The Effect of the Recovery Act on Consumer Spending
Bill Dupor (FRB of St Louis) Marios Karabarbounis (FRB of Richmond) Marianna Kudlyak (FRB of San Francisco) M. Saif Mehkari (University of Richmond)
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The analysis and conclusions set forth do not reflect the views of the institutions to which the authors are affiliated.
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The Research Question: If the government purchases $1 worth of goods by how many dollars does private consumption increase or decrease?
– Very old question but literature still lacks consensus • Ramey and Shapiro (1998), Blanchard and Perotti (2002), Gali et al. (2007) • consumption multipliers from slightly negative to 0.5.
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What we do We analyze the effect of the Recovery Act on consumer spending. Very large program! Spending component around $228 Billion.
Million $ 0
(0-10]
(10-20] (20-50]
(50-100]
(100-500] >500
Notes: Recovery Act Data (2009-2012): $ amounts awarded per county (Authors’ calculations). 3 / 28
What is new? • We use regional variation to estimate the consumption response to
gov. spending. — Large literature using regional variation to study effects of gov. programs (Chodorow-Reich et al. (2012), Conley and Dupor (2013), Nakamura and Steinsson (2014)). — We look explicitly at local consumer spending: combine micro-level data on retail purchases (Nielsen Store/HomeScan) and auto purchases (auto balances from Equifax).
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What is new? • We use regional variation to estimate the consumption response to
gov. spending. — Large literature using regional variation to study effects of gov. programs (Chodorow-Reich et al. (2012), Conley and Dupor (2013), Nakamura and Steinsson (2014)). — We look explicitly at local consumer spending: combine micro-level data on retail purchases (Nielsen Store/HomeScan) and auto purchases (auto balances from Equifax).
• Translate estimates using a HA-NK model with many regions — aggregate local to national (Nakamura and Steinsson (2014), Beraja, Hurst and Ospina (2016)). — Novel part: we also allow for heterogeneity/incomplete markets within regions→ heterogeneity in MPC’s.
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Our preliminary findings •• The spending component of the Recovery Act increased local consumer spending. 1% increase in gov. spending increased the growth rate of • retail spending by 0.033 p.p. • auto spending by 0.060 p.p
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Our preliminary findings •• The spending component of the Recovery Act increased local consumer spending. 1% increase in gov. spending increased the growth rate of • retail spending by 0.033 p.p. • auto spending by 0.060 p.p
•• Our quantitative model implies that • Local variation in wages → positive local consumption multiplier. • Monetary policy response affects both the local and the aggregate consumption multiplier.
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Data Sources and Empirical Analysis
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A narrative/quantitative IV approach
To overcome endogeneity we find components of the Act allocated in a way that was uncorrelated with local business cycle • do a detailed reading of the Act, federal codes and regulations cited
in the act and implementation guidances written by agencies tasked with allocating funds (100s of pages!) • catalogue 7 programs that are exogenous to local business cycle • Examples: water quality assistance grants and Dept. of Education’s
Special Education Fund
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Components of the Recovery Act used in the construction of the instrument
Federal Department/Agency
Total Amount Fraction included Authorized in IV ($Billions) (%)
Environmental Protection Agency General Services Administration Department of Transportation Department of Education Department of Energy Department of Justice Department of Defense All other Agencies
6.7 4.8 39.3 71.6 33.3 3.5 4.3 62.3
87.5 98.3 16.7 15.6 43.5 72.4 87.1 0.0
All Departments/Agencies
228.0
20.2
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Nielsen HomeScan Data Panel of approximately 60,000 households/ 40,000 stores for period 2002-2013. Information on amount spent/total sales and area of residence/store location (zip code). Store Type Grocery Discount store Warehouse club Drug store Department store Online Shopping Hardware/Home Improv. Dollar Store Apparel Stores
Spending 32.9% 20.5% 8.5% 4.2% 3.9% 3.0% 2.9% 1.7% 1.6%
Convenience store Electronics store Gas mini mart Pet store Restaurant Office supplies store Quick serve restaurants Liquor store Home furnishings
1.5% 1.1% 1.0% 0.8% 0.7% 0.7% 0.6% 0.6% 0.5%
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Equifax 5% random sample of all U.S. consumers with credit records: approx. 10 million individuals. Information on household debt: mortgages, credit cards, student loans, and auto loans. Use change in auto balance as a proxy for auto spending. 180 160 140 120 100 80 60 40
# Loans (2010=100) Passenger Car Registration in U.S. (FRED)
20 0
2000
2002
2004
2006
2008
2010
2012
2014
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Econometric Model
∆ log Cj = a + β × log Gj + Xj Φ0 + Dj + εj with
∆ log Cj =
2012 X
{log Cj,t − log Cj,2008 }
t=2009
Xj : county’s population, per-capita change in county income between 2008-2012 Dj : state dummy
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Retail and Auto Spending Effects of the Recovery Act
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Percentage Change in County-Level Retail Spending -.05 .03 .11 .19 .27 .35
Relationship between Recovery Act spending and retail spending, county-level
5
6 7 8 County-Level Recovery Act Spending (in log)
9
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1% increase in government spending increases retail spending growth rate between 0.016-0.037 p.p. Spending Category
Retail Spending (Nielsen, Store-level) OLS
Recovery Act Spending Partial F stat. County Controls/State F.E.
