D O C U M E N T O

D E T R A B A J O

Instituto de EconomíaD

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I N S T I T U T O D E E C O N O M Í A

www.economia.puc.cl • ISSN (edición impresa) 0716-7334 • ISSN (edición electrónica) 0717-7593

Fiscal Rules and Fiscal Performance: World Evidence

Klaus Schmidt-Hebbl y Raimundo Soto.

5172018

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Fiscal Rules and Fiscal Performance: World Evidence *

Klaus Schmidt-Hebbel **

Raimundo Soto ***

Final Version, September 2017

Abstract

This paper presents world evidence on the contribution of fiscal rules to fiscal performance.

The paper identifies the links between fiscal institutions, rules, and performance measures,

and reviews the relevant literature on fiscal performance and the potential contribution of

different fiscal rules to performance. The expected contribution of rules to stabilization

objectives is ambiguous, depending on policymakers´ objective functions and constraints, as

well as on particular design and implementation features of rules. Rules have expected effects

on other policy objectives (solvency and government size) that are less ambiguous. This

paper’s empirical models identify the potential contribution of three types of rules –

expenditure, budget balance, and debt rules, using de facto and de jure measures – to four

indicators of fiscal performance, controlling for other determinants. The empirical analysis is

conducted using an econometric approach designed to deal with potential endogeneity,

dynamic responses, and unobserved heterogeneity. The models are tested on a world sample

for 115 countries, spanning 1985-2015. De jure expenditure rules (but not other rules) are

found to reduce expenditure procyclicality. Yet procyclicality of budget balances is not

affected by any rule. All three types of rules raise government budget balances but they have

no significant effects on government debt. The latter results are broadly robust to using

alternative de facto rule measures. The results are also generally robust to nested testing of

differential effects in country sub-groups, i.e., no systematic effects of rules on fiscal

performance were found for small states and for Latin American countries.

* Prepared for the World Bank LCR Regional Study on Fiscal Rules and Economic Size. We

thank Fernando Blanco, Fritzi Koehler-Geib, Emilia Skrok, and Carlos Végh for helpful

discussions and comments provided to an earlier draft. We also thank Isaac Martinez for very

able research assistance. The usual disclaimer applies.

** Instituto de Economía, P. Universidad Católica de Chile, email: kschmidt-hebbel@uc.cl

*** Instituto de Economía, P. Universidad Católica de Chile, email: raimundo.soto@gmail.com

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1 Introduction

Fiscal rules and restrictions that govern the conduct of fiscal policy have been in place

in a small number of industrial countries for a long time. However, only since the 1990s

adoption of fiscal rules started spreading world-wide, as part of significant reforms of fiscal

frameworks in many industrial and emerging/developing countries (Figure 1).

Figure 1 Number of Countries with Fiscal Rules in Place, 1985-2015

Source: IMF Fiscal Rules Dataset, 2016.

Reforms of fiscal institutions and fiscal rules have been motivated by several (often

complementary) objectives: (i) making fiscal policy design and execution more resilient to

principal-agent and other political-economy problems that cause deficit bias, (ii)

strengthening fiscal solvency and sustainability (i.e., attaining sustainable levels of

government deficits and public debt), (iii) contributing to macroeconomic or cyclical

stabilization, by reducing fiscal policy pro-cyclicality or raising fiscal policy counter-

cyclicality, and (iv) improving intergenerational fairness (IMF, 2013).

The latter objectives are shared by most fiscal policy makers worldwide. So why do

some countries adopt fiscal rules while others do not? Recent research has shed light on

political, institutional, and economic conditions that explain the likelihood of adopting fiscal

rules and maintaining them over time (Elbadawi et al. 2015, Schmidt-Hebbel and Soto 2017).

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This paper focuses on the original motivation of policy makers when putting in place a

fiscal rule: the attainment of several fiscal policy objectives. There is little empirical research

available on the possible contribution of fiscal rules to the attainment of fiscal objectives. This

study fills this void.

Of the four policy objectives identified above, this study will focus on the contribution

of fiscal rules on two objectives: fiscal solvency and sustainability, and cyclical stabilization. It

will do so by identifying the statistical contribution of fiscal rules to four different empirical

measures of fiscal performance that are proxies for the policy objectives of solvency and

sustainability, and cyclical stabilization. Countries have adopted four types of fiscal rules (as

depicted in Figure 1): rules on government budget, debt, expenditure, and/or revenue. There

are also two different measures of fiscal rules available: legal or de jure rules and measures of

rule compliance or de facto rules (using the recent dataset on fiscal rules compliance by

Belinga et al. 2017).

Specifically, this paper will address the following questions:

(1) Do fiscal rules promote good fiscal performance? (2) Does the type of fiscal rule matter affect for its impact on fiscal performance? (3) Which other fiscal institutions, political institutions, macroeconomic regimes,

development conditions, and macroeconomic conditions affect fiscal performance and sustainability?

(4) Which factors matter for the effectiveness of de jure fiscal rules? I.e., do fiscal rules interact with other institutional and economic variables in their impact on fiscal performance?

(5) Does the effect of fiscal rules on fiscal performance depend on country size? (6) Is there a difference between the impact of de jure and de facto fiscal rules?

The paper’s next section discusses the multi-causal links between fiscal institutions

(including rules) and fiscal performance, defines measures of fiscal performance and types of

fiscal rules, reviews selectively the relevant literature on optimal fiscal policy, derives a policy

function from government optimization in a small open economy, and presents a taxonomy of

fiscal rules and their relation to fiscal policy objectives. Section 3 reviews selectively the

empirical evidence on the determinants of fiscal performance and the contribution of fiscal

rules to fiscal performance, as well as the reverse causality from performance to rules. Section

4 addresses empirically the question if small economies are structurally different from larger

economies, which is an important issue for the subsequent empirical research. The empirical

methodology is discussed in section 5, addressing the main econometric issues, including the

key issue of potential endogeneity of fiscal rules to fiscal performance. The subsequent

empirical research is conducted using a three-stage instrumental-variable approach designed

to deal with the challenges of assessing the impact of fiscal rules: potential endogeneity,

dynamic response, and unobserved heterogeneity. Section 6 deals with the creation of

suitable instruments for a variety fiscal rules, as well as for other fiscal institutions, such as

sovereign wealth funds. Section 7 presents the core research results on the contribution of

different types of de jure fiscal rules to the four measures of fiscal performance, controlling for

a relevant number of other performance determinants. Section 8 reports comparable results

for the contribution of de facto rules to fiscal performance. Section 9 concludes.

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2 Fiscal Rules and Fiscal Performance: Measures, Interactions, and

Policy Optimality

A fiscal policy rule is one among several components of a fiscal framework adopted to

strengthen fiscal performance. How is fiscal performance linked to fiscal institutions

(including rules), considering country conditions? How do we define fiscal performance and

which are the main types of fiscal rules? How are optimal policy functions (not fiscal rules)

derived in the analytical literature? Which are the expected effects of different sub-categories

of fiscal rules on different fiscal performance measures? We address these questions in this

section.

2.1 Interactions between Fiscal Performance, Fiscal Institutions, Economic Conditions, and Political-Economy Features

Fiscal institutions and fiscal performance measures are linked in several causal

directions and affected by other variables and conditions. These complex multi-factor and

multi-causal relations are depicted in Diagram 1. Fiscal performance (measured by debt and

deficit levels, public debt prices or yields, and policy cyclicality) is influenced by fiscal

institutions (including rules, independent fiscal councils or agencies, and sovereign wealth

funds), cyclical and structural conditions (business cycles, economic shocks, per capita GDP,

natural resources, and country size, among others), political-economy distortions (caused by

principal-agent problems, political cycles, high discount rates, interest groups, etc.), and

political institutions (democracy, institutional quality, checks and balances, electoral rules,

among others).1

Reverse causality from fiscal performance to fiscal institutions is observed in the

design, adoption, and functioning of fiscal institutions, which are all shaped by fiscal

performance measures and other economic and political determinants.

2.2 Measures of Fiscal Performance

Fiscal policy seeks attainment of several different objectives, ranging from

macroeconomic goals (fiscal sustainability and solvency, output stability, and size of

government), to microeconomic and sector goals (more efficiency in resource allocation

across sectors and regions, removing distortions, and supplying public goods), and to social

goals (reducing poverty and raising equity). The structure of public expenditure and taxation

is geared to attaining micro, sector, and social goals. In contrast, setting aggregate levels of

government spending, revenue (including taxes), and/or debt levels are the conventional

instruments of choice to attain macroeconomic goals. We focus on the latter in this paper.

There are different approaches to assess directly a country’s fiscal sustainability and

solvency. Some measures are test results based on debt stationarity conditions (Milesi-

Ferretti and Razin, 1996), typically applied to one specific country, with detailed information

1 Here we include fiscal rules as one of several fiscal institutions, defined in a broad sense. In a narrow sense, fiscal institutions are government entities such as government agencies and funds. A fiscal framework comprises institutions (in a narrow sense), fiscal rules, and the legal and regulatory acts that define the latter and constrain fiscal policy decisions.

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about its public finance structure and on detailed projections of key future macroeconomic

and financial variables that affect its fiscal stance. Considering the wide range of reasonable

assumptions and projections that underlie a fiscal sustainability study, it is not surprising that

different parties come to different conclusions regarding sustainable debt and deficit levels.

(E.g., the ongoing discussions of Greece’s fiscal sustainability among creditor institutions).

Diagram 1 Fiscal Performance, Institutions, and Other Conditions

Cyclical and Structural Conditions

- Shocks/business cycles

- Per capita income

- Natural resources

- Market failures and rigidities

- Country size

Fiscal Institutions

- Fiscal rules

- Fiscal responsibility acts (constitutional or legal)

- Independent fiscal councils / agencies

- Sovereign Wealth Funds

Fiscal Performance

- Deficit levels

- Debt levels

- Debt prices / premiums

- Policy cyclicality

Political Institutions

- Democracy

- Institutional quality

- Checks and balances

- Electoral rules

Political-Economy Distortions

- Principal-agent problems

- Political cycles

- High discount rates

- Interest groups

- Common-pool problems

- …

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An alternative gauge of fiscal sustainability is derived from the public debt limit,

beyond which (without extraordinary measures) debt would be unsustainable. Ghosh et al.

(2013) propose estimation of the debt limit from the reaction function of a country’s primary

(or non-interest) government budget balance to past government debt levels. Rolling time-

series estimation of the reaction function could reveal time-varying estimates of the debt

limit. Fiscal space is the difference between the debt limit and current debt. As both the debt

limit and current debt change over time, fiscal space varies accordingly.

Market measures of government debt prices or yields (sovereign debt yields or CDS

spreads) are an alternative, market-based gauge of fiscal sustainaibility.

No systematic information for large panel datasets is available for estimates of

sustainable public-sector debt and deficit levels (based on estimates a la Milesi-Ferretti and

Razin) and for estimates of debt limits and fiscal space (a la Ghosh et al. 2013). The same is

true for sovereign debt yields and CDS spreads.

In the absence of the latter, we will use in this paper the conventional approach of

using GDP ratios of government budget balances and government balance levels as proxy

indicators of fiscal sustainability and solvency.

Regarding fiscal policy cyclicality, we focus on two measures: the correlation between

government expenditure and GDP, and the correlation between the government balance and

GDP. For the first (second) measure, a positive (negative) correlation reflects pro-cyclical

fiscal policy.2

2.3 Types of Fiscal Rules

As discussed above, fiscal rules (among other fiscal institutions) are put in place in

order to strengthen fiscal policy objectives, which are more difficult to attain in their absence

due to several market failures and frictions, and to political-economy constraints. In this

sense, fiscal rules are second-best policies that potentially make more likely attainment of

fiscal objectives.

Before turning to the types of fiscal rules considered in this paper, it is key to consider

three issues about fiscal rules that are not much addressed in the analytical and the policy

literature.

Difference between fiscal (target) rules and behavioral fiscal rules

A fiscal (target) rule sets a quantitative target for a fiscal aggregate (for example, the

budget balance).3 In contrast, a behavioral fiscal rule is a policy function that links a fiscal

target (for example, the budget balance target) to a fiscal policy instrument (for example,

government expenditure), controlling for other variables that affect the economy, the budget

balance, and the fiscal policymaker’s objective function. This is analogous to the conduct of

2 Both are ex-post measures of overall policy cyclicality, combining the cyclical stance of discretionary and non-discretionary government expenditure (and implicitly those of government revenue, in the case of fiscal deficits). 3 We follow in this paper the conventional usage of “fiscal rule” instead of the more precise term “fiscal target rule”. As discussed below, terminology of policy targets and behavioral policy functions is used correctly in the realm of monetary policy.

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monetary policy, where a policy function for the policy instrument (for example, the interest

rate in a Taylor rule) is co-determined by a target rule (for example, the inflation target),

controlling for other variables that affect the economy.

The analytical and policy literature (reviewed below) derives fiscal policy rules. Of

course, policy rules are unobservable variables – they may be hypothesized about or inferred

from econometric estimation, but they are not measured or reported as observable time

series. However, fiscal rules are publicly available, and hence will be used in this paper.

On fiscal rules

In order to clarify interpretation of the subsequent analysis and empirical results, next

we highlight several key issues related to design and implementation of fiscal rules.

Different fiscal objectives can be attained by different target rules. For example, the goal

of fiscal sustainability can be achieved by a rule on either the budget balance or the level

of debt.

Sometimes two (or more) different rules are defined for the same objective. The two (or

more) targets may or may not be consistent.

Inconsistency between two (or more) rules is less likely when the rule is set

asymmetrically, i.e., when it is defined as either a ceiling or a floor for two (or more)

targets. For example, the EU Maastricht target rules for government debt and deficits are

defined as ceilings.

When fiscal indicators are not close to their target values, governments face fiscal policy

space. In this case, targets are not restrictive and do not constrain fiscal policy behavior.4

The relation between fiscal objectives and fiscal rules gets even more complicate when

considering that governments aim at different policy objectives and use different rules

simultaneously. For example, attaining fiscal sustainability, i.e., lowering debt by running

budget surpluses when current debt exceeds the debt target, constrains heavily the use of

government expenditure as a counter-cyclical policy tool, at least during the transition

period toward lower debt. We illustrate this issue deriving a simple optimal fiscal policy

rule in section 2.5 below.

For any rule, the attainment of the fiscal objective depends on how the specific rule is

defined. For example, a budget balance rule has very different implications for the

cyclicality of fiscal policy, depending if it is defined on a year-by-year basis or over the

business cycle. We discuss this issue in more detail in section 2.6 below.

A final relevant issue refers to the government level for which a fiscal rule is defined.

Supranational rules are internationally agreed rules among members of an international

agreement or an economic union. National rules are adopted by individual countries as a

results of their sovereign decision or in obedience of a supranational rule. Separate fiscal

rules are adopted at sub-national levels (at state, provincial or municipality levels). We

limit our analysis below to national fiscal rules.

De jure and de facto rules

4 An alternative use of the term fiscal space is defined by the difference between a financially relevant limit and the actual level of a fiscal indicator, such as the debt limits defined by Ghosh et al. (2013).

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Having a fiscal rule in place does not ensure compliance with the rule. One reason of

non-compliance with a fiscal rule is the existence of fiscal space. As discussed above, if a

government’s debt level falls significantly below its debt ceiling, it may decide to exceed

significantly its deficit target. On the other hand, governments often violate their rules – either

by deviating significantly from them or by exceeding significantly the corresponding ceilings –

as a result of economic or political restrictions. The widely documented, frequent violations of

deficit and debt rules by EU member countries are the best example of non-compliance with

de jure rules.

Types of fiscal rules

Now we turn to the fiscal rules used in this paper. We consider four types of fiscal

rules: on budget balance, government expenditure, government revenue, and government

debt. Our main focus in this paper is on measures of de jure rules. However, as an extension,

we will also consider measures of de facto rules.

Which are the links between particular fiscal rules and the main fiscal policy

objectives? Here we follow Schaechter et al. (2012). Debt rules and current budget balance

rules, and in particular when they are defined as debt and deficit ceilings, aim at achieving

fiscal (or debt) sustainability. Some forms of budget balance rules (structural balance rules),

expenditure rules, and revenue rules (those that limit the use of cyclical or natural resource

windfall revenue) aim at macroeconomic stabilization. Finally, expenditure rules (ceilings)

and revenue rules (floors) aim at limiting the size of government.

