Jamus Jerome Lim

The Political Economy of Fiscal Procyclicality

Keywords: fiscal procyclicality, political economy, financial access, dcc-garch models JEL Classification: D72, E62, F34, H63

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Introduction

In December 2017, the United States Congress passed the Tax Cuts and Jobs Act. The law led to a swelling of the budget deficit by $780 billion in 2018 (and added another $2.3 trillion to the national debt), and famously occurred amid already solid late-cycle growth conditions. This example is not unique to the latest U.S. administration. In the midst of the first Obama term, a failure to extend the Great Recession-related stimulus package led to spending cutbacks even in a weak economy. And more recently in Europe, German policymakers-citing its infamous schwarze Null balanced-budget rule-remained reticent to calls for fiscal support in 2019, even as the economy was on the skids.

Fiscal procyclicality-the tendency to enact expansionary fiscal policy during a boom and vice versa-is by no means confined to advanced economies. Smaller economies as diverse as the Congo, Paraguay, the Philippines, and Saudi Arabia have, at numerous instances in their recent history, chosen to expand government expenditures even while enjoying already-strong economic performance (and contracted in the face of recession). Yet economic theory militates in favor of countercyclical-or at least acyclical-fiscal policy, which renders the relatively widespread nature of procyclicality somewhat of a puzzle.

The pervasiveness and persistence of fiscal procyclicality has led to the emergence of two major schools of thought on why we observe procyclicality in practice. The first stakes the argument on constraints to financial access faced by governments, due to reasons such as credit frictions [START_REF] Aizenman | Optimal Tax and Debt Policy with Endogenously Imperfect Creditworthiness[END_REF], incomplete markets [START_REF] Cuadra | Fiscal Policy and Default Risk in Emerging Markets[END_REF], or commitment difficulties [START_REF] Bauducco | Optimal Fiscal Policy in a Small Open Economy with Limited Commitment[END_REF]. The second places the onus on political economy complications, such as electoral rules [START_REF] Persson | Constitutional Rules and Fiscal Policy Outcomes[END_REF], political polarization (Ilzetzki 2011), or corruption [START_REF] Alesina | Why is Fiscal Policy Often Procyclical?[END_REF]). Yet despite the distinctiveness of these two competing explanations, there has been little effort at systematically comparing the relative contributions of each to the overall phenomenon of procyclicality.

In this paper, we take on the question of evaluating competing explanations for fiscal cyclicality. 1Our approach first constructs measures of the time-varying relationship between fiscal spending and economic activity that is robust to a non-normal data generating process. In particular, we model the variations in the fiscal-output relationship as a multivariate generalized autoregressive conditional heteroskedasticity (garch) process exhibiting dynamic conditional correlation (dcc). We then jointly evaluate the relative importance of financial access and political economy explanations for procyclical behavior, at both the cross-sectional as well as panel levels.

Overall, our evidence favors a positive effect due to political economy-as proxied by a measure of political participation-for procyclical fiscal policy, consistent with theory. This effect is distinct from the more common phenomenon of political business cycles (which attributes economic fluctuations to political drivers), since business cycles per se tend to occur more infrequently than events on the political calendar. The effects of financial access-as proxied by the outstanding debt burden-are weaker and more mixed. We also find that the debt-related positive association for government consumption becomes negative with respect to government expenditure, suggesting that the public investment component of spending, if financially-unconstrained, could actually be countercyclical in nature, or that political influence operates via the transfer payments channel.

Interestingly, secondary analyses also reveal that much of the politics-induced procyclicality in our full sample is driven by advanced, rather than developing, economies.

Existing empirical approaches often rely on estimates of cyclicality based either on discrete partitions of the data (e.g. correlations over a given time period), or on temporally-static coefficient estimates obtained from regressions of policy on a measure of cyclically-adjusted output.

The problem with the first approach is that such partitions are arbitrary (and hence may be either artificially oversensitive or insensitive to temporal changes in correlations), while the concern with the second is that estimates may suffer from endogeneity (from reverse causality as income itself reacts to fiscal policy, omitted variables due to common unobserved confounders, or measurement error bias since the output gap is unobserved) along with imprecision (because heteroskedasticity resulting from the volatility of the business cycle can bias correlation estimates2 ). Taken together, these measurement issues cast doubt on whether the estimated procyclical relationship is genuine.

In comparison, our dcc approach explicitly accounts for time-varying idiosyncratic changes in the evolution of the fiscal-output relationship, embeds all available historical information, while simultaneously allowing for heteroskedastic and leptokurtic features in the underlying data generating process.

Moreover, our reliance on a multivariate garch model means that we are able to directly accommodate the possibility that financial or political drivers alter our measure of procyclicality, without the need to secure plausible instrumental variables for either government spending or output. Although garch methods nevertheless inherit the identification problem associated with endogeneity, our approach allows us to adopt an agnostic stance on whether we have satisfied exclusion restrictions for potential candidate instruments, and instead focus on how the conditional correlation changes when these factors are introduced.

The question of whether fiscal policy tends to be procyclical has been directly taken on in a number of empirical papers. The majority of these papers find fairly strong evidence of procyclical behavior in developing economies [START_REF] Alesina | Why is Fiscal Policy Often Procyclical?[END_REF][START_REF] Calderón | Do Good Institutions Promote Countercyclical Macroeconomic Policies?[END_REF][START_REF] Frankel | On Graduation from Fiscal Procyclicality[END_REF][START_REF] Gavin | Fiscal Policy in Latin America[END_REF]. In contrast, while some authors have argued that advanced economies tend to subscribe to countercyclical policies [START_REF] Galí | Fiscal Policy and Monetary Integration in Europe[END_REF][START_REF] Lane | The Cyclical Behaviour of Fiscal Policy: Evidence from the OECD[END_REF], others find little evidence of cyclicality in either direction [START_REF] Bashar | The Cyclicality of Fiscal Policy: New Evidence from Unobserved Components Approach[END_REF][START_REF] Talvi | Tax Base Variability and Procyclical Fiscal Policy in Developing Countries[END_REF]. Moreover, even among OECD economies, certain conditions-notably political concentration-may increase the likelihood of running procyclical fiscal policies [START_REF] Lane | The Cyclical Behaviour of Fiscal Policy: Evidence from the OECD[END_REF]. Many of these analyses have been limited to a set of similar economies, for a comparatively brief time period. One of the advances we make in this paper is to expand our working sample to as many as 44 advanced and developing economies, with time spans for some nations extending to as long as 1800-2015. The extended temporal coverage is especially important, since it allows us to encapsulate many more occurrences of business cycles.

Importantly, many of these papers have sought to empirically account for how either financial imperfections or political confounders can explain procyclical fiscal choices. Most papers in the former category have found evidence that underscores the importance of financial access. [START_REF] Gavin | Fiscal Policy in Latin America[END_REF], for example, demonstrate that reliance on emergency finance appears to spike during bad times, supporting the notion that borrowing constraints become more binding during contractions. [START_REF] Kaminsky | When It Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies[END_REF] document that both fiscal policy as well as capital inflows are procyclical in emerging markets-consistent with the idea that reduced financing may be a relevant channel-while [START_REF] Aizenman | Fiscal Space and Government-Spending and Tax-Rate Cyclicality Patterns: A Cross-Country Comparison, 1960-2016[END_REF] find that governments in more indebted countries (and thereby less able to obtain additional credit) tend to spend more in good times, and vice versa.

Studies in the latter group have likewise ascertained how political economy matters. [START_REF] Lane | The Cyclical Behaviour of Fiscal Policy: Evidence from the OECD[END_REF] and [START_REF] Alesina | Why is Fiscal Policy Often Procyclical?[END_REF], for instance, find that when political competition is greater, fiscal policy often ends up exacerbating the business cycle (competition in these respective papers are captured by measures of political constraints and political participation, respectively), while government corruption can further exacerbate the degree of procyclicality. [START_REF] Abbott | Political Pressure and Procyclical Expenditure: An Analysis of the Expenditures of State Governments in Mexico[END_REF] also find that the coincidence of political party control at federal and state levels increases the likelihood that state legislatures accommodate rent seeking [START_REF] Abbott | Political Pressure and Procyclical Expenditure: An Analysis of the Expenditures of State Governments in Mexico[END_REF]. More generally, [START_REF] Calderón | Do Good Institutions Promote Countercyclical Macroeconomic Policies?[END_REF] show that higher levels of institutional quality better equip policymakers to resist pressures to engage in procyclical fiscal expenditures.

One common shortcoming among most of these "determinants" studies is that they seldom test for the relevance of the financial access and political economy channels side-by-side. Part of the reason is the difficulty in addressing endogeneity, which requires finding valid instruments. One example is [START_REF] Brückner | Government Spending Cyclicality: Evidence from Transitory and Persistent Shocks in Developing Countries[END_REF], who utilize weather shock instruments to resolve the endogeneity issues afflicting the government spending response. 3 In contrast, our approach here sidesteps this thorny problem by, first, deriving correlations from the conditional variance-covariance matrix of error terms (instead of from estimated coefficients, which may be more subject to simultaneity bias); and, second, by directly embedding our variables of interest into the multivariate system, and comparing the resulting changes in conditional correlations.

Fiscal procyclicality has also been touched on, albeit tangentially, in the literature on fiscal multipliers (see, for example, Auerbach & Gorodnichenko 2012b,a;[START_REF] Candelon | Fiscal Policy in Good and Bad Times[END_REF][START_REF] Favero | Country Heterogeneity and the International Evidence on the Effects of Fiscal Policy[END_REF]. In most such instances, multipliers are conditional on, inter alia, the state of the business cycle. Consequently, the fact that recession (expansion)-phase multipliers are larger (smaller) indirectly suggests a diminished benefit to the pursuit of procyclical policy.4 However, the unique conditions surrounding business cycle turning points caution against such generous interpretations against procyclicality on the basis of such indirect evidence.

Theoretical Background

There is remarkable consistency in what economic theory claims for the behavior of fiscal policy over the business cycle. Neoclassical theory has long maintained, on the basis of intertemporal smoothing, that any shocks to the tax base should be offset by adjustments to fiscal balances to maintain expected constancy in tax rates [START_REF] Barro | On the Determination of Pubic Debt[END_REF]). 5 The Keynesian prescription, likewise, implies that optimal fiscal policy should seek to return a post-shock economy to equilibrium via either automatic stabilizers or, if necessary, discretionary action [START_REF] Blanchard | Rethinking Macroeconomic Policy[END_REF]. Either way, fiscal deficits (surpluses) would accompany economic expansions (contractions) in a countercyclical fashion.

Yet the routine violation of these standard implications in practice has led to efforts at formulating models that give rise to procyclicality in fiscal policy. These fall into two main families:

Those that rely on imperfections in credit markets that inhibit countries from borrowing during downturns (or promote overspending during expansions) to smooth the cycle; and those that introduce political economy frictions that systematically push effected policy away from the socially optimal outcome. The basic principle that undergirds models where financial access is the culprit behind procyclicality lies in the notion that governments always face some form of binding liquidity constraint (so expenditures would otherwise be greater in the absence of this constraint). During booms, the improved ease of financial access then leads to increased public borrowing and spending; during busts, funding becomes prohibitively expensive or evaporates entirely, which compels budgetary rationalization and prevents deficit-financed stimulus.

The mechanisms that govern the endogenous access to credit can vary. This could arise due to inefficiencies in tax collection and differences in creditor bargaining power [START_REF] Aizenman | Optimal Tax and Debt Policy with Endogenously Imperfect Creditworthiness[END_REF], or because of an inability to commit to a risk-sharing arrangement with the rest of the world [START_REF] Bauducco | Optimal Fiscal Policy in a Small Open Economy with Limited Commitment[END_REF], or because imperfect enforcement affects the sovereign default risk premium faced by economies [START_REF] Cuadra | Fiscal Policy and Default Risk in Emerging Markets[END_REF], especially when such repayment capacities are compromised by exchange rate volatility [START_REF] Bi | Fiscal Limits in Developing Countries: A DSGE Approach[END_REF]. Importantly, the accumulation of government liabilities may erode the financial depth of an economy by lowering aggregate liquidity and/or the valuation of the country's assets, due to crowding out [START_REF] Caballero | Fiscal Policy and Financial Depth[END_REF]. If so, the extent of financial access-and, consequently, the ability to conduct countercyclical fiscal policy-diminishes as the debt stock grows.

