WPS5220
Policy Research Working Paper 5220
Estimating the Fiscal Multiplier
in Argentina
Paloma Anós-Casero
Diego Cerdeiro
Riccardo Trezzi
The World Bank
Latin America and the Caribbean
Economic Policy Sector
February 2010
Policy Research Working Paper 5220
Abstract
Argentina's government has resorted to fiscal policy as a factors, including the higher propensity of households to
countercyclical tool to mitigate the negative impact of save during the economic downturn, the implementation
the current economic downturn on aggregate demand. lag of public expenditures, particularly of capital
Empirical results based on a vector error correction model expenditures, and the narrow tax base that limits the
suggest, however, that the fiscal multiplier is relatively impact of countercyclical revenue measures on domestic
small and short-lived. This could reflect a number of demand.
This paper--a product of the Economic Policy Sector, Latin America and the Caribbean--is part of a larger effort in the
department to estimate the fiscal multiplier in LAC countries. Policy Research Working Papers are also posted on the Web
at http://econ.worldbank.org. The author may be contacted at panoscasero@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
ESTIMATING THE FISCAL MULTIPLIER IN
ARGENTINA
Authors: Paloma Anós-Casero, Diego Cerdeiro and Riccardo Trezzi
1. INTRODUCTION
The ongoing global downturn and financial turmoil have led many countries to
resort to fiscal stimulus to cushion the impact on domestic economic activity. In turn,
this has reignited the long-standing debate among economists about the effectiveness of
fiscal policy as a counter-cyclical tool. Supporters of an active role for fiscal policy
suggest that economies lack an efficient mechanism to return to full potential. Critics, on
the other hand, argue that economic agents will offset the impact of fiscal policy on
aggregate demand through changes in their saving behavior. A middle-of-the-road view
holds that fiscal policy can be effective provided certain conditions hold, including sound
macroeconomic fundamentals, nominal wage and price stickiness, and/or economic
agents with finite horizons and liquidity constraints.
Argentina's policy makers have also resorted to fiscal stimulus packages to mitigate
the negative impact of the current economic slowdown on aggregate demand (see Table 1
for details).
Table 1 - Response to the Financial Crisis Countercyclical Measures
Fiscal costs
Countercyclical fiscal
Details (US$)
measures
(-)=savings
Credit lines US$2.7bn
to the agricultural sector US$500mn
to the manufacturing sector US$380mn
to the SMEs US$880mn
to those buying their first car US$910mn
to those buying appliances US$1mn
The newly created SIPA (Social Security fund)
conducted an auction to allocate time deposits across
Other subsidized credits for No immediate
different banks. The winning bids will be those that
consumption fiscal costs,
charge less to finance these activities. The cost is paid
by future retirees.
Safety Nets US$400mn
Lump-sum payment to ARS200 lump sum payment for retirees (for 2008
US$ 290mn
retirees only)
Other programs US$110mn
PIT/exemptions/deductions US$1.2bn
From 28 percent to 23 percent for wheat, 25 percent to
Lower export taxes US$ 170mn
20 percent for corn
The elimination of the tax brackets put in place in
2001 would benefit those who earn (after some Social
Income tax reduction US$850mn
Security taxes) more than ARS7000 (US$2060) per
month
Infrastructure Investment US$3.6bn
4-year infrastructure spending plan of about AR$71 bn
(US$20bn). Fiscal impact will depend on the sources
and details of financing. Most of the capital
expenditures to be announced for 2009 are likely to be
already included in the budget. It is assumed that 40
Program of public works US$1.5bn
percent of the additional revenues from the
nationalization of private pension funds will be used to
finance additional infrastructure expenditures (about
AR$6bn or about 1.5 bn US$)
Other programs in
US1.9bn
Infrastructure
Other Measures -US$760mn
Capital repatriation Fiscal impact unclear
Tax moratorium 2 percent of annual tax collection (estimated) -US$760mn
Employment formalization No fiscal effect expected
Total fiscal costs for 2009 US$ 2bn
2
But the implementation of countercyclical fiscal policies can be challenging,
particularly for emerging economies. For start, economic fluctuations tend to be more
intense for these countries. A typical recession in Argentina reduces output by an average
of 6.2 percent. In the OECD, output falls by 2 percent, on average. As the tax base is
narrower and revenues are largely dependent on commodity exports, external shocks and
fluctuations in economic activity often resulted in large changes in government revenues.
