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Flexible Fiscal Rules and Countercyclical Fiscal Policy1

Author(s):
Martine Guerguil, Pierre Mandon, and Rene Tapsoba
Published Date:
January 2016
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I. Introduction

The aftermath of the recent Great Recession has seen renewed calls for the use of fiscal policy as a countercyclical instrument, owing to the large and protracted growth and employment costs of the crisis, the limited power of monetary policy when interest rates are at the zero-lower-bound, and the perceived potential for increased public investment to avoid a “secular stagnation” in this environment. At the same time, calls have also abounded for a more decisive strengthening of fiscal institutions, and particularly of fiscal rules, as an instrument to ensure prudent fiscal management and bring public debt ratios to safer levels. There is a tension between the two recommendations, as fiscal rules have often been associated with a procyclical bias, and activist fiscal policy with a weakening of fiscal discipline.2

Empirical studies have generally found fiscal rules to be discipline-enhancing (Alesina and Bayoumi, 1996; Bohn and Inman, 1996; Brzozowski and Gorzelak, 2010; Debrun and others, 2008; Fatas and Mihov, 2006; de Haan and others, 1999; Hallerberg and von Hagen, 1997; Manasse, 2006; Perotti and Kontopoulos, 2002; and Tapsoba, 2012). However, the evidence regarding their impact on the cyclical stance of fiscal policy is largely inconclusive. On the one hand, a number of papers have concluded that governments subject to fiscal rules are more prone to procyclical fiscal behavior (Alesina and Bayoumi, 1996; Alt and Lowry, 1994; Lane, 2003; Levinson, 1998; Poterba, 1994; Roubini and Sachs, 1989; and Sorensen and others, 2001). Among spending categories, investment outlays have been found to be more procyclical—countries under pressure to reduce their budget deficits find it politically easier to cut public investment than current outlays (Arezki and Ismail, 2013; Blanchard and Giavazzi, 2004; Dessus and others, 2013). However, other empirical work found that numerical fiscal rules had been associated with less procyclical fiscal behaviors in the European Union (Galí and Perotti, 2003 and Manasse, 2006).

More recently, Ayuso-i-Casals and others (2007), Bova and others (2014), and Combes and others (2014) concluded that fiscal rules could be associated with more countercyclical fiscal policy, provided their design allowed for flexibility, including proper escape clauses, cyclically-adjusted targets, or the extension over several years of the timeframe needed for assessing the compliance with the rule. In a close vein, Bergman and Hutchison (2015) pointed out that fiscal rules are very effective in curbing procyclical fiscal policy once a minimum threshold of government efficiency or quality has been reached.

This paper tries to expand our understanding of the links between fiscal rules and the cyclicality of fiscal policy on three counts. First, it differentiates among types of fiscal rules and explores whether more flexible rules are associated with more, or less, cyclicality. Second, it looks in parallel at the cyclicality of overall spending and that of investment spending. This is important because of the potential growth-enhancing properties of public investment, especially during periods of economic slack and when investment efficiency is high (Afonso and Furceri, 2010; Barro, 1990; IMF, 2014; and Lucas, 1988). And third, it uses propensity scores-matching techniques, borrowed from the microeconomic literature on impact analysis, to handle the self-selection issue that arises from the fact that a country’s decision to introduce a fiscal rule may well be correlated with factors that also affect the cyclical stance of its fiscal policy.

We find that not all fiscal rules have the same impact on the cyclicality of fiscal policy: the design of the rule matters.

  • Among standard rules, budget balance rules are associated with countercyclical changes in overall spending and in investment spending. Expenditure rules are associated with countercyclical changes in overall spending, but with procyclical changes in investment spending, as cuts in the latter during bad times are more politically palatable. Debt rules do not appear to affect the cyclical stance of either overall or investment spending.
  • Flexibility in design seems however, to have the strongest impact. Specifically, investment-friendly rules, or those where public investment or other priority outlays are excluded from the perimeter of the rule, are associated with larger countercyclical movements in both overall public spending and investment public spending. The inclusion of cyclical adjustment features in spending rules yields broadly similar results. The adoption of cyclically-adjusted BBRs is associated with countercyclical movements in overall spending, but with procyclical movements in investment spending. The introduction of escape clauses in fiscal rules does not seem to affect the cyclical stance of fiscal policy.
  • We also confirm that structural factors, including past debt-to-GDP ratio, the level of development, the volatility of terms of trade, natural resources endowment, government stability, the legal enforcement, and monitoring arrangements backing the rule, can influence the link between fiscal rules and countercyclicality. The results are robust to a wide set of alternative specifications.

These findings suggest that an expenditure rule, and to a lesser extent a budget balance rule, may cohabit with countercyclical fiscal policy when investment spending or other priority spending is excluded from the rule target. These findings are in line with recent studies, which concludes that the introduction of investment-friendly rules could help increase investment spending without necessarily undermining fiscal discipline and public debt sustainability, should investment efficiency be high (Blanchard and Giavazzi, 2004; IMF, 2014; IMF 2015b). However, the larger countercyclicality of fiscal policy found in the present paper to be associated with investment-friendly rules is not synonymous with superiority of investment-friendly rules compared with other types of rules. Indeed, investment-friendly fiscal rules may give rise to creative accounting practices, as the lack of a clear-cut conceptual distinction between current expenditure and investment expenditure may provide an incentive for opportunistic misclassification of unproductive expenditures as ‘investment,’ with a view to circumventing the binding constraint of the fiscal rule (IMF, 2014; Serven, 2007)

Of particular importance, fiscal rules have traditionally been enacted to counter the deficit bias and foster fiscal discipline, though a large body of literature has emphasized unpleasant side effects, including procyclicality (Blanchard and Giavazzi, 2004) and lower quality spending (Peletier, Dur, and Swank, 1999). This paper aims at assessing whether certain design features of fiscal rules can alleviate those side effects. Our empirical analysis shows that design matters, which carries potentially important implications for the design of fiscal rules in cases where these side effects are believed to be large. The operational challenge of course is to amend rules in a way that does not jeopardize their effectiveness, an issue we plan to take up in future research.

The rest of the paper is structured as follows. Section 2 introduces the dataset and highlights key stylized facts. Section 3 describes the methodological approach. Section 4 discusses the results and their robustness. Section 5 explores whether structural factors could affect the results. Section 6 concludes and draws some policy implications.

II. Data and Stylized Facts

Fiscal rules, or “permanent constraints on fiscal policy, expressed in terms of a summary indicator of fiscal performance” (Kopits and Symansky, 1998), have multiplied over the past decades. A quick glance at Figure 1 illustrates that by the end of 2012, 80 countries had some type of fiscal rule in place, compared to less than a dozen in the early 1990s. Fiscal rules are usually differentiated by the type of fiscal indicator that they target. Budget balance rules are most common, followed by debt rules, expenditure rules, and revenue rules far behind.

Figure 1.Fiscal Rules Adoption over Time (worldwide)*

*Note: this includes FR countries not retained in the sample considered in this analysis

Over time, fiscal rules have become increasingly flexible in their design (Budina and others, 2012). Investment-friendly rules, which exclude public capital spending from the constraint, are the oldest form of flexible rule: they were adopted by some advanced and developing economies as early as the 1970s and 1980s. Investment-friendly rules seek to give space for potentially growth-enhancing public investment while maintaining fiscal discipline. Investment-friendly rules have been criticized for justifying fiscal laxity and encouraging opaque “creative” accounting, but have attracted renewed interest in the 2000s, as evidence emerged that standard fiscal rules were often associated with sizable cuts in public investment and the emergence of “infrastructure gaps” (Servén, 2007; Blanchard and Giavazzi, 2004).

Other approaches have been explored to introduce flexibility within a fiscal rule. Some rules exclude other specific types of spending, such as social transfers or interest payments, from the constraint. Some rules define their targets in cyclically adjusted or structural terms, to allow flexibility to respond to the cycle. More recently, a growing number of fiscal rules have come to include escape clauses that allow for temporary deviations in the case of a large, unexpected shock. Overall, by 2012, 63 countries, or close to 80 percent of those using fiscal rules, had incorporated some form of flexibility in their rules. Of these, 45 had rules with escape clauses, 31 had rules that excluded investment or other priority spending, and 14 had rules that defined targets in cyclically adjusted or structural terms.

In practice, there are sizable overlaps between rules, making an assessment of the impact of a specific rule feature particularly challenging. For example, investment-friendly and cyclically-adjusted features are mostly associated with budget balance rules (Figure 2): budget balance rules account for 34 of the 36 cases of investment-friendly rules, and all of the 15 cases of cyclically-adjusted rules. By contrast, investment-friendly and cyclical adjustment features were less often enacted in the presence of spending rules: spending rules account for 14 of the 36 cases of investment-friendly rules and 8 out of the 15 cases of cyclically-adjusted rules.3

Figure 2.Overlaps between Standard Rules (BBR and ER) and Flexible Rules (IR and SR)

1/The numbers refer to the sample retained in this study.

A. Dataset and Measure of Cyclicality

To explore the impact of fiscal rules on the procyclicality of fiscal policy, we use a broad, unbalanced panel of 167 countries over the period 1990–2012; the scarcity of reliable fiscal data prior to 1990, especially for developing countries and ex-Soviet Union members, prevents the use of a longer data period. Out of this sample, 82 countries had fiscal rules in place for at least one year between 1990 and 2012 (Table 1).4 Among these “fiscal rule (FR) countries,” 36 countries introduced investment-friendly rules that shield public investment or other priority spending from the perimeter of the rule. The remaining 85 countries in the sample did not adopt any form of fiscal rule throughout the chosen period. To ensure reasonable comparability across groups, the sample of non-FR countries excludes countries with a real per capita GDP lower than that of the poorest FR country, and a smaller population than the smallest FR country.5

Table 1.Fiscal Rule Countries (number)
As of end 2012Over a minimum of one year over 1990–2012
WorldSampleWorldSample
Countries with fiscal rules 180778582
Countries with budget balance rules (BBRs)264647774
Countries with debt rules (DRs)365626764
Countries with expenditure rules (ERs)424242929
Countries with revenue rules (RRs)57777
Countries with investment-friendly rules (narrow definition)619182423
Countries with investment-friendly (broad definition: IRs)731303836
Countries with cyclically-adjusted balance rules (CARs)814141515
Countries with rules containing well defined escape clauses (CRs)45454545
Source: IMF.

The sum of categories may be larger than the total as some countries use multiple rules.

The rule targets the budget balance, usually as a percent of GDP.

The rule targets the level of public debt or public borrowing.

The rule targets the level or growth rate of public spending.

The rule targets the level of public revenue.

Public investment is excluded from the target of the rule.

Public investment and/or other specified spending categories are excluded from the target of the rule.

The target of the rule is defined in cyclically adjusted or structural terms.

Source: IMF.

The sum of categories may be larger than the total as some countries use multiple rules.

The rule targets the budget balance, usually as a percent of GDP.

The rule targets the level of public debt or public borrowing.

The rule targets the level or growth rate of public spending.

The rule targets the level of public revenue.

Public investment is excluded from the target of the rule.

Public investment and/or other specified spending categories are excluded from the target of the rule.

The target of the rule is defined in cyclically adjusted or structural terms.

