brazil's structural fiscal balance - banco ita

ABSTRACT

In this paper, we provide new, updated estimates for Brazilian public sector’s structural primary fiscal balance. Our structural primary fiscal balance series differ markedly from unadjusted budget results. The numbers point to a tightening fiscal stance in the first part of the 2000s and an easing fiscal stance in the latter part of the decade. Our calculations also reveal a considerable fiscal effort in 2011 (up to Q3). According to our estimates, judging the fiscal stance only from the standpoint of unadjusted primary fiscal balance can lead to misleading conclusions about policymaking.

Our results confirm Brazil’s pro-cyclical fiscal drive evidenced in the literature, with a negative correlation between the policy stance (measured by the structural balance) and the action of automatic stabilizers such as tax collection (measured by the cyclical balance). We also note a recurrent use of budget-enhancing one-off operations in times of fiscal consolidation or cyclical downturns.

We conclude that the current policy setting – based on non-structural primary balance targets – produces a pro-cyclical fiscal policy bias: in booming years, it leads to overspending; in recession years, it leads to tightening and a search for extraordinary revenues.

The current fiscal framework needs to include incentives to raise public savings in expansion years. The use of structural primary balance targets could make fiscal policy “lean against the wind”. In our view, this new policy setting would help boost public savings and investment, increasing potential GDP growth.

The opinions expressed in this Working Paper are those of the author(s), not necessarily those of Itaú Unibanco.

The author thanks Ilan Goldfajn and Samuel Pessoa for their comments, Aurelio Bicalho and Giovanna Rocca for the potential GDP and long-term commodity price series, as well as Ítalo Franca and Kim Vanderbilt for the editing.

BRAZIL’S STRUCTURAL FISCAL BALANCE

Mauricio Oreng* [email protected]

* Economist - Itaú Macro Research Team

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Brazil’s Structural Fiscal Balance © April 2012 - Working paper nº6

INDEX

1. Introduction…………...…………………..…………………....3

2. Definitions and Literature Review.…………..…………......6 2-A. Structural Fiscal Balance: Definition, Applications, Limitations………..….6 2-B. The Literature on Budget Adjustment Applied to Brazilian Data………...…8

3. Methodology …………….……………………….……….….10 3-A. Estimating the Structural Fiscal Balance…………………..…………..……...10 3-A.1. The Aggregated Approach (IMF)………………………………………….......……………..….…..10 3-A.2. The Indirect, Disaggregated Approach (OECD)…..………………….…….………..…………….11 3-A.3. The Direct, Disaggregated Approach (ECB)………………………………………………............13

3-B. Pre-Modeling Choices: Budget Concept and Scaling…...……....…...……..14 3-C. The Dataset ……….....…………………………………………………….….…....15 3-C.1. Setting Up the Public Sector Budget Database …………......….….….….….….….….….….… 15 3-C.2. Adjusting the Budget Data……..…………..…..………………………………………………........16 3-C.3. Sampling: Revenue Classes, Tax Bases, Time Window ……………………………………..….20

3-D. Estimation Procedures……………………………...………….………..….…… 21 3-D.1. Overview…………………………………………..…………......…..….….….….….….….…..…… 21 3-D.2. Estimating Revenue Elasticity Parameters…..………………….………………………….….......23 3-D.3. Trend Filtering: Finding the Equilibrium Path for the Tax Bases .…….......……………….…….27

4. Results……………..…………………………………………..29 4-A. Elasticity Estimates……………………………………………………..…….…...29

4-B. Structural Primary Fiscal Balance Results….…………………………….…..33 4-C. Dissecting Budget Results…………………………………………….………....35 4-C.1.The Structural Balance for Central Government and Regional Governments…......………...…35 4-C.2.Structural Fiscal Balance: Cyclical Balance vs. Structural Balance………………..………........36 4-D. The Fiscal Impulse……………………………………………….……….………..40 4-E. Simulating Alternative GDP and Commodity Trends.…………….………....43 4-F. Comparing Our Estimates with the Literature ………………..………..……..45

5. Concluding Remarks………..……….….….…………..…...48

Appendix…….……………………………………………….…..49

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1. INTRODUCTION

Consistently elevated primary budget results have strengthened Brazil’s fiscal position over the

last decade or so. There are two caveats to the overall positive assessment of the budget

performance. First, fiscal results have been helped by the double tailwinds of increasing

commodity prices and easy external financing, a phenomenon that Eyzaguirre et al (2011) argue

to have occurred in most Latin American economies. In the Brazilian case, in particular,

favorable demographics and rising formalization have provided additional support to revenues.

Second, at times fiscal policy has resorted to non-recurring revenues to meet the fiscal targets.

The use of temporary revenues is closely linked to Brazil’s pro-cyclical fiscal policy bias, as

evidenced by the literature, as in Blanco et al (2006) and Mello et al (2006). The pro-cyclical bias

precludes fiscal policy from smoothing out economic cycles and, in practice, produces less-

efficient fiscal spending, as investment is always the adjusting variable.

In this paper we provide new structural primary fiscal balance estimates for Brazil’s public sector,

combining procedures already tested with additional techniques (including the use of a

methodology never before applied on Brazilian data).

Our main findings can be summarized as follows:

We estimate fiscal revenues’ elasticity with respect to GDP at 1.5 for 2002–2011.

Key revenue categories – such as income taxes, indirect taxes and social security

contributions – proved to be quite activity-elastic. Brazilian primary surplus tends to

rise by 0.45% of GDP for each additional point of economic growth (as of 2010). In

2003, this budget sensitivity was at 0.40, short of the average for OECD (0.44) and

Europe (~0.50), but above estimates for the U.S., Japan (~0.34) and Korea (0.22). In all,

our estimates point to a considerable budget cyclicality for an emerging economy.

Our models indicate that commodity prices have a significant role as budget

drivers. We find a significant impact of raw material prices on broad fiscal takings and

on revenue categories such as corporate and personal income taxes, financial

transactions taxes and dividend receipts. This confirms asset-related wealth effects on

fiscal revenues, occurring through corporate profits, consumer spending, capital gains

and bank lending. Still, activity trends matter more for tax collection than

commodity-price swings.

Our estimates point to pronounced changes in the structural primary fiscal balance since

the 2000s, contrasting with relatively stable official (unadjusted) budget results.

o The fiscal stance was quite tight in the early 2000s. A mix of higher taxes and

controlled spending prompted the structural primary balance to rise steadily from

2001, peaking at around 4.5% of GDP in late 2003.

o In the latter part of the 2000s, cyclical tailwinds from solid economic growth and higher

commodity prices facilitated the delivery of a stable primary-surplus target. In response,

the fiscal stance was relaxed at a pace of 0.5% of GDP per year from 2004 onwards,

driven by fast-rising expenditures at both federal and regional levels. As an

upshot, the structural primary surplus narrowed continuously, reaching around 1%

of GDP in 2010. In that year, the fiscal impulse totaled more than 1% of GDP.

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o Our numbers point to an important removal of budget stimulus last year (up to Q3)

– notably at the federal level – due to both slower expenses and higher revenues.

We estimate a fiscal drag on top of 1% of GDP for 2011 (up to Q3), taking the

annual structural primary surplus up to just above 2% of GDP by September.

Exhibit-0: Primary Fiscal Results: Observed, “Recurring” and Structural Budget Balance

Source: Itaú

We note a highly negative correlation between the structural balance (measuring the

fiscal policy stance) and the cyclical balance (measuring the action of automatic

stabilizers, mainly tax collection): the coefficient is -0.9 for the period of 2000–2011. In

practice, this means that whenever tax collection boomed (weakened), the fiscal stance

turned easier (tighter). This evidences a highly pro-cyclical fiscal policy bias, which

probably follows the adoption of constant unadjusted primary balance targets.

We highlight the importance of a rigorous database clean-up to avoid

overestimation of Brazil’s structural balance. Our adjusted budget database –

following IMF recommendations and criteria to exclude temporary budget-

enhancing drivers – shows that extraordinary revenues have played a visible role

in Brazil’s fiscal performance. On average, one-off transactions raised the primary

fiscal surplus by about 0.6% of GDP in the beginning of the fiscal adjustment

(early 2000s) and in the post-Lehman period (2008–2010). In 3Q11, about half of the

gap between our annual structural balance estimate and the official data stems from the

database adjustment, as reflected in the ―recurring‖ annual primary balance of 2.7% of

GDP estimated for the same period. There is a significant qualitative difference

between the sources of non-recurring revenues obtained across the 2000s: in the

past, concessions were the focus; now, most one-off transactions comprise

capital transfers and tax debt settlements.

In this paper, we estimate Brazil’s structural primary fiscal balance under two methodologies: a

widely used aggregated method – ―the IMF approach‖; and a disaggregated procedure – ―the

ECB approach‖. The IMF method strips out the impact of GDP and asset-price cycles1 (e.g., our

paper uses oil costs as a proxy) on broad revenue aggregates. In the ECB approach, one splits

government takings into various classes and remove the cyclical influences from revenue bases,

1Similarly to Gobetti et al (2010), we adapt the IMF methodology for Brazilian data using Brent oil prices as a proxy for

asset (or commodity) price cycles. This followed tests with other raw material prices such as iron ore, soybean and the

composite CRB index. Oil-related revenues account for no less than 5% of total federal takings.

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

2000-I 2001-I 2002-I 2003-I 2004-I 2005-I 2006-I 2007-I 2008-I 2009-I 2010-I 2011-I

Observed Budget Result Recurring Budget Result Structural Budget Result (Baseline)

% GDP

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which could be activity subcomponents (e.g., retail sales, wage bill, bank lending, industrial

production) or financial/commodity indicators (we used Brent oil price and the CRB index). While

the IMF methodology is simpler, the ECB approach accounts for output-composition effects –

when asymmetrical cycles in key activity drivers (e.g., wage bill, retail sales) imply different

revenue outcomes for the same pace of total GDP growth.

We work with a subset of Brazilian public-sector budget data: a complete dataset on federal

revenues and spending, partial information on regional governments’ budget (basically, on tax

collection), and the fiscal balance from state-owned firms. The latter was not subject to structural

adjustment, given the evidence of no cyclical swings. We also decided not to structurally adjust

government expenditures, considering the pro-cyclical pattern in unemployment insurance

outlays in recent years. After a thorough treatment of the budget database for one-off fiscal

operations, we estimate revenue elasticity parameters via regression analysis. We sought to

deploy parsimonious models and intensive testing, so as to obtain well-behaved residuals and

consistent estimates. To estimate equilibrium values for the various tax bases, we use two de-

trending procedures: a statistical procedure (Hodrick-Prescott Filter) and a ―theory-based‖

procedure, using the production-function framework for GDP and mean historic GDP ratios for

activity subcomponents. Commodity prices are only de-trended by HP Filter.

This paper is organized as follows: In section 2, we discuss the concept of structural fiscal

balance and survey the previous applications of cyclically-adjusted or structural fiscal balance to

the Brazilian economy. In section 3, we detail the most widely used methods to estimate the

structural fiscal balance. We also explain our research procedures, such as: database treatment,

definitions and specifications; econometric techniques. In section 4, we present the results:

budget elasticity parameters, structural balance and fiscal impulse estimates, and a stress

simulation. In the section 5, we bring the concluding remarks. The Appendix has more details on

the definitions, research procedures, data series, models and econometric tests.

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2. Definitions and Literature Review

2-A. Structural Fiscal Balance: Definition, Applications, Limitations

The structural fiscal balance (SFB) is defined as the share of budget results consistent with

output at potential and asset prices at long-term equilibrium. To illustrate the concept, let’s first

assume a simplified model, where: (1) there are no government expenses; (2) tax collection is a

linear function of output, and does not depend on asset prices. Let Y denote actual GDP and Y*

denote potential GDP (Exhibit-1).

Take Y = Y’’ as the actual level of output for a given year, and Y* = Y’ (<Y’’) as the long-term

consistent level of output (i.e., potential GDP) for the same year. The structural fiscal balance

(SFB) is determined by the level of revenues consistent with potential output, represented by

R(Y’). Since the economy is moving ahead of trend, observed revenues top structural revenues,

or R(Y’’) > R(Y’). The gap between observed revenues and structural revenues – implicitly, a

function of the output gap – denotes the cyclical fiscal balance (CFB), obtained by residual. In

our example, the cyclical balance is positive, which means a positive contribution of the

economic cycle (through automatic activity stabilizers, such as tax collection) to the fiscal result.

Exhibit-1: The Concept of Structural Fiscal Balance

Source: Itaú

This concept can be easily extended to a more realistic situation, where asset prices affect tax

collection and budget results incorporate government expenses. The structural fiscal balance

(SFB) is expressed as a function of structural revenues (R) and expenditures (E):

SFB = R(Y, A) – E

The structural fiscal balance (SFB) is the part of the fiscal performance more likely to be

permanent, determined by long-term consistent levels of activity and revenues. Meanwhile, the

cyclical fiscal balance (CFB) is the portion of fiscal results stemming from business and financial

cycles, which are temporary by nature.

By filtering out cyclical influences, the SFB reflects the impact of the government’s active fiscal

policy stance on observed budget results, by means, for instance, of taxation and spending

policies. By the same token, the CFB denotes the influence of automatic stabilizers such as tax

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collection (and, when is the case, unemployment benefits) resulting from the gaps in real activity

and asset prices. The CFB can also be understood as the passive fiscal policy response to

economic conditions, revealing the impact of cyclical conditions on the fiscal performance.

Conceptually, the structural fiscal balance (SFB) builds on a widely used tool to adjust fiscal

results in the past: the cyclically adjusted fiscal balance. The difference between these concepts

is that the SFB adjusts budget results not only for real activity cycles, but also for the impact of

relevant asset-price cycles. The narrower approach of the cyclically adjusted fiscal balance may

lead to biased estimates for budgetary elasticity parameters and the influences of automatic

stabilizers. Thus, the conclusion is that the structural balance is a better instrument to measure

the actual fiscal policy stance, as it minimizes double-counting and omitted variable problems,

which may potentially harm cyclically adjusted balance estimates.

Inspired by Brezdek et al (2003), we highlight five elements that temporarily or permanently

condition a country’s fiscal performance:

1) Business cycles2;

2) Asset-price cycles;

3) One-off events (e.g., windfall revenues, extraordinary spending);

4) Demographics;

5) Discretionary policy actions;

There are plenty of examples illustrating the budgetary impact of these variables. A strong

(weak) economy will drive tax collection higher (lower) and spending in unemployment benefits

down (up). Booming (plunging) natural-resource, real-estate or other asset prices may

substantially lift (weigh on) revenues. Concessions of public services may also add to non-tax

government income in a particular year. Disasters may prompt temporary reconstruction

expenses for some time. Countries running a pay-as-you-go social security system will a face a

low (high) burden of transfers to retirees when the dependence ratio is low (high).

If well estimated, the structural fiscal balance is expected to strip budget results off of influences

from factors 1, 2 and 3. Such estimates should be valuable information for governments, as the

nature of the drivers at work will signal the likely robustness (or weakness) of budget results

overtime. Policymakers must distinguish between transitory and permanent forces behind the

fiscal performance, for this breakdown will tell if current budget decisions, laws, or policy rules

are indeed consistent with long-term debt sustainability. Thus, the structural balance can be an

important policy tool to preserve a country’s fiscal solvency.

However, for many reasons, one should not take a structural balance estimate for a given period

as a completely fixed number to extrapolate too many years ahead in the future. First, just as for

potential output and equilibrium asset valuations, the structural balance may change overtime in

accordance with a country’s fundamentals. Second, to the extent that the output gap and trend

asset price estimates entail some uncertainty, given the imperfection in de-trending procedures,

structural balance estimates should be taken as an indicative value (Bouthevillain et al, 2001).

Third, structural balance methodologies do not eliminate the impact of demographics, which may

alter a country’s fiscal position across generations. Fourth, structural balance estimates suffer a

post-hoc (or ―incentive‖) bias, for the resulting adjustment in revenues occurs after spending

2 For the sake of precision, we define cycles as deviations of a variable from its trend or equilibrium level.

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decisions were taken, not necessarily under the same set of incentives as in a structural balance

policy setting.

The comprehensive approach proposed by structural balance methodologies fits perfectly well

with emerging economies. In Brazil and other developing countries, the fluctuation in resource-

related revenues may materially affect fiscal performance. There are many Latin American

examples, such as Chile, where copper-driven revenues have a significant impact, and Mexico,

which is largely dependent on oil-related proceeds.

But financial cycles may also influence the fiscal performance in advanced economies. In Spain,

for instance, home-price indexes have been used as a cyclical proxy to structurally adjust fiscal

results, so as to capture the budgetary effects of a housing boom.

The volatile nature of financial conditions suggests that revenues from this source may fail at

some point, demanding a cautious approach on current fiscal policy decisions. In such cases, a

structural fiscal balance approach can provide guidance to improve the discussions and

policymaking.

2-B.The Literature on Budget Adjustment Applied to Brazilian Data

We highlight four noteworthy studies. Bevilaqua and Werneck (1997) inaugurated this literature

using a measure of fiscal impulse – methodology considered to be a forerunner of the structural

fiscal balance – inspired by Blanchard (1990). They adapt the methodology for the Brazilian

case, by using GDP instead of unemployment as a cyclical proxy and additionally adjusting

budget results for inflation. Their estimates point to a more expansionary fiscal stance than

suggested by the actual budget results for the period of 1989–1996. The results also suggest a

less significant fiscal consolidation in 1994 and a stronger fiscal impulse in 1995, as compared

with unadjusted data. In all, this work provides a first evidence of Brazil’s pro-cyclical fiscal policy

bias.

Mello et al (2006) use the OECD methodology to estimate the cyclically adjusted primary budget

balance, and reach conclusions of a similar nature. The authors find a primary surplus

sensitiveness to the business cycle (as measured by the output gap) of 0.32 for data spanning

1995–2005, lower than the OECD average but in line with the U.S. and Japan. This estimate

results from a 0.38 sensitivity for central government revenues and a -0.06 sensitivity for

expenditures. The study points to a greater cyclical sensitivity of Brazil’s fiscal performance

compared with other EMs, which probably follows the country’s higher share of government

spending as a percentage of GDP. They find a highly pro-cyclical bias in Brazil’s discretionary

fiscal decisions, especially in times of economic downturns – reflecting corrective actions to

improve fiscal solvency in times of crises; they note that mandatory spending is pro-cyclical in

upturns too, posing risk to the fiscal adjustment, according to the authors.

Rocha (2009) uses a different framework to dissociate the impact of fiscal stabilizers from the

effects of discretionary policy decisions in Brazil. In this paper, the author takes for granted the

estimated budget elasticity from Mello et al (2006) and applies it to a fiscal policy Taylor rule,

where the actual budget balance is a function of the structural fiscal balance and the output gap.

Using data from 1995–2005, the structural balance is obtained as a residual. The paper also

tests policy rules relating the actual primary fiscal balance to the output gap, government debt

level and lagged fiscal balance. The author concludes that fiscal policy has always been pro-

cyclical in the sampling period, although it has turned less so since the approval of the Fiscal

Responsibility Act (Lei de Responsabilidade Fiscal) in 2000.

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Gobetti et al (2010) use the IMF methodology to estimate Brazil’s structural fiscal balance for the

period of 1997–2010. The authors structurally adjust government revenues for the cycles of

GDP and oil price. Structural oil revenues are estimated either by regression or a deterministic

rule. No spending adjustment is made, as the authors find evidence of pro-cyclical behavior in

unemployment insurance outlays. Trend series are obtained via econometric procedures, such

as HP and Kalman filters. Elasticity estimates result from OLS regressions, Markov-switching

regressions (allowing for discrete regime changes) and Kalman filter (allowing for a continuous

space of regimes). The paper concludes that there have been two different periods for fiscal

policy: one of contraction (1997–2005) and one of expansion (2006 onwards). Results point to a

slight pro-cyclical bias in discretionary fiscal policy decisions.

Our paper provides new, updated estimates for the Brazilian public sector’s structural primary

fiscal balance. We test alternative approaches and procedures compared with previous

applications, while replicating a few. The main methodological differences are as follows: (1) we

make a thorough database adjustment to strip out large enough one-off budget operations; (2)

we employ two structural balance techniques: the IMF’s aggregated approach and the ECB’s

disaggregated methodology – the latter applied to Brazilian data for a first time; (3) we combine

standard statistical filtering (i.e., Hodrick-Prescott) with theory-based de-trending procedures,

especially on activity-related tax bases; (4) we use parsimonious econometric procedures to

estimate revenue elasticity, taking particular care with residuals properties; (6) we break down

the origin of the fiscal impulse into revenue, spending and government firms, and also reveal

what cyclical variables had greater influence in fiscal results.

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3. METHODOLOGY

3-A. Estimating the Structural Fiscal Balance

Estimating the structural fiscal balance is equivalent to breaking down the observed budget

result into a cyclical and a structural component, as described below (lower-case letters denote

variables written as a percentage of nominal GDP):

(1) f = fs + fc

f is the observed fiscal balance, fs the structural fiscal balance (SFB), and fc the cyclical fiscal

balance (CFB). We can rewrite expression (1) as follows:

(2) f = t – g = (ts + tc) – (ts + gc) wheret denotes observed revenues; ts structural revenues and tc cyclical revenues. The same

applies for g.

Broadly speaking, the structural fiscal balance approaches treat revenues more carefully than

expenditures. In general, government receipts are endogenous in the short run (i.e., conditioned

more by the economic and social environments than by policymaking) while spending is

predominantly exogenous in the short run (i.e., chosen by policymakers). Thus, except for

discretionary changes in the tax code, revenues tend to play a greater role as an automatic

stabilizer, whereas expenditures usually reflect policy decisions.

