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EUI, Florence October 2013 More Habits than Choice? Government Spending in the Oecd by Riccardo Fiorito University of Siena 1. GOALS Length, depth and spread of the Great Recession (2008-2013?) - and the differences in the recovery pace - revitalized the debate on fiscal stimuli and spending multipliers. Yet, this debate does not seem conclusive: not simply because results widely differ but also -and even more - because one should evaluate first if there are (and which are) spending variables under some policy control. Following Coricelli and Fiorito (2013) [henceforth: CF (2013)], I will discuss here the following preliminary question: Which is the government spending share that can be considered discretionary in a number of Oecd countries? This will be evaluated linking discretion to reversibility and temporariness of government expenditure, without estimating its effect on the economy. The analysis focusses on government spending since revenues normally reflect their cyclical tax bases, and discretion is virtually confined to occasional tax rate changes and negligible lump-sum receipts. Empirically, we use detailed, annual, general government NIPA variables (1980-2011) for 15 Oecd countries, chosen only when providing data long enough for a minimal time-series analysis: Austria, Belgium, Denmark, Finland, France, Iceland, Ireland, Italy, Japan, Netherlands, Norway, Spain, Sweden, UK and the US. 2 2. FISCAL DISCRETION IN THE EMPIRICAL LITERATURE Isolating discretionary policies is complicated as every spending component combines automatic, inertial and discretionary elements. The empirical research, however, is roughly based on three main approaches: (1) cyclically adjusted government balances (2) estimated residuals from feedback equations (3) event chronology (“narrative approach”). 2.1 Cyclically adjusted balances Output gap elasticity is used to remove automatic stabilization from actual (CAB) or primary (CAPB) balances (Blanchard, 2003; Girouard and André, 2005). However: CAP (CAPB) do not account for recessions nor for differences in government size. Have a limited ability to remove cyclical fluctuations as Table 1 shows comparing CAPB and unadjusted balances: in most cases, CAPB is less volatile but not less persistent and correlation among the two (stationary) variables is very high. 3 Table 1- Primary Balances and Cyclically Adjusted Primary Balances (CAPB) Country Range (1) Primary Balances (2) CAPB Correlation Volatility Persistence Volatility Persistence (1)-(2) Austria 1990-2011 1.44 8.4 (5) 1.10 8.5 (5) .84 Belgium 1990-2011 3.80 54.1 (8) 3.41 46.8 (8) .97 Denmark 1990-2011 3.82 49.4 (8) 2.93 49.7 (8) .97 Finland 1990-2011 3.99 45.5 (8) 2.54 51.6 (8) .95 France 1990-2011 1.54 24.3 (8) 1.12 37.2 (8) .88 Iceland 1990-2011 4.32 9.7 (5) 4.06 5.2 (5) .94 Ireland 1990-2011 6.48 15.7 (5) 3.75 35.7 (5) .84 Italy 1990-2011 3.37 89.0 (8) 3.81 75.0 (8) .97 Japan 1990-2009 3.34 60.3 (7) 3.00 64.5 (7) .97 Netherlands 1990-2011 2.20 12.5 (8) 2.00 6.9 (8) .91 Norway 1990-2012 2.35 46.1 (8) 2.36 43.3 (8) 1.00 Spain 1990-2011 3.63 37.8 (8) 2.84 37.4 (8) .96 Sweden 1990-2012 4.09 58.1 (8) 3.13 43.1 (8) .95 UK 1990-2011 3.36 38.7 (8) 2.90 44.4 (8) .96 US 1990-2011 3.17 31.5 (8) 2.71 40.3 .98 Source: Oecd, Economic Outlook. Primary balances and CAPB are percentage ratios to actual and potential GDP, respectively. Persistence is given by the LB test. 4 2.2 Residuals from estimated spending equations Fatás and Mihov (2003) first estimated gov.t consumption/gdp changes as a function of real GDP changes and of a set of controls in a panel of developed and developing countries. Afonso et al. (2008) follow a similar approach, estimating expenditure and revenues in levels. More recently, Corsetti et al. (2012) estimate an Oecd panel where regression controls include government debt and exchange rate variables. In practice, all these studies estimate a government consumption equation and interpret its residuals as a measure of the discretionary spending component. However: Approximating discretion via residuals, confines discretionary spending to an unpredictable