Download Output Gap, Recessions and Fiscal Discretion

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.
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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.
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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.
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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.
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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).
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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
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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.
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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:
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(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.
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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.
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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.
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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.
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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.
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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
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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.
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 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.
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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.
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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.
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