As compelling as the financial access argument may be, especially for developing economies, many have come to question why countries do not simply either self-insure through reserve accumulation, or why lenders do not extend credit to governments if they were certain that doing so would ultimately enable counteryclical policy that would help the economy exit recession [START_REF] Alesina | Why is Fiscal Policy Often Procyclical?[END_REF]. This has led to political economy justifications for procyclicality.

Models where political economy feature as an explanation for procyclicality introduce political distortions of some form in order to justify deviations from the Ramsey optimum. 6 Such distortions mean that self-serving demands for public goods or tax relief tend to be myopic: rising during good times, and falling otherwise. Governments acting to satisfy these political pressures gives rise to procyclicality.7

Political distortions emerge from two main channels. Special interest pressures-either because a common-pool problem incites competition among politically influential groups over redistributive fiscal transfers [START_REF] Tornell | The Voracity Effect[END_REF] or public investment funds [START_REF] Park | Choosing the Size of the Public Sector Under Rent Seeking from State Coffers[END_REF], or because tax revenues end up being directly appropriated by corrupt governments to fund political rent distribution in contrast to the socially-optimal fiscal policy [START_REF] Alesina | Why is Fiscal Policy Often Procyclical?[END_REF])-can influence the government in power, promoting expenditure excess. Alternatively, electoral competition may also induce overspending, especially when there is a high degree of political (Ilzetzki 2011;[START_REF] Talvi | Tax Base Variability and Procyclical Fiscal Policy in Developing Countries[END_REF] or social [START_REF] Woo | Why Do More Polarized Countries Run More Procyclical Fiscal Policy?[END_REF] polarization. Even constitutional rules governing elections may play a role in influencing the size and composition of government [START_REF] Kantorowicz | Electoral Systems and Fiscal Policy Outcomes: Evidence from Poland[END_REF][START_REF] Persson | Constitutional Rules and Fiscal Policy Outcomes[END_REF]).8 Whatever the channel, political turnover can condition the extent to which fiscal policy moves in a procyclical manner.

Empirical Methodology

Measuring procyclicality

The conventional approach to measuring procyclicality in the literature has been to either compute the (static) unconditional correlation coefficient for a given country i for the time period between t and t + n:

ρ u i,t,t+n = cov (G i , Y i ) σ 2 G i σ 2 Y i , (1)
where G and Y are measures of the government fiscal policy stance and the state of the business cycle, respectively; or to run regressions of

G i,t = α i + ρ r i,t,t+n Y i,t + X i,t β i + i,t , (2)
where X is a set of potential controls, and ∼ N 0, σ 2 is an i.i.d. disturbance term. It is possible to obtain rolling (dynamic) correlations by running regressions for subsamples between t and t + n, t + 1 and t + n + 1, and so on. 9 In the absence of additional adjustments, the estimates ρu and ρr will typically be biased [START_REF] Boyer | Pitfalls in Tests for Changes in Correlations[END_REF][START_REF] Forbes | No Contagion, Only Interdependence: Measuring Stock Market Comovements[END_REF]. 10

Procyclicality is then assessed as a positive value-which, for (1), is strictly less than or equal to unity-although in practice there is probably difficult to determine whether a large, positive (but insignificant) coefficient is more reflective of procyclicality as compared to a positive, significant one that is smaller in magnitude. In this paper, we adopt a different strategy for computing correlations, premised on the conditional variance-covariance matrix that emerges from a garch model.

To obtain these conditional correlations, we first apply the dcc-garch model proposed by [START_REF] Engle | Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models[END_REF], represented by the system

Z i,t = X i,t Γ + i,t , (3a)
i,t = η 1/2 i,t ν i,t , (3b)
η i,t = δ 1/2 i,t ρ c i,t δ 1/2 i,t , (3c)
ρ c i,t = diag (θ i,t ) -1/2 θ i,t diag (θ i,t ) -1/2 , ( 3d
)
θ i,t = (1 -λ 1 -λ 2 ) ρ c + λ 1 ˜ i,t-1 ˜ i,t-1 + λ 2 θ i,t-1 , (3e)
where Z = [G Y ] is the 2 × 1 vector of dependent variables, η 1/2 is a Cholesky factor of the timevarying conditional covariance matrix η, δ is a diagonal matrix of conditional variances in which each nonzero component evolves according to a univariate garch (1,1) model, 11 and ρ c is a matrix

of conditional quasicorrelations. ν ∼ N σ 2 ν is a 2 × 1 vector of i.i.d. innovations, while ˜ ∼ (0, 1) is a 2 × 1 vector of standardized errors.
The properties of the system (3) have been discussed extensively elsewhere (c.f. Aielli 2013) and will not be reiterated here. Instead, we merely note three features that are useful for our application.

First, the regression specification (3a) is multivariate, in that it treats both the fiscal stance G 9 An alternative (and more straightforward) approach, which we adopt in this paper, is to compute rolling correlations by repeating the exercise for the static correlation coefficient (1) with analogous slices into subsamples.

10 Both are biased in the presence of heteroskedasticity, and if regressions are applied to ρr without the Cochrane-Orcutt (or other serial correlation correction) procedure, inference will be biased further.

11 Specifically, the variances of each diagonal element j evolve according to σ 2 D,j,i,t = φ0,j,i + φ1,i 2 j,i,t-1 + φ2,j,iσ 2 D,j,i,t-1 . φ1 is known as the arch parameter, while φ2 is known as the garch parameter.

and cycle state Y as dependent, while also permitting the inclusion of additional independent variables in the matrix X. Second, it is dynamic, in that not only the conditional covariance matrix δ follows a univariate garch process, but the matrix ρ c likewise evolves according to (3e). Third, the ρ c matrix in (3e) turns out to be a weighted average of the unconditional mean of θ and the unconditional covariance matrix of standardized errors ˜ [START_REF] Aielli | Dynamic Conditional Correlation: On Properties and Estimation[END_REF]. Since this weighted expression is neither these two unconditional terms independently, the parameters of interest embedded in ρ c are generally referred to as (conditional) quasicorrelations. These are calculated, for an element in row k of column l for the sample between t and t + n, as

ρ c kl,i,t,t+n = θ kl,i θ kk,i θ ll,i = cov (G i , Y i ) σ 2 G i σ 2 Y i . (4)
Although (4) offers a conditional correlation sensitive to temporal dynamics, it remains a timeinvariant representation of the procyclicality relationship. To obtain dynamically evolving measures of this correlation, we fit the estimated model ( 3) and obtain in-sample predictions of the conditional variance-covariance matrix, after which we can derive the time-varying conditional correlation:

ρ d kl,i,t = θ kl,i,t θ kk,i,t θ ll,i,t = cov (G i,t , Y i,t ) σ 2 G i,t σ 2 Y i,t . (5)
The dynamic conditional estimate of correlations represented by ρc and ρd offer several distinct advantages relative to the standard approaches described earlier. First and foremost, real GDP movements over a sufficiently long time horizon are likely to suffer from heteroskedasticity in the underlying distribution; this in turn can lead to increased estimates of procyclicality, even if the true relationship between spending and output remain unaltered [START_REF] Forbes | No Contagion, Only Interdependence: Measuring Stock Market Comovements[END_REF]. Second, this measure of procyclicality is not dependent on the idiosyncratic time frame. Deriving changes in correlation over arbitrary subsample splits can introduce the same sort of bias inherent in computing conditional correlations in the presence of heteroskedasticity [START_REF] Boyer | Pitfalls in Tests for Changes in Correlations[END_REF]. Third, the timevarying correlations in (5) allow us to capture whether the fiscal-output relationship may have strengthened (or weakened) over time, rather than simply verify the presence of a continuous linkage, regardless of the state of the business cycle.

The system (3) is estimated via repeated maximum likelihood over a maximum of 16,000 iterations, using the Newton-Raphson algorithm.

Fiscal policy indicators

We construct our measures of fiscal policy along three main dimensions. The first is real government final consumption (G c ), and the second is real total expenditure (G e ). Fluctuations between the two are often due to gross public capital formation, and hence the latter series provides additional insight into government investment dynamics that are unavailable when considering consumption alone. 12 Both series are expressed in real terms using appropriate deflators, and constitute fiscal policy from the spending side of the government budget.

We obtain the cyclical component of these variables by passing each of these series through a

Hodrick & Prescott (1997) (HP) filter 13 to extract the cyclical component, which we treat as a cyclically-adjusted spending indicator. 14 We repeat the same exercise for real GDP to derive our corresponding measure of the state of the business cycle. 15

Our third approach introduces instead an indicator of fiscal policy that does not require calculating deviations from trend. This is the primary expenditure share of output (G p ). This measure nets our interest payments from expenditures-thereby better capturing the discretionary component of government spending-and divides it by GDP. Since this measure is already normalized by output-hence, increases in the primary expenditure share constitute real fiscal impulses-the corresponding business-cycle concept here is no longer deviations in the level of GDP, but rather real GDP growth, which is what we employ.

The extent of fiscal procyclicality is then captured by the degree to which either the cyclicallyadjusted or GDP-share fiscal spending measures, when set against their corresponding businesscycle state measures such as the cyclical deviation of real GDP or its growth rate, yield high estimates of ρ u , ρ r , ρ c , and/or ρ d .

In our robustness checks, we also consider three different fiscal balance measures, namely the deficit/GDP ratio, the cyclical deviations of the fiscal balance, and the primary deficit/GDP. These alternative dependent variables are detailed in the data data appendix.

Econometric model

After obtaining our different measures of procyclicality, we subject these to a straightforward regression of our procyclicality measure on our two main determinants of interest:

ρ m i,t = χ 0 + χ t + χ i + W χ + χ P P olEc i,t-1 + χ F F inAcc i,t-1 + ε i,t , (6)
12 The other major components of expenditure are transfer payments and interest expenses. The former tends to be less volatile, although it is a legitimate target for political-economic pressures. The latter tends to be both relatively stable and largely exogenous once the debt has been incurred. 13 As the data are annual, we follow [START_REF] Ravn | On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations[END_REF] and adopt a smoothing parameter of 6.25.

14 One known concern with the HP filter is that the endpoints for the filter tend to be suboptimal. There are several reasons to believe, however, that this is less an issue in our particular application. First, the poor calibration tends to be most problematic when drawing real-time inferences or rendering forecasts, while our use here is to detrend the respective series for subsequent secondary analysis. Second, the filter was applied to both spending and output; to the extent that there is an endpoint bias, it would apply with equal force to both measures, and hence give rise to a correspondingly diminished effect on the subsequent correlation calculations. Third, the filter is applied to only two of the three baseline metrics we consider. We nevertheless performed a sensitivity check by truncating the first and final three years of each series and recalculating the dynamic conditional correlations. The qualitative results we report do not change much as a result of this truncation, although the sample size is severely compromised, leading to noisier estimates. These results are available on request.

15 While we have retained the standard approach in the literature for identifying the cyclical component of fiscal policy and output, other approaches are possible. [START_REF] Bashar | The Cyclicality of Fiscal Policy: New Evidence from Unobserved Components Approach[END_REF], for example, impose an unobserved components model to disengtangle the correlations in cycles from correlations in slopes of the relevant variables.

where m ∈ {u, r, c, d} is one of our four candidate measures of procyclicality, and P olEc and F inAcc are proxy measures of political economy and financial access, respectively, lagged one period to alleviate the most egregious instances of simultaneity bias. In our baseline, we use political participation (the Polity index 16 ) to capture political-economic influences, and the outstanding debt burden (public debt to GDP) to represent financial access. W represents a set of additional controls potentially related to the business cycle, while χ 0 , χ t , and χ i represent a constant term, along with time and country fixed effects. ε ∼ N 0, Ω is a matrix of variances that allows for two-way clustering of by country and time. 17,18 Our controls are designed to address three other main demand-side channels by which other forms of stabilization policy (other than fiscal policy) may alter the fiscal-output relationship.