shock, in a non-VAR framework. The fact that estimated residuals are white does not imply per se discretion. On the contrary, it is plausible that discretionary spending aims at improving – though temporarily - the economy: i.e. that discretionary interventions should only be less cyclical than automatic stabilizers are. 5 2.3 Event studies The ‘narrative’ approach measures directly discretion interpreting laws, policy interventions or intentions revealed – say - by presidential speeches: See: Ramey and Shapiro (1998); Romer and Romer (2010) postwar reconstruction on tax legislation in the US; Finally, Ramey (2011) shows that events GC SVAR shocks. Limits of the event approach: Policy statements (even Laws!) are policy intentions that not always result in budget decisions. Often, proposals are not precise enough in terms of the involved funding. Further, approved budget decisions do not necessarily imply that actual spending is the same as the approved spending in the reference year (Elmendorf, 2011). Linguistic problems confine analysis to the US only, despite two recent IMF studies (IMF, 2010; Devries et al., 2011) provide cross-national evidence that have been recently used for instrumenting (IV) government spending (Jordà and Taylor, 2013). Finally, events are sort of dummies that cannot reconstruct how fiscal policy works in the light of expenditure composition and of inside and outside lags (Blinder, 2006). 6 3. DISCRETIONARY SPENDING: A NEW MEASURE Discretionary and non-discretionary spending are artificial constructs, having no reference to actual data. Yet, they are important for evaluating stabilization policies. This happens because automatic stabilizers (Table 2) do not require policy makers to know current state of the economy and, especially, to have political consensus. Discretionary spending assumes instead knowledge of the current and perspective economic conditions. Discretion is generally based on the political commitment to improve, more or less immediately, the state of the economy. Table 2 – Fiscal Policy Lags SPENDING AUTOMATIC DISCRETIONARY INSIDE LAG: None Recognition + Decision B* C A B B* C From perception to decision (A B) OUTSIDE LAGS: From actual spending (B*) to (B’ = B + e); e = Lag between decision macroeconomic effects (C) and actual spending 7 3.1 Conceptual requirements Our measure of discretionary spending (CF, 2013) rests on economic rather than on legal grounds: a few, intuitive, assumptions, that are approximated and tested via simple univariate properties of each spending component. i) DISCRETION CANNOT BE (TOO) INERTIAL Since actual policy decisions do not simply update or confirm past decisions, discretionary spending should be less persistent than automatic stabilizers. ii) DISCRETION DOES NOT IMPLY OBLIGATIONS This requirement is meant to exclude a priori those variables, mostly characterized by several types of obligations (e.g. debt payments, wages, pensions). iii) DISCRETION MUST BE TEMPORARY AND REVERSIBLE Policy interventions should reflect discrete and reversible choices, i.e. temporary spending decisions that democratic governments can legally start and cancel. Commonly used measures of discretionary spending DO NOT satisfy these criteria. 8 3.2 Empirical implementation Besides debt, government spending includes many types of obligations (wages, pensions etc.), regardless of their legal, contractual or plainly moral nature. Empirically, the absence or the presence of less stringent obligations should not only imply a lower persistence but also a higher volatility relative to more automatic types of spending We consider the volatility and persistence of each spending variable by taking cyclical variables (HP-filter) in volume to look at their stationary time-series properties. The persistence is evaluated via the Ljung-Box (LB) statistics, whose value increases with the dependence on past. Volatility (VOL) is measured via the SDEV of all zero-mean variables. Combining LOW persistence and HIGH volatility should provide a reasonable signal for detecting discretion as earlier defined, independently of any theoretical assumption. This is not a contradiction