 0.002 0.001 -0.001 -0.002 -0.003 -0.004 0.02 0.015 0.01 0.005 -0.005 -0.01<br>24<br>23<br>22<br>24<br>21<br>23<br>20<br>22<br>19 21<br>18 20 GDP<br>17 19<br>16 Tax Revenues 18<br>15 17<br>Government Expenditure<br>16<br>14<br>15<br>13<br>14<br>12<br>13<br>11<br>12<br>10<br>11<br>9 10<br>10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>Figure 17a: Responses to a shock to government expenditure, Malaysia 8 9 9<br>7 8 8<br>6 7 7<br>5 6 6<br>4 5 5<br>4 4<br>3<br>3 3<br>2<br>2<br>2<br>1 1<br>1<br>0 0<br>0<br>0.06 0.05 0.04 0.03 0.02 0.01 -0.01 -0.02 0.05 0.04 0.03 0.02 0.01 -0.01 -0.02 0.004 0.002 -0.002 -0.004 -0.006 -0.008 -0.01<br><!-- End of picture text -->
<!-- Start of picture text --> Inflation<br>Interest Rate Exchange Rate<br>10 11 12 13 14 15 16 17 18 19 20 21 22 23 249 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9 9<br>8<br>8 8<br>7<br>6 7 7<br>5 6 6<br>4 5 5<br>3 4 4<br>2 3 3<br>1 2 2<br>1 1<br>0<br>0 0<br>0.002 0.0015 0.001 0.0005 0.005-0.00050.004 0.003 0.002 0.001 -0.001 -0.002 -0.003 -0.004 -0.005 0.02 0.015 0.01 0.005 -0.005 -0.01 -0.015<br>24<br>23<br>22<br>24<br>21<br>23<br>20<br>22<br>19<br>21<br>18 20 GDP<br>17 19<br>16 Tax Revenues 18<br>15 17<br>14 Government Expenditure 16<br>13 15<br>14<br>12<br>13<br>Figure 17b: Responses to a shock to tax revenues, Malaysia 11<br>12<br>10<br>11<br>9 10 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>8<br>9 9<br>7 8 8<br>6 7 7<br>5 6 6<br>4 5 5<br>4<br>3 4<br>3<br>3<br>2 2<br>2<br>1 1<br>1<br>0 0<br>0<br>0.015 0.01 0.005 -0.005 -0.01 -0.015 -0.02 -0.025 -0.03 -0.035 0.08 0.06 0.04 0.02 -0.02 -0.04 0.006 0.004 0.002 -0.002 -0.004 -0.006 -0.008 -0.01<br><!-- End of picture text -->
<!-- Start of picture text --> 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9<br>Tax increase<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0.0% -0.1% -0.2% -0.3% -0.4% -0.5% -0.6% -0.7% -0.8% -0.9%<br>24<br>Malaysia 23<br>22<br>21<br>20<br>19<br>18<br>17<br>16<br>15<br>14<br>13<br>12<br>11<br>10<br>G increase 9<br>8<br>7<br>6<br>5<br>4<br>Figure 17c: Implied Changes in Debt-to-GDP ratio in BP (dotted) and FG (solid) VARs,<br>3<br>2<br>1<br>1.0% 0.9% 0.8% 0.7% 0.6% 0.5% 0.4% 0.3% 0.2% 0.1% 0.0%<br><!-- End of picture text -->
|1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Inflation**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Interest Rate**<br> 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Exchange Rate**<br>**nt expenditure, South Africa**|
|---|
|24<br>-0.007<br>-0.005<br>-0.003<br>-0.001<br>0.001<br>0.003<br>-0.001<br>-0.0008<br>-0.0006<br>-0.0004<br>-0.0002<br>0<br>0.0002<br>0.0004<br>0.0006<br>0.0008<br>-0.1<br>-0.08<br>-0.06<br>-0.04<br>-0.02<br>0<br>0.02<br>0.04<br>1<br>**vernme**|
|3 <br>**o**|
|1<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 2<br>**Government Expenditure**<br>1 2 3 4 5<br>6<br>7<br>8<br>9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**Tax Revenues**<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>**GDP**<br>**Figure 18a: Responses to a shock to g**|