2.4 Optimal Fiscal Policy

The analytical literature on fiscal rules derives behavioral or policy functions from

optimization in an economy constrained by particular economic or political frictions. Some of

the behavioral fiscal rules are derived from the optimization of the government’s objective

function while others arise from the maximization of consumer welfare.

Leith and Wren-Lewis (2005) analyze optimal counter-cyclical fiscal policy, when

using tax and spending instruments, aimed at maximizing consumer welfare in an economy

with real and nominal frictions. The authors argue that political-economy distortions often

lead governments to mismanage short-term stabilization tools as an excuse to neglect long-

term sustainability. They argue that a well-designed explicit fiscal rule is an effective fiscal

institution to maintain fiscal sustainability.

Halac and Yared (2014) derive an optimal expenditure function for a government with

time-inconsistent preferences with a bias toward current spending. The government chooses

an expenditure rule as a trade-off between desire to commit to not overspend against its

desire to have flexibility to react to privately observed shocks to the value of spending. When

shocks are persistent and large, the rule does not preempt the government from deviating

from the ex-ante optimal rule, leading to erosion of fiscal discipline over time. This relevant

result provides an analytical foundation for the observed fact that fiscal rule enforcement may

be weak, leading to discrepancies between de jure and de facto rules when political-economy

frictions are present.

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A different strand of the literature on optimal policy analyzes the interaction of

optimal fiscal and monetary rules in general equilibrium. Benigno and Woodford (2003)

derive optimal behavioral fiscal and monetary rules in an economy that is neoclassical in the

sense of fiscal policy (i.e., taxes are distortionary) and that is Neo-Keynesian in the sense that

prices and wages adjust slowly. Eser (2009) addresses similar interactions between optimal

fiscal and monetary policy for different benchmark economies.

The above cited literature is for closed economies. Végh (2013) extends the analysis of

optimal fiscal policy to open economies, focusing on the objective of macroeconomic

stabilization of a benevolent government that satisfies intertemporal consistency and

maximizes consumer welfare. In the benchmark case of full access to international capital

markets, no uncertainty, and no political-economy restrictions, full intertemporal

consumption smoothing is achieved by a government setting a constant consumption tax rate

(its policy instrument) over time. As a result, government spending (considered exogenous in

this model) is acyclical, while tax revenue and the budget balance are optimally procyclical,

implying fiscal policy countercyclicality. Then two traditional explanations of fiscal policy

procyclicality, observed in many developing countries, are modelled by Végh: imperfect

access to international capital markets (governments are cut off from international credit in

bad times, hence they are forced to cut spending and raise taxes) and weak domestic fiscal

institutions or political distortions that induce governments to undersave in good times and

oversave in bad times.

Under imperfect access to international markets, higher government spending will

have to be financed both by higher domestic and external borrowing (which drives up the

domestic interest rates above international levels) and higher tax rates. When uncertainty

about future government spending is added to incomplete capital markets, an increase in

uncertainty raises even further the procyclicality of taxes. However, under full access to

capital markets, uncertainty does not make optimal fiscal policy procyclical.

Analogously, under full access to international capital markets, a domestic political

distortion will be reflected in larger demands for government spending during good times

(when tax revue is large), which governments will not be able to resist. As a result, a second-

best optimal fiscal policy arises: in good times larger temporary tax revenue is not fully saved

(as it would be in the first-best case) but is partly spent in the form of higher government

spending and lower tax rates. Hence political-economy factors lead to procyclical fiscal policy.

2.5 A simple optimal fiscal policy rule for an open economy

We develop a simple framework for optimal fiscal policy, deriving a policy function for

government spending from stochastic government optimization subject to its budget

constraint, in a stochastic environment for an open economy characterized by key behavioral

equations.

We consider a government that pursues three fiscal policy objectives at the same time:

macroeconomic stability, expenditure stability, and debt sustainability. It does so by optimally

setting its one and only policy instrument: government expenditure.

The government objective function is an expected loss function in the quadratic

deviations of three arguments: the output gap (defined by the ratio of current to trend

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output), the deviation of the ratio of current government expenditure to current output from

the ratio of the expenditure target to trend output, and the deviation of the ratio of current

debt to current output from the ratio of the debt target to trend output (notation is defined

below):

(1) 𝐸𝑡 [∑ 𝛽𝑠 {𝛼 (𝑌𝑠

𝑌𝑠∗)

2+ 𝛿 (

𝐺𝑠

𝑌𝑠−

�̅�𝑠

𝑌𝑠∗)

2

+ 𝜔 (𝐷𝑠

𝑌𝑠−

�̅�𝑠

𝑌𝑠∗)

2

}∞𝑠=𝑡 ]

Note that the government’s objective function embodies the short-term objective of

macroeconomic stability (the first argument) and the longer-term objectives of policy

sustainability and government solvency, reflected both in its preference of setting its

instrument (expenditure) close to its quantitative targets for spending and debt (the second

and third arguments). No political-economy arguments are included that could bias policy

toward fiscal procyclicality.

The government budget constraint is:

(2) 𝐷𝑠+1 − 𝐷𝑠 = 𝐺𝑠 − 𝑇𝐴𝑠 + 𝑟𝑠𝐷𝑠

Tax revenue is linked to current output and subject to a stochastic i.i.d. shock:

(3) 𝑇𝐴𝑠 = 𝑘𝑌𝑠 + 휀1𝑠

Output is positively affected by government spending and negatively affected by the

real interest rate, and is subject to an i.i.d. shock. The aggregate demand equation is written in

terms of the output gap and the ratio of current to target government expenditure:

(4) (𝑌𝑠

𝑌𝑠∗) = 𝛾 (

𝐺𝑠

�̅�𝑠) − 𝜂𝑟𝑠 + 휀2𝑠

Trend output grows at a constant exogenous rate but is also subject to a stochastic iid

shock:

(5) 𝑌𝑠+1∗ = (1 + 𝜌)𝑌𝑠

∗ + 휀3𝑠

The domestic interest rate deviates from the international rate proportional to the

deviation of the ratio of current debt to current income from the ratio of target debt from

trend income:

(6) 𝑟𝑠 − 𝑟𝑠∗ = 𝜙 (

𝐷𝑠

𝑌𝑠−

�̅�𝑠

𝑌𝑠∗)

The government defines the following expenditure target:

(7) �̅�𝑠 = 𝜇𝑌𝑠∗

and the following debt target:

(8) �̅�𝑠 = 𝜐𝑌𝑠∗

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where 𝑌𝑠 is output, 𝑌𝑠∗ is trend output, 𝐷𝑠 is public debt, �̅�𝑠 is target debt, 𝐺𝑠 is government

expenditure, �̅�𝑠 is target expenditure, 𝑇𝐴𝑠 is tax revenue, 𝑟𝑠 is real interest rate. 휀1𝑠, 휀2𝑠 y 휀3𝑠

are iid stochastic tax revenue, output, and trend output shocks, respectively. Model

parameters are 𝛽, 𝛿, 𝜔, 𝑘, 𝛾, 𝜂, 𝜌, 𝜙, 𝜇, 𝜐.

Minimizing the government loss function (1) subject to equations (2)-(8) yields a

government expenditure rule of the general form:

(9) 𝐺𝑠 = 𝐺(휀1𝑠, 휀2𝑠 , 휀3𝑠, … )

where the signs of the partial derivatives of G with respect to the three stochastic shocks are

ambiguous in general. In particular, an aggregate demand shock 휀2𝑠 has an ambiguous effect

on government spending. The reason is that the government faces a trade-off in setting

spending because its loss function is complex. It depends on three different arguments, which

are affected in opposite ways – directly and indirectly – by government expenditure.

Now let’s restrict the general model to one where the interest rate is exogenous and

constant and the second and third terms of the government’s loss function are shut off,

implying:

(10) 𝛿 = 𝜔 = 𝜂 = 𝜙 = 0

The solution of this restricted government optimization yields the following

government expenditure rule:

(11) 𝐺𝑡

𝑌𝑡=

𝜇

𝛾

1

(𝑌𝑡𝑌𝑡

∗)(

𝐸𝑡(𝑌𝑡+1𝑌𝑡+1

∗ )

1+𝜌+𝜀3𝑡

(1+𝜌)𝑌𝑡−1∗ +𝜀3𝑡−1

) −𝜇

𝛾휀2𝑡

This restricted spending rule reflects unambiguous counter-cyclical behavior, as

spending is now a negative function of the aggregate demand shock.

We conclude that when the government is limited by using only one instrument to

pursue several policy objectives at the same time – like the three objectives embedded in

equation (1) – the corresponding policy trade-offs can lead to pro-cyclical fiscal policy.

However, when the use of the policy instrument is geared only to macroeconomic

stabilization, optimal policy is countercyclical, reflected in countercyclical government

expenditure.

2.6 Relations between types of fiscal target rules and fiscal performance measures

Now we turn to the expected relations between particular fiscal target rules and policy

objectives, proxied by measures of fiscal performance. We identify the three main fiscal policy

objectives and their corresponding measures of fiscal performance as columns in Table 1.

Then we identify four family of fiscal target rules and particular target rules in each family as

rows in the table. Table cells identify expected sign of the effect of a particular target rule on

the corresponding performance measure. For example, an annual budget balance rule that is

enforced every year independently of cyclical conditions increases cyclicality of government

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spending (+). In contrast to the latter, a structural budget balance rule that is set as an average

numerical target over the business cycle, but is allowed to vary pro-cyclically, induces

counter-cyclicality of government spending (–).

How do we obtain the sign summary in Table 1? We simply use the government

budget constraint (equation (2) above), complemented by the assumption that tax revenue is

closely linked to output (automatic stabilizer) while expenditure is largely discretionary.

Hence we exclude other government objectives and restrictions by market failures and

political constraints, taken up in the literature on behavioral fiscal rules. In addition we

assume that quantitative target rules are binding, i.e., there is no fiscal space.

Table 1 Relation between Fiscal Rules and Policy Objectives

(A)

Macroeconomic

Stabilization:

Cyclicality of

government

spending

(correlations with

GDP)

(B)

Fiscal

sustainability

and solvency:

Government

deficit and debt

levels (ratios to

GDP)

(C)

Size of

Government:

Government

expenditure and

revenue levels

(ratios to GDP)

1. Budget Balance Rules (–)

Current BBR (annual) (+) (–)

Current BBR (average over the cycle) (+), (0) or (–)

Structural BBR (annual) 0

Structural BBR (average over the

cycle) (–)

2. Debt Rules (–)

Current DR (annual) (+)

Current DR (average over the cycle) (+), (0) or (–)

3. Expenditure Rules (–) (–)

Current ER (annual) (0)

Current ER (average over the cycle) (–)

4. Revenue Rules (–) (–)

Current RR (annual)

Current RR (average over the cycle)

Note: column 1 refers to cyclicality of government spending for illustrative purpose. Alternatively, the

policy objective of macroeconomic stabilization can be reflected by the cyclicality of tax rates or the

government budget.

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The summary description in Table 1 suggest the following general results. First, for

macroeconomic stability (column 1), what matters is the particular, not the general rule.

Second, budget balance or debt targets that are enforced on a yearly basis induce pro-

cyclicality of spending, while structural or over-the-cycle rules allow for counter-cyclicality.

This ambiguous effect a fiscal on the cyclical behavior of government spending, depending on

the particular form the rule is defined, complements the previously discussed analytical

finding that the cyclical nature of a fiscal rule depends on the existence of capital-market and

political-economy distortions (Végh 2013). This ambiguity is not found in the case of the links

between rules and debt or deficit levels (for the policy objective of fiscal sustainability and

solvency, column 2) and expenditure or tax levels (size of government, column 3), assuming

that the corresponding numerical targets are well defined for achieving fiscal sustainability

and limiting government size.

3 Previous Empirical Evidence on Fiscal Rules and Fiscal

Performance

We review selectively the empirical evidence on the determinants of fiscal

performance and on the contribution of fiscal rules to fiscal performance, as well as the

reverse causality from performance to rules.

3.1 Fiscal Performance

We focus selectively on the empirical evidence regarding the role of different groups

of determinants of fiscal policy performance. Most of this evidence is based on econometric

estimations for reduced-form equations for two sets of fiscal performance measures (debt and

deficit ratios to GDP, and cyclical correlations between fiscal variables and GDP), performed

on cross-country data. The sets of determinants of fiscal performance variables include

political-economy variables, political institutions, fiscal institutions, macroeconomic variables

(business-cycle measures, trade openness, financial integration, growth, per capita GDP),

among others.

Government debt and deficits

Nerlich and Reuter (2015) conduct a comprehensive empirical analysis of the

contribution of fiscal rules and fiscal space to fiscal performance in EU countries, controlling

for a significant number of co-determinants. The government primary balance ratio to GDP is

reported to increase with financial openness, trade openness, and future age dependency, and

to decline with growth and government fragmentation. The government debt ratio to GDP

increases with future age dependency and government fragmentation, and declines with trade

openness.

For a world sample of low-income countries, Dabla-Norris et al. (2010) report that

their measures of budget institutions development (both the aggregate budget institutions

index and several of its components) raise the government primary balance and lower official

13

public external debt ratios to GDP. Macroeconomic control variables do not robustly

contribute to fiscal performance.

Eyraud and Lusinyan (2013) provide evidence on the negative contribution of vertical

fiscal integration (defined as the share of subnational net borrowing and transfers received

from general government) to the general government primary balance ratio to GDP in OECD

countries. This means that less responsibility of subnational governments for self-financing

their expenditure leads to weaker fiscal performance. The output gap (a control variable)

raises the government primary balance ratio, which suggests fiscal policy countercyclicality,

possibly due to automatic stabilizers.

Eslava (2010) presents results for government deficits in the LAC region (without

controlling for macroeconomic factors), showing that deficit ratios to GDPs are higher under

parliamentary systems and are lowered by higher levels of fiscal accountability.

Escolano et al. (2012) analyze fiscal performance (measured mostly by the primary

general government cyclically-adjusted balance ratio to GDP) in EU member countries. The

government balance is raised by the level of government debt, is lowered by the output gap

(reflecting procyclical fiscal policy), and is raised by spending decentralization to subnational

government levels.

Cyclicality of fiscal policy

Fiscal policy cyclicality is measured in empirical work as a rolling correlation

coefficient or a regression coefficient between a fiscal performance aggregate (in level or in its

cyclical component) and GDP (in level or its cyclical component).

Végh (2013, pp. 484-6) reports for a world sample cross-country correlations

between the cyclical components of government spending and GDP, and between the cyclical

components of tax rates and GDP, suggest that fiscal policy tends to be countercyclical in

industrial economies and procyclical in emerging-economy and developing countries Further

evidence suggests that the causality is largely from the GDP cycle to government spending and

taxes, and not the reverse (Ilzetzki and Végh 2008, Végh and Vulletin 2012).

Several explanations are offered to explain these stylized facts on fiscal policy

cyclicality. First, developing and emerging economies face more stringent capital market

imperfections. They tend to be cut off from international financial markets in bad times

(Aizenman et al., 2000) and, more generally, they face more incomplete financial markets

(Riascos and Végh 2005). Second, they have weaker fiscal institutions in place, making fiscal

policy more prone to agency and political-economy problems that imply spending more in

good times and less in bad times (Talvi and Végh 2005).

Calderón and Schmidt-Hebbel (2003) and Calderón et al. (2004) identify a causal

factor that drives the difference in fiscal policy cyclicality within a sample of developing

countries: the foreign debt premium or spread, a measure of the cost of foreign lending and of

weak policy credibility. They find that at low (high) spreads, fiscal policy, measured by the

cyclical component of government spending is countercyclical (procyclical). Related work by

Calderón et al. (2016) focuses on different measures of institutional quality and their relation

to policy cyclicality. For a world sample of 112 industrial and developing countries, they

14

report that at high (low) levels of institutional quality, fiscal policy is countercyclical

(procyclical).

Nerlich and Reuter (2015) estimate fiscal policy procyclicality as a time-varying

regression coefficient for growth in an equation for government spending that controls for

other variables. Then the authors regress the estimated procyclicality coefficients, finding that

fiscal space (the difference between the debt limit and current debt, a la Ghosh et al. 2013)

and trade openness raise procyclicality, while growth and age dependency reduce cyclicality.

Finally, Dabla Norris et al. (2010) report that their measures of budget institutions

development reduce fiscal policy cyclicality in low-income countries.