if volatility originates from abrupt spending decisions rather than from long transmission of the interventions over time since the persistence of a time-series affects also its variance, as we can see in the simplest AR(1) case: 9 (1) y(t) = ρ*y(t-1) + e(t) , e(t) ~ iid (0, σ2), 0 < σ2 < ∞, (2) Var(y) = σ2/(1 - ρ2) , 0 < ρ < 1. To account for this possible link between persistence and variance , we introduced a “deflated volatility” indicator, correcting volatility by persistence (LB) to better approximate the volatility due to abrupt (discretionary) interventions only: (3) DVOL = VOL/LB. 10 3.3 Candidate variables Candidate variables satisfying in principle our criteria are the following: Subsidies (TSUB), Purchases (CGNW) and Capital spending (KT = IG + TKPG). We exclude from discretion not only Interest payments, but also Compensation of the employees and Transfers (SSPG), which are in most cases dominated by pensions. This means that we exclude from discretionary spending the non-pension component (mainly unemployment and welfare benefits), which behaves more as a persistent cyclical component than as an occasional labor market intervention. Thus, our virtual government discretionary spending (GD) is obtained adding - both in nominal and real terms - the following components where Capital spending (KT) sums separate Fixed investment (IG) and Capital transfers (TKPG): (4) GD = TSUB + CGNW + IG + TKPG. 11 4. RESULTS Results refer to single variables first (for details, see Table 4 in CF cit.) and then to discretionary (GD) and non-discretionary (GN) aggregates (Tables 3-4, here). 4.1 Background data Let me summarize first a few background data on government spending composition and patterns in the Oecd sample [Tables 2a, 2b and 3 in CF cit.]: Overall, discretionary spending declines over time. Yet, the Great Recession reversed this tendency, mainly in those countries having lower deficits and debt -> FISCAL SPACE. Government consumption (CG = WAGES + CGNW) is about everywhere the largest variable (about 40%-50%), followed almost everywhere by Social security (about 30%40%) summing Pensions and Welfare expenditures. Usually, pensions dominate except than in the Northern countries, in Ireland and in the UK. Capital spending is the third (about 10%), much smaller and volatile, component. Investment is low, except for Japan. Finally, government size [i.e. (spending+revenues)/GDP] tends to fall over time and is rather small in Japan where, however, the (gross) debt/GDP ratio is exceptionally high. 12 4.2 Discretionary spending by variable Leaving more details to CF (Table 4), I summarize here our major results: While there are obvious differences by variable and country, most results conform to the assumption that discretionary spending should be more volatile, and less persistent than automatic spending. In general, persistence results differ more by country and by variable Institutions. Deflating volatility from persistence makes stronger this overall evidence. In most cases Capital transfers (KT) are the most volatile spending variable. This is maintained using the deflated volatility index (DVOL). Conversely, Fixed investment has some persistence, induced by the time-to-build technology. The last discretionary variable in our scheme is given by Subsidies, a small component. Given the policy nature of this variable, cross-countries differences are not surprising. Within non-discretionary spending, the Welfare component is generally characterized by a high degree of cyclicality (Adema et al., 2011) which is inconsistent with the standard view that this type of expenditure should reflect temporary interventions only. 13 4.3 Aggregate discretionary spending Discretionary spending (GD) is about 1/3 of total spending (Table 3). This share is much larger than that typically obtained from estimated residuals, but it is also much smaller than it was typically assumed in the earlier econometric models. Although there are significant differences across countries, the non-discretionary share (GN) is generally increasing over time until the Great Recession reversal. Looking at the components of discretionary spending, patterns across countries are similar: Purchases are about 2/3 of discretionary spending and are always and everywhere the largest component, generally increasing in the last period. Capital spending ranks second, at about the 20% of the aggregate, with country differences reflecting unusual episodes (Ireland) or structural patterns (US, Japan). In all cases, Subsidies are the last and smallest discretionary component (5% - 10%) . Conversely, Non-discretionary spending (GN) differs more markedly among countries, partially because of Social security and Interest spending differences. 