The structural adjustment of revenues is applied to a large share of government takings, after

stripping the non-cyclical income. The adjustment can be made in an aggregated way (i.e.,

applied to total cyclical revenues) or in a disaggregated fashion (i.e., treating each revenue

group separately). As for expenditures, unemployment transfers are the only spending category

usually considered as a candidate for structural adjustment. Other expenses are often assumed

to be government-controlled, moving independently from the cycles.

In this section, we survey the main structural fiscal balance approaches. We provide an overview

of the IMF’s, the OECD’s and the ECB’s methodologies. Refer to Bornhorst et al (2011) for a

more detailed discussion.

3-A.1. The Aggregated Approach (IMF)

One can estimate the structural fiscal balance following an aggregated approach, as described

in Bornhorst et al (2011). The simplicity of this method makes it a good starting point to

understand the different SFB methodologies.

The method starts with an assumption that the ratio of structural revenues (expenditures) to

observed revenues (expenditures) is proportional to the ratio of potential output to observed

output, as well as the ratio of trend asset prices to observed asset prices. The output and asset-

price ratios are adjusted for the elasticity of the revenue (expenditure) gap with respect to the

gaps in output and asset prices (upper-case variables denote values adjusted for seasonality

and inflation). In formal terms:

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(3) Ts = T .( Y* )

T,Y. ( A* )

T,A Y A

Where Y denotes actual output and Y* potential output; A stands for actual asset prices and A*

means the long-term (or trend) asset prices. T,Y and T,A are the elasticity of revenues with

respect to output or asset-price gaps. Expression (3) can also be applied for expenditures.

The original IMF work by Hagemman (1999) proposes estimating elasticity parameters for each

tax-collection category (e.g., personal and corporate income taxes, indirect taxes and social

security contributions). Bornhorst et al (2011) present an aggregated treatment of fiscal

variables, fully compatible with the previous work. The authors highlight that, in many situations,

using broad fiscal measures instead of disaggregated data prompts little loss of accuracy.

One can generalize the IMF methodology to allow for lagged impact of business and asset gaps

on cyclical revenues or expenditures. In those cases, expression (3) should be adjusted for the

respective lag structure.

Trend values for Y* and A* can be obtained either with statistical filters (e.g., Hodrick-Prescott,

linear trend) or calculated by theory-based assumptions (e.g., production function, ―equilibrium‖

or mean ratios, econometric models).

The elasticity of revenues (T,Y or T,A) with respect to output and asset prices can be taken from

the literature (when available) or estimated through econometric procedures. Sometimes,

researchers impose unit elasticity of revenue cycles with respect to business cycles – implying

that the revenue gap moves exactly in line with the output gap.

While most revenues are subject to structural adjustment, unemployment insurance is the only

expenditure category to adjust, because of its autonomous reaction to employment fluctuations.

One commonly finds empirical work using the assumption of zero elasticity for all expenditures.

In these cases, all government expenses are supposedly discretionary (or independent of

business and financial cycles), being fully regarded as structural. Formally, it implies that Gs = G.

A positive feature of the IMF methodology is simplicity. The use of readily available data and the

parsimonious specifications facilitate the estimation process and cross-country comparisons.

The main shortcoming of this approach, however, is the possible influence of composition effects

– when activity sub-components (e.g., consumer spending, employment, new loans), react

differently through the cycle and affect fiscal revenues in an unequal manner. As Bornhorst et al

(2011) put it, when output cycles are relatively homogeneous or when tax elasticity changes little

across revenue classes, the aggregated method still can provide a fairly good approximation.

3-A.2. The Indirect, Disaggregated Approach (OECD)

The OECD methodology entails a more comprehensive treatment than the IMF’s method, with a

separate adjustment for each revenue class. Here, we present an extended version of the

OEDC approach, which allows for the impact of asset-price cycles, as proposed by Girouard and

Andre (2005). Formally:

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M

(4) fs = (tis ) – g

i=1

wherei indexes the M revenue categories.

Researchers usually classify revenues in four groups: social security contributions, household

income taxes, corporate income taxes and indirect taxes (e.g., VAT, excise taxes, import duties).

Each of those has its own economic base: wage bill, corporate earnings, consumer spending

and imports, respectively. In the case of government outlays, only unemployment benefits are

subject to treatment, with the jobless rate serving as the base.

The starkest contrast between the OECD and the IMF approaches is the two-stage estimation of

elasticity parameters. Letting Bi be the macroeconomic base for tax class i, elasticity parameters

can be broken down into two factors: Ti,Bi, the elasticity of tax revenues with respect to the

relevant tax base and BiY, the elasticity of tax base with respect to output gap. The same can be

applied to expenditures.

(5) Ti,Y = Ti,Bi . BiY According to Bezdek et al (2003), this breakdown makes it easier to interpret the cyclical

sensitivity of taxes (or spending). Combining equations (3) and (5), we get the following

expression, used to calculate the structural tax collection in the OECD approach:

(6) Ti

s = Ti .[ (Y* )

BiY. ( A* )

BiA]

TiBi Y A

In the OECD approach, there are three ways to assign numbers to parameters related to the

revenue elasticity with respect to the base (Ti,Bi): deriving, calibrating or assuming. So, the

base-revenue elasticity can be derived from the tax code, taken from previous studies, or simply

hypothesized. In some cases (e.g., indirect taxes), it makes sense to impose unit elasticity –

which means a proportionality between tax proceeds and the base. However, that may not be

appropriate in other situations. An example: income taxes tend to be progressive, with higher tax

rates for higher income levels.

Bezdek et al (2003) advise against the use of regression analysis to estimate tax elasticity with

respect to the base. The authors believe that it is impossible to track all the history in countries

facing frequent changes in the tax law. Without controlling for modifications in the tax code,

elasticity estimates may be biased. To overcome this problem, the authors propose the use of a

unit elasticity assumption3. Bezdek et al (2003) see no problem, however, with using regressions

to estimate the elasticity of the tax base with respect to real activity or asset-price cycles (BY,

BA).

3 To overcome this problem, one can use dummy variables in regressions so as to control for changes in the tax

legislation. In our view, this strategy is superior to assuming an ad-hoc unit base-elasticity of revenues.

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The advantage of the OECD methodology is the precision (at least in theoretical terms) of

elasticity estimates following the separate treatment for each class of tax proceeds and the

breakdown of sensitivity parameters. The detailed approach provides a deeper knowledge of the

cyclical reaction of different taxes, which may be important information for regional government

finances (Bornhorst et al, 2011).

The shortcoming of the OECD methodology is its large data requirements. In most cases, micro

data – not always available – is needed to derive elasticity from the tax codes. Moreover,

assuming a specific (e.g., unit elasticity) relationship between tax revenues and their bases can

lead to distorted results, especially in countries where economic formalization is on the rise

(producing a tax elasticity greater than one). Another important deficiency in the OECD

methodology is that it does not account for output composition effects.

3-A.3. The Direct, Disaggregated Approach (ECB)

A number of real activity variables, such as consumer spending, industrial output and wage bill,

may fluctuate differently than broad output, both in magnitude and timing. Bearing this fact in

mind, Bouthevillain et al (2001) propose to shift the structural-balance focus out of the output

gap and towards cyclical movements in other variables composing the relevant macro picture for

fiscal aggregates. As Bezdek et al (2003) put it, the ECB approach builds on the OECD and IMF

methodologies, which do not allow for the possibility of output composition effects.

When cycles are not fully synchronized, composition effects may matter. In these circumstances,

the elasticity of tax collections with respect to GDP will change overtime, for the movements in

activity aggregates affect tax proceeds (or government spending) in different ways. Thus, the

use of actual macroeconomic bases instead of GDP should (at least theoretically) improve the

quality of elasticity estimates and thereby the accuracy of the structural adjustment.

A standard example illustrates: suppose GDP grows at the same rate in two scenarios, one led

by (a) exports, another one led by (b) consumer spending. No doubt tax collection would move

faster in case (b), for the tax burden on local spending is usually greater than on exports.

Another example: owing to backward-looking wages or firms’ labor hoarding, the job market

usually responds with a lag and in a different magnitude to changes in the economic cycle

measured by the GDP. In that case, social security contributions and personal income taxes,

more dependent on payrolls, may take longer to lose momentum when a booming cycle

reverses. In both of these cases, it is important that the structural revenues estimates reflect

these facts.

In the ECB methodology, structural revenues are estimated as follows (the same applying for

expenditures):

(7) Tis = Ti . ( Bi* )

TiBi. ( Ai* )

TiAi

Bi Ai

Where TiBi is the elasticity of tax category i with respect to its macroeconomic base and TiAi is the

elasticity of tax category i with respect to a relevant asset price.

Expression (7) allows for the set of tax bases to include numerous real activity or asset-price

proxies, in different lag structures. The elasticity parameters can be obtained via econometric

procedures (OLS, for instance).

14


The disadvantage of the ECB approach is the greater reliance on statistical filtering techniques,

since there are less established theory-based methodologies to tell cycles from trends in macro

aggregates other than GDP. In other words, de-trending turns a bigger problem in this method.

3-B. Pre-Modeling Choices: Budget Concept and Scaling

Some decisions have to be made before estimating the structural balance. Firstly, we confined

our work to the realm of the primary fiscal balance (i.e., savings before interest payments), which

in our view reflects more clearly fiscal policy decisions and the impact of economic cycles.

The country’s relatively high share of short-term debt linked to the benchmark interest rate

makes interest payments and nominal budget results highly correlated with monetary policy.

While this correlation suggests that there are cyclical elements affecting the nominal balance,

several difficulties cloud the structural adjustment of interest outlays. Fedelino et al (2009)

contend that interest rate moves do not always react automatically or predictably in line with the

economic cycle. The authors advocate against cyclically adjusting nominal spending (i.e.,

primary plus interest outlays).

In the Brazilian case, the difficulties of structurally adjusting interest expenses are yet bigger.

One reason is the changing macroeconomic conditions leading to a downward shift in real

interest rates across the last decade. That further increased the uncertainty surrounding

estimates of equilibrium interest rate4 and, as a consequence, the structural level of interest

outlays. A second challenge is the consequences of a changing composition of government

assets: higher international reserves and greater outstanding Treasury loans to BNDES have

raised the spreads between the benchmark Selic rate and the actual cost of rolling over the

public debt5. Among these methodological difficulties, we decided to leave the structural

adjustment of the nominal balance as a future step.

Scaling is also a relevant theme for the structural balance. In this paper, we present our

structural balance estimates as a share of potential output. In our view, that improves the

comparability of results, as that is the way most researchers report their estimates. An easy

alternative is to use the observed nominal GDP as a deflator, which facilitates communication to

the public. As Fedelino et al (2011) stress, there is a trade-off between economic rigor and

convenience, and we have clearly chosen the former. However, our results are insensitive to

scaling. Appendix 1 shows the derivation of our nominal potential GDP series and its (small)

impact on our structural balance estimates.

4 For a discussion on Brazilian equilibrium interest rates, refer to Goldfajn and Bicalho (2011).

5 Refer to Garcia (2007) and Afonso (2011) for a discussion of the fading relationship between the policy rate and yields

on net public debt in Brazil.

15


3-C. The Dataset

3-C.1. Setting Up the Public Sector Budget Database

We calculate the structural balance for the entire Brazilian public sector. Our sample uses inputs

from different data sources, including the Finance Ministry’s (―above the line‖6) and the Central

Bank’s official (―below the line‖6) statistics. This is necessary to overcome the scarcity of

information on revenues and expenditures at a sub-national level. To present our results, we use

the BCB’s fiscal accounts structure and fill the information gaps with other data, in order to make

our structural balance estimates fully comparable with the BCB’s official primary fiscal balance

statistics7.

As for the central government, the Treasury data on revenues and spending suffices for this

work.

For sub-national entities, however, the lack of data makes sampling quite cumbersome. There is

incomplete information on revenues, and no information on spending. We took a similar

approach to Gobetti et al (2010): we structurally adjust a subset of regional revenues, available

mostly at the states level. In order to create a relevant dataset of regional government revenues,

we use the National Treasury’s data on federal transfers and the figures on states’ tax receipts

published by the Finance Ministry’s National Council of Economic Policy (Confaz). Confaz

reports the collection of ICMS (value-added) tax, IPVA (car ownership) tax and other regional

government levies.

There are shortcomings in this strategy. First, our dataset only covers a part of total regional

revenues. We are leaving behind, for instance, some municipal taxes and regional government

firms’ dividends. Moreover, the data on states’ tax receipts is subject to frequent revisions,

making our structural regional revenues estimates relatively fluid in the very short term. Despite

these problems, this presents the best available data on a significant share of public sector

revenues outside the central government.

We calculate regional government expenditures as residual from the gap between our revenue

subset and the ―below the line‖ regional primary budget surplus calculated by the Central Bank.

Data restriction is even more acute for government-owned companies, where no data on

revenues or spending is available. This is a manageable problem, though, considering the low

share of this government sphere in the public-sector fiscal results – especially after the removal

of Petrobras and Eletrobras8 from the statistics. In fact, we decided not to structurally adjust the

primary balance from this entity, considering the lack of cyclicality in its headline budget results.

Importantly, our structural primary fiscal balance results are reported including the (unadjusted)

values for the statistical discrepancy of central government results and government-owned firms’

balance, which are clearly non-cyclical variables. The idea, as mentioned before, is to guarantee

comparability of our estimates with the official data.

6 In the ―above the line‖ criterion, the fiscal balance is calculated through the gap between revenues and spending. In the

―below the line‖ methodology, the fiscal balance is calculated through changes in net government indebtedness,

excluding the impact of the FX rate. For more details on Brazil’s fiscal statistics, refer to Gerin (2010). 7Our budget database is available on the following link:

http://www.itaubba-economia.com.br/content/interfaces/cms/anexos/ITABBA_WP_6_Annex.pdf 8 The Central Bank’s revised fiscal statistics stripped of Petrobras and Eletrobras start in December 2002. Here, we

appended to this series the one excluding only Petrobras, to enlarge our series history. From 2002 to 2009, the average

absolute gap between the ex-Petrobras and ex-Petrobras/Eletrobras series is small (0.06% of GDP).

http://www.itaubba-economia.com.br/content/interfaces/cms/anexos/ITABBA_WP_6_Annex.pdf

16


3-C.2. Adjusting the Budget Data

According to Bornhorst et al (2011), adjusting the budget data for temporary drivers is crucial to

generate trustworthy structural fiscal balance results. Database clean-up not only filters out

operations taking place for a limited period of time – which, by definition, might not be regarded

as structural – but also prevents biased elasticity parameters.

Given the sizeable amount of extraordinary government income (seldom mixing with ordinary tax

collection), especially in recent years, tracking and removing one-off operations and accounting

events has become a critical step to adapt the structural fiscal balance methodologies to

Brazilian data. Unfortunately, the incomplete information on regional governments’ and

government-owned firms’ budgets prompted us to make adjustments only at the federal level.

But since the central government accounts for the bulk of the public-sector fiscal balance, and

considering the reduced room to maneuver of non-federal government entities, the lack of

database treatment at sub-national level causes little damage to our estimates.

In this paper, we follow a set of guidelines proposed by Bornhorst et al (2011) on how to treat

temporary fiscal transactions in the structural balance framework. According to the authors, one

should remove large operations with no sustained impact on inter-temporal budget positions

(i.e., fiscal solvency). The list of examples coincides with many situations found in the Brazilian

case: concession revenues, tax amnesty proceeds, court decisions and capital transfers.

According to the IMF’s technical recommendations, those operations should be taken out of the

structural balance results.

The recommendations to exclude certain fiscal operations apply mostly to revenues. As for

spending, a careful approach is necessary before stripping out ―temporary‖ expenses that may

prove hard to reverse, as Bornhorst et al (2011) highlight. An example: increased spending

during an economic crisis may be followed by a slow fiscal consolidation in the recovery. Since

structural fiscal results indicate the discretionary policy stance, the impact of fiscal decisions

(e.g., to spend more) should be reflected in the statistics, even if meant to be temporary. We

adopted the following set of IMF-recommended criteria before deciding whether to keep, remove

or include certain fiscal transactions in our structural balance database:

(1) Type of operation: we take out capital transfers, temporary tax amnesty receipts and

proceeds from court decisions. We group amnesty programs and court-decision revenues in

a class we call ―tax debt settlement‖;

(2) Persistence of the operation: we exclude one-off events lasting for less than a year.

(3) Size of the operation: in general, fiscal transactions worth more than 0.1% of GDP9 in

aggregate.

All one-off operations subject to adjustment in our database meet these requirements. Appendix

2 lists all operations (among those that we tracked) matching these criteria. While we recognize

these operations make a subset in the wide space of potential transactions eligible for treatment,

these are the largest ones disclosed to the public. A more thorough analysis of the budget would

demand years of screening and research, making this work virtually impossible.

To make sense of the one-off transactions that we treated, we group those in classes.

9 Some operations worth less than 0.1% of GDP were subject to treatment, for they are repetitions of other similar

transactions. In aggregate, those add to more than 0.1% of GDP.

17


Tax amnesty and court decisions:

Tax Debt Settlement: includes proceeds from a tax amnesty program inaugurated in 2009

(under the Law 11,941/2009). The implementation of this program was finalized in 2011.

Our database strips out large down-payments of tax debt incurred in November 2009 and

June 2011. We kept in the sample recurring monthly revenues related to the program

(around BRL 700 million per month until 1H11, BRL 1 billion per month afterwards). The

―tax debt settlement‖ group also considers revenues stemming from court rulings or tax

dispute deals. That’s the case for a BRL 5.8 billion inflow related to an agreement between

a mining company and the Revenue Service;

Capital transfers:

Petrobras: net revenues booked after the capitalization deal as of September 2010

(BRL 31.9 billion);

CEF: deposits in court held for a long time (i.e., before 1998) at ―Caixa Economica Federal‖

(CEF) and finally transferred to the Treasury in 2009;

FND: scattered, unusual profit remittances from a regional development fund – ―Fundo

Nacional de Desenvolvimento‖ (FND) in August 2008, March 2009 and June 2009;

Eletrobras: dividend receivables sold by the federal government to BNDES. We assume

partial repayment of this debt (by means of recent dividends paid by the firm);

SWF: transactions related to the capitalization of the sovereign wealth fund (―Fundo

Soberano do Brasil‖) and its stock purchases10

later on;

Exhibit-2 displays the net reduction of the federal budget result caused by the treatment of each group of one-off operations.

Exhibit-2: Database Adjustment: Capital Transfers and Tax Debt Proceeds (BRL billions, %)

Sources: Press sources, Brazilian Sovereign Wealth Fund, Brazil Revenue Service, National Treasury, Itaú

10

According to the Central Bank accounting methodology, stock purchases (even in the case of government-controlled

companies) are booked as public spending.

CEF - 8.9

(13%)

SWF - 1.9 (3%)

FND - 6.0

(9%)

Tax Debt - 15.1

(23%)Eletrobras - 2.8

(4%)

Petrobras - 31.9

(48%)

18


In addition to the database adjustment for capital transfers and tax debt settlements, we made

other corrections to the budget database. Inspired by IMF guidelines presented in Bornhorst et al

(2011), we stripped out all concession revenues booked by the Treasury since 1997. The idea is

that this type of operation is non-recurring, even if it means an anticipation of (at least part of) a

future flow of persistent revenues.

Secondly, we also adjust the budget series for a distortion generated by a cash management

mechanism called ―restos a pagar processados‖11

(disbursement deferrals or RPPs henceforth).

In a nutshell, disbursement deferrals occur when the government postpones the actual cash

outflow related to goods formally purchased or services officially hired (i.e., when invoices are

issued or when a contract is signed). This mechanism allows the government to make expenses

– usually late in the fiscal year12

– and push the disbursement to subsequent periods. As

spending is computed on a cash basis, this procedure allows the government to reallocate the

primary budget results across fiscal years. Our correction follows the assumption that

government spending impacts aggregate demand at the time of the commitment13

, and not

when disbursements occur (assuming normal liquidity conditions).

In order to account for the calendar-year distortions caused by budget deferrals, we take the

annual data on RPPs shown in the National Treasury’s Budget Execution Report14

. Owing to

some data limitations15

, we had to make a few simplifying assumptions: first, we assume all

deferrals refer to expenses in the last quarter; second, we disregard RPP cancellations, taking

the changes in their stock measured in January as a value for the whole year; third, we assume

zero spending deferrals before 2000 (when the series start).

These assumptions mean that we compute expenditure leftovers for (the Q4 of) years when

deferrals increase, subtracting them from the spending registered in Q4 of the following year.

The opposite is true when deferrals are paid off. Since these are reasonable hypotheses and the

values involved are small, this type of adjustment has more impact on the methodological rigor

of our analysis than on structural fiscal balance estimates themselves.

There are interesting conclusions to draw from the database adjustment process16

. A first

conclusion is that temporary revenues have been a widely used fiscal policy instrument in Brazil

over the last decade. For the time spanning 1997 to 2011, the net value of our budget

adjustments totaled 0.39% of GDP, basically reflecting the impact of non-recurring revenues like

concessions, capitals transfers, tax amnesty or accounting events.

As Exhibit-3 shows, the use of temporary fiscal drivers can be split into three phases: (1) 1997–

2001: transactions added to 0.60% of GDP, with concession revenues accounting for 90% of

total; (2) 2002–2007: the net impact of temporary operations tumbled to just below 0.1% of GDP;

(3) 2008–2011: large capital transfers, tax debt settlements and other accounting events took

the net level of non-recurring transactions back to around 0.6% of GDP.

11

For more details on the concept of ―restos a pagar‖, please refer to http://www.lrf.com.br/ (only in Portuguese). 12

In Brazil, the calendar year coincides with the fiscal year. 13

This stage of budget spending process is called ―liquidação‖ (settling, in English), preceding the cash payment. 14

This report can be found in the Brazilian Treasury’s web site:

http://www.tesouro.fazenda.gov.br/contabilidade_governamental/relatorio_resumido.asp 15

As an example of problems in the RPP data, social security payments as of December 2008 were mistakenly added to the stock of RPPs (―restos a pagar processados‖). This event boosted the statistics by about BRL 21 billion in early 2009. Our database is already adjusted for this event. 16

More details on the database adjustment can be found in Appendix 2.

http://www.lrf.com.br/

19


These numbers reveal a shortcoming of the current fiscal policy framework. The mechanism in

place induces policymakers to search for temporary budget-enhancing operations, so as to

deliver the fiscal target. This strategy often takes place in times of worsening economic

conditions, when tax collection disappoints.