3.2 Fiscal Rules and Fiscal Performance

We start by reviewing the scant evidence on the contribution of fiscal rules to fiscal

performance. Then we turn to the reverse causality: evidence on the contribution of fiscal

performance measures to the adoption of fiscal rules.

From rules to fiscal policy cyclicality

Eslava (2010) reports that fiscal rules reduce the levels of government debt and

deficits in Latin American and Caribbean countries.

In their above mentioned work, Nerlich and Reuter (2015) report several fiscal

performance measures that are significantly affected by having fiscal rules in place,

controlling for many other determinants (several were mentioned above). First, fiscal space is

increased by having any fiscal rule in place, and in particular a budget balance and an

expenditure rule. Second, an expenditure rule raises the government primary balance and

reduces government debt. Third, discretionary current expenditure is raised and

discretionary current revenue is lowered by having any fiscal rule in place. However, this

result may be spurious as it refers to having any fiscal rule in place, because the expected sign

of the effect of any rule should depend on which rule is implemented, as discussed above.

Finally, the effects of fiscal rules on procyclicality are reported to be positive for a budget

balance rule and negative for an expenditure rule, but the magnitude of the latter effects is

diminished by the size of fiscal space, a variable that interacts with fiscal rules. Also in this

case the results do not seem to be fully consistent with the ambiguity of the effects of general

rules on policy cyclicality, as extensively discussed above.

Reverse Causality: from Fiscal Performance to Rules

An issue that is of significance for our estimations below on the effects of fiscal rules

on different measures of fiscal performance is potential reverse-causality running from fiscal

performance to the implementation of rules. In fact, in previous work, we have reported

significant contributions of two fiscal performance measures (government budget balance

and procyclicality of government expenditure) to the likelihood of having a national fiscal rule

in place, controlling for a host of other macroeconomic, structural, institutional, and political

conditions. These results underscore the importance of instrumenting properly fiscal rules in

our econometric analysis, as reported below.

15

4 Are Small Economies Different?

There is an extensive literature documenting the notion that small states share

characteristics that are absent in larger economies (Favaro, 2008). Most of the world’s small

states are very young, having achieved independence in the past 50 years. Political

independence meant that services formerly provided by colonial institutions had to be

supplied by fledgling national institutions. Furthermore, there have always been questions

about the implications of a small domestic market for small states’ income levels and growth

potential. A small domestic market limits capacity to exploit economies of scale and diversify

risk. Other features of small states are that they receive more foreign direct investment and

worker remittances, and tend to have relatively larger governments with higher debt and

deficit levels.

Yet small states as a group tend to grow as fast as other emerging economies.

Compensating factors for their size limitation include their reliance on and proximity to world

markets (despite their larger export concentration), their relatively higher endowments of

human and physical resources; and the policies, institutions, and regulations that many small

states have adopted to facilitate their integration into world markets.

Moreover, Easterly and Kraay (2000) conclude that, controlling for location, small

states have higher per capita GDP levels than other states. This income advantage is largely

due to a productivity advantage, which is evidence against the notion that small states suffer

from an inability to exploit returns to scale. While small states have similar per-capita growth

rates than other economies, they suffer from higher instability, which is in part due to their

larger terms-of-trade volatility, due to their larger openness.

Based on current data, Table 2 confirms several features small states that were

identified in previous papers. We split countries along the 1.5 million inhabitants threshold

level (the World Bank’s limit of a small state) and conduct simple mean tests to identify the

main differences between large and small economies. Small countries have higher levels of

per capita GDP and enjoy higher government stability, while their political checks and

balances and political participation level (a measure of democracy) are slightly less

developed. Small states are significantly more open to foreign trade, while their export base

tends to be less diversified, although they enjoy lower natural resource rents. Small

economies display a degree of financial openness that is similar to other countries are

similarly open regarding capital-market integration equally open in terms of financial

markets. They are more likely to adopt more rigid exchange-rate regimes despite receiving

much larger inflows of worker remittances. Small and large states share similar levels of tax

revenue instability and inflation levels, and there is no evidence that business cycles are more

pronounced in smaller economies.

16

Table 2 Stylized facts of Small versus Large Economies

Variable

Large Economies

(more than 1.5

million inhabitants)

Small States

(less than 1.5 million

inhabitants)

Mean Test

large–small

Mean Std. Dev Mean Std. Dev

Business Cycles (index) 0 0.03 0 0.03 -0.14

Checks and Balances (index) 2.76 1.77 2.38 1.35 7.09*

Dependency Ratio (% population) 60 0.12 60 0.12 -0.04

Development Level (GDP per capita) 2,582 1.65 4,891 1.4 -12.18*

Exports Concentration (index) 2.49 0.61 3.30 0.55 -13.46*

Financial Development (% of GDP) 40% 0.88 37% 0.94 2.55*

Financial Openness (index) 0.47 0.37 0.44 0.35 2.16*

Fiscal Revenue Instability (index) 0.07 0.09 0.07 0.08 -1.00

Fixed Exchange Rate (index) 0.29 0.45 0.62 0.49 -19.54*

Government Stability (index) 2.0 2.11 3.5 2.3 -4.58*

Political Participation (index) 5.05 4.07 4.17 4.29 4.81*

Price Instability (annual inflation) 9.9 0.15 5.7 0.08 12.24*

Resource Rents (US$ per capita) 7,476 1.9 5,249 2.86 3.43*

Sovereign Wealth Funds (index) 0.05 0.12 0.05 0.15 0.60

Trade Openness (% of GDP) 64 0.55 107 0.41 -32.74*

Workers Remittances (% of GDP) 0.83 1.96 1.34 1.86 -6.10*

(*) significant at 95% confidence or higher.

Source: own elaboration based on data from World Bank Database.

The fact that small states exhibit structural differences compared to larger economies

suggests that fiscal rules might affect small economies differently. It also suggests the need to

control for economic size in our subsequent statistical analysis. While we use an arbitrary

threshold level to identify small states – a population of 1.5 million, as noted above – we

obtain similar econometric results when employing a continuous variable for population size.

5 Empirical Methodology

Empirical assessment of the impact of rules on fiscal outcomes (balance, debt, and

procyclicality) has to address three relevant issues: (a) fiscal rules tend to display high

persistence, thereby requiring the use of dynamic models, (b) countries are highly

idiosyncratic regarding unobservable features in their budget structures, indicating the

existence of individual country effects, and (c) reverse causation and endogeneity are very

likely, since governments may decide to implement rules as a result of their fiscal stance.

Standard dynamic panel data models of the Arellano-Bond family can be used to address the

first two issues but their results are hampered by the third. Therefore we complement

17

dynamic panel data techniques with a novel approach to building a consistent instrument for

the choice of implementing a fiscal rule.

From a methodological viewpoint, unveiling the impact of rules on fiscal performance

can be casted as the study of whether a treatment (implementing a fiscal rule) has any

discernible effect on fiscal performance (FP) of country “i” at time “t”, which we denote

generically by 𝐹𝑃𝑖𝑡. A general model would be of the form:

(12) 𝐹𝑃𝑖𝑡 = 𝑓(𝛼𝑖 , 𝑥𝑖𝑡 , 𝐷𝑖𝑡 , 𝐹𝑃𝑖𝑡−1)

where parameter 𝛼𝑖 reflects cross sectional heterogeneity (i.e., individual effects), 𝑥𝑖𝑡

represents the set of fundamentals – other than a fiscal rule, FR – that determine fiscal

outcomes, and 𝐷𝑖𝑡 denotes the implementation of a FR. The presence of 𝐹𝑃𝑖𝑡−1 indicates the

dynamic nature of our models, which captures the inertia of fiscal performance variables.

Identifying causal effects from observational data is often quite difficult as it depends

crucially on the validity of the implicit or explicit identification assumptions that are

particular to the corresponding empirical approach. As noted by Jordá and Taylor (2016), the

divergence of results in fiscal policy analysis can be the result of poor identification conditions

and, indeed, much of the variation in results is likely to be the consequence of endogenous

factors being either ignored or inadequately treated by the econometrician. In the context of

panel data, this would indicate the need of properly specifying the determinants of fiscal

performance, as well as controlling for potential endogeneity of the treatments. Furthermore,

modelling treatment is not as straightforward as it might seem, since it requires separating

the determinants of fiscal outcomes from those that led to the enactment of such rules. In

what follows we first address the issue of the modelling strategy for the treatments (i.e., the

implementation of the fiscal rule) and later the issue of potential endogeneity of fiscal rules

and other controls that co-determine fiscal outcomes.

5.1 Fiscal Rules as Treatments

In principle, the analysis of the effects of policy variables on outcomes of interest

ought to consider the intensity of such policies by using continuous variables. The imposition

of a fiscal rule as well as any other fiscal institutions (e.g., fiscal councils, sovereign wealth

funds), on the contrary, can be viewed as a once-and-for-all event (under the assumption that

such rule does not change in time). In this context it seems appropriate to use a dummy

variable (𝐷𝑖𝑡) taking value 1 whenever the rule is in place and zero otherwise. We further

assume that such policy is determined by 𝐷𝑖𝑡 = 𝐷(𝑤𝑖𝑡 , 𝜓𝑖, 휀𝑖𝑡) where 𝑤𝑖𝑡 is the conditioning

set (historical data), 𝜓𝑖 refers to the parameters of the implied policy function in country i and

휀𝑖𝑡 is an idiosyncratic source of random variation. Historical data would include past

observations on fiscal performance and other variables that may inform about the presence of

the rule. Therefore, 𝐷(𝑤𝑖𝑡 , 𝜓𝑖, . ) refers to the systematic component of policy determination.

18

Following Angrist et al. (2013) we define the potential outcomes of a policy as given

by 𝐹𝑃𝑖,𝑡+ℎ(𝐷𝑖𝑡) − 𝐹𝑃𝑖,𝑡, i.e., the value that the observed performance variable 𝐹𝑃𝑖,𝑡+ℎ − 𝐹𝑃𝑖,𝑡

would take when the rule is not enacted (𝐷𝑖𝑡 = 0) and when it is (𝐷𝑖𝑡 = 1). Therefore the

difference 𝐹𝑃𝑖,𝑡+ℎ − 𝐹𝑃𝑖,𝑡 refers to the cumulative change in outcome from 𝑡 to 𝑡 + ℎ.

Considering that the impact of a fiscal rule on the government stance might take time to yield

its potential effects, we use five-year averages in our subsequent econometric analysis.

Consider now expressing equation (12) in linear form:

(13) 𝐹𝑃𝑖𝑡 = 𝛼𝑖 + 𝛽𝑖𝑥𝑖𝑡 + 𝜃𝐷𝑖𝑡 + 𝛾𝐹𝑃𝑖𝑡−1 + 𝑢𝑖𝑡

Standard panel data estimators, such as pooled or fixed-effects models, are

inappropriate to parameterize equation (13) because there would be correlation between the

individual effect and the error term. The pooled estimator, which ignores heterogeneity

altogether, induces correlation between the residual and the lagged endogenous variable. For

the fixed effects estimators, on the other hand, Nickell (1981) shows that the demeaning

process which subtracts the individual’s mean value of the variables creates a correlation

between regressors and the error term. The resulting correlation biases the estimate of the

coefficient of the lagged dependent variable which is not mitigated by increasing the number

of individual units.

One solution to this problem involves taking first differences (∆) of the original model.

(14) ∆𝐹𝑃𝑖𝑡 = 𝛽𝑖∆𝑥𝑖𝑡 + 𝜃∆𝐷𝑖𝑡 + 𝛾∆𝐹𝑃𝑖𝑡−1 + ∆𝑢𝑖𝑡

The first difference transformation removes the individual effect (and any other

constant) but it comes at the cost of inducing correlation between the differenced lagged

dependent variable and the disturbance process (the former contains 𝐹𝑃𝑖𝑡−1 and the error

term contains 𝑢𝑖𝑡−1). But with the individual fixed effects swept out, instrumental variables

estimators are available. As shown by Anderson and Hsiao (1981), we may construct

instruments for the lagged dependent variable from the second and third lags of 𝐹𝑃𝑖𝑡, either in

the form of differences or lagged levels. As argued by Blundell and Bond (1998) and others,

the instrumental variables approach does not exploit all of the information available in the

sample. By doing so in a generalized method of moments (GMM) context, we may construct

more efficient estimates of the dynamic panel data model. Monte Carlo studies have shown

that estimated asymptotic standard errors of the efficient two-step GMM estimator can be

severely biased downward in small samples. Therefore we, use Windmeijer’s (2005) finite

sample correction in order to obtain robust estimators.

Beyond the unbiased estimation of the parameters of the model, appropriate

estimation of 𝜃 requires 𝐹𝑃𝑖,𝑡+ℎ(𝐷𝑖𝑡) − 𝐹𝑃𝑖,𝑡 to be orthogonal to 𝐷𝑖𝑡|𝑤𝑖𝑡 (for all h). This

conditional independence assumption plays an important role and allows us to identify the

average causal effect of a policy intervention relative to a baseline on the outcome variable at

time t+h using local projections (Jordá and Taylor, 2016):

19

(15)

𝐸[(𝐹𝑃𝑖,𝑡+ℎ(𝐷𝑖𝑡 = 1) − 𝐹𝑃𝑖,𝑡) − (𝐹𝑃𝑖,𝑡+ℎ(𝐷𝑖𝑡 = 0) − 𝐹𝑃𝑖,𝑡)] =

𝐸[𝐸(𝐹𝑃𝑖,𝑡+ℎ − 𝑦𝑖,𝑡|𝐷𝑖𝑡 = 1; 𝑥𝑖,𝑡) − 𝐸(𝐹𝑃𝑖,𝑡+ℎ − 𝐹𝑃𝑖,𝑡|𝐷𝑖𝑡 = 0; 𝑥𝑖,𝑡)] =

𝜃ℎ[(𝐷𝑖𝑡 = 1 − 𝐷𝑖𝑡 = 0)]

If the conditional independence assumption fails, OLS applied to equation (15) will

deliver a biased and inconsistent estimate of 𝜃. This is a case known as the endogenous dummy

variable problem (Heckman, 1978).5 This is most likely the case in the context of fiscal rules

since they are imposed precisely as the result of the perceived need of enacting additional

mechanism to achieve or secure policy outcomes. Instrumental variables can be brought in to

fix this inconsistency, but need to meet two well-known conditions. First, they need to be

independent of the unobserved selection mechanism. Second, the instruments 𝑧𝑖𝑡 need to be

predictive for 𝐷𝑖𝑡. Assuming these two conditions are met, estimation of the response to policy

interventions using local projections will deliver a consistent estimate of 𝜃.

Estimation of these conditional expectations can be simplified considerably when a

model for the policy variable 𝐷𝑖𝑡 is available. Angrist and Kuersteiner (2011) refer to the

predicted value from such a policy model the “policy propensity score”. The policy propensity

score acts as a dimension-reduction device and is meant to ensure the estimation of the policy

response (the average treatment effect in microeconomics parlance) is consistent under the

main assumption.

5.2 On the Endogeneity of Fiscal Rules

A legitimate concern is whether policy instruments, such as fiscal rules, are truly

exogenous in models of fiscal performance. While one would expect that the adoption of new

rules and institutions would induce better fiscal outcomes, it is nevertheless possible that said

policy instruments are adopted after an economy has already improved its fiscal indicators or

is firmly on a path of consolidating them. If there is reverse causality, the estimation of the

impact of fiscal rules on performance will be biased (the estimator is inconsistent).

This problem of reverse causality is an important issue that cannot be adequately

addressed by the standard time-series practice of using lagged values and GMM conditions for

the potentially endogenous regressor (such as Arellano and Bond, 1991). We deal with the

possible endogeneity of fiscal rules using instrumental variables. Assuming that we have a set

of valid instruments 𝑧𝑖𝑡 for adopting a fiscal rule (not including the elements that determine

fiscal performance in 𝑥𝑖𝑡), we can consistently estimate our models by the following three-

stage procedure:

5 Assume for simplicity that all projection slopes but 𝜃 are zero. Then, if 𝑐𝑜𝑣(𝐷𝑖𝑡 , 𝑢𝑖𝑡) ≠ 0, the estimator

of interest will be: �̂� = 𝜃 +𝑐𝑜𝑣(𝐷𝑖𝑡,𝑢𝑖𝑡)

𝑉𝑎𝑟(𝐷𝑖𝑡). The direction of OLS inconsistency is determined by the sign of

𝑐𝑜𝑣(𝐷𝑖𝑡 , 𝑢𝑖𝑡). For example, if countries are more likely to have a fiscal rule when performance is good, we have that 𝑐𝑜𝑣(𝐷𝑖𝑡 , 𝑢𝑖𝑡) > 0 and the OLS estimator will over-estimate the true value (asymptotically).