Our data only captures the existence of a rule, but not the actual degree of implementation and observance of the rule, for which comprehensive, homogenous data is unfortunately not available. Information on the features of fiscal rules and the dates they were in place come from the 2013 vintage of the IMF Fiscal Affairs Department’s Fiscal Rule Dataset; detailed information on the sample can be found in Appendices 13. Data on total public spending and public investment spending, used to calculate the cyclical stance of fiscal policy, comes from the IMF Fiscal Affairs Department’s expenditure database. Appendix 4 documents the sources and definitions of the variables used in this study. Descriptive statistics are in Appendix 5.

To measure the cyclicality of the fiscal stance, we compute country-specific, time-varying cyclicality coefficients. This approach allows capturing the fact that a government’s reaction to business cycle fluctuations may vary over time or differ between the up and down phases of the cycle. Following Aghion and Marinescu (2008), we estimate the fiscal reaction function (1) with Local Gaussian-Weighted Ordinary Least Squares (LGWOLS):

with ɛitN(0,σ2ωt(τ)) and ωt(τ)=1σ2πexp((τt)22σ2).

Subscripts i and t refer to country and time dimensions; ΔlogY refers to the growth rate of real GDP;6 and ΔlogG stands for the growth rate of public spending (total spending or investment spending).7 The β^it coefficient captures the cyclical behavior of public spending, which is found to be countercyclical if βit < 0, procyclical if βit > 0, acyclical otherwise. Accordingly, the higher β^it the more procyclical (or less countercyclical) total public or investment spending is.

To ensure an unbiased estimate of β^it, we extend equation (1) in three ways: we include the lagged value of the dependent variable, to capture the inertia in public spending; we run equation (1) with 2SLS to address possible reverse causality between changes in public spending and in real GDP; and we add a vector of covariates (X) to mitigate omission bias. As a result, (1) becomes

Specifically, the change in real GDP is instrumented with its lagged values, and vector X includes the lagged debt-to-GDP ratio, government stability, volatility of terms of trade, trade openness and financial openness, and inflation rate.

Stylized facts

Figure 3 suggests that total public spending was countercyclical on average during 1990–2012 in FR countries as well as in non-FR countries (the coefficient is negative in both cases). However, the degree of countercyclicality was much more pronounced for FR countries. In contrast, investment spending was procyclical in both FR countries and non-FR countries, but more procyclical in the former. This is in line with the findings of a large body of literature that showed public investment spending to be largely procyclical: it expands during booms but falls during slumps (Arezki and Ismail, 2013; Blanchard and Giavazzi, 2004; Dessus and others, 2013).

Figure 3.FRs and Procyclicality of Public Spending (1990–2012)

Figure 4 shows the level of cyclicality coefficients among FR countries before and after the adoption of a fiscal rule. It suggests that the adoption of a fiscal rule was associated with a subsequent strengthening of the countercyclicality of public spending, and a reduction in the procyclicality of investment spending.

Figure 4.Procyclicality of Public Spending in FR countries (1990–2012)

As illustrated in Figures 5 to 7, the results vary with the type of rules adopted. The changes in the coefficients go in the same direction, but are quite more marked, after the adoption of an investment-friendly rule (Figure 5). But for cyclically-adjusted balance rules (CAR) or well-designed escape clauses (CR), different patterns emerge: the adoption of a cyclically-adjusted balance rule was associated with a subsequent reduction in the countercyclicality of public spending as a whole and a reduction in the procyclicality of investment spending, while the adoption of rules with escape clauses was associated with a reduction in the countercyclicality of overall spending and a strengthening in the procyclicality of the investment spending. By and large, the adoption of fiscal rules seems to reduce the procyclicality of public spending as well as that of investment spending, but investment-friendly rules seem to be associated with a stronger impact.

Figure 5.Public Spending Procyclicality in IR Countries (1990–2012)

Figure 6.Public Spending Procyclicality in CAR Countries (1990–2012)

Figure 7.Public Spending Procyclicality in Escape Clause Rule Countries (1990–2012)

However, these stylized facts only show simple correlations, and do not address possible self-selection problems: if fiscal rules, and more specifically flexible rules, are only adopted by countries with strong fiscal positions, and thus with the capacity to undertake countercyclical policies, the results are biased.

III. Methodological Approaches

We use propensity score matching (PSM), a method borrowed from the impact analysis literature, to address possible self-selection issues.8 PSM consists of pairing countries that adopted a given policy measure (in our case, a fiscal rule) with countries that have not done so, but share certain characteristics associated with both the adoption of the policy measure and the outcome of interest (in our case, the cyclicality of the fiscal policy stance). These characteristics are synthesized in a propensity score that reflects the estimated probability for a country to adopt the given policy measure, conditional upon the defined characteristics. The propensity score is used to identify a control group (of countries not having adopted fiscal rules) that serves as counterfactual to the treatment group (of countries having adopted fiscal rules). Assuming that the variables used to measure the outcome of interest (here, the cyclicality of the fiscal policy stance) are statistically independent of the policy decision (establishment of a fiscal rule), given common characteristics between the treatment group and the control group, then the difference in outcome between the two groups (known in the literature as average treatment effect on the treated, or ATT) can be attributed to the presence of the fiscal rule.9 More specifically, in this study, the average difference in the cyclicality coefficient (as defined above) between the matched FR countries and the non-FR countries, appropriately weighted by the propensity score distribution of the sample, will be used to estimate the causal effect of fiscal rules on the cyclical stance of fiscal policy.

The ATT can be expressed as follows:

Where FRi stands for a binary variable equaling 1 if country i has a fiscal rule in place, and 0 otherwise. βi1 | FRi = 1 captures the procyclical behavior of fiscal policy if country i has adopted a fiscal rule, βi0 | FRi = 1 measures the fiscal policy procyclicality that would have been observed should country i had not introduced a fiscal rule. Equation (3) therefore compares the outcome value observed in the treatment group (FR countries) with the outcome value that would have been observed in the same countries should they had not adopted a FR.

With the propensity score (PS) expressed as P(Xi) = E[FRi | Xi] = Pr(FRi = 1 | Xi), where X is a vector of observable variables associated with the decision to adopt a fiscal rule, and P(Xi) < 1 (so that there are comparable control countries, or non-FR countries, for each treated country, or FR country), equation (3) can be rewritten as:

A. Propensity Scores

We estimate the propensity scores with a probit model, and a dummy for a given fiscal rule as the dependent variable.10 We use different dummies to capture the distinct impact of different fiscal rules: FR for any type of fiscal rule; BBR for budget balance rules; DR for debt rules; ER for expenditure rules; IR for investment-friendly rules (whereby public investment and priority sector spending are explicitly shielded from the target under the rule); CAR when the target of the rule is specified in cyclically-adjusted or structural terms; and CR if the rule includes clearly defined escape clauses.11 Because of the overlap between different categories of rules, we also intersect some of these dummies (e.g., ER * IR, CAR * BBR) when relevant.

To ensure robust results, we use seven different algorithms for country matching, in line with the existing literature (Tapsoba, 2012): the nearest-neighbor matching with replacement, which matches each treated country to the n control countries having the closest PS (we consider n = 1 and n = 3); the radius matching, which matches a FR country to the FR countries with PS falling within a radius (or caliper) of length r (we consider a wide radius r = 0.05, a medium radius r = 0.03 and a narrow radius r = 0.01); the regression-adjusted local linear matching, which consists of pairing covariates-adjusted outcomes for the treatment group with the corresponding covariates-adjusted outcomes for the control group using local linear regression weights (Heckman and others, 1998); and (Epanechnikov) kernel matching, which matches a treated country to all control countries weighted proportionately by their closeness (in terms of PS) to the treated country. Since the matching estimator has no analytical variance, we compute standard errors by bootstrapping, in line with Dehejia and Wahba (2002).

We also use two diagnostic tools to check the validity of the conditional independence assumption, and thus of the matching.

  • First, we follow Rosenbaum and Rubin (1985) and report key statistics to assess the balancing properties of the matched versus unmatched observations. For the conditional independence assumption to hold (i.e., no evidence of significant differences between the FR countries’ and non-FR countries’ observable characteristics within the matched data), the standardized bias score and the p-value associated with its t-test statistic have to stand below the 5% rule of thumb and above the critical threshold of 10%, respectively, (see Caliendo and Kopeinig, 2008; Lechner, 1999; or Sianesi, 2004).
  • Second, we use Rosenbaum (2002) bounding sensitivity tests to check whether unobserved heterogeneity could pollute the results: the ATTs could be biased if countries that appear similar in terms of observed covariates actually differ in terms of important unmeasured covariates that influence both the procyclical behavior of fiscal policy and the decision to introduce a fiscal rule. The bounding sensitivity tests identify the size over which unobserved heterogeneity could impair the results (see Appendix 6 for a detailed presentation of the methodology).

B. Control Variables and Robustness Checks

We use a range of control variables to account for macroeconomic and politico-institutional factors associated in the literature with the adoption of fiscal rules and the cyclicality of fiscal policy. As a reminder, the PS estimation does not aim at finding the best statistical model for explaining the probability of FR adoption, but to control, to the extent possible, for variables that could influence both FR adoption and the outcome variable (fiscal policy procyclicality).12 The selection of variables included in the probit model follows closely this central principle. As macroeconomic indicators, we include the past debt-to-GDP ratio, the rate and volatility of economic growth, and the rate of inflation. As political factors also play a pivotal role in the cyclicality of fiscal policy, we include indicators of political stability and the degree of democracy. Finally, on the institutional front, we include the type of presidential regime, the use of majority electoral rules, federal status and participation to a currency union. Appendix 7 provides details on the empirical literature and expected signs for the control variables.

As robustness checks, we augment the probit model with additional macroeconomic and institutional variables, including the squared value of past public debt (in view of a possible non-linearity in the influence of the debt dynamics); the fiscal balance; trade openness; financial openness; the level of development (seized by per capita real GDP); natural resources endowment; institutional quality (proxied by the quality of bureaucracy); the ruling party’s ideology; the degree of government polarization; the size of the population; the dependency ratio (captured by the share of the population aged 65 and above) ;13 the presence of an IMF program; and a dummy for the occurrence of a crisis.

IV. Fiscal Rules and Procyclicality of Public Spending

A. Propensity Scores

Table 2 displays the probit estimates of propensity scores for different fiscal rules. In column 1, wherein the existence of any fiscal rule is the dependent variable, most coefficients are significant and bear the expected signs: the lagged debt-to-GDP ratio, growth instability, inflation, presidential-type regime, and majoritarian election rules are found to affect negatively and significantly the probability of adopting a fiscal rule, while stronger growth performance, political stability, democracy, federal states, and currency union membership enhance significantly the likelihood of joining the club of FR countries. Results remain broadly similar when budget balance rules, debt rules, expenditure rules, investment-friendly rules, cyclically-adjusted balance, and rules with well-established escape clauses, respectively, are the dependent variables (columns 2 to 7).