|-0.02<br>-0.01<br>0<br>0.01<br>0.02<br>0.03<br>0.04<br>0.05<br>0.06<br>-0.02<br>-0.015<br>-0.01<br>-0.005<br>0<br>0.005<br>0.01<br>-0.001<br>0<br>0.001<br>0.002<br>0.003<br>0.004|
<!-- Start of picture text --> Inflation<br>Interest Rate Exchange Rate<br>10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>8 9<br>8<br>7 8<br>7<br>6 7<br>6<br>5 6<br>5<br>4 5<br>4<br>3 3 4<br>2 2 3<br>1 1 2<br>0 1<br>0<br>0.004 0.003 0.002 0.001 -0.001 -0.002 -0.003 0.002-0.004 0.0015 0.001 0.0005 -0.0005 -0.001 0.12 0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04<br>24<br>23<br>22<br>21<br>20<br>19<br>18<br>GDP<br>17<br>16 Tax Revenues<br>15<br>Government Expenditure<br>14<br>13<br>12<br>11<br>10<br>Figure 18b: Responses to a shock to tax revenues, South Africa<br>9<br>10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>8 9<br>9<br>7 8 8<br>6 7 7<br>5 6 6<br>5 5<br>4<br>4 4<br>3<br>3 3<br>2 2 2<br>1 1 1<br>0.025 0.02 0.015 0.01 0.005 0 -0.005 -0.01 -0.015 -0.02 0.025 0.02 0.015 0.01 0.005 0 -0.005 -0.01 0.002 0.001 0 -0.001 -0.002 -0.003 -0.004<br><!-- End of picture text -->
<!-- Start of picture text --> 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br>9<br>Tax increase<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0.3% 0.2% 0.1% 0.0% -0.1% -0.2% -0.3% -0.4%<br>24<br>23<br>South Africa 22<br>21<br>20<br>19<br>17 18<br>16<br>15<br>14<br>13<br>12<br>10 11<br>G increase 9<br>8<br>7<br>6<br>5<br>4<br>Figure 18c: Implied Changes in Debt-to-GDP ratio in BP (dotted) and FG (solid) VARs,<br>3<br>2<br>1<br>0.2% 0.1% 0.1% 0.0% -0.1% -0.1% -0.2%<br><!-- End of picture text -->
|Employment<br>Output<br>Average<br>Wage elasticity of<br>Profit share<br>Elasticity<br>Elasticity of<br>Output elasticity of<br>Tax Revenue share of<br>Output Elasticity<br>PIT<br>SS<br>ofGDP<br>ofWages<br>Employment<br>PIT<br>SS<br>CIT<br>PIT<br>ss<br>CIT<br>Sales<br>ofTax Revenue<br>Argentina<br>1.82<br>0.95<br>0.52<br>0.590<br>0.400<br>0.83<br>0.62<br>0.00°<br>6.5%<br>20.6%<br>12.9% 60.0%<br>0.78<br>(0.41)<br>(0.23)<br>Australia<br>1.58<br>-<br>0.40<br>0.033<br>0.370<br>0.39<br>-<br>0.00°<br>57.0%<br>-<br>17.2% 25.8%<br>0.48<br>(0.06)<br>(0.14)<br>Brazil<br>1.95<br>0.92<br>0.45<br>0.00°<br>0.010<br>0.01<br>0.01<br>2.21<br>11.9% 34.0%<br>17.9% 36.2%<br>0.76<br>-<br>(0.07)<br>Belgium<br>1.49<br>1.00<br>0.38<br>1.690<br>0.090<br>0.32<br>0.24<br>1.277<br>33.7% 33.3%<br>6.2%<br>26.8%<br>0.53<br>(0.81)<br>(0.05)<br>Bulgaria<br>1.64<br>0.98<br>0.51<br>0.044<br>0.681<br>0.73<br>0.71<br>0.00°<br>11.4% 31.9%<br>9.4%<br>47.3%<br>0.78<br>(0.09)<br>(0.17)<br>Canada<br>1.28<br>0.75<br>0.31<br>0.170<br>0.530<br>0.64<br>0.59<br>5.43"<br>42.7%<br>15.8%<br>10.4%<br>31.0%<br>1.24<br>(0.19)<br>(0.09)<br>Chile<br>2.25<br>0.77<br>0.36<br>0.00°<br>0.080<br>0.08<br>0.08<br>2.64<br>7.5%<br>8.5%<br>23.0% 