These include monetary policy (proxied by the change in the money supply), exchange rate policy (captured by the change in the nominal exchange rate), and trade policy (approximated by the trade balance). 19 χ P and χ F represent our coefficients of interest. A priori, the theories discussed in Section 2 suggest that greater participation would give rise to a stronger influence of politics on fiscal policy procyclicality (a positive coefficient). Theory also implies that a greater debt burden is likely to result in reduced financial access, and hence a greater tendency toward procyclical behavior (a positive coefficient).

Data matters

Our data are drawn from a number of distinct sources. The majority of the fiscal measures, as well as macro controls, are from the World Development Indicators (WDI), supplemented (especially for recent years) by the World Economic Outlook (WEO) database. Although primary expenditure data are available in the latter, we exploit the much longer temporal coverage available in the [START_REF] Mauro | A Modern History of Fiscal Prudence and Profligacy[END_REF] dataset, along with the real growth series there. In general, G c coverage is up to a maximum range of 1960-2015, G e coverage is up to 1980-2015, and G p coverage 16 Although the polity measure captures only one dimension of political economy, it has the important advantage that it is available for many countries over a long time period. In Section 5.2, we expand our measures to include a proxy for special interest pressure (corruption), and we also consider a number of other alternative measures of political participation in our robustness checks.

17 More formally, this is given by Ω

= N T i=1 N T t=1 Ii,t Wi W t εi εt,
where Ii,t is an indicator that takes on unity when i, t share the same cluster, and zero otherwise; and W = [W P olEc F inAcc].

18 Given the nature of the time-varying nature of the dependent variable, clustering by year is self-evident. Our decision to cluster at the country, instead of a higher level (e.g. region), is due to the likelihood that intra-country correlation is likely to still be present even after the inclusion of fixed effects [START_REF] Cameron | A Practitioner's Guide to Cluster-Robust Inference[END_REF], because correlation estimates are constructed from the full set of country-level observations. We view this selection-based justification as the most defensible reason for clustering at the country level. Nevertheless, we also consider estimates with standard errors that are either clustered by year and region, or with Eicker-Huber-White-corrected robust standard errors. In either case, our qualitative conclusions are unchanged, with significance slightly weaker in the former case, and slightly stronger in the latter.

19 One important practical consideration directing our selection of these variables is to minimize sample attrition due to missing one or more of these additional controls. Thus, for example, we rely on the change in the money supply to proxy shocks from monetary policy, instead of changes to other possibly more common indicators such as the interest rate. Most controls were also sourced from the WDI or WEO, although notably our corruption measure (used in our discussion) was drawn from the International Country Risk Guide, private credit was from the Macrohistory database [START_REF] Jordà | Macrofinancial History and the New Business Cycle Facts[END_REF], and fiscal rules rely on the Fiscal Rules dataset first compiled by [START_REF] Schaechter | Fiscal Rules in Response to the Crisis-Toward the "Next-Generation" Rules: A New Dataset[END_REF].

In general, all the main explanatory variables and their controls were transformed with an inverse hyperbolic sine transformation prior to the regressions (this avoids the negative valueproblem that plagues the more common logarithmic transform). Additional data and definitions, along with minor data cleaning procedures, are available in the data appendix.

Results

Preliminaries

Before proceeding to our main findings, it is useful to document the extent to which heteroskedasticity was an issue with the data. We perform two sets of tests: country-specific Breusch-Pagan/Cook-Weisberg tests, and panel-level LR tests, for regressions of each of the fiscal variables on output. For the full panel, the χ 2 statistics using any of the three fiscal variables are all highly significant. For the country cross-sections, the tests were frequently significant for G c and G e ; only in the case of G p did most of the tests turn out insignificant. 21 On balance, these tests reveal that heteroskedasticity is a likely problem in our data, which justifies our application of the garch model (this set of results are available on request).

Estimates of procyclicality

Table 1 reports our estimates of procyclicality. The top panel reports the two static forms (the unconditional Pearson's coefficient, ρ u ) and the conditional quasicorrelation between standardized errors (ρ c ). The bottom panel computes the time-varying rolling correlation, ρ u , calculated over moving 10-year windows, along with the means of the predicted dynamic conditional correlation (ρ d ). In each instance, we report averages for the full sample, along with averages by income group.

For the world as a whole, fiscal policy appears to be procyclical, on average. Regardless of our choice of fiscal instrument or measurement approach, correlations tend to be positive; we take this to mean that the evidence is in favor of mild procyclicality, with correlations ranging from 0.1 to 20 One alternative to the use of public debt is external debt (owed to nonresidents of a country). The issue with relying on this series, and other variant debt measures more generally, is that doing so tends to decimate our sample size. External debt data, for instance, are available mainly from the 1990s, and for mostly developing economies, which would leave the working sample much smaller.

21 One potential reason for this distinction is that Gp is measured as a share of GDP (rather than deviations from trend); consequently, the risk of bias arising from heteroskedasticity owing to fluctuations in the business cycle may be reduced. Nevertheless, given the importance of heteroskedasticity at the panel level (even for Gp), we continue to believe that garch corrections remain relevant for all indicators. 0.2. The exception is the primary expenditure share vis-à-vis growth, which is consistently negative for the unconditional correlations of G p (whether using a static or rolling measure), as compared to the conditional estimates, which are not just positive but quite large in magnitude. 22 While this exception may be surprising at first glance,23 in most cases the positive conditional estimate is more consistent with the G c and G e variants from the same country (and other estimates of fiscal procyclicality in the literature), and hence strike us as more reasonable. 24 On balance, we view the plausibility, consistency, and stability of the estimates obtained from the dcc-garch model as evidence in favor of relying on such conditional correlations to more accurately pin down the true fiscal-output relationship.

Advanced economies also tend to demonstrate less procyclical behavior, relative to developing ones. In half the cases, the calculated correlation is actually negative; in most of the remaining instances where the coefficient is positive, it nevertheless tends to be lower than the comparable figure for developing countries (and in the two cases where it is higher, it is so by only a hair).

This result is broadly consistent with the broader literature [START_REF] Alesina | Why is Fiscal Policy Often Procyclical?[END_REF][START_REF] Calderón | Do Good Institutions Promote Countercyclical Macroeconomic Policies?[END_REF][START_REF] Frankel | On Graduation from Fiscal Procyclicality[END_REF].

It is also worth noting that among the dynamic calculations, the variability of correlations tend to be quite large, relative to the mean. This is not entirely surprising, and is reflective of the wide fluctuations in procyclical behavior both between countries (as demonstrated by the large standard deviations), as well as within them (not directly captured by the table, but this will be evident below).

The between-country variability in procyclicality is even more evident when we compare the distribution of static conditional correlations (ρ c ) by income group (Figure 1). Even when we restrict the bounds to a 25th/75th percentile distribution, the within-group interquartile range is more than twice as large among developing economies, especially in the first half of the 20th century. While this variation has narrowed since that time, it remains higher than that of advanced economies at virtually any time in their history.

Another important takeaway from this graphical representation of procyclicality is that we see little evidence of diminishing trends over time. Procyclicality in the advanced world has remained largely stable, while rising in the developing world. While this feature of the data may seem opposed to the claim made by some that procyclicality has diminished in the recent past [START_REF] Frankel | On Graduation from Fiscal Procyclicality[END_REF][START_REF] Jalles | Fiscal Rules and Fiscal Counter-Cyclicality[END_REF]), the divergence between the two measures of central tendency in our developing world subsample is actually consistent with this fact: that there has been some evidence of graduation (which accounts for the decline in mean relative to the median), but this occurs too infrequently to shift the median.

Cross-sectional regressions

As a first step, we consider regressions at the cross-section, using static measures of correlations, both unconditional ρ u (Table 2, upper panel) and conditional, ρ c (Table 2, lower panel). For each fiscal policy measure, we run a specification that uses only data from the initial year for each country (odd-numbered columns), and a specification that averages the observations over the full sample period (even-numbered columns). 25 conditional correlation (vcc-garch) model. These alternative models differ from our baseline dcc-garch model in terms of the parameter restrictions imposed on the correlation matrix. The resulting conditional correlations, which are reported in the appendix, are not substantively different from the baseline dcc-garch estimates. 25 Since we are working with a cross-section, we deviate from the model outlined in (6) and simply apply OLS, with bootstrapped standard errors given the relatively small size of the sample in most cases.

13 The dependent variable is the static unconditional or conditional correlation between the cyclical components of economic activity and government spending listed in the first row of each panel. All other variables are expressed using the inverse hyperbolic sine transformation. A constant term was included in all regressions, but not reported. Bootstrapped standard errors, replicated over 16,000 iterations, are given in parentheses. Goodness-of-fit measures report the unadjusted R 2 . * indicates significance at 10 percent level, * * indicates significance at 5 percent level, and * * * indicates significance at 1 percent level. 1891 1906 1921 1936 1951 1966 1981 1996 2011 Conditional correlation Source: Author's calculations. Notes: Shaded area represents the upper (75th) and lower (25th) percentile, and solid (dashed) line represents the median (mean), in the distribution of the conditional correlations between primary expenditure and GDP growth, for any given year.

(b) Developing

Figure 1: Distribution of static conditional correlations for advanced economies, 1800-2011 (left) and developing economies, 1861-2011 (right), between primary expenditure and GDP growth. Both groups exhibit procyclicality, on average, although developing economies do display systematically higher correlations since 1950.

The main impression one receives from these regressions are that neither political economy nor financial access appears to be important for fiscal procyclicality. Many specifications-especially those where correlations are conditional-give rise to insignificant coefficients. And when coefficients are significant, they tend to produce signs that are inconsistent with theory and intuition.

For example, a higher initial level of political participation appears to be associated with less procyclicality (specification C1); while this could imply that greater participation inspires policymakers to adopt better stabilization policy, it is more likely reflecting the fact that higher-income economies are more likely to be democratic, and these same economies usually run less procyclical fiscal policies (due to omitted variable bias). Similarly, the possibility that a less financiallyconstrained economy chooses to exploit this enhanced access to reduce fiscal expenditures during booms could be the result of enlightened policymaking, or it may simply be a reflection of how economies able to sustain higher levels of debt are also those more likely to adopt countercyclical fiscal policies in the first place (an issue of selection). 26These issues could be addressed by the inclusion of additional controls, but are also easily remedied by applying the within estimator with appropriate fixed effects. Just as important, introducing the panel dimension will ensure the the analysis takes into account time-varying correlations, which-as evident in Figure 1-is a feature of the data. Accordingly, we discount this set of results and turn to our panel analysis.

Baseline panel analysis

Table 3 reports our baseline results. As before, we consider all three fiscal policy metrics, although we now rely on the the respective dynamic conditional correlations (ρ d ) as our dependent variable. For each measure, we include specifications where we variously control for only country fixed effects, both country and time fixed effects, and fixed effects alongside additional controls. 27We offer several remarks about these results. First, the evidence suggests that political economy appears to matter relatively more than financial access, insofar as conditioning the dynamic correlation between fiscal policy and output is concerned. The coefficient on the polity variable is consistently positive, and frequently significant;28 in contrast, the coefficient on debt switches signs when for government consumption versus expenditures (whether as a cyclical deviation or share of GDP). 29 The magnitudes suggest an elasticity of the political economy effect that ranges from 0.01 to 0.08 percent (among the significant coefficients). 30

Second, the switch in signs for the debt constraint from positive for G c to negative for G e and G p could imply that public investment-which is included in the latter two measures but not in the first-could well be countercyclical, especially if financial constraints are binding. 31 If we accept this to be the case, governments may choose to scale back on infrastructure or R&D expenditures (elements of government investment) during a boom, for fear of crowding out private sector efforts while simultaneously reserving capital for an actual downturn; conversely, during a recession, it is the loss of financing that inhibits the deployment of public investment as a means of stabilization policy. Government consumption may well be less sensitive to such considerations, and hence either exacerbates the cycle, or is acyclical, at best. However, the fact that polity tends to be statistically significant for these latter two measures could alternatively imply that political pressure for procyclical spending operates along the investment channel as well, a result that has some support in the literature [START_REF] Gupta | Now or Later? The Political Economy of Public Investment in Democracies[END_REF]. Another possibility is that there could be politically-induced procyclicality from transfer payments (transfers are included in expenditures but not consumption), which is consistent with some empirical evidence [START_REF] Manacorda | Government Transfers and Political Support[END_REF]. 32,33 Third, the somewhat stronger results we obtain from primary expenditures vis-à-vis growth also speak to the possibility that recurring fiscal liabilities-in this case, interest payments-may not feature strongly in the spending decisions of governments. That is, netting out the contributions of such liabilities appears to heighten the sensitivity of the spending proxy to the effects of political economy and financial access. This could be because these conditioning variables tend to operate on the discretionary element of expenditures.