Table 2.Probit Estimates of the Propensity Scores
Dependent Variable[1][2][3][4][5][6][7]
FRBBRDRERIRCARCR
Log. Debt-to-GDP ratio (lagged)−0.096***−0.024−0.087**−0.0240.059−0.494***0.013
(0.037)(0.039)(0.041)(0.048)(0.041)(0.075)(0.041)
Growth instability−0.132***−0.148***−0.192***0.075−0.106*−0.217**0.184***
(0.051)(0.055)(0.056)(0.058)(0.058)(0.106)(0.051)
Economic growth0.088*0.0840.0020.104−0.036−0.093−0.089
(0.053)(0.053)(0.057)(0.066)(0.057)(0.094)(0.055)
Inflation rate−0.469***−0.519***−0.173**−0.362***−0.434***−0.190*−0.315***
(0.066)(0.066)(0.069)(0.076)(0.064)(0.114)(0.069)
Government stability0.538***0.2130.372*1.076***0.551***0.335−0.113
(0.203)(0.209)(0.212)(0.263)(0.213)(0.347)(0.223)
Degree of democracy2.104***2.063***1.991***2.046***0.915***13.25***1.282***
(0.124)(0.127)(0.128)(0.183)(0.118)(1.758)(0.128)
Presidential-type regime−0.232***−0.479***−0.531***−0.123−0.173**−0.144−0.191**
(0.075)(0.077)(0.092)(0.087)(0.085)(0.122)(0.087)
Majoritarian election rule−0.269***−0.445***−0.074−0.289***−0.062−0.391***−0.444***
(0.086)(0.084)(0.096)(0.101)(0.094)(0.140)(0.105)
Federal State0.407***0.416***0.323***0.387***0.399***0.607***0.290***
(0.082)(0.083)(0.090)(0.083)(0.086)(0.118)(0.089)
Currency Union member1.206***1.194***1.584***0.1330.716***−0.336***1.041***
(0.075)(0.074)(0.075)(0.085)(0.076)(0.115)(0.076)
Number of observations2,6182,6182,6182,6182,6182,6182,618
Pseudo R20.2810.2950.3550.1410.1350.3230.209
Note: In brackets the robust standard errors. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively. Constant terms are include but not reported.
Note: In brackets the robust standard errors. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively. Constant terms are include but not reported.

Matching results

Table 3 reports the matching results associated with the propensity scores from the presence of any type of fiscal rule (estimates from column 1 of table 2 above). Regarding the cyclical behavior of total public spending, the coefficients are negative (indicating countercyclicality) but small, and significant in five cases (out of seven pairing methods). When looking at the impact on public investment spending (bottom panel of Table 3), the coefficients are also negative and larger, though they are significant only in four out of the seven matching cases. This would suggest that the introduction of a fiscal rule is not associated with a clear-cut reduction in the pro-cyclicality of fiscal policies.

Table 3.Matching Results: All Fiscal Rules
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT−0.262*−0.159−0.163−0.188**−0.202**−0.272***−0.199**
(0.141)(0.129)(0.107)(0.089)(0.096)(0.093)(0.090)
Number of Treated observations846846841846846846846
Number of Control observations1,5751,5751,5751,5751,5751,5751,575
Total number of observations2,4212,4212,4162,4212,4212,4212,421
Dependent variable: Procyclicality of Public Investment Spending
[2] ATT−0.0993−0.434−0.415−0.449*−0.423*−0.508**−0.428*
(0.340)(0.289)(0.265)(0.234)(0.241)(0.231)(0.229)
Number of Treated observations725725722725725725725
Number of Control observations1,4731,4731,4731,4731,4731,4731,473
Total number of observations2,1982,1982,1952,1982,1982,1982,198
Standardized biases (p-value)0.0550.3200.2030.3330.4350.0550.414
Rosenbaum Bounds Sensitivity Tests1.72.32.52.8332.9
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.

Results could vary, however, according to the type of fiscal rule. Table 4 reports the estimated ATTs for BBRs, DRs, and ERs.

  • For BBRs, the results clearly suggest that the adoption of rules is conducive to more countercyclicality for both overall spending and investment spending: coefficients are negative (indicating countercyclicality) and mostly significant (in six out the seven matching techniques), and larger for the latter.
  • DRs appear unrelated to the cyclical behavior of fiscal policy: all the estimated coefficients turned statistically insignificant for overall spending as well as for investment spending.
  • Regarding ERs, coefficients are negative and significant (in six out seven pairing techniques) for overall spending, but positive and significant (in five out seven pairing techniques) for investment spending. The finding that ERs are associated with countercyclical changes in total public spending is in line with the conclusions of previous studies that showed that ERs help curb pressures for additional spending in the presence of budgetary windfalls (Ayuso-i-Casals and others, 2007; and European Commission, 2006). But the procyclical behavior of investment spending associated with ER suggests that when investment outlays are not specifically shielded, they are more likely than other spending to be cut in downturns (and expanded in booms)—a finding also in line with the literature.
Table 4.Matching Results: BBRs, DRs, and ERs
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
BBR as treatment variable
[1] ATT−0.320**−0.221−0.262**−0.214**−0.197**−0.325***−0.196**
(0.140)(0.135)(0.112)(0.100)(0.092)(0.101)(0.091)
Number of Treated observations742742704742742742742
Number of Control observations1,6841,6841,6841,6841,6841,6841,684
Total number of observations2,4262,4262,3882,4262,4262,4262,426
DR as treatment variable
[2] ATT0.0550.065−0.0010.0040.009−0.080.0094
(0.140)(0.117)(0.107)(0.010)(0.096)(0.099)(0.097)
Number of Treated observations629629622627629629629
Number of Control observations1,8021,8021,8021,8021,8021,8021,802
Total number of observations2,4312,4312,4242,4292,4312,4312,431
ER as treatment variable
[3] ATT−0.269−0.314**−0.246**−0.251***−0.250***−0.294***−0.248***
(0.201)(0.157)(0.098)(0.088)(0.093)(0.089)(0.092)
Number of Treated observations253253253253253253253
Number of Control observations2,2192,2192,2192,2192,2192,2192,219
Total number of observations2,4722,4722,4722,4722,4722,4722,472
Dependent variable: Procyclicality of Public Investment Spending
BBR as treatment variable
[1] ATT−0.893***−0.856***−0.857***−0.813***−0.745***−0.810***−0.756***
(0.338)(0.293)(0.274)(0.245)(0.232)(0.215)(0.252)
Number of Treated observations617617562617617617617
Number of Control observations1,5651,5651,5651,5651,5651,5651,565
Total number of observations2,1822,1822,1272,1822,1822,1822,182
DR as treatment variable
[2] ATT−0.182−0.136−0.151−0.174−0.130−0.253−0.146
(0.386)(0.341)(0.311)(0.312)(0.305)(0.325)(0.312)
Number of Treated observations527527520527527527527
Number of Control observations1,6781,6781,6781,6781,6781,6781,678
Total number of observations2,2052,2052,1982,2052,2052,2052,205
ER as treatment variable
[3] ATT0.5070.4980.460*0.488*0.510**0.503**0.506**
(0.442)(0.353)(0.254)(0.263)(0.226)(0.230)(0.234)
Number of Treated observations216216216216216216216
Number of Control observations2,0092,0092,0092,0092,0092,0092,009
Total number of observations2,2252,2252,2252,2252,2252,2252,225
Standardized biases (p-value)0.7040.9931.0000.9990.9880.7040.992
Rosenbaum Bounds Sensitivity Tests1.11.21.41.51.51.51.5
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.

Table 5 shows the results for investment-friendly rules. The coefficients are negative, significant and larger both for public spending as a whole and especially for investment spending. Among the whole set of rules, investment-friendly rules are the ones thus associated with the strongest and broadest countercyclicality. The results broadly hold when using a narrower definition of IR countries (see Appendix 8 for details).

Table 5.Matching Results: Investment-friendly Rules (IRs)
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT−0.401**−0.352**−0.277**−0.277**−0.279**−0.301***−0.279***
(0.168)(0.151)(0.121)(0.118)(0.110)(0.102)(0.104)
Number of Treated observations391391390391391391391
Number of Control observations2,0812,0812,0812,0812,0812,0812,081
Total number of observations2,4722,4722,4712,4722,4722,4722,472
Dependent variable: Procyclicality of Public Investment Spending
[2] ATT−1.035**−1.155***−1.173***−1.193***−1.167***−1.115***−1.170***
(0.433)(0.370)(0.339)(0.316)(0.340)(0.307)(0.330)
Number of Treated observations349349349349349349349
Number of Control observations1,8761,8761,8761,8761,8761,8761,876
Total number of observations2,2252,2252,2252,2252,2252,2252,225
Standardized biases (p-value)0.6310.8930.7420.8350.8570.6310.859
Rosenbaum Bounds Sensitivity Tests1.21.21.21.31.31.21.3
Note: bootstrapped standard errors (with 500 replications) In brackets the. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: bootstrapped standard errors (with 500 replications) In brackets the. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.

To further probe this finding, we run the same exercise excluding investment-friendly rule countries from the treatment group (Table 6), and see a spectacular reversal in the results: most coefficients lose significance, and when they are significant (for public investment spending), they are positive. This suggests that the countercyclicality evidenced for fiscal rules as a whole was in fact largely driven by the presence of investment-friendly rules.

Table 6.Matching Results: Excluding IR Countries from the Treatment Group
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT0.0780.0030.0035−0.003−0.0000.012−0.002
(0.132)(0.108)(0.072)(0.068)(0.069)(0.066)(0.070)
Number of Treated observations457457457457457457457
Number of Control observations2,0052,0052,0052,0052,0052,0052,005
Total number of observations2,4622,4622,4622,4622,4622,4622,462
Dependent variable: Procyclicality of Public Investment Spending
[2] ATT0.708**0.698**0.581***0.626***0.643***0.615***0.645***
(0.361)(0.278)(0.212)(0.191)(0.188)(0.189)(0.189)
Number of Treated observations363363362363363363363
Number of Control observations1,8541,8541,8541,8541,8541,8541,854
Total number of observations2,2172,2172,2162,2172,2172,2172,217
Standardized biases (p-value)0.0370.1010.3180.3700.5160.0370.469
Rosenbaum Bounds Sensitivity Tests1.21.41.41.51.51.51.5
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.

The inclusion of an investment-friendly feature in ERs and BBRs seems to increase the scope for countercyclical stances. For example, when IRs and ERs overlap (IR*ER), that is, when the treatment group comprises countries with expenditure rules that also exclude investment or priority spending from the ceilings, the adoption of rules is associated with negative coefficients that are larger than in the case of IRs or ERs alone—particularly for overall spending (Table 7). When IRs and BBRs overlap (Table 8), the coefficients are also negative, and particularly larger for public investment spending.

Table 7.Matching Results: IRs and ERs Jointly as Treatment Group
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT−1.080***−0.974***−0.906***−0.866***−0.783***−0.774***−0.793***
(0.316)(0.254)(0.180)(0.170)(0.152)(0.162)(0.152)
Number of Treated Obs.118118112118118118118
Number of Control Obs.2,0812,0812,0812,0812,0812,0812,081
Total Observations2,1992,1992,1932,1992,1992,1992,199
Dependent variable: Procyclicality of Investment Spending
[2] ATT−0.727−0.923**−1.070***−1.092***−1.066***−1.018***−1.070***
(0.507)(0.451)(0.381)(0.357)(0.353)(0.346)(0.337)
Number of Treated Obs.111111110111111111111
Number of Control Obs.1,8761,8761,8761,8761,8761,8761,876
Total Observations1,9871,9871,9861,9871,9871,9871,987
Standardized biases (p-value)0.8480.8210.8500.7500.7790.8480.775
Rosenbaum Bounds Sensitivity Tests1.61.7222.12.12.1
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Table 8.IRs and BBRs Jointly as Treatment Group
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT−0.275−0.365**−0.216*−0.214*−0.213**−0.223**−0.214*
(0.182)(0.154)(0.113)(0.112)(0.105)(0.107)(0.112)
Number of Treated Obs.316316315316316316316
Number of Control Obs.2,0812,0812,0812,0812,0812,0812,081
Total Observations2,3972,3972,3962,3972,3972,3972,397
Dependent variable: Procyclicality of Investment Spending
[2] ATT−1.599***−1.644***−1.715***−1.717***−1.713***−1.650***−1.709***
(0.454)(0.417)(0.397)(0.339)(0.345)(0.382)(0.356)
Number of Treated Obs.282282282282282282282
Number of Control Obs.1,8761,8761,8761,8761,8761,8761,876
Total Observations2,1582,1582,1582,1582,1582,1582,158
Standardized biases (p-value)0.7600.8780.8870.9390.9460.7600.949
Rosenbaum Bounds Sensitivity Tests1.51.41.51.41.41.41.4
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5% and 1%, respectively.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5% and 1%, respectively.