60.9%<br>1.23<br>-<br>(0.09)<br>Colombia<br>2.23<br>1.01<br>0.31<br>0.00°<br>0.210<br>0.21<br>0.21<br>2.76<br>34.2%<br>20.7%<br>7.1%<br>38.1%<br>0.69<br>-<br>(0.51)<br>Croatia<br>1.62<br>1.00<br>0.35<br>0.00°<br>0.171<br>0.17<br>0.17<br>0.00°<br>9.3%<br>35.0%<br>6.7%<br>49.0%<br>0.57<br>-<br>(0.06)<br>Cyprus<br>1.96<br>1.00<br>0.43<br>0.426<br>0.100<br>0.18<br>0.14<br>175°<br>11.8%<br>35.2%<br>15.9%<br>37.2%<br>0.72<br>(0.23)<br>-<br>Czech Rep<br>1.47<br>1.00<br>0.29<br>0.158<br>0.220<br>0.27<br>0.25<br>0.00°<br>12.4%<br>43.6%<br>12.2%<br>31.8%<br>0.46<br>(0.30)<br>(0.39)<br>Estonia<br>1.20<br>1.00<br>0.30<br>0.795<br>0.112<br>0.22<br>0.20<br>4.02°<br>24.1%<br>39.3%<br>5.6%<br>31.0%<br>0.67<br>(0.29)<br>(0.12)<br>France<br>1.72<br>0.92<br>0.35<br>0.140<br>0.290<br>0.36<br>0.33<br>0.00°<br>16.0%<br>46.8%<br>6.4%<br>30.8%<br>0.52<br>(0.06)<br>(0.22)<br>Germany<br>1.43<br>0.83<br>0.34<br>0.380<br>0.110<br>0.17<br>0.14<br>2.26°<br>27.4% 39.4%<br>4.7%<br>28.5%<br>0.49<br>0.581<br>0.046<br>Hungary<br>1.39<br>0.98<br>0.35<br>0.227<br>0.239<br>0.31<br>0.29<br>1.92°<br>15.3%<br>37.9%<br>6.6%<br>40.2%<br>0.69<br>(0.23)<br>(0.37)<br>Latvia<br>1.18<br>1.00<br>0.46<br>0.00°<br>0.343<br>0.34<br>0.34<br>0.86"<br>20.3%<br>34.1%<br>7.4%<br>38.3%<br>0.63<br>-<br>(0.70)<br>Lithuania<br>1.29<br>1.00<br>0.40<br>0.029<br>0.867<br>0.90<br>0.89<br>0.00°<br>28.0%<br>34.4%<br>6.6%<br>31.0%<br>0.87<br>(0.14)<br>(0.27)<br>Mexico<br>1.55<br>0.97<br>0.60<br>0.105<br>0.040<br>0.05<br>0.04<br>1.64<br>17.3%<br>13.9%<br>22.2%<br>46.5%<br>0.84<br>(0.05)<br>(0.10)<br>Peru<br>2.96<br>1.00<br>0.59<br>0.00°<br>0.390<br>0.39<br>0.39<br>1.42<br>7.8%<br>12.1%<br>16.0%<br>64.1%<br>0.95<br>-<br>(0.12)<br>Poland<br>1.29<br>0.95<br>0.49<br>0.012<br>0.342<br>0.35<br>0.35<br>4.40°<br>17.4%<br>38.0%<br>8.2%<br>36.3%<br>0.92<br>(0.70)<br>(0.20)<br>Slovakia<br>1.64<br>0.97<br>0.49<br>0.604<br>0.241<br>0.48<br>0.38<br>1.217<br>10.2% 42.7%<br>9.1%<br>38.0%<br>0.70<br>(0.31)<br>(0.10)<br>Slovenia<br>1.54<br>1.00<br>0.47<br>0.00°<br>0.129<br>0.13<br>0.13<br>1.80°<br>17.0%<br>41.5%<br>4.8%<br>36.7%<br>0.53<br>-<br>(0.08)<br>South Africa<br>1.26<br>-<br>0.29<br>0.001<br>0.427<br>0.43<br>-<br>2.40<br>42.7%<br>-<br>30.1%<br>27.2%<br>1.18<br>(0.05)<br>(0.31)<br>UK<br>1.53<br>1.02<br>0.30<br>0.470<br>0.140<br>0.24<br>0.21<br>1.43"<br>32.0%<br>20.6%<br>10.5%<br>36.9%<br>0.64<br>(0.21)<br>(0.04)<br>USA<br>1.35<br>0.85<br>0.30<br>0.190<br>0.300<br>0.38<br>0.35<br>3.86°<br>42.3%<br>27.7%<br>10.0%<br>19.9%<br>0.84<br>(0.18)<br>(0.03)<br>* Elasticities equal to zero due because tax payments and realization of profits are not contemporaneous.<br>2 Output elasticities of quarterly gross operating surplus.<br>3 Output elasticities of profits.<br>4 Output elasticitiy of profits factored bythe share of profit income that pays tax at quarterly frequency (75%)<br>> Elasticities set to zero as estimated parameter is negative.|
|---|
**Table 2: Tax Revenue Elasticities: Annual Estimates**