20

(1) In the first stage, we estimate by maximum likelihood a probit model of the

determinants of having a fiscal rule in place and compute the predicted probability FR̂.

The dependent variable is binary, taking value 1 if the fiscal rule is in place and value 0

otherwise. The predicted value of this model is a valid, yet inefficient, instrument for

the fiscal rule.

(2) In the second stage, we regress the fiscal rule on FR̂ and 𝑥𝑖𝑡 using OLS and compute

the fitted values FR̿̿̿̿ . This step ensures the conditional independence assumption that

is crucial to identify in a causal manner the role of rules on fiscal performance (Adams

et al., 2009, Wooldridge, 2002, p. 623).

(3) In the third stage, we regress a fiscal performance variable, 𝐹𝑃𝑖𝑡, on 𝑥𝑖𝑡 and the fitted

values of the second stage, FR̿̿̿̿ . In this stage we control for a number of independent

variables – including institutions, exchange rate and monetary regimes, financial

development, political variables and overall economic development – in order to

isolate the true contribution of rules to the four different measures of fiscal

performance.

This procedure is different from the “pseudo-IV” procedure of running an OLS

regression of 𝐹𝑃𝑖𝑡 on FR̂ and 𝑥𝑖𝑡. In the latter case, consistency is not guaranteed unless the

first stage is correctly specified, and the standard errors need to be adjusted. There are many

advantages of our approach. First, it takes the binary nature of the endogenous variable into

account. Although the two-stage least squares consistency of the second stage does not hinge

on getting the functional form right in the first stage (see Angrist and Krueger, 2001), two-

stage least squares leads to biased estimates in finite samples and it is not known how

misspecification in the first stage may affect this bias. Second, unlike some of the alternative

procedures, it does not require the binary response model of the first stage to be correctly

specified. Third, although some regressors are generated in the first stage, the standard IV

standard errors are still asymptotically valid (Wooldridge 2002).

6 Empirical Analysis I: Instrumenting Fiscal Rules

In the first part of our empirical analysis we generate an instrument for the presence

of fiscal rules (as well as for the other potentially endogenous fiscal institution, sovereign

wealth funds), which we then use to obtain an unbiased estimate the effect of rules on

outcomes. We use annual data for the period 1985-2015. Macroeconomic data tend to be very

and unreliable before 1985, especially for fiscal variables in developing and emerging-market

economies, as a result of changes in definitions and measurement methods, structural

changes, and lack of data. Our sample is restricted to 120 countries but they represent all

world regions (Appendix Table 1 lists sample countries). The total number of observations is

around 2,750, depending on data availability.

21

In the first stage of our methodology, we estimate a probit model for the incidence of

FRs, defined as a binary dummy variable taking value 1 if there is a fiscal rule in place and 0

otherwise:

(16) 𝑃𝑟𝑜𝑏(𝐹𝑅𝑖𝑡 = 1|𝑥𝑖𝑡 , 𝑤𝑖𝑡) = 𝜙(𝜋𝑖 + 𝜌𝑥𝑖𝑡 + 𝑤𝑖𝑡)

where ϕ(·) is the cumulative distribution function for a standardized normal random variable,

𝑤𝑖𝑡 is the vector of instruments, and 𝑥𝑖𝑡 is a vector of other control variables. It is important to

highlight that our IV approach does not require this specification to be correct nor the

estimation to be efficient. It only requires the instruments to be correlated with the

probability of having a fiscal rule in place.

For independent variables in equation (16), the literature suggests several potential

determinants of the likelihood of implementing a fiscal rule. Following the results in Schmidt-

Hebbel and Soto (2017), we group these potential determinants of fiscal rules in five areas:

Institutional Variables

Fiscal rules are likely to be the outcome of particular political regimes and institutions.

By constraining policymakers in the design and execution of the budget, in a way that is

relatively transparent and subject to open monitoring, fiscal rules reflect more transparency,

stronger democratic accountability, less discretion, and less corruption. Therefore, our first

political determinant is a standard measure of political participation or democracy (see

Appendix Table 2 for the sources and definitions), for which we expect a positive sign.

At the constitutional level, the distinction between federal and unitary government is

likely to make a difference in the adoption of fiscal rules and, more importantly, in the type of

rule selected. In federal countries, the fiscal sovereignty of federal governments is weaker

than that enjoyed by central governments in unitary countries. The vast literature on fiscal

federalism attests to the important differences in the conduct and outcome of fiscal policy

between federal and unitary countries (e.g. Feld and Schnellenbach, 2010). We expect federal

governments to be more likely to adopt fiscal rules –in particular, a budget balance rule—than

unitary governments, because they strengthen their bargaining position with respect to the

federated states or provinces. Likewise, federal economies ought to be less likely to adopt a

revenue rule over which the central government has little control. For this reason, we include

a binary dummy variable taking value 1 if there is a federal government and 0 otherwise,

which we label as federalism.

There is evidence suggesting that rules reflect an implicit contract between

governments and voters. In other words, they signal a government commitment to maintain

mutually agreed standards of fiscal discipline (Debrun and Kumar, 2007). Therefore, we

include a measure of political checks and balances.

As the political instability of governments makes it difficult to pre-commit to rules,

FRs are more likely to be adopted and continued over time under conditions of government

22

stability. Hence we include a government stability measure as a potential regressor and expect

a positive effect on the existence of a fiscal rule. We use the International Country Risk Guide

(ICRG) measure which defines government stability as a combination of a government’s

ability to carry out its declared program and its ability to stay in office.

Exchange Rate and Monetary Regimes

Two types of exchange rate regime are likely to affect the adoption of fiscal rules. A

super-hard exchange rate regime in the form of absence of a national currency due to the use

of an international currency as a result of monetary union membership is likely to raise the

likelihood of adoption of supranational fiscal rules that are then implemented at the national

level. This is to reduce the incidence of moral hazard in member countries’ conduct of

sovereign fiscal policy. Therefore, we include monetary union membership as a regressor.

Regarding the connection of fiscal rules and exchange regimes, the literature has

explored mainly the reversed causality, i.e., stemming from fiscal discipline to exchange

regime stability. The conventional view is that pegs provide more fiscal discipline than floats

(Giavazzi and Pagano, 1988 and Frenkel et al., 1991). If governments adopt a lax fiscal policy

under a fixed exchange rate, this would lead to a speculative attack on reserves and,

consequently, result in currency devaluation and a large political cost that the policymaker

would prefer to avoid by imposing discipline on fiscal outcomes. Alternatively, Tornell and

Velasco (2000) stress that, under reasonable conditions (linked to governmental uncertainty

about re-election and lack of access to capital markets), more fiscal discipline is attained

under floats, where fiscal mismanagement leads to devaluation and inflation in the short term.

Under pegs, unsustainable fiscal policy leads to higher debt and lower reserves in the short

term, postponing the costs of devaluation and inflation to the future. Hence we include as a

second exchange rate regime measure a binary variable for a fixed exchange rate regime. We

use a de jure measure taking value 1 if the country has a fixed exchange system and zero

otherwise. Fixed exchange systems include dollarization, currency boards, and monetary

unions. Data was taken from Reinhart and Rogoff (2004) and extended to 2015 using IMF

country reports.6 Considering the preceding arguments, the expect effect of a hard peg of the

currency on the likelihood of having a fiscal rule in place is ambiguous.

Turning now to monetary policy, there is significant theoretical and policy consensus

that the absence of fiscal dominance is a pre-condition for the success of inflation targeting

(see Minea and Villieu, 2009). Fiscal dominance –the need to rely on central bank resources,

ultimately seigniorage– is less likely when a government commits to a fiscal rule. Lucotte

(2012) finds that inflation targeting has a significant positive effect on public revenue

collection in thirteen emerging economies. Hence we include a discrete variable for countries

in which monetary policy is based on an inflation-targeting regime (inflation target). We

expect that an inflation-targeting regime increases the likelihood of having a fiscal rule in

place.

6 We also tested our models using a de facto measure of exchange regime based on observed movements in the nominal exchange rate following Shambaugh (2015) classification. The results are qualitatively similar but the sample is shorter so that we prefer the de jure measure.

23

Capital Account Openness and Financial Development

Financial development could have a positive influence on the likelihood of having

fiscal rules in place through two channels. First, both domestic financial development and

stronger integration into world capital markets increase governments’ access to domestic and

external debt financing and subject governments to closer scrutiny of fiscal sustainability on

the part of financial market analysts and rating agencies. This strengthens the case for

adopting fiscal rules that commit governments to fiscal prudence and solvency. Second, if

domestic financial markets are deeper, and integration into world capital markets is full and

comprehensive, governments will be more likely to access domestic or external funding

during cyclical downturns. This reinforces governmental adoption of fiscal rules that

minimize fiscal pro-cyclicality or strengthen fiscal counter-cyclicality. Therefore, we include

one variable that reflects domestic financial development and another that measures

international financial integration or financial openness as potential determinants of having

fiscal rules in place.

Overall Economic Development

We use real GDP per capita to control for the overall development level. Some studies

focus on the reverse causality, i.e. on the impact of fiscal rules on economic growth (e.g.

Castro, 2011). Here we focus on the causality from the level of development to the likelihood

of having a fiscal rule in place. This hypothesis embodies the stylized fact that governments in

richer economies have more human and financial resources available to undertake the

complex tasks of adopting, complying with, monitoring, and evaluating the operation of a

fiscal rule.

Next we include the population dependency ratio, i.e. the ratio of the population under

15 and over 64 years of age to those falling within the age range 15–64. As the ratio rises, so

does the expected demand for higher government spending on social programs in support of

the young and the elderly (for childcare, education, pensions, and health). This makes it more

attractive for governments to commit to a fiscal rule for prudential reasons (e.g., to avoid

accumulating unsustainable government debts) thereby increasing the likelihood of putting

one in place.

External Fiscal Shocks

One main role of fiscal rules is to smooth out the effect of transient macroeconomic

phenomena from government budgets. We control for the two main external shocks that have

been documented to have significant impact on fiscal variables: international workers’

remittances and natural resource rents. On one hand, evidence indicates that remittances

appear to be strongly procyclical vis-à-vis sending country income and tend to be spent on

consumption of both imported and domestically produced goods, rather than on investment

(Abdih et al., 2012). Therefore, shocks in sending countries are transmitted via remittances to

the public finances –specifically, tax revenues—of receiving countries. On the other hand, in

countries where the resource revenue constitutes a large component of total government

revenues, commodity price fluctuations will have a direct impact on public spending

24

(Bjørnland and Thorsrud, 2015). Many resource-rich countries are therefore advised to adopt

some type of fiscal policy framework (i.e., a fiscal spending rule), which, if operated

countercyclically, should shelter the economy from commodity price fluctuations prevent

over-spending on the part of the government. Consequently, we include in our models both a

measure of natural resource rents and the amount of workers remittances received by a

country (both as share of GDP).

Our econometric models update and extend previous studies by Schmidt-Hebbel and

Soto (2017), Kumar et al. (2009), and Calderón and Schmidt-Hebbel (2008a). We lag the

independent variables in order to avoid simultaneity problems. The results of the estimations

are presented in Table 3. First, note that the null hypothesis of the poolability test is strongly

rejected in all models, indicating that there exists country heterogeneity in the form of

individual effects and that a panel probit model is an adequate specification. Second, the

models present a rather high fit to the observed data, as shown in relatively high pseudo R2

indicators. This would ensure that our instruments are highly –but not perfectly—correlated

to fiscal rules. Third, note that most right-hand side variables are statistically significant and

that the evidence supports the above discussion on the determinants of having a fiscal rule in

place. Naturally, not all variables have a bearing on implementing a particular rule but, when

significant, the estimated parameters have the expected sign. Recall, nevertheless, that when

creating an instrument the only consideration is that right-hand side variables must be

correlated with fiscal rules and that neither the model must be well specified nor the

estimated parameters must have an economic interpretation.

As shown in Table 3, our results reveal that some variables strongly influence the

probability of observing a fiscal rule in place in a country, independently of the type of rule.

Economies that feature lower levels of resource rents, higher population dependency, and

larger financial openness are more likely to have implemented any of the four specific fiscal

rules we consider. Likewise, countries that conduct monetary policy using modern inflation-

targeting schemes or belong to a monetary union are also more likely to have fiscal rules in

place.

Other variables influence selectively the choice of particular fiscal rules. For example,

more developed economies are more likely to adopt expenditure and/or revenue rules, but

not budget balance rules and debt rules. Finally, some variables have opposite effects on the

selecting different rules; for example, federal constitutions raise the likelihood of adopting

budget balance rules and lower the likelihood of adopting revenue rules.

In order to save space, we present the empirical results of our second stage in

Appendix Table 3. In addition to the first-stage fundamentals, the models include the

predicted probability of having a fiscal rule in place, (𝐹�̂�). While results are in general in line

with those reported in Table 3, the estimated parameters for the different 𝐹�̂� are highly

significant, indicating the importance of this second stage for obtaining efficient instruments

of fiscal rules.

Finally, we use the same methodological approach to generate instruments for

another potentially endogenous fiscal institution: sovereign wealth funds. These are state-

25

owned investment vehicles that invest globally in various types of assets ranging from

financial to real to alternative assets. Around one half of the sovereign wealth funds in the

world have been designed and implemented for fiscal stabilization goals. We follow Elbadawi

et al. (2017) to select the appropriate set of fundamentals and generate an instrument for the

presence of external sovereign wealth funds. The detailed discussion and empirical results are

presented in Appendix 1.

Table 3 Stage 1: Probit Regression Models for Fiscal Rules

------------------------------------------------------------------------------------------------------------ National Rules --------------------------------------------------------------------------- Any Rule Any Expenditure Revenue Budget Balance Debt ------------------------------------------------------------------------------------------------------------ Political 0.157*** 0.0873 0.283** 0.707** 0.0113 -0.0390 Participation (3.15) (1.14) (2.24) (2.34) (0.15) (-0.68) Federalism -4.720* 0.780 -0.147 -3.616*** 4.051*** 1.091 (-1.85) (0.60) (-0.13) (-2.82) (3.01) (0.97) Checks and 0.141*** 0.0552 -0.117 0.0576 0.163*** 0.0142 Balances (3.36) (1.44) (-1.51) (0.39) (3.18) (0.14) Government -0.00372 0.0622* -0.00448 0.182* -0.0257 0.00262 Stability (-0.12) (1.73) (-0.11) (1.95) (-0.57) (0.05) Monetary Union 13.79*** 1.273*** 1.165*** 1.923** -3.097* -1.668** (6.23) (4.19) (3.08) (2.21) (-1.81) (-2.13) Fixed Exchange 1.102*** -0.0879 -0.477 0.636 -0.418 1.062*** Regime (3.82) (-0.36) (-1.61) (1.03) (-0.89) (2.77) Inflation Target 1.516*** 1.333*** 0.603* -0.388 1.304*** 1.773*** (5.38) (5.43) (1.89) (-0.61) (4.48) (4.87) Financial 2.334*** 1.387*** 2.486*** -1.629 1.801*** -0.198 Openness (5.98) (3.38) (4.37) (-1.35) (3.14) (-0.40) Financial -0.322* 0.421** 0.0678 1.000** 0.629*** 0.358 Development (-1.69) (2.55) (0.29) (2.14) (2.63) (1.42) Economic 1.834*** 0.559 1.284* 2.315*** 0.208 -0.934** Development (4.84) (1.49) (1.77) (3.23) (0.59) (-2.31) Dependency 22.56*** 27.94*** 17.01*** -10.99 31.08*** 31.84*** Ratio (6.38) (7.14) (4.42) (-1.29) (6.51) (7.77) Resource -4.244*** -1.376*** -0.802 -1.703*** -0.778** -0.0172 Rents (-10.74) (-2.65) (-1.53) (-3.93) (-2.06) (-0.05) Workers 0.343*** 0.0127 0.156 1.012*** 0.163 -0.181* Remittances (3.82) (0.17) (1.39) (2.61) (1.57) (-1.79) Constant -173.4*** -146.0*** -101.6*** -14.63 -150.3*** -131.0*** (-11.12) (-9.97) (-5.25) (-0.47) (-7.94) (-9.54) ------------------------------------------------------------------------------------------------------------ Poolability Test 3.971*** 3.265*** 2.606*** 3.556*** 3.492*** 3.176*** (18.16) (10.42) (4.57) (12.79) (14.08) (13.46) ------------------------------------------------------------------------------------------------------------ Observations 2744 2744 2744 2744 2744 2744 Number of Countries 118 118 118 118 118 118 Pseudo R squared 0.79 0.71 0.62 0.70 0.71 0.69 ------------------------------------------------------------------------------------------------------------ t statistics in parentheses * p<0.1, ** p<0.05, *** p<0.01

26

7 Empirical Analysis II: Assessing the Impact of Fiscal Rules

In the first part of this section we discuss in more detail the measures of fiscal

performance we use in the empirical analysis. In subsequent subsections, we estimate

econometric dynamic panel-data models to evaluate how fiscal rules affect the fiscal stance.