Table 9 and Table 10 look at the impact of other flexible rules. Cyclically-adjusted balance rules are associated with negative coefficients (indicating countercyclicality) but these are significant only for public spending as a whole, not for investment spending (Table 9). A possible interpretation is that to meet the target, policymakers tend to avoid procyclical adjustments in current outlays by cutting capital outlays, as the latter are not specifically shielded in the design of CAR. In contrast, the results in Table 10 suggest that rules with escape clauses do not protect from a procyclical fiscal stance: the coefficients are negative for overall spending, positive for investment spending, but in neither case are they significant.

Table 9.Matching Results: Cyclically-adjusted Balance Rules (CARs)
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT−0.676***−0.559***−0.498***−0.502***−0.509***−0.491***−0.505***
(0.241)(0.190)(0.119)(0.107)(0.093)(0.094)(0.098)
Number of Treated observations143143143143143143143
Number of Control observations2,3142,3142,3142,3142,3142,3142,314
Total number of observations2,4572,4572,4572,4572,4572,4572,457
Dependent variable: Procyclicality of Public Investment Spending
[2] ATT−0.408−0.432−0.388−0.593−0.564−0.674**−0.584
(0.605)(0.497)(0.405)(0.363)(0.344)(0.339)(0.362)
Number of Treated observations130130128130130130130
Number of Control observations2,0842,0842,0842,0842,0842,0842,084
Total number of observations2,2142,2142,2122,2142,2142,2142,214
Standardized biases (p-value)0.4050.7000.9790.9550.8780.4050.891
Rosenbaum Bounds Sensitivity Tests1.21.111.21.41.51.3
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Table 10.Matching Results: Rules with Escape Clause (CRs)
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT−0.193−0.196−0.119−0.152−0.139−0.167*−0.143
(0.148)(0.124)(0.101)(0.097)(0.097)(0.101)(0.098)
Number of Treated observations376376375376376376376
Number of Control observations2,0862,0862,0862,0862,0862,0862,086
Total number of observations2,4622,4622,4612,4622,4622,4622,462
Dependent variable: Procyclicality of Public Investment Spending
[2] ATT0.4410.4570.5190.4510.5080.4820.497
(0.467)(0.370)(0.347)(0.337)(0.326)(0.313)(0.320)
Number of Treated observations276276271276276276276
Number of Control observations1,9401,9401,9401,9401,9401,9401,940
Total number of observations2,2162,2162,2112,2162,2162,2162,216
Standardized biases (p-value)0.0880.7490.9720.9580.9850.0880.984
Rosenbaum Bounds Sensitivity Tests1.11.31.51.51.61.61.5
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.

The inclusion of a cyclical adjustment feature in ERs and BBRs has mixed effects on their countercyclical impact. The combination of ER and CAR gives statistically significant negative coefficients for both overall and investment spending (Table 11); these coefficients are larger than seen for ERs alone, and largely similar to those seen for the combination of ER with IR. The combination of a BBR with CAR yields larger negative coefficients for overall spending, but lower (and barely significant) coefficients for investment spending (Table 12), which seems to confirm the intuition that unless specifically shielded in the rule, investment outlays will be policymakers’ preferred adjustment variable, even when the rule target is defined in cyclically adjusted terms.

Table 11.Matching Results: CARs and ERs Jointly as Treatment Group
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT−0.813**−0.849***−0.598***−0.582***−0.621***−0.763***−0.615***
(0.368)(0.307)(0.209)(0.174)(0.158)(0.171)(0.167)
Number of Treated Obs.67676767676767
Number of Control Obs.2,3142,3142,3142,3142,3142,3142,314
Total Observations2,3812,3812,3812,3812,3812,3812,381
Dependent variable: Procyclicality of Investment Spending
[2] ATT0.110−0.989−1.304**−1.375***−1.337***−1.382***−1.329***
(0.926)(0.727)(0.556)(0.475)(0.408)(0.404)(0.442)
Number of Treated Obs.59595959595959
Number of Control Obs.376376376376376376376
Total Observations435435435435435435435
Standardized biases (p-value)0.1780.9080.9881.0001.0000.1781.000
Rosenbaum Bounds Sensitivity Tests1.11.33331.53
Note: bootstrapped standard errors (with 500 replications) In brackets the. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: bootstrapped standard errors (with 500 replications) In brackets the. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Table 12.Matching Results: CARs and BBRs Jointly as Treatment Group
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT−0.676***−0.559***−0.498***−0.502***−0.509***−0.491***−0.505***
(0.232)(0.198)(0.117)(0.101)(0.098)(0.093)(0.102)
Number of Treated Obs.143143143143143143143
Number of Control Obs.2,3142,3142,3142,3142,3142,3142,314
Total Observations2,4572,4572,4572,4572,4572,4572,457
Dependent variable: Procyclicality of Investment Spending
[2] ATT−0.408−0.432−0.388−0.593−0.564*−0.674*−0.584*
(0.606)(0.479)(0.389)(0.374)(0.337)(0.362)(0.349)
Number of Treated Obs.130130128130130130130
Number of Control Obs.2,0842,0842,0842,0842,0842,0842,084
Total Observations2,2142,2142,2122,2142,2142,2142,214
Standardized biases (p-value)0.4050.7000.9790.9550.8780.4050.891
Rosenbaum Bounds Sensitivity Tests1.21.11.11.21.41.51.3
Note: bootstrapped standard errors (with 500 replications) In brackets the. *, **, and *** indicate the significance level of 10%, 5% and 1%, respectively.
Note: bootstrapped standard errors (with 500 replications) In brackets the. *, **, and *** indicate the significance level of 10%, 5% and 1%, respectively.

In sum, we find that not all flexible fiscal rules accommodate a countercyclical fiscal stance. Investment-friendly rules, and more broadly rules that exclude some categories of spending from the rule target, are the ones more clearly associated with countercyclical changes in both total and investment public spending.14 The inclusion of cyclical adjustment features in ERs yields similar results. The results are mixed for cyclically-adjusted BBRs: the introduction of the latter is associated with countercyclical movements in overall spending, but with procyclical changes in investment spending. The introduction of escape clauses in FRs does not seem to have any impact on the cyclical stance of public spending. Investment-friendly ERs and BBRs, and cyclically-adjusted ERs, therefore appear as the most effective in taming the procyclical bias in public spending.

B. Robustness Checks

Diagnostic tests, reported at the bottom of Tables 312, confirm the robustness of the above results. The p value associated with the standardized biases is above the critical threshold of 10 percent in the large majority of cases. The cutting points from Rosenbaum sensitivity tests hover between 1.2 and 3, large enough levels compared to the findings in the literature (Rosenbaum, 2002; and Aakvik, 2001).15

Appendix 9 shows the results obtained using a probit model augmented to account for possible covariates of investment-friendly rules.16 The propensity scores (Table A9.1) remain quantitatively and qualitatively similar across columns The matching results using these “augmented” probit estimates (Table A9.2) all have similar sign as, and are close in magnitude to, those obtained from the non-augmented model in Table 5.

V. The Role of Strucural Factors

Structural factors can magnify or mitigate the impact of a fiscal rule on the cyclical stance of fiscal policy. To explore their potential role, we look at possible non-linearity in the ATTs, through a control function regression approach. Building on Lin and Ye (2009) and Tapsoba (2012), we use the following OLS regression:

where Cycl.it refers to the procyclicality of total spending (or alternatively investment spending); IRit to the investment-friendly rule dummy variable;17pscoreit stands for the estimated PS from the baseline probit model and is included as a control function; Xit is a vector of macroeconomic, political and institutional factors that could give rise to heterogeneity in the ATT; ui and Vt refer to country and time fixed effects, respectively, while εit refer to the stochastic disturbance term. ψ, the coefficient of the interactive term between IR and Xit, catches the heterogeneity features of the treatment effect of IRs.

Table 13 and 14 report the results for total spending and investment spending, respectively. In each table, Column 1 shows the results of a simple OLS linking IRs adoption to the procyclicality of total spending (investment spending) while accounting for the estimated pscoreit. The β coefficient catches the mean difference in procyclicality between countries having enacted IRs and those that have not. In both cases, it is negative and significantly different from zero, and the magnitudes are close to the coefficients from the matching exercise in Table 5 above (-0.263 for total spending and -1.182 for investment spending). The following columns show the ψ coefficients of the interactive term between an investment-friendly rule and a given structural factor.18