||Wage ela|sticity of|Profit<br>Elasticity|Output<br> Elasticity of|Output<br> Elasticity of||Output el|asticity o|f|Tax Revenue share of|Average<br>Output Elasticity|
|---|---|---|---|---|---|---|---|---|---|---|---|
||PIT<br>|SS<br>|of CIT|<br>Wages|<br>Profits|PIT<br>|SS<br>|CIT<br>|VAT<br>|PIT<br>SS<br>CIT<br>VAT|<br>of Tax Revenue|
||A|B|C|D|E|F=AD|G=BD|H=FC|I|J<br>K<br>L<br>M|FJ + GK + HL + IM|
|_Argentina_|1.82|0.95|1.00|1.325<br>(0.26)|0.578<br>(0.18)|2.41|1.26|0.58|1.206<br>(0.12)|7.5%<br>23.6% 14.8% 54.1%<br>5.2%<br>16.6% 10.4% 37.9%|1.22|
|_Australia_|1.58|-|1.00|0.905<br>(0.12)|1.263<br>(0.18)|1.43|-|1.26|0.835<br>(0.07)|61.9%<br>-<br>18.5%<br>5.5%<br>43.4%<br>0.0%<br>13.0%<br>3.9%|1.48|
|_Brazil_|1.95|0.92|1.15|1.238<br>(0.19)|1.274<br>(0.17)|2.42|1.14|1.46|1.001<br>(0.14)|13.9% 39.5% 20.5%<br>-<br>9.7%<br>27.7% 14.4%<br>0.0%|1.33|
|_Belgium_|1.49|1.00|1.00|0.782<br>(0.12)|1.163<br>(0.30)|1.17|0.78|1.16|0.787<br>(0.08)|46.2% 45.6%<br>8.5%<br>22.9%<br>32.3% 31.9%<br>5.9%<br>16.0%|0.95|
|_Bulgaria_|1.64|0.98|1.02|0.960<br>(0.24)|0.571<br>(0.15)|1.57|0.94|0.58|0.993<br>(0.27)|15.3% 42.9% 12.7% 45.4%<br>10.7% 30.0%<br>8.9%<br>31.8%|0.80|
|_Canada_|1.28|0.75|1.00|0.817<br>(0.68)|1.167<br>(0.62)|1.02|0.58|1.16|0.810<br>(0.07)|50.0% 19.0% 12.0% 19.0%|0.92|
|_Chile_|2.25|0.77|1.00|0.996<br>(0.12)|1.692<br>(0.66)|2.24|0.77|1.69|1.038<br>(0.09)|9.1%<br>10.3% 27.8% 60.3%<br>6.4%<br>7.2%<br>19.4% 42.2%|1.28|
|_Colombia_|2.23|1.01|0.95|0.868<br>(0.10)|1.623<br>(0.31)|1.93|0.88|1.54|0.992<br>(0.10)|46.8% 28.3%<br>9.7%<br>46.8%<br>32.7% 19.8%<br>6.8%<br>32.8%|1.34|
|_Croatia_|1.62|1.00|1.00|1.104<br>(0.40)|1.623<br>(0.51)|1.79|1.10|1.62|0.873<br>(0.33)|12.8% 48.2%<br>9.2%<br>49.3%<br>8.9%<br>33.8%<br>6.5%<br>34.6%|1.12|
|_Cyprus_|1.96|1.00|1.07|0.931<br>(0.07)|1.481<br>(0.22)|1.82|0.93|1.58|1.229<br>(0.15)|15.1% 45.3% 20.4% 28.1%<br>10.6% 31.7% 14.3% 19.6%|1.25|
|_Czech Rep_|1.47|1.00|1.00|0.950<br>|1.132<br>|1.39|0.95|1.13|0.843<br>|17.5% 61.2% 17.1% 25.6%<br>|1.02|
|||||(0.15)|(0.18)||||(0.16)|12.2% 42.9% 11.9% 17.9%||
|_Dominican Rep._|2.40|0.00|1.00|1.062<br>(0.00)|1.324<br>(0.00)|2.55|-|1.32|0.993<br>(0.15)|18.8%<br>0.0%<br>39.1% 15.1%<br>13.2%<br>0.0%<br>27.4% 10.6%|1.57|
|_Estonia_|1.20|1.00|1.00|0.993<br>(0.14)|1.223<br>(0.22)|1.19|0.99|1.22|1.105<br>(0.10)|30.1% 49.1%<br>7.0%<br>37.9%<br>21.1% 34.4%<br>4.9%<br>26.6%|1.09|