Recessions can cost the Treasury an average of 9 percent of its revenues, compared with
six percent for the OECD as a whole. Additionally, automatic stabilizers are weak when
compared to those in developed economies, and discretionary government spending
might take several months to be implemented. Another complicating factor common to
most LAC economies is the increased cost of borrowing during recessions, as creditors
are wary of buying emerging-market bonds during bad times 1. This, in turn, prevents
emerging-country governments from smoothing the cycle by borrowing, as their rich
counterparts can afford to do.
This paper empirically analyzes the effectiveness of fiscal policy in stimulating
economic activity in Argentina, drawing on a vector error correction model to derive
the fiscal multiplier.
2. BRIEF LITERATURE REVIEW
Empirical studies based on structural vector autoregression models conclude that
fiscal multipliers have declined over time and, in some cases, may even have been
negative (on this specific point see Perotti, 2005). According to Eskesen (2009), these
results, which differ widely across countries, likely reflect: (i) increasing leakage through
the trade channel due to higher openness of economies; (ii) a decline in the share of
liquidity constrained households due to better access to credit; and (iii) a sharper focus of
monetary policy on price stability.
In his study for OECD countries, Perotti (2005) finds that the effects of fiscal policy on
GDP are small. He finds that only for the U.S. prior to 1980 the fiscal multiplier for
government expenditures was larger than one. The figures below summarize these
findings.
1
During the 2001-02 crisis interest rate shifted from 15.4 percent in June to 132.6 percent in November 2001.
3
Effect on GDP of a shock to government spending of 1 p.p. of GDP
cumulative response at 12 quarters (source: Perotti, 2005)
4
1960-1980
3
1980-2001
2
1
0
-1
-2
-3
Australia Canada Germany United Kingdom United States
Effect on GDP of a cut in net taxes of 1 p.p. of GDP
cumulative response at 12 quarters (source: Perotti, 2005)
3
1960-1980
2
1980-2001
1
0
-1
-2
-3
Australia Canada Germany United Kingdom United States
Studies drawing on aggregate dynamic macro models conclude that fiscal
multipliers are in the range of 0.3 to 1.2 percent, and that expenditure measures
appear to have a larger effect than tax measures (Hemming et al 2002, Botman 2006).
Other studies find that government investment has the largest impact on economic
activity and inflation. However, the size of the estimated multipliers depends on
assumptions about, among other things, the monetary regime, labor supply elasticities
and liquidity constraints (IMF, 2008).
3. METHODOLOGY
This paper focuses on two measures: a general government tax cut and a general
government expenditure increase. The expenditure variable is defined as total
purchases of goods and services, meaning government consumption plus government
investment. The revenue variable is defined as total tax revenues minus transfers.
The traditional approach in the literature consists in estimating impulse reaction
functions using a vector autoregressive (VAR) model (see for example Blanchard and
Perotti, 1999 or Bernanke and Mihov, 1998 for monetary policy). Fiscal multipliers are
than computed summing the first four, eight or 12 periods of the reaction functions. The
basic reduced VAR specification is as follows: Z t = ( L) Z t -1 + u t (1) where Z t is a three-
dimensional vector ( Z t = [Yt , Et , Rt ]' ) in the logarithms of quarterly observations for
government consumption, government revenues and private consumption.
4
U t [u ty , u te , u tr ] is the corresponding vector of reduced form residuals, which in general
will have no-cross correlations, and can be represented as follows:
(2)u ty = ey u te + ry u tr + vty
(3)u te = y u ty + re vte + vte
e
(4)u tr = ry u ty + er vte + vtr
However, as is well known in the literature, the reduced form residuals represented
by equations (2), (3) and (4) have little or no economic significance. In fact, they are
linear combination of the underlying "structural" tax, spending and private consumption
shocks. Equation (2) states that unexpected movements in output can be due to
unexpected movement in taxes and/or unexpected movements in government spending.