This tension between the different components government expenditure has implications for our understanding of how to model the key transmission channels theoretically, not least because many existing models of procyclical behavior tend to focus on electoral competition. Since this generally appeals to aspects of government consumption or transfer payments, such models could miss out on an equally important channel-public investment-by which political economy forces 30 For a linear-arcsinh specification, it can be shown that the elasticity of ρ in response to a variable X, ξρX is approximately ξρX ≈ β ρ , where β is the coefficient estimate of the linear-arcsinh model [START_REF] Bellemare | Elasticities and the Inverse Hyperbolic Sine Transformation[END_REF]. 31 Although we are unable to definitively corroborate this claim, we do find evidence that public investment is countercyclical in the presence of financial constraints, while political economy pressures do not appear to be as important. These results, which are based on a more limited dataset, are reported and discussed in greater detail in the appendix.

32 A third possibility is that this is a statistical artifact that results from sample coverage differences across the three measures. We attempt to rule out this contrivance by re-running the regressions using only the observations that overlap when each respective baseline specification (P2, P5, and P8) is considered. With one exception (the subsample for P8 leads to the coefficient on the polity measure flipping signs when Gc is the dependent variable), the results remain qualitatively unchanged from those reported in Table 3. These additional results are available on request.

33 A final possibility is that multicollinearity may be present-perhaps because the polity and debt variables exhibit comovement-and this violation of standard assumptions leads to the changed signs. We believe that this is unlikely, given the low correlation between the two variables (ρ = 0.07). Nevertheless, as an additional check, we enter these two variables separately to ascertain that the sign switch is not because of collinearity issues. Doing so does not generally alter the signs nor the significance of the coefficients on the variables of interest (results are available on request).

operate. The contrast between the cyclical patterns of expenditures versus consumption, or between recurrent versus discretionary spending, may also potentially reconcile why some empirical studies (e.g. [START_REF] Andrikopoulos | Fiscal Policy and Political Business Cycles in the EU[END_REF]) find that fiscal policy is stabilizing, while others find the opposite result (e.g. [START_REF] Castro | Politically Driven Cycles in Fiscal Policy: In Depth Analysis of the Functional Components of Government Expenditures[END_REF], even when examining the same group of countries.

Although we have focused on government expenditure for this baseline (for reasons documented in the introduction), it is worth pointing out that the conditioning effect of polity and debt, while occasionally statistically significant, do appear to matter less when using fiscal balance measures instead as our dependent variable of interest (these are included as robustness checks, details of which are reported in the appendix). One possible explanation for this is that procyclicality has a tendency to operate on the spending margin. 34 In particular, if governments exercise the (rational) policy of countercyclicality on the revenue side-subject to them facing no constraints on the revenue front-this choice could mute the effects of procyclical spending.

Finally, we recognize that these results are obtained from a relatively narrow set of economies (between 29 and 44), and may not extend to a more diverse sample. While we have made an effort to ascertain that our results are not unduly affected by sample size fluctuations, the compromise we make by relying on time-series methods to generate our procyclical measures means that external validity questions inevitably arise. 35 Even so, we view the incorporation of many more cycles into the working sample that we do have as an important advance.

Endogeneity concerns

Notice that, in contrast to existing approaches in the literature, the formulation in (6) only accounts for endogeneity by way of controlling for unobserved heterogeneity. Endogeneity may of course arise instead from reverse causality. The lagged implementation of the P olEc and F inAcc proxies should go some way toward alleviating immediate concerns regarding simultaneity bias, especially given the slow-evolving nature of both variables, which are stocks rather than flows.

Still, one may hold residual doubt about this form of endogeneity. One strategy, commonly employed in the literature, is to deploy instrumental variable techniques. This is challenging in our context, because even if we were able to secure instruments that convincingly satisfy the exclusion restriction, it is unlikely that these instruments would be sufficiently long-dated, given the temporal coverage of our sample. 34 There are theoretical and empirical reasons that support this claim. Theoretically, both government transfers and consumption tend to be easier to alter than tax rate changes, since the former typically only require budget-line adjustments which-while dependent on their magnitude and existing fiscal rules-may be independently executed by the fiscal authority or executive. In contrast, revenue-side changes almost always require legislative approval as well. Empirically, both taxes and revenue are countercylical or acyclical in our sample. The correlation between the tax rate (as a share of profit) and the cyclical component of GDP is extremely low, always insignificant, and almost uniformly negative; in the government consumption and expenditure subsamples, for example, these are ρ (τ, Y ) = -0.020, p = 0.65 and ρ (τ, Y ) = -0.023, p = 0.62, respectively, and in the primary expenditure subsample, this is ρ (R, Y ) = -0.038, p = 0.03. These low correlations are largely independent of development status. 35 In addition to the sample restrictions we document in footnote 32, we also re-ran the regressions for the first two specifications of each spending measure set, using the restricted subsample from the final specification, to check if the findings using the larger sample held. In almost all cases, the results were qualitatively unchanged (for Gp, however, the coefficients on debt fell out of significance). These results are also available on request.

These caveats aside, it is possible to make some progress in terms of understanding the extent to which either political economy or financial access influences procyclicality. After all, the correlations ρ c and ρ d are not derived from simply taking, at face value, the average effect of output fluctuations on fiscal policy, after controlling for political participation or the debt constraint. Rather, we can instead ask whether politics or finance affects the variance-covariance matrix derived from the error terms in (3e). This distinction is important, because it means that we can observe how the calculated correlations change after embedding our variables of interest directly into the multivariate dcc-garch specification.

The strategy we employ here is to alternately36 include either polity or debt as a dependent variable in (3), then calculate the changes in the dynamic correlation ρ d relative to that without the added variable. Two-sided t tests of the difference-in-means are then computed, by country. These are reported in Table 4, along with the total share of the sample for which the difference-in-means are significant.37 The results largely corroborate the qualitative conclusions from our panel analysis. Including either political economy or financial access into the model results in significant changes in the dynamic conditional correlations obtained between 50-100 percent of the time. The significant divergences appear more frequently when polity is included, with some exceptions, mainly when using the primary expenditure (although the difference in significant shares is much smaller than those for total expenditure). In addition, the divergences were often insignificant for government expenditure.

The income group-disaggregated results also suggest that financial access might be a more significant driver of procyclicality in developing countries insofar as government consumption is concerned, while political economy factors feature more in its effects on government expenditure.

Overall, the results lend some modest additional support to the notion that political economy appears to matter more than financial access in governing fiscal procyclicality.

These divergences are well-captured visually. Figure 2 graphs the predicted dynamic conditional correlations between government consumption and output, together with the further inclusion of either polity or debt as an additional dependent variable, for Finland and Greece. 38 For Finland, it is clear that significant separation in the occurs after taking into account the effects of financial access, which underscores the value of directly conditioning on our variables of interest; in contrast, there is little difference after accounting for either politics or finance.

Robustness

In the appendix, we consider three sets of robustness checks that rely on different measures of the key variables of interest or fiscal policy measure. First, we use a more narrowly-defined measure for political participation ( democracy instead of polity); second, we use an alternative estimate of the debt constraint (fiscal space instead of debt); and third, we use various measures of fiscal balance (the fiscal deficit or the primary balance). While these different measures may, arguably, be more nuanced proxies for the effects effects we seek, we find that the results are broadly unchanged, relative to our baseline.

Discussion

One fascinating result in Section 4 is how procyclicality responds differently to political economy effects versus financial access constraints. More specifically, the former tends to be uniform in its (positive) influence, whereas the latter is more ambiguous, depending on the fiscal policy measure in question. We have speculated that this could be due to differential responses in government consumption as opposed to investment. In this section, we probe whether this result is due to income-a quintessential distinction drawn in the literature-or whether additional political or financial channels may be responsible. (bottom), 1960-2018, between the cyclical components of government consumption and GDP. In Greece, there is little difference in correlations after accounting for either political economy or financial access in a multivariate garch model, whereas significant separation in correlations occurs for Finland when financial access is included.

Contrasting procyclicality in advanced versus developing economies

Given the pervasiveness of the advanced/developing distinction in the literature, we would be remiss not to consider our main results in the context of a sample split in that manner. One important point to underscore is that we are not analyzing whether procyclicality is higher or lower for each group; rather, the subsample split only allows us to evaluate whether polity and/or debt are more pronounced in their effects on procyclicality within each income group. As discussed in Section 4.2, procyclicality is clearly higher in developing countries. Hence the contribution from the estimated coefficients to the level of the conditional correlations are not directly comparable across the two groups. With that qualification in mind, Table 5 replicates the specification with both country and time fixed effects (P2, P5, and P8), by income group.39 The dependent variable is the dynamic conditional correlation between the cyclical components of economic activity and government spending listed in the first row. All other variables are expressed using the inverse hyperbolic sine transformation and lagged one period. A constant term was included in all regressions, but not reported. Standard errors, clustered over country and year, are given in parentheses. Goodness-of-fit measures report the adjusted R 2 and within R 2 . * indicates significance at 10 percent level, * * indicates significance at 5 percent level, and * * * indicates significance at 1 percent level.

There seems to be stronger evidence that political economy matters more in advanced economies (while estimates are uniformly positive, they tend to be measured with more noise for developing countries). This is surprising, since one typically expects political economy influences to matter more in developing nations. In contrast, there is just one case where financial access enters with a (highly) significant coefficient, although as was the case with government expenditure before, it carries a negative sign. Overall, while splitting our sample into the two income groups does little to alter the qualitative takeaways from our baseline results, the comparative importance of political economy among advanced economies comes across as somewhat of a paradox. 40 Yet it need not be. This result serves to partially validate theories of the political business cycle, albeit with some additional nuance. If political economy forces operate on fiscal policy mainly in high-income, democratic nations, then such pressures likely originate around the electoral cycle. This would then imply a comparatively tighter relationship between political and economic cycles in more democratic nations than one might expect from the full-sample results. While this is the case in our data, there is another possibility: since elections tend to occur at a higher frequency than business cycle movements, our results could also imply that such political influences could alternatively be giving rise to mini-cycles of activity around the broader business cycle. Such minicycles could still be procyclical, but need not be as tightly linked to just the frequency of the longer-duration business cycle.

Relatedly, the fiscal multiplier literature has repeatedly found that recessionary states tend to give rise to larger multipliers (Auerbach & Gorodnichenko 2012b,a;[START_REF] Candelon | Fiscal Policy in Good and Bad Times[END_REF]. We extend this line of reasoning to consider whether procyclicality responds more in a contraction.

We restrict the sample to just the trough 41 and the year thereafter, and repeat the analysis, by income group. Despite the much smaller sample sizes involved, our qualitative findings are unchanged. However, the magnitude of the coefficients did increase relative to the full sample. This provides some limited validation that, much like fiscal multipliers, procyclicality appears to be more pronounced in a recessionary context (these results are available on request).

Moving on to developing countries, it is natural to question whether there is quantitative evidence of graduation from procyclicality among developing countries [START_REF] Frankel | On Graduation from Fiscal Procyclicality[END_REF]. In Section 4.2, we have already offered some aggregate evidence that this phenomenon may not be all that widespread. Here, we examine the argument more systematically, by examining whether there has been any palpable change in procyclicality before and after the year 2000. Indeed, we do find some evidence of graduation, but only in a limited fashion. The positive coefficient for G c falls in advanced economies and even turns negative in developing ones. And while a similar reversal occurs for G p in high-income countries, we otherwise find little systematic evidence of widespread graduation. This underscores the fact that graduands from procyclicality still remain few and far between (these results are available on request).