|_France_|1.72|0.92|1.00|0.678<br>(0.10)|1.487<br>(0.26)|1.17|0.62|1.49|0.918<br>(0.07)|19.8% 58.0%<br>7.9%<br>25.4%<br>13.9% 40.6%<br>5.6%<br>17.8%|0.85|
|_Germany_|1.43|0.83|1.00|0.515<br>(0.15)|1.267<br>(0.29)|0.74|0.43|1.27|0.602<br>(0.12)|38.1% 54.6%<br>6.5%<br>24.7%<br>26.6% 38.2%<br>4.6%<br>17.3%|0.60|
|_Hungary_|1.39|0.98|1.00|0.768<br>(0.27)|1.153<br>(0.00)|1.06|0.75|1.15|0.985<br>(0.25)|20.6% 51.3%<br>9.0%<br>32.2%<br>14.5% 35.9%<br>6.3%<br>22.6%|0.91|
|_Latvia_|1.18|1.00|1.00|1.421<br>(0.25)|1.176<br>(0.17)|1.67|1.41|1.18|1.233<br>(0.16)|27.1% 45.5%<br>9.8%<br>35.8%<br>19.0% 31.9%<br>6.9%<br>25.1%|1.40|
|_Lithuania_|1.29|1.00|1.10|1.287<br>(0.16)|1.310<br>(0.27)|1.66|1.29|1.44|1.020<br>(0.11)|33.9% 41.6%<br>8.0%<br>36.3%<br>23.8% 29.1%<br>5.6%<br>25.4%|1.32|
|_Malaysia_|3.58|1.00|1.10|0.971<br>(0.00)|1.288<br>(0.00)|3.48|0.97|1.42|1.131<br>(0.12)|18.9%<br>0.0%<br>47.8% 14.6%<br>13.2%<br>0.0%<br>33.5% 10.2%|1.84|
|_Mexico_|1.55|0.97|1.00|1.151<br>(0.13)|1.401<br>(0.26)|1.79|1.11|1.40|1.379<br>(0.16)|22.4% 17.9% 28.7% 40.0%<br>15.7% 12.6% 20.1% 28.0%|1.47|
|_Peru_|2.96|1.00|1.00|0.796<br>(0.10)|1.376<br>(0.12)|2.35|0.80|1.38|0.797<br>(0.05)|9.1%<br>14.1% 18.7% 58.8%<br>6.3%<br>9.8%<br>13.1% 41.2%|0.82|
|_Poland_|1.29|0.95|1.00|1.001<br>(0.18)|1.106<br>(0.20)|1.29|0.95|1.11|0.871<br>(0.09)|22.9% 49.9% 10.8% 28.5%<br>16.0% 35.0%<br>7.5%<br>19.9%|1.01|
|_Slovakia_|1.64|0.97|1.00|0.796<br>(0.18)|1.153<br>(0.00)|1.30|0.77|1.15|0.763<br>(0.09)|13.9% 58.7% 12.5% 32.1%<br>9.8%<br>41.1%<br>8.8%<br>22.5%|0.87|
|_Slovenia_|1.54|1.00|1.00|1.298<br>(0.11)|1.153<br>(0.00)|2.00|1.30|1.15|0.772<br>(0.12)|22.1% 53.9%<br>6.2%<br>36.8%<br>15.5% 37.7%<br>4.3%<br>25.8%|1.26|
|_South Africa_|1.26|-|1.00|1.104<br>(0.10)|1.874<br>(0.28)|1.39|-|1.87|1.267<br>(0.15)|51.7%<br>-<br>36.4% 23.0%<br>36.2%<br>0.0%<br>25.5% 16.1%|1.52|
|_Thailand_|2.46|0.72|1.05|0.773|1.068|1.90|0.55|1.12|0.883|19.5%<br>-<br>30.5% 37.2%|1.07|
|||||(0.08)|(0.32)||||(0.04)|13.6%<br>0.0%<br>21.4% 26.1%||
|_UK_|1.53|1.02|1.00|0.831<br>(0.09)|1.391<br>(0.32)|1.28|0.85|1.39|1.056<br>(0.09)|39.6% 25.5% 13.0% 25.2%<br>27.8% 17.8%<br>9.1%<br>17.6%|1.13|
|_USA_|135|085|100|0809|1638|109|069|164|0930|529%366%131%166%|100|
||.|.|.|.<br>(0.05)|.<br>(0.52)|.|.|.|.<br>(0.06)|. . . .<br>37.1% 25.7%<br>9.2%<br>11.6%|.|
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# **Table 4: Tax Changes and GDP**