The regression analysis is conducted for around 120 economies over 1985-2015, using

averages of five-year periods to reduce the impact of temporary events. Each country has a

minimum of three and a maximum of seven non-overlapping five-year observations (the

panel is slightly unbalanced due to missing data). A minimum of three observations per

country is required to run the instrumental variable methodology outlined above. Since one

observation is reserved for instrumentation, the first five-year period corresponds to 1990-

1994. The total number of observations is around 430, depending on data availability.

7.1 Measures of fiscal performance

For our empirical analysis we focus on four measures of fiscal performance. Our

selection of measures is determined by data availability and our reviews carried out in

sections 2 and 3. Hence we focus our analysis on two indicators of procyclicality (government

expenditures and fiscal balances) and two of sustainability (fiscal balance and debt).

We measure fiscal procyclicality as the rolling correlation between the cyclical

components of government expenditure and of GDP, both at constant prices. We also use a

similar measure for fiscal balance procyclicality. The underlying data is from World

Development Indicators and the IMF World Economic Outlook database for general

government expenditure, fiscal balance, and GDP. To estimate the cyclical components, we de-

trend all variables using a bandpass filter (Hodrick and Prescott, 1997) and then compute ten-

year rolling correlations of the cyclical components of government consumption and GDP. We

also use alternative windows and periods for computing the rolling correlations (3 and 5

years) to check for robustness.

We measure the government budget balance as a ratio to GDP on annual basis. For

government balances some countries report data for the central government while others

include subnational government units and report data for the general government. We use the

IMF Fiscal Database as the main source for our measure of the fiscal balance of the central

government, supplemented, when necessary, by country data from ECLAC, the African

Development Bank, and the Asian Development Bank.

For government debt we use the IMF’s measure of “General government gross debt”,

comprising all liabilities requiring future payments of interest and/or principal. This measure

(expressed as ratio to GDP) includes debt liabilities in the form of SDRs, currency and

deposits, debt securities, loans, insurance, pensions and standardized guarantee schemes, and

other accounts payable.

27

7.2 Determinants of Fiscal Performance

In order to isolate the contribution of fiscal rules to fiscal performance, we control for

other determinants of performance, as suggested by theory and previous evidence, reviewed

in section 3. Our control variables include other fiscal institutions, political institutions, the

level of development, macroeconomic regimes, and macroeconomic controls (including

cyclical conditions). The fiscal implications of many of these variables were discussed above;

here we keep their description to a minimum.

Access to borrowing by governments

Capital account openness and financial development largely determine the ability of

governments to borrow money in domestic and external markets to fulfill their financial

needs. Measuring access to foreign borrowing is not an easy task but accessing funds and

covering financial needs should be easier in economies with more open capital accounts. We

use the de jure measure of financial openness developed by Chinn and I to (2008),which we

introduced above. Likewise, borrowing from internal sources should be easier in countries

with higher financial development. We proxy the latter using the level of domestic credit to

private sector (as share of GDP), reported by the World Development Indicators. Sovereign

wealth funds, in particular those aimed at stabilizing the fiscal stance, are increasingly popular

and we control in our regressions with the instrumented dummy variable approach discussed

above.

Institutional determinants of fiscal performance

Fiscal performance is co-determined by political conditions. Among the latter we have

include political participation, which we proxy with the Polity2 measure of democracy

compiled by the Polity IV Project 2016. Political budget cycles are recognized in many

democracies, as politicians use fiscal policy for reelection purposes. An alternative would be

to use the ICRG democracy index, which differs in terms of country and time coverage.

Democratic horizontal accountability requires that checks and balances and oversight

mechanisms are in place to exercise control over government budgets. We use as proxy

variable the measure of checks and balances developed by World Bank (2016). This index is a

quantitative measure of the institutional constraints faced by authorities in the operation of

the government. In addition to political variables, we include measures of the perceived

political stability of government, as measured by the ICRG stability index, and expect it to be

positively correlated to fiscal outcomes. Alesina and Tabelini (1990) provide several

explanations for the observation that fiscal performance is worse in unstable countries.

Exchange-rate and monetary regimes

Fiscal policy does not operate in isolation from other macroeconomic policies,

particularly monetary and exchange regimes. A standard result in the literature is that the

stabilizing effects of fiscal policy are higher the more inflexible is the exchange-rate regime.

Therefore we use two variables to address this issue: dummy variables for de jure and de facto

measures of fixed exchange-rate regimes and for membership of a monetary union.

28

Revenue Instability

Lack of revenue forecastability could be one of the reasons behind fiscal procyclicality.

It is difficult for policymakers to predict the exact timing of the business cycle and the

associated levels of revenue collection. Forecasting revenues is also difficult when the tax base

is volatile or when revenue is largely from natural resource rents that on volatile

international prices of commodities. Policymakers determine fiscal policy under a veil of

ignorance about the state of the economy. Talvi and Végh (2005) argue that finance ministers

of all countries tend to underestimate fiscal revenues to avoid political spending pressures. On

the other hand, Frankel (2011) found empirical evidence for 33 advanced and emerging

market countries that official forecasts of the budget balance and GDP growth are overly

optimistic. We proxy this lack of forecastability using a measure of fiscal revenue instability,

namely the three-year rolling coefficient of variation of tax revenue as share of GDP (see

Appendix Table 2).

External Shocks

External shocks impact fiscal performance significantly. We control for three

structural features observed in many developing economies that raise vulnerability of their

public finances to foreign shocks. Worker remittances can induce cycles in public finances via

tax revenue. We also control for the role of natural resource revenues, as well as for the

concentration of exports (as exports are more concentrated, commodity price fluctuations

become relatively more important and can induce significant cyclicality in revenues and fiscal

balances).

Cyclical Conditions

We control for temporary cyclical conditions that affect fiscal performance,

considering three potential causes of fiscal performance: domestic business cycles, resource-

rent cycles, and price instability. We use the measures discussed in the previous section.

Business cycles and price instability (inflation) can influence fiscal outcomes by affecting the

domestic tax base. Resource-rent cycles arising primarily from commodity price fluctuations

and affect several categories of revenue inn commodity-exporting countries.

Overall Development

Overall development level is measured by per capita GDP. The data is from World

Development Indicators (2017). We also include the population dependency ratio. As

discussed above, an important determinant of the dynamics of government expenditures is

the change in the demand for government spending of the young and the elderly, for childcare,

education, pensions, and health.

In the following subsections we present the econometric results. Note that we exclude

models for revenue rules given that only eight countries have implemented such rules and

their experience is not informative for emerging countries since six of them are developed

29

economies.7 Our econometric results are presented as follows: in all cases we report a “base

model” including the variables found to be important determinants of fiscal performance in

previous literature. We next report the results of a “clean model”, i.e, the base model less

those variables that in our sample of counties and years are found to be statistically and

economically irrelevant. We then provide models testing separately the role of three types of

rules: budget balance, debt and expenditure rules. We test the rules separately to avoid

collinearity. We finally extend the models to allow for differential effects in small states and in

Latin American countries.

7.3 Main Results on Procyclicality of Government Expenditures

First we study the role of fiscal rules in reducing the procyclicality of government

expenditures; a negative estimated coefficient would indicate that rules are effective. Table 4

reports the main results for our estimated panel-data, instrumental-variable estimation for

the procyclicality of government expenditures. The results for Model 1 (our base model)

strongly justify our empirical approach. First, there is significant inertia in procyclicality, thus

justifying the use of a dynamic model. Arellano-Bond type tests indicate that after including

two lags, the model is properly specified. The first two lags of the dependent variable are

significant and exhibit opposite signs indicating that the dynamics are highly persistent and

quite cyclical. Our results for the control variables replicate in a more formal environment

those of previous studies. Among the key results we find that:

Countries at higher higher income levels exhibit lower degrees of procyclicality. The

estimated coefficient of -0.335 indicates that the average small state (with a per capita

GDP that exceeds that of larger states, as reported in Table 2) would benefit from

around 40% less procyclicality than the average large economy.

Economies with more stable governments have lower procyclicality. This would

support Rogoff’s (1990) hypothesis that political business cycles can have a

deleterious effect on government finances. The parameter is nevertheless small and

the differential impact of small states vis-à-vis larger economies is negligible.

A more open capital account is associated with higher procyclicality but the

differential effect between small states and larger economies is close to zero since

there are only minor differences in financial openness.

Price instability plays a significant role in inducing higher procyclicality. Halving

annual inflation from 10% to 5% lowers procyclicality by five percentage points,

which is a sizable effect, considering that given that the sample average inflation is

9.9%.

7 Countries that have long-standing revenue rules include Australia (1996-2015), the Netherlands (1992-2015), and Kenya (1997-2015). The rest of the countries, which include Denmark, France, Lithuania, Iran, and Belgium, have had revenue rules for around a decade or less.

30

Reducing instability of government revenue by 50% – for example by adopting

multiannual budgets or implementing stabilization funds – eliminates half of the

observed procyclicality in government expenditures.

Not surprisingly, the business cycle is a key determinant of fiscal procyclicality: the

estimated parameter is large and statistically significant.

We found no effects of resource rents, worker remittances, the exchange regime, and

export concentration on procyclicality, and we therefore drop these variables from the

subsequent analysis.

Models 3 to 5 in Table 4 test for the direct impact of the three fiscal rules on

procyclicality. Estimated parameters for variables other than fiscal rules do not change

significantly (in size or statistical significance) in comparison to the Model 2.

All measures of fiscal rules used here (and in subsequent tables) are the instrumented

or fitted values estimated in stage 2, as reported in section 6 and Appendix Table 3. All

estimated parameters are negative, indicating that rules have stabilizing effects on

government expenditures, reducing procyclicality of government expenditure. However, only

the result for expenditure rules is statistically significant (with a p-value lower than 5%) and

economically large. The latter coefficient estimate suggests that countries implementing an

expenditure rule could reduce expenditure procyclicality by 40%.

Both from our analysis of different subcategories of rules (in Column (A), Table 1) and

the review of the analytical literature (in section 3), we expect that the effects of general rules

(such as our measures of balanced budget, debt, and expenditure rules) on government

expenditure cyclicality is ambiguous. Our results are in line with this expected ambiguity of

general rules: we find that two rules have no effects on expenditure cyclicality, while the third

rule has a significant and large negative impact on cyclicality.

In what follows we focus only on expenditure rules. First we interact the expenditure

fiscal rule with our population dummy (taking value 1 if the country has less than 1.5 million

inhabitants) to test for differential effects of rules in small states. Model 6 reports that the

estimated parameter is insignificant, thus indicating that there are no differential effects of an

expenditure rule for a small state. In Model 7 we test the proposition that the stabilizing

effects of an expenditure rule are different in Latin American economies, compared to other

regions. Our results reject the hypothesis as the parameter turns out to be insignificant.

31

Table 4 Determinants of the Procyclicality of Government Expenditure

------------------------------------------------------------------------------------------------------------------------ Base Model Clean Model BBR Model DR Model ER Model ER SS ER LAC ER SWF

(1) (2) (3) (4) (5) (6) (7) (8) ------------------------------------------------------------------------------------------------------------------------ 1st Lag Expenditure 0.639*** 0.665*** 0.659*** 0.664*** 0.652*** 0.637*** 0.652*** 0.677*** Procyclicality (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 2nd lag Expenditure -0.203*** -0.195*** -0.190*** -0.188*** -0.194*** -0.187*** -0.193*** -0.221***

Procyclicality (0.003) (0.002) (0.003) (0.003) (0.004) (0.005) (0.003) (0.001) Development -0.335** -0.210* -0.208* -0.205* -0.128 -0.132 -0.132 -0.0829 Level (0.028) (0.078) (0.070) (0.067) (0.273) (0.273) (0.248) (0.550) Government -0.0284 -0.0303* -0.0296 -0.0298* -0.0285 -0.0261 -0.0293 -0.0256 Stability (0.110) (0.089) (0.117) (0.096) (0.142) (0.183) (0.128) (0.220) Business Cycles 6.000** 6.800*** 6.860*** 6.910*** 6.593*** 6.358*** 6.568*** 6.074*** (0.010) (0.005) (0.005) (0.004) (0.006) (0.007) (0.007) (0.010) Financial 0.533*** 0.401** 0.406** 0.406** 0.522*** 0.493** 0.534*** 0.565*** Openness (0.002) (0.011) (0.048) (0.022) (0.005) (0.010) (0.003) (0.003) Price 0.812 0.710 0.711 0.730* 0.710 0.697 0.700 0.708 Instability (0.108) (0.102) (0.100) (0.091) (0.111) (0.121) (0.113) (0.101) Revenue 0.912** 0.826** 0.810* 0.808* 0.550 0.509 0.576 0.546 Instability (0.017) (0.046) (0.054) (0.058) (0.188) (0.222) (0.177) (0.196) Exports -0.241 -0.330** -0.325** -0.326** -0.332** -0.332** -0.334** -0.230 Concentration (0.134) (0.031) (0.038) (0.039) (0.035) (0.033) (0.036) (0.157) Workers 0.0470 Remittances (0.151) Fixed Exchange -0.120 Regime (0.225) Resource Rents 0.257 Cycles (0.298) Dependency Ratio 1.124 (0.174) Budget Bal. Rule -0.0537 (0.905) Debt Rule -0.0562 (0.944) Expenditure Rule -1.434** -1.472** -1.362* -1.970** (0.034) (0.031) (0.054) (0.034) Expenditure Rule* 1.469 Small State (0.519) Expenditure Rule* -0.496 LAC (0.694) Sovereign Wealth -0.944* Fund (0.068) Expenditure Rule* 3.634 Sov. Wealth Fund (0.187) Constant -0.115 4.228*** 4.173*** 4.156*** 3.614** 3.649** 3.668** 2.499 (0.973) (0.005) (0.007) (0.006) (0.026) (0.023) (0.025) (0.195) ------------------------------------------------------------------------------------------------------------------------ Observations 408 414 414 414 414 414 414 414 Number of Countries 111 111 111 111 111 111 111 111 Arellano Bond Tests -4.10*** -4.54*** -4.50*** -4.58*** -4.47*** -4.42*** -4.45*** -4.73*** -1.36 -1.59 -1.61 -1.65* -1.37 -1.33 -1.30 -1.27 Sargan Test 20.68 21.55 22.14 22.21 21.84 21.39 21.81 21.07 ------------------------------------------------------------------------------------------------------------------------ Note: Dependent variable is the 10-year rolling correlation of HP-filtered expenditures of general government to HP-filtered GDP. The econometric method used is System Dynamic Panel-data Models with Instrumental Variables. Fiscal rules and Sovereign Wealth Fund are instrumented using the results in section 6. p-values in parentheses. * p<0.1, ** p<0.05, *** p<0.01.

32

Finally, in Model 8 we investigate whether our expenditure rule instrument could be

representing another fiscal institution, namely a sovereign wealth fund. The coefficients of

both the expenditure rule and the sovereign wealth fund are large, negative and statistically

significant, indicating that both fiscal institutions are important in reducing fiscal

procyclicality. Furthermore, the estimated coefficient for the expenditure rule in Model 5 is

smaller than that in Model 8 and the interaction term is insignificant, which leads to conclude

that our initial result (Model 5) on the stabilizing effect of an expenditure rule is robust.

7.4 Main Results on Procyclicality of Fiscal Balance

Now we replicate the previous analysis to assess the procyclicality of the fiscal

balance. Note that here the stabilizing role of fiscal rules would be reflected in an increase in

the cyclicality measure, i.e., the government budget balance ought to increase whenever the

economy expands to be considered a countercyclical policy. The results for Base Model 1 in

Table 5 indicate that there is significant inertia in fiscal balances, as expected. The opposite

signs of the first two lags of the dependent variable reveal the complex dynamics of an

adjustment that is both persistent and cyclical. Again, Arellano-Bond type tests indicate that

two lags must be included to have a properly specified model.