Table 13.Heterogeneity of Treatment Effect of IRs on the Procyclicality of Total Public Spending
Variables[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]
Pscore0.956***1.651***1.481***1.220***0.964***1.174***0.912***1.483***1.090***0.995***1.331***1.288***1.168***0.976***1.130***
(0.197)(0.250)(0.256)(0.286)(0.225)(0.270)(0.222)(0.257)(0.223)(0.278)(0.280)(0.270)(0.257)(0.221)(0.247)
IR−0.263***3.050***−0.716−0.493***−0.146−0.262**−1.583***0.079−0.273**−0.2500.074−0.408***−0.375***−0.244**−0.275
(0.099)(0.839)(0.663)(0.132)(0.122)(0.119)(0.610)(0.393)(0.111)(0.176)(0.091)(0.143)(0.079)(0.103)(0.249)
Macroeconomic Factors
IR* Lagged debt-to-GDP ratio−0.830***
(0.206)
IR* Log real per capita GDP0.051
(0.069)
IR* Volatility of terms of trade0.026**
(0.013)
IR* Bad times dummy−0.381**
(0.151)
IR*Log of natural rents−0.010
(0.083)
Political Factors
IR* Government stability1.913**
(0.794)
IR* Democracy−0.411
(0.421)
IR* Election−0.040
(0.234)
Design
IR* Monitoring−0.279**
(0.114)
IR* Enforcement−0.512***
(0.144)
IR* Coverage−0.164**
(0.0814)
IR* Legal basis−0.295**
(0.127)
IR*Year of major change−0.599***
(0.117)
National IR−0.389***
(0.098)
Country fixed effectsYesYesYesYesYesYesYesYesYesYesYesYesYesYesYes
Time fixed effectsYesYesYesYesYesYesYesYesYesYesYesYesYesYesYes
Observations2,4722,4722,4721,8792,4722,4672,4722,4722,3582,4722,4722,4722,4722,4722,472
Adjusted R-squared0.0080.0180.0060.0040.0060.0040.0060.0100.0060.0030.0070.0070.0060.0060.006
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively. Constant terms, as well as vector X variables in isolation (without interaction with IR) are included but not reported for space purpose.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively. Constant terms, as well as vector X variables in isolation (without interaction with IR) are included but not reported for space purpose.
Table 14.Heterogeneity of Treatment Effect of IRs on the Procyclicality of Investment Spending
Variables[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]
Pscore1.960***2.236***1.789***0.7001.959***1.748***1.708***2.372***1.936***0.6870.5281.837***0.7612.226***1.909***
(0.466)(0.571)(0.577)(0.633)(0.489)(0.615)(0.488)(0.633)(0.471)(0.658)(0.645)(0.629)(0.572)(0.534)(0.499)
IR−1.182***−2.848*2.855*−0.783**−1.346***−1.706***−1.824−1.371−1.182***−2.369***−2.338***−1.149***−0.392−1.190***−1.083
(0.311)(1.551)(1.673)(0.374)(0.434)(0.361)(1.509)(1.303)(0.388)(0.531)(0.499)(0.375)(0.265)(0.316)(0.756)
Macroeconomic Factors
IR* Lagged debt-to-GDP ratio0.414
(0.401)
IR* Log real per capita GDP−0.463***
(0.174)
IR* Volatility of terms of trade−0.014
(0.035)
IR* Bad times dummy−0.381**
(0.151)
IR*Log of natural rents0.374*
(0.199)
Political Factors
IR* Government stability0.904
(2.003)
IR* Democracy0.235
(1.370)
IR* Election−0.138
(0.679)
Design
IR* Monitoring−0.373
(0.403)
IR* Enforcement−0.234
(0.404)
IR* Coverage−1.163**
(0.569)
IR* Legal basis−1.062***
(0.404)
IR*Year of major change0.448
(0.466)
National IR−1.190***
(0.312)
Country fixed effectsYesYesYesYesYesYesYesYesYesYesYesYesYesYesYes
Time fixed effectsYesYesYesYesYesYesYesYesYesYesYesYesYesYesYes
Observations2,2252,2252,2251,7512,2252,2202,2252,2252,2162,2252,2252,2252,2252,2252,225
Adjusted R-squared0.0100.0120.0130.0060.0080.0100.0130.0110.0090.0170.0180.0080.0120.0080.008
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5% and 1%, respectively. Constant terms, as well as vector X variables in isolation (without interaction with IR) are included but not reported for space purpose.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5% and 1%, respectively. Constant terms, as well as vector X variables in isolation (without interaction with IR) are included but not reported for space purpose.

On the macroeconomic side, potential sources of heterogeneity include past debt-to-GDP ratio, the level of development (proxied by per capita real GDP), the volatility of terms of trade, and the position over the business cycle (captured by a dummy for bad times, equaling one if the output gap is negative, zero otherwise).19 The results indicate that investment-friendly rules are more effective in curbing fiscal procyclicality in countries with high past debt-to-GDP ratio, suggesting that these rules are more helpful in avoiding the procyclical bias when the financing constraints are tighter. The countercyclical-enhancing effect of investment-friendly rules appears more pronounced in bad times: in other words, it is easier, politically, to use capital outlays to stimulate the economy when faced with a contractionary shock, than to shield capital outlays from demand-cooling spending cuts in a boom. In addition, highly volatile terms of trade, which makes it harder to save during good times, seem to limit the procyclicality-reducing effect of IRs, especially on overall spending (the coefficient of the interactive term is not significant for investment spending). The significantly negative coefficient of the interaction between investment-friendly rule and real per capital GDP for investment spending suggests that investment-friendly rules have more of an impact on investment spending in more advanced economies. Finally, the significantly positive coefficient of the interaction between investment-friendly rules and natural resource rents indicate that the voracity effect is stronger in resources-rich countries (Tornell and Lane, 1994; and Lane, 2003), hampering the ability of investment-friendly rules to rein in the procyclical bias in investment spending. This echoes the recent conclusion of the IMF Fiscal Monitor that commodity-exporting countries need to strengthen their ability to run countercyclical fiscal policies by further building fiscal buffers in good times, as the economic cycle moves together with the commodity cycle (IMF, 2015c).

Among political factors, government stability seems to mitigate the cycle-friendly property of IRs on public spending. This might stem from the fact that the ability of a government to stay in power makes it easier for it to resist spending pressures and to build fiscal buffers during booms, so as to spend more during bad times. In contrast, the coefficients of the interaction of either democracy or the election cycle (presence of elections in a given year) with an investment-friendly rule are not statistically significant, suggesting that these factors do not affect the effectiveness of IRs on the cyclicality of public spending.

Finally, we explore the influence of design and implementation features of fiscal rules on their countercyclical impact, and find that they all have significant impact. Formal monitoring and enforcement modalities, as well as strong legal basis for the rule, are associated with large negative coefficients, suggesting that these parameters magnify the countercyclical effect of IRs on overall spending (but less so for investment spending). In addition, a wider coverage of the rule increases its impact on the countercyclicality of public spending. The large negative sign of the interaction between an investment-friendly rule and the number of major changes in the design of the rule suggests that such rules have been frequently amended to increase their countercyclical impact. Finally, national investment-friendly rules have a stronger countercyclical effect than supranational ones, likely because the former are more binding on national fiscal authorities than the latter (Prakash and Cabezon, 2008; Tapsoba, 2012; Budina et al, 2012; and Dessus and others, 2013 for the West African Economic and Monetary Union).

VI. Conclusions

This paper uses propensity score matching methods on a broad panel of 167 advanced and developing economies over the period 1990–2012 to explore the relationship between fiscal rules and the cyclicality of the fiscal stance. It isolates the impact of different types of rules, and particularly that of flexible rules, which adapt the target in view of cyclical circumstances or policy priorities. The main findings, robust to a wide set of alternative specifications, are as follows.

  • In line with the results of some recent studies, the paper finds that FRs in general are associated with a weak reduction in the procyclicality of fiscal policy. However, not all rules have the same impact: the design of the rule matters.
  • Among standard rules, budget balance rules are associated with countercyclical changes in overall spending and in investment spending. The effects are mixed for expenditure rules: the introduction of the latter is associated with countercyclical changes in overall spending, but with procyclical changes in investment spending. Debt rules have no effect on the cyclical behavior of public spending.
  • Flexibility in design seems however to have the strongest impact. Specifically, investment-friendly rules, or more broadly rules that exclude some categories of spending from the rule target, are associated with enhanced countercyclicality of both overall spending and investment spending. The countercyclical effect of investment-friendly rules seems stronger in bad times and when the rule is enacted at the national level. Inclusion of cyclical adjustment features in ERs yields broadly similar results. The enactment of cyclically-adjusted BBRs is associated with countercyclical movements in overall spending, but with procyclical changes in investment spending. The introduction of escape clauses in fiscal rules does not seem to affect the cyclical stance of fiscal policy.
  • Country heterogeneity, such as past debt-to-GDP ratio, the level of development, the volatility of the terms of trade, natural resources endowment and government stability influences the procyclicality-reducing role of investment-friendly rules. So do the legal and enforcement arrangements surrounding the rule.

These results suggest that when it comes to enhancing the countercyclicality of fiscal policy, flexibility in the definition of the spending aggregate, and more particularly shielding public investment from the effect of the rule, is particularly effective. Flexibility through off cycle targets has more limited impact. However, improving fiscal discipline is the primary goal of fiscal rules. As such, the larger countercyclicality of fiscal policy associated with investment-friendly rules is not a synonymous of superiority of investment-friendly rules compared with other types of rules.

Indeed, countercyclicality does not guarantee fiscal soundness. Our analysis does not explore whether countercyclical movements were symmetric (expanding in bad times, falling in good times), or if investment-friendly rules provided sufficient incentives for governments to save during good times so as to be able to maintain or expand capital spending in bad times without putting the overall budget balance or public debt ratio at risk. There is evidence that governments tend to use fiscal policy as a stabilizing instrument more actively in bad times than in good times, with potentially adverse impact on public debt ratios (IMF, 2015a; and Celasun and others, 2015).

Investment-friendly fiscal rules may indeed give rise to creative accounting practices, as the lack of a clear-cut conceptual distinction between current expenditure and investment expenditure may provide an incentive for opportunistic misclassification of unproductive expenditures as ‘investment’, with a view to circumventing the binding constraint of the fiscal rule (IMF, 2014; and Serven, 2007). Countercyclicality in public investment spending will deliver the desired impacts on growth, only if accompanied with an improvement in efficiency through a strengthening of public investment management framework (IMF, 2014; IMF, 2015b; and Warner, 2014). In countries with serious debt sustainability concerns, the growth-enhancing impact of public investment may fail to reduce budgetary pressures should the tax base be limited or tax administration be weak. Rebuilding fiscal buffers should be a priority in those countries, as mounting fiscal risks might lead to market pressure. More importantly, data is lacking to explore the link between countercyclicality and compliance with (or breach) of the rule targets. Furthermore, the coefficients of procyclicality used in this study are based on government spending, and do not account for changes in tax rates, owing to the lack of comprehensive and homogenous database on tax rates, especially in developing countries, which are predominant in our sample. These are promising avenues for further research.