Equation (4) states that unexpected movements in taxes within a quarter can be due to
one of the following factors: the response to unexpected movements in private
consumption (captured by ry u ty ), the response to structural shocks in spending (captured
by er vte ), and to structural shocks to taxes, captured by vtr . Finally, vte and vtr are the
mutually uncorrelated structural shocks to government expenditure and revenues that we
seek to identify. In order to do so, we follow a four-step procedure.
In the first step, the reduced form residuals are estimated from the VAR specified in
equation (1). Given the fact that the reduced form residuals are correlated with the
structural shocks, it is necessary to apply further restrictions in order to identify the
coefficients y and y . However, since ordinary least squares in equation (2) and (4)
e r
would not generate consistent estimates y and y , external estimates are used. 2
r e
With these estimates, in the second step, the cyclically adjusted fiscal shocks, u e ,adj and
y
u y ,adj can be calculated. Expressed in more detail:
r
(5)u te ,adj u te - y u ty = re vtr + vte
e
(6)u tr ,adj u tr - y u ty = er vte + vtr
r
In the third step, assuming that structural revenue shocks have no impact on structural
spending shock (meaning that re = 0 ), the structural fiscal shocks are identified.
Equation (5) reduces to u te ,adj = vte = u te . This simply implies that structurally adjusted
expenditure shocks are effectively assumed equal to the cyclically adjusted expenditure
2
The elasticity of expenditure with respect to changes in economic activity is assumed to be close to zero
within the quarter, as commonly assumed in many other empirical studies. The elasticity of revenues to
economic activity is estimated to be 0.45 for Argentina.
5
shocks. Finally, equation (6) can be estimated using ordinary least squares to get
consistent estimates of er . 3
In the fourth step, the coefficients ey and ry in equation (2) can be determined using
the structural uncorrelated fiscal shocks vte and vtr as instruments for u te and u tr ,
respectively.
Combined, the four steps allow us to estimate the A and B matrices of the standard
relation between the reduced-form innovations and the structural shocks Au t = Bvt ,
where:
1 - ey - ry 1 0 0
A = - y
e
1
0 and B = 0 1 0 which brings to:
- y
r
0 1 0 er
1
-1
1 - ey - ry 1 0 0 vty
u t = - y
e
1 0 0 1
0 vte
- y
r
0 1 0 er 1 vtr
With this, impulse responses are computed using Monte Carlo simulations based on
10,000 replications.
Given the non-stationary properties of the underlying series, we test for co-integrated
relationships among variables. If no co-integrated relationships are found, a Vector Auto
Regressive (VAR) model, specified in first log differences, is estimated. If there are co-
integrated relationships, a vector error correction (VEC) model is estimated, without
imposing additional restrictions in order to avoid over-identification. In its basic form, a
VEC model can be represented as:
y t = y t -1 + y t -1 + t
Where is the long-run matrix which contains the equilibrium correction terms. As
usual, the number of co-integrating vectors is given by the number of linearly
independent rows of , namely the rank of .
3
er is estimated to be 0.7541 for Argentina.
6
4. ESTIMATING THE FISCAL MULTIPLIER
Consistent with the visual inspection of the data, no evidence for co-integration was
found among the variables (see annex). Figure 1 shows diverging patterns of
government revenues and private consumption 4. Thus, we specify a VAR model 5 and
estimate the impulse response functions of shocks to both government expenditures and
revenues, specifying the variables in log differences to eliminate the non-stationary
properties. Finally, in order to estimate the fiscal multiplier, we calculate the cumulative
response of private consumption to each of these shocks by adding the first four, eight
and twelve lagged quarter responses.
Figure 1. Tax Revenues, Government Expenditure and Private Consumption
Source: National Authorities. Series seasonally adjusted.