With regards to control variables, the results are the following:

Procyclicality of fiscal balances does not depend on development levels (the estimated

parameter is negative but statistically insignificant). This result invalidates the notion

that poor, small states are in worse condition to manage their fiscal stance.

Countries with high levels of checks and balances on the operation of the government

tend to have a more stable fiscal stance on average (the estimated parameter is

positive and statistically significant). Again this would be in line with Rogoff’s (1990)

political business cycle model, where incumbents cut taxes or increase spending prior

to elections to increase their chance of reelection but are forced to subsequently

increase taxes or cut spending once reelected, thus inducing fiscal procyclicality.

In line with the literature we find a positive association between business cycles and

price instability and the procyclicality of fiscal balances, but the estimated coefficients

are statistically insignificant at 90% confidence. This could be the results of averaging

over five-year periods which dampens the variance of regressors. The positive

parameter estimated for the effect of having a fixed exchange rate on fiscal balances

would be also in line with this explanation because a credible fixed exchange would

lower the depreciation risk and thereby the cost of funding. Higher dependency ratios

are associated with higher procyclicality.

33

Table 5 Determinants of the Procyclicality of Fiscal Balance

------------------------------------------------------------------------------------------------------------------------ Base Model Clean Model BBR Model DR Model ER Model BBR LAC DR LAC ER LAC

(1) (2) (3) (4) (5) (6) (7) (8) ------------------------------------------------------------------------------------------------------------------------ 1st Lag Budg. Bal. 0.522*** 0.487*** 0.471*** 0.458*** 0.489*** 0.477*** 0.465*** 0.477*** Procyclicality (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 2nd lag Budg. Bal -0.164** -0.176*** -0.164*** -0.173*** -0.183*** -0.174*** -0.174*** -0.169*** Procyclicality (0.015) (0.005) (0.009) (0.005) (0.002) (0.004) (0.004) (0.004) Development -0.0344 -0.0252 0.00853 -0.00418 -0.0381 -0.000725 -0.00716 0.0121 Level (0.823) (0.879) (0.956) (0.981) (0.806) (0.996) (0.966) (0.938) Checks and 0.0229** 0.0211** 0.0225** 0.0218** 0.0199* 0.0216** 0.0219** 0.0226* Balances (0.022) (0.030) (0.016) (0.024) (0.089) (0.022) (0.022) (0.062) Financial 0.0955 0.0792 0.0756 0.0732 0.0892 0.0856 0.0869 0.0959* Development (0.164) (0.170) (0.175) (0.201) (0.157) (0.126) (0.134) (0.085) Fixed Exchange 0.250** 0.241** 0.244** 0.243** 0.240** 0.249** 0.248** 0.254** Regime (0.030) (0.032) (0.032) (0.033) (0.029) (0.028) (0.026) (0.023) Revenue -0.750** -0.826** -0.794** -0.813** -0.790** -0.879** -0.852** -0.896** Instability (0.012) (0.027) (0.021) (0.021) (0.028) (0.020) (0.021) (0.015) Dependency Ratio 2.448*** 1.875*** 1.836*** 1.738** 1.864*** 1.500** 1.503** 1.241** (0.000) (0.002) (0.007) (0.015) (0.001) (0.018) (0.031) (0.043) Sovereign Wealth 0.303 0.544* 0.454 0.481 0.454 0.546 0.549* 0.556 Fund (0.468) (0.079) (0.180) (0.121) (0.176) (0.103) (0.098) (0.103) Business Cycles 3.218 (0.157) Price 0.337 Instability (0.341) Financial 0.0580 Openness (0.791) Workers -0.0361 Remittances (0.214) Resource -0.0157 Rents (0.737) Exports 0.132 Concentration (0.407) Budget Bal. Rule 0.239 -0.107 (0.470) (0.789) Budget Bal. Rule* -4.510* Small State (0.072) Debt Rule 0.439 -0.0497 (0.428) (0.936) Debt Rule* -2.147 Small State (0.542) Expenditure Rule 0.374 -0.321 (0.526) (0.614) Expenditure Rule* 5.586 Small State (0.209) Budget Bal. Rule* 0.919 LAC (0.130) Debt Rule*LAC 1.313 (0.144) Expenditure Rule* 2.864** LAC (0.025) Constant -10.75*** -7.735*** -7.856*** -7.339*** -7.653*** -6.436*** -6.400*** -5.516** (0.000) (0.000) (0.000) (0.001) (0.000) (0.003) (0.006) (0.011) ------------------------------------------------------------------------------------------------------------------------ Observations 389 415 415 415 415 415 415 415 Number of Countries 108 110 110 110 110 110 110 110 Arellano Bond Tests -3.71*** -4.09*** -3.95*** -3.93*** -4.18*** -4.13*** -4.08*** -4.12*** 0.20 0.35 0.28 0.27 0.46 0.25 0.29 0.19 Sargan Test 19.39 25.10* 23.86 23.68 23.76 23.08 23.80 22.25 ------------------------------------------------------------------------------------------------------------------------

Note: Dependent variable is the 10-year rolling correlation of HP-filtered fiscal balance to HP-filtered GDP. The econometric method used is System Dynamic Panel-data Models with Instrumental Variables. Fiscal rules and Sovereign Wealth Fund are instrumented using the results in section 6. p-values in parentheses. * p<0.1, ** p<0.05, *** p<0.01.

34

Two additional results are in line with those reported for expenditure procyclicality.

First, we confirm that sovereign wealth funds not only have a stabilizing effect on

expenditures but also on fiscal balances.8 Second, we found that higher instability in

fiscal revenues not only induces procyclicality of expenditures but also the

procyclicality of fiscal balances.

Turning now to the role of rules in the procyclicality of fiscal balances, in Model 3 the

evidence indicates that budget balance rules – which ought to be directly linked to the

stability of the government stance – in fact have a positive but statistically insignificant

estimated coefficient. Likewise, the estimated coefficients for the debt and expenditures rules

in Models 4 and 5 are also positive but statistically insignificant. Note also that the estimated

coefficients for the rest of variables do not change in any manner, suggesting that this result

does not arise from collinearity problems. Our models also test the hypothesis that fiscal rules

in small states would have a differential effect on the procyclicality of the fiscal stance. It can

be seen that all of the parameters are statistically zero, except for budget balance rules which

display a negative estimate, implying that in small economies this type of rule could backfire.

This reinforces the above conclusion that general fiscal rules do not contribute to more stable

fiscal balances and that this effect in general is not conditioned by economic size.

We also find evidence that expenditure rules can have a significant positive effect on

the stability of fiscal balances on the 11 Latin American countries that, at one point or another

of their recent history, have had rules in operation. As shown in Models 6, 7, and 8 of Table 5,

the interactions between our instrumented fiscal rules and a regional dummy for Latin

America (LAC) are all positive but only the coefficient of the expenditure is highly significant.

Leaving aside the issue of statistical significance, note that the point estimates of these effects

are also asymmetrical being expenditure rules more effective in reducing the procyclicality of

fiscal balances than budget balance rules. The evidence on the beneficial effect of rules in

Latin America is entirely derived from large economies (the smallest is Panama with 3.5

million inhabitants) and thus cannot be extended directly to small states. Nevertheless, to the

extent that Latin American economies share similar institutional setups and development

levels, the results is indicative of the potential gains of fiscal rules in this region.

7.5 Main Results on Fiscal Balances

We now turn to the results for our models on the role of fiscal rules in affecting fiscal

balances and sustainability. We deem an economy to be more sustainable the lower is the

fiscal deficit and, therefore, a negative estimated coefficient would indicate that rules are

effective. As shown in Table 6, and in agreement with the previous models, there is a high

level of inertia in fiscal balances. Understandably, adjusting fiscal imbalances –particularly,

high deficits—requires a lot of effort on the part of authorities as well as political muscle, and

it usually takes time. The main results for our standard controls are as follows:

8 There are only nine small economies with a SWF (Bahrain, Brunei, Gabon, Kuwait, Oman, Qatar, Trinidad & Tobago, United Arab Emirates). Interaction dummies are statistically insignificant.

35

We found the standard result in the literature that more stable countries in political

terms as well as those with fixed exchange rates tend to have more conservative fiscal

policies and, on average, higher fiscal balances. However, we found no effects of the

development level itself, indicating that a balanced budget is not a privilege of

advanced economies.

Business and resource-rent cycles play a significant role in affecting fiscal balances,

even if in a transitory manner. We found no significant effects of fiscal revenue

instability on fiscal balances: the latter result is an interesting finding.

The empirical evidence indicates that countries with fixed exchange regimes tend to

have lower fiscal deficits or higher surpluses, a result that could be in part explained

because resource-rich economies (e.g., oil exporters) usually have large fiscal

surpluses and have historically preferred to peg their currencies either to the British

pound or the US dollar.

We find that larger inflows of worker’s remittances deteriorate fiscal balances. This is

in line with the analytical results of Chami et al. (2009) which found that, by

increasing consumption and the revenue base, remittances may allow the government

to carry more debt or incur in more expenditures.

Models 3, 4, and 5 in Table 6 test whether fiscal rules have a positive impact on fiscal

balances: the estimated coefficients for all types of rule are positive and statistically

significant. These econometric results confirm the predictions of our theory, as summarized in

Table 1. According to our theoretical model, all fiscal rules should lower the fiscal deficit or,

conversely, improve the fiscal balance. This is precisely our empirical result. It is, therefore,

directly to conclude that having a fiscal rule in place leads to improve fiscal balances and

thereby supports more sustainable fiscal policies. These models also test for possible effects

of fiscal rules according to the size of the economy; we found negative but statistically

insignificant effects, except for the debt rule.

Models 6, 7, and 8 indicates that there are insignificant differential effects of fiscal

rules in Latin American countries vis-à-vis the rest of the world. For non-Latin American

countries we find that all rules have the expected positive results of improving the fiscal

balance but the opposite result and with similar magnitude – is found for Latin American

countries. The latter are very imprecisely estimated, as shown in relatively low p-values, but

one has to bear in mind that there is a small number of countries in the region with fiscal

rules. One possible explanation is that LA countries had initially imposed budget rules with

strict fiscal surpluses and later had relaxed the requirement, aiming at budgets in balance or

in deficits. This is Chile’s experience, for example. In such case, a negative correlation could be

observed between the budget balance rule and fiscal balances. Alternatively, the rule had not

been binding and credible and had not been obeyed (non-compliance). The available data on

36

de jure fiscal rules preclude us from investigate the issue. However, we return to this issue

below using data on compliance.

Table 6 Determinants of Fiscal Balance

------------------------------------------------------------------------------------------------------------------------ Base Model Clean Model BBR Model DR Model ER Model BBR LAC DR LAC ER LAC (1) (2) (3) (4) (5) (6) (7) (8) ------------------------------------------------------------------------------------------------------------------------ 1st Lag Fiscal 0.376*** 0.508*** 0.526*** 0.515*** 0.513*** 0.542*** 0.510*** 0.503*** Balance (0.006) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 2nd lag Fiscal -0.109* -0.143** -0.131** -0.139*** -0.132** -0.128** -0.138** -0.132** Balance (0.062) (0.013) (0.016) (0.009) (0.019) (0.022) (0.015) (0.027) Development 0.374 -0.0334 -0.524 -0.396 -0.504 -0.677 -0.439 -0.532 Level (0.719) (0.961) (0.419) (0.537) (0.478) (0.341) (0.518) (0.468) Government 0.277** 0.363*** 0.370*** 0.360*** 0.356*** 0.401*** 0.341*** 0.359*** Stability (0.026) (0.003) (0.003) (0.003) (0.004) (0.001) (0.004) (0.004) Fixed Exchange 1.874** 2.591*** 3.088*** 2.723*** 2.611*** 3.052*** 2.559*** 2.605*** Regime (0.018) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Business Cycles 48.65*** 67.70*** 69.09*** 67.15*** 68.05*** 69.08*** 68.28*** 67.91*** (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Cycles in 4.772*** 4.531*** 5.214*** 4.831*** 4.971*** 5.020*** 4.601*** 4.674*** Resource Rents (0.002) (0.005) (0.002) (0.003) (0.002) (0.002) (0.003) (0.003) Price 6.523* 7.588** 8.859** 8.949** 8.490** 9.058** 8.094** 8.246** Instability (0.064) (0.039) (0.021) (0.013) (0.026) (0.033) (0.037) (0.049) Workers -0.316 -0.477** -0.410** -0.503** -0.421* -0.396* -0.479** -0.443* Remittances (0.104) (0.036) (0.048) (0.019) (0.051) (0.098) (0.041) (0.063) Dependency Ratio 0.364 (0.961) Exports 0.249 Concentration (0.788) Revenue -3.020 Instability (0.269) Financial 0.490 Openness (0.709) Budget Bal. Rule 5.595** 7.387** (0.031) (0.014) Budget Bal. Rule* -21.05 Small State (0.103) Debt Rule 7.985** 9.890** (0.022) (0.015) Debt Rule* -39.83** Small State (0.044) Expenditure Rule 6.774* 6.617 (0.095) (0.136) Expenditure Rule* -20.64 Small State (0.524) Budget Bal. Rule* -8.394 LAC (0.139) Debt Rule*LAC -11.83 (0.126) Expenditure Rule* -2.272 LAC (0.832) Constant -11.73 -5.644 -2.446 -3.237 -2.251 -1.368 -2.681 -2.053 (0.689) (0.347) (0.661) (0.567) (0.705) (0.823) (0.657) (0.744) ------------------------------------------------------------------------------------------------------------------------ Observations 406 477 477 477 477 477 477 477 Number of Countries 113 121 121 121 121 121 121 121 Arellano Bond Tests -1.15 -2.88*** -2.90*** -2.95*** -2.90*** -2.94*** -2.89*** -2.79*** 0.07 -0.56 -0.60 -0.52 -0.60 -0.68 -0.48 -0.61 Sargan Test 63.92 49.85 50.62 47.70 57.10 49.59 44.68 48.28 ------------------------------------------------------------------------------------------------------------------------ Note: Dependent variable is fiscal balance (as share of GDP). The econometric method used is System Dynamic Panel-data Models with Instrumental Variables. p-values in parentheses. Fiscal rules are instrumented using the results in section 6. * p<0.1, ** p<0.05, *** p<0.01

37

7.6 Main Results on Government Debt

Our last set of results assesses the effects of fiscal rules on the stock of government

debt, which we collect in Table 7. Clearly, a negative estimated coefficient would indicate that

rules are effective in reducing debt levels and, thereby, in improving fiscal sustainability. It

can be seen that, contrary to the rest of fiscal outcome indicators, we have included only one

lag of the dependent variables as the dynamics in this case are simpler: there is significant

persistence, as is expected from a slow adjusting stock variable, but no evidence of cycles

whatsoever. Given that variable of interest is expressed as share of real GDP, debt levels

indirectly indicate debt sustainability: since tax revenue is positively correlated with GDP

levels, a lower level of debt to GDP ought to be easier to service and, therefore, more

sustainable.

With regards to the standard set of controls used in this paper, the following elements

stand out:

Development levels do not appear to correlate in any form to government debt.

However, financial development levels are positively associated with debt levels,

perhaps reflecting that when domestic financial markets are deeper, the government

finds it easier or less expensive to recourse to debt.

We found that countries endowed with more natural resources do not seem to carry

more debt. Although there are examples of countries which have mismanaged natural

resource revenues (mainly during the oil boom of the 1970s), our evidence is

inconsistent with the notion of “boom based borrowing” and “debt overhang” as

discussed by Manzano and Rigobon (2007).

Higher government stability is associated with lower government indebtedness, as

indicated by the estimated negative parameter, which is only marginally insignificant

but in subsequent regression shows up invariably significant.

A higher dependency ratio is very negatively associated to debt levels.

As expected, higher inflation levels tend to be associated with lower debt levels for

two reasons. First, governments can substitute inflationary tax for debt. Second,

governments find it costlier to finance debt in high inflation environments. We also

obtain the customary results that debt levels are lower in countries with fixed

exchange regimes.

We now examine the results for fiscal rules in Table 7. Models 3, 4, and 5 test for the

direct effect of fiscal rules: all estimated parameters are insignificantly different from zero.