# Dependent Variable: Change in GDP

||Full S|ample|Develop|ingcountries|
|---|---|---|---|---|
||OLS|Arellano-Bond|OLS|Arellano-Bond|
|PIT|**-0.14**<br>**(.055)**|**-0.16**<br>**(.081)**|**-0.20**<br>**(.080)**|<br>**-0.37**<br>**(.091)**|
|CIT|**0.043**<br>**(.028)**|**-0.084**<br>**(.063)**|**0.018**<br>**(.060)**|<br>**0.034**<br>**(.061)**|
|VAT|**0.38**<br>**(.129)**|**0.51**<br>**(.55)**|**0.44**<br>**(.24)**|<br>**0.55**<br>**(.33)**|
|Lagged<br>GDP||0.35<br>(.043)||0.37<br>(.10)|
|UR|-00079<br>(.00036)|0.0035<br>(0.0011)|-0.015<br>(.0012)|<br>0.003 (.0017)|
|Trading<br>Partners'<br>Growth<br>|1.52 (.31)|2.95 (.23)|1.62<br>(.34)|<br>2.77<br>(.44)|
|n|335<br>|308|188|166|
||Significant at 1%<br>Significant at 5%<br>Significant at 10%||||
# **Table 5: Shares of Debt Denominated in Foreign Currency**

||Selected years||
|---|---|---|
|Country|Year|Share|
|Brazil|2001<br>2008|40%<br>23%|
|Chile|2002|93%|
||2009|37%|
|Estonia|2004|100%|
|Malaysia|2003|20%|
|Sth Afi|1994|20%|
|ou rca|2008|42%|
# **Data Appendix**