In Argentina, the fiscal multiplier is small and short-lived. A (one standard deviation)
shock to government expenditures, which represents additional AR$ 1.17 million in
government expenditures, is estimated to increase private consumption by nearly 0.5%
within the same year. Put it differently, each AR$ 1 million of additional expenditures,
private consumption increases 0.39%. The multiplier of revenue measures is also small.
The cumulative impact on real private demand of a (one standard deviation) shock in tax
revenues, which represents AR$ 1.16 million, is close to zero. The fiscal multiplier
evaporates quickly (lasting one year). It should be noted that the coefficients after the
fourth quarter should be interpreted with caution since only within the first year
coefficients are statistically significant. The limited effectiveness of revenue measures
could be related to a narrow tax base since the informal sector is not negligible. The small
and short-lived cumulative impact 6 of increases in expenditures may reflect a number of
factors, including a high propensity to save among households (possibly, in part,
reflecting the lack of a comprehensive social safety net) and the implementation lag of
public investment programs.
4
During the period that spans from 1993Q1 through 2003Q1 (end of the most recent financial crisis), the seasonally-adjusted series
for private consumption increased by 1.72 percent (from 157,762.6 to 160,482.1), while government tax revenues increased 38.6
percent (from 8,207.2 to 11,380.7). The substantial increase in both government revenues and expenditures in 1997Q1 was not
associated with a similar movement in private consumption. Finally, an exceptional increase (80.1 percent) in government tax
revenues during 1998 was associated with a negligible 0.007 percent increase in private consumption.
5
It includes two dummy variables, corresponding to 1998-crisis and to the crisis in 2001-2002.
6
In fact, only the first four quarters are associated to statistically significant coefficients.
7
That the fiscal multiplier is small is not a surprising result. The small size of the fiscal
multiplier in Argentina is comparable to the size of fiscal multipliers in other LAC
economies (Perry et al., 2008). In many industrialized countries, the fiscal multipliers can
range from less than zero to at most three (Perotti and Blanchard, 2005). In the US, Barro
(2009) estimates that the multiplier of the increase in government expenditures at the
peak of World War II (1943-44) was 0.8. But when Barro estimates the fiscal multiplier
associated with peacetime government purchases in the US, he obtains a number close to
8
zero. The evidence for Argentina and elsewhere points to one of the key challenges of
fiscal policy as a counter-cyclical tool: the relatively small size and short-lived nature of
the fiscal multiplier.
Finally, we estimate the fiscal multiplier in Peru, to compare the results obtained for
Argentina. The Johansen's procedure suggests one co-integrated relationship, consistent
with the visual inspection of the data for Peru (see Figure 2). Thus, we estimate a VECM
to estimate fiscal multipliers, that are calculated on a cumulative basis over the first four,
eight and twelve quarters (see annex for details).
Figure 2. Peru :Tax Revenues, Government Expenditure and Private Consumption
Source: National Authorities. Series seasonally adjusted.
The fiscal multiplier in Peru is also small, close to zero, and short-lived. The impact
of one standard deviation shock in government expenditure on private domestic demand
is close to zero.
9
ANNEX
Johansen's Procedure ARGENTINA
Date: 05/20/09 Time: 14:56
Sample (adjusted): 1993Q4 2008Q3
Included observations: 60 after adjustments
Trend assumption: Linear deterministic trend
Series: PRIV_CONSSA GOV_EXPSA GOV_REVSA
Lags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None 0.253503 26.17100 29.79707 0.1237
At most 1 0.129025 8.629179 15.49471 0.4008
At most 2 0.005661 0.340648 3.841466 0.5595
Trace test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None 0.253503 17.54182 21.13162 0.1479
At most 1 0.129025 8.288531 14.26460 0.3501
At most 2 0.005661 0.340648 3.841466 0.5595
Max-eigenvalue test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
VAR results for ARG, variables specified in log differences.