This would indicate that having a debt rule in place is not tantamount to debt control or

improved sustainability, an issue to which we return below. In Model 3 we obtain a negative

and significant differential effect of a budget balance rule on debt levels in small states,

suggesting that small economies benefit from such rule. We found no differential effects of

38

expenditure and debt rules in Latin America countries vis-à-vis the rest of the world, but

found that budget balance rules tend to be associated with higher debt levels in Latin

American economies.

Table 7 Determinants of Government Debt

------------------------------------------------------------------------------------------------------------------------ Base Model Clean Model BBR Model DR Model ER Model BBR LAC DR LAC ER LAC (1) (2) (3) (4) (5) (6) (7) (8) ------------------------------------------------------------------------------------------------------------------------ 1st Lag Government 0.688*** 0.709*** 0.720*** 0.684*** 0.714*** 0.709*** 0.696*** 0.717*** Debt (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Development 8.027 7.185 5.105 9.623 8.738 8.309 9.659 8.766 Level (0.217) (0.245) (0.411) (0.197) (0.207) (0.152) (0.151) (0.222) Government -0.759 -0.771 -0.871 -0.718 -1.055 -0.987 -0.491 -0.991 Stability (0.604) (0.522) (0.466) (0.579) (0.464) (0.398) (0.704) (0.480) Dependency Ratio -167.7* -189.1** -161.0** -202.3** -189.3*** -179.5** -203.0*** -188.7*** (0.053) (0.012) (0.032) (0.015) (0.007) (0.012) (0.008) (0.007) Financial 18.38** 20.77*** 20.22*** 20.61*** 19.57*** 20.52*** 20.88*** 19.63*** Development (0.020) (0.000) (0.000) (0.002) (0.002) (0.000) (0.001) (0.002) Price -271.3** -263.8*** -251.2** -281.2*** -267.8** -261.4*** -258.5** -263.7*** Instability (0.013) (0.007) (0.015) (0.008) (0.010) (0.006) (0.012) (0.009) Fixed Exchange -19.67** -19.77*** -19.16** -20.11*** -19.38*** -20.86*** -19.40*** -19.68*** Regime (0.026) (0.007) (0.016) (0.009) (0.009) (0.007) (0.010) (0.009) Financial -24.08* -26.05** -16.86 -31.15*** -22.20 -26.17** -31.39*** -23.31 Openness (0.070) (0.029) (0.186) (0.007) (0.139) (0.022) (0.007) (0.101) Resource -0.975 Rents (0.744) Workers -1.005 Remittances (0.658) Exports 8.844 Concentration (0.296) Budget Bal. Rule -11.85 -35.21 (0.697) (0.270) Budget Bal. Rule* -256.8*** Small State (0.003) Debt Rule 19.35 3.420 (0.658) (0.945) Debt Rule* 114.0 Small State (0.616) Expenditure Rule -25.49 -28.18 (0.476) (0.430) Expenditure Rule* -117.0 Small State (0.630) Budget Bal. Rule* 65.76* LAC (0.099) Debt Rule*LAC 80.26 (0.189) Expenditure Rule* 22.73 LAC (0.847) Constant 654.1* 794.7*** 691.3** 832.2*** 783.9*** 750.0*** 831.4*** 781.0*** (0.051) (0.004) (0.012) (0.005) (0.002) (0.004) (0.003) (0.002) ------------------------------------------------------------------------------------------------------------------------ Observations 366 390 390 390 390 390 390 390 Number of Countries 116 116 116 116 116 116 116 116 Arellano Bond Tests -1.23 -1.14 -1.22 -1.12 -1.14 -1.17 -1.11 -1.16 Sargan Test 27.51 32.09 31.58 34.00 30.91 30.26 33.99 31.03 ------------------------------------------------------------------------------------------------------------------------ Note: Dependent variable is Government Gross Debt (as share of GDP). The econometric method used is System Dynamic Panel-data Models with Instrumental Variables. p-values in parentheses. Fiscal rules are instrumented using the results in section 6. * p<0.1, ** p<0.05, *** p<0.01

39

Three considerations on the role of general de jure fiscal rules are important to

highlight at this point. First, having any general fiscal rule in place does not ensure that it will

contribute to a particular fiscal policy objective. In the absence of data on particular sub-types

of rules, the contribution of general rules to macroeconomic stabilization is uncertain. Second,

having a fiscal rule in place does not necessarily imply that such rule is optimally designed or

even appropriate for a country in a particular situation. For example, fiscal rules imposing

non-binding restrictions or with non-credible design (e.g., without properly designed escape

clauses) would have no positive effects (and perhaps even negative effects) on fiscal

outcomes. Unfortunately, our dataset does not allow us to address this important issue and

our econometric models implicitly assume that de jure rules are well designed. Third, having a

rule in place does not necessarily mean that it is properly enforced by the authorities.

Countries may choose to bypass or ignore altogether de jure fiscal rules, implying partial or

full non-compliance. This leads to exploring the empirical relevance of compliance of

countries in their implementation of de jure rules, which we do in the following section.

8 De Facto and de Jure Rules

In this section we first address the issue of de jure vs. de facto fiscal rules. Two

practical considerations arise when using measures of compliance. First, we use the slightly

more restricted data on fiscal rule compliance collected by Belinga et al. (2017) in our

empirical models; out of the more than 80 countries with de jure national fiscal rules in 2015

only around 70 countries have data on compliance or de facto rules. Second, the data is likely

to be noisy since it corresponds to the assessment made by country economists and other

experts on the degree of compliance of the rules in any given country and for every year in the

sample. Noise inevitably arises because the data corresponds to the judgment of different

individuals with varying degrees of information and, in most cases, it would be memory-based

or the product of ex-post analysis rather than direct witnessing of events. Data on compliance

has been coded as 1 if there was compliance on any given year and zero otherwise. The latter

would include countries without fiscal rules as well as those that do not comply with the rules

they chose to implement.

We replicate the models in Tables 4 to 7 using the data on compliance instead of de

jure rules. We include in our models both the rules by themselves as well as in interaction

with the dummy variable for Latin America, the latter interacted only for budget balance and

expenditure rule compliance since there is no data for debt compliance in Latin American

economies. We also include the dummy for small states. The results are presented in Table 8,

where we omit the results for standard controls for space reasons and focus only on the de

facto rules. Column 1 presents the result on expenditure procyclicality. It can be seen that all

de facto rules and the small-state dummies are statistically insignificant. The result is very

similar to that of de jure rules, except that in the latter the expenditure rule was significantly

counter-cyclical. With regards to the procyclicality of fiscal balance, we found that de facto

rules have no direct impact, as was the case of de jure rules: estimated parameters are

statistically insignificant (see Column 2). Likewise, the interaction between de facto rules and

40

small-state dummies are also statistically insignificant, suggesting that small-size economies

are not intrinsically different than their larger counterparts vis-à-vis procyclicality.

Table 8 Estimated Results of Models using Fiscal Rule Compliance

------------------------------------------------------------------------------------------------------------------------ Exp. Pro. Bal. Pro. F. Balance Debt Exp. Pro. Bal. Pro. F. Balance Debt (1) (2) (3) (4) (5) (6) (7) (8) ------------------------------------------------------------------------------------------------------------------------

Standard Controls Omitted Compliance with -0.0340 0.192 3.099*** -9.704* -0.0569 0.223 2.971*** -9.917 BR (0.766) (0.113) (0.000) (0.097) (0.689) (0.102) (0.000) (0.101) Compliance with 0.245 -0.210 -1.595* -7.610 0.256 -0.258* -1.462* -4.269 DR (0.388) (0.200) (0.074) (0.343) (0.367) (0.095) (0.081) (0.622) Compliance with 0.00854 0.173 1.110 12.33 -0.0732 0.266* 0.208 4.771 ER (0.958) (0.265) (0.232) (0.284) (0.676) (0.061) (0.836) (0.562) Compliance with -0.200 -0.0356 3.011 -41.76 BR*Small State (0.592) (0.915) (0.358) (0.372) Compliance with 1.302 -161.4 DR*Small State (0.905) (0.413) BR Compliance* 0.131 -0.0969 1.032 5.799 LAC (0.568) (0.616) (0.443) (0.483) ER Compliance* 0.550 -0.566 3.466 100.1*** LAC (0.189) (0.382) (0.155) (0.000) DR Compliance* -2.117 -5.442 LAC (0.166) (0.711) Constant 2.749 -7.539*** -4.220 562.4 2.594 -8.445*** -2.341 625.3 (0.160) (0.001) (0.500) (0.199) (0.201) (0.000) (0.725) (0.154) ------------------------------------------------------------------------------------------------------------------------ Observations 414 415 477 279 414 415 477 279 Number of Countries 111 110 121 115 111 110 121 115 ------------------------------------------------------------------------------------------------------------------------ Note: The econometric method used is System Dynamic Panel-data Models with Instrumental Variables. Fiscal rules and

Sovereign Wealth Fund are instrumented using the results in section 6.p-values in parentheses, * p<0.1, ** p<0.05, *** p<0.01

Column 3 of Table 8 collects the results for the models testing the effects of de facto

rules on fiscal balances. It can be seen that compliance with the budget balance rule actually

improves significantly the fiscal stance, as expected. This effect was not visible when using the

de jure rule, and it is a significant contribution of the new dataset. The de facto expenditure

rule has no discernible effect on the fiscal stance, but the debt rule has a unexpected negative

correlation to the fiscal balance. Note that because we cannot instrument compliance, we can

at best analyze our econometric results in terms of correlations and not of causality, as

discussed in section 5. The last set of results on compliance refer to the effect of rules on

government debt and are presented in column 4 of Table 8. Again, the debt rule even if

complied has no effects in lowering debt level and, on the contrary, it is the compliance with a

budget balance rule lowers the debt by a sizable amount.

We extend our analysis to test for differential effects of the facto rules on fiscal

outcomes in Latin American economies and present the results in the last four columns of

Table 8. It can be seen that de facto rules and the LAC dummy are statistically insignificant in

both procyclicality models (expenditures and fiscal balances) thus leading to the conclusion

that LAC economies are not different than other countries of the world in this regard.

Likewise, our results for fiscal balances do not change with the inclusion of the LAC

41

interaction, leading to the conclusion that there are no regional asymmetries. Finally, we

disregard the estimated model for the government debt on the grounds that there is not

enough density for ER in LAC, even if the parameters appears to be highly significant.

9 Conclusions

This paper presents world evidence on the contribution of fiscal rules to fiscal

performance. The paper starts by identifying the links between fiscal institutions, rules, and

performance measures and reviewing the relevant analytical and empirical literature on fiscal

performance, and on the potential contribution of different fiscal rules to performance. Our

analysis indicates that the impact of rules on fiscal outcomes (balance, debt, and

procyclicality) have three defining characteristics: (a) they tend to display high persistence,

thereby requiring the use of dynamic models, (b) countries are highly idiosyncratic vis-à-vis

unobservable features in their tax and revenue structures, prompting the existence of

individual country effects, and (c) there may be reverse causation and endogeneity, since

governments may decide to implement rules as a result of their fiscal stance.

Our methodology assumes that unveiling the impact of rules on fiscal performance can

be casted as the study of whether a treatment (implementing a fiscal rule) has had any

discernible effect – the treatment effect – on fiscal performance. Identifying causal effects

from observational data is often quite difficult as it depends crucially on the validity of the

implicit or explicit identification assumptions. In our context of panel data, this would indicate

the need of properly specifying the determinants of fiscal performance as well as controlling

for the potential endogeneity of the treatments. Furthermore, modelling the treatment is not

as straightforward as it might seem as it requires separating the determinants of fiscal

outcomes from those that led to the enactment of such rules. We use standard dynamic panel

models to address the first two characteristics. In order to avoid our results to be hampered

by endogeneity running from fiscal outcomes to treatments, we complement dynamic panel

data techniques with a novel approach to building a consistent instrument for the choice of

implementing a fiscal rule.

Our conclusions on the role of fiscal rules on performance are based on two

considerations. First, the inherent differences between small states and larger economies in

terms of economic structure and size, and the external shocks they face. Second, the

econometric evidence obtained for a group of 115 economies in the period 1980-2015

regarding the impact of rules on four dimensions of fiscal performance: government

expenditures procyclicality, fiscal balance procyclicality, fiscal balances and government debt.

Using the World Bank definition of a small state (population below 1.5 million) we

found that they have higher development levels but enjoy more government stability, even if

political checks and balances are slightly less developed. We also confirm that small states are

significantly more open to foreign trade, their export base is less diversified, and that natural

resources are less important. Small states are equally open in terms of financial markets but

tend to prefer fixed exchange rate systems despite receiving much larger inflows of worker

42

remittances. Despite the abovementioned differences, large and small states share similar

business cycles, inflation levels, and even similar levels of tax revenue instability. In summary,

small states share only a few characteristics with larger economies so that one should expect a

potentially different impact of fiscal rules according to economic size.

One general conclusion from the econometric evidence is that there is significant

inertia in fiscal balances, with complex dynamics and adjustments paths that are both

persistent and cyclical. Therefore, the impact of policy shocks as well as of implementing fiscal

rules or any other fiscal institution takes a relatively long time to materialize, be it in the form

of reduced procyclicality or higher sustainability. Should small states in Latin America decide

to implement fiscal rules – and should these rules be properly designed – the beneficial

results would accrue only in the medium to long run.

A second general conclusion from our evidence is that, while fiscal rules are important

instruments to improve the fiscal stance of an economy, other variables can be equal or more

important determinants of fiscal performance. Elements that improve the fiscal performance

of a country include having a properly designed and functioning system of checks and

balances in the government; achieving higher levels of political stability; fostering the

integration of the economy with globalized financial markets; and maintaining lower and

stable levels of inflation. The flip-side of these results indicate that absent these elements, the

benefits of implementing fiscal rules may be severely hampered.

A third general conclusion is that fiscal institutions other than rules also play an

important role in reducing procyclicality and/or increasing sustainability. We found that

reducing the instability of government revenues would eliminate a significant portion of fiscal

procyclicality in government expenditures and of fiscal balances. Among the alternatives

policies to stabilize revenues are, for example, adopting multiannual budgets, diversifying the

tax base away from natural resource exports or tourism, and implementing revenue-

stabilization funds. By themselves, these measures would certainly improve matters in a cost-

effective manner; implemented in conjunction with appropriate fiscal rules their impact

would be much enhanced.

A fourth general conclusion is that cyclical phenomena are quite important

determinants of the fiscal stance and, most likely, they should be dealt with outside the realm

of fiscal rules. We found that business cycles, resource cycles induced by commodity price

fluctuations, and changes in workers’ remittances have a deleterious impact on fiscal

performance. Dealing with these elements would call for instruments better equipped to deal

with transitory shocks than fiscal rules which tend to focus on medium-to-long term goals

(e.g., structural balance or debt targets) and use rather inflexible instruments such as annual

budgets. In this regard, we found that sovereign wealth funds have an important stabilizing

effect on fiscal balances and are instrumental in reducing procyclicality.

Turning now to the evidence regarding the role of fiscal rules we found that there are

some important differences in terms of effectiveness between de jure and de facto rules. The

former refer to a rule that imposes a long-lasting constraint on fiscal policy through numerical

limits on budgetary aggregates. Such constraints can be easily identified and measured;

43

furthermore, an instrumental variables procedure is used in our study to avoid biasing or

estimation as a consequence of endogeneity. Of course, compliance with such constraints is an

entirely different matter: the de facto measure of fiscal rules collected by Belinga et al. (2017)

and used in our study provides important information regarding the actual operation of the

rules but its measurement based on experts’ opinions is rather subjective.

With regards to de jure rules, a descriptive summary of results is found in the

following table. It can be seen that two rules deliver their intended primary result:

expenditure rules reduce expenditure procyclicality and budget balance rules improve fiscal

balances. Debt rules, on the contrary, do not affect debt levels in a significant manner. One

explanation for this result is that debt rules are only binding from above when indebtedness

reaches a certain target, prior to that level the rule is ineffective. Also no rule has a direct

impact on the procyclicality of fiscal balances, perhaps because rules target either one

component (expenditures) or the level of fiscal deficit and in achieving any of such targets

might induce volatile instead of controlling it. We also find that debt and budget balance rules

do not have effects outside their primary target, but expenditure rules not only reduce fiscal

procyclicality but also improve on fiscal balances.

