Post on 14-Sep-2020
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GOVERNMENT SPENDING MULTIPLIERS IN GOOD TIMES AND IN BAD:
EVIDENCE FROM U.S. HISTORICAL DATA
Valerie A. Ramey
UC San Diego & NBER Sarah Zubairy
Texas A&M University
Discussion by
Yuriy Gorodnichenko UC Berkeley & NBER
MOTIVATION Key policy questions
What is the size of the government spending multiplier? o Previous work: multiplier 1 (wide “confidence” bands)
MOTIVATION Key policy questions
What is the size of the government spending multiplier? o Previous work: multiplier 1 (wide “confidence” bands)
What is the size of the government spending multiplier IN RECESSIONS?
MOTIVATION Key policy questions
What is the size of the government spending multiplier? o Previous work: multiplier 1 (wide “confidence” bands)
What is the size of the government spending multiplier IN RECESSIONS?
Challenges of estimating state-dependent multipliers
A handful of recessions in the post-WWII data & relatively little variation in G o RZ: construct long, quarterly time series: 1880-2013.
Post-WWII data: standard Pre-WWII data: many sources + interpolate annual series into quarterly
MOTIVATION Key policy questions
What is the size of the government spending multiplier? o Previous work: multiplier 1 (wide “confidence” bands)
What is the size of the government spending multiplier IN RECESSIONS?
Challenges of estimating state-dependent multipliers
A handful of recessions in the post-WWII data & relatively little variation in G o RZ: construct long, quarterly time series: 1880-2013.
Post-WWII data: standard Pre-WWII data: many sources + interpolate annual series into quarterly
Identification of exogenous, unanticipated shocks to government spending o RZ: News shocks (extend Ramey (QJE 2011)) about military gov’t spending
MOTIVATION Key policy questions
What is the size of the government spending multiplier? o Previous work: multiplier 1 (wide “confidence” bands)
What is the size of the government spending multiplier IN RECESSIONS?
Challenges of estimating state-dependent multipliers
A handful of recessions in the post-WWII data & relatively little variation in G o RZ: construct long, quarterly time series: 1880-2013.
Post-WWII data: standard Pre-WWII data: many sources + interpolate annual series into quarterly
Identification of exogenous, unanticipated shocks to government spending o RZ: News shocks (extend Ramey (QJE 2011)) about military gov’t spending
Nonlinear models: sensitive estimates + how to model feedback/dynamics? o RZ: Use Jorda (2005) projection method as in AG (2012)
RESULTS
Output responds more strongly in “slack times” (unemployment rate > 6.5%)
RESULTS
Output responds more strongly in “slack times” (unemployment rate > 6.5%)
Government spending responds more strongly in “slack times”
o Multipliers ≡ ∑∑ are similar in “slack times” and “no-slack times”
RESULTS
Output responds more strongly in “slack times” (unemployment rate > 6.5%)
Government spending responds more strongly in “slack times”
o Multipliers ≡ ∑∑ are similar in “slack times” and “no-slack times”
Little variation/weak identification in post-WWII data
Multipliers are similar at the zero lower bound (ZLB) and outside ZLB
o More challenges for multipliers at ZLB
RESULTS
Output responds more strongly in “slack times” (unemployment rate > 6.5%)
Government spending responds more strongly in “slack times”
o Multipliers ≡ ∑∑ are similar in “slack times” and “no-slack times”
Little variation/weak identification in post-WWII data
Multipliers are similar at the zero lower bound (ZLB) and outside ZLB
o More challenges for multipliers at ZLB
A GREAT PAPER!
RESULTS
Output responds more strongly in “slack times” (unemployment rate > 6.5%)
Government spending responds more strongly in “slack times”
o Multipliers ≡ ∑∑ are similar in “slack times” and “no-slack times”
Little variation/weak identification in post-WWII data
Multipliers are similar at the zero lower bound (ZLB) and outside ZLB
o More challenges for multipliers at ZLB
A GREAT PAPER!
Why are the RZ results different from the results in Auerbach-Gorodnichenko and others?
Measurement Specification Estimation Identification
RZ APPROACH
…
RZ APPROACH ⇒
⇒
RZ APPROACH ⇒
⇒
Multiplier at horizon : ≡,
,,,
RZ APPROACH ⇒
⇒
Multiplier at horizon : ≡,
,,,
RZ APPROACH ⇒
⇒
Multiplier at horizon : ≡,
,,,
Instrumental variable interpretation: Regress on and use as an IV.
RZ APPROACH ⇒
⇒
Multiplier at horizon : ≡,
,,,
Instrumental variable interpretation: Regress on and use as an IV.