Exhibit-3: Database Adjustment: Capital Transfers and Tax Debt Proceeds (BRL billions, %)


The second conclusion is methodological: the database treatment is a key step to estimate

Brazil’s structural fiscal balance. When comparing the primary fiscal balance implicit in this new

database after all the adjustments (i.e., a series that we call ―recurring‖ primary surplus) with the

official data (Exhibit-4) the ―recurring‖ surplus decouples from the actual fiscal result in two

phases of budget-enhancing transactions. Sometimes, the gap reaches a full 1.5% of GDP,

underscoring the risk of overestimating the structural balance when research skips this

(cumbersome, but necessary) phase.

Exhibit-4: Primary Fiscal Balance (over twelve months, % GDP)

Sources: National Treasury, Itaú

0.06%

0.58%

0.09%

0.60%

0.39%

0.00%

0.22%

0.00%

0.51%

0.09%

0.54%

0.16%

0.06%0.01% 0.03%

-0.02%-0.1%

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

1997-2011 1997-2001 2002-2007 2008-2011

Total adjustment Capital Transfers & Tax DebtConcessions Spending Deferrals

% GDP

3.2%

2.7%

0.5%

1.5%

2.5%

3.5%

4.5%

1999-III 2001-III 2003-III 2005-III 2007-III 2009-III 2011-III

Observed Balance Recurring Balance

% GDP, annually

20


Since structural balance procedures will be applied to this rebuilt budget data series, it is fair to

say that the recurring budget balance is the starting point (or the basic material) for structural

balance calculations.

3-C.3. Sampling: Revenue Classes, Tax Bases, Time Window

Revenue classification is a significant step in the sampling process. Categorizing matters for two

reasons: first, it helps tell cyclical and non-cyclical revenues apart. The latter are not subject to

cyclical adjustment. Second, the disaggregated structural fiscal balance approach needs this

segmentation to give separate treatment to different classes of government income. We chose

to group government receipts according to similarities in their bases, which should help yield

more consistent elasticity and structural balance estimates.

Using our sample of ―recurring‖ public sector revenues, we regroup government takings based

on a prior knowledge of revenue classes and their respective bases. As an example, we set up a

class of ―personal income taxes‖ – adding withholding levies to other types of household income

tax. In the case of corporate income tax, we combined IRPJ (firms’ income tax) and CSLL

(contributions on profits), for both are levied on companies’ earnings. For payroll taxes, we put

together social security contributions from the private sector (INSS) and deductions from public

sector workers’ pay (CPSS).

In the aggregated model, we created two groups of federal revenues: the first, for which we

couldn’t find evidence of a cyclical pattern, includes personal income taxes on capital gains,

income taxes on remittances abroad, and the ―CIDE‖ fuel tax. Those taxes make up the ―non-

cyclical federal revenue‖ category, which is regarded as entirely structural (i.e., not subject to

cyclical treatment). All other federal takings were included in the aggregated group of ―cyclical

federal revenues,‖ which is subject to our structural adjustment procedures. As for regional

revenues, we structurally adjust all receipts composing our database in the aggregated model.

Revenue classification in the disaggregated approach is only a more-fragmented version of the

revenue grouping used in the aggregated method. We split cyclical federal revenues into ten

classes, federal transfers into three groups, and regional revenues in three segments. Poor

estimation results obtained for non-constitutional (i.e., ―other‖) transfers and regional (i.e.,

states’) property taxes led us to treat these groups as structural (non-cyclical). Thefinal grouping

of federal and regional revenues is shown in Appendix 3.

For the tax base dataset, we found nine variables best characterizing cycles of economic activity

and asset prices with significant impact on government revenues. These variables are used in

the best models resulting from our elasticity estimation procedures (to be described ahead).

The set of tax bases is used in accordance with the approach. The aggregated models take real

GDP, Brent crude oil price and retail sales as inputs17

. The disaggregated model employs a

larger set of tax bases (or regressors): retail sales, real wage bill, industrial output, industrial

orders, new loans, imports, Brent18

crude oil price and CRB commodity price index. Additionally,

a number of dummy variables are employed in OLS models used in the elasticity estimation, in

order to control for structural changes in the tax series. Appendix 3liststhe group of economic

17

Originally, the IMF’s aggregated approach proposed the use of GDP as the cyclical activity variable. However, the

large share of ICMS (value-added tax) in total states revenues led us to test retail sales as an aggregate activity

measure. In the end, that improved the quality of our models, as we explain ahead. 18

Just as in Gobetti et al (2010), we used Brent prices as the proxy, since this is the benchmark for the National Oil Agency (ANP).

21


variables used a revenue base, showing our treatment of these series so as to prepare them for

the subsequent steps in the structural balance estimation.

As for the sample size, we estimate tax elasticity parameters for the period spanning 2002 to

201019

. Given the fluid lags involved in the transmission of macroeconomic conditions to tax

collection, we opted to use quarterly data in our analysis.

3-D. Estimation Procedures

3-D.1. Overview

We estimate Brazil’s structural fiscal balance with two alternative methodologies. Following the

IMF-styled aggregated approach, we structurally adjust total (cyclical) government receipts for

real GDP, retail sales and oil-price cycles. In the ECB-inspired disaggregated approach, we

separately adjust segmented revenue classes according to the cycles in numerous tax bases

working as proxy for business and financial conditions, such as industrial production and

manufacturing orders, wage bill, retail sales, imports, new loans and the CRB index.

There are two reasons for using two different methodologies to estimate the structural balance:

firstly, to guarantee robustness of the results; secondly, to test (and account for) the relevance of

output-composition effects in Brazilian budget results.

As shown in Appendix 4, the cycles of GDP and other activity subcomponents in Brazil are

moderately well correlated20

, suggesting some relevance of output-composition effects, yet to a

limited extent. In any case, the change in the composition of Brazilian economic growth over the

years – with domestic spending becoming significant as a driver – was a good reason to apply

the ECB methodology on Brazilian data for a first time.

Let i index public sector entities, with ―Fed‖ standing for the federal government21

, ―Reg‖ for

regional governments, ―Fir‖ for state-owned firms. We can re-write expression (1) as follows:

(8) f – fc = fs =ifsi = fs

Fed + fsReg + f Fir

Expression (8) shows the aggregation of the public sector’s structural primary fiscal balance

estimates. It results from the addition of structural fiscal results recorded by the central

government, regional governments (i.e., states and municipalities) and state-owned firms.

The absence of subscript in the term denoting government firms’ primary surplus means that we

are taking those as fully structural (or non-cyclical). We found no evidence of swings in state-

owned firms’ fiscal balance related to economic cycles (Appendix 5). This assumption has

negligible effect on our estimates, since annual government firms’ balance has averaged out at

0.13% of GDP since 2002 according to the new series stripped of Petrobras and Eletrobras. In

19

The choice of the time window took two criteria into consideration: (1) data scarcity: retail sales and bank lending series begin in 2000. Government-owned firms’ primary balance data, free of Petrobras and Eletrobras, begin in 2002; (2) series breaks: Chow breakpoint tests pointed to structural changes occurring early in the 2000sfor various revenue categories. The decision to pick 2002 as a cutoff date for all models meant to guarantee comparability of results across methodologies. 20

This exercise uses the average of our estimates for trend variables in the two filtering approaches that we tested: a

statistical one (HP Filter) and a ―theory-based‖ one, which we detail ahead. 21

Our structural balance calculations for the federal government include the statistical discrepancy between the primary

fiscal results calculated by the Treasury and the Central Bank (BCB). As discussed earlier, the objective is to maintain

the comparability between our structural balance estimate and the official BCB data. The statistical error has averaged

just 0.06% of GDP from 2003 to 2010.

22


practice, our structural primary fiscal balance estimates for the public sector will reflect the fiscal

stance of the general government (i.e., central and regional governments).

Suppose a general government entity i has J types of cyclical revenues (i.e., subject to structural

adjustment) and K categories of non-cyclical revenues (i.e., not subject to structural adjustment).

Then, the structural primary fiscal balance for entity i will be calculated as:

J K

(9) fis = ri

s,j +ris,k - e

* j=1 k=1

Note that expression (9) changes slightly the notation of revenues and expenditures presented

in Section 3.A, where ―t‖ denoted revenues and ―g‖ expenditures. Here ‖r― means receipts after

the stripping out of one-off revenues (denoted by ot), and ‖e― means expenditures after the

database treatment (spending one-offs, denoted by og). Formally (for all revenues):

(10.A) r = t – ot | e = g – og (10.B) R = T – Ot | E = G – Og Just as in Gobetti et al (2010), we do not make structural adjustment to government spending.

Brazilian data points to a pro-cyclical pattern in unemployment benefit expenses – the only

expenditure class usually subject to cyclical adjustment (Appendix 5). This result, which

contradicts economic intuition, probably follows the increased job-market formalization and the

Brazilian government policies to boost the minimum wage.

We calculate the structural level of revenue category j using the following general formula:

W

(11) RAs,j = Rj

A .(Bjw* / Bj

w)

Rj,Bw w=1

where Rj

A is the seasonally-adjusted real level

22 of Rj, defined in expression (10.B). B

jw is the

level of the base for revenue j, already adjusted for seasonality and inflation, with Bjw* being its

long-term consistent values. Our notation for he index w is general enough to comprise the

representation of an activity variable or commodity price, in different lags. Importantly,

expression (11) considers a same variable in different lags as a different tax base, with structural

balance calculations reflecting exactly the same lag structure as the one used in elasticity

estimation models23

.

In formula (11), we assume that the structural amount of revenue j is a function of the ratio

between the revenue base’s equilibrium level to its observed value (accounting for the due lags).

This ratio is adjusted by the elasticity of revenue j with respect to the base. In the particular case

of non-cyclical receipts, the elasticity equals zero, implying that RA

s,j = RjA (i.e., structural

revenues equal actual revenues).

22

To present our estimates, we convert structural revenues back to nominal values and then calculate those as

percentage of nominal potential GDP. 23

In some empirical works, researchers structurally adjust revenues using the steady-state elasticity (i.e., adding lagged

elasticity parameter estimates). In our view, this simplification of the lag structure distorts structural balance estimates.

23


We also structurally adjust federal transfers to states and municipalities, just as we do for

revenues. The idea is to produce reliable estimates of government entities’ contributions to the

structural fiscal balance.

To calculate the structural balance, we have to assign values for the two families of unknown

parameters in expression (11): the elasticity of revenues with respect to their bases (Rj,Bw); and

the series of trend (equilibrium) values for the tax bases (Bjw*). In the next sections, we provide

details on the procedures followed to estimate these parameters in both aggregated and

disaggregated methodologies.

3-D.2. Estimating Revenue Elasticity Estimates

In order to estimate the parameters representing the elasticity of revenues with respect to the tax

bases, we had three alternatives: assuming ad-hoc values, using parameters from the literature

or using econometric procedures. We chose the latter for the following reasons: first, we believe

that Brazil’s changed macroeconomic conditions may have altered, at least for some time, the

magnitude of tax collections’ responses to the cycles. In this case, new estimates are needed,

especially for the most recent period. Secondly, since the ECB’s approach had never been

tested to Brazilian data, there are no disaggregated-enough elasticity estimates in the literature.

The choice of the econometric tools used in this work reflects our will to guarantee transparency,

consistency and meaningfulness of elasticity parameters and structural fiscal balance estimates.

The idea behind these guidelines is to lend credibility to our results, considering the high

uncertainty related to the estimation of non-observable economic variables such as the structural

balance.

We depart from a general framework of autoregressive distributed lags (ADL) regression

models:

(12) Ln (RjA) =j0 + q

jq Ln (RA

j,q)+ wjw Ln (Bj

w)+ Where Rj

A is the seasonally-adjusted real level

24 of Rj. Additionally, B

jw is the level of the base

for revenue j, already adjusted for seasonality and inflation. is a white-noise error term. As in

expression (11), subscript w indexes tax bases (i.e., activity components or commodity prices) in

different lags, meaning that a same variable in a different time is a different base.

Since the subscript q refers to the number of lagged dependent variables, RA

j,q is the q-th lag for

regressand RjA. In our models, we first sought to use equations with no lagged dependent

variables, so as to avoid excessive parameterization25

. But in a few cases, the inclusion of one

lagged dependent variable improved the model’s performance.

We use OLS regressions to estimate the model’s parameters (’s). Since both dependent and

explanatory variables are in logs, estimates for jw are the elasticity of revenue j with respect to

the base w (RjBw).

24

To present our estimates, we convert structural revenues back to nominal values and then calculate their ratio to

nominal potential GDP. 25

Statistically significant slopes for lagged dependent variables generate a persistent impact of shocks affecting the tax

bases. This prompts the inclusion of multiple tax bases in structural balance calculations, so as to reflect the lag

structure.

24


To avert ex-ante specification problems, we ran models based on economic intuition and

previous knowledge of Brazil’s tax structure26

. Difficulties increased especially the case of the

disaggregated approach, as more detailed information on Brazil’s complex tax system was

required to find a satisfactory set of tax bases for each revenue class.

In the aggregated methodology, we worked with a narrower list of tax bases, for the method

assumes GDP as the standard measure of business cycle.

The selection of our tax elasticity models was based on a qualitative, multi-dimensional

judgment of parameter, residual and specification tests. We sought models generating theory-

(and/or reality-) consistent slopes, with residuals as close as possible to a white noise, and with

stable parameters. We embraced Box-Jenkins’ modeling philosophy and sought to use the

fewest parameters in our regressions. Below, we explain the rationale for model selection,

revealing the criteria and tools used to assure that the chosen models had the desired

properties:

- Significant, meaningful OLS estimates (necessary condition to accept the model): we

only accepted models with statistically valid, theory-consistent parameter estimates. In

order to test the robustness of the (ad-hoc) relationships, we ensured that most models

showed statistically similar parameter values when the lag structure is changed by one

period27

(i.e., either one lag or one lead);

- Well-behaved residuals (necessary condition to accept the model): a key dimension in our

choosing process. We only considered models with no evidence of serial correlation

(according to Breusch-Godfrey LM test28

) or conditional heteroskedasticity (based on ARCH

LM tests).

- Validity, robustness, stability of specifications: we ran different specification tests. To

detect omitted variables problems, incorrect functional forms, and correlation between

regressors and residuals, we ran Ramsey’s RESET tests. Rejection of the null hypothesis

that the model is well specified led us to discard it. Thus, ―approval‖ in the RESET test was

a necessary condition for model acceptance. To test coefficients’ stability, we ran sub-

sample regressions (using 2007 as a cutting date) and statistically compared the results29

.

We also tested stability by running Chow’s break-point and forecast tests for different sub-

samples. In some cases, test results and/or prior information pointed to structural breaks,

leading us to resort to dummy variables to control for such regime changes.

We ran most regressions in levels, after certifying for the presence of unit roots and

cointegration relationships (always assuming similar specifications as in the respective models:

in most cases with a constant and no trend). While most variables proved to be I(1), there were

a few stationary (I(0)) dependent variables. In these cases, we used the first differences of I(1)

regressors.

26

All the structural fiscal balance methodologies discussed in this paper use ad-hoc specifications to estimate tax

elasticity parameters. 27

Result based on a double-tailed t-statistic parameter test. 28

We also considered Durbin Watson statistics as a secondary test for serial correlation. In models with lagged

dependent variables, however, we disregarded this test, given the changed probability distribution in these cases. 29

We use a two-tailed t-statistic test to verify the equivalence of individual parameter estimates in sub-sample

regressions, assuming independence of parameter estimates and an asymptotically normal distribution.

25


It is interesting to compare our estimation strategy with alternative approaches to illustrate the

rationale behind our procedures. One of the possibilities was to obtain elasticity parameters from

a combination of multiple regressions. In our view, the numerous structural breaks present in the

tax series, particularly more visible in disaggregated revenue models, hamper the use of such

econometric mining techniques. The latter might lead to inconsistent parameters and inaccurate

results.

Another alternative was to estimate elasticity parameters by regressing (the logarithm of)

revenues on the output gap and other (activity, commodity or asset-price) cycles, instead of their

levels. This strategy might produce misleading results, for two reasons. First, the imprecision in

trend filtering techniques could directly affect residuals and slopes, adding more noise to

structural balance results. Second, regressing I(1) variables (e.g., revenues) in I(0) variables

(e.g., cycles) harms the consistency of elasticity estimates. Since both use of levels or cycles as

regressands are consistent with the structural balance assumption represented by expression

(11) – if we assume that cyclical elasticities are unchanged in equilibrium – we opted for a

method that makes more sense econometrically.

The strengths of our procedures:(1) the simplicity of econometric procedures, improving

transparency of results, and (2) an artisan modeling approach focused on good large-sample

properties and economic validity of estimates. The main shortcomings of our approach: (1) the

failure to allow for endogenous regime30

changes and (2) the absence of steady-state

convergence rules31

.

Below, we present noteworthy specifics about the econometrics for each structural fiscal balance

methodology. Appendix 7 and Appendix 8 bring further details on all tax-revenue models (i.e.,

estimates, residuals and tests).

Aggregated Approach

The models for central government revenues (Model I.1) and total federal transfers (Model I.2)

boasted satisfactory results, with intuitive elasticity estimates. The results confirm the statistically

positive relationship between federal revenues and GDP or oil prices. The latter was the asset

price exhibiting the best explanation power for broad federal receipts (among the CRB index,

iron ore and soybean costs, as well as the terms of trade). In our view, the significant revenue

impact of oil costs reveals the influence of financial and commodity cycles on the budget

performance.

The models for transfers to states and municipalities also show a statistically positive

relationship between transfers and collection of income taxes. The proportionality reflected in a

unit-elasticity estimate suggests an accurate replication of Brazil’s revenue-sharing framework.

For these models, there was no need to use dummy variables to control for regime changes.

Results were not so good for the states’ tax models that used GDP as base (Model I.3).

Although the initially chosen model looks well-specified, with residuals neither serially correlated

nor heteroskedastic (signaling consistent estimates), robustness tests pointed to certain

30

Another criticism applying to every econometric approach estimating budget elasticity is the possible endogeneity of explanatory variables, as Bouthevillain et al (2001) point out. 31

We do not include error-correction terms in our regressions for two reasons: first, to avoid an excessive number of

parameters, which could damage to the stability of slope coefficients and the easy interpretation of results. Second,

considering recent changes in economic formalization and, possibly, in tax-collection technology, we believe Brazilian

tax collection must be far from a steady-state (as we know it).

26


parameter instability. For some reason, a structural break could have taken place sometime

between 2007 and 2010.

We decided to try an alternative model for states’ revenues (I.3A), making an amendment to the

IMF-styled approach as we apply it to Brazilian data. We replaced GDP as a proxy for the

business cycle and introduced retail sales as an instrumental variable. The choice of retail sales

was based on the large share of ICMS (VAT) in total tax receipts by state governments (it

accounts for nearly 90% of states’ tax collection in our sample). The replacement markedly

improved the robustness of elasticity estimates. Moreover, the model’s result looks quite

reasonable, as the elasticity of states’ tax collection with respect to retail sales stands very close

to unit, reflecting the expected proportionality of indirect taxes.

In light of these results, we embraced this alternative model for states’ revenues (using retail

sales as tax base) in our aggregated methodology. That means a bit of mixing between the IMF

and ECB approaches32

.

Disaggregated Approach

Due to necessary adaptations to the Brazilian data, our procedures entailing the application of

the ECB’s disaggregated methodology were also a bit hybrid, as we combined one element of

two from the IMF methodology. While the disaggregated methodology proposes to regress

revenues on bases comprising activity subcomponents (i.e., activity indicators other than GDP),

some models posted better results when we included (complementarily) GDP in the set of

regressors. That was the case for payroll taxes (Model II.1), corporate taxes (Model II.3) and

other federal revenues (Model II.10).

As for the elasticity estimates, the disaggregated models posted encouraging results, with all

revenue categories confirming the assumed (a priori) relationship with their bases (i.e., positive

or negative correlation). Some key examples: the wage bill was confirmed to be statistically

significant for payroll taxes (Model II.1) and personal income taxes (Model II.4); demand

variables such as retail sales and industrial orders confirmed the positive relationship with

federal sales tax (Model II.2) and states’ VAT taxes (Model II.13); industrial production and

imports were also empirically confirmed as drivers for IPI taxes on industrial products (Model

II.5) and import duties (Model II.6).

Another important aspect of the disaggregated results is the clearer evidence of the important

role played by commodity cycles in fiscal budget results. The ECB-inspired models confirm a

broad-based revenue impact from raw material prices, as the CRB index (one of the proxies

tested) posted a statistically meaningful relationship with corporate earnings taxes, personal

income taxes and financial transaction taxes. Moreover, our estimates also confirmed that oil

prices play a significant role in federal dividends, probably reflecting the direct impact of oil on

profit transfers from Petrobras, and the indirect effect of commodity (and other asset) prices on

federal bank earnings (and dividends). Oil prices also proved to be significant for federal

royalties, reflecting the oil exploration framework currently in place.

Most models scored fairly well in residual and specification tests, signaling good large-sample

properties of elasticity coefficients. However, the modeling of segmented tax classes in the

disaggregated approach clearly prompted some loss of robustness and stability of coefficients,

compared with the aggregated approach. This was particularly the case for the models of import

tax (Model II.6), constitutional transfers (Model II.11) and states’ VAT revenues (Model II.13).

32

In Appendix 10, we compare structural balance results using GDP and retail sales as a base for states’ revenues.