# Counties
IV
OLS
IV
0.016 0.037∗ 0.029∗ 0.033∗ (0.020) (0.022) (0.017) (0.017) — No 365
132.4 No 365
— Yes 365
213.4 Yes 365
Note: the Table reports the regression estimates based on Store-level data. We weight by population and we cluster standard errors at the state level. State
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1% increase in government spending increases retail spending growth rate between 0.022-0.032 p.p. Spending Category
Retail Spending (Nielsen, HomeScan) OLS
Recovery Act Spending Partial F stat. County Controls/State F.E.
# Counties
IV
OLS
IV
0.022∗ 0.029∗∗ 0.032∗ 0.023 (0.013) (0.014) (0.017) (0.019) — No 525
166.9 No 525
— Yes 524
220.4 Yes 524
Note: the Table reports the regression estimates based on HomeScan data. We weight by population and we cluster standrard errors at the state level.
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Total Non-durable elasticity We translate our Nielsen elasticity into a nondurable elasticity. • Nielsen type purchases from the CEX: food at home, alcohol and
beverage, deteregents, cleaning products and other household products, small appliances, and personal care products • Total non-durables: Nielsen + food away from home, a broader set of housing supply purchases, utility bills and public transportation (on average 4.6 times larger). non-durable Nielsen log Ci,t = a + β × log Ci,t + Xi,t Φ0 + εi,t
β = 0.55: the nondurable elasticity is around half our Nielsen coefficient.
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Percentage Change in County-Level Auto Spending 0 .1 .2 .3 .4 .5
Relationship between Recovery Act spending and auto spending, county-level
5
6 7 8 County-Level Recovery Act Spending (in log)
9
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1% increase in government spending increases auto spending growth rate between 0.045-0.060 p.p. Spending Category
Auto Spending (Equifax) OLS
Recovery Act Spending Partial F stat. County Controls/State F.E.
# Counties
Placebo Regressions
IV
OLS ∗∗∗
IV
0.015 0.061 0.045 0.060∗∗∗ (0.024) (0.023) (0.014) (0.015) — No 2999
205.9 No 2930
∗∗∗
— Yes 2936
162.8 Yes 2872
Heterogeneity
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Model
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Description of the Economy • i = {1, .., N} regions (N = 2 for this presentation).
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Description of the Economy • i = {1, .., N} regions (N = 2 for this presentation). • Each region is an open Huggett economy: continuum of households
making consumption, working, and saving decisions.
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Description of the Economy • i = {1, .., N} regions (N = 2 for this presentation). • Each region is an open Huggett economy: continuum of households
making consumption, working, and saving decisions. • Each region produces a final good using local and foreign
intermediate inputs. – 1st spillover: trade across regions
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Description of the Economy • i = {1, .., N} regions (N = 2 for this presentation). • Each region is an open Huggett economy: continuum of households
making consumption, working, and saving decisions. • Each region produces a final good using local and foreign
intermediate inputs. – 1st spillover: trade across regions • Federal government supplies a bond. Buys final goods from all
regions and finances local expenditures using federal taxes. – 2nd spillover: federal fiscal authority
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Description of the Economy • i = {1, .., N} regions (N = 2 for this presentation). • Each region is an open Huggett economy: continuum of households
making consumption, working, and saving decisions. • Each region produces a final good using local and foreign
intermediate inputs. – 1st spillover: trade across regions • Federal government supplies a bond. Buys final goods from all
regions and finances local expenditures using federal taxes. – 2nd spillover: federal fiscal authority • Monetary authority sets the nominal rate. – 3rd spillover: currency union
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Households
( Vi (x, b, φ) = max 0
c,b ,h
)
1−θ
(1 − h) c 1−σ +ψ 1−σ 1−θ
+ β
X
Γx,x 0 Vi (x 0 , b 0 , φ0 )
x0
s.t. c + (1 + πi,t+1 )b 0 = wi si xh − T (wi si zh) + (1 + Rt−1 + κI[b<0] )b + θ(x)Di b0 ≥ b