Vector Autoregression Estimates
Date: 05/21/09 Time: 14:36
Sample (adjusted): 1993Q4 2008Q3
Included observations: 60 after adjustments
Standard errors in ( ) & t-statistics in [ ]
D(LOG(PRIV_C D(LOG(GOV_E D(LOG(GOV_R
10
ONSSA)) XPSA)) EVSA))
D(LOG(PRIV_CONSSA(-1))) 0.643741 1.757509 1.819987
(0.14466) (1.01856) (0.97272)
[ 4.45018] [ 1.72548] [ 1.87102]
D(LOG(PRIV_CONSSA(-2))) -0.152693 0.470141 0.747600
(0.15219) (1.07164) (1.02341)
[-1.00329] [ 0.43871] [ 0.73050]
D(LOG(GOV_EXPSA(-1))) -0.039537 -0.616201 -0.152425
(0.03644) (0.25658) (0.24504)
[-1.08500] [-2.40157] [-0.62205]
D(LOG(GOV_EXPSA(-2))) -0.014673 -0.255629 -0.250986
(0.03580) (0.25210) (0.24075)
[-0.40983] [-1.01401] [-1.04251]
D(LOG(GOV_REVSA(-1))) 0.052093 0.095097 -0.295705
(0.03730) (0.26263) (0.25081)
[ 1.39665] [ 0.36210] [-1.17900]
D(LOG(GOV_REVSA(-2))) 0.019451 0.024786 -0.023454
(0.03651) (0.25707) (0.24550)
[ 0.53278] [ 0.09642] [-0.09554]
C 0.005087 -0.000273 -0.002308
(0.00301) (0.02120) (0.02025)
[ 1.68930] [-0.01289] [-0.11397]
DUMMY98 -0.008476 0.098150 0.092226
(0.00956) (0.06730) (0.06427)
[-0.88686] [ 1.45847] [ 1.43502]
DUMMY01 -0.018267 -0.003741 0.017975
(0.01343) (0.09459) (0.09033)
[-1.35977] [-0.03955] [ 0.19898]
R-squared 0.518550 0.250246 0.230159
Adj. R-squared 0.443028 0.132637 0.109400
Sum sq. resids 0.014482 0.718021 0.654846
S.E. equation 0.016851 0.118654 0.113314
F-statistic 6.866250 2.127784 1.905932
Log likelihood 164.7394 47.63172 50.39469
Akaike AIC -5.191314 -1.287724 -1.379823
Schwarz SC -4.877163 -0.973572 -1.065671
Mean dependent 0.006997 0.012353 0.013774
S.D. dependent 0.022579 0.127404 0.120072
Determinant resid covariance (dof adj.) 1.57E-08
11
Determinant resid covariance 9.66E-09
Log likelihood 298.2462
Akaike information criterion -9.041539
Schwarz criterion -8.099084
Johansen's Procedure PERU
Date: 05/20/09 Time: 14:33
Sample (adjusted): 1981Q2 2008Q3
Included observations: 110 after adjustments
Trend assumption: Linear deterministic trend
Series: PRIV_CONSSA TAX_REVSA GOV_EXPSA
Lags interval (in first differences): 1 to 4
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.194352 35.72291 29.79707 0.0092
At most 1 0.080222 11.95094 15.49471 0.1593
At most 2 0.024711 2.752365 3.841466 0.0971
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.194352 23.77196 21.13162 0.0207
At most 1 0.080222 9.198577 14.26460 0.2700
At most 2 0.024711 2.752365 3.841466 0.0971
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
VECM results for PERU, variables specified in log.