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Appendix 1. Full Sample
Treatment GroupControl Group
Antigua and BarbudaChadGabonKenyaPakistanSt. LuciaAlbaniaComorosIran, Islamic Rep. MoldovaSolomon IslandsUkraine
ArgentinaChileGermanyLatviaPanamaSt. Vincent & the GrenadinesAlgeriaCongo, Dem. Rep.IraqMongoliaSouth AfricaUnited Arab Emirates
ArmeniaColombiaGreeceLithuaniaPeruSwedenAngolaDjiboutiJordanMoroccoSudanUruguay
AustraliaCongo, Rep.GrenadaLuxembourgPolandSwitzerlandAzerbaijanDominican Rep.KazakhstanNepalSurinameUzbekistan
AustriaCosta RicaGuinea-BissauMalaysiaPortugalTogoBahamas, TheEgypt, Arab Rep.KiribatiNicaraguaSwazilandVanuatu
BelgiumCote d’IvoireHong KongMaliRomaniaUnited KingdomBahrainEl SalvadorKorea, Rep.OmanSyrian Arab Rep.Venezuela, RB
BeninCroatiaHungaryMaltaRussiaUnited StatesBangladeshFijiKuwaitPapua New GuineaTajikistanVietnam
BotswanaCyprusIcelandMauritiusSenegalBarbadosGambia, TheKyrgyz Rep.ParaguayTanzaniaYemen, Rep.
BrazilCzech RepublicIndiaMexicoSerbiaBelarusGeorgiaLao PDRPhilippinesThailandZambia
BulgariaDenmarkIndonesiaNamibiaSingaporeBelizeGhanaLebanonQatarTongaZimbabwe
Burkina FasoEcuadorIrelandNetherlandsSlovak RepublicBhutanGuatemalaLesothoSamoaTrinidad and Tobago
Cabo VerdeEquatorial GuineaIsraelNew ZealandSloveniaBoliviaGuineaMacedonia, FYRSao Tome and Prin.Tunisia
CameroonEstoniaItalyNigerSpainBosnia and HerzegovinaGuyanaMadagascarSaudi ArabiaTurkey
CanadaFinlandJamaicaNigeriaSri LankaCambodiaHaitiMaldivesSeychellesTurkmenistan
Central African Rep.FranceJapanNorwaySt. Kitts and NevisChinaHondurasMauritaniaSierra LeoneUganda
Appendix 2. Types of Fiscal Rules (only for FR countries in our sample)
CountriesBBRDRERRRCARCRIRSup.CountriesBBRDRERRRCARCRIRSup.
Antigua and Barbuda19981998YesIsrael1992 (2010)2005 (2010)Yes
Argentina20002000YesItaly19921992YesYes
Armenia2008Jamaica20102010Yes
Australia1985 (1998)199819851985YesJapan1947 (1998)2006 (2010)Yes
Austria1995 (1998)1995YesYesKenya19971997
Belgium199219921993 (1998)1995 (1999)YesYesLatvia20042004YesYes
Benin20002000YesYesYesLithuania20041997 (2004)20082008YesYes
Botswana2003Luxembourg1990 (2004)1990YesYesYes
Brazil20002000YesYesMalaysia19591959 (2009)Yes
Bulgaria200620032006 (2010)YesYesYesMali20002000YesYesYes
Burkina Faso20002000YesYesYesMalta20042004YesYes
Cameroon2002 (2008)2002YesYesMauritius2008Yes
Canada1998 (2006)1998 (2006)1998 (2006)Mexico2006 (2009)YesYes
Cabo Verde19981998Namibia20012010
Central African Rep.2002 (2008)2002YesYesNetherlands1992199219941994YesYesYes
Chad2003 (2008)2002YesYesNew Zealand19941994Yes
Chile2001 (2010)YesNiger20002000YesYesYes
Colombia20112000YesNigeria2007
Congo. Rep.20022002YesYesNorway2001Yes
Costa Rica2001YesPakistan20052005YesYes
Côte d’Ivoire20002000YesYesYesPanama2002(2008)2002(2008)Yes
Croatia201220092012YesYesYesPeru2000 (2003)2000 (2003)Yes
Cyprus20042004YesYesPoland2004 (2008)1999 (2004)2011YesYes
Czech Republic20042004YesYesPortugal19921992YesYes
Denmark1992 (2011)19921994 (2009)2001 (2012)YesYesYesRomania200720072010YesYes
Ecuador2003 (2010)2003 (2010)2010YesRussia2007
Equatorial Guinea2002 (2008)2002YesYesSenegal20002000YesYesYes
Estonia19932004YesYesSerbia20112011Yes
Finland1995 (2011)1995 (2011)2003 (2011)YesYesYesSingapore19651991 (2008)Yes
France199219921998 (2011)2006 (2011)YesYesSlovak Republic20042004YesYes
Gabon2002 (2008)2002YesYesSlovenia20042000 (2005)YesYes
Germany1969 (2009)19921982 (2008)YesYesYesSpain1992 (2006)19922011YesYesYes
Greece19921992YesYesSri Lanka20032003
Grenada1998 (2006)1998YesSt. Kitts and Nevis19981998Yes
Guinea-Bissau20002000YesYesYesSt. Lucia19981998Yes
Hong Kong1997YesSt. Vincent and the Grenadines19981998Yes
Hungary2004 (2012)2004 (2012)2010YesYesSweden1995 (2000)19951997YesYes
Iceland2004Switzerland2003Yes
India2004YesTogo20002000YesYesYes
Indonesia1967 (2004)2004United Kingdom1992 (2010)1992 (2010)YesYesYes
Ireland19921992United States19861990 (2011)Yes
Note: BBR= Budget Balance Rule; DR= Debt Rule; ER= Expenditure Rule; RR= Revenue Rule; BBR= Budget Balance Rule; DR= Debt Rule; ER= Expenditure Rule; RR= Revenue Rule; CAR= cyclically-adjusted balance rule (defined in terms of cyclically-adjusted balance); CR= Rule with well-defined escape clauses; IR= Investment-friendly rule; Sup = Supranational rule; Major Change= Year of last major change for ECs in brackets. Dominica, Kosovo and Liberia, adopted FRs, but are excluded from our sample because of data limitation on key variables for the study. Source: IMF Fiscal Affairs Department’s Fiscal Rule Database (2013).
Note: BBR= Budget Balance Rule; DR= Debt Rule; ER= Expenditure Rule; RR= Revenue Rule; BBR= Budget Balance Rule; DR= Debt Rule; ER= Expenditure Rule; RR= Revenue Rule; CAR= cyclically-adjusted balance rule (defined in terms of cyclically-adjusted balance); CR= Rule with well-defined escape clauses; IR= Investment-friendly rule; Sup = Supranational rule; Major Change= Year of last major change for ECs in brackets. Dominica, Kosovo and Liberia, adopted FRs, but are excluded from our sample because of data limitation on key variables for the study. Source: IMF Fiscal Affairs Department’s Fiscal Rule Database (2013).
Appendix 3. Investment-friendly Rules Adoption Worldwide Through 2012 (Over a Minimum of One Year)
CountryMax. number of rulesNational RulesSupranational Rules
All rules are concernedSome rules are concernedAll rules are concernedSome rules are concerned
Argentina22000-2008
Benin22000-
Brazil22000-
Bulgaria52003-
Burkina Faso22000-
Cameroon22002-
Central African Republic22002-
Chad22002-
Congo, Rep.22002-
Costa Rica12001-
Cote d’Ivoire22000-
Croatia52012-
Denmark51994-
Ecuador22003-
Equatorial Guinea22002-
Finland51995-
Gabon22002-
Germany41985-2009
Guinea-Bissau22000-
Hong Kong SAR, China12002-
India12004-2008
Israel21992-2008
Japan11947-
Kosovo*12008-2009
Liberia*12009-
Luxembourg41990-2004
Malaysia21959-
Mali22000-
Mexico12009-
Netherlands42007-20101994-2006
New Zealand21994-
Niger22000-
Pakistan22005-
Senegal22000-
Spain42006-
Togo22000-
United Kingdom41997-
United States11986-2002; 2011-
Total19668
Note: *Countries that are not in the sample, owing to data limitation.Source: IMF Fiscal Rule Dataset (2013)
Note: *Countries that are not in the sample, owing to data limitation.Source: IMF Fiscal Rule Dataset (2013)
Appendix 4. Sources and Definitions of Data
VariableDefinitionSources
Government Total SpendingTotal public spending, as GDP percentage.World Economic Outlook (2014)
Cyclicality of Total SpendingResponsiveness of total public spending to GDP variation (>0: procyclical; =0: acyclical; <0: countercyclical)Author’s construction
Public Capital SpendingTotal public investment, as GDP percentage.World Economic Outlook (2014)
Cyclicality of Capital SpendingResponsiveness of public investment spending to GDP variation (>0: procyclical; =0: acyclical; <0: countercyclical)Author’s construction
FR DummyDummy equaling 1 if a numerical constraint is imposed on any fiscal aggregates at time t, 0 otherwise.
Investment-friendly Rule DummyDummy equaling 1 if a numerical constraint is imposed on fiscal aggregates, excluding public investment or priority sector spending, at time t, 0 otherwise.
Flexible Rule DummyDummy equaling 1 if a flexible rule (in the form of investment-friendly rule, cyclically-adjusted budget balance rule, or rules with escape clauses), is in place at time t, 0 otherwise.
Fiscal Rule Database, IMF’s Fiscal Affairs Department (2012)
MonitoringDummy equaling 1 if monitoring mechanism outside the government for the fiscal rule exists, 0 otherwise.
EnforcementDummy equaling 1 if formal enforcement procedures for the fiscal rule exist, 0 otherwise.
CoverageDummy equaling 1 if FR targets the general government or wider, 0 otherwise.
Writing Legal BasisDummy equaling 1 if FR is statutory, written in international treaty, or added in Constitution; 0 otherwise.
Degree of DemocracyLinear interpolation of freedom house political right index and polity2 indexFreedom House (2014); PolityIV (2013)
Quality of BureaucracyIndex ranging from 0 to 4 (normalized 0-1) and measuring the institutional strength and expertise that a country’s bureaucracy has to govern without drastic changes in policy or interruptions in government services.
International Country Risk Guide (ICRG, 2009)
Government StabilityIndex ranging from 0 to 12 (normalized 0-1) and measuring the ability of government to stay in office and to carry out its declared program(s). The higher the index, the more stable the government is.
Right-Wing DummyBinary variable equaling 1 if right-wing government is in place (in non-authoritarian regime), 0 otherwise.Database of Political Institutions (2012) and own calculations
Federal DummyBinary variable equaling 1 if country has a federal state form, 0 otherwise.Perspective Monde (2014); CIA WorldFactbook (2014)
Presidential DummyBinary variable equaling 1 if country has a presidential form of government, 0 otherwise.Cheibub and others (2010); Perspective Monde (2014)
Majoritarian elections rule DummyBinary variable equaling 1 if country has a majoritarian electoral system in place, 0 otherwise.Bormann and Golder (2005); Perspective Monde (2014)
Real GDPGDP at constant prices.
Per Capita Real GDPPer capita GDP at constant prices.
Penn World Table (PWT 8.0)
Output Growth RateAnnual change in real output
Volatility of Output GrowthStandard deviation of real output growth.Author’s calculation
Inflation RateAnnual change in CPI, normalized as (inflation/1+inflation), to mitigate the influence of hyperinflation episodesWorld Development Indicators (2014) and Author’s calculation
Financial OpennessChinn and Ito index of financial liberalization.Chinn and Ito (2006)
Trade OpennessSum of imports and exports divided by GDPPenn World Table (PWT 8.0)
Debt-to-GDP RatioGross general government debt, as GDP percentage.Ali Abbas et al. (2010)
Appendix 5. Descriptive Statistics
VariableNmeanminmaxsd
Government Total Spending (%GDP)354131.3260204.1712.952
Cyclicality of Total Spending3472−0.0707−3.9573.877.5926383
Public Capital Spending (%GDP)31436.0770.03340.0274.775
Cyclicality of Capital Spending31180.417−9.1329.8901.707
Fiscal Rule Dummy38410.285010.451
Investment-friendly Rule Dummy38410.120010.325
Flexible Rule Dummy38410.193010.395
Monitoring38410.199010.399
Enforcement38410.183010.387
Coverage38410.132010.338
Writing Legal basis38410.213010.409
Degree of Democracy38410.685010.327
Quality of Bureaucracy29060.564010.279
Government Stability29060.6650.08310.165
Right-Wing Ideology Dummy36910.197010.397
Federal State Dummy38410.135010.341
Presidential-type regime Dummy38410.173010.379
Majoritarian elections rule Dummy38410.187010.390
Real GDP38313.04e+111.21e+081.33e+131.07e+12
Per capita real GDP38319099.797197.63987716.7313650.74
Output Growth Rate38170.357−0.9940.9930.636
Volatility of Output Growth37884.0850.027142.0719.392
Inflation rate38170.695−0.9740.9980.427
Financial openness34350.309−1.8642.4391.572
Trade Openness380188.35711.087531.73752.046
Debt–to-GDP ratio356964.6230.77817822092.9264.830
Appendix 6. The Problem of Unobserved Heterogeneity

The underlying idea is straightforward. Let us consider that the decision to adopt FRs is determined not only by a vector of observable covariates (X), as this was the case hitherto, but also by unobservable covariates u, scaled such that 0 ≤ u ≤ 1. Consequently, the probability of enacting FRs becomes

Recall that the odds for a country to introduce FR are defined by the ratio P /1 − P.