where 0 2 − log x 0 = ρ log x + η 0 , with η ∼ iid N(0, ση ). − s is regional productivity (constant)
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Final good firm Firm i’s preferences for local and foreign inputs are given by γii 0 ∀i 0 . " Yi =
N X
γ
1 ii 0
Z x j
i 0 =1
−1 ii 0 j
# −1
Firm i’s demand for input j located at i 0 is: xii 0 j = γii 0
pi 0 j Pi
− Yi
Price aggregate is " Pi =
N X i 0 =1
Z γii 0 j
1 # 1−
pi1− 0j
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Intermediate good firms The total demand for intermediate good firm j located at region i will be yij =
N X
xi 0 ij
i 0 =1
Sticky price model: max ∗ pi,j,t
∞ X
((1 − λ)β)
s
∗ pi,j,t+s yi,j,t+s − Wi,t+s Li,j,t+s
s=0
∗ pijt = Pit −1
PN
1+ 0 0 0 0 Γi it+1 i 0 =1 γi i Qi 0 it wit Yi t + (1 − λ)β(1 + πi t+1 ) PN 0 0 0 0 i 0 =1 γi i Qi 0 it [Yi t + (1 − λ)β(1 + πi t+1 ) ∆i t+1 ]
with Γi 0 ,i,t = wi,t Yi 0 ,t + (1 − λ)β(1 + πi 0 ,t+1 )1+ Γi 0 ,i,t+1 ∆i 0 ,i,t = Yi 0 ,t + (1 − λ)β(1 + πi 0 ,t+1 )ε ∆i 0 ,i,t+1 Yi 0 ,t = Ci 0 ,t + Gi 0 ,t 20 / 28
Government and Monetary Authority The government buys final goods from every region Gi = {G1 , ..., GN }. • finances this expenditure using labor income taxes
¯ • issues government bonds b. Government budget constraint: Z X X XZ (1 + πi,t+1 ) bi0 − (1 + R)b¯ = Gi − T (wi si xh) i
φ
i
i
φ
The monetary authority sets the nominal rates based on a simple Taylor rule Rt = Rss + φˆ πt Equilibrium
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Calibration
Parameter Risk Aversion Discount Factor Labor supply elasticity Disutility of labor Persistence of x Variance of innovation to x Variance of innovation to s Tax parameter Elasticity of substitution Price reset probability Dividend ownership Stock of liquid assets Borrowing limit Borrowing spread Taylor rule coefficient
Notation σ β 1/θ ψ ρ ση2 σs2 τ λ θ B¯ b κ φ
Value
Target / Source
1 – 0.96 Nominal rate=2% 0.5 EFK-2013 0.3 Hours worked=40% 0.92 PSID 4.0% PSID 2.5% IRS 0.27 G/Y=20% 6 NS-2014 0.3 CER-2011 – Risky asset ownership (SCF) 1.2× Income SCF 0.25× Labor Income KMV-2016 0.17 19%: Net worth < 0 1.5 –
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Cross sectional Results Figure: MPC heterogeneity Statistic
Households with b < 0 Liquid Assets/Income Mean 25th percent. 50th percent. 75th percent. 90th percent. 99th percent. Liquid Assets Gini
SCF Model (1998-2007) 19.2%
20.7%
1.20 0.01% 0.04 0.32 1.68 18.8 0.93
1.21 0.98% 0.17 1.20 3.72 12.2 0.77
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
[ Average MPC= 0.16 ]
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Percentage Deviation from Steady-State
Local government spending shock
Government Spending
Taxes 0.014
Region 1 Region 2
0.014
0.01 0.006
0.006
0.002 -0.002
-0.002
-0.006 -0.01
-0.01 0
2
4
6
8
10 12 14 16 18 20
0
2
4
6
8
10 12 14 16 18 20
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Percentage Deviation from Steady-State
Local government spending shock
×10 -3Inflation and nominal interest rate
3
Inflation R1 Inflation R2 Nominal Rate
2
×10 -3
Real Wage
9 7 5 3
1 1 -1
0
-3 -1
-5 0
2
4
6
8
10 12 14 16 18 20
0
2
4
6
8
10 12 14 16 18 20
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Percentage Deviation from Steady-State
Local government spending shock
4
×10 -3
×10 -3
Consumption
Bond Holdings
2.6 3 1.8
2
1
1
0.2
0 -1
-0.6
-2
-1.4
-3
-2.2
-4
-3 0
2
4
6
8
10 12 14 16 18 20
0
2
4
6
8
10 12 14 16 18 20
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Consumption Change– by Skill Group
Change from Steady-State
3.5
×10 -4
Region 1
5.5 4.5 3.5 2.5 1.5 0.5 -0.5 -1.5 -2.5 -3.5 -4.5 -5.5
Household Income after Taxes Consumption Asset Holdings
2.5 1.5 0.5 -0.5 -1.5 -2.5 -3.5 1
2
3
4
Skill Level
5
6
7
×10 -4
1
Region 2
2
3
4
5
6
7
Skill Level
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Consumption Change– Decomposition
Consumption (% Change)
2
×10 -3
Region 1
1
1
0
0
-1
-1
-2
-2
×10 -3
Region 2
Benchmark Wages Inflation Nominal Rate Dividends Taxes
-3 0
2
4
6
8
10 12 14 16 18 20
0
2
4
6
8
10 12 14 16 18 20
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Quantitative Results
Multiplier
Local
Local
Data
Consumption Wage Inflation
0.20 1.01 0.00
Aggregate Model
Empirical Evidence
0.29 2.56 0.66
-0.72 0.61 0.60
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Quantitative Results-Specifications
Taylor rule coeff.