Additional channels for political economy and financial access

Having exhausted these avenues with respect to different slices of the data, we move to adding additional variables that could offer some leverage to improving our understanding of the different channels by which political economy and financial access may operate. For the former, we now 40 It is important to underscore the fact that the more significant coefficient on the political economy variable for fiscal procyclicality in advanced economies does not imply that procyclicality, per se, is pronounced in such economies (in fact, it is the opposite, as shown in Section 4.2.

41 Trough turning points were identified algorithmically using the [START_REF] Bry | Cyclical Analysis of Time Series: Selected Procedures and Computer Programs[END_REF] procedure.

include corruption alongside polity; for the latter, we supplement debt with private credit.42

Corruption allows us to potentially separate the pressures emanating from electoral competition versus those due to special interest lobbying. Lobbying, whether through campaign contributions or via information delivery, can easily alter the trajectory of fiscal policy choices [START_REF] Grossman | Special Interest Politics[END_REF][START_REF] Hillman | Public Finance and Public Policy: A Political Economy Perspective on the Responsibilities and Limitations of Government[END_REF]. Lobbying pressures are typically most effective in the presence of corruption; including the level corruption measure into (6) thereby allows us to evaluate whether special interest politics matters, and entering corruption as an interaction permits the assessment of the extent to which the effects of political participation on procyclicality may be further conditioned by lobbying activity.

Since our focus on financial constraints is restricted to public debt, interacting this with its private counterpart allows us to evaluate if the total burden of debt is what matters for procyclicality (as opposed to just the public share). There is evidence that the growth rate of total national debt is what matters for growth [START_REF] Lim | Growth in the Shadow of Debt[END_REF], and this could well be the case for fiscal procyclicality. If crowding out is present, this could potentially mitigate an unchecked expansion of public-sector liabilities.

Table 6 produces these results. The direct effects of lobbying are positive (based on the statistically significant coefficient on corruption in column I3), which indicates that, ceteris paribus, special interest pressures facilitate procyclical fiscal choices. However, the negative coefficient on the interaction term implies that when such pressures are greater, they could mitigate the tendency of policymakers to veer toward procyclicality. Alternatively, more robust electoral competition can prevent special interest groups from channeling expenditures in a procyclical direction. 43 This suggests that the two could be institutional substitutes, and hence may (paradoxically) offset each other in their effort to influence the distribution of government spending. That said, even at the highest levels of special interest lobbying, the total effect of electoral competition on fiscal procyclicality never turns significantly negative.44

Governments also appear to condition their procyclical choices on the pervasiveness of private debt. While more financially developed economies enable governments to pursue procyclical policy (as evidenced by the statistically significant sign on the credit variable in column I4), the negative and significant interaction term indicates that governments do restrain themselves-and exercise countercyclical fiscal policy-when the private sector is already heavily extended. Or, conversely, the private sector may internalize a Ricardian equivalence-type argument and reduce its borrowing when it observes that public-sector indebted is high. In either case, the two forms of debt appear The dependent variable is the dynamic conditional correlation between the cyclical components of economic activity and government spending listed in the first row. All other variables are expressed using the inverse hyperbolic sine transformation and lagged one period. A constant term was included in all regressions, but not reported. Standard errors, clustered over country and year, are given in parentheses. Goodness-of-fit measures report the adjusted R 2 and within R 2 . * indicates significance at 10 percent level, * * indicates significance at 5 percent level, and * * * indicates significance at 1 percent level.

to be substitutes, at least at the margin, in terms of their conditional effect on procyclicality.45

Are fiscal rules a panacea?

A relatively recent literature has emerged that has argued for the promise of fiscal rules as a potential solution to procyclicality [START_REF] Hayek | Do Fiscal Rules Constrain Fiscal Policy? A Meta-Regression-Analysis[END_REF]. Analogous to the rules-versus-discretion debate in monetary policy, the premise here is that such constraints can curb governmental tendencies toward procyclical action. The resistance to such claims lies in the fact that the nature of fiscal policy-being a function of societal choices regarding resource allocation-means that it may be far less amenable to technocratic management than its monetary counterpart.

In this subsection, we briefly explore how fiscal rules may alter the conclusions within our framework of analysis. Table 7 includes a numerical score for the prevalence of different types of fiscal rules (first three columns), and the interaction of these rules with our two main regressors of interest (latter three columns). 46

Rules only

Conditioned on rules The dependent variable is the dynamic conditional correlation between the cyclical components of government spending and economic activity listed in the first row. All other variables are expressed using the inverse hyperbolic sine transformation and lagged one period. A constant term was included in all regressions, but not reported. Standard errors, clustered over country and year, are given in parentheses. Goodness-of-fit measures report the adjusted R 2 and within R 2 . * indicates significance at 10 percent level, * * indicates significance at 5 percent level, and * * * indicates significance at 1 percent level.

G c G e G p G c G e G p (F1) (F2) (F3) (F4) (F5) (F6)
Unfortunately, the results presented in Table 7 do not lend support to the claim that the imposition of fiscal rules may be able to resolve the problem of procyclicality. For starters, the presence of fiscal rules, ipso facto, is associated with greater procyclicality, not less. And although this sign reverses when rules are further conditioned on both polity and debt, the conditional effect of rules on either turns out to further exacerbate, rather than diminish, the independent effects of these factors (the coefficient on the interaction term is positive when statistically significant). 47 46 The usual caveat here is that our sample suffers from significant attrition once we include this variable. We have also considered specifications that include the additional short-term controls from our panel baseline-at even greater detriment to our sample size-and the qualitative results remain essentially unaltered. 47 One possibility is that these results are being driven by the countries within the European Monetary Union (EMU), which face a common-pool problem that exacerbates the temptation to engage in public deficit financing, Although perhaps unexpected, these results should not be all that surprising. Others have found that compliance with fiscal rules tends to be tepid [START_REF] Caselli | Do Fiscal Rules Cause Better Fiscal Balances? A New Instrumental Variable Strategy[END_REF][START_REF] Reuter | National Numerical Fiscal Rules: Not Complied With, But Still Effective?[END_REF]. 48 Moreover, there are potential empirical issues that we do not resolve that take us beyond the scope of this paper; for example, it is possible that different rules have different effects [START_REF] Guerguil | Flexible Fiscal Rules and Countercyclical Fiscal Policy[END_REF], and this is washed out by our aggregate approach. Alternatively, the endogeneity of fiscal rules means that a more careful approach is required to identify causal effects [START_REF] Hayek | Do Fiscal Rules Constrain Fiscal Policy? A Meta-Regression-Analysis[END_REF]. Still, the fact that we uncover a positive effects of rules-in terms of both levels and interactions-indicates that a naive reliance on the presence of rules alone would be insufficient to deliver the sort of countercyclical actions that rational policymaking would call for.

Conclusion

This paper has offered an alternative approach to measuring the procyclicality of fiscal policy, using a dcc-garch model. This novel approach allows us to directly embed key explanatory factors-political economy and financial access-into the computation of dynamic conditional correlations between government spending and output.

We find that fiscal procyclicality owes more to political economy drivers, especially electoral pressures in advanced economies. In contrast, while we occasionally find that constraints due to limited financial access may matter, the evidence in favor of this explanation is more mixed.

Moreover, the effects of political participation and the burden of public debt may be conditioned by other channels. In particular, we find that special interest pressures may serve as a substitute for electoral competition and hence condition the procyclicality of fiscal expenditures, just as high levels of private debt may end up offsetting the tendency toward excess public spending. Importantly, we are able to tease out these relationships despite a fairly wide variation in spatial and temporal coverage, which speaks to the overall strength of the results and lends credibility to our methodology. Future work can exploit our time-series approach to expand the analysis to include additional, structural factors-such as social capital or productivity-that could further condition the cyclicality of fiscal policy. We leave such work, along with theoretical justifications of the mechanisms at play, to future research.

A.1.2. Summary statistics

* Austria †, ‡ Finland * , †, ‡ Poland * , †, ‡ Azerbaijan * France †, ‡ Portugal * , †, ‡ Bahrain †, ‡ Greece * , †, ‡ Rwanda * , †, ‡ Bangladesh * Guatemala †, ‡ Saudi Arabia * Bolivia& * Honduras †, ‡ Senegal * Botswana * India †, ‡ Singapore †, ‡ Brazil †, ‡ Italy * South Africa * Bhutan †, ‡ Kenya * , †, ‡ Spain * Bulgaria †, ‡ Mali * Sudan * , †, ‡ Canada * , †, ‡ Mauritania * Suriname †, ‡ Cape Verde †, ‡ Mauritius * Sweden * , †, ‡ Chad †, ‡ Moldova * Tanzania * Chile * , †, ‡ Montenegro †, ‡ Trinidad & Tobago †, ‡ Congo †, ‡ Morocco * United States †, ‡ Costa Rica * , †, ‡ Namibia * Uruguay * Croatia * , †, ‡ Nicaragua * , †, ‡ Yemen †, ‡ Cyprus †, ‡ Norway * , †, ‡ Denmark * , †, ‡ Panama *
Countries included in the Gc subsample. † Countries included in the Ge subsample. ‡ Countries included in the Gp subsample.

A.2. Details of additional robustness checks

In this annex, we report the results for robustness checks where we vary: the measure of political participation (left panel), the debt constraint (middle panel), and the fiscal balance (right panel). For the first, we substitute polity with democracy, under the notion that it is the sort of political competition inherent in democracies that matter more for inducing procyclical behavior, which may or may not be relevant in an autocracy. 49,50 For the second, we substitute total debt with the ratio of public debt to tax revenue, which is a potential measure of fiscal space (c.f. [START_REF] Aizenman | Fiscal Space and Government-Spending and Tax-Rate Cyclicality Patterns: A Cross-Country Comparison, 1960-2016[END_REF]. For the third, we compute three different measures of the fiscal balance, which embeds the revenue side of the government balance sheet, and may better capture the possibility that it is net fiscal position that matters. These measures are the DCC between the fiscal deficit/GDP ratio and real GDP growth (G d ), the cyclical deviation of the real fiscal balance and cyclical deviations of real GDP (G d ), and the primary deficit/GDP and real growth rate (G b ). 51 For the same reasons as before, we report the specifications with country and time fixed effects, but excluding additional controls.

As can be seen in Table A.6, the results remain broadly robust to our changes. In some cases, the coefficients for either democracy or fiscal space fall out of significance relative to the baseline, but in other (unreported) specifications-either when including only country fixed effects, or with additional controls-certain coefficients become significant. Overall, however, the effects from political economy are more consistently significant, as in the baseline.

When using the various fiscal balance measures, the coefficients are both negative. This is in accord with a priori theory for the political economy effect; that is, heightened political participation gives rise to larger deficits, consistent with reduced spending discipline and greater procyclicality (in other words, if procyclicality were present, the correlation between deficits and economic activity would be negative). However, when the debt constraint is more binding, deficits tend to be larger as well. In our view, this is less likely due to an enhanced ability to spend, but rather a diminished ability to raise revenue (that is, the effect operates along the revenue rather than expenditure margin). This is likely to be the case even with our limited efforts to address simultaneity (by including debt with a lag), mainly because the effects of a high debt burden on the budget balance are likely to be persistent.

As discussed in the main text, the reduced statistical significance of the political economy and financial access proxies when using the correlation of activity to fiscal balances could be due to the fact that revenues may be counter cylical; if governments choose to slash taxes during contractions (or hike them during expansions), this additional countercyclicality on the revenue side would diminish the procyclicality effects of spending.

Finally, it is worth noting that the sample sizes, especially when using the alternative fiscal access measure, are substantially smaller, as is the country coverage (in all but one case). This provides some justification for not utilizing these metrics in our baseline results.

In footnote 29, we mention that we perform a series of robustness checks on the sensitivity of 49 Although not reported, further decompositions of polity into its three constituent indexes-executive recruitment, executive constraint, and political competition-reveal that recruitment and competition tend to be more material for procyclicality. Since the executive recruitment score also includes a subcomponent representing the competitiveness of recruitment-whereas the constraints subindex is essentially about decision rules-we conclude that the degree of political competition (in any aspect of government) is the key mechanism that gives rise to procyclicality. 50 We also considered, as an alternative, a measure of constraints imposed by different government branches with veto power over policy changes [START_REF] Henisz | The Institutional Environment for Economic Growth[END_REF]. The qualitative results obtained are largely similar to the other instances of robustness reported here, and are available on request.

