Identifying Government Spending Shocks: It’s all in the timing By Valerie A. Ramey

Identifying Government Spending Identifying Government Spending Shocks:Shocks:

It’s all in the timingIt’s all in the timing

Valerie A. RameyValerie A. Ramey

What is the Impact of an Increase in Government Spending?

Traditional Keynesian

Increase in Government Spending leads to:

↑ Output ↑ Consumption

↑ Hours ↑ Real Wage

↓ Investment

Neoclassical Theory: Impact of Increased Gov.

Aiyagari, Christiano, & Eichenbaum (1992), Baxter & King (1993)

Key mechanism: Negative wealth effect increases labor supply

↑ Output ↓ Consumption

↑ Hours ↓ Real Wage

↓ or ↑ Investment

New Keynesian Theory

Rotemberg & Woodford (1992): Countercyclical markups

Devereux, Head & Lapham (1996): increasing returns

Gali, Lopez-Salido, Valles (2007): sticky prices, noncompetitive labor markets, rule-of-thumb consumers

Ravn, Schmitt-Grohe, Uribe (2006): deep habits

Empirical Evidence on the Impact of ↑ G

• Papers using VAR techniques for identification

e.g. Rotemberg and Woodford (1992), Blanchard and Perotti (2002), Fatás and Mihov (2001), Perotti (2004), Montford and Uhlig (2005), Galí, López-Salido, and Vallés (2006)), Perotti(2007)

↑ output ↑ hours ↑ consumption ↑ real wages ↓ investment • Papers using Ramey-Shapiro war dates

e.g. Ramey and Shapiro (1998), Edelberg, Eichenbaum, and Fisher (1999), Burnside, Eichenbaum, and Fisher (2004), and Cavallos (2005)).

↑ output ↑ hours ↓consumption ↓ real wages ↓ or ↑ investment

• Other Event Studies

e.g. Giavazzi & Pagano’s (1990), Cullen & Fishback (2006), Barro & Redlick (2009)

↓consumption or no effect

Outline of talk1. Overview of behavior of government spending2. Generate results using the two methods on

the same data and time period3. Compare the timing of war dates to the

timing of VAR shocks4. Present theoretical model that explains both

results5. Show that changing the timing on the war

dates produces the same results as the VAR6. Introduction of 2 new “news” variables and

extension of sample back to 1939

Red lines are Ramey-Shapiro military dates + 9/11.

(numbers in thousands of 2005 dollars)

1950 1960 1970 1980 1990 2000 2010year

Real Defense Spending Per Capita

1950 1960 1970 1980 1990 2000 2010year

Real Government Spending Per Capita

Some Historical Perspective

Real Defense Spending Per Capita

(thousands of 2005 dollars)

1940 1950 1960 1970 1980 1990 2000 2010year

Components of Government SpendingFraction of nominal GDP

1950 1960 1970 1980 1990 2000 2010year

defense nondefense federalstate & local

Two Leading Identification Methods

1. VAR method Order government spending first in a VAR, use standard Choleski

decomposition to identify shocks to government spending.

2. Ramey-Shapiro Dates

Augment system with a dummy variable that takes the value of unity at times when a major political event caused Business Week to begin forecasting large increases in defense spending. Shocks to the dummy variable rather than actual government spending are identified as the shock.

Ramey-Shapiro Dates: The dummy variable takes the value of unity at 4 dates.

1950:3: North Korea invaded South Korea in late June 1950.

1965:1 Johnson began air strikes against N. Vietnam in Feb. 1965.

1980:1 The USSR invaded Afghanistan on Dec. 24, 1979.

*2001:3 Terrorists struck the World Trade Center and the Pentagon on 9/11.

Framework for Comparison

X includes: total govt spending, GDP, Barro-Redlick tax rate, total hours (including military), nondurable + services consumption, private fixed investment, product wage in private business.

Log per capita (except BS tax, product wage)

Quarterly data: 1947:1 – 2008:4, 4 lags, quadratic trend

ttt UXLAX 1)(

Identification

Standard VAR: order government spending first

War dates: also include current and 4 lags of Ramey-Shapiro war dates variable augmented with 9/11.