**1. Quarterly Public Finance Database:** This dataset includes quarterly time series for total government revenue, tax revenue, revenues from social security, total expenditures (current and capital), interest payments and transfers.  The data refer to the consolidated general government or the consolidated central government (including social security), depending on the existence of quarterly accounts for the general government in each country.  Data are from national sources, with the exception of the UK and Canada from the OECD Economic Outlook and Belgium, Bulgaria, Czech Republic, Cyprus, France, Germany, Hungary, Poland, Slovakia and Slovenia from Eurostat.  Links to these sources are provided below.  For the Latin American and Asian countries in the sample the data correspond to central government accounts. For the remaining countries the data refers to the Consolidated General Government.

**2. Annual Tax Revenue Breakdown:** This dataset contains the breakdown of tax revenues by their components: Personal Income Tax Revenue, Corporate Income Tax Revenue, Value Added Tax Revenues (or Sales Tax Revenues, for countries with no VAT) and Social Security Contribution Revenues.  This database was constructed separately from the Quarterly Public Finance Dataset since these series are not always reported together and, many times, the tax revenue breakdown is in annual frequency.  For Latin American and Asian countries, where Central Government is being considered, only federal tax revenues are included.  Data was taken from national sources, with the exception of Argentina, Chile, Colombia and Mexico from ECLAC; Canada, UK, France, Germany, Belgium, Czech Republic, Poland, Slovakia and Hungary from OECDstats and Cyprus from the IMF’s IFS.

**3. Statutory Tax Rates:** The data related to the statutory tax rates, comprised by tax brackets and rates of Personal Income Tax, Corporate Income Tax, Social Security and VAT, comes from the OECD Tax Database for OECD countries ( _www.oecd.org/tax_ ); from the annually tax books “Individual Taxes: Worldwide Summaries” and “Corporate Taxes: Worldwide Summaries”, published by _PriceWaterhouseCoopers_ until 2005; from the _PWC_ tax webpage which has the latest statutory tax information ( _www.pwc.com/gx/en/worldwide-tax-summaries_ ); and also from the _KPMG_ Individual and Corporate Tax Rate Survey from 2006-2009.

- a. **Personal Income Tax** is generally progressive and composed by several brackets, but there are a few exceptions of countries and years in which a flat rate applies (Bulgaria and Czech Republic after 2008).

- b. **Corporate Income Tax** is usually given by a flat rate on profits, although there are also a few exceptions. Brazil has a progressive tax schedule with two brackets and Bulgaria had a progressive tax schedule in the years 19992001. For Lithuania (after 2003), Cyprus (1995-2004), Malaysia and Thailand have different tax rates according to the firm size.  In Argentina and Colombia, if the resulting chargeable corporate tax is small enough, there is a complementary tax on assets.  According to the perceived regressiveness/progressiveness of the tax schedule the imputed profit elasticities in those cases vary from 1.0 to 1.1.

- c. **VAT (or Sales Tax)** although there are many deductions according to the nature of each product, we consider the standard flat rate.  In the USA, where there are State, County and Municipal sales taxes, we consider the national average sales-tax rate.

- d. **Social Security** usually has regressive tax rates with several brackets.  UK is the only exception, since a high tax credit offsets the effect of regressiveness from the tax brackets.  The income elasticities of social security tax revenues, computed in a similar way to the personal income tax elasticities, range from 0.75-1.10.

4. **Gini Coefficients:** The Gini Coefficients were used to log-normalize of the labor earnings distribution for the computation of personal income tax and social security elasticities. Series on Gini coefficients were taken from the World Bank’s Povcal Net database for developing countries and national sources for high-income countries with the exception of France, Germany and Belgium, taken from Eurostat. Complementary data from the World Bank’s World Development Indicators were also been used to fill in missing observations and check for consistency. We interpolated remaining missing observations by a regression of the Gini coefficient on GDP for existing years. This allows for predictable shifts in the income distribution due to cyclical conditions to further inform the output elasticity of tax revenues.
5. **Average labor earnings:** Data is taken from national household income surveys with the exception of Croatia and the UK from the Eurostat and the ILO; Malaysia from the Povcal Net database; and Hungary, Poland, Slovenia and South Africa from the ILO.