27


There was also evidence of possible structural breaks taking place in social security

contributions (Model II.1), financial transaction taxes (Model II.7) and other state levies (Model

II.14).

Modeling federal firms’ dividends (II.8) was particularly cumbersome, due to severe structural

breaks taking place after 200933

. As an upshot, the dividends model proved unstable, which led

us to treat as outliers the abnormally large structural dividends in 2Q09 and 4Q1034

. We

maintained the structural balance of dividends despite these shortcomings, because: (1)

elasticity estimates still seemed consistent and (2) we felt it was important to maintain a minimal

structural adjustment to such a highly cyclical revenue source.

Brazil’s ever-changing tax code prompted us to make corrections for structural breaks in many

disaggregated models, which was done through dummy variables. That was the case, for

instance, for federal sales taxes (Model II.2) – following the changes in Cofins legislation (turning

it into a non-cumulative levy) in 2004. Other examples are the impact of the extinction of the

CPMF (financial transaction) tax, the IPI tax exemptions (on manufactured products) in 2009

(Model II.5), the increase in job formality changing the dynamics of social security contributions,

and the evidence of minor structural changes in some revenue classes after the 2009 crisis. In

most cases, these dummy corrections resulted in consistent and relatively robust estimates of

elasticity coefficients.

Overall, the results of the disaggregated models were satisfactory, especially for the most

important revenue categories, which bodes well for the accuracy of our structural fiscal balance

estimates. However, the disaggregated approach resulted in a certain loss of robustness in the

elasticity estimates, as compared with the aggregated approach.

3-D.3. Trend Filtering: Finding the Equilibrium Path for the Tax Bases

The last element needed to estimate Brazil’s structural fiscal balance under the IMF and ECB

methodologies is the path of trend (or equilibrium) values for the different tax bases

characterizing the business and financial cycles. In this paper, we estimate long-term trends of

activity indicators and commodity prices in two different ways: (1) via statistical filtering; (2) via

economic (or ―theory-based‖) de-trending.

In the statistical approach, we use the Hodrick-Prescott (HP) Filter, setting the value of

parameter at 1,600 (as recommended for quarterly data). We performed this procedure for all

revenue bases in both methodologies. The choice of the HP filter is based on its ready

availability and wide use in the literature, which improves the transparency and trustworthiness

of our structural fiscal balance estimates.

33

The biggest methodological problem here is the upward shift in dividend revenues, particularly reflecting the steep

increase in the size of the loan portfolio of BNDES, the national development bank. This expansion was supported by

elevated Treasury loans in recent years. It is debatable whether these dividends should be treated as one-off or not, but

we decided to keep them in our structural primary-balance estimates, as the bank is likely to maintain (if not increase)

the size of its portfolio and this magnitude of dividends for longer than a year (criterion we used to tell temporary from

recurring revenues). Broadening the reach of our structural-balance procedures to the concept of nominal deficit might

provide a better treatment for such high BNDES dividends, as the cost of the Treasury loans will impact structural

interest payments. This broader treatment would better measure the net impact of these BNDES operations on the

public sector’s fiscal position. For more on the Treasury loans to BNDES, refer to Mansueto Almeida’s personal web

page: http://mansueto.wordpress.com/. 34

Our estimates had pointed to a spike in structural dividend revenues in those quarters. We decided to override the

structural adjustment in these periods, and assume all dividend revenues in that period were non-cyclical.

28


The ―economic‖ de-trending approach entails heterogeneous methods that vary according to the

variable used as a base. As a general rule, these procedures are oriented by economic theory or

intuition, instead of statistical techniques. An illustrative example of what we call economic

filtering is the calculation of potential (or trend) GDP using a production function framework (in

this paper, we use the Itaú Macro Research Team’s series of potential GDP estimated through

the production function method).

For the other variables characterizing business conditions in the disaggregated methodology35

,

our strategy was more intuitive. We were inspired by numerous works in the literature, as

surveyed by Bornhorst et al (2011), using benchmarks set upon historical ratios to determine

equilibrium values for some bases. We also assume a cointegration relationship between GDP

and activity subcomponents, meaning that these variables should not drift away from each other

for too long.

The long-term consistent levels of these business-cycle variables will be set upon their average

ratios to real GDP for the time spanning 1Q01 to 3Q11. In formal terms, the equilibrium level for

tax base w (for any class of revenue, anytime) is calculated via the following expression:

(13) Bw* =wY* Where w is the average ratio of tax base w to real GDP in the sampling period, and Y* is the potential output estimated under the production function framework (to keep the coherence of this type of economic filtering). Expression (13) shows that the equilibrium level for any base (i.e., activity subcomponent) in any period will be determined by the product between potential GDP in the same period and the observed average ratio. As an example, the trend value estimated for retail sales in 1Q11 will be determined by the average historical ratio of retail sales to real GDP (for the time spanning 1Q01 to 3Q11) multiplied by the estimated level for real potential GDP in the same quarter.

Since our procedure defines a constant, linear relationship between the business cycle variables

used as tax base and trend output (at a rate determined by the average historical ratio), the

resulting equilibrium levels for activity subcomponents will accompany the moves in potential

output, which makes perfect sense economically. Moreover, despite possible fluctuations in the

share of consumption in total GDP, or in the participation of labor income in total income, the use

of average ratios doesn’t seem to be a strong assumption, given the relatively low quarterly

volatility of these ratios in our sample (Exhibit-5). The only exception is the imports’ GDP-ratio

series, the increased volatility of which reflects the currency movements (remember, we use the

inflation-adjusted BRL-value of imports)

Despite the statistical sufficiency of our method – as we update average ratios (and equilibrium

levels) as new data comes in –one shortcoming of this procedure is that it may take a while to

reflect sudden structural changes in the relationship between activity subcomponents and GDP

in the short run. However, this problem also applies for other de-trending procedures.

35

The set of variables include: wage bill, retail sales, imports (in BRL), new loans, industrial orders and output.

29


Exhibit-5: Activity Variables: Fluctuations Relative to Real GDP

Sources: Itaú, IMF, Bloomberg, IBGE, BCB, CNI ** Imports expressed in BRL terms

Owing to the (yet) greater uncertainty regarding the estimation of equilibrium levels for asset

prices, and also following some non-intuitive results obtained in our tries, we decided not to use

econometric filters to de-trend oil and commodity prices, in both methodologies.

To eliminate idiosyncratic distortions and at the same time reflect different movements captured

by each de-trending methodology, our structural fiscal balance estimates use a combination of

trend values obtained from both economic and statistical de-trending methods. That applies for

both aggregated and disaggregated methodologies. Most trend values will be determined by the

average between statistical and economic filters (with the exception of prices of commodities –

Brent oil and CRB indexes – which are only de-trended via statistical methods, the HP filter).

The final series for all tax bases used in our models are displayed in Appendix 9.

4. RESULTS

4-A. Elasticity Estimates

Our estimates from the aggregated methodology point to a steady-state36

GDP elasticity of

revenues of 1.48 for the public sector37

, resulting from an elasticity of 1.45 for the central

government and 1.55 for regional governments. Our estimate falls a tad below the values found

in Gobetti et al (2010): between 1.6 and 1.8 using the OLS approach and around 2.0 using

Markov-Switching models. The evidence of elastic revenues for the period of 2002 to 2011

reflects, among other factors, the rising formalization of the economy and the increased

contribution of domestic spending to total GDP growth.

36

Steady-state elasticity means the sum of elasticity coefficients for different lags of a same regressor, accounting for

possible lags of the dependent variable. It denotes the all-time impact of the explanatory variable on the regressand. 37

Since government firms’ primary balance is assumed to be non-cyclical (i.e., zero elasticity), the revenue elasticity for the public sector is the same as the one for the general government, which includes federal and regional governments.

80

90

100

110

120

130

140

2001-I 2002-I 2003-I 2004-I 2005-I 2006-I 2007-I 2008-I 2009-I 2010-I 2011-I

retail sales w ages ind.orders new loans ind.output imports

2007=100, ratio of each variable to real GDP

30


Revenue elasticity estimates allow us to quantify the cyclical sensitivity of the primary fiscal

balance. Since we assume budget outlays do not depend on economic cycles (i.e., meaning

zero GDP elasticity), the sensitivity of the public sector’s primary balance is obtained by

multiplying the elasticity of revenues with respect to output by the ratio of government revenues

to GDP. Using the data as of 2010, our results show that the public sector’s primary surplus

tends to rise by 0.45% of GDP for every additional percentage point of GDP growth. Entity-wise,

the GDP sensitivity of the primary fiscal balance recorded by the central government is 0.32; for

regional governments, 0.13 (Exhibit-6).

Exhibit-6: Sensitivity of Primary Budget Balance With Respect to GDP

Source:Itaú

As Exhibit-6 shows, Brazil’s cyclical primary-balance elasticity rose to 0.45 in 2010, from 0.40 in

2003. This increase reflects a higher share of government spending and revenues in the

economy.

To compare our results with those from the literature, we use the ratio of government revenues

relative to the economy as of 2003. Our budget sensitivity estimate for that year stands at 0.40,

below the value obtained in Gobetti et al (2010), 0.43, but topping the one found in Mello et al

(2006), 0.32. The gap from Mello et al (2006) is influenced by the fact that these authors deploy

a positive elasticity of expenditures, downwardly impacting their estimates by about 0.06.

For other countries, estimates from Girouard and Andre (2005) showed that, in 2003, the

average budget sensitivity in the group of OECD economies was 0.44. Exhibit-7 shows: the

output-sensitivity of Brazilian budget results stands below those found for European countries

like Sweden, Netherlands, France, Italy and Germany (0.55—0.51), while topping the estimates

for non-European OECD economies like the U.S., Japan (0.33-0.34) and Korea (0.22). Our

estimates for Brazil stood close to the ones obtained for advanced commodity exporters, like

Canada and Australia (0.38-0.39).

It is important to bear in mind that the results obtained in Girouard and Andre (2005) are

produced under a cyclically adjusted fiscal balance framework, which implies no treatment for

the budgetary impact of financial (or commodity) cycles. This suggests that the GDP elasticity of

budget results estimated for some of the countries shown in this cross-section (e.g., Spain,

Australia, Canada) might be overestimated relative to our Brazil results.

2003

2007

2010

Ratios from

the year of

Primary Fiscal Balance Sensitivity

0.12

0.12

0.13

Public Sector Central Government Regional Government

0.40

0.44

0.45 0.32

0.32

0.28

31


Exhibit-7: General Government Budget Sensitivity to Output

Source: Itau, Mello and Moccero (2006), Girouard and Andre (2005)

These numbers suggest that Brazilian fiscal results are considerably sensitive to business

cycles. This probably reflects the country’s relatively high level of tax collection and public

expenses as share of the economy, compared with its emerging-market peers. Policy-wise, this

large budget cyclicality signals the need to create fiscal buffers to protect the budget from abrupt

reversals in economic conditions.

Our aggregated structural revenue models also show that tax receipts are significantly affected

by commodity prices, with the cost of oil capturing quite well the fiscal impact of fluctuations on

commodity prices and financial conditions38

. Our estimates point to a statistically significant

elasticity of broad federal revenues with respect to Brent oil price, equal to 0.08(Exhibit-8).

In our view, the lower revenue elasticity with respect to oil, compared with GDP, is due to two

facts: first, the higher volatility of raw material costs; second, Brazil’s relatively diversified

economy, with natural resources being only part of the business picture.

The significance of oil prices in public sector revenues contrasts with the fact that oil-related

receipts have accounted for no more than 5% of total federal takings in recent years. In our

interpretation, the significant impact of oil cycles on government revenues reflects more than just

the direct effects on dividends, royalties and corporate taxes. The high correlation of oil price

with other financial variables (such as equities and other commodity prices), suggests that oil

costs also work as a proxy for the broad indirect fiscal impact from wealth effects related to

commodity and financial cycles. The latter influences government revenues via consumer

spending, capital gains and corporate investment.

38

As pointed out in previous sections, we alternatively tested the CRB commodity index, prices of other commodities (iron ore, soybean) and the terms of trade as proxies for the impact of financial cycles on government budget results. We obtained the best results in terms of explanation power and residual properties using oil costs.

0.550.53 0.53 0.53

0.51

0.47 0.46 0.45 0.44 0.44

0.39 0.38

0.34 0.33

0.22

0.40

0.44

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

Sw

eden

Fra

nce

Italy

Neth

erlands

Germ

any

Gre

ece

Port

ugal

UK

Pola

nd

Spain

OE

CD

Avg.

Bra

zil

Austr

alia

Canada

US

Japan

Kore

a

Our estimate using

2003-data

32


Exhibit-8: Activity Variables: Fluctuations Relative to Real GDP

Source: Itaú

Elasticity estimates from disaggregated models also bring interesting insights, basically

confirming the elevated cyclicality of key revenue categories in Brazil. Given the lack of previous

work applying the ECB’s approach to Brazilian data, we use as benchmark the estimates from

Bouthevillain et al (2001) for European Union economies39

. We compared estimates for social

security contributions, corporate income taxes, personal income taxes and indirect taxes.

As Exhibit-9 shows, our revenue elasticity estimates with respect to the most commonly used

bases stood just a tad below the average of European economies (see column ―One Base‖).

But our disaggregated revenue models incorporate more explanatory variables than our

European counterpart. Since we worked with structural adjustment instead of cyclical

adjustment, as in Bouthevillain et al (2001), we used commodity-price proxies as additional tax

bases for each tax category. When we add all tax elasticity estimates with respect to all

variables, just as an approximation (see column ―More Bases‖), it appears that the cyclical

sensitivity of various revenue classes in Brazil is larger than in Europe.

Exhibit-9: Disaggregated Elasticity Estimates – An International Comparison

Source: Itaú E.U. estimates taken from Bouthevillain et al (2001) * Refers to our long-term (or steady-state) revenue elasticity estimates. ** This column’s results mean a simple addition of cyclical elasticity coefficients with no discrimination for different explanatory variables. While these estimates are not addable, rigorously speaking, these numbers provide an indicative value for the total cyclical sensitivity of tax revenue

classes when we incorporate more tax bases into the models. *** Weighted average between elasticity estimates for federal sales taxes and states’ VAT.

39

While Bouthevillain et al (2001) estimate the cyclically adjusted fiscal balance using the same ECB methodology, there are important differences in the estimation of budget elasticity parameters compared to our work. The European application obtains elasticity parameters both via econometric analysis and derivation from the tax code. Additionally, Bouthevillain et al (2001) use a framework of cyclically adjusted balance, with no treatment for financial cycles’ impact on fiscal results.

1.48

0.08

0.00

0.50

1.00

1.50

2.00

GDP Oil Price

Elasticity of General Government Revenues With Respect to....

E.U. BRAZIL Average Range One Base More Bases**

Social security contributions Wages Wages 1.0 0.9 - 1.0 0.9 1.8

Corporate earnings taxes Profits GDP 1.2 0.7 - 1.5 1.0 1.9

Direct household taxes Wages Wages 1.5 1.2 - 2.6 1.4 1.7

Indirect taxes Consumption Retail sales 1.0 0.7 - 1.2 0.9*** 1.2***

TAX CATEGORYTAX BASES E.U. Results BRAZIL Results*

33


4-B. Structural Primary Fiscal Balance Results

This subsection presents and discusses our Brazilian public sector’s structural primary fiscal

balance estimates for both aggregated and disaggregated approaches. A methodological

analysis of our results can be found in Appendix 10, as we explain the reasons for the final

configuration of models, which yielded the numbers shown below.

Exhibit-10 displays our estimates, comparing those with the official (unadjusted) data. In order to

eliminate idiosyncrasies from each methodology, we display our baseline estimate, which takes

the average between estimates under the aggregated and disaggregated approaches.

Exhibit-10: Structural Primary Fiscal Balance Estimates (vs. the Official or Unadjusted Data)

Source: Itaú (A) Twelve months to September 1/ A few disaggregated models structurally adjust revenues based on cycles of activity subcomponents with a short data series (e.g., retail sales, bank lending). In these cases, to estimate results for the year of 2000, we filled the information gaps with structural balance estimates using GDP as the revenue base. The GDP-elasticity results follow the re-estimation of the model with GDP replacing the respective tax base.

A first noteworthy point about the numbers is the relative robustness of our estimates across the

two different structural balance approaches. In the quarterly series, the maximum absolute gap

between our aggregated and disaggregated estimates is 1.2% of GDP, which occurs in late

2009 and early 2010. These data points should be seen as outliers – reflecting the impact of a

sudden increase in BNDES dividends after large loans from the National Treasury.

For the time spanning 1Q00 to 3Q11 (on a quarterly frequency), the average absolute gap

between aggregated and disaggregated estimates is 0.36% of GDP (or 0.30%, disregarding the

―outliers‖). For annual data (using a same time window), the difference stands below 0.2% of

GDP for about half of the years in the sample; for a third of those years, the gap is 0.3%–0.5% of

GDP; for only two calendar-years did the difference lie within 0.6%–0.8% of GDP. In all, these

numbers suggest that estimates from our aggregated and disaggregated methods are quite

similar for most of the time

2000 2.1% 2.2% 2.1% 3.2%

2001 2.6% 2.2% 2.4% 3.4%

2002 3.4% 3.2% 3.3% 3.2%

2003 4.3% 4.6% 4.5% 3.3%

2004 4.2% 4.4% 4.3% 3.7%

2005 4.0% 4.0% 4.0% 3.8%

2006 3.4% 3.9% 3.6% 3.2%

2007 2.7% 3.3% 3.0% 3.3%

2008 2.5% 2.3% 2.4% 3.4%

2009 2.0% 2.8% 2.4% 2.0%

2010 0.8% 1.4% 1.1% 2.8%

2011(A) 2.1% 2.2% 2.1% 3.5%Average

2000-2011 2.8% 3.0% 2.9% 3.2%

Baseline (Mean)

Estimate

Official

(Unadjusted) Data% GDP

Aggregated

Approach

Disaggregated

Approach1/

34


The similarity of results under both approaches is favored by adaptations we made to apply the

aggregated and disaggregated methodologies to Brazilian data40

. These adaptations aimed at

improving the meaningfulness and robustness of the results. Additionally, the small estimate gap

also reveals a relatively low influence from output composition effects in Brazil’s structural

balance, confirming ex-ante expectations following pre-tests (refer to Appendix 4).

Our final results show that, for the period spanning 1Q00 to 3Q11, the average structural primary

balance recorded by Brazil’s public sector was 2.9%, a bit short of the unadjusted primary

balance average, 3.2%. This means that the average annual impact of cyclical fiscal revenues

(including one-off transactions) on budget results has been 0.3% of GDP since 2000.

The relatively small difference in the average of structural and official budget balance hides

periods of considerable decoupling between these series across the last decade (Exhibit-11).

Exhibit-11: Structural Fiscal Balance Estimates for Brazil’s Public Sector (Quarterly Data)

Source: Itaú

From 2000 to 2008, the unadjusted primary surplus stood around 3.5% of GDP, with a low

variance in this period. In the post-Lehman period, the observed primary fiscal balance suddenly

tumbled to 1.1% of GDP in 3Q09, and then rapidly initiated a staggered recovery: to 2.0% of

GDP in 4Q09, 2.8% in 4Q10 and 3.5% in 3Q11.

In fact, as opposed to a relatively stable path of the observed primary surplus up to 2008, our

baseline structural primary fiscal balance estimates point to significant changes in the fiscal

policy stance across the decade.

Our baseline numbers signal a significant contraction in the early days of Brazil’s fiscal

adjustment. As the official data show, the policy stance turned tighter in 1999 with the reversal of

primary fiscal deficits. According to our structural balance estimates, the fiscal contraction

continued into the first half of the 2000s and, after a temporary drop in late 2000 and early 2001,

40

Key examples are: the use of retail sales as a tax base in the aggregated approach and the inclusion of GDP in the set of revenue bases in the disaggregated methodology. Both turn our procedures a bit hybrid, as compared with the pure theoretical or empirical propositions by the IMF and ECB.

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

20

00

-III

20

01

-III

20

02

-III

20

03

-III

20

04

-III

20

05

-III

20

06

-III

20

07

-III

20

08

-III

20

09

-III

20

10

-III

20

11

-II

Aggregated Approach Disaggregated Approach

BASELINE Official (unadjusted) data

% GDP

35


became steadily more pronounced, reaching 2.4% of GDP in end-2001, 3.3% in end-2002, and

peaking at 4.5% in end-2003.

After touching 4.6% in 2004, the structural primary fiscal balance started to come down. In the

beginning of this process, the recalibration of a very tough fiscal stance was slow, with the

efforts easing at a pace of 0.3% of GDP per year. The structural primary balance fell to 3.6% in

end-2006. From 2007 onwards, the government’s intention to boost federal investment (e.g., by

creating the PAC growth-accelerating program), during an improvement in cyclical revenues,

caused a faster decline in the structural primary balance. The pace of decrease reached 0.7% of

GDP per year, with the structural balance falling to 2.8% of GDP in end-2008. The process

accelerated further after the Lehman collapse, as the government chose to use fiscal tools to

avert a steeper local slowdown. In 2010, the structural primary fiscal balance reached a low of

1.1% of GDP, according to our estimates.

Better results appeared in 2011, especially in the second half. Our calculations point to a

recovery in the structural primary surplus last year, with our baseline estimate standing at 2.1%

of GDP forQ3 (last data point).

The large swings in our structural fiscal balance estimates reveal the misleading conclusions

one can make by assessing the stance of fiscal policy in Brazil only through the official

(unadjusted) primary budget results.

4-C. Dissecting Budget Results

Our structural balance estimates allow us to look at recent fiscal policy results from different

angles, producing interesting insights. In this section, we analyze the contributions of

government entities to the public-sector structural primary fiscal balance, and break down the

public sector’s observed fiscal balance between a structural and a cyclical component.