To a large extent, the effectiveness of fiscal rules in achieving their intended objective

is independent of economic size except for a few cases: small states seem to benefit more in

terms of improved sustainability from budget balance and debt rules. We also found evidence

that, while an expenditure rule might not reduce expenditures procyclicality differently in

Latin America than in the rest of the world, it can act as a stabilizing tool for fiscal balances.

When studying the same issues but using de facto instead of de jure fiscal rules the

econometric results do not change significantly and offer only one additional insights, namely

that compliance with expenditure rules might allow Latin American countries higher levels of

government debt.

Table 10 Summary of Results

Fiscal Outcomes Budget Balance Rule Debt Rule Expenditures Rule

de jure de facto de jure de facto de jure de facto

Procyclicality of government expenditures

Is there any effect on procyclicality? No No No No Reduced Reduced Small states are more/less procyclical? No No No No No No LAC countries are more/less procyclical? No No No No No No Procyclicality of fiscal balances

Is there any effect on procyclicality? No No No No No No Small states are more/less procyclical? More No No No No No LAC countries are more/less procyclical? No No No No Less No Fiscal Balance

Do fiscal balances improve? Yes Yes Yes Yes Yes No Small states have higher/lower balances? No No Lower No No No LAC countries have higher/lower balances? No No No No No No Government debt

Is debt reduced? No No No No No No Small states have higher/lower debt? Lower No No No No No LAC countries have higher/lower? No No No No No Higher

44

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Appendices

Appendix Table 1 Country List

Albania El Salvador Kuwait Qatar

Algeria Estonia Latvia Romania

Argentina Ethiopia Lebanon Russian Federation

Armenia Finland Libya Saudi Arabia

Australia France Lithuania Senegal

Austria Gabon Luxembourg Serbia

Azerbaijan Gambia, The Madagascar Seychelles

Bangladesh Germany Malawi Sierra Leone

Belarus Ghana Malaysia Slovak Republic

Belgium Greece Mali Slovenia

Bolivia Guatemala Mexico South Africa

Botswana Guinea Moldova Spain

Brazil Guinea-Bissau Morocco Sri Lanka

Bulgaria Haiti Mozambique Suriname

Burkina Faso Honduras Namibia Sweden

Cameroon Hong Kong Netherlands Switzerland

Canada Hungary New Zealand Syria

Chile Iceland Nicaragua Tanzania

China India Niger Thailand

Colombia Indonesia Nigeria Togo

Congo, Dem. Rep. Iran, Islamic Rep. Norway Trinidad & Tobago

Congo, Rep. Ireland Oman Tunisia

Costa Rica Israel Pakistan Turkey

Cote d'Ivoire Italy Panama Uganda

Croatia Jamaica Papua New Guinea Ukraine

Cyprus Japan Paraguay United Kingdom

Czech Republic Jordan Peru United States

Denmark Kazakhstan Philippines Uruguay

Dominican Republic Kenya Poland Venezuela

Ecuador Korea, Rep. Portugal Vietnam

Egypt Yemen

Appendix Table 2 Definitions and Sources of Variables used in Regression Analysis

Variable Definition and construction Source

Dependency Ratio

Population between 15 and 64 years of age as

share of total population

World Bank World Development

Indicators

50

Exchange Rate Regime Dummy variable taking value 1 if the country

is dollarized, has a currency board, or belong s

to a monetary union. Any other system is not

considered as a fixed regime.

Reinhart and Rogoff (2004) de jure

classification, extended using IMF

country reports.

Export Concentration Herfindahl’s index of 26 exported goods

categories, measured annually in US dollars,

using data from UN COMTRADE database.

Soto (2016)

Federalism Dummy variable = 1 if the country defines

itself formally as a federal entity.

Information from Forum of

Federations web page.

Financial Development Domestic credit to private sector (% of GDP World Development Indicators by

the World Bank

Financial Openness Index based on the binary dummy variables

that codify the tabulation of restrictions on

cross-border financial transactions reported in

the IMF's Annual Report on Exchange

Arrangements and Exchange Restrictions

Chinn and Ito database (2014).

Fiscal Balance General Government primary balance Databases of the IMF, World Bank,

ECLAC, OECD, Asian Development

Bank and African Development

Bank

Fiscal Rules A fiscal rule is defined as a permanent

constraint on fiscal policy through simple

numerical limits on budgetary aggregates.

IMF Fiscal Rules Dataset, 1985-

2015.

Government Gross Debt All liabilities that require payments of interest

and/or principal by the debtor to the creditor

at a date or dates in the future.

IMF World Economic Outlook

Database

Government Revenue

Instability

Three-year rolling coefficient of variation of

real government revenue (including taxes,

social contributions, grants receivable, and

other revenue).

Databases of the IMF, World Bank,

ECLAC, OECD, Asian Development

Bank and African Development

Bank

Government Stability Assessment of a government’s ability to carry

out its declared program and stay in office.

The risk rating is the sum of three

subcomponents (government unity, legislative

strength and popular support).

International Country Risk Guide

database, from PRS Group.

Inflation Measured by the log change of the consumer

price index or GDP deflator.

World Development Indicators by

the World Bank

Inflation Targeting Dummy variable: 1 if the central bank operates

formally an inflation targeting scheme, and 0

otherwise.

Central Bank News webpage and

database.

Monetary Union Binary variable that takes value 1 if the

country formally belongs to a monetary union

and 0 otherwise.

Own elaboration.

Natural Resource Rents Rents are the difference between the value of

production for a stock of minerals at world

prices and their total costs of production.

World Development Indicators by

the World Bank

Political Accountability Checks and balances indicators Cruz et al. (2016)

Political Participation Democracy and Polity2 indices of the Polity IV

database

Developed by Integrated Network

for Societal Conflict Research

(INSCR).

Procyclicality of Fiscal

Balance

Ten-year rolling correlation of HP-filtered

fiscal balance to HP-filtered GDP (both in

constant 2000 US$).

Databases of the IMF, World Bank,

ECLAC, OECD, Asian Development

Bank and African Development

Bank

51

Procyclicality of Government

Expenditures

Ten-year rolling correlation of HP-filtered

expenditures of general government to HP-

filtered GDP (both in constant 2000 US$).

Expenditure consists of total expense and the

net acquisition of nonfinancial assets.

IMF Government Finance Statistics

and World Economic Outlook

Databases.

Real per capita GDP Ratio of total GDP to total population. GDP is in

1985 PPP-adjusted US$. Growth rates are

obtained from constant 1995 US$ per capita

GDP series.

World Development Indicators by

the World Bank

Remittances Workers' remittances and compensation of

employees, received.

World Development Indicators by

the World Bank

Sovereign Wealth Funds Dummy variable taking value 1 if the country

has an international sovereign wealth fund,

and 0 otherwise.

Own elaboration.

Terms-of-trade shocks Log difference of the terms of trade. Terms of

trade are defined as customary.

World Development Indicators by

the World Bank

Trade Volume Exports + Imports (as share of GDP) World Development Indicators by

the World Bank

52

Appendix Table 3 Stage 2: Linear Regression Models for Fiscal Rules

------------------------------------------------------------------------------------------------------------ National Rules --------------------------------------------------------------------------- Any Rule Any Expenditure Revenue Budget Balance Debt ------------------------------------------------------------------------------------------------------------ Political 0.0153*** 0.00539** 0.00111 0.000972 0.000172 0.000513 Participation (5.30) (2.00) (0.58) (0.79) (0.08) (0.26) Federalism 0.0105 0.0799*** 0.00295 -0.00755 0.134*** 0.128*** (0.45) (3.77) (0.19) (-0.78) (7.14) (8.48) Checks and 0.0192*** 0.00623 0.0104*** 0.00690*** -0.000743 -0.00235 Balances (3.90) (1.36) (3.14) (3.30) (-0.19) (-0.71) Government -0.00137 0.00356 -0.000610 0.00148 -0.00299 0.00294 Stability (-0.37) (1.04) (-0.25) (0.94) (-1.04) (1.19) Monetary Union 0.378*** 0.0256 0.0312* 0.0627*** -0.0377** 0.00388 (13.13) (1.11) (1.89) (6.17) (-2.03) (0.24) Fixed Exchange 0.112*** -0.0318* 0.00772 -0.0142* -0.0647*** 0.00634 Regime (5.67) (-1.72) (0.58) (-1.70) (-4.21) (0.48) Inflation Target 0.192*** 0.194*** 0.0764*** -0.00119 0.158*** 0.126*** (8.40) (9.09) (5.09) (-0.13) (9.06) (8.35) Financial 0.361*** 0.302*** 0.0907*** 0.0426*** 0.218*** 0.0639*** Openness (14.21) (12.65) (5.17) (3.96) (11.03) (3.77) Financial -0.0192* 0.0176 -0.0249*** 0.0159*** 0.0183** 0.0271*** Development (-1.67) (1.63) (-3.22) (3.24) (2.05) (3.51) Economic 0.0201** -0.00229 0.0374*** 0.00469 -0.0268*** -0.0511*** Development (1.97) (-0.24) (5.39) (1.09) (-3.41) (-7.51) Dependency 0.248** 0.134 -0.129* -0.100** 0.202** 0.481*** Ratio (2.26) (1.34) (-1.81) (-2.22) (2.45) (6.51) Resources -0.0500*** -0.0313*** -0.0247*** -0.00249 -0.00261 0.0234*** Rents (-5.99) (-4.75) (-5.49) (-0.92) (-0.53) (5.56) Workers -0.000497 -0.0195*** -0.00537* 0.000757 -0.0147*** 0.000597 Remittances (-0.11) (-4.69) (-1.81) (0.40) (-4.28) (0.20) Any rule -0.0661** (instr.) (-2.10) Any national -0.0685** Rule (instr.) (-1.98) Exp. Rule 0.181*** (instr.) (3.71) Revenue Rule 36.65*** (instr.) (13.42) Budget Balance -0.0911** Rule (instr.) (-2.08) Debt Rule -0.260*** (instr.) (-2.82) Constant -1.982*** -1.009*** -0.150 0.316* -0.627** -1.251*** (-4.55) (-2.67) (-0.57) (1.91) (-2.06) (-4.59) ------------------------------------------------------------------------------------------------------------ Observations 2744 2744 2744 2744 2744 2744 Number of Countries 118 118 118 118 118 118 ------------------------------------------------------------------------------------------------------------ t statistics in parentheses * p<0.1, ** p<0.05, *** p<0.01

53

Appendix 1 Generating Instruments for Sovereign Wealth Funds

Following the results of Elbadawi et al. (2017) we first estimate a probit model for a

binary variable taking value 1 if country “I” has a sovereign wealth fund in place at time “t”

and zero otherwise. The literature suggests the existence of several potential determinants of

the likelihood of accumulating net foreign assets in the form of sovereign wealth funds (Beck

and Fidora, 2008).

The first area relates to export proceeds. Earlier commodity price booms illustrate the

adverse effect on competitiveness of large real appreciations induced by using these windfall

gains for domestic expenditures, particularly when the gains are transitory (Céspedes and

Velasco, 2012). Consequently, some countries have sought to deal with these concerns by

saving a share of the gains in SWFs. We thus control for the level of foreign trade (as share of

total GDP), export concentration (measured by the Herfindhal index, as computed by Soto

2016), and the size of natural resource rents (from the World Bank Database).

The second area of relates to macroeconomic stance and general development level.

More developed countries, on average, tend to have better macroeconomic management

which, more often than not, include policies of accumulating foreign reserves. Diversification

of these reserves into potentially higher-yielding assets have usually entailed transferring

them from the control of the central bank to the Treasury or to quasi-public entities, such as

SWFs, with the mandate to pursue financial strategies aiming at higher long-run returns

(Aizenman and Glick, 2009). We control for development levels using real per capita GDP in

US$. We also control more directly for macroeconomic mismanagement using the inflation

rate as a measure of price instability.

A third group of factors behind the growth of SWFs relates to government revenue

structures and, particularly, tax revenue instability. A large number of SWFs had been set up

to insulate the budget and economy from commodity price volatility and external shocks.

Their investment horizons and liquidity objectives resemble central banks reserve managers,

in view of their role in countercyclical fiscal policies to smooth boom/bust cycles. We control

for government revenue instability using the coefficient of variation of tax revenues computed

as a rolling three-year. We also control for the fact that countries with federal structures may

find it more appealing –and perhaps more challenging—to set up a SWF as a way to increase

independence of the federal government from the subnational units. A dummy variable is

used in the empirical analysis.

Finally, we also control for idiosyncratic factors in the GCC economies. Gulf economies

have not only allocated a significant fraction of their wealth in SWFs but have also been the

pioneers in their implementation, following Kuwait’s initiative of 1953. These resource-rich

economies are subject to the highly volatile oil prices; SWFs partly serve the purpose of

stabilizing government revenues which would otherwise mirror the volatility of oil and

commodity prices (Barnett and Ossowski, 2002). We use a dummy to identify GCC countries.

54

Column 1 in Appendix Table 4 reports the results of estimating probit regressions of

the incidence of FRs for 146 countries using annual data in the period 1984-2015.9 As

reported, more open countries that enjoy higher levels of resource rents and have

concentrated exports tend to have a higher probability of implementing SWFs. Likewise, more

developed economies –which usually also have lower levels of price instability—are more

likely to have established sovereign wealth funds. Finally, the evidence confirms that

instability in tax revenues is an important reason for governments to implement SWFs and

the preference of the oil-rich economies of the GCC for this type of fiscal institution. We use

these results to generate a prediction of the likelihood of having a SWF in place for each year

of the period 1984-2015, conditional on the observed values of fundamentals. This predicted

variable is a consistent instrument for SWF but is not efficient. Efficiency obtains when the

SWF dummy is filtered against the fundamentals and the instrument obtained in the previous

step. Results are contained in Panel B of Table 1.

Appendix Table 4

Instrumenting Sovereign Wealth Funds

Sovereign Wealth Fund

1st stage

Sovereign Wealth Fund

2nd stage

Economic Development 2.827*** 0.046***

Resource Rents 2.183*** 0.026***

Price Instability -0.057*** 0.000

Trade Volume 1.707*** 0.158***

Exports Concentration 0.926** 0.087***

Revenue Instability 3.865** 0.102*

Federalism -3.855*** 0.231***

GCC country 5.510*** -0.011

Constant -103.050*** -2.533***

First Stage 𝑺𝑾�̂� - 0.157***

Observations 3135 3135

Countries 146 146

Note: * p<0.1, ** p<0.05, *** p<0.01

9 We also controlled for political factors and national governance using data from ICRG but the results proved to be statistically and economically insignificant.

55

Contents

1 Introduction ................................................................................................................................................................ 1

2 Fiscal Rules and Fiscal Performance: Measures, Interactions, and Policy Optimality ................. 3

2.1 Interactions between Fiscal Performance, Fiscal Institutions, Economic Conditions, and

Political-Economy Features ................................................................................................................................. 3

2.2 Measures of Fiscal Performance ................................................................................................................ 3

2.3 Types of Fiscal Rules ....................................................................................................................................... 5

2.4 Optimal Fiscal Policy ....................................................................................................................................... 7

2.5 A simple optimal fiscal policy rule for an open economy ............................................................... 8

2.6 Relations between types of fiscal target rules and fiscal performance measures ............. 10

3 Previous Empirical Evidence on Fiscal Rules and Fiscal Performance .......................................... 12

3.1 Fiscal Performance ....................................................................................................................................... 12

3.2 Fiscal Rules and Fiscal Performance ..................................................................................................... 14

4 Are Small Economies Different? ..................................................................................................................... 15

5 Empirical Methodology ...................................................................................................................................... 16

5.1 Fiscal Rules as Treatments ....................................................................................................................... 17

5.2 On the Endogeneity of Fiscal Rules ....................................................................................................... 19

6 Empirical Analysis I: Instrumenting Fiscal Rules .................................................................................... 20

7 Empirical Analysis II: Assessing the Impact of Fiscal Rules ................................................................ 26

7.1 Measures of fiscal performance ............................................................................................................... 26

7.2 Determinants of Fiscal Performance ..................................................................................................... 27

7.3 Main Results on Procyclicality of Government Expenditures ..................................................... 29

7.4 Main Results on Procyclicality of Fiscal Balance .............................................................................. 32

7.5 Main Results on Fiscal Balances .............................................................................................................. 34

7.6 Main Results on Government Debt ......................................................................................................... 37

8 De Facto and de Jure Rules ................................................................................................................................ 39

9 Conclusions .............................................................................................................................................................. 41

References .................................................................................................................................................................... 44

Appendices ................................................................................................................................................................... 49