Download The Government Spending Multiplier: Evidence from County Level

The Government Spending Multiplier: Evidence from
County Level Data
Christopher A. Erickson, Ph.D.
Department of Economic, Applied Statistics,
and International Business
College of Business
New Mexico State University
Box 30001, MSC 3CQ
Las Cruces, NM 88003
[email protected]
August 27, 2012
Acknowledgements
I would like to acknowledge the able assistance of Eduardo Saucedo and Fred Owensby.
The Government Spending Multiplier: Evidence from county level data
By Christopher A. Erickson, Ph.D.
Executive Summary
The passage of the American Recovery and Reinvestment Act (ARRA) of 2009 has
brought fiscal policy to the forefront once again. The size of the “multiplier” of government
spending becomes of critical importance for determining the effect of ARRA. Yet there is
considerable controversy on this issue with estimates varying from .8 to 1.2. This study seeks to
estimate the impact of ARRA on local economic development by exploiting county level data. In
particular, this study relies on data obtained from the Census to estimate the government
spending multiplier. The spending associated with ARRA represents a relatively unique
circumstance in that the expenditure occurred at a time during which the economy was in a sharp
contraction and monetary policy was especially accommodating. Thus provides a particular pure
test of the use of activist fiscal policy.
The main results are presented in Table 1, which appears at the end of this paper. Two
different versions of the model were estimated. In each model, lagged log difference of personal
income, logged personal income (not differenced), log difference of state personal income and
the change in the unemployment rate are included as control variables. In Column 1 of Table 1 is
reported the ordinary least square results with each source of government earnings is entered
separately, allowing estimation of the elasticity for each of local, state, federal military and
federal civilian earnings.1 Column 2 is also estimated using ordinary least squares, but in this
case, total government earnings are used with no distinction made as to the level of government.
1
Government sector earnings are calculated using revenue received from the governmental unit. So federal civilian
government earnings is the budget allocated to the county to support federal civilian activity in that county.
Page 1 of 12
There is reasonable consistency between the models, which gives confidence that the estimates
are reasonable. The coefficient on ARRA spending is most troubling. The coefficient is
marginally significant and positive in the first model (Column 1), but insignificant in the second
model (Column 2), although the coefficients are similar in magnitude between the two models.
Table 2 presents the implied government spending multipliers (GSMs) based on the
estimated elasticities calculated at the mean value of county personal income and of the
respective measures of government earnings. The results are reasonable and fall within the range
found by other authors, as indicated in the literature review. The multiplier on ARRA is small
and economically insignificant. At least within the one-year sample we have here, it appears that
ARRA was not very effective.
Page 2 of 12
The Government Spending Multiplier: Evidence from county level data
By Christopher A. Erickson, Ph.D.
Introduction and Literature Review
The passage of the American Recovery and Reinvestment Act (ARRA) of 2009 has
brought fiscal policy to the forefront once again. The size of the “multiplier” of government
spending becomes of critical importance for determining the effect of ARRA.2 This study
estimates the impact of ARRA on local economic development by exploiting county level data.
In particular, data from Recovery.gov will be used to estimate the short run government spending
multiplier. Because ARRA expenditures are determined exogenous to local economic
conditions, the stimulus plan provides a natural experiment to study fiscal policy impact.
Because of the detailed information provided by recovery.gov, it is possible to isolate the impact
of government spending on the local economy.
The spending associated with ARRA represents a relatively unique circumstance in that
the expenditure occurred at a time during which the economy was in a sharp contraction and
monetary policy was especially accommodating. We would expect the government spending to
have maximum impact under these circumstances, thus, providing a particularly favorable
environment to test fiscal policy for those who believe that fiscal policy can be used to stabilize
the economy. Should the government spending multiplier be found to be small, it would be a
strong argument against activist fiscal policy. Either way, the results will be interesting.
There is considerable controversy as to the multiplier. Estimates vary from Barro (1981)
who found a multiplier of .8 to Ramey (2010) who found a multiplier of 1.2. Hall (1986), using
annual data back to 1920, found a slightly negative effect of government purchases on
The multiplier determines the effect of a dollar of spending on income. Specifically, 𝑑𝑌 = 𝑚 𝑑𝐺, then m is the
multiplier.