4-C.1. The Structural Balance for Central Government and Regional Governments

For the intra-government composition of the structural balance, our results show relatively

coordinated movements across general government entities (i.e., including federal and regional

governments, and excluding state-owned firms). Despite a decoupling between structural fiscal

results posted by federal and regional governments in 2011 – as the federal government

recently tightened the fiscal stance without similar initiatives by states and municipalities –

Exhibit-12 shows a historically close link between the structural balance results recorded by

these government levels.

The correlation between the structural primary fiscal balance of federal and regional

governments stands around 0.85 for the whole sample (i.e., 2000–2011). This means that

whenever the central government’s fiscal policy took on a tighter (looser) stance, regional

governments have generally followed suit. Our annual structural balance series show that this

synchrony is seen in 7 out of 11 years in our sample.A simple regression (using quarterly data

from 2000 to 2011) shows that for every percentage point increase in the federal government’s

structural primary fiscal surplus, regional governments increased their structural balance by

about 0.62 percentage point, on average (Appendix 12).

36


Exhibit-12: Structural Primary Fiscal Balance (Baseline) Estimates By Entities

Source: Itaú

It is possible that the incomplete data on regional governments’ budgets has some influence on

this high correlation of structural balance across general government entities. Still, in our view,

the similarity between the fiscal policy stance of central and regional governments reflects some

aspects of the fiscal framework currently in place. One example is the exposure to similar

budgetary shocks – as federal and regional government share an important amount of tax

collections. Another factor is the limited room for fiscal policy maneuvering by states and

municipalities in the wake of the Fiscal Responsibility Law, enacted in 2000. This new legislation

left regional governments more dependent on decisions taken at the federal level, which seems

to have successfully improved intra-government fiscal policy coordination. Refer to Appendix 12

for more details on the structural fiscal balance registered at the regional level.

4-C.2. Structural Fiscal Balance: Cyclical Balance vs. Structural Balance

This subsection breaks down the primary fiscal balance, revealing the impact of cyclical drivers

on Brazil’s budget performance recently. For the sake of simplicity and intuition, we work only

with aggregated methodology estimates. Appendix 13 shows results using baseline estimates.

The structural fiscal balance (SFB) – which is the share of budget results consistent with output

and asset prices in equilibrium – measures the fiscal policy stance. The cyclical fiscal balance

(CFB) – which reflects the impact of business and financial cycles on budget results –reveals the

impact of automatic stabilizers (e.g., tax collection), also interpreted as a passive fiscal policy

response41

.

41

Since we estimate the CFB as residual (i.e., observed balance minus structural balance), our calculations for the CFB will be influenced by variables not necessarily linked to the cycles of activity and commodity prices. The key examples: (1) non-recurring fiscal transactions; (2) small distortions generated by the use of potential GDP (instead of actual GDP) in the scaling of structural budget result.

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

20

00

-III

20

01

-III

20

02

-III

20

03

-III

20

04

-III

20

05

-III

20

06

-III

20

07

-III

20

08

-III

20

09

-III

20

10

-III

20

11

-III

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

Federal Government (left) Regional Governments (left) Public Sector (right)

% GDP % GDP

37


BOX: Estimates for the Output Gap and Oil Price Cycles

For a better understanding of the (estimated) cycles impacting our structural balance estimates, we present in

Exhibit-13 our calculations for real GDP and (Brent) oil price gaps. The technical procedures were already

discussed in Section 3-D, so that a few points need to be clarified now.

Exhibit-13.A: Trend GDP and the Output Gap (De-trending via Production Function and HP Filter)

Exhibit-13.B: Equilibrium Oil Price and the Oil Cycles (De-trending Only by HP Filter)

Source: IBGE, Bloomberg, Itaú

At a first glance, our de-trending procedures seem to have produced trend estimates with the welcomed property

of prompting cycles in either direction. Another important feature is that output and oil price gaps seem to have

properly replicated economic and financial conditions observed for each period: below-trend values for 2001–

2003, overheating and exuberance for 2007–2008, widening gaps in the post-Lehman period, and above-trend

levels in 2010.

Our potential output series– estimated via the average of statistical and economic trend filters– points to an

average trend growth around 3.5% annually for 2000–2011. For the early 2000s, our estimate moves at an annual

clip just below 2%, accelerating throughout the period to just above 4% in 3Q11. Our oil price trend – estimated

only via HP filter – starts with a nominal value of US$ 20 per barrel, reaching up to USD 94 in the end of the time

window. The average oil price trend though the period is estimated at USD 54 per barrel.

70.0

80.0

90.0

100.0

110.0

120.0

20

00

-III

20

01

-III

20

02

-III

20

03

-III

20

04

-III

20

05

-III

20

06

-III

20

07

-III

20

08

-III

20

09

-III

20

10

-III

20

11

-III

-5.0%

-2.5%

0.0%

2.5%

5.0%

Output Gap (right)

GDP (left)

Potential GDP (left)

2007=100 %

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

20

00

-III

20

01

-III

20

02

-III

20

03

-III

20

04

-III

20

05

-III

20

06

-III

20

07

-III

20

08

-III

20

09

-III

20

10

-III

20

11

-III

-45.0%

-30.0%

-15.0%

0.0%

15.0%

30.0%

45.0%

60.0%

Oil Price Gap (right)

Brent Oil Price (left)

Trend Oil Price (left)

2007=100 %

38


Exhibit-14.A: Structural vs. Cyclical Primary Fiscal Balance Estimates (Aggregated Estimate)

Exhibit-14.B: Contributions to the Cyclical Primary Fiscal Balance (Aggregated Estimate)

Source: Itaú * For some years, contributions shown in Exhibit-14.B may not add, because we use nominal potential GDP as deflator (instead of actual nominal GDP, as in the official series). The annual impact stands within 0.1% of GDP for the entire time window.

The results show that, for the years of 2000–2001, the budgetary impact of economic cycles

added to nearly1% of GDP, accounting for a third of the official (unadjusted) average primary

budget results. About 60% of the cyclical budget balance in that period stems from concession

takings, which were used to improve fiscal results at the time. As explained earlier, these

revenues were stripped out of our structural budget database. The remaining 40% of the cyclical

balance reflects favorable activity and oil price cycles estimated for the year 2000. Our estimates

show a reasonable structural balance in the early 2000s – averaging at 2.3% of GDP –indicating

a tightening fiscal policy stance (see Exhibit-14.A and Exhibit-14.B).

2.1%2.6%

3.4%

4.3% 4.2% 4.0%3.4%

2.7% 2.5%2.0%

0.8%

2.1%

1.2%

0.0%

0.8%

-0.2%

1.2%

-0.5%

-1.1%

-0.1%-0.2%

0.7% 0.9%

2.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Structural Fiscal Balance Cyclical Fiscal Balance Observed Fiscal Balance

% GDP

0.6% 0.6%

0.1% 0.1%

0.6%

1.4%

0.5%

0.4%

-0.2%

-1.2%

-0.2% -0.5%

0.6%

0.3%

-0.3%

0.1%

0.1% -0.1%

0.9%

-0.4%-0.4%

0.8%

0.4%

-0.1%

0.3%

-0.1%

-0.2%

0.2%

-0.1%

0.2%

0.1%

-0.3%

0.2%

0.1%

0.1%

-0.2%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Non-Recurring Revenues Activity Cycle Commodity Cycle

% GDP

39


For the years of 2002–2006, the cyclical impact on observed primary fiscal balances was

negative on total. That was especially true in 2003 – when we estimate the CFB pressured down

the official budget result by about 1.1% of GDP, following a recession that took place in that

year. The structural balance rose to 4.3% of GDP in 2003 from 3.4% in the previous year,

according to our aggregated estimates. From 2004 on, cyclical winds turned gradually less

harmful – with commodity price back on the rise (outdoing its trend in 2005–2006) and the

economy moving closer to equilibrium. Amid improving cyclical revenues, delivering stable

budget results (as targeted) became a less-difficult task, paving the way for a less-tight fiscal

stance. As an upshot, the structural balance dropped to 3.4% by end-2006.

Economic conditions for Brazil kept improving in 2007–2008. According to our trend estimates,

GDP and oil price gaps were largely positive in this period, signaling that the overheating

created significant tailwinds for budgetary results. There was a positive cyclically led fiscal

balance of 0.8% per year, out of which 0.6% stemmed from an overheated economy and 0.2%

from a commodity price spike. In our calculations, 2008 was the year when the commodity cycle

helped Brazilian budget results the most, adding around 0.3% of GDP.

As the cyclical push from automatic stabilizers made the achievement of a constant fiscal target

a good deal easier, especially compared with the uphill situation faced in the beginning of the

decade, the government continued to ease the fiscal stance: the structural balance fell to 2.5%

of GDP in end-2008, the lowest level since 2001.

In the post-Lehman period, policymakers further eased the fiscal stance to avert local

contamination from the global recession. In 2009, the structural balance declined by 0.5% of

GDP42

(to 2.0%), and a local slowdown brought about a lower cyclical balance. In fact, the action

of automatic stabilizers was the main reason for the decline in the observed surplus to 2.0% of

GDP in 2009, from 3.4% in 2008.

An important feature of Brazil’s fiscal policy in the after-crisis is the use of non-recurring budget

operations to improve fiscal results, especially in 2009 and 2010. Based on these temporary

budget-enhancing transactions, the government maintained a relatively fast pace of expenses.

In our calculations, the impact of one-off fiscal operations rose to 0.6% of GDP in 2009 and

peaked at 1.4% of GDP in 2010. One-off revenues and accounting events provided the greatest

contribution for the rise in the official primary fiscal balance. The latter touched 2.8% of GDP in

2010 (from 2.0% in 2009), reflecting the impact of the activity cycle on tax collection – +0.8% of

GDP in 2010 (from -0.5% in 2009) – and despite the 1.2% decline in the structural primary

surplus (reaching 0.8%).

In 2011, fiscal policy changed gears. Most of the increase in the observed primary budget

balance – to 3.2% of GDP in 3Q11 from 2.8% in 4Q10 – was due to a tighter fiscal stance by the

federal government. As an upshot, the public sector’s structural balance rose by 1.3 percentage

points, to 2.1% of GDP, according to our aggregated methodology estimates. While one-off

revenues remained at a relatively high historical level (0.5% of GDP) and although the impact of

activity drivers on automatic stabilizers remained considerable (0.6% of GDP), the total cyclical

contribution to the primary budget result in 2011 was lower than in 2010.

In sum, the breakdown of the official primary budget balance (into structural and cyclical

components) confirms the negative relationship between the fiscal policy stance and the action

42

This relatively timid budgetary action, reflected in a smooth decline in the structural fiscal balance, contrasts with bolder off-budget fiscal interventions. The main example is the significant Treasury loans to BNDES, as the government decided to use a mix between fiscal and quasi-fiscal instruments to stimulate the economy in 2009.

40


of automatic stabilizers (i.e., tax collection). In fact, we calculate a current correlation coefficient

of -0.91 between the structural and cyclical fiscal balances. The latter evidences the highly pro-

cyclical bias in Brazil’s fiscal policy over the last twelve years. The establishment of constant

primary fiscal balance targets, a key feature of the current policy framework, is probably the

main element behind this fact. This setting allows governments to ease the fiscal stance in times

of favorable cycles (and plentiful tax collection) and brings the need to increase efforts in periods

of adverse economic and financial cycles (prompting lackluster revenues).

Lastly, our numbers also reveal that the activity cycles play a far more important role as a fiscal

driver than commodity-price cycles (as denoted by oil costs). Our estimates suggest that the

budgetary impact of activity drivers have been more than three times as large as the effect of

raw material prices.

4-D. The Fiscal Impulse

The structural fiscal balance reflects the budgetary impact of discretionary policy decisions. This

implies that the changes in the structural fiscal balance denote the fiscal impulse given to the

economy. Formally, we calculate it as follows:

(14) I(t) = – [ fs(t) – fs(t-1) ] Where I(t) is the fiscal impulse in year t, fs(t) is the structural primary fiscal balance estimate for

year t. Positive values signal fiscal expansion and negative values mean fiscal contraction.

According to our results, the budgetary fiscal impulse from the public sector was strongly

negative in 2001, 2002 and 2003. For these years, the estimated fiscal drag was around0.8% of

GDP. Our calculations point to another significant fiscal contraction in 2011, totaling1.3% of GDP

(see Exhibit-15.A).

Exhibit-15.A: Public Sector’s Budget Fiscal Impulse or Drag (%GDP) – (Aggregated Estimate)

-0.6%

-0.8%

-0.9%

0.1% 0.2%

0.7% 0.7%

0.2%

0.5%

1.2%

-1.3%-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fiscal Impulse

% GDP

41


Exhibit-15.B: Breaking Down the Budget Fiscal Impulse (Aggregated Estimate)

Source: Itaú

When we compare these two periods of fiscal contraction (2001–2003 and 2011), we see

differences both in the magnitude and in the composition of the tightening. Exhibit-15.Bbreaks

the fiscal impulse down according to its sources: revenue, expenditure, and government-owned

firms’ balance. The graph shows that the fiscal consolidation of 2011 (up to Q3) is almost evenly

explained by higher structural revenues and lower expenditure stimulus. This contrasts with the

composition of the fiscal contraction seen in 2001–2003. In 2001–2002, the adjustment was

largely led by revenues, with higher taxes prompting an annual drag of nearly 1.5% of GDP on

average. In 2003, the focus was on spending, which contributed with a drag of 1.1% of GDP.

There is another distinct feature of the fiscal consolidation of 2011. Unlike 2001–2003, the tight

stance in 2011 follows a largely expansionary policy in 2010. Our estimates point to a fiscal

impulse of 1.2% of GDP for that year, the strongest in our series (started in 2001). Thus, the

2011 adjustment seems to be just a normalization of the budgetary stimulus given to the

economy in the previous year.

Interestingly, the expansionary fiscal stance of 2010 contrasts with a relatively low impulse

estimated for 2009:0.5% according to our aggregated methodology. This number has to be put

into perspective, as a good deal of stimulus took place via quasi-fiscal instruments, especially

BNDES lending. This explains why the budgetary fiscal expansion was not so high for such a

recession year. The large Treasury loans to BNDES (about BRL 100 billion, or 3.1% of GDP, in

2009) prompted a sharp increase in the development bank’s portfolio and, as a consequence, a

brisk rise in dividend paid to the Treasury (worth about BRL 14 billion, 0.45% of GDP, in 2009).

We did not treat these dividends as one-off, for this type of transaction did not match the ex-ante

removal criteria that we adopted based on IMF recommendations. Therefore, this operation

accounts for most of the spurious fiscal drag caused by revenues for that year, totaling 0.6% of

GDP. Since the expenditure impulse was 1.2% of GDP in 2009, compared with 0.8% in 2010,

the remaining noise created by dividend revenues probably explains why the total impulse was

shy of the stimulus seen in 2010. The difficulties in treating quasi-fiscal operations suggest that

there is probably some remaining noise in our structural balance estimates for the year of 2009.

-0.9%

-1.9%

-0.6%

0.3%

-0.7%-1.1%

-0.5%

1.2%0.8%

0.7%

0.1%-0.2%0.0%

-0.1%-0.9%

-0.6%

1.1% 0.8%0.3%

0.2%0.8% 0.5%

0.3%

0.0%

0.0%

0.1%

-0.1%-0.1%

-0.1%

0.0%

0.2%

0.4%

0.2%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Revenues Expenditures Government-Owned Firms

% GDP

42


Exhibit-15.B shows that a lax stance towards expenditures was the key element behind the

decline in the structural balance, as estimated for 2004 to 2010.Our calculations show that the

average fiscal impulse from public-sector spending was 0.6% of GDP per year in this period.

That contrasts with an average drag of 0.1% per year on the revenue side resulting, for instance,

from a tightening in Cofins rules in 2005 and an ―easing‖ due to the expiration of the CPMF tax in

2008.Our calculations show that the spending impulse was especially strong in 2005–2006 and

2009–2010: it was worth about 1.0% of GDP annually for these years.

We also broke down the fiscal impulse by government entities, in order to observe the fiscal

stance of different public-sector spheres and how these added to the overall structural balance.

Exhibit-16 shows that the fiscal contraction as of 2001–2003 was evenly shared by the federal

and regional governments: both contributed to the fiscal consolidation by about 0.5% of GDP per

year. These numbers contrast with a fiscal expansion of 0.3% of GDP registered by government-

owned firms43

.

Federal and regional governments were also equally accountable for the reduction of the

primary structural fiscal balance in subsequent years. The fiscal impulse averaged 0.5% per

year for the period of 2004–2010, with each government level recording an average annual

fiscal impulse of about 0.25% of GDP in that period. However, in 2011 (up to Q3), there was a

decoupling in the fiscal impulse from federal and regional governments, as the central

government tightened the fiscal stance and prompted a fiscal drag of 1.4% of GDP. Meanwhile,

regional governments eased the policy stance by about 0.1% of GDP.

The numbers on the fiscal impulse by government entities highlight the phenomenon described

in section 4-C.1– the high correlation of structural results by federal government, and states and

municipalities. In our view, this reflects a greater policy coordination following the Fiscal

Responsibility Law (2000), which limited the room for fiscal maneuvering at the regional level.

Exhibit-16: The Fiscal Impulse by Government Entities (Aggregated Estimate)

Source: Itaú

43

The fiscal expansion of state-owned companies in 2001–2002 might bea spurious one, reflecting the removal of Petrobras and Eletrobras from the fiscal statistics in 2002. These companies used to add around 0.3%–0.5% of GDP per year to the consolidated primary fiscal surplus.

-0.3%

-0.8%

-0.3%-0.1%

0.1%0.4% 0.2%

-0.4%

0.8% 0.7%

-1.4%-0.5%

-0.3%-0.8%

0.3%

0.3%0.2% 0.6%

-0.2%

0.4%

0.1%

0.1%

-0.1%

0.0%

0.1%

-0.1%0.0%

0.0%0.3%

-0.1%

0.2%

0.4%0.2%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Central Government Regional Governments Government-Owned Firms

% GDP

43


4-E. Simulating Alternative GDP and Commodity Trends

Considering the usual uncertainty involving the estimation of unobservable economic variables,

such as potential GDP, equilibrium asset (or commodity) prices and the structural fiscal balance

itself, it is important to observe the impact of different trajectories of potential GDP and trend oil

prices in our structural primary surplus estimates.

We begin with alternative paths of potential output. We proposed two different series: the first

one runs 2.5% above the equilibrium values previously estimated (which we call ―main trend‖) for

all times; the other one runs 2.5% below, for the same period. The 2.5% gap follows an arbitrary

rule: it’s two times as high as the average absolute output gap seen in quarterly data from 2000

to 2011.

These alternative potential GDP trends imply non-extreme values, setting reasonable

boundaries to simulate structural balance results with other assumptions for steady-state

economic growth (Exhibit-17.A). One should view these alternative trends as upper and lower

limits for reasonable assumptions for the evolution of potential GDP across the tested period.

As expected, a linear shift in output trends caused a similar displacement in our structural

primary balance estimates, maintaining the same shape as the curve shown in our main

(aggregated) results (Exhibit-17.B). In this simulation, the gap between the estimates assuming

the optimistic and pessimistic trends is 1.9% of GDP for the tested period; in the last data point,

3Q11, the structural primary surplus estimates a range between 1.1% and 3.1% of GDP (the

aggregated model’s estimate is 2.1%).

Exhibit-17.A: Alternative Trajectories for Potential GDP

70

80

90

100

110

120

mar/97 mar/99 mar/01 mar/03 mar/05 mar/07 mar/09 mar/11

Lowest Trend Main Trend Highest Trend Actual GDP

2007=100, SA

44


Exhibit-17.B: Structural Balance for Alternative Potential Output Paths (Aggregated Estimate)

Source: Itaú

We also tested the impact of different equilibrium paths for Brent oil price. We basically followed

the same criterion used for potential GDP: The alternative equilibrium trends are set with a gap

from the main trend that is twice as high as the average absolute quarterly oil-price gaps from

2000 to 2011. This implies that these alternative trend oil price series stand 35% above and

below the equilibrium values used in our models (i.e., the ―main trend‖). Exhibit-18.A shows

these simulated equilibrium paths for oil prices.

Similarly to the potential GDP simulation, the shape of the structural balance results did not

change assuming these alternative oil-price trends. The gap between the highest and the lowest

result stood around 1.2% of GDP for the whole observation period (as compared with a gap of

1.9% for the GDP simulation). The structural primary fiscal balance for 3Q11 lies between 1.3%

and 2.6% of GDP in this simulation of alternative equilibrium oil-price paths (Exhibit-18.B).

Exhibit-18.A: Alternative Trajectories for Brent Oil Price

1.1%

2.1%

3.2%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

2000-I

2001-I

2002-I

2003-I

2004-I

2005-I

2006-I

2007-I

2008-I

2009-I

2010-I

2011-I

Highest Trend Main Trend Lowest Trend

% GDP

0

45

90

135

180

mar/97 mar/99 mar/01 mar/03 mar/05 mar/07 mar/09 mar/11

Lowest Trend Main Trend Highest Trend Actual Oil Price

2007=100, SA

45


Exhibit-18.B: Simulated Structural Balance for Brent Oil Price Paths (Aggregated Estimate)

Source: Itaú

We also tested scenarios combining these alternative paths for equilibrium GDP and oil costs

altogether. The outcome is a 3Q11 structural balance estimate ranging from 0.5% to 3.5% of

GDP, with a similar range (i.e., around 3pps) valid for almost the entire sampling window.

Overall, the simulations show that alternative paths of potential GDP growth and oil price may

have a significant impact on structural balance estimates. In this exercise, we conclude that a

2.5% increase (decrease) in potential GDP levels may produce a change of one percent rise

(fall) in primary structural fiscal balance estimates. For oil price, a 35% rise (drop) in equilibrium

Brent costs may upwardly (downwardly) impact structural balance estimates by 0.6% of GDP.