14 Table 3 - Discretionary (GD) and non-discretionary spending (GN) by period Country Austria 1980-89 GD GN GY - 1990-99 GD GN GY 31.0 69.0 49.8 2000-08 GD GN GY 33.8 66.2 47.4 2009-11 GD GN GY 34.8 65.2 49.1 Belgium 29.7 70.3 57.0 27.1 72.9 50.8 31.6 68.4 47.6 33.9 66.1 50.3 Denmark 25.0 75.0 54.5 24.2 75.8 54.3 27.0 73.0 49.2 29.2 70.8 54.1 Finland 32.7 67.3 40.2 28.4 71.6 50.8 28.8 71.2 44.1 30.0 70.0 48.8 France 33.0 67.0 47.6 32.2 67.8 50.0 31.9 68.1 49.3 32.7 67.3 52.4 Iceland - - - 42.2 57.8 40.6 41.5 58.5 41.6 40.0 60.0 46.4 Ireland - - - 30.1 69.9 37.7 38.3 61.7 32.1 43.2 56.8 50.2 Italy 30.6 69.4 47.8 25.7 74.3 51.2 29.5 70.5 46.5 30.3 69.7 49.5 Japan 46.6 53.4 33.3 49.7 50.3 35.7 47.0 53.0 38.3 - - - Netherlands 34.2 65.8 56.4 37.0 63.0 49.9 45.6 54.4 42.7 51.4 48.6 48.4 Norway - - - 32.0 68.0 47.7 31.6 68.4 40.7 33.0 67.0 42.7 Spain 37.6 62.4 38.8 33.0 67.0 43.0 36.2 63.8 47.9 35.7 64.3 43.7 Sweden 30.5 69.5 57.8 30.1 69.9 59.1 31.2 68.8 49.8 35.1 64.9 49.0 United Kingdom United States 31.2 68.8 44.3 30.2 69.8 40.6 33.3 66.7 38.5 36.5 63.5 45.5 29.8 70.2 35.5 26.4 73.6 35.2 28.5 71.5 34.2 28.8 71.2 39.9 Source: Oecd EO90 cit. Average data; GD = Discretionary spending; GN = Non-discretionary spending; GY = Gen. Govt. Spending/Nominal GDP 15 4.4 Comparing Different Aggregates In the following Table 4, I compare the persistence and volatility indicators in each country relative not only to discretionary and non-discretionary components but considering also total and primary aggregates. Discretionary spending (GD) is always more volatile than non-discretionary spending (GN) and also more volatile than primary (GP) and total government spending (GT). Furthermore, GN is generally less volatile than primary and total government spending. While total expenditure by definition cannot separate its components, it is interesting to note that GP – i.e. the first empirical proxy for discretion – does not comply with our criteria, since not only is more volatile but often is also more persistent than GN. Deflating volatility from persistence, the DVOL index does not affect the volatility ranking: discretionary spending volatility is still the highest in all cases but Norway (GPFG fund?), confirming our priors even in a stronger way. 16 The persistence indicators, reported in Table 4, broadly support the volatility results, though in a weaker way. In ten out of fifteen cases, GN aggregate is more persistent then GD aggregate. In the remaining cases, however, inertia in discretionary spending is either the same as non-discretionary spending (in Spain), or higher (in Belgium, Italy, Norway and Sweden). By looking at the individual variables, this result could either reflect a rising share of somewhat inertial purchases or the time needed to activate and complete fixed investment decisions. 17 Table 4 – Persistence and Volatility of Different Types of Government Spending Austria (1) (2) (3) = (1)/(2) Belgium (1) (2) (3) =(1)/(2) Denmark (1) (2) (3) = (1)/(2) 1990-2011 Vol LB DVOL 1980-2011 Vol LB DVOL 1980-2011 Vol LB DVOL GD 4.28 5.03 .85 GD 3.54 17.3 .20 GD 2.16 8.1 .27 GN 1.28 7.24 .18 GN 1.16 16.0 .07 GN 1.30 26.2 .05 GP 1.55 7.77 .20 GP 1.62 12.9 .13 GP 1.31 28.2 .05 GT 1.41 6.14 .23 GTOT 1.49 4.36 .34 GTOT 1.28 27.8 .05 Finland France Iceland 1980-2011 1980-2011 1990-2011 GD 2.22 13.8 .16 GD .91 6.6 .14 GD 11.4 5.1 2.23 GN 1.54 17.6 .09 GN .69 12.1 .06 GN 2.31 5.6 .41 GP 1.14 13.5 .08 GP .61 3.8 .16 GP 5.77 3.9 1.48 GT 1.22 23.1 .05 GT .56 10.5 .05 GT 5.34 4.5 1.17 Ireland Italy Japan 1990-2011 1980-2011 1980-2008 GD 16.7 