2
Page 3 of 12
consumption. An important determinate of the empirical multiplier is the treatment of the timing
of the effect of government spending. Traditionally, the assumption has been that the effect of
spending occurs in the quarter in which the spending occurs. Others, especially Ramey (2010)
and Romer and Romer (2010), argue that the impact of government spending occurs at the time
of announcement, which they study by analyzing news reports to determine when positive
congressional action is expected.
Those studies that assume that the effects of government spending are simultaneous with
the actual expenditure of spending typically find that a positive government spending results in
increased GDP, hours worked, consumption and real wages (Rotemberg and Woodford, 1992;
Blanchard and Perotti, 2002; Fatas and Mihov, 2001; Monacelli and Uhlig, 2002; Perotti, 2005;
Caldara and Kamps, 2006; Gali, Lopez-Salido, and Valles, 2007). By contract, studies that
assume that government spending affects the economy via an announcement effect, find that
while government spending raises GDP and hours, it lowers consumption and the real wage
(Ramey and Shapiro, 1998; Edelberg, Eichenbaum and Fisher, 1999; Burnside, Eichenbaum, and
Fisher, 2004; Cavallo, 2005; Romer and Romer, 2010). Papers investigating an announcement
effect using event study methodology generally show negative effect of government spending on
private consumptions (Giavazzi and Paganom, 1990; and Cullen and Fishback, 2006).
The ambiguous results found in the empirical literature are reflected in published
theoretical models. These models fall into two general categories: Neoclassical models that
assume markets clear, and new Keynesian models that assume sticky prices. With Neoclassical
models such as those of Aiyagari, Christiano, and Eichenbaum (1992) and Baxter and King
(1993), a permanent increase in government spending financed by nondistortionary means (such
as a lump sum tax) creates a negative wealth effect. Households respond by decreasing
Page 4 of 12
consumption and increasing labor supply, causing output to rise. Increased labor supply causes
the real wage to fall and a rise in the real return to capital in the short run. A higher real return
causes capital accumulation, which ultimately causes the real wage to return to its original value.
At the new steady state, consumption is lower and hours worked are higher. The neoclassical
model is at odds with the empirical results of studies using contemporaneous timing.
The new Keynesian approach, which is characterized by sticky prices in the short run,
seeks to explain a rise in consumption, the real wage, and productivity found in most empirical
analyses. For example, Drautzburg and Uhlig (2010), Rotemberg and Woodford (1992) and
Devereux, Head and Lapham (1996) use models with imperfect competition and increasing
returns to explain the rise in real wages and productivity. Gali, Lopez-Salido, and Valles (2006)
showed that only an “ultra-Keynesian” can explain how consumption and real wages can rise
when government-spending increases, thereby highlighting the many special features required to
explain a positive correlation between consumption and government spending. Typically, New
Keynesian models are neoclassical in nature in the long run; therefore, long-run issues tend to be
of second order in importance raised here, are surely not a matter of principle, but a matter of
degree.
The Model
Clearly, from the discussion above, economists have no consensus model for evaluating
the effects of macroeconomic effects of government spending. To access the empirical effects of
the ARRA, following Barro and Redlick (2011), the following model was estimated:
(1)
(y(i,t) – y(t-1,i))/y(i,t-1) = β0 + β1 ((g(i,t) – g(i,t))/g(i,t-1)] + β2z(i,t)
Page 5 of 12
In the equation, y(i,t) is per capita real personal income for county i in year t, g(i,t) is per
capital real ARRA expenditures for county i in year t, and z(i,t) is a vector of county level
controls for year t. The above would be estimated using data on government expenditure from
available through Recovery.gov, which includes ARRA expenditures at the county level, by zip
code. Data on county level for control variables will be obtained from Bureau of Economic
Analysis (www.bea.gov), and the Bureau of the Census (www.census.gov). In Eq. (1), the main
variable of interest is β1, which gives the elasticity of personal income with respect to
government spending.
Methodology and Results
The Eq (1) was estimated using cross sectional techniques with a potential n= 3143—the
number of counties in the United States. After accounting for missing data, the sample size was
reduced to 2766. Personal income and earnings are available for multiple years, so growth rates
could be calculated, but data for ARRA expenditures by county was only available for 2009, so
eq (1) could be estimated only for that one year.