The simulations reveal that structural balance estimates are more dependent on equilibrium

GDP levels than on oil price trends. The greater GDP sensitivity is quite intuitive, as Brazil has a

relatively well-diversified economy, where raw materials play a significant role but are far from

accounting for the bulk of the story.

4-F. Comparing Our Estimates with the Literature

Considering the limited amount of published work on Brazil’s structural fiscal balance in recent

years, we take Gobetti et al (2010) as a benchmark to our results.

Our structural primary fiscal balance estimates are qualitatively similar to the ones obtained by

those researchers, despite some important differences in key procedures followed in this paper.

For the period of 1Q00–2Q10, the shape of the structural balance curve estimated in Gobetti et

al (2010) resembles our baseline results (Exhibit-19). The absolute gap of mean estimates for

the general government is, on average, 0.4% of GDP.

Our methodology points to more pronounced fiscal policy cycles and impulses. There are

significant differences in specific years. For 2000–2001, for instance, we calculate that the

structural primary balance for the general government (in our case, using baseline estimates44

)

was 1.4% of GDP, while estimates in the benchmark paper stand around 2.2%. This gap likely

reflects our decision to remove concession revenues from the structural balance database.

44

As explained earlier, our baseline estimates refer to the average between the structural primary fiscal results obtained from aggregated and disaggregated methodologies.

1.3%

2.1%

2.6%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

2000-I

2001-I

2002-I

2003-I

2004-I

2005-I

2006-I

2007-I

2008-I

2009-I

2010-I

2011-I

Highest Trend Main Trend Lowest Trend

% GDP

46


Exhibit-19: Comparing Our Estimates With Gobetti et al (2010)

1/

Sources: Itaú, Gobetti et al (2010) 1/

We used our baseline structural primary fiscal balance estimates for the general government, so as to improve the comparability of results

Another decoupling occurs in 2003–2004, when our structural fiscal balance estimates point to

an average surplus of 4.2% of GDP; Gobetti et al (2010) estimate 3.7% of GDP.

For 2008–2009, the authors estimate an average structural result of 3.0% of GDP, which

compares with our numbers – around 2.3% of GDP. Once again, the difference in the estimates

seems to reflect the database adjustments (to control for the impact of one-offs) made in this

current work, which affected the structural results in the post-Lehman period.

Overall, this comparison highlights one of the most important methodological differences in our

work: the thorough treatment for one-off fiscal operations enhancing budget results, which

affected the numbers especially in the early and late 2000s.

As Exhibit-20 shows, the database adjustment to budgetary one-offs has played a key role in our

structural balance estimates. We calculate that this procedure accounted for nearly half of the

1.2 percentage point gap between our structural balance estimate (2.1% of GDP in 3Q11) and

the observed surplus (3.2% of GDP in 3Q11). Considering the sizeable temporary budget-

enhancing operations that took place in Brazil in recent years, our estimates demonstrate that a

detailed database treatment is a key step to estimate Brazilian structural balance.

1,0%

1,5%

2,0%

2,5%

3,0%

3,5%

4,0%

4,5%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

ITAU Estimates Estimates from Gobetti et al (2010)

% GDP

47


Exhibit-20: Primary Budget Balance: Observed, “Recurring,” Structural Primary Fiscal Results

Sources: Central Bank, Itaú

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

2000-I 2001-I 2002-I 2003-I 2004-I 2005-I 2006-I 2007-I 2008-I 2009-I 2010-I 2011-I

Observed Budget Result Recurring Budget Result Structural Budget Result (Baseline)

% GDP

48


5. Concluding Remarks Our main findings can be summarized as follows:

For each additional point of GDP growth, Brazil’s primary budget surplus tends to rise by

0.45% of GDP. The country’s budget sensitivity is short of European economies but tops

estimates for the U.S. and Japan, suggesting a high cyclicality for an emerging economy. We

find evidence of elastic revenues with respect to economic conditions, possibly reflecting a

rising formalization and a more internally led growth.

Elasticity estimates confirmed the budgetary significance of commodity prices, which work as

a proxy for the wealth effect from swings in financial conditions in our models. However,

business cycles proved to have far greater influence on automatic stabilizers and therefore

on budget results in Brazil.

Our structural primary-balance estimates differ significantly from the official unadjusted

budget data, pointing to a significant tightening in the first part of the 2000s, a visibly easing

policy stance in the latter part of the 2000s and a tightening in 2011. The numbers show that

watching the fiscal stance only through the official (unadjusted) data can lead to misleading

conclusions.

Structural balance estimates for central and regional governments point to a highly correlated

fiscal stance by these government entities. The structural balance correlation largely tops the

one for official budget results. This synchronized fiscal stance suggests increased policy

coordination after the implementation of the Fiscal Responsibility Law and the debt deal

between the Federal government and local governments.

We note a highly negative correlation between the structural balance (measuring the

government’s fiscal stance) and the cyclical balance (measuring the fiscal contribution from

automatic stabilizers). This confirms Brazil’s pro-cyclical fiscal policy drive, which in our view

reflects the incentives behind the pursuit of constant, unadjusted primary targets.

Based on IMF’s database clean-up criteria to remove one-off fiscal-enhancing transactions

before applying structural balance methods, we observe the use of fiscal one-offs in times of

fiscal consolidation or when cyclical revenues fail.

This paper confirms Brazil’s highly pro-cyclical policy bias. The current policy setting, based on

the pursuit of stable, non-structural primary balance targets, generates incentives for this

behavior. It seems that policymakers calibrate fiscal efforts so as to fill the gaps between targets

and the budgetary balances that result from the fiscal impact of automatic stabilizers (tax

collection, mainly). That’s probably why we observe overspending in booming years and a

search for extraordinary revenues in recession years.

The current fiscal framework needs to include incentives for a truly counter-cyclical fiscal action.

In the Brazilian case, the use of structural primary balance targets seems to be a good way to

prompt fiscal policy to lean against the wind (instead of going along with it). In our view, this new

policy drive would help boost public savings and increase potential GDP growth.

49


APPENDIX Appendix 1 – Derivation of Nominal Potential GDP

Our structural balance estimates are shown as percentage of nominal potential GDP. In this

section, we describe how we calculate this unobservable variable.

One can break down nominal aggregate spending as follows:

(1) Nt = Yt .Pt

In equilibrium:

(2) N t* = Yt*. P t*

N is nominal GDP (N* the nominal potential GDP); Y denotes the real GDP (and similarly, Y*

stands for the real potential output); P is the GDP price deflator (with P* as steady-state price

level, assuming no shocks since the beginning of the series). We can rewrite expression (2) as:

(3) N t* = Yt .Pt.Yt* .Pt* .(Yt)-1

. (Pt)-1

By merging (1) and (3), the nominal potential GDP is determined as follows:

(3) N t* = Nt .ht.rt

Where ht is the ratio of potential output to actual output in period t and rt is the ratio between the

steady-state price level and the actual price level in the same period. We use our own potential

GDP series to calculate the value of ht. The equilibrium price level could be estimated by

accumulating the inflation rate implied by the Central Bank’s target. However, since the target is

set upon consumer inflation, this procedure would be distorted by changes in relative prices

caused by, say, spikes in commodity costs. The latter affect the GDP deflator more intensely

than the CPI. Hence, we assume actual prices are always in equilibrium, setting rt at unity.

Our structural balance results were not very sensitive to the choice of actual or potential nominal

GDP for scaling, as the graph shows. In fact, the use of potential nominal GDP impacts quarterly

structural balance estimates by no more than 0.1% GDP in average.

Potential or Actual Nominal GDP as Deflator – Little Difference

Source:Itaú

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

0.15%

2002-I 2003-I 2004-I 2005-I 2006-I 2007-I 2008-I 2009-I 2010-I 2011-I

Aggregated Approach Disaggregated Approach

% GDP

50


Appendix 2 – Details of Database Adjustment

One-Offs: Capital Transfers, Tax Amnesty and Court Decisions Subject to Treatment


Exclusion or

Inclusion?

On which

line?

Budget

Impact

(R$ billion)

Budget

Impact

(%GDP)

ago-08

Atypical profit taking from the National Development Fund (FND),

measured by actual value minus the past twelve-month average

(the latter is a proxy for "permanent" flow)

FND Yes Excluded Expenditures -1.8 -0.1%

dez-08Spending with the capitalization of the Sovereign Wealth Fund

(SWF)SWF Yes Excluded Expenditures 14.2 0.5%

mar-09





mai-09Tranfers of deposits in court from Caixa Economica to the

TreasuryCEF Yes Excluded Revenues -0.6 0.0%

jun-09





ago-09 Tranfers of deposits in court from Caixa Economica to Treasury CEF Yes Excluded Revenues -1.7 -0.1%

out-09Tranfers of deposits in court from Caixa Economica to Treasury

(estimated for the month)CEF Yes Excluded Revenues -3.3 -0.1%

nov-09Tranfers of deposits in court from Caixa Economica to Treasury

(estimated for the month)CEF Yes Excluded Revenues -3.3 -0.1%

nov-09

Proceeds from a tax amnesty program (Law 11.941), estimated as

the total inflow deduced of average monthly payments (the latter is

a proxy for the "permanent" flow)

Tax Debt Yes Excluded Revenues -2.3 -0.1%

dez-09Federal government sells Eletrobras dividend rights to BNDES

(federal banks are out of public sector fiscal statistics)Eletrobras Yes Excluded Revenues -3.5 -0.1%

fev-10Eletrobrás dividend payment transferred to BNDES by the federal

government. This is our own estimate based on market dataEletrobras No Included Revenues 2.1 0.1%

jul-10Federal government buys Banco do Brasil shares tapping

resources from the SWFSWF No Included Expenditures -1.5 0.0%

ago-10Federal government sells Eletrobras dividend rights to BNDES

(federal banks are out of public sector fiscal statistics)Eletrobras Yes Excluded Revenues -1.4 0.0%

set-10 Booking of revenues from the Petrobras capitalization deal Petrobras Yes Excluded Revenues -74.8 -2.0%

set-10 Booking of expenditures from the Petrobras capitalization deal Petrobras Yes Excluded Expenditures 42.9 1.1%

set-10Federal government buys Petrobras shares tapping resources

from the SWFSWF No Included Expenditures -14.6 -0.4%

jun-11


the total inflow deduced of estimated average monthly payments

(we assumed R$ 1 billion per month)

Tax Debt Yes Excluded Revenues -5.8 -0.2%

jul-11


the total inflow deduced of estimated average monthly payments

(we assumed R$ 1 billion per month)

Tax Debt Yes Excluded Revenues -1.3 0.0%

jul-11Payment of tax debt owed by a mining company ending, a judicial

disputeTax Debt Yes Excluded Revenues -5.8 -0.2%

TOTAL (2008-2011) -66.6 -0.5%

Adjustment done to raw federal budget database

Month DescriptionClass of

Adjustment

Computed in

official

statistics?

51


The Net Budgetary Impact of Database Adjustments: Annual Data


(A) Past twelve months; (B) Year-to-date (up to September) * Positive values mean upward budget impact after the adjustment; negative values mean lower results after the adjustment. ** Data on spending deferrals (“restos a pagar processados”) start in 2000, so that we assume zero deferrals for 1997 to 1999. *** We correct the spending deferrals series for a distortion affecting the data from 2009 onwards, which relates to a mistaken computation of INSS pension outlays in the last month of 2008 as a delayed payment. We subtract BRL 21.2 billion from the stock of budget deferrals registered in the official data for each year since 2009. The value of this adjustment is equivalent to the total pension outlays in December 2008.

For more on our budget database, refer to the following link: http://www.itaubba-economia.com.br/content/interfaces/cms/anexos/ITABBA_WP_6_Annex.pdf

Year GDP

R$ billions % GDP R$ billions % GDP R$ billions % GDP R$ billions % GDP R$ billions

1997 -1.5 -0.2% 0.0 0.0% -1.5 -0.2% - - 939.1

1998 -9.4 -1.0% 0.0 0.0% -9.4 -1.0% - - 979.3

1999 -9.2 -0.9% 0.0 0.0% -9.2 -0.9% - - 1,065.0

2000 -7.1 -0.6% 0.0 0.0% -5.2 -0.4% -1.9 -0.2% 1,179.5

2001 -5.7 -0.4% 0.0 0.0% -4.4 -0.3% -1.3 -0.1% 1,302.1

2002 -2.2 -0.1% 0.0 0.0% -1.8 -0.1% -0.4 0.0% 1,477.8

2003 -4.7 -0.3% 0.0 0.0% -0.4 0.0% -4.3 -0.3% 1,699.9

2004 2.3 0.1% 0.0 0.0% -1.3 -0.1% 3.6 0.2% 1,941.5

2005 -1.3 -0.1% 0.0 0.0% -0.8 0.0% -0.5 0.0% 2,147.2

2006 -1.3 -0.1% 0.0 0.0% -1.0 0.0% -0.4 0.0% 2,369.5

2007 -3.4 -0.1% 0.0 0.0% -2.1 -0.1% -1.4 -0.1% 2,661.3

2008 7.8 0.3% 12.5 0.4% -6.1 -0.2% 1.4 0.0% 3,032.2

2009 -18.4 -0.6% -18.9 -0.6% -3.1 -0.1% 3.6 0.1% 3,239.4

2010 -50.9 -1.4% -47.3 -1.3% -1.2 0.0% -2.5 -0.1% 3,770.1

2011(B) -14.7 -0.5% -12.9 -0.4% -1.8 -0.1% 0.0 0.0% 3,052.3

2011(A) -17.7 -0.4% -12.9 -0.3% -2.4 -0.1% -2.5 -0.1% 4,076.3

Total '97-11 -119.7 -0.4% -66.6 -0.2% -49.1 -0.2% -4.0 0.0% 30,856.4

Total '97-01 -32.8 -0.6% 0.0 0.0% -29.6 -0.5% -3.2 -0.1% 5,465.0

Total '02-07 -10.7 -0.1% 0.0 0.0% -7.4 -0.1% -3.4 0.0% 12,297.3

Total '08-11 -76.2 -0.6% -66.6 -0.5% -12.2 -0.1% 2.6 0.0% 13,094.0

Total adjustment Capital Transfers | Tax Debt Concessions Spending Deferrals

http://www.itaubba-economia.com.br/content/interfaces/cms/anexos/ITABBA_WP_6_Annex.pdf

52


Appendix 3 – Revenue Classes and Tax Bases

Our Re-Grouping of Public-Sector Revenues


Regressors: The List of Revenue Bases (use on a Quarterly Frequency)


Tax DescriptionStructurally

Adjusted?

I. CYCLICAL FEDERAL REVENUES Taxes co-moving with activity or commodity prices YES

I-1. PAYROLL TAXESSocial security contributions in both private and public sectors (RGPS, CPSS). Includes

"Salário Educação" contributions.YES

I-2. SALES TAXES Cofins, PIS/PASEP, Income tax - Miscellaneous ("IRRF - Outros Rendimentos"). YES

I-3. CORPORATE INCOME TAXES Corporate tax ("IR - Pessoa Jurídica"), earnings contributions (CSLL). YES

I-4. PERSONAL INCOME TAXESHouseholds ("IR - Pessoa Física"), labor income ("IRRF - Rendimentos do Trabalho")

minus tax returns ("Restituições").YES

I-5. INDUSTRIAL OUTPUT TAXES All types of IPI ("Imposto sobre Produtos Industrializados"). YES

I-6. IMPORT DUTIES Import Tax ("Imposto de Importação"). YES

I-7. FINANCIAL TRANSACTION TAXES IOF, CPMF (discontinued in 2008). YES

I-8. DIVIDENDS Dividends from federal government companies ("Dividendos da União"). YES

I-9. ROYALTIESFederal government rights on certain concessions ("Cota parte de compensações

financeiras").YES

I-10. OTHER REVENUES Other taxes, contributions, revenues. Includes Central Bank revenues. YES

II. NON-CYCLICAL FEDERAL REVENUES Taxes found to be insensitive to the cycles NO

II-1. PAYROLL TAXESIncome tax on capital remittances ("IRRF - Remessas ao Exterior") and income tax on

capital gains ("IRRF - Rendimentos do Capital").NO

II-2. FUEL TAX CIDE ("Contribuição de Intervenção no Domínio Econômico") NO

III. FEDERAL TRANSFERS Shared revenues and other transfers to states & cities YES

III-1. CONSTITUTIONAL TRANSFERSMostly comprising shared revenues of IPI (industrialized goods taxes) and IR (income

taxes).YES

III-2. ROYALTIES TRANSFERS According to law 9,478/97 YES

III-3. OTHER TRANSFERSCompensation for certain tax breaks on exports ("Lei Complementar 87e 1152").

Transfers of CIDE, "Salário Educação", "Fundef/Fundeb" (education-related). NO

IV. STATES TAX COLLECTION The sample of regional revenues with largely available data YES

IV-1. VAT REVENUES Revenues from ICMS ("Imposto sobre Circulação de Mercadorias e Serviços"). YES

IV-2. PROPERTY TAXES Revenues from IPVA ("Imposto sobre a Propriedade de Veículos Automotores"). NO

IV-3. OTHER TAXESRevenues from ITCD ("Imposto de Transmissão Causa-Mortis e Doação") and other

levies at local level.YES

Cod.

Macro Variables Data Original Seasonal Adj. Series Base

(Tax Bases) Provider / Source Frequency Method (QoQ) Start Year Index

GDP IBGE Quarterly IBGE's 1991 2007=100 -

Wage Bill IBGE Monthly Our own (X12) 1991 2007=100Old survey appended to new survey

to enlarge series

Retail Sales IBGE Monthly Our own (X12) 2000 2003=100 -

Industrial Orders CNI Monthly Our own (X12) 1992 2003=100Old survey appended to new survey

to enlarge series

New Bank Loans BCB Monthly Our own (X12) 2000 2007=100 We use daily averages

Industrial Production IBGE Monthly Our own (X12) 1991 2007=100 -

Imports - BRL terms Funcex Monthly Our own (X12) 1993 2007=100 -

Oil Price (Brent) Bloomberg Daily Our own (X12) 1946 2007=100 -

CRB Commodity Price Index Bloomberg Daily Our own (X12) 1950 2007=100 -

Notes:

53


Appendix 4 –GDP and Activity Cycles: Case for Disaggregating?45

According to Bornhorst et al (2011), the correlation and synchrony between the gap in output and in other activity variables is an early signal for the possible presence of output-composition effects in budget results and structural balance estimates. If those cycles are highly correlated, the results from the aggregated approach should be similar to those from the disaggregated methodology. The graphs below point to a statistically significant, yet moderate correlation between the cycles of GDP and variables such as the wage bill, retail sales, bank lending, imports (in BRL), industrial production, and industrial orders

46. On average, the contemporary correlation between these cycles

is 0.48, with largely unequal pair-wise results (see graphs below). An example: the correlation between GDP and industrial output cycles is 0.82, while the correlation between GDP and wage cycles is 0.20. At first, the data suggest that output-composition effects should have a moderate impact on Brazil’s structural fiscal balance estimates. But these correlations signal that it may be worth trying the ECB method for Brazilian data. The final outcome confirmed expectations of limited composition effects.

Cycles in Real GDP and Other Activity Subcomponents

Sources: BCB, IBGE, CNI, Funcex, Itaú

45

The cycles displayed in these charts are estimated via statistical and economic filters. 46

All variables are in volume or inflation-adjusted terms.

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

mar/01 mar/03 mar/05 mar/07 mar/09 mar/11

-12.0%

-9.0%

-6.0%

-3.0%

0.0%

3.0%

6.0%

9.0%

12.0%

GDP cycles (left) Retail sales cycles (right)

correlation: 0.35

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%


-12.0%

-9.0%

-6.0%

-3.0%

0.0%

3.0%

6.0%

9.0%

12.0%

GDP cycles Wage cycles

correlation: 0.20

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%


-40.0%

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

GDP cycles (left) Cycles of imports in BRL (right)

correlation: 0.47

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%


-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

GDP cycles (left) Bank loan cycles (right)

correlation: 0.55

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%


-16.0%

-12.0%

-8.0%

-4.0%

0.0%

4.0%

8.0%

GDP cycles (left) Ind.output (right)

correlation: 0.82

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%


-16.0%

-12.0%

-8.0%

-4.0%

0.0%

4.0%

8.0%

12.0%

GDP cycles (left) Ind.orders (right)

correlation: 0.49

54


Appendix 5 – Cyclicality of Jobless Benefits and State Firms’ Budget Balance

The graphs below illustrate why we chose not to structurally adjust federal unemployment

benefit expenses and government-owned firms’ primary fiscal balance.

Graph A: Pro-Cyclical Pattern in Jobless Insurance Outlays

Sources: National Treasury, IBGE, Itaú

Graph A shows the positive correlation between jobless insurance and GDP, revealing its unintuitive pro-cyclical behavior.

Graph B: No Evidence of Cyclical Swings in Government Firms’ Budget Results

Sources: BCB, IBGE, Itaú

Graph B shows that government firms’ primary balance (excluding Petrobras and Eletrobras) is unrelated to economic growth.

3.6

3.8

4.0

4.2

4.4

4.6

4.8

5.0

4.30 4.35 4.40 4.45 4.50 4.55 4.60 4.65 4.70 4.75 4.80

Real GDP (Log, SA)

Re

al

Jo

ble

ss B

en

efits

(L

og

, S

A)

-8

-4

0

4

8

12

70 75 80 85 90 95 100 105 110 115 120

Real GDP (2007=100, SA)Go

vt.

Fir

ms P

rim

ary

Ba

lan

ce

(R

$ b

illio

n,

IPC

A-A

dj, S

A)

55


Appendix 6 – Unit-Root and Cointegration Tests Aggregated Approach

Unit Root Tests

*** Crosses the 1% significance level | ** Crosses the 5% significance level | * Crosses the 10% significance level

Cointegration Tests

-0.44 -0.03 -0.03 -10.73 ***

-1.39 -1.16 -1.16 -12.32 ***

0.21 0.27 0.27 -6.27 ***

1.38 1.13 1.13 -5.74 ***

-0.54 -1.14 -1.14 -5.27 ***

-1.80 -1.33 -1.33 -14.64 ***

Total Federal Transfers

Total Income Taxes

Series Level Lag length:10 (Level) Lag length:1 (Level) Lag length:1 (1st diff.)