Accordingly, for a matched pair of countries i and j, the odds ratio is Pi/(1Pi)Pj/(1Pj), which, if assuming a logistic distribution, can be rewritten as follows

Given that countries i and j are matched on the basis of their observable covariates (X), it follows that Xi = Xj, which simplifies the odds ratio to exp [γ(uiuj)]. Given the bounds imposed on u, it results that the odds ratio also turn bounded as follows

A given value of γ will therefore set the extent to which a difference in the probability of FRs adoption between countries i and j, namely any deviation from the “free of hidden bias” case, may be attributable to the unobservable heterogeneity.

From the foregoing, two cases wherein the odds ratio is equal to 1, implying that the probability of FRs adoption is free of a hidden bias, emerge: (i) unobserved heterogeneity plays no role on the cyclicality of fiscal policy, that is γ = 0, or (ii) unobserved covariates are not different (ui = uj). The influence of unobserved characteristics is active when eγ takes values different from 1. Thus, eγ = k indicates that two countries that are similar in terms of their observable characteristics could differ in their odds of introducing FRs by as much as a factor of k.

For each value of eγ, the Rosenbaum bounds calculate the significance level of the null hypothesis that the ATT is equal to zero. Accordingly, it becomes possible to identify the tipping point from which the hidden bias should cause us to question the validity of our estimated ATT. The higher the level of cut-off point is, the more robust the linkage between FRs adoption and the procyclical stance of fiscal policy is.

Appendix 7. Control Variables

Control variables were selected on the basis of the key drivers behind the adoption of fiscal rules and the cyclical properties of fiscal policy identified in the literature. As a reminder, the aim is not to define the best statistical model explaining the probability of fiscal rule adoption or the emergence of a countercyclical fiscal stance. Rather, it is to control, to the extent possible, for variables that could influence both FR adoption and fiscal policy procyclicality. On that basis, the following variables were selected:

  • Macroeconomic indicators include the past debt-to-GDP ratio, rate and volatility of economic growth, and rate of inflation—variables identified as key drivers of the cyclical properties of fiscal policy in the existing literature (Aghion and Marinescu, 2008; Alesina and others, 2008; Frankel and others, 2013; Mpatswe and others, 2011; Talvi and Végh, 2005; and Tornell and Lane, 1994). As the adoption of a fiscal rule is expected to be more likely in fiscally healthier countries, given the need for credibility about the enforceability of the rule (Calderón and Schmidt-Hebbel, 2008; IMF, 2009; and Tapsoba, 2012), we expect a negative correlation between the likelihood of fiscal rule adoption and the past debt-to-GDP ratio. Besides, the debt-to-GDP ratio might influence a government’s leeway for engaging into countercyclical fiscal policy (Combes and others, 2014; Frankel and others, 2013; and Mpatswe and others, 2011). Likewise, good macroeconomic fundamentals are seen as key ingredients for a credible introduction of fiscal rules (Budina and others, 2012; and IMF, 2009), and we expect a positive association between the probability of fiscal rule adoption and the rate of economic growth, and a negative one with growth instability and the inflation rate.
  • Political factors also play a pivotal role in the cyclicality of fiscal policy (Alesina and others, 2008; and Frankel and others, 2013), so indicators of political stability and the degree of democracy were included. The expected sign of government stability is ambiguous a priori: on the one hand, greater government stability may lower the deficit bias and improve fiscal discipline, setting the stage for adoption of a fiscal rule; on the other, government instability might foster fiscal rule adoption with a view to maintaining fiscal discipline despite frequent government changes (Tapsoba, 2012). In a similar vein, we expect a positive association between the degree of democracy and the probability of enacting rules-based fiscal frameworks, as democratic processes go hand-in-hand with political rights and inclusion of citizens in policymaking, which in turn should raise the appetite for constraining fiscal policy discretion (Acemoglu and others, 2003; and Gerring and others, 2005).20
  • Institutional factors also matter for the cyclical stance of fiscal policy (Aghion and Marinescu, 2008; Alesina and others, 2008; Ayuso-i-Casals and others, 2007; Bergman and Hutchison, 2015; and Mpatswe and others, 2011). We include the type of presidential regime, the use of majority electoral rules, federal status and participation to a currency union. A presidential form of government and majority electoral rules are expected to be negatively correlated with the likelihood of adoption of a fiscal rule, because presidential-type regimes imply a greater rigidity between political branches in the decision-making process (Gerring and others, 2005, 2009), while majority electoral rules are less conducive to the formation of coalition governments (Austen-Smith and Banks, 1999): both features are not very amenable to constraining policymaking through fiscal rule adoption. By contrast, federal status is expected to increase the probability of introducing fiscal rules, as it implies a stronger vertical separation of power (Gerring and others, 2005) and a stronger interest in controlling negative fiscal spillovers across government levels through binding constraints on their respective fiscal discretion (Huber and others, 1993; and Swank, 2002). Finally, we expect a positive correlation between currency union membership and fiscal rule adoption, as monetary unions are usually governed by supranational fiscal rules aiming at preventing free-riding behaviors between member states. The presence of these supranational rules may in turn catalyze the implementation of national rules (Debrun and others, 2008; and IMF, 2009).
Appendix 8. Matching Results Using a More Restrictive Definition of IRs as Treatment Variable

To better nail down the influence of investment-friendly rules on spending procyclicality, we re-estimate the ATT using a more restrictive definition of the investment-friendly rule dummy. Basically, we discard from the latter, observations on Finland, Israel, Netherlands (during 2007–2010), and the United States, as the priority items excluded from the applicability perimeter of their rule are not really related to investment.21 Spain (post–2011) and the United Kingdom (post–2011) are also excluded, because their investment exclusion is voided since 2011. We also rule out WAEMU countries, as the WAEMU-wide investment-friendly rule shields only externally-financed capital spending from the fiscal aggregates covered by the rule. The results, reported below are qualitatively similar to the baseline, bolstering the evidence on the IRs-induced countercyclicality on public spending.

Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Dependent variable: Procyclicality of Total Public Spending
[1] ATT−0.273−0.184−0.169*−0.203**−0.230***−0.280***−0.222***
(0.189)(0.149)(0.0991)(0.0842)(0.0826)(0.0839)(0.0799)
Number of Treated observations245245245245245245245
Number of Control observations2,0142,0142,0142,0142,0142,0142,014
Total number of observations2,2592,2592,2592,2592,2592,2592,259
Dependent variable: Procyclicality of Public Investment Spending
[2] ATT−1.868***−1.858***−1.814***−1.811***−1.807***−1.768***−1.805***
(0.422)(0.365)(0.326)(0.304)(0.308)(0.309)(0.324)
Number of Treated observations222222221222222222222
Number of Control observations1,7981,7981,7981,7981,7981,7981,798
Total number of observations2,0202,0202,0192,0202,0202,0202,020
Standardized biases (p-value)0.7640.9690.9950.9950.9890.7640.992
Rosenbaum Bounds Sensitivity Tests2.12.32.52.52.52.52.5
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Appendix 9.1 Robustness
Appendix 9.1. Augmented Probit Model
Dependent variable[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]
Investment rule
Log. debt-to-GDP ratio (lagged)0.059−3.601*0.0520.120***0.0480.078*0.0560.077*0.026−0.0120.0690.0580.069*0.074*0.009
−0.041(2.103)−0.041−0.043−0.043−0.041−0.041−0.041−0.043−0.045−0.043−0.047−0.041−0.041−0.042
Growth instability−0.106*−0.0983*−0.0959*−0.0995*−0.110*−0.141**−0.114*−0.121**−0.053−0.0992*−0.123**−0.141**−0.118**−0.085−0.092
−0.058−0.058−0.058−0.059−0.058−0.058−0.059−0.058−0.059−0.059−0.059−0.067−0.058−0.057−0.057
Economic growth−0.036−0.038−0.033−0.057−0.038−0.05−0.037−0.052−0.057−0.017−0.049−0.054−0.046−0.074−0.111*
−0.057−0.057−0.057−0.059−0.057−0.058−0.057−0.057−0.058−0.057−0.057−0.062−0.057−0.058−0.059
Inflation rate−0.434***−0.436***−0.438***−0.423***−0.454***−0.479***−0.451***−0.397***−0.458***−0.478***−0.384***−0.433***−0.387***−0.441***−0.500***
−0.064−0.065−0.065−0.068−0.066−0.067−0.066(0.0662)−0.065−0.067−0.066−0.072−0.066−0.064−0.068
Government stability0.551***0.560***0.589***0.569***0.585***0.675***0.566***0.472**0.732***0.675***0.498**0.447*0.493**0.466**0.469**
(0.213)(0.214)(0.217)(0.220)(0.215)(0.222)(0.214)(0.213)(0.219)(0.220)(0.216)(0.231)(0.214)(0.217)(0.217)
Degree of democracy0.915***0.895***0.953***1.009***1.019***1.544***1.016***0.949***1.065***1.321***0.865***0.627***0.834***0.947***0.975***
(0.118)(0.120)(0.120)(0.119)(0.124)(0.141)(0.118)(0.122)(0.124)(0.151)(0.116)(0.138)(0.119)(0.119)(0.121)
Presidential-typre regime−0.173**−0.181**−0.191**−0.197**−0.193**−0.222***−0.198**−0.134−0.194**−0.263***−0.160*−0.081−0.139−0.185**−0.174**
−0.085−0.085−0.085−0.089−0.087−0.085−0.086−0.087−0.086−0.091−0.087−0.09−0.086−0.086−0.087
Majoritarian electoral rule−0.062−0.069−0.079−0.089−0.083−0.175*−0.071−0.027−0.195**−0.156−0.0410.022−0.032−0.072−0.039
−0.094−0.094−0.094−0.097−0.097−0.099−0.094−0.094−0.093−0.098−0.094(0.106)−0.094−0.095−0.095
Federal State0.399***0.398***0.387***0.422***0.422***0.387***0.425***0.421***0.1360.493***0.424***0.331***0.416***0.413***0.419***
−0.086−0.086−0.088−0.089−0.091−0.085−0.089−0.086−0.092−0.089−0.089−0.096−0.087−0.087−0.086
Currency Union Member0.716***0.728***0.709***0.656***0.715***0.774***0.707***0.749***0.814***0.623***0.743***0.649***0.753***0.713***0.724***
−0.076−0.076−0.076−0.078−0.075−0.078−0.074−0.077−0.079−0.077−0.076−0.084−0.077−0.076−0.076
Squared debt-to-GDP ratio (lagged)1.772*
(1.016)
Trade openness−0.762
(0.841)
Financial openness−0.0252
(0.0206)
Log. per capita real GDP−0.029
−0.024
Log. natural rents0.212***
−0.033
Quality of bureaucracy−0.162
(0.131)
Right-wing ideology−0.194**
−0.083
Log. Population size0.161***
−0.021
Dependency ratio3.896**
(1.654)
IMF program0.061
−0.071
Government polarization0.191***
−0.046
Elections0.0572
−0.077
Crisis−0.343***
(0.115)
Overall fiscal balance−0.00839***
−0.002
Observations2,6182,6182,6002,4482,6182,6132,6182,5952,6182,6182,5742,2612,5952,5962,594
Pseudo R20.1350.1360.1350.1370.1320.1360.1490.1360.1540.1460.1330.1410.1350.1380.143
Note: robust standard errors in brackets. *,**, and *** indicate the significance level of 10%, 5%, and 1%, respectively. Constant terms are included but not reported.
Note: robust standard errors in brackets. *,**, and *** indicate the significance level of 10%, 5%, and 1%, respectively. Constant terms are included but not reported.
Appendix 9.2. Robustness Matching Results (with IRs as Treatment Variable)
Nearest-neighbor matchingRadius matchingLocal linear regression matchingKernel matching
n= 1n= 3r= 0.01r= 0.03r= 0.05
Robustness checks
ATT on Procyclicality of Total Public Spending
[1] Adding squared public debt (lagged)−0.175−0.206−0.288**−0.299**−0.297**−0.323***−0.299**
(0.185)(0.157)(0.129)(0.118)(0.118)(0.102)(0.116)
[2] Adding trade openness−0.361**−0.273*−0.276**−0.278***−0.287***−0.308***−0.283***
(0.166)(0.152)(0.117)(0.106)(0.100)(0.100)(0.106)
[3] Adding financial openness−0.275*−0.346**−0.268**−0.281***−0.282***−0.308***−0.281***
(0.164)(0.152)(0.116)(0.108)(0.106)(0.106)(0.105)
[4] Adding log. per capita real GDP−0.442***−0.269*−0.283***−0.285***−0.281***−0.304***−0.281***
(0.168)(0.146)(0.109)(0.104)(0.0997)(0.108)(0.106)
[5] Adding log. natural rents−0.0916−0.124−0.169−0.217**−0.233**−0.240**−0.226**
(0.172)(0.137)(0.107)(0.103)(0.102)(0.0947)(0.0996)
[6] Adding quality of bureaucracy−0.229−0.332**−0.304***−0.302***−0.285***−0.305***−0.288***
(0.180)(0.146)(0.109)(0.0988)(0.101)(0.103)(0.100)
[7] Adding right-wing ideology−0.334*−0.295**−0.292**−0.304***−0.302***−0.320***−0.303***
(0.183)(0.149)(0.118)(0.115)(0.112)(0.106)(0.112)
[8] Adding log. population size−0.267−0.258*−0.242**−0.236**−0.240**−0.245**−0.238**
(0.165)(0.134)(0.102)(0.0975)(0.105)(0.103)(0.107)
[9] Adding dependency ratio−0.165−0.201−0.213*−0.280**−0.307***−0.338***−0.302***
(0.172)(0.150)(0.111)(0.116)(0.109)(0.100)(0.105)
[10] Adding IMF program−0.179−0.235−0.296***−0.299***−0.297***−0.305***−0.295***
(0.185)(0.157)(0.113)(0.113)(0.106)(0.103)(0.105)
[11] Adding government polarization−0.213−0.283**−0.263**−0.254**−0.252**−0.234**−0.252**
(0.163)(0.141)(0.111)(0.112)(0.106)(0.104)(0.105)
[12] Adding elections−0.180−0.278*−0.283**−0.300***−0.298***−0.311***−0.297***
(0.172)(0.154)(0.114)(0.113)(0.108)(0.103)(0.108)
[13] Adding crisis−0.289*−0.316**−0.272**−0.289***−0.292***−0.303***−0.290***
(0.169)(0.146)(0.116)(0.111)(0.109)(0.105)(0.111)
[14] Adding overall balance−0.540***−0.397***−0.304***−0.295***−0.298***−0.311***−0.298***
(0.162)(0.138)(0.115)(0.111)(0.105)(0.0982)(0.104)
ATT on Procyclicality of Public Investment Spending
[1] Adding squared public debt (lagged)−1.055***−1.221***−1.113***−1.112***−1.094***−1.072***−1.098***
(0.407)(0.366)(0.338)(0.314)(0.319)(0.298)(0.300)
[2] Adding trade openness−1.127**−1.158***−1.207***−1.219***−1.194***−1.142***−1.198***
(0.441)(0.366)(0.318)(0.315)(0.319)(0.301)(0.313)
[3] Adding financial openness−0.919**−1.098***−1.207***−1.204***−1.173***−1.138***−1.179***
(0.430)(0.384)(0.338)(0.325)(0.307)(0.326)(0.304)
[4] Adding log. per capita real GDP−0.883**−1.068***−1.182***−1.192***−1.166***−1.114***−1.169***
(0.426)(0.369)(0.323)(0.323)(0.292)(0.293)(0.315)
[5] Adding log. natural rents−1.235***−1.180***−1.142***−1.141***−1.146***−1.162***−1.143***
(0.403)(0.349)(0.309)(0.287)(0.325)(0.321)(0.292)
[6] Adding quality of bureaucracy−1.007**−1.205***−1.191***−1.179***−1.155***−1.103***−1.159***
(0.406)(0.372)(0.330)(0.321)(0.317)(0.318)(0.313)
[7] Adding right-wing ideology−0.984**−1.304***−1.221***−1.153***−1.125***−1.115***−1.131***
(0.435)(0.382)(0.325)(0.327)(0.326)(0.315)(0.332)
[8] Adding log. population size−1.318***−1.273***−1.297***−1.336***−1.318***−1.245***−1.319***
(0.405)(0.396)(0.334)(0.315)(0.312)(0.329)(0.319)
[9] Adding dependency ratio−1.490***−1.324***−1.256***−1.259***−1.223***−1.202***−1.223***
(0.410)(0.374)(0.310)(0.343)(0.332)(0.323)(0.312)
[10] Adding IMF program−1.349***−1.118***−1.156***−1.132***−1.092***−1.060***−1.099***
(0.473)(0.389)(0.351)(0.329)(0.331)(0.330)(0.324)
[11] Adding government polarization−2.265***−2.424***−2.364***−2.401***−2.373***−2.321***−2.377***
(0.450)(0.432)(0.387)(0.384)(0.404)(0.408)(0.377)
[12] Adding elections−1.332***−1.190***−1.160***−1.186***−1.244***−1.281***−1.230***
(0.490)(0.411)(0.393)(0.363)(0.359)(0.344)(0.364)
[13] Adding crisis−1.232***−1.256***−1.187***−1.150***−1.120***−1.089***−1.127***
(0.441)(0.415)(0.363)(0.323)(0.338)(0.322)(0.347)
[14] Adding overall balance−1.116***−0.976***−1.162***−1.157***−1.124***−1.068***−1.128***
(0.393)(0.348)(0.321)(0.328)(0.313)(0.314)(0.301)
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
Note: Bootstrapped standard errors (with 500 replications) in brackets. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.
*

CERDI and School of Economics, University of Auvergne, France.

The paper was written when Martine Guerguil was Deputy Director in the IMF’s Fiscal Affairs Department.

1

We are very grateful to Jean-Louis Combes and Alexandru Minea for their valuable comments and suggestions. The paper also benefited from useful discussions and comments from Vitor Gaspar, Abdelhak Senhadji, Paulo Medas, Marialuz Moreno Badia, Xavier Debrun, Mousse Sow, Sampawende Tapsoba, Dell’Erba Salvatore, Tigran Poghosyan, Estelle Liu, and participants at the IMF’s Fiscal Affairs Department seminars. All remaining errors are our own.

2

On procyclical bias, a petition signed by 1,100 economists and 11 Nobel laureates in the New-York Times claimed that attempts to strictly keep the budget balanced (in US states) would aggravate recession (see Levinson, 1998). More recently, the former French Minister of Economy, Arnaud Montebourg, stated during the 2014 summer that the binding fiscal rules underpinning the European Stability and Growth Pact are responsible for the painful job crisis in which the Euro area is stuck (http://www.lemonde.fr/politique/article/2014/08/23/arnaud-montebourg-les-choix-politiques-ne-sont-pas-figes_4475668_823448.html).

3

These figures on examples of overlaps between rules are derived from the sample retained in this study.

4

We focus on numerical fiscal rules and leave procedural rules aside.

5

The poorest FR country in our sample is Niger, with an average real per capita GDP of US$ 270.23, while the smallest FR country in terms of population size is St. Kitts and Nevis, with an average population of 46,647 inhabitants.

6

We use the growth rate of real GDP instead of the output gap itself, given data constraints for measuring properly the latter in developing countries, a large share of our sample.

7

We use LGWOLS computations to mobilize all observations available for each country at each date t while weighting them proportionally to their closeness to the considered period t. In line with the recent literature, we use public expenditure, rather than the fiscal balance or tax revenue, as a proxy for the fiscal policy trend because its evolution is less endogenous, and thus likely to capture more accurately the non-cyclical stance of fiscal policy (See Bova and others, 2014; Dabla-Norris and others, 2010; Frankel and others, 2013; Ilzetzki and Végh, 2008; Kaminsky and others, 2004; and Mpatswe and others, 2011). We set the smoothing parameter (σ) to 5, in line with Aghion and Marinescu (2008), but the results are qualitatively robust to changes in this parameter.

8

See Dehejia and Wahba, 2002; Heckman and others, 1998; and Rosenbaum and Rubin, 1983. Similar applications of the PSM to macroeconomic studies can be found in the literature on the effects of Inflation targeting (see e.g., Lin and Ye, 2009; and Minea and Tapsoba, 2014).

9

The assumption of conditional independence is required because variables that influence the outcome may also matter for the decision to implement the policy, leading to a self-selection bias (Dehejia and Wahba, 2002; and Heckman and others, 1998): a simple comparison of the mean value of the outcome between the two groups would yield biased ATTs. But if, conditional upon observable covariates X, the procyclicality coefficients (β0 and β1) are independent of the treatment variable (β0FR | X and β1fr | x), differences in outcomes between the control group and the treatment group are attributable to FRs adoption (Caliendo and Kopeinig, 2008).

10

The results remain qualitatively unchanged with a logit model.

11

We do not include revenue rules given the very small number of countries using such rules.

12

According to the conditional independence assumption, omitting in the probit regression variables that systematically affect FRs adoption but do not matter for the procyclical stance of fiscal policy has little influence on the results (Persson, 2001).

13

Results remain broadly unchanged when using the share of the population aged below 14, or the sum of the population aged below 14 and aged above 65.

14

Further overlaps between different rules, in line with Figure 2, confirm that IRs and CARs are the main drivers of the counter-cyclical properties associated with the implementation of FR. These results are available upon request to the authors.

15

The cutting points of the Rosenbaum sensitivity tests indicate the level beyond which the ATT is no longer significant at the 5% threshold—a cutting point of 1.5 indicates that the unobserved heterogeneity would have to raise the odds of adopting a fiscal rule by 50 percent for the ATT to lose significance. Tipping points typically range between 1.1 and 2.2.

16

We report only the results when investment-friendly rule is the treatment variable for space purpose. Results for other treatment variables are available upon request to the authors.

17

Results for other fiscal rules are not reported here for space purposes. They are available upon request.

18

In the equation underlying Tables 13 and 14, the X variables are entered in isolation (without being interacted with IR) on top of the interactive terms. For space purposes, these coefficients are not reported (only those for the interactive terms are).

19

Results based on an alternative proxy for bad times, namely a dummy equaling one if the output growth rate is below the sample median output growth rate, and zero otherwise, remain qualitatively unchanged and are available upon request to the authors.

20

Such constraints are critical for tackling the root causes of fiscal profligacy, including notably issues related to polarized social preferences (Talvi and Végh, 2005; and Woo, 2009), conflicts of interest between political parties (Alesina and Tabellini, 1990), agency problem between voters and politicians (Alesina and others, 2008; and von Hagen, 2005), and the common-pool problem (Alesina and Perotti, 1994; and von Hagen, 2005).

21

Finland excludes social security transfers, Israel, security spending, the Netherlands (during) 2007-10, interest payments, and the US, entitlement payments.

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