Download By Habits or Choice? Discretionary Spending in the Oecd 1. GOALS

Riccardo Fiorito
By Habits or Choice? Discretionary Spending in the Oecd
Treasury Department
Rome, June 2013
1. GOALS
• Length, depth and spread of the last international contraction (2008-?) - and the
differences in the recovery pace (Oecd, 2013) - made and make popular the debate on
fiscal stimuli and spending multipliers.
• Yet, this debate does not seem conclusive: probably because before evaluating the
effect of government spending on the economy, 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 similar countries?
• This will be evaluated linking discretion to reversibility and temporariness of
government expenditure and without estimating the effect of this or other type of
spending on the economy.
• The analysis focusses on government spending as 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) 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). Despite their wide
use by international organizations, CAB (CAPB):
• Do not account for recessions nor for differences in government size.
• Have a limited ability to remove cyclical fluctuations as Table 1 clearly shows comparing
adjusted and unadjusted balances.
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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
1980-2011
3.80
54.1 (8)
3.41
46.8 (8)
.97
Denmark
1980-2011
3.82
49.4 (8)
2.93
49.7 (8)
.97
Finland
1980-2011
3.99
45.5 (8)
2.54
51.6 (8)
.95
France
1980-2011
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
1980-2011
3.37
89.0 (8)
3.81
75.0 (8)
.97
Japan
1980-2009
3.34
60.3 (7)
3.00
64.5 (7)
.97
Netherlands
1980-2011
2.20
12.5 (8)
2.00
6.9 (8)
.91
Norway
1980-2012
2.35
46.1 (8)
2.36
43.3 (8)
1.00
Spain
1980-2011
3.63
37.8 (8)
2.84
37.4 (8)
.96
Sweden
1980-2012
4.09
58.1 (8)
3.13
43.1 (8)
.95
UK
1980-2011
3.36
38.7 (8)
2.90
44.4 (8)
.96
US
1980-2011
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 Ljung-Box 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.
• 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 estimated 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 policy decisions, taking discretion from Laws,
policy interventions or intentions:
• See: Ramey and Shapiro (1998); Romer and Romer (2010) postwar reconstruction on
tax legislation in the US. Recently, Ramey (2011) evaluates “spending news”.
Limits of the event approach:
• Policy statements (even Laws!) are policy intentions that not always result in approved
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.
• 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 do not require policy makers to know the
current state of the economy and, more importantly, 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
From perception to decision (A
B)
None
Recognition + Decision
OUTSIDE LAGS:
B’ C
A B
B’ C
INSIDE LAG:
From actual spending to
(B’ = B + e); e = Lag between decision
macroeconomic effects (B’
C)
and actual spending
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3.1 Conceptual requirements
Our measure of discretionary spending (CF, 2013) rests on economic rather than legal grounds.
Namely, we rest on a few, intuitive, assumptions, that are approximated and tested via simple
univariate properties of each government spending component.
i)
DISCRETION CANNOT BE 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 should help 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 by the Ljung-Box (LB) statistics, whose value increases with
the dependence on past. Volatility 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 particular 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 timeseries 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).
• 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
(e.g., mainly unemployment and welfare benefits), which behave 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 an 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 (see for details Table 4 in CF cit.) and then to
discretionary and non-discretionary aggregates (Tables 3-4, here).
4.1 Background data
To help interpretation, I 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 share declined over time, although the Great Recession
reversed this tendency, mainly in those countries with smaller deficits and debt.
• Government consumption (CG = WAGES + CGNW) is about everywhere the largest
spending variable (≈40%-50%), followed almost everywhere by Social security (≈30%40%) which contains Pensions and Welfare expenditures. Usually, pensions dominate
except than in the Northern countries, Ireland and the UK. Capital spending is the third
(≈10%), much smaller and volatile, component. Investment is low except for Japan.
• Finally, Government size tends to fall over time and is rather small in Japan where,
however, the (gross) debt/GDP ratio is indeed very high.
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4.2 Discretionary spending by variable
Leaving more details to CF Table 4, I summarize here major results:
• While there are obvious differences by variable and country, most of the reported
statistic conform to our assumption that discretionary spending should be more volatile,
and less persistent than automatic spending is.
• In general, persistence results differ more by country and by variable.
• Deflating volatility from persistence makes stronger this overall evidence.
• In most cases Capital transfers (KT) are the most volatile spending variable and 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. 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
• The discretionary spending share (GD) is about 1/3 of total spending (Table 3). This is
much larger than the share obtained from estimated residuals, but is much smaller than
typically assumed in the earlier econometric models.
• Although there are significant differences across countries, the non-discretionary share
(GN) is generally increasing over time though this is reversed in the Great Recession.
• 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 third and smallest discretionary component (5%- 10%) .
• The components of non-discretionary spending (GN) differ 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
1980-89
GN GY
-
GD
31.0
1990-99
GN GY
69.0 49.8
GD
33.8
2000-08
GN GY
66.2 47.4
GD
34.8
2009-11
GN GY
65.2 49.1
Austria
GD
-
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: OEC,D 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 Table 4 we compare the persistence and volatility indicators relative to both discretionary
and non-discretionary government spending aggregates in each country, considering also
total and primary spending.
• Regarding the comparison between primary and total government spending, the main
finding is that discretionary spending (GD) is always more volatile than nondiscretionary spending (GN), and more volatile as well than primary and total
government spending.
• Furthermore, non-discretionary spending is generally less volatile than primary (GP)
and total government spending (GT).
• While total expenditure by definition cannot separate its components, it is interesting to
note that primary spending (GP) – i.e. the first empirical proxy for discretion – does not
comply with our criteria, since not only is more volatile than the GN aggregate but
often is also more persistent.
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• 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,
confirming our priors even in a stronger way.
• The persistence indicators, reported in Table 4, broadly support the volatility results,
though in a weaker way. In ten out of fifteen cases, the non-discretionary spending
aggregate is more persistent then the discretionary 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
• In most countries, discretionary spending is about 1/3 of the total. The majority of
spending is more automatic and historically rises, with the exception of last crisis.
• 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.
• Our evidence suggests only that economic policy changes are not too frequent,
regardless if they are good or bad.
• Probably, economic policy should focus more on reducing the reasons of persistence.
•
Inertia contributes to make government spending procyclical since built-in stabilizers
do not refer to theoretical “cycles” but to the way in which actual economies evolve: in
the Oecd, about 90% of cases growing and facing contractions otherwise (Fiorito, 2013).
•
This procyclical (or acyclical) pattern stimulates spending when it is not necessary and
prevents 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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