The logic extends to state-dependent multipliers
and as IVs.
RZ APPROACH ⇒
⇒
Multiplier at horizon : ≡,
,,,
Instrumental variable interpretation: Regress on and use as an IV.
The logic extends to state-dependent multipliers
and as IVs.
Single equation approach
:
:
FIRST STAGE FIT: FULL SAMPLE
Note: controls are included. F-stat in the figure is capped at 45.
Include WWII0
510
1520
2530
3540
45fir
st-s
tage
F-s
tatis
tic
0 2 4 6 8 10 12 14 16 18 20horizon h
RecessionExpansion
FIRST STAGE FIT: EXCLUDE WWII
Note: controls are included. F-stat in the figure is capped at 45.
Exclude WWII0
510
1520
2530
3540
45fir
st-s
tage
F-s
tatis
tic
0 2 4 6 8 10 12 14 16 18 20horizon h
RecessionExpansion
FIRST STAGE FIT: RECESSION
Horizon 8
1939.5
1940.25
1940.751941
1941.25
1941.75
1942.25
b = 0.77 (0.11)
b = 0.45 (0.39)
0.1
.2.3
.4.5
.6(G
t+h-G
t-1)/Y
t-1
0 .1 .2 .3 .4 .5 .6 .7 .8shock
WWII observationsExclude WWIIFit all observationsFit excl. WWII obs
FIRST STAGE FIT: RECESSION
Horizon 8
Question: which shocks should one use to design/assess the fiscal stimulus in 2009?
1939.5
1940.25
1940.751941
1941.25
1941.75
1942.25
b = 0.77 (0.11)
b = 0.45 (0.39)
0.1
.2.3
.4.5
.6(G
t+h-G
t-1)/Y
t-1
0 .1 .2 .3 .4 .5 .6 .7 .8shock
WWII observationsExclude WWIIFit all observationsFit excl. WWII obs
RAMEY-ZUBAIRY VS. BLANCHARD-PEROTTI Ramey-Zubairy:
use military spending shocks as the instrument
RAMEY-ZUBAIRY VS. BLANCHARD-PEROTTI Ramey-Zubairy:
use military spending shocks as the instrument
Blanchard-Perotti
use as the instrument
RAMEY-ZUBAIRY VS. BLANCHARD-PEROTTI Ramey-Zubairy:
use military spending shocks as the instrument
Blanchard-Perotti
use as the instrument
o First-stage fit for 0 is perfect 1
RAMEY-ZUBAIRY VS. BLANCHARD-PEROTTI Ramey-Zubairy:
use military spending shocks as the instrument
Blanchard-Perotti
use as the instrument
o First-stage fit for 0 is perfect 1 Alternative IV (Auerbach-Gorodnichenko):
≡ a professional forecast as of time 1 of government spending at time
RAMEY-ZUBAIRY VS. BLANCHARD-PEROTTI Ramey-Zubairy:
use military spending shocks as the instrument
Blanchard-Perotti
use as the instrument
o First-stage fit for 0 is perfect 1 Alternative IV (Auerbach-Gorodnichenko):
≡ a professional forecast as of time 1 of government spending at time
Strength of 1st stage: RZ vs. BP BP (AG) instrument is nearly impossible to beat over short horizons. RZ can perform better over longer horizons b/c it measures present values.
CHALLENGES IN CONSTRUCTING AND ANALYZING LONG-TIME SERIES
Data quality is likely to vary o Linear interpolation ⇒ Attenuate differences between recession/expansion
CHALLENGES IN CONSTRUCTING AND ANALYZING LONG-TIME SERIES
Data quality is likely to vary o Linear interpolation ⇒ Attenuate differences between recession/expansion
Regime changes o Balanced budget provisions o Gold standard
CHALLENGES IN CONSTRUCTING AND ANALYZING LONG-TIME SERIES
Data quality is likely to vary o Linear interpolation ⇒ Attenuate differences between recession/expansion
Regime changes o Balanced budget provisions o Gold standard
Structural changes o Changes in the volatility of government spending
% CHANGE IN REAL PER CAPITA GOVERNMENT SPENDING
-.50
.5dl
og(g
over
nmen
t spe
ndin
g p.
c.)