φ = 1.5
φ = 0.0
φ = 0.0
Reset Probability
λ = 0.3
λ = 0.3
λ = 0.0
Multiplier
(L)
(A)
Data
Consumption Wage Inflation
IRF Specification 2
0.20 1.01 0.00
(L)
(A)
(L)
(A)
0.15 2.29 0.00
0.10 3.47 0.00
Model
0.29 2.56 0.66
–0.72 0.61 0.60
0.14 2.43 0.16
0.50 4.42 0.47
IRF Specification 3
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Thank you!
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Placebo regressions We randomly re-assign government spending of a county to another county within the same income group/state
Spending Category
Retail Spending
Auto Spending
(Nielsen)
(Equifax)
OLS Randomly Assigned Rec. Act Spending
IV
-0.001 -0.002 (0.002) (0.002)
OLS
IV
0.001 -0.005 (0.002) (0.004)
County Controls/State F.E.
Yes
Yes
Yes
Yes
# Counties
524
524
2990
2990
Back
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Elasticities broken down by income group Spending Category
Retail Spending
Auto Spending
(Nielsen)
(Equifax)
OLS Recovery Act Spending Rec. Act Spending × Low Income Rec. Act Spending × Medium Income Rec. Act Spending × High Income
IV
0.027 0.017 (0.018) (0.020) —
—
0.005 0.005 (0.005) (0.005) -0.007 -0.007 (0.005) (0.005)
OLS ∗∗∗
IV
0.048 (0.015)
0.056∗∗∗ (0.016)
—
—
0.011 (0.007) 0.010 (0.005)
0.012∗ (0.006) 0.011∗∗ (0.005)
County Controls/State F.E.
Yes
Yes
Yes
Yes
# Counties
525
525
2999
2930
Back
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Percentage Deviation from Steady-State
Percentage Deviation from Steady-State
Results φ = 0.0, λ = 0.3 ×10 -3Inflation and nominal interest rate
3
×10 -3
2
Real Wage
9
Inflation R1 Inflation R2 Nominal Rate
7 5 3
1 1 -1
0
-3 -1
-5 0
4
2
4
6
×10 -3
8
10
12
14
16
18
20
0
2
4
6
×10 -3
Consumption
8
10
12
14
16
18
20
14
16
18
20
Bond Holdings
2.6 3 1.8
2
1
1
0.2
0 -1
-0.6
-2
-1.4 -2.2
-3 -4
-3 0
2
4
6
8
10
12
14
16
18
20
0
2
4
6
8
10
12
Back
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Percentage Deviation from Steady-State
Percentage Deviation from Steady-State
Results φ = 0.0, λ = 0.0 ×10 -3Inflation and nominal interest rate
3
×10 -3
2
Real Wage
9
Inflation R1 Inflation R2 Nominal Rate
7 5 3
1 1 -1
0
-3 -1
-5 0
4
2
4
6
×10 -3
8
10
12
14
16
18
20
0
2
4
6
×10 -3
Consumption
8
10
12
14
16
18
20
14
16
18
20
Bond Holdings
2.6 3 1.8
2
1
1
0.2
0 -1
-0.6
-2
-1.4 -2.2
-3 -4
-3 0
2
4
6
8
10
12
14
16
18
20
0
2
4
6
8
10
12
Back
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Characterizing the model
"R
Z yij = yi = j
X i0
γi 0 i
j
pij
Pi 0
#− Yi 0
" − # X pij∗ = λ + (1 − λ)(1 + πit ) · γi 0 i Qi0 it Yi 0 Pi 0 i
• Total demand for intermediate inputs of region i is a weighted sum of final goods Y of all regions. • If demand for final good Yi0 increases then yi will increase depending on the preference of i 0 for i’s inputs γi 0 i and also on the relative price of final good Qi0 ,i . • This will affect local inflation πi and real wage wi .