# 6. **Complementary Data for the Panel Analysis**

- a. Trade Shock: The trade shock series are based on data from the IMF’s Direction of Trade database.

- b. Unemployment Rate: The Unemployment rate data have been collected in the IMF’s World Economic Outlook database.

|Argentina|_www.mecon.gov.ar/hacienda_<br>_www.indec.gov.ar_<br>Useful|Mexico<br>Links for Data Sources|_www.apartados.hacienda.gob.mx_<br>_www.inegi.org.mx_|
|---|---|---|---|
|Australia|_www.abs.gov.au_|Peru|_www.bcrp.gob.pe_|
|Belgium|_statbel.fgov.be/en_||_www.mef.gob.pe_|
|Brazil|_www.tesouro.fazenda.gov.br_||_www.inei.gob.pe_|
||_www.receita.fazenda.gov.br_|Slovakia|_www.statistics.sk_|
||_www.ipeadata.gov.br_|Slovenia|_www.stat.si_|
|Bulgaria|_www.nsi.bg_||_www.mf.gov.si_|
|Canada|_www.statcan.gc.ca_|South Africa|_www.reservebank.co.za_|
|Chile|_www.dipres.cl_|Thailand|_nso.go.th/en_|
||_www.bcentral.cl_|UK|_www.ifs.org.uk_|
|Colombia|_www.banrep.gov.co_<br>_www.dane.gov.co_|USA|_www.bea.gov_<br>_www.dol.gov_|
|Croatia|_www.mfin.hr/en_|World Economic Outlook|_www.imf.org/weo_|
|Cyprus|_www.mof.gov.cy_|Direction of Trade|_www.imfstatistics.org/DOT_|
|Czech Rep|_www.czso.cz_|Povcal Net|_www.worldbank.org/poverty_|
|Dominican R.|_www.hacienda.gov.do_|World Development Indicators|_data.worldbank.org_|
||_www.one.gob.do_|Eurostat|_epp.eurostat.ec.europa.eu_|
|Estonia|_www.stat.ee/en_|International Finance Statistics|_www.imfstatistics.org_|
|France|_www.insee.fr/en_|OECDstats|_stats.oecd.org_|
|Latvia|_www.csb.gov.lv_|CEPALstats|_websie.eclac.cl/sisgen_|
|Lithuania|_www.stat.gov.lt/en_|LABORsta|_laborsta.ilo.org_|
|Malaysia|_www.statistics.gov.my_<br>_www.anm.gov.my_|OECD Economic Outlook|_www.sourceoecd.org_|
||<br>Time Series Cove|rage for the SVAR||
|---|---|---|---|
|Argentina|1993Q1 - 2009Q3|Germany|1999Q1 - 2009Q1|
|Australia|1959Q3 - 2009Q3|Hungary|1999Q1 - 2009Q3|
|Belgium|1991Q1 - 2009Q3|Latvia|1999Q1 - 2009Q3|
|Brazil|1997Q1 - 2009Q3|Lithuania|1999Q1 - 2009Q3|
|Bulgaria|1999Q1 - 2009Q3|Malaysia|1999Q1 - 2009Q3|
|Canada|1970Q1 - 2009Q3|Mexico|1993Q1 - 2009Q3|
|Chile|1989Q1 - 2009Q3|Peru|1993Q1 - 2009Q3|
|Colombia|1995Q1 - 2009Q3|Poland|1993Q1 - 2009Q3|
|Croatia|2004Q3 - 2009Q3|Slovakia|1993Q1 - 2009Q3|
|Cyprus|1995Q1 - 2009Q3|Slovenia|1993Q1 - 2009Q3|
|Czech Rep|1999Q1 - 2009Q3|South Africa|1990Q1 - 2009Q3|
|Dominican Rep|2002Q1 - 2009Q3|Thailand|1993Q4 - 2009Q3|
|Estonia|1999Q1 - 2009Q3|United Kingdom|1970Q1 - 2009Q3|
|France|1991Q1 - 2009Q1|United States|1947Q1 - 2009Q3|