51 Additional details on the definition and construction of these variables are provided in the data appendix.

the results in the specifications with controls (P3, P6, and P9 of Table 3). Our approach is to systematically consider every possible combination of the controls, incrementally building up from just a single variable (AP1-AP9), to permutations of two controls (AP10-AP18). These results are shown in the top three panels of Table A .7. In all cases, the debt variable does not enter with a statistically significant coefficient, whereas the polity variable does so on a number of occasions. Looking more carefully at the rightmost columns, it is clear that the inclusion of the change in the money supply tends to compromise the significance of the polity variable. This could be either due to sample changes, or because even purely fiscal operations may be accompanied by changes in the money supply as a consequence [START_REF] Cochrane | The Fiscal Theory of the Price Level[END_REF][START_REF] Hamburger | Deficits, Money and Inflation[END_REF]. To better isolate the effect of monetary policy, then, we substitute the money supply-which we chose as a proxy for monetary policy to maximize sample coverage-with the real interest rate, either singly (AP19-AP21) or with the other two cyclical policy controls (AP22-AP24). In either case, the political economy effect turns out to be positive and significant (with financial access remaining insignificant).

In footnote 31, we point to how public investment appears to be cyclical, especially when financial constraints bind. We test this claim using two proxies for public investment. First, we calculate the expenditure-deflated difference of government expenditure and consumption (G c -G e ), as a proxy for changes in public investment (this approach is imperfect because expenditures also include transfer and interest payments; however, if both are fairly constant, then the difference between the two variables will derive mainly from changes to public investment). 52 The cyclical component is then extracted from this constructed series, and the dynamic conditional correlation is computed vis-à-vis the cyclical component of real GDP. Second, we obtain a (limited) series for the public share of gross fixed capital formation (G k ), available from the OECD for up to 41 economies. 53 Since this is a share, following the logic of the argument for the primary expenditure share, we obtain the dynamic conditional correlation relative to the real growth rate.

Table A.9 reports our results for these proxies for the three baseline specifications analogous to Table 3. We offer several observations. First, we find some evidence that, in the presence of debt constraints, government investment does appear to be countercyclical. The coefficients on the debt variable are consistently negative, and statistically significant when using the OECD data (albeit applicable to only a small number of countries). Second, the coefficients on the polity measure, while positive as before, tend to be insignificant. This suggests, as implied in the text, that political economy pressures are less likely to matter for public investment. Finally, the important caveat that colors all these findings is that they are based on a much diminished sample: a maximum of 18 economies (and only 37 years) for the (G e -G c ) proxy, but as little as 7 economies (over 42 years) for the G k proxy. Consequently, we would not be confident in entirely dismissing the role of political economy factors in influencing public investment, although it is likely to be of second-order importance.

52 There is a secondary, technical problem with combining both series: the data for government consumption are in constant local currency units (LCU) (having been deflated by a national expenditure deflator), while that for government expenditure is in nominal LCU, which is then deflated by the GDP deflator. The base year for these deflators differ. While care was taken to ensure that comparable series were used for the baseline (GDP in constant LCU for the former, and GDP in current LCU, suitably deflated, for the latter; and further ensuring that the corresponding series all relied on the same data source), this is not the case for the combined series. While these differences should not be too consequential, it is worth recognizing that the potential for measurement error exists as a result.

53 However, for the majority of these countries, data only begin in 1995 (running through 2018), with longer durations only for 6 economies. As a consequence, the GARCH model fails to converge in the majority cases, depriving us of many countries.

In footnote 24, we allude to alternative calculations of the conditional correlation between the primary expenditure share G p and real growth. We consider two alternatives: the constant conditional correlation (ccc-garch) model, and the varying conditional correlation (vcc-garch) model. These alternative models differ from our baseline dcc-garch model in terms of the parameter restrictions imposed on the correlation matrix. For the former, conditional correlation parameters that weight nonlinear combinations of the conditional variance are held constant; for the latter, these follow a garch-like process. The resulting conditional correlations are reported in Table A.8. Tests of equality of means between the different measures relative to our baseline are not statistically different at the 10 percent level.

In footnote 47, we describe additional robustness checks where we consider whether the EMU economies are driving the result that fiscal rules exacerbate procyclicality. More generally, EMU countries may be more subject to politically-driven fiscal cycles, a finding that has some precedence in the literature (c.f. Castro & Martins 2018). To verify if this is the case, we conduct two sets of tests. First, we introduce an indicator variable for economies in the EMU and examine whether the coefficient on this EMU effect is positive and significant (in this case, the sample retains both non-EMU as well as EMU economies prior to entry). Second, we restrict our analysis to only EMU economies, and check whether the interaction between the EMU indicator and fiscal rules is positive and significant. As reported in Table A.10, in either case, the EMU effect turns out to be statistically insignificant. These results indicate that the positive effect of rules on procyclicality are not driven by the EMU effect. The dependent variable is the dynamic conditional correlation between the cyclical components of economic activity and government spending listed in the first row. All other variables are expressed using the inverse hyperbolic sine transformation (except for change variables, which are expressed as percentage changes) and lagged one period. TB = trade balance, MS = money supply, FX = exchange rate, IR = real interest rate. A constant term was included in all regressions, but not reported. Standard errors, clustered over country and year, are given in parentheses. Goodness-of-fit measures report the R 2 and within R 2 . * indicates significance at 10 percent level, * * indicates significance at 5 percent level, and * * * indicates significance at 1 percent level.

G c G e G p G c G e G p G d G d G b (AR1) (AR2) (AR3) (AR4) (AR5) (AR6) (AR7) (AR8) (

A.3. Additional figures

In this appendix, we include a number of additional figures to complement those provided in the main text. Figure A.1 complements Figure 2 in the main text, where dynamic conditional correlations in the bivariate and multivariate models are plotted for Chile and Nicaragua, to illustrate mid-period switches and trends in procyclicality, respectively.

In Section 5.2, we focus the discussion on the conditional effects of including additional variables related to the political economy and financial access channels. Figure A.2 demonstrates that the total effects of polity and public debt never turns significantly negative (these are calculated with the specifications that yield significant coefficients of interest, I3 and I4, respectively). Marginal effects are are reported in Table A .11. When evaluated at the means, the marginal effect of polity on ρ d (G p ) is negative but statistically indistinguishable from zero; similarly, the effect of debt on ρ d (G c ) is positive but likewise statistically insignificant (these apply as well to the marginal effects of corruption and private credit, although the former is marginally significant). For a hypothetical "average" economy, then, neither political economy nor financial access variables appear to matter for fiscal procyclicality, which underscores the importance of taking into account total effects as well as effects by income-group subsample (both of which were addressed in the main text). The dependent variable in the regressions is the dynamic conditional correlation between the primary expenditure and economic growth (political economy channel) and the cyclical component of government consumption and output (financial access channel). Variables were expressed using the inverse hyperbolic sine transformation and lagged one period. Standard errors are given in parentheses. * indicates significance at 10 percent level, * * indicates significance at 5 percent level, and * * * indicates significance at 1 percent level. (bottom), 1960-2018, between the cyclical components of government consumption and GDP. In Chile, correlations after accounting for financial access in a multivariate garch model are initially very low, before following the bivariate model more closely. Over time, procyclicality is falling in Chile, a result confirmed by others [START_REF] Frankel | A Solution to Fiscal Procyclicality: the Structural Budget Institutions Pioneered by Chile[END_REF]. In comparison, procyclicality is rising in Nicaragua, and this appears to be largely due to financial access reasons.

47

- and public debt, conditional on private credit (bottom), on the dynamic conditional correlation of primary expenditure and growth, and the cyclical components of government consumption and GDP, respectively. Dashed maroon lines represent the 90 percent confidence bands. Special interest lobbying is proxied with corruption, and private credit with domestic credit to the private sector. Both interaction effects are negative, suggesting that (respectively) electoral competition and lobbying, as well as public and private debt, are substitutes. At very high levels of either corruption and private credit, the respective total effects turn negative, but neither are statistically significant.

  is up to 1862-2011.The Polity index (as well as democracy indicator, used in robustness checks) are from latest version of the[START_REF] Marshall | Polity IV, 1800-1999[END_REF] Polity IV database, while the public debt data are from[START_REF] Abbas | Historical Patterns and Dynamics of Public Debt-Evidence From a New Database[END_REF], which is also offers the longest temporal coverage.20

  's calculations. Notes: Shaded area represents the upper (75th) and lower (25th) percentile, and solid (dashed) line represents the median (mean), in the distribution of the conditional correlations between primary expenditure and GDP growth, for any given year.

  The dependent variable is the dynamic conditional correlation between the cyclical components of economic activity and government spending listed in the first row. All other variables are expressed using the inverse hyperbolic sine transformation (except for change variables, which are expressed as percentage changes) and lagged one period. A constant term was included in all regressions, but not reported. Standard errors, clustered over country and year, are given in parentheses. Goodness-of-fit measures report the R 2 and within R 2 . * indicates significance at 10 percent level, * * indicates significance at 5 percent level, and * * * indicates significance at 1 percent level.

Figure 2 :

 2 Figure 2: Dynamic conditional correlations for Finland (top) and Greece(bottom), 1960-2018, between the cyclical components of government consumption and GDP. In Greece, there is little difference in correlations after accounting for either political economy or financial access in a multivariate garch model, whereas significant separation in correlations occurs for Finland when financial access is included.

  [START_REF] Abbas | Historical Patterns and Dynamics of Public Debt-Evidence From a New Database[END_REF]

  Figure A.1: Dynamic conditional correlations for Chile (top) and Nicaragua(bottom), 1960-2018, between the cyclical components of government consumption and GDP. In Chile, correlations after accounting for financial access in a multivariate garch model are initially very low, before following the bivariate model more closely. Over time, procyclicality is falling in Chile, a result confirmed by others[START_REF] Frankel | A Solution to Fiscal Procyclicality: the Structural Budget Institutions Pioneered by Chile[END_REF]. In comparison, procyclicality is rising in Nicaragua, and this appears to be largely due to financial access reasons.

  Figure A.2: Total effect of electoral competition, conditional on special interest lobbying (top) and public debt, conditional on private credit (bottom), on the dynamic conditional correlation of primary expenditure and growth, and the cyclical components of government consumption and GDP, respectively. Dashed maroon lines represent the 90 percent confidence bands. Special interest lobbying is proxied with corruption, and private credit with domestic credit to the private sector. Both interaction effects are negative, suggesting that (respectively) electoral competition and lobbying, as well as public and private debt, are substitutes. At very high levels of either corruption and private credit, the respective total effects turn negative, but neither are statistically significant.

Table 1 :

 1 Static and dynamic measures of fiscal procyclicality, 1810-2018 (maximum) †

				Static
		Unconditional	Conditional
		G c	G e	G p	G c	G e	G p
	All	0.19	0.14	-0.06	0.20	0.10	0.66
	Advanced	0.11	-0.04	-0.11	0.21	-0.04	0.63
	Developing 0.22	0.23	-0.01	0.20	0.20	0.70
				Dynamic
			Rolling		Conditional
		G c	G e	G p	G c	G e	G p
	All	0.17	0.11	-0.14	0.18	0.07	0.57
		(0.27) (0.33) (0.21)	(0.22) (0.34) (0.17)
	Advanced	0.08	-0.06	-0.24	0.16	-0.10	0.57
		(0.28) (0.36) (0.14)	(0.23) (0.44) (0.11)
	Developing 0.21	0.19	-0.04	0.19	0.17	0.56
		(0.25) (0.29) (0.22)

(0.22) (0.22) (0.23) † Static correlations are the average across all countries for a given income group, and may be computed with different start/end years. Dynamic correlations are the average of within-country means across all countries for a given income group. Parentheses are standard deviations calculated from within-country distributions. Gc coverage is up to 1960-2018, Ge coverage is up to 1980-2018, and Gp coverage is up to 1810-2011.