Phillips-Perron Augmented Dickey-Fuller

Total Cyclical Federal Revenues

GDP

Oil Price (Brent)

Total States' Tax Collection

Sample: 2002Q1 2011Q1

MODEL I.1 MODEL I.3Series: LOG_CYCL_REV_SA LOG_GDP_SA LOG_OILBRENT_SA Series: LOG_STATETAXTOTAL_SA LOG_GDP_SA

Lags interval: 1 to 4 Lags interval: 1 to 2

Selected (0.05 level*) Number of Cointegrating Relations by Model Selected (0.1 level*) Number of Cointegrating Relations by Model

Data Trend: None None Linear Linear Quadratic Data Trend: None None Linear Linear Quadratic

Test Type No Intercept Intercept Intercept Intercept Intercept Test Type No Intercept Intercept Intercept Intercept Intercept

No Trend No Trend No Trend Trend Trend No Trend No Trend No Trend Trend Trend

Trace 0 1 1 1 1 Trace 0 1 0 2 2

Max-Eig 0 1 1 1 1 Max-Eig 1 1 0 0 0

MODEL I.2 MODEL I.3 (alternative)Series: LOG_TRANSF_TOTAL_SA LOG_INCTAXTOTAL_SA Series: LOG_STATETAXTOTAL_SA LOG_RETSALES_SA






Trace 2 2 1 0 2 Trace 2 1 1 2 2

Max-Eig 2 2 1 0 0 Max-Eig 2 1 1 2 2

56


Disaggregated Approach

Unit Root Tests


2.55 1.80 1.80 -8.33 ***

-1.26 -1.24 -1.24 -7.21 ***

-1.13 -0.40 -0.49 -12.46 ***

-1.38 -0.51 -0.51 -14.98 ***

-1.91 -1.49 -1.49 -6.32 ***

-1.33 -1.54 -1.54 -5.39 ***

-2.38 -2.27 -2.27 -7.25 ***

-6.08 *** -5.89 *** -5.89 *** -8.67 ***

-7.42 *** -4.20 *** -4.57 *** -4.82 ***

-1.49 -1.97 -1.57 -8.21 ***

-0.90 -0.57 -0.57 -13.34 ***

-4.43 *** -3.56 ** -3.56 ** -7.01 ***

-0.02 -0.09 -0.09 -6.70 ***

0.25 0.08 0.08 -8.46 ***

0.77 0.59 0.30 -4.28 ***

1.18 0.83 0.83 -4.87 ***

2.11 1.45 1.45 -3.83 ***

Industrial Orders -0.72 -0.78 -1.15 -5.95 ***

0.93 -0.27 -0.27 -4.66 ***

New Bank Loans -1.39 -1.44 -1.35 -4.63 ***

Industrial Production -0.04 -0.18 -0.71 -5.78 ***

Imports Spending in BRL -1.64 -1.47 -1.87 -6.43 ***

CRB Commodity Price Index

Job Formality

Retail Sales

Federal Corporate Income Taxes

Federal Industrial Output Taxes

Federal Import Taxes

Federal Financial Transaction Taxes

Federal Companies' Dividends (up to '08)

Federal Royalties

Other Federal Revenues

Federal Constitutional Transfers

Federal Payroll Taxes

Federal Sales Taxes

Federal Personal Income Taxes

Wage Bill

Federal Royalty Transfers

States Value-Added Taxes

Other States Taxes

Series Level Lag length:10 (Level) Lag length:1 (Level) Lag length:1 (1st diff.)

Phillips-Perron Augmented Dickey-Fuller

57


Cointegration Tests

Sample: 2002Q1 2011Q1

MODEL II.1 MODEL II.8Series: LOG_WAGES_SA GDP LOG_FORMAL_SA LOG_PAYRTAX_SA Series: LOG_OILBRENT_SA LOG_DIVIDENDS_SA



Sample: 2002Q1 2008Q4 (28 obs.)

Data Trend: None None Linear Linear Quadratic

Test Type No Intercept Intercept Intercept Intercept Intercept Data Trend: None None Linear Linear Quadratic

No Trend No Trend No Trend Trend Trend Test Type No Intercept Intercept Intercept Intercept Intercept

Trace 2 3 2 2 2 No Trend No Trend No Trend Trend Trend

Max-Eig 2 3 2 2 2 Trace 0 1 1 1 1

Max-Eig 0 1 1 1 1

MODEL II.2 MODEL II.9Series: LOG_ORDERS_SA LOG_RETSALES_SA LOG_SALESTAX_SA Series: LOG_OILBRENT_SA LOG_ROYALTIES_SA






Trace 1 2 2 2 3 Trace 0 1 1 0 1

Max-Eig 1 1 2 0 0 Max-Eig 0 1 1 1 1

MODEL II.3 MODEL II.10Series: LOG_LOANS_SA LOG_CRB_SA LOG_CORPTAX_SA GDP Series: GDP LOG_OTHERREV_SA






Trace 2 2 1 2 2 Trace 2 1 1 2 2

Max-Eig 2 2 2 1 1 Max-Eig 0 1 1 0 2

MODEL II.4 MODEL II.11Series: LOG_WAGES_SA LOG_CRB_SA LOG_PERSINCTAX_SA Series: LOG_TRANSFCONST_SA LOG_IPITAX_SA LOG_INCTAXTOTAL_SA






Trace 3 1 1 1 3 Trace 1 1 1 1 2

Max-Eig 1 1 1 0 0 Max-Eig 1 1 1 1 1

MODEL II.5 MODEL II.12Series: LOG_IP_SA LOG_IPITAX_SA Series: LOG_TRANSF_ROYAL_SA LOG_ROYALTIES_SA






Trace 2 1 0 0 0 Trace 1 2 2 1 2

Max-Eig 0 1 0 0 0 Max-Eig 1 2 0 0 2

MODEL II.6 MODEL II.13Series: LOG_IMPORTS_SA LOG_IMPTAX_SA Series: LOG_STATETAXTOTAL_SA LOG_ORDERS_SA LOG_RETSALES_SA






Trace 0 1 1 2 2 Trace 3 2 2 2 3

Max-Eig 0 1 1 2 0 Max-Eig 1 2 2 1 1

MODEL II.7 MODEL II.14Series: LOG_LOANS_SA LOG_CRB_SA LOG_IOFTAX_SA Series: LOG_STATEOTHER_SA GDP

Sample: 2002Q1 2007Q4 (24 obs.) Lags interval: 1 to 1

Lags interval: 1 to 1 Selected (0.05 level*) Number of Cointegrating Relations by Model

Selected (0.05 level*) Number of Cointegrating Relations by Model


Data Trend: None None Linear Linear Quadratic Test Type No Intercept Intercept Intercept Intercept Intercept

Test Type No Intercept Intercept Intercept Intercept Intercept No Trend No Trend No Trend Trend Trend

No Trend No Trend No Trend Trend Trend Trace 1 1 0 0 2

Trace 1 3 1 0 0 Max-Eig 1 0 0 0 0

Max-Eig 1 3 0 0 0

Sample: 2008Q1 2011Q1 (13 obs.)

Lags interval: 1 to 1

Selected (0.01 level*) Number of Cointegrating Relations by Model


Test Type No Intercept Intercept Intercept Intercept Intercept

No Trend No Trend No Trend Trend Trend

Trace 1 1 1 2 1

Max-Eig 1 1 1 2 1

58


Appendix 7 – Models: Estimation Details Aggregated Approach Models (Class I)

Estimation Method: Least Squares All models use Newey-West HAC Standard Errors & Covariance RESET

47 test up to 2 lags

#

All independent variables further lagged by one period (relative to the original model)


#

Independent variable further lagged by one quarter (relative to the original model)


Chow breakpoint and forecast tests applied to regressions without the correction for structural breaks by the dummies #

Some independent variables with a one-quarter lead (relative to the original model)


47

The RESET tests the null hypothesis of no specification problems. By the same token, Breusch-Godfrey and ARCH

tests verify the null hypothesis of no joint serial correlation or conditional heteroskedasticity in the residuals.

Variable Slope Slope Slope Slope

Constant -2.48 -14.81 *** -2.05 -0.32 -2.26 -0.48 -2.16 -0.78

GDP 1.45 30.41 *** 1.34 0.34 1.44 0.17 1.37 0.76

OIL PRICE 0.05 3.00 *** 0.03 0.35 0.04 0.33 0.05 0.07

OIL PRICE (-4) 0.04 2.64 ** 0.08 -1.60 0.01 0.65 0.06 -0.95

Adjusted R-squared: 0.99

Durbin-Watson Statistic: 2.08 Lags (3): 0.86 Lags (3): 0.22

Ramsey RESET Test (P-value): 0.76 Lags (6): 0.91 Lags (6): 0.36

Chow Breakpoint Test (5% sig.): breaks detected until 2002 Lags (9): 0.92 Lags (9): 0.61

Chow Forecast Test (5% sig.): no breaks from 2006 onwards Lags (12): 0.80 Lags (12): 0.67

MODEL I.1 - Dependent Variable: TOTAL FEDERAL CYCLICAL REVENUES

Same regressors, time-window,

different lags #

t-Stat z-Stat z-Stat z-Stat

Sample: 2002Q1 2010Q4 Sub-Sample: 2002Q1 2006Q4 Sub-Sample: 2007Q1 2010Q4

Breusch-Godfrey LM Test: ARCH Test:


Constant -0.14 -0.67 -0.19 0.09 0.21 -1.10 0.04 -0.53

TOTAL INCOME TAX 1.03 22.77 *** 1.04 -0.09 0.96 1.09 1.00 0.49




Chow Breakpoint Test (5% sig.): breaks from 2001 to 2005 Lags (9): 0.64 Lags (9): 0.95


MODEL I.2 - Dependent Variable: TOTAL FEDERAL TRANSFERS

Sample: 2002Q1 2010Q4 Sub-Sample: 2002Q1 2006Q4 Sub-Sample: 2007Q1 2010Q4Same regressors, time-window,

different lags #




Constant -2.50 -13.86 *** -2.28 -0.47 -4.51 5.09 *** -2.80 1.11

GDP (-1) 1.92 11.26 *** 1.73 0.68 1.48 1.98 ** 1.19 3.38 ***

GDP (-2) -0.36 -2.06 ** -0.23 -0.43 0.51 -3.99 *** 0.42 -3.65 ***

DUMMY2003 * GDP (-1) -0.01 -3.53 *** 0.00 0.00 0.00 0.00 - -

DUMMY2009 * GDP (-1) 0.01 4.81 *** 0.97 0.00 0.97 0.00 - -




Chow Breakpoint Test (5% sig.): breaks in the whole sample Lags (9): 0.21 Lags (9): 0.96



MODEL I.3 - Dependent Variable: TOTAL STATES' TAXES

z-Stat


different lags #


t-Stat z-Stat z-Stat

59


#



Disaggregated Approach Models (Class II) Estimation Method: Least Squares All models use Newey-West HAC Standard Errors & Covariance RESET

48 test up to 2 lags

Chow breakpoint and forecast tests applied to regressions without the correction for structural breaks by the dummies Structural breaks likely caused by the increase in job formality since 2004 #

All Independent variable further lagged by one quarter (relative to the original model)


Chow breakpoint and forecast tests applied to regressions without the correction for structural breaks by the dummies Structural breaks likely caused by the impact of changes in the Cofins tax code from 2004 onwards #



48

The RESET tests the null hypothesis of no specification problems. By the same token, Breusch-Godfrey and ARCH

tests verify the null hypothesis of no joint serial correlation or conditional heteroskedasticity in the residuals.


Constant 0.00 -0.01 -0.71 1.62 0.19 -0.70 -0.15 0.63

RETAIL SALES 0.94 29.70 *** 1.09 -1.65 0.90 0.70 0.98 -0.69



Ramsey RESET Test (P-value): 0.40 Lags (6): 0.10 * Lags (6): 0.95

Chow Breakpoint Test (5% sig.): breaks up to 2004 Lags (9): 0.31 Lags (9): 0.88




MODEL I.3 (alternative) - Dependent Variable: TOTAL STATES' TAXES


different lags #


Constant -3.85 -6.94 *** -4.94 1.17 -4.12 0.34 -4.00 0.17

WAGE BILL 0.86 3.33 *** 0.60 0.80 1.01 -0.46 0.82 0.09

GDP (-4) 0.96 2.58 ** 1.47 -1.12 -0.19 3.04 *** 1.04 -0.12

DUMMY2004 * JOB FORMALITY 0.02 2.77 *** 0.02 0.82 1.29 -3.77 *** 0.03 -0.35




Chow Breakpoint Test (5% sig.): breaks in the whole sample Lags (9): 0.98 Lags (9): 0.59




MODEL II.1 - Dependent Variable: FEDERAL PAYROLL TAXES


different lags #


Constant -2.11 -2.03 * -2.57 0.12 -2.27 0.12 -2.55 0.31

DUMMY2004Q2 * IND. ORDERS (-1) 1.18 3.59 *** 1.14 0.05 1.19 -0.02 1.36 -0.41

RETAIL SALES 1.39 6.06 *** 1.49 -0.12 1.51 -0.16 1.48 -0.30

DUMMY2004Q2 * RETAIL SALES (-1) -1.16 -3.46 *** -1.12 -0.04 -1.26 0.12 -1.33 0.39


Durbin-Watson Statistic: 1.10 Lags (3): 0.08 * Lags (3): 1.00




z-Stat z-Stat z-Stat


MODEL II.2 - Dependent Variable: FEDERAL SALES TAXES


different lags #

t-Stat

60


#



#

All independent variables with a one-quarter lead (relative to the original model)


Chow breakpoint and forecast tests applied to regressions without the correction for structural breaks by the dummies Structural breaks likely caused by the IPI tax exemptions in the post-Lehman Brazilian recession #

Independent variable with a one-quarter lead (relative to the original model)



Constant -4.11 -4.67 *** -2.74 -0.34 -1.64 -1.22 -4.15 0.03

GDP 0.99 2.40 ** 0.10 0.47 0.85 0.22 1.25 -0.43

NEW BANK LOANS (-4) 0.66 3.16 *** 0.98 -0.60 0.30 1.05 0.31 1.03

CRB INDEX (-2) 0.26 2.10 ** 0.55 -0.45 0.22 0.21 0.35 -0.44


Durbin-Watson Statistic: 2.08 Lags (3): 0.98 Lags (3): 0.03 **






MODEL II.3 - Dependent Variable: FEDERAL CORPORATE INCOME TAXES


different lags #


Constant -3.72 -4.65 *** -14.28 3.06 *** -2.32 -1.13 -3.63 -0.07

WAGE BILL (-1) 2.95 4.17 *** 3.14 -0.18 1.33 1.05 2.56 0.39

CRB (-2) 0.45 2.72 ** 1.05 -1.79 * 0.43 0.10 0.58 -0.61

WAGE BILL (-5) -1.60 -2.42 ** -0.01 -1.34 -0.25 -1.01 -1.36 -0.25








different lags #


MODEL II.4 - Dependent Variable: FEDERAL PERSONAL INCOME TAXES



Constant -1.00 -1.87 * 0.03 -1.04 -1.06 0.04 -0.69 -0.33

IND. PROD. TAXES (-1) 0.64 5.97 *** 0.65 -0.02 0.44 1.49 0.48 0.70

IND. PRODUCTION 1.01 9.74 *** 0.78 0.51 1.03 -0.15 0.88 0.40

IND. PRODUCTION (-3) -0.44 -2.72 ** -0.44 0.02 -0.24 -0.72 -0.21 -1.25

DUMMY'09EXPT * IND. PRODUCTION -0.01 -3.61 *** - - -0.02 2.27 ** -0.01 -0.07






MODEL II.5 - Dependent Variable: FEDERAL INDUSTRIAL PRODUCTION TAXES


different lags #




Constant -0.51 -2.94 *** -0.23 -0.50 -0.52 0.03 -0.10 -1.01

IMPORT TAX (-1) 0.33 3.31 *** 0.41 -0.60 0.59 -1.50 1.24 -2.75 ***

IMPORTS 0.78 8.90 *** 0.64 1.28 0.53 1.58 -0.21 3.31 ***

DUMMY2009 * IMPORTS 0.02 4.05 *** - - - - -0.01 1.77 *




Chow Breakpoint Test (5% sig.): breaks detected after 2009 Lags (9): 0.78 Lags (9): 0.98

Chow Forecast Test (5% sig.): breaks detected after 2009 Lags (12): 0.75 Lags (12): 0.94

z-Stat



MODEL II.6 - Dependent Variable: FEDERAL IMPORTS TAXES


different lags #

61


Chow breakpoint and forecast tests applied to regressions without the correction for structural breaks by the dummies Structural breaks possibly caused the global crisis (slowing global demand causing rising imports penetration) #



Chow breakpoint and forecast tests applied to regressions without the correction for structural breaks by the dummies Structural breaks possibly caused by macro-prudential measures aimed at slowing capital flows (increasing sensitiveness to financial conditions) #

Some independent variables further lagged by one quarter (relative to the original model)


Chow breakpoint and forecast tests applied to regressions without the correction for structural breaks by the dummies Structural breaks possibly caused by Treasury loans to BNDES, which boosted the bank’s size and also the volume of dividends paid #

Independent variable with a two-quarter lead (relative to the original model)


#




Constant 1.60 9.61 *** 1.42 0.43 1.42 0.41 1.64 -0.09

FINANCIAL TRANSACTION TAXES (-1) 0.20 6.93 *** 0.15 0.33 0.19 0.23 0.19 0.06

DUMMYCPMF * NEW BANK LOANS (-1) 0.15 36.08 *** 0.33 -1.70 * 0.35 -1.95 * 0.15 0.02

CRB INDEX (-1) 0.30 7.57 *** 0.21 1.07 0.03 6.73 *** 0.30 0.00

DUMMY2010 * CRB INDEX (-1) 0.03 8.87 *** - - - - 0.03 0.08




Chow Breakpoint Test (5% sig.): breaks detected since 2008 Lags (9): 0.50 Lags (9): 0.04 **

Chow Forecast Test (5% sig.): breaks detected since 2008 Lags (12): 0.64 Lags (12): 0.13


MODEL II.7 - Dependent Variable: FEDERAL FINANCIAL TRANSACTION TAXES


different lags #



Constant 4.04 34.77 *** - - - - 4.14 -0.53

OIL PRICE (-2) - 1ST DIFF. 2.39 2.59 ** - - - - 0.30 2.03 **

DUMMY2007 0.36 2.07 ** - - - - 0.43 -0.24




Chow Breakpoint Test (5% sig.): breaks detected since 2008 Lags (9): 0.53 Lags (9): 0.96




MODEL II.8 - Dependent Variable: FEDERAL DIVIDENDS


different lags #


Constant 0.78 2.17 ** 1.43 -1.01 0.73 0.07 1.14 -0.47

ROYALTIES (-1) 0.80 8.96 *** 0.60 1.20 0.84 -0.26 0.72 0.39

OIL PRICE (-1) - 1ST DIFF. 0.65 14.27 *** 0.53 0.85 0.68 -0.30 0.16 3.59 ***

@TREND 0.00 1.97 * 0.01 (1.20) 0.00 0.65 0.00 0.10




Chow Breakpoint Test (5% sig.): no breaks from 2001 onwards Lags (9): 0.60 Lags (9): 0.92




MODEL II.9 - Dependent Variable: FEDERAL ROYALTIES


different lags #

62


Chow breakpoint and forecast tests applied to regressions without the correction for structural breaks by the dummies Structural breaks possibly caused by a search for extra revenues in the after-Lehman period #



#

Independent variables further lagged by one quarter (relative to the original model)


#




Constant -2.40 -3.36 *** 0.72 -2.42 ** -2.54 0.03 -2.45 0.05

GDP (-1) 1.52 9.59 *** 0.82 2.44 ** 1.55 -0.04 1.53 -0.04

DUMMY2009Q3 0.15 2.42 ** 0.00 0.00 0.12 0.21 0.14 0.15




Chow Breakpoint Test (5% sig.): breaks detected since 2005 Lags (9): 0.92 Lags (9): 0.80




MODEL II.10 - Dependent Variable: OTHER FEDERAL REVENUES


different lags #


Constant -0.43 -2.27 ** -0.86 0.83 -0.55 0.43 -0.40 -0.08

TOTAL INCOME TAX 0.92 19.29 *** 0.77 1.23 1.03 -1.34 0.80 1.15

IND. PROD. TAXES 0.17 3.09 *** 0.42 -1.86 * 0.09 1.14 0.29 -0.83




Chow Breakpoint Test (5% sig.): no breaks from 2000 onwards Lags (9): 0.54 Lags (9): 0.80


MODEL II.11 - Dependent Variable: FEDERAL CONSTITUTIONAL TRANSFERS




different lags #


Constant -1.32 -5.39 *** -1.50 0.57 0.18 -5.55 *** -0.49 -1.83 *

ROYALTIES 1.27 25.30 *** 1.31 -0.55 0.96 5.61 *** 1.10 1.81 *


Durbin-Watson Statistic: 0.87 Lags (3): 0.01 *** Lags (3): 0.54

Ramsey RESET Test (P-value): 0.13 Lags (6): 0.03 ** Lags (6): 0.68




z-Stat


different lags #


MODEL II.12 - Dependent Variable: FEDERAL ROYALTIES TRANSFERS

63


# Independent variable further lagged by one quarter (relative to the original model)


# Independent variable further lagged by one quarter (relative to the original model)


Dummy Index: find below the definition of the dummy variables used in models to control for structural breaks (in most cases, reflecting changes in the tax code):

DUMMY2003 = {―1‖ from 2003 on; ―0‖ otherwise}


DUMMY2004Q2 = {―1‖ from 2Q04 on; ―0‖ otherwise}



DUMMY2009Q3 = {―1‖ from 3Q09 on; ―0‖ otherwise}

DUMMY’09EXPT49

= {―1‖ from 1Q09 to 1Q10; ―0‖ otherwise}


49

This dummy was designed to capture the period of tax exemptions on manufactured products (especially cars) during

the 2008–2009 crisis.