Back
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Equilibrium-Transition N 0 N N d N N We are looking to solve for {Ci,t }N 1 , {Li,t }1 , {Bi,t }1 , {Yi,t }1 , {yi,t }1 , {wi,t }1 ,
{
pi,j,t Pi,t
N }N 1 , {πi,t }1 , {Qii 0 t
N ∀i, i 0 } pairs, {Di,t }N ˆt , and τt for 1 , {φi,t }1 , Rt , π
t = {T , ∞} where T is the time of the policy change. A total of 11 × N + 3 equations. These are:
1-3) {Ci , Lsi , Bi0 }
all satisfy the households’ problem.
4) Final good i equals local consumption by households and the government: Yi = Ci + Gi ∀i = 1, N 5) Regional GDP {y1d , ..yNd } is given by " # X pij∗ − γi 0 i Qi0 it Yi 0 ∀i = 1, N + (1 − λ)(1 + πit ) · yid = λ Pi 0 i
6) The real wage is set to equalize labor demand and supply yid = Lsi ∀i = 1, N
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Equilibrium-Transition (cc’ed) 7) The reset price
pij∗ Pi
satisfies PN 1+ 0 0 0 Γi 0 it+1 i 0 =1 γi i Qi 0 it wit Yi t + (1 − λ)β(1 + πi t+1 )
∗ pijt = P Pit − 1 Ni0 =1 γi 0 i Qi0 it [Yi 0 t + (1 − λ)β(1 + πi 0 t+1 ) ∆i 0 t+1 ] ∀i = 1, N
8) The inflation rates πi,t satisfy # " X pi∗0 j 1− 1− 1− −1 Qi 0 i + (1 − λ)(1 + πi 0 t ) 1= γii 0 Qi 0 i ∀i = 1, N λ Pi 0 9) Relative final good price pairs Qii 0 t satisfy Qi 0 it Qi 0 i(t+1)
=[
Cit σ Ci 0 t+1 σ ] ·[ ] Ci 0 t Cit+1
10) Dividends are given by " # X pij∗ 1− Di = λ + (1 − λ)(1 + πit )−1 · [γi 0 i Qi0 i Yi 0 − wi Li ] ∀i = 1, N Pi 0 i
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Equilibrium-Transition (cc’ed)
11) The stationary regional measures φi,t evolve based on the policy functions and the transition matrices described in the model. 12) Government budget clears X X X X X µi (1 + πi,t+1 )Bi0 − (1 + R)( µBi ) = µi G i − µi Ti − µi Fi,ss i
i
i
i
i
13) Interest rate is given by a standard Taylor rule: Rt = Rss + φˆ πt PN 14) National inflation rate is given by: π ˆt = i=1 wi πi,t , where the weights are the calculated based on the relative economic size of each region Yi,ss . Back
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Percentage Change in State-Level Retail Spending -.05 .03 .11 .19 .27 .35
Recovery Act and retail spending, state-level
6
Spending Category
6.5 7 State-Level Recovery Act Spending (in log)
Back
Recovery Act Spending Partial F stat. State Controls/Census F.E. # States
7.5
Retail Spending (Nielsen, Store-level) OLS IV OLS IV 0.00 0.134 0.103∗ 0.273∗∗ (0.057) (0.160) (0.052) (0.108) — 132.4 — 28.3 No No Yes Yes 46 46 46 46
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-.1
.02
County-Level PCE Inflation Rate .03 .04 .05
Percentage Change in Zip-Level Wages -.05 0 .05 .1
.06
Wages and Inflation
5
6 7 County-Level Recovery Act Spending (in log)
Category
Recovery Act Spending Partial F stat. County Controls/State F.E. # Zip codes
8
5
6 7 8 County-Level Recovery Act Spending (in log)
Wages (IRS)
Inflation (BLS)
OLS 0.025∗∗∗ (0.007)
IV 0.020∗∗ (0.008)
OLS -0.000 (0.001)
IV -0.002 (0.002)
— Yes 14602
272.7 Yes 14602
— Yes 1116
166.5 Yes 1111
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