Table 2 :

 2 Cross-sectional regressions for fiscal procyclicality, 1800-1995 (maximum) †

				Unconditional
		G c		G e		G p
		(C1)	(C2)	(C3)	(C4)	(C5)	(C6)
	Polity	-0.041	-0.005	-0.043	-0.031	-0.012	0.003
		(0.013) * * *	(0.012)	(0.025) *	(0.013) * *	(0.012)	(0.020)
	Debt	-0.143	-0.028	0.035	0.013	-0.049	-0.093
		(0.046) * * *	(0.028)	(0.066)	(0.029)	(0.029) *	(0.046) * *
	R 2	0.298	0.012	0.088	0.034	0.093	0.121
	Estimation OLS	OLS	OLS	OLS	OLS	OLS
	Errors	B/strapped B/strapped	B/strapped B/strapped	B/strapped B/strapped
	Obs.	39	148	32	160	43	54
				Conditional
		G c		G e		G p
		(C7)	(C8)	(C9)	(C10)	(C11)	(C12)
	Polity	-0.045	-0.041	-0.008	-0.039	-0.013	-0.024
		(0.022) * *	(0.034)	(0.028)	(0.041)	(0.010)	(0.016)
	Debt	-0.075	-0.010	0.066	0.061	0.023	0.031
		(0.068)	(0.054)	(0.077)	(0.065)	(0.028)	(0.039)
	R 2	0.138	0.059	0.026	0.052	0.049	0.038
	Estimation OLS	OLS	OLS	OLS	OLS	OLS
	Errors	B/strapped B/strapped	B/strapped B/strapped	B/strapped B/strapped
	Obs.	39	39	32	33	43	36
	†

Table 3 :

 3 Panel regressions for fiscal procyclicality, 1801-2016 (unbalanced) †

Table 4 :

 4 Difference in means for dynamic conditional correlations after controlling for polity or debt, 1862-2015 (unbalanced) † Differences are the mean changes in dynamic conditional correlations within each country, averaged across all countries for a given income group, and may be computed with different start/end years. Significant shares indicate the number of countries where the t test for differences in means are significant at the 5 percent level. Gc coverage is up to 1960-2015, Ge coverage is up to 1980-2015, and Gp coverage is up to 1862-2011.

				Political economy
			G c		G e		G p
		Diff.	Sig. (%)	Diff.	Sig. (%)	Diff.	Sig. (%)
	All	-0.008	59	-0.028	57	-0.380	90
	Advanced	-0.013	60	-0.016	60	-0.467	88
	Developing -0.002	58	-0.039	50	-0.292	100
				Financial access
			G c		G e		G p
		Diff.	Sig. (%)	Diff.	Sig. (%)	Diff.	Sig. (%)
	All	-0.038	59	-0.057	29	-0.441	100
	Advanced	-0.068	50	0.022	40	-0.352	100
	Developing -0.009	67	-0.136	0	-0.529	100
	†

Table 5 :

 5 Panel regressions for fiscal procyclicality by income group subsample †

			Advanced			Developing
		G c	G e	G p	G c	G e	G p
		(S1)	(S2)	(S3)	(S4)	(S5)	(S6)
	Polity	0.019	0.033	0.005	0.003	0.022	0.011
		(0.007) * *	(0.012) * *	(0.004)	(0.013)	(0.009) * * (0.013)
	Debt	0.050	-0.046	-0.037	0.018	-0.011	0.010
		(0.034)	(0.045)	(0.009) * * *	(0.041)	(0.035)	(0.045)
	Fixed effects:
	Time?	Yes	Yes	Yes	Yes	Yes	Yes
	Country?	Yes	Yes	Yes	Yes	Yes	Yes
	R 2 (adj.)	0.481	0.775	0.442	0.428	0.472	0.498
	R 2 (within)	0.022	0.008	0.020	0.001	0.009	0.007
	Estimation	FE	FE	FE	FE	FE	FE
	Errors	Clustered Clustered Clustered	Clustered Clustered Clustered
	Ctry (yr)	20 (57)	17 (37)	23 (211)	24 (57)	24 (35)	17 (72)
	Obs.	889	501	2,396	985	578	779
	†

Table 6 :

 6 Conditioning on additional political economy and financial access channels †

			Political economy	Financial access
		G c	G e	G p	G c	G e	G p
		(I1)	(I2)	(I3)	(I4)	(I5)	(I6)
	Polity	-0.035	0.064	0.085	0.019	-0.173	0.003
		(0.032)	(0.028) * *	(0.046) *	(0.008) * *	(0.924)	(0.006)
	Corruption	-0.063	-0.051	0.212
		(0.061)	(0.060)	(0.074) * * *
	Polity ×	0.020	-0.036	-0.044
	corruption	(0.020)	(0.016) * *	(0.023) *
	Debt	0.016	-0.032	-0.076	0.471	-1.257	-0.047
		(0.043)	(0.027)	(0.035) * *	(0.148) * * * (0.986)	(0.067)
	Pte credit				0.503	-1.119	-0.016
					(0.199) * *	(0.959)	(0.075)
	Debt ×				-0.093	0.232	0.002
	pte credit				(0.036) * *	(0.189)	(0.016)
	Fixed effects:
	Time?	Yes	Yes	Yes	Yes	Yes	Yes
	Country?	Yes	Yes	Yes	Yes	Yes	Yes
	R 2 (adj.)	0.463	0.713	0.521	0.507	0.560	0.432
	R 2 (within)	0.005	0.018	0.110	0.028	0.025	0.014
	Estimation	FE	FE	FE	FE	FE	FE
	Errors	Clustered Clustered Clustered	Clustered Clustered Clustered
	Ctry (yr)	41 (32)	34 (32)	40 (27)	11 (57)	8 (37)	16 (141)
	Obs.	1,172	876	1,023	598	262	1,699
	†

Table 7 :

 7 Accounting for fiscal rules †

Table A .

 A 1: Summary statistics for main variables of interest, full panel †

	Table A.2: Correlation matrix for main variables of interest A.1.3. Sample, definitions and sources
				G c	G e Table A.4: Sample of countries † G p Y Ẏ	Polity Debt
	Variable Real gov consumption 6,275 N Real gov expenditure 5,383 Primary exp/GDP 4,411 Real output 5,383 Debt -0.06 Polity -0.07 Ẏ 0.00 G c 1.00 G e 0.97 Albania †, ‡ G p -0.13 Angola †, ‡ Y 0.95 Australia *	Mean 9863.32 17638.57 19.64 85289.33 -0.06 0.12 -0.03 0.43 0.08 -0.05 Ecuador †, ‡ 1.00 -0.20 El Salvador * 1.00 0.98 -0.13 Estonia& *	Std Dev Full sample 62086.90 117129.20 13.73 619755.30 -0.05 -0.09 -0.04 -0.03 0.03 1.00 Paraguay †, ‡ Min 0.01 0.01 0.56 0.03 -0.01 1.00 Peru * 1.00 Philippines	1.00	Max 11000000.00 66.04 1836263.00 828714.20
	Real growth			4,411	3.46	4.88	-35.33	53.13
	Polity			5,521	3.0	7.0	-10.0	10.0
	Public debt/GDP	5,051 Table A.3: Correlation between different static and dynamic pro-54.86 53.80 0.06 High income cyclicality measures †	2092.92
	Real gov consumption 2,225 Real gov expenditure 1,781	4477.51 7631.38 G c	20634.67 36092.22 G e	G p	0.24 0.02	241919.40 368143.20
	Primary exp/GDP	2,966 corr (ρ u , ρ c )	21.64 0.60	15.27 0.75	0.56 -0.24	66.04
	Real output			2,225	27127.45 (0.00)	134859.00 (0.00)	2.00 (0.00)	1597514.00
	Real growth		2,966 corr ρ r , ρ d	3.22 0.67	4.79 0.69	-35.33 0.09	53.13
	Polity			1,857	7.2 (0.00)	5.7 (0.00)	-10.0 (0.00)	10.0
	Public debt/GDP			1,846	47.71	35.24	0.06	283.96
						Developing
	Real gov consumption 4,050	12822.19	75594.22	0.01	828714.20
	Real gov expenditure	3,563	22833.81	141413.10	0.01	1836263.00
	Primary exp/GDP		1,445	15.55	8.46	1.21	48.19
	Real output			3,563	114247.80	752586.80	0.03	11000000.00
	Real growth			1,445	3.96	5.02	-26.48	38.09
	Polity			3,664	0.9	6.6	-10.0	10.0
	Public debt/GDP			3,205	58.98	61.64	1.03	2092.92

Table A .

 A 6: Robustness regressions for fiscal procyclicality, 1801-2015 (unbalanced) †

	Fiscal balance
	Financial access
	Political economy

  The dependent variable is the dynamic conditional correlation between the cyclical components of economic activity, and either government spending or the fiscal balance, as listed in the second row. All other variables are expressed using the inverse hyperbolic sine transformation and lagged one period. A constant term was included in all regressions, but not reported. Standard errors, clustered over country and year, are given in parentheses. Goodness-of-fit measures report the adjusted R 2 Table A.7: Panel regressions for fiscal procyclicality, 1801-2016 (unbalanced), with alternate controls †

	Gc (AP1) 0.010 (0.008) 0.038 (0.030) Ctry (yr) 44 (56) Polity Debt Control Obs. 1,797 (AP7)	Ge (AP2) 0.020 (0.008) * * (0.009) * Gp (AP3) 0.016 -0.011 -0.039 (0.030) (0.032) TB 39 (37) 40 (51) 977 1,622 (AP8) (AP9)	Yes Yes Yes	Yes Yes Yes	0.523 0.600 0.392 Gc (AP4) 0.024 0.003 0.002 0.010 (0.009) FE FE FE 0.023 (0.041) 37 (55) 1,485 (AP10) (AP11) Clustered Clustered Clustered 68 (36) 38 (37) 38 (211) 961 3,112 Ge (AP5) 0.011 (0.009) -0.018 (0.028) ∆MS 35 (37) 871 1,670	Gp (AP6) 0.015 (0.010) -0.028 (0.037) 29 (50) indicates significance at 10 percent 1,208 . * (AP12)
	Fixed effects: 0.013 (0.009) * * (0.008) 0.019 -0.038 -0.021 (0.025) (0.030) ∆FX 41 (37) 40 (50) 979 1,583 (AP13) (AP14) 0.010 (0.007) 0.025 (0.034) Ctry (yr) 44 (55) Polity Debt Control Obs. 1,755 (AP15) Time? Yes Yes Yes Yes Yes Yes Polity 0.010 0.025 0.014 (0.008) (0.010) * * (0.009) Debt 0.020 -0.043 -0.016 (0.035) (0.029) (0.034) Control TB, ∆FX Ctry (yr) 44 (55) 39 (37) 40 (50) Obs. 1,697 875 1,480 (AP19) (AP20) (AP21) Polity -0.005 0.031 0.036 (0.015) (0.012) * * (0.015) * * Debt 0.015 -0.024 -0.042 (0.047) (0.025) (0.027) Control IR Ctry (yr) 31 (55) 27 (35) 26 (50) Obs. 910 604 722 †	Country? Yes Yes Yes Yes Yes Yes	R 2 (adj.) 0.469 0.707 0.446 0.447 0.727 0.458 0.009 R 2 (within) 0.010 0.001 0.021 0.012 0.014 0.032 (0.009) Estimation FE FE FE FE FE FE 0.018 (0.040) 37 (55) 1,467 (AP16) (AP17) Errors Ctry (yr) Obs. Clustered Clustered Clustered Clustered Clustered Clustered 44 (57) 41 (37) 40 (211) 41 (44) 36 (37) 40 (39) 1,874 1,081 3,228 782 1,181 0.016 (0.010) -0.014 (0.029) TB, ∆MS † 33 (37) 772 0.010 0.011 (0.009) (0.009) 0.023 -0.024 (0.041) (0.028) ∆FX, ∆MS 1,172 37 (55) 35 (37) 1,485 860 (AP22) (AP23) -0.005 0.048 (0.015) (0.014) * * * (0.015) * * level, * * and within R 2 0.016 (0.010) -0.027 (0.041) 29 (50) 1,170 (AP18) 0.017 (0.010) -0.015 indicates significance at 1 percent level. (0.038) 29 (50) 1,197 (AP24) 0.034 0.010 -0.027 -0.038 (0.048) (0.027) (0.039) TB, IR, ∆FX 31 (55) 25 (35) 26 (50) 887 497 678 indicates significance at 5 percent level, and * * *
		43

* *

Table A .