3.94 4.25 GD 2.90 16.4 .18 GD 2.88 12.3 .23 GN 1.72 7.23 .24 GN 1.84 13.9 .13 GN 1.88 12.6 .15 GP 7.7 2.80 2.75 GP 1.01 3.3 .30 GP 2.3 14.4 .16 GT 7.34 2.90 2.53 GT 1.15 5.6 .20 GT 2.54 12.8 .20 Netherlands Norway Spain 1980-2011 1980-2011 1980-2011 GD 3.87 11.3 .34 GD 1.79 26.2 .07 GD 3.46 14.6 .24 GN 1.31 15.2 .09 GN 1.02 12.6 .08 GN 1.83 14.5 .13 GP 1.97 12.9 .15 GP 1.11 26.4 .04 GP 1.93 23.6 .08 GT 1.81 10.8 .17 GT 1.16 20.2 .06 GT 1.84 18.6 .10 Sweden UK US 1980-2011 1980-2011 1980-2011 GD 3.33 11.3 .29 GD 4.05 10.5 .39 GD 1.83 12.7 .14 GN .98 4.6 .21 GN 1.31 17.4 .08 GN .80 17.0 .05 GP 1.57 11.8 .13 GP 1.87 9.9 .19 GP 1.23 24.6 .05 GT 1.49 9.5 .16 GT 1.64 10.0 .16 GT .90 22.6 .04 Source: Oecd EO90 cit. All indicators refer to deflated cyclical deviations from an annual HP trend; Vol is the standard deviation of the cyclical data. LB(p) is the Ljung-Box statistics where p = T/4 is the number of autocorrelations and T is the number of data points. The LB(p) statistics is used as an indicator of persistence; Dvol = Vol/LB is an indicator of deflated volatility, i.e. of the volatility not induced by the estimated persistence. 18 5. CONCLUSIONS Discretionary spending is about 1/3 of general government spending which generally is more automatic. Automatic spending historically rises, with the exception of last crisis and in the light of the debt size Fiscal space The fact that policy decisions involve only 1/3 of total spending does not say too much about the quality or the efficacy of the decision: discretion could be good or bad. Discretion implies here only temporary and reversible feature, evaluated on the basis of univariate, detailed, spending variables. Our assumptions are widely confirmed. This work should be preliminary before evaluating spending ‘multipliers’ since standard exogeneity analysis does not apply well discretion reacts also to past events. The dominance of inertia in government spending can explain its procyclical (or acyclical) pattern: stimulating outlays when are not necessary and preventing spending in the few recession cases. Especially when fiscal constraints, due to past inertia, make difficult – since not credible promises of temporary spending. 19 REFERENCES Adema W., P. Fron and M. Ladaique (2011), “Is the European Welfare State Really More Expensive? Oecd WP # 124. Afonso A., L. Agnello and D. Furceri (2010), “Fiscal Policy Responsiveness, Persistence and Discretion”, Public Choice, 145, 503-30. Blanchard O.J. (1990), “Suggestions for a New Set of Fiscal Indicators”, Oecd WP # 79. Blinder A.S. (2006), “The Case Against the Case Against Discretionary Fiscal Policy”, in Kopcke, R. , G. Tootell and R. Triest (eds.), The Macroeconomics of Fiscal Policy, The MIT Press, 25-61. Coricelli F. and R. Fiorito (2013), “Myths and Facts about Fiscal Discretion: A New Measure of Discretionary Expenditure”, Documents de Travail du Centre d’Economie de la Sorbonne. Corsetti G., A. Meier and G.J. Müller . (2012), “What Determines Govenment Spending Multipliers?”, IMF Working Paper 12/150. Devries P., R. Guajardo, D. Leigh and A. Pescatori (2011), “A New Action-Based Data Set of Fiscal Consolidation”, IMF Working Paper 11/128. 20 Elmendorf, (2011), “Discretionary Spending”, Congressional Budget Office, United States. Fatás A. and I. Mihov (2003), “The Case for Restricting Fiscal Policy Discretion”, Quarterly Journal of Economics, 118(4), 1419-47. Fiorito R. (2013), “Business Cycles and Recessions in the Oecd Area”, Modern Economy, 4, 203-08. Girouard N. and C. André (2005), “Measuring Cyclically Adjusted Budget Balances for Oecd Countries”, Oecd WP # 434. IMF (2010), “Will It Hurt? Macroeconomic Effects of Fiscal Consolidation”, in World Economic Outlook, Ch. 3, October, 93-124. Ramey, V.A. (2011), “Identifying Government Shocks: It’s All in the Timing”, Quarterly Journal of Economics, 1-50. Ramey, V.A. and M. D. Shapiro (1998), “Costly Capital Reallocation and the Effects of Government Spending”, Carnegie-Rochester Conference on Public Policy, 48(1), 145-94. Romer C. and D. Romer (2010), “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks”, American Economic Review, 100: 763-801. 21