The main results are presented in Table 1, which appears at the end of this paper. Two
different versions of the model were estimated. In each model, lagged log difference of personal
income, logged personal income (not differenced), log difference of state personal income and
the change in the unemployment rate are included as control variables. In Column 1 of Table 1 is
reported the ordinary least square results with each source of government earnings is entered
separately, allowing estimation of the elasticity for each of local, state, federal military and
Page 6 of 12
federal civilian earnings.3 Column 2 is also estimated using ordinary least squares, but in this
case, total government earnings are used with no distinction made as to the level of government.
There is reasonable consistency between the three models estimated, which gives
confidence that the estimates are reasonable. The coefficient on ARRA spending is most
troubling. The coefficient is marginally significant and positive in the first model (Column 1),
but insignificant in the second model (Column 2), although the coefficients are similar in
magnitude between the two models.
. Table 2 presents the implied government spending multipliers (GSMs) based on the
estimated elasticities calculated at the mean value of county personal income and of the
respective measures of government earnings.4 The results from ordinary least squares (Column 1
and Column 2) are reasonable and fall within the range found by other authors, as indicated in
the literature review. The multiplier on ARRA is small and economically insignificant. At least
within the one-year sample we have here, it appears that ARRA was not very effective. The
problem here may be that ARRA data reflects more projects approved rather than projects
actually undertaken. This, of course, is precisely the criticism of ARRA put forward by
opponents of the program—that actually expenditure on projects were delayed, mitigating the
impact of ARRA on the economy.
3
Government sector earnings are calculated using revenue received from the governmental unit. So federal civilian
government earnings is the budget allocated to the county to support federal civilian activity in that county.
𝑌𝑃
4
The formula for calculating the government spending multiplier is 𝐺𝑆𝑀 = 𝛽̂𝑖 where 𝛽̂𝑖 is the estimated
𝐺𝑖
coefficient on government spending of type i, Gi is government spending of type i, and YP is personal income.
Page 7 of 12
References
Aiyagari, S. Rao, Lawrence Christiano, and Martin Eichenbaum, “The output, employment,
and interest rate effects of government consumption,” Journal of Monetary Economics, vol.
30(1), pages 73-86, 1992.
Barro, Robert, “Output effects of government purchases,” Journal of Political Economy,
89(6): 1115, 1981.
Barro, Robert and Charles J. Redlick, “Macroeconomic effects from government Purchases
and Taxes,” The Quarterly Journal of Economics (2011) 126, 51–102.
Baxter, Marianne and Robert G. King, “Fiscal policy in general equilibrium,” The American
Economic Review, 83, 3: 315-334, Jun., 1993.
Blanchard, Olivier, and Roberto Perotti, “An Empirical Characterization of the Dynamic
Effects of Changes in Government Spending and Taxes on Output,” Quarterly Journal of
Economics, 117 (2002), 1329–1368.
Burnside, Craig, Martin Eichenbaum, and Jonas Fisher, “Fiscal shocks and their
consequences,” Journal of Economic Theory 115: 89-117, 2004.
Caldara, Dario and Christophe Kamps, “What are the Effects of Fiscal Policy Shocks? A
VAR-based Comparative Analysis,” Working Paper, September 2006..
Cavallo, Michele, “Government Employment Expenditure and the Effects of Fiscal Policy
Shocks,” Federal Reserve Bank of San Francisco Working Paper 2005-16, 2005.
Christiano, Lawrence, Martin Eichenbaum, and Charles Evans, “Nominal rigidities and the
dynamic effects of a shock to monetary policy,” Journal of Political Economy, 113: 1—45,
2005.
Cullen, Joseph, and Price V. Fishback, “Did Big Government Largesse Help the Locals: The
Implications of WWII Spending for Local Economic Activity, 1939-1958.” NBER Working
Paper No. 12801, 2006.
Drautzburg, Thorsten and Harald Uhlig, “Fiscal Stimulus and Distortionary Taxation,”
Draft, University of Chicago, 2010.
http://www.frbatlanta.org/documents/news/conferences/10fiscal policy uhlig.pdf
Edelberg, Wendy, Martin Eichenbaum, and Jonas D. M. Fisher, “Understanding the Effects
of a Shock to Government Purchases,” Review of Economic Dynamics, 2 (1999), 166–206.