1950:3, 1965:1, 1980:1, 2001:3

Set shock size so that peak response of government spending is the same across specifications

Comparison of Identification Methods VAR shocks in top row; War dates in bottom row

68% bands

0 4 8 12 16 20quarter

government spending

0 4 8 12 16 20quarter

total hours

0 4 8 12 16 20quarter

government spending

0 4 8 12 16 20quarter

70 4 8 12 16 20

quarter

total hours

Comparison of Identification Methods VAR shocks in top row; War dates in bottom row

68% bands

0 4 8 12 16 20quarter

consumption, ndur + serv

0 4 8 12 16 20quarter

investment, nonres + res

0 4 8 12 16 20quarter

real wages

0 4 8 12 16 20quarter

real wages

Why Do these Two Methods Give Such Different Results?

It’s All in the timing

I will argue that the shocks identified by the VAR are mostly anticipated and this explains all of the difference in the results.

1948 1949 1950 1951 1952 1953 1954 1955

Defense Spending During Korean War

1964 1965 1966 1967 1968 1969 1970

Defense Spending During Vietnam War

1948 1949 1950 1951 1952 1953 1954 1955

VAR Shocks During Korean War

1964 1965 1966 1967 1968 1969 1970

VAR Shocks During Vietnam War

1950 1951 1952 1953 1954fiscal year

actual Jun 50 forecastAug 50 forecast Sep 50 forecast

Business Week Defense Forecasts

1965 1966 1967 1968 1969fiscal year

actual Jan 65 forecastAug 65 forecast Sep 66 forecastJan 67 forecast May 67 forecast

Business Week Defense Forecasts

1978 1980 1982 1984 1986

Defense Spending During Carter-Reagan

1999 2000 2001 2002 2003 2004

Defense Spending During 9/11

1978 1980 1982 1984 1986

VAR Shocks During Carter-Reagan

1999 2000 2001 2002 2003 2004

VAR Shocks During 9/11

1980 1982 1984 1986fiscal year

actual Jan. 1979 forecastJan. 1980 forecast Jan. 1981 forecastOct. 1981 forecast

OMB Defense Forecasts

2001 2002 2003 2004 2005 2006

actual Apr. 2001 forecastFeb. 2002 forecast Feb. 2003 forecastFeb. 2004 forecast

OMB Defense Forecasts

Survey of Professional Forecasters

Forecasts of Real Federal Spending Growth from Quarter t-1 to t+4

1999 2000 2001 2002 2003 2004

Are VAR shocks anticipated? Yes.

Hypothesis Tests p-value in parenthesis

Do War dates Granger-cause VAR shocks? Yes (0.017)

Do one-quarter ahead Professional Forecasts Granger-cause VAR shocks? 1981:3 – 2008:4

Yes (0.025)

Do four-quarter ahead Professional Forecasts Granger-cause VAR shocks? 1981:3 – 2008:4

Yes (0.016)

Do VAR shocks Granger-cause War dates? No (0.148)

A simple model shows how getting the dates wrong can lead to faulty

inferences.

Very simple model with government spending and lump-sum taxation.

Simulate a defense spending process similar

to data. The key is that the new spending is

announced two quarters before it happens, similar to what we saw in the data.

028.0,ln25.0ln18.0ln4.1constant ln

008.0,ln95.ln

01.0,ln95.ln

)023.01(1

)1log()log(33.067.0)(

egttttt

egGFGFGFGF

tKtItKtGtItCtY

tNttCUtKtNtZtY

Theoretical Effect of an Increase in Government Spending(announced two quarters in advance)

0 4 8 12 16 20quarter

government spending

0 4 8 12 16 20quarter

consumption

0 4 8 12 16 20quarter

investment

0 4 8 12 16 20quarter

real wages

Estimation of VARs on Simulated Data Actual Spending Identification in top row; News in bottom row

68% bands

0 4 8 12 16 20quarter

government spending

0 4 8 12 16 20quarter

government spending news

0 4 8 12 16 20quarter

Estimation of VARs on Simulated Data Actual Spending Identification in top row; News in bottom row

68% bands

0 4 8 12 16 20quarter

consumption

0 4 8 12 16 20quarter

investment

0 4 8 12 16 20quarter

real wages

0 4 8 12 16 20quarter

consumption

0 4 8 12 16 20quarter

investment

20 4 8 12 16 20

quarter

real wages

If the argument is right, then delaying the military dates should produce the standard VAR results

Procedure: delay Ramey-Shapiro dates to time of first big VAR defense shock.