Vector Error Correction Estimates
Date: 05/20/09 Time: 14:40
Sample (adjusted): 1981Q1 2008Q3
12
Included observations: 111 after adjustments
Standard errors in ( ) & t-statistics in [ ]
Cointegrating Eq: CointEq1
LOG(PRIV_CONSSA(-1)) 1.000000
LOG(GOV_EXPSA(-1)) -5.539632
(1.08443)
[-5.10835]
LOG(TAX_REVSA(-1)) 4.116692
(1.03463)
[ 3.97889]
C 2.489465
D(LOG(PRIV_C D(LOG(GOV_E D(LOG(TAX_R
Error Correction: ONSSA)) XPSA)) EVSA))
CointEq1 0.003664 0.075820 -0.017105
(0.00584) (0.01777) (0.01415)
[ 0.62711] [ 4.26768] [-1.20873]
D(LOG(PRIV_CONSSA(-1))) -0.003520 0.580140 -0.482135
(0.10315) (0.31366) (0.24984)
[-0.03412] [ 1.84956] [-1.92976]
D(LOG(PRIV_CONSSA(-2))) -0.142206 0.012405 -0.171618
(0.09339) (0.28398) (0.22620)
[-1.52273] [ 0.04368] [-0.75871]
D(LOG(PRIV_CONSSA(-3))) -0.223590 -0.085381 0.061868
(0.08841) (0.26883) (0.21413)
[-2.52911] [-0.31760] [ 0.28893]
D(LOG(GOV_EXPSA(-1))) 0.075170 -0.089658 0.024927
(0.03794) (0.11536) (0.09188)
[ 1.98151] [-0.77723] [ 0.27129]
D(LOG(GOV_EXPSA(-2))) 0.066604 -0.192547 -0.048395
(0.03599) (0.10943) (0.08716)
[ 1.85083] [-1.75960] [-0.55523]
D(LOG(GOV_EXPSA(-3))) 0.014277 0.138317 0.057988
(0.03345) (0.10170) (0.08101)
[ 0.42687] [ 1.36003] [ 0.71583]
D(LOG(TAX_REVSA(-1))) 0.013674 -0.229854 -0.155426
(0.04470) (0.13592) (0.10827)
[ 0.30591] [-1.69107] [-1.43559]
13
D(LOG(TAX_REVSA(-2))) 0.136474 -0.072110 0.216053
(0.04604) (0.14001) (0.11152)
[ 2.96396] [-0.51502] [ 1.93729]
D(LOG(TAX_REVSA(-3))) -0.021874 -0.349052 0.237864
(0.04481) (0.13626) (0.10853)
[-0.48816] [-2.56166] [ 2.19160]
C 0.011261 0.014617 0.013869
(0.00376) (0.01143) (0.00910)
[ 2.99672] [ 1.27925] [ 1.52385]
DUMMY87 -0.028946 -0.029857 -0.080481
(0.01310) (0.03982) (0.03172)
[-2.21016] [-0.74971] [-2.53711]
DUMMY90 -0.021113 -0.094835 0.038780
(0.01708) (0.05193) (0.04136)
[-1.23639] [-1.82632] [ 0.93759]
R-squared 0.320937 0.382791 0.216285
Adj. R-squared 0.237786 0.307214 0.120320
Sum sq. resids 0.102162 0.944644 0.599334
S.E. equation 0.032287 0.098180 0.078203
F-statistic 3.859701 5.064932 2.253783
Log likelihood 230.4833 107.0373 132.2892
Akaike AIC -3.918618 -1.694366 -2.149355
Schwarz SC -3.601286 -1.377033 -1.832023
Mean dependent 0.006284 0.007022 0.006045
S.D. dependent 0.036982 0.117956 0.083379
REFERENCES
Blanchard, Olivier and Roberto Perotti, 2002, "An Empirical Characterization of the
Dynamic Effects of Changes in Government Spending and Taxes on Output,"
Quarterly Journal of Economics 117, 13291368.
Hemming Richard, Michael Kell, and Selma Mahfouz, 2002, "The Effectiveness of
Fiscal Policy in Stimulating Economic Activity--A Review of the Literature," IMF
Working Paper 02/208 (Washington: International Monetary Fund).
Perotti, Roberto, 2005, "Estimating the Effects of Fiscal Policy in OECD Countries,"
CEPR Discussion Paper 4842 (London: Centre for Economic Policy Research).
14
, 2007, "In Search of the Transmission Mechanism of Fiscal Policy," NBER
Working Paper 13143 (Cambridge, Massachusetts: National Bureau of Economic
Research).
Shapiro, Matthew D. and Joel Slemrod, 2003, "Consumer Response to Tax Rebates,"
American Economic Review, Vol. 93 (1), 381396
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