 A 11: Marginal effects of polity and debt, evaluated at means †

		dy dx	CI
	Polity	-0.001	[-0.026, 0.024]
		(0.013)
	Corruption	0.098	[-0.010, 0.207]
		(0.055)
	Debt	0.010	[-0.088, 0.107]
		(0.050)
	Credit	0.098	[-0.125, 0.322]
		(0.114)
	†

Our definition of "fiscal" in this paper is, consistent with the literature, mainly focused on the variations in the spending side of the fiscal ledger, which we regard as the more relevant margin for typical applications of countercyclical stabilization policy. While revenue instruments-such as the tax rate-occasionally feature as discretionary fiscal policy, tax policy changes are rarer in practice, and consequently tend to be less amenable to analysis at business-cycle frequencies. Nevertheless, we also consider three fiscal balance measures in our robustness checks, which implicitly capture variations on the revenue side.

Although coefficient estimates do not suffer from such bias, correlations require the use of second moments, which are biased in the presence of heteroskedasticity.

While the use of instrumental variables is relatively common in the fiscal multiplier literature (e.g.[START_REF] Acconcia | Mafia and Public Spending: Evidence on the Fiscal Multiplier from a Quasi-Experiment[END_REF][START_REF] Barro | Macroeconomic Effects From Government Purchases and Taxes[END_REF][START_REF] Ramey | Identifying Government Spending Shocks: It's All in the Timing[END_REF], its use in the fiscal cyclicality context is comparatively less common.

Relatedly, papers that condition multipliers on debt (e.g.[START_REF] Eggertsson | Debt, Deleveraging, and the Liquidity Trap: A Fisher-Minsky-Koo Approach[END_REF] Huidrom et al. forthcoming;[START_REF] Ilzetzki | How Big (Small?) Are Fiscal Multipliers?[END_REF]) yield lower estimates when fiscal space is limited, which implies that the outstanding debt burden may itself alter the calculus behind countercyclical fiscal policy at the margin.

 5 And even in the absence of shocks, Ricardian equivalence would suggest that any increases in government expenditure would simply be offset by concomitant declines in private demand; at the extreme, such public expenditures may shift demand from producers' goods to consumers' goods and even prolong stagnation[START_REF] Hayek | Do Fiscal Rules Constrain Fiscal Policy? A Meta-Regression-Analysis[END_REF]. In this case, fiscal policy should at best be acyclical.

Under the assumption, standard in public finance, that this first-best allocation is socially desirable.

There is a much older tradition that has explored political business cycles[START_REF] Andrikopoulos | Fiscal Policy and Political Business Cycles in the EU[END_REF][START_REF] Castro | Politically Driven Cycles in Fiscal Policy: In Depth Analysis of the Functional Components of Government Expenditures[END_REF][START_REF] Potrafke | Political Cycles and Economic Performance in OECD countries: Empirical Evidence from 1951-2006[END_REF]. However, many of these only go part of the way toward explaining procyclicality, since there is no ex ante reason why business cycles-which are usually less frequent than elections and other regular fluctuations in political activity (such as turnovers in political appointments), especially in advanced economies-need to closely adhere to such political calendars. A spinoff literature on political budget cycles[START_REF] Rogoff | Elections and Macroeconomic Policy Cycles[END_REF][START_REF] Shi | Political Budget Cycles: Do They Differ Across Countries and Why?[END_REF], while related, is also distinct, since papers in this vein typically examine spending patterns surrounding election events, but are generally less concerned with off-election periods.

Although these two mechanisms feature in most political economy explanations of fiscal procyclicality, they are not the only ones that could matter, of course;[START_REF] Barseghyan | Fiscal Policy Over the Real Business Cycle: A Positive Theory[END_REF], for instance, model legislative politics as a source of political distortion.

While the magnitudes for the Gp correlation may be on the high side, for the analysis that follows, we are somewhat less interested in the actual levels-which are evidently sensitive to the specific metric used-but more in their changes over time, and especially when conditioned on other covariates.

This is especially the case since the country-specific heteroskedasticity tests, as reported in Section 4.1, tend to indicate little issue with heteroskedasticity for Gp. However, a few mitigating factors should be kept in mind. First, heteroskedasticity is an issue for the panel at large. Second, the positive conditional estimate is more consistent with the other measures of spending procyclicality. Third, conditional correlations are more sensitive to not only heteroskedasticity (which afflicts the standardized residuals in (3e)), but also temporality (via lags of the unconditional mean in (3e), as well as the time-varying conditional variance matrix (3c)). The upshot of this is that the conditional correlation embed all historical information in generating dynamic correlations, rather than just a partial history (as is the case for rolling correlations). Fourth, while rolling correlation estimates ρr do address changes over time, these tend to be swing sharply, changing from negative to positive over the course of several years.

As an additional check on whether the conditional correlations are sensitive to the estimation methodology, we rerun estimates of ρc for Gp using two alternatives: a constant conditional correlation (ccc-garch) or a varying

A separate, technical concern is that the sample size varies between the unconditional and conditional correlations. We do not view this as a likely problem, since the results when using just observations from the initial year (odd-numbered columns), which do not suffer from this sample size change, are qualitatively similar and largely analogous to the results from the full sample period (even-numbered columns). To further verify that sample changes are not an issue, we reran the regression with the full sample restricted to only the countries in the initial-year sample. With the exception of the Gp measure, the results are qualitatively identical. For Gp, the sign change on the coefficient on debt between the unconditional and conditional correlations is indeterminate, since the coefficients on the latter are statistically insignificant.

 27 We view the respective middle specifications for each as our baseline, because it controls for the two most important dimensions of unobserved heterogeneity (and correlated residuals along those same dimensions) for our particular sample, without sacrificing sample

coverage.28 In some cases, even though the coefficient is insignificant at standard levels, they approach significance; for instance, for specifications P2 and P4, p = 0.12 and p = 0.17,

respectively.29 One potential concern regarding the results in Table3is that the significance of the coefficients on specifications with controls (P3, P6, and P9) appear to be weaker. We believe that this is the consequence of a combination of sample attrition and the inclusion of the control for changes in the money supply. We systematically test the sensitivity of the results to controls by re-running the regressions using all possible combinations of controls, as well as substituting the change in the money supply with the interest rate. As shown in the appendix and discussed in greater detail there, the main takeaway from this exercise is that the coefficient on the polity variable tends to exhibit much more stability relative to debt, consistent with the conclusions we draw here.

We do not do so simultaneously because we run into severe degree-of-freedom problems that prohibit convergence in the estimation.

In principle, it may be possible to replicate this approach using fixed effect panels, by comparing the coefficient estimates before and after including the additional variable of interest, via a Hausman test. However, doing so could mask important period-to-period changes in the relationship (a coefficient that changes significantly with each incremental year may nevertheless average no change over the full sample period). Second, while the two-step method is an indirect way of evaluating effect changes due to an added variable, our method accounts for how this additional variable alters the fiscal-output relationship directly, since our conditional (quasi) correlations are, by construction, derived from residuals.

For additional context, we also provide equivalent charts for Chile and Nicaragua, which illustrate mid-period switches and trends in procyclicality, in the appendix.

The observation count for the subsamples do not precisely match those for the full sample due to the need to drop singletons in the subsamples, which is a finite-sample corrections necessary in multi-way fixed effects models that also include multi-way clustering. Keeping these observations will lead to an underestimation of standard errors[START_REF] Cameron | Robust Inference With Multiway Clustering[END_REF]

We are hardly the first to consider the inclusion of these additional variables, which have appeared in the literature under various pretexts. For corruption, see[START_REF] Calderón | Do Good Institutions Promote Countercyclical Macroeconomic Policies?[END_REF] or[START_REF] Alesina | Why is Fiscal Policy Often Procyclical?[END_REF]; and for private credit, see[START_REF] Furceri | Fiscal Counter-Cyclicality and Productive Investment: Evidence from Advanced Economies[END_REF].

Either outcome is possible because fiscal procyclicality does not require that spending be directed toward the same beneficiaries; hence, special interest groups may benefit from procyclical spending in some cases, but the broader electorate may benefit in others.

This result is easily verified visually, and a figure that does so is supplied in the appendix. The appendix also discusses the marginal effects of each, when evaluated at the respective means.

As was the case previously, at no level of private credit does the total effect of debt on procyclicality turn significantly negative; a figure that verifies this fact visually is likewise provided in the appendix.

† Middle and bottom panels correspond to summary statistics by income group. Summary statistics are for the untransformed variables, allowing the maximum coverage available for a given fiscal policy measure, but statistics may vary depending on the available sample for a given specification.

† Significance level of each correlation coefficient reported correspondingly below, in parentheses.

† The EMU is defined as the economies that joined the European Monetary Union, according to their date of accession. The dependent variable is the dynamic conditional correlation between

Financial support from the ESSEC Centre de Recherche is acknowledged, and helped pay for much of the falafel and hummus consumed over the course of this project. A (much) earlier discussion on this topic benefited from feedback by Ayhan Kose and Raju Huidrom; subsequent comments were received from Fred Giesenow and Elena Seghezza (discussants), Arye Hillman

Highlights

• Assess political economy and financial access explanations for fiscal procyclicality

• Model procyclical fiscal-output relationship using a DCC-GARCH process

• Stronger evidence that polity, not debt, is associated with procyclicality

• Results driven by advanced economies along the expenditure margin

• Fiscal rules appear to amplify rather than mitigate procyclicality

Conflict of Interest

The author has declared all funding sources in the title footnote, and has no conflicts of interest to report.

Online Appendix (Not for Publication)

A.1. Data appendix

In this annex, we provide additional information about the data. We first detail a number of adjustments made to the raw data (Section A.1.1). We report a number of standard summary statistics (Section A.1.2). Finally, we provide a detailed table of the sample coverage, along with sources and definitions (Section A.1.3).

A.1.1. Data adjustments

Here, we document a number of data cleaning procedures we applied. These were effected mainly to remove gaps in the series, although in some instances they involve removing outliers or replacing known erroneous observations. • For real GDP: data for Kuwait between 1990-1991 from WDI were replaced with data from the WEO.

• For primary balance/GDP: datum for Dominica in 2002 was interpolated with fiscal balance/GDP; datum for Estonia in 2010 were interpolated with fiscal balance/GDP; datum for Ghana in 1981 was replaced with missing; data for Kiribati in 1992 and 2000 were interpolated with year; datum for St Kitts and Nevis in 1996 was interpolated with fiscal balance/GDP; data for Marshall Islands in 2002 and 2012 were interpolated with fiscal balance/GDP; datum for Swaziland in 1985 was interpolated with fiscal balance/GDP; datum for St Vincent and the Grenadines in 2002 was interpolated with fiscal balance/GDP.

• For the subcomponents of the polity measure, instances of foreign interruption (-66) were converted to missing, and interregna (-77) were replaced with 0 (consistent with the transformation of the raw Polity index to the continuous Polity2 index). Cases of transition (-88) were filled in with the Polity2 score, adjusted to the corresponding subcomponent's respective weight (executive recruitment: 4/10, executive constraints: 7/20, political competition: 1/2).

• The 2006 observation of government revenue for Zambia (from the WDI) was replaced with 21.8 percent, drawn from the WEO data. The dependent variable is the dynamic conditional correlation between the cyclical components of economic activity and government investment listed in the first row. All other variables are expressed using the inverse hyperbolic sine transformation (except for change variables, which are expressed as percentage changes) and lagged one period. A constant term was included in all regressions, but not reported. Standard errors, clustered over country and year, are given in parentheses. Goodness-of-fit measures report the R 2 and within R 2 . * indicates significance at 10 percent level, * * indicates significance at 5 percent level, and * * * indicates significance at 1 percent level.

Rules only

Conditioned on rules