Actual Delayed Dates

1950:3 1951:11965:1 1965:31980:2 1981:42001:3 2003:2

Empirical Results from Delaying the Timingof the Ramey-Shapiro Military Dates

Estimates on Actual Data

0 4 8 12 16 20quarter

government spending

0 4 8 12 16 20quarter

total hours

0 4 8 12 16 20quarter

real wages

A New Measure of Defense Shocks

The simple dummy variable incorporates only a small part of the information available in the narrative record.

Thus, I created a new variable: the present discounted value of the forecasted changes in defense-related spending. This is what matters for the wealth effect.

I created the variable by reading mostly Business Week, but also the New York Times, Washington Post, and Wall Street Journal from January 1939 to December 2008.

Business Week 5/25/40, p. 60: “The German drive to the English Channel this week assured quick adoption of the President’s program to speed up war preparations. But the proposed expenditure of less than $3.5 billion in the coming fiscal year is only a small beginning; of that, business men can now be certain… In the 1919 fiscal year costs ran to $11 billion. A major war effort in the ‘40s would come higher… since we have started six years behind, a vast outlay is required if we are to attain military parity with Hitler’s industrial machine. In a major war at least four times the $3.5 billion we plan to spend in 1941 would be needed, and quite conceivably five to six times that – or anywhere from 20% to 30% of the peacetime national income. However, it is not possible to jump immediately up from a $3.5 billion to a $14 billion military effort. It takes time to shift a nation from a peace economy to a war-preparation economy and thence to a war economy. Right now we are at the very beginnings of a war-preparation economy.”

Business Week 10/25/41: “Expect dramatic developments in the defense program in the next few weeks. Plans were under way before the Kearny incident and the sinking of two more American ships but they have been speeded by this week’s shocks and by the heartening reports on Russia’s capacity to hold out, brought home by the Harriman mission. Beginning this week, war production –and it’s ‘war,’ not ‘defense’ …-becomes the No. 1 item on the business docket.” p. 7

“Already, Washington is taking cognizance of the imminence of a shooting war…A year ago, the government thought of armament expenditures of $10 billion a year; six months ago the goal was $24 billion; as recently as last month $36 billion was regarded as a desirable but hard-to-achieve outlay; but now an annual expenditure of $50 billion is begin seriously discussed – not as the desirable goal, but as an inescapable necessity.” p. 13

Defense News:PDV of Expected Change in Spending as a % of lagged

1940 1950 1960 1970 1980 1990 2000 2010year

Explanatory Power of New Defense Shock Seriesfor Growth of Real Per Government Spending

Columns 1 and 2 from regression on current and 4 lags of shock. Column 3 is from full VAR.

R-squared F-statistic Marginal F-statistic

1939:1 – 2008:4 0.410 37.58 9.00

1947:1 – 2008:4 0.518 51.15 19.65

1955:1 – 2008:4 0.037 1.60 0.429

Framework for Defense News VAR

X includes: defense news variable (as a % of lagged GDP), total govt spending, GDP, Barro-Redlick tax rate, 3-month T-bill rate

6th variable is rotated in.

Newly constructed Quarterly data: 1939:1 – 2008:4, 4 lags, quadratic trend

ttt UXLAX 1)(

VAR with Defense News Variable: 1939-2008(red lines: 68%; green lines: 95%)

0 4 8 12 16 20quarter

government spending

0 4 8 12 16 20quarter

total hours

0 4 8 12 16 20quarter

manufacturing wage

0 4 8 12 16 20quarter

3 month Tbill rate

0 4 8 12 16 20quarter

average marginal income tax rate

VAR with Defense News Variable: 1939-2008(red lines: 68%; green lines: 95%)

0 4 8 12 16 20quarter

real baa bond rate

0 4 8 12 16 20quarter

nondurable consumption

0 4 8 12