Eggertsson, Gauti and Michael Woodford, “The zero interest-rate bound and optimal
monetary policy,” Brookings Panel on Economic Activity, 2003.
Fatas, Antonio, and Ilian Mihov, “The Effects of Fiscal Policy on Consumption and
Employment: Theory and Evidence,” CEPR Discussion Paper No. 2760, 2001.
Page 8 of 12
Gali, Jordi, J.David Lopez-Salido and Javier Valles , “Understanding the Effects of
Government Spending on Consumption,” Journal of the European Economics Association,
5:1, 227-270, 2007.
Giavazzi, Francesco and Marco Pagano, “Can Severe Fiscal Contractions be
Expansionary?” NBER Macroeconomics Annual, 1990.
Hall, Robert E. “The Role of Consumption in Economic Fluctuations,” in The American
Business Cycle: Continuity and Change, Robert J. Gordon, ed. Chicago: NBER and The
University of Chicago Press, 1986.
Monacelli, Tommaso and Roberto Perotti , “Fiscal policy, wealth effects and markups,”
National Bureau of Economic Research Working Paper 14584, 2008.
Perotti, Roberto, “Estimating the Effects of Fiscal Policy in OECD Countries,” CEPR
Discussion Paper 4842, January 2005.
Ramey, Valerie A. and Matthew D. Shapiro, “Costly capital reallocation and the effects of
government spending,” Carnegie-Rochester Conference Series on Public Policy, 48: 145194, June 1998.
Ramey, Valerie A. “Identifying government spending shocks: it’s all in the timing,”
forthcoming, Quarterly Journal of Economics, 2011.
Romer, Christina D., and David H. Romer, “The Macroeconomic Effects of Tax Changes:
Estimates Based on a New Measure of Fiscal Shocks,” American Economic Review 100
(2010), 763–801.
Rotemberg, Julio, and Woodford, Michael, “Oligopolistic Pricing and the Effects of
Aggregate Demand on Economic Activity,” Journal of Political Economy, 100, 1153–1297,
1992.
Woodford, Michael (2009), “Convergence in Macroeconomics: Elements of the New
Synthesis,” American Economic Journal: Macroeconomics, Vol. 1, No. 1, 267-279.
Page 9 of 12
Table 1: Impact of Government Spending on County Personal Income
Dependent Variable: Personal Income
Ordinary Least Squares
Variablea
Model 1
Constant
Model 2
0.05167
6.87998
ARRA Spending
0.05474
***
0.00199
1.82463
Federal Gov't Contracts
Federal Gov't Civilian Earnings
***
0.00137
*
0.00666
2.06922
7.55892
1.18032
0.01369
**
3.95971
***
0.02789
2.27044
Federal Gov't Military Earnings
*
0.04502
2.17896
State Gov't Earnings
*
0.01847
2.09557
Local Gov't Earnings
*
0.05039
2.33593
*
Total Gov't Earnings
0.16983
6.53053
Lagged Personal Income
-0.34014
-20.73302
Log Personal Income
-0.43869
***
-0.00556
-4.83478
State Personal Income
Change in the Unemployment Rate
***
Adjust R-Bar
Durbin-Watson
N
Df
t-statistic given in bold
0.22861
1.8387
2766
2755
* Significance level between 5% and 10%
**Significance level between 1% and 5%
Page 10 of 12
***
-3.65614
***
0.52769
***
-0.05841
-11.27912
-26.91167
-0.0045
0.53559
12.56123
***
11.28338
***
-0.06062
***
-11.00317
0.26068
1.8657
2766
2755
***
*** Significance level between less than 1%
a. Variables are expressed as log differences unless indicated otherwise. All variables are by county except for state personal
income.
Table 2: Implied Government Spending Multiplier
(GSM)
Variablea
Model 1
Model 2
ARRA Spending
0.0872
0.0081
Federal Gov't Contracts
0.0269
0.0036
Federal Gov't Civilian Earnings
1.1244
Fedearl Gov't Military Earnings
3.1265
State Gov't Spending
0.6989
Local Gov't Earnings
0.6888
Total Gov't Earnings
1.2612
Page 11 of 12