0.1
.2.3
st.d
ev. d
log(
G p
.c.),
5 y
ear m
ovin
g w
indo
w
1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
CHALLENGES IN CONSTRUCTING AND ANALYZING LONG-TIME SERIES
Data quality is likely to vary o Linear interpolation ⇒ Attenuate differences between recession/expansion
Regime changes o Balanced budget provisions o Gold standard
Structural changes o Changes in the volatility of government spending o Secular trend in the size and composition of the government
SHARE OF GOVERNMENT SPENDING IN GDP
0.1
.2.3
.4.5
Gov
ernm
ent s
pend
ing
shar
e in
GD
P
1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
CHALLENGES IN CONSTRUCTING AND ANALYZING LONG-TIME SERIES
Data quality is likely to vary o Linear interpolation ⇒ Attenuate differences between recession/expansion
Regime changes o Balanced budget provisions o Gold standard
Structural changes o Changes in the volatility of government spending o Secular trend in the size and composition of the government ⇒ avoid using variables in levels, use differences or/and growth rates
RZ: ∑ ln ∑ ln ∑
Alt.: ∑ Δ ln ∑ Δ ln ∑
NORMALIZATION
Typical approach: Δ log Δ log ⇒
NORMALIZATION
Typical approach: Δ log Δ log ⇒
Alternative approach: ⇒
NORMALIZATION
Typical approach: Δ log Δ log ⇒
Alternative approach: ⇒
Δ log
NORMALIZATION
Typical approach: Δ log Δ log ⇒
Alternative approach: ⇒
Δ log
Potential concerns
and are correlated because shows up in the denominator
varies systematically over the business cycle
NORMALIZATION
Notes: post 1960 data; potential GDP is from the CBO.
-.015
-.01
-.005
0.0
05G
/Y
0 5 10 15Quarters since the start of a recession
Normalized by actual GDP Normalized by potential GDP
MULTIPLIERS: RAMEY-ZUBAIRY
Spec: baseline, IV implementation
-.50
.51
1.5
22.
5M
ultip
lier
0 1 2 3 4 5 6 7 8 9 10 11horizon
ExpansionRecession
MULTIPLIERS: BLANCHARD-PEROTTI
Spec: IV implementation, include more lags, normalize by potential GDP, controls
include variables in growth rates rather than levels.
These estimates are similar to the Auerbach-Gorodnichenko results.
-.50
.51
1.5
22.
5M
ultip
lier
0 1 2 3 4 5 6 7 8 9 10 11horizon
ExpansionRecession
EQUALITY OF MULTIPLIERS OVER THE BUSINESS CYCLE
0.1
.2.3
.4.5
.6.7
.8.9
1p-
valu
e(R
eces
sion
=Exp
ansi
on)
0 1 2 3 4 5 6 7 8 9 10 11horizon h
Blanchard-Perotti Ramey-Zubairy
CONCLUDING REMARKS We need more variation/data to identify G shocks and estimate their effects Cross-state variation (e.g., Nakamura and Steinsson 2014) Natural experiments (e.g., Joshua Hausman 2013) Asset prices and high frequency data (e.g., Johannes Wieland 2012)
CONCLUDING REMARKS We need more variation/data to identify G shocks and estimate their effects Cross-state variation (e.g., Nakamura and Steinsson 2014) Natural experiments (e.g., Joshua Hausman 2013) Asset prices and high frequency data (e.g., Johannes Wieland 2012)
We need to study responses of other variables to understand the workings of G shocks Consumption, investment, durables/non-durables Prices, wages, interest rates Employment, capacity utilization Export, import, exchange rates
CONCLUDING REMARKS We need more variation/data to identify G shocks and estimate their effects Cross-state variation (e.g., Nakamura and Steinsson 2014) Natural experiments (e.g., Joshua Hausman 2013) Asset prices and high frequency data (e.g., Johannes Wieland 2012)
We need to study responses of other variables to understand the workings of G shocks Consumption, investment, durables/non-durables Prices, wages, interest rates Employment, capacity utilization Export, import, exchange rates
We need better theory to guide our empirical analyses RZ: Michaillat (2014) is the only modern macro model with state-dependent multipliers Most models are linearized or close to linear
CONCLUDING REMARKS We need more variation/data to identify G shocks and estimate their effects Cross-state variation (e.g., Nakamura and Steinsson 2014) Natural experiments (e.g., Joshua Hausman 2013) Asset prices and high frequency data (e.g., Johannes Wieland 2012)
We need to study responses of other variables to understand the workings of G shocks Consumption, investment, durables/non-durables Prices, wages, interest rates Employment, capacity utilization Export, import, exchange rates
We need better theory to guide our empirical analyses RZ: Michaillat (2014) is the only modern macro model with state-dependent multipliers Most models are linearized or close to linear
“The problem with QE is it works in practice but it doesn’t work in theory.” – Bernanke