Constant -0.93 -3.08 *** -1.43 1.07 -1.34 0.75 0.59 -3.61 ***

INDUSTRIAL ORDERS (-1) 0.52 5.80 *** 0.38 1.22 0.67 -0.98 0.03 3.62 ***

RETAIL SALES 0.90 11.34 *** 0.89 0.08 0.87 0.27 1.23 -2.35 **

RETAIL SALES (-3) -0.27 -2.65 ** -0.01 -2.29 ** -0.30 0.19 -0.44 0.86







different lags #

z-Stat

ARCH Test:


Breusch-Godfrey LM Test:

MODEL II.13 - Dependent Variable: STATES' VAT TAXES



Constant -2.99 -1.77 * -0.14 -1.28 -9.51 1.28 -3.03 0.02

OTHER STATES' TAXES (-1) 0.70 6.78 *** 0.84 -1.06 0.46 0.97 0.70 0.02

GDP 0.96 2.03 * 0.19 1.26 2.61 -1.23 0.97 -0.02






z-Stat

MODEL II.14 - Dependent Variable: OTHER STATES' TAXES


different lags #



64


Appendix 8 – Graphs: Models’ Residuals Aggregated Approach Models (Class I)

Disaggregated Approach Models (Class II)

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MODEL II.7

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MODEL II.14

67


Appendix 9 – Graphs: Activity & Commodity Cycles50

50

We use the average of economic and statistical filters for activity variables; only statistical filter for oil and CRB.

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68


Appendix 10 – Assessing the Results From a Methodological Perspective

In this section, we assess the main features of our structural primary fiscal balance estimates for the Brazilian public sector. The properties of our results under each approach and method justify the decisions about the final configuration of models leading to the estimates reported in Section 4. We evaluate our framework’s performance from two standpoints: (1) the robustness of results across structural balance approaches and de-trending methods; and (2) the efficacy in stripping out cyclical components from the raw data.

We departed from a ―purer‖ framework for the IMF’s aggregated methodology, using GDP as the base for all revenue models. We also estimated the structural balance in both approaches using both statistical and economic de-trending techniques for activity and commodity cycles. To calculate a theory-based equilibrium for oil and commodity prices (in the economic filtering), we used the output from Itaú’s commodity-price models, assuming global GDP at potential for the whole period. The results from this initial setting are shown in Graph (A).

Graph (A): Structural Balance Estimates by Approach, De-trending Technique: The First Try

Source: Itaú

A first impression from the chart is that all estimates point to a similar pattern for the evolution of the structural balance across the 2000s: a tightening stance early in the decade, a lax policy drive in its latter part, a partial recovery in 2011. However, these estimates showed very large amplitude of results (i.e., gap between maximum and minimum estimates) – around 2.0%–2.5% of GDP for the period between 2003 and 2007. A good deal of this amplitude is due to the fact that estimates from the disaggregated method using economic de-trending behaved as true outliers. Two changes were made to overcome this problem. Firstly, we adapted the aggregated

approach to use retail sales as a tax base in the states’ revenue model (replacing GDP, as

proposed in the ―pure‖ IMF methodology). Second, we decided not to apply the economic filter

on commodity prices, as unintuitive cycles for raw material prices resulted from this procedure,

helping distort (to the upside) structural balance results.

The changes made produced more narrowly distributed results, as the disaggregated methodology with economic filtering posted lower structural balance estimates, more in line with economic intuition (and estimates from the other alternative methodologies and procedures). In fact, the amplitude for the period of 2003 to 2007 was reduced by about one percent, ranging around 1.0%–1.5% of GDP. Thus, the adaptations further improved the robustness of our results.

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Baseline Estimate

% GDP

69


Graph (B): Structural Balance Estimates by Approach, De-trending: The Final Setting

Source: Itaú

Despite the similarity of estimates across structural balance techniques, we noted that the results from the aggregated methodology proved less dependent on the filtering techniques than the disaggregated approach. This is a natural result, as de-trending is more challenging for activity subcomponents than for GDP. There are more established theoretical procedures to estimate broad output equilibrium (e.g., the production-function approach) than methods to de-trend activity subcomponents. To further improve the robustness of our estimates, we use what we called a ―combined filter‖,

which means the average of equilibrium values estimated via statistical and economic filters51

.

As Graph (B) shows, the use of average equilibrium estimates prompted a convergence of

results: the amplitude fell sharply, to an average of 0.4% of GDP for the whole period.

Graph (C): Structural Balance Estimates: IMF and ECB Approaches with “Combined” Filtering

Source: Itaú

The relatively low discrepancy of results from aggregated and disaggregated methodologies using average trend estimates (i.e., the ―combined‖ filter) gave us the ―green light‖ to report the average of estimates from both methodologies as our baseline result. While simplicity would call for the use of the aggregated methodology only–since it is much easier to estimate – using a

51

As previously mentioned, in the case of oil prices and the CRB index, we only deploy the statistical de-trending (HP filter).

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2010-I

2011-I

Aggregated Approach / Economic Filter Aggregated Approach / Statistical Filter

Disaggregated Approach / Economic Filter Disaggregated Approach / Statistical Filter

Baseline Estimate

% GDP

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

2000-I

2001-I

2002-I

2003-I

2004-I

2005-I

2006-I

2007-I

2008-I

2009-I

2010-I

2011-I

Aggregated Approach (IMF) Disaggregated Approach (ECB) Baseline

%GDP

COMBINED FILTERING

70


combination of both approaches allows our results to also reflect possible output-composition effects, an exclusive feature of the disaggregated methodology. Another important property to observe in our estimates is how ―cycle-free‖ our structural balance estimates have turned out, which signals the efficacy of our procedures. Graph (D) relates the official public-sector primary surplus data (as well as our own structural primary fiscal balance estimates) to real GDP growth, so as to identify cyclical influences. The linear regressions shown in the charts illustrate the relationship between these series, showing the GDP influences on the official data and our structural balance estimates. For the period spanning 2001 to 2009, the observed quarterly data point to a large correlation between activity conditions (as measured by real GDP growth) and the official primary budget results, as shown in the large slope (0.22) and R

2coefficient (0.59). Our structural balance

estimates under both approaches have successfully removed this cyclical component present in the unadjusted data, as suggest the zeroing of the R

2 coefficients and the brisk reduction of

slopes (to a range of -0.01–0.08). In all, the numbers show that not only are our structural balance results relatively robust across

methodologies, but they are also efficient in removing cyclical components of the unadjusted

series (for a period showing this influence). Overall, the final results were quite satisfactory.

Graph (D): Checking Structural Balance Estimates for Cyclical Influences (Combined Filtering)

Source: Itaú

y = 0.22x + 0.02

R2 = 0.59

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

-4.0% -2.0% 0.0% 2.0% 4.0% 6.0% 8.0%

GDP (%p.a.)

Pri

mary

Fis

cal

Bala

nce (

%G

DP

)

observed data

y = 0.05x + 0.03

R2 = 0.01

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

-4.0% -2.0% 0.0% 2.0% 4.0% 6.0% 8.0%

GDP (%p.a.)

Str

uctu

ral

Pri

mary

Bala

nce

(%G

DP

)

aggregated approach

y = -0.01x + 0.03

R2 = 0.00

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

5.5%

-2.0% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0%

GDP (%p.a.)

Str

uctu

ral

Pri

mary

Bala

nce

(%G

DP

)

disaggregated approach

y = 0.08x + 0.03

R2 = 0.04

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

-4.0% -2.0% 0.0% 2.0% 4.0% 6.0% 8.0%

GDP (%p.a.)

Str

uctu

ral

Pri

mary

Bala

nce

(%G

DP

)

baseline estimate

71


Appendix 11 – Structural Revenue X Observed Revenue (Baseline Estimate) Aggregated Approach Models (Class I)

Disaggregated Approach Models (Class II)

50

60

70

80

90

100

110

120

130

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAFederal Cyclical Revenues

50

60

70

80

90

100

110

120

130

140

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SATotal Federal Transfers

50

60

70

80

90

100

110

120

130

140

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SATotal States Revenues

50

60

70

80

90

100

110

120

130

140

150

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAPayroll Taxes

50

60

70

80

90

100

110

120

130

140

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAFederal Sales Taxes

20

40

60

80

100

120

140

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SACorporate Income Taxes

20

40

60

80

100

120

140

160

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAPersonal Income Taxes

72


60

70

80

90

100

110

120

130

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAIndustrial Production Taxes

60

80

100

120

140

160

180

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAImport Duties

20

30

40

50

60

70

80

90

100

110

120

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAFinancial Transaction Taxes

0

100

200

300

400

500

600

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAFederal Dividends

0

20

40

60

80

100

120

140

160

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAFederal Royalties

50

70

90

110

130

150

170

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAOther Federal Revenues

50

60

70

80

90

100

110

120

130

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAConstitutional Transfers

0

20

40

60

80

100

120

140

160

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SARoyalty Transfers

60

70

80

90

100

110

120

130

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAStates VAT

20

60

100

140

180

220

1999-I 2001-I 2003-I 2005-I 2007-I 2009-I 2011-I

Structural Observed

2007=100, SAOther States Taxes

73


Appendix 12 – Structural Fiscal Balance by Government Entities Structural Primary Fiscal Balance Estimates

Source: Itaú (A) Past twelve months to September 1/ A few disaggregated models structurally adjust revenues based on cycles of activity subcomponents with a short data series (e.g., retail sales, bank lending). In these cases, to estimate results for the year 2000, we filled the information gaps with structural balance estimates using GDP as the revenue base. The GDP elasticity used in these results follow the re-estimation of the model with GDP replacing the tax base.

2/ Public sector results account for the (non-cyclical) discrepancy between the BCB and Treasury’s estimate for central government balance.

Graph A: Relationship Between STRUCTURAL Primary Balance Estimates Across Government Entities (%GDP) – 2000 to 2011 (Quarterly Data)

Source: Itaú

Graph B: Relationship Between OBSERVED (Unadjusted) Primary Balance Estimates Across Government Entities (%GDP) – 2000 to 2011 (Quarterly Data)

Source: Itaú

Public

Sector2/

Central

Govt.

Regional

Govt.

Govt.

Firms2/

Public

Sector2/

Central

Govt.

Regional

Govt.

Govt.

Firms2/

Public

Sector2/

Central

Govt.

Regional

Govt.

Govt.

Firms2/

Public

Sector

Central

Govt.

Regional

Govt.

Govt.

Firms

2000 2.1% 0.9% 0.2% 1.0% 2.2% 1.0% 0.3% 1.0% 2.1% 0.9% 0.3% 1.0% 3.2% 1.7% 0.5% 1.0%

2001 2.6% 1.2% 0.7% 0.7% 2.2% 1.1% 0.4% 0.7% 2.4% 1.1% 0.5% 0.7% 3.4% 1.7% 0.8% 0.9%

2002 3.4% 2.0% 1.1% 0.3% 3.2% 2.0% 0.8% 0.3% 3.3% 2.0% 0.9% 0.3% 3.2% 2.2% 0.7% 0.3%

2003 4.3% 2.3% 1.9% 0.2% 4.6% 2.9% 1.6% 0.2% 4.5% 2.6% 1.7% 0.2% 3.3% 2.3% 0.8% 0.2%

2004 4.2% 2.4% 1.6% 0.1% 4.4% 2.9% 1.2% 0.1% 4.3% 2.6% 1.4% 0.1% 3.7% 2.7% 0.9% 0.1%

2005 4.0% 2.2% 1.4% 0.2% 4.0% 2.5% 1.2% 0.2% 4.0% 2.4% 1.3% 0.2% 3.8% 2.6% 1.0% 0.2%

2006 3.4% 1.9% 1.2% 0.2% 3.9% 2.5% 1.1% 0.2% 3.6% 2.2% 1.1% 0.2% 3.2% 2.2% 0.8% 0.2%

2007 2.7% 1.6% 1.0% 0.0% 3.3% 2.2% 1.1% 0.0% 3.0% 1.9% 1.0% 0.0% 3.3% 2.2% 1.1% 0.0%

2008 2.5% 2.0% 0.4% 0.1% 2.3% 1.9% 0.4% 0.1% 2.4% 1.9% 0.4% 0.1% 3.4% 2.4% 1.0% 0.1%

2009 2.0% 1.3% 0.6% 0.0% 2.8% 1.5% 1.1% 0.0% 2.4% 1.4% 0.9% 0.0% 2.0% 1.3% 0.7% 0.0%

2010 0.8% 0.6% 0.2% 0.1% 1.4% 0.9% 0.4% 0.1% 1.1% 0.8% 0.3% 0.1% 2.8% 2.1% 0.6% 0.1%

2011(A) 2.1% 2.0% 0.1% 0.0% 2.2% 1.8% 0.3% 0.0% 2.1% 1.9% 0.2% 0.0% 3.5% 2.8% 0.7% 0.0%Average

2000-2011 2.8% 1.7% 0.9% 0.2% 3.0% 1.9% 0.8% 0.2% 2.9% 1.8% 0.8% 0.2% 3.2% 2.2% 0.8% 0.3%

Baseline (Mean) Estimate Official (Unadjusted) Data% GDP

Aggregated Approach Disaggregated Approach1/

y = 0.62 x - 0.00

R2 = 0.71

0.0%

0.5%

1.0%

1.5%

2.0%

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%

Central Government

Re

gio

na

l G

ov

ern

me

nts

y = 0.13 x + 0.01

R2 = 0.14

0.0%

0.5%

1.0%

1.5%

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%

Central Government

Re

gio

na

l G

ov

ern

me

nts

74


Appendix 13 – Budget Balance Breakdown and Fiscal Impulse Details Using Baseline Estimates A comparison between graphs shown in Exhibit-14.A (or B) and Exhibit-14.Ax (or Bx), as well as Exhibit-15.A (or B) and Exhibit-15.Ax (or Bx), shows that the decomposition of the budget balance (between structural and cyclical components) as well as the breakdown of fiscal impulses are quite robust to the use of aggregated or baseline estimates. In sections 4-C.2 and 4-D, we use results obtained from the aggregated methodology, so as to make intuition clearer. In this section, we present the same class of results using our baseline estimates.

Exhibit-14.Ax: Structural vs. Cyclical Primary Balance Estimates (BASELINE ESTIMATE)

Exhibit-14.Bx: Contributions to the Cyclical Primary Fiscal Balance (BASELINE ESTIMATE)

Source: Itaú

2.1% 2.4%

3.3%

4.5% 4.3% 4.0%3.6%

3.0%2.4% 2.4%

1.1%

2.1%

1.1%

-0.4%

1.0%

-0.1%

1.1%

-0.6%

-1.2%

-0.4%-0.2%

0.3% 1.0%

1.7%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Structural Fiscal Balance Cyclical Fiscal Balance Observed Fiscal Balance

% GDP

0.6% 0.6%

0.1% 0.1%

0.6%

1.4%

0.5%

0.3%

-0.1%

-1.4%

-0.5% -0.8%

0.5%

0.3%

-0.3%

0.1%

0.1% -0.1%

0.8%

-0.7%-0.5%

0.4%

0.1%

0.2%

0.4%

-0.3%

-0.1%

0.1%

-0.2%

0.0%0.1%

-0.2%

0.1% 0.1%0.1%

0.0%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Non-Recurring Revenues Activity Cycle Commodity Cycle

% GDP

75


Exhibit-15.Ax: Public Sector’s Budget Fiscal Impulse or Drag (%GDP) - (BASELINE ESTIMATE)

Source: Itaú

Exhibit-15.Bx: Breaking Down the Budget Fiscal Impulse (BASELINE ESTIMATE)

Source: Itaú

-0.3%

-0.9%

-1.2%

0.2%0.3%

0.4%

0.6% 0.6%

0.0%

1.3%

-1.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Fiscal Impulse

% GDP

-0.7%

-2.0%

-1.1%

0.4%

-0.4%

-1.1%

-0.5%

1.2%0.8%

1.1%

0.1%-0.2%-0.3%

-0.4%-0.8%

-0.6%

1.1% 0.8%0.3%

0.2%0.8% 0.5%

0.3%

0.0%

0.1%

0.1%

-0.1%

-0.1%0.0%

0.0%

0.2%

0.4%

0.2%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Revenues Expenditures Government-Owned Firms

% GDP

76


REFERENCES Afonso, José Roberto R.; November 2011, ―A redução dos juros pelo Banco Central diminuirá no mesmo ritmo o custo da dívida do governo?‖, mimeo (only in Portuguese) Bevilaqua,Afonso; Werneck, Rogerio L.F.; October 1997; ―Fiscal Impulse in the Brazilian Economy, 1989-1996‖, PUC-Rio Economics Dept., Discussion Paper No.379 Bezdek, Vladimír;Dybczak, Kamil; Krejdl, Ales; 2003, ―Cyclically Adjusted Fiscal Balance – OECD and ESCB Methods‖, Czech Journal of Economics and Finance, 53 Blanchard, Olivier; April 1990, ―Suggestions for a New Set of Fiscal Indicators‖, OECD Economics and Statistics Department, Working Paper No. 79 Blanco, Fernando; Herrera, Santiago; September 2006, ―The Quality of Fiscal Adjustment and the Long-Run Growth Impact of Fiscal Policy in Brazil‖, World Bank, Policy Research Working Paper No. 4004 Bornhorst, Fabian; Dobrescu, Gabriela; Fedelino,Annalisa; Gottschalk, Jan; Nakata, Taisuke; April 2011, ―When and How to Adjust Beyond the Business Cycle? A Guide to Structural Fiscal Balances‖, IMF Technical Notes and Manuals (Washington: International Monetary Fund) Bouthevillain, Carine; Cour-Thimann, Philippine; van den Dool, Gerrit; Hernandez de Cos, Pablo; Langenus, Geert, Mohr, Matthias; Momigliano, Sandro; Tujula, Mika; 2001, ―Cyclically-Adjusted Budget Balances: An Alternative Approach,‖ ECB Working Paper No. 77 (Frankfurt: European Central Bank). Eyzaguirre, Nicolás; Kaufman, Martin; Phillips, Steven; Valdés, Rodrigo; April 2011, ―Managing Abundance to Avoid a Bust in Latin America‖, IMF Staff Discussion Note (Washington: International Monetary Fund) Fedelino, Annalisa; Ivanova, Anna; Horton, Mark; November 2009, ―Computing Cyclically Adjusted Balances and Automatic Stabilizers‖, IMF Technical Notes and Manuals (Washington: International Monetary Fund) Garcia, Márcio G.P.; August 2007, ―Dívida Pública e Reservas Cambiais‖, mimeo (only in Portuguese) Girouard, Nathalie, and Christophe André; 2005, ―Measuring Cyclically Adjusted Budget Balancesfor OECD Countries,‖ OECD Economics Department, Working Paper No. 434, (Paris: OECD) Gerin, Diretoria de PolíticaEconômica do Banco Central do Brasil; March 2010, ―IndicadoresFiscais‖ (Fiscal Indicators), Central Bank of Brazil’s Frequently Asked Series, Central Bank of Brazil, http://www.bcb.gov.br, (only in Portuguese) Gobetti, Sergio Wulff; Gouvêa, Raphael Rocha; Schettini, Bernardo Patta; December 2010, ―Resultado Fiscal Estrutural: Um Passo Para a Institucionalização de Políticas Anticíclicas no Brasil‖ (Structural Fiscal Balance: A Step Towards The Institutionalization of Anti-cyclical Policies in Brazil), Discussion Paper No. 1515, IPEA Instituto de Pesquisa Econômica Aplicada, http://www.ipea.gov.br, (only in Portuguese) Goldfajn, Ilan; Bicalho, Aurélio; February 2011; ―A Longa Travessia Para a Normalidade: Os Juros Reais no Brasil‖; Textos Para Discussão no. 2; Departamento de Pesquisa Macroeconômica do Itaú Unibanco, (only in Portuguese)

http://www.bcb.gov.br/

http://www.ipea.gov.br/

77


Hamilton, James D.; 1994; ―Time Series Analysis‖; Princeton University Press Hagemann, Robert; July 1999, ―The Structural Budget Balance: The IMF’s Methodology‖, IMF Working Paper No. 99/95 (Washington: International Monetary Fund) Marcel, Mario; Tokman, Marcelo; Valdés, Rodrigo; Benavides, Paula; December 2001, ―Balance Estructural: La Base de La Nueva Regla de Política Fiscal Chilena‖ (Structural Balance: The Basis of The New Chilean Fiscal Policy Rule), Economía Chilena, Vol. 4 No.3 (pages 5-27), Central Bank of Chile, http://www.bcentral.cl, (only in Spanish) Mello, Luiz de; Moccero, Diego; 2006, ―Brazil’s Fiscal Stance during 1995-2005: The Effect of Indebtedness of Fiscal Policy Over the Business Cycle‖, OECD Economics Department Working Paper No. 485, http://www.oecd.org (OECD, Paris). Noord, P. van den; January 2000; ―The size and role of automatic fiscal stabilizers in the 1990s and Beyond‖; OECD Economics Department Working Paper No. 230 (OECD, Paris). Rocha, Fabiana; Setember 2009; ―Política Fiscal Através do Ciclo e Operação dos Estabilizadores Fiscais‖, Revista Economia – Anpec, Vol.10, No.3, p.483-499, Brasília (DF), (only in Portuguese)

http://www.bcentral.cl/

http://www.oecd.org/

brazil's structural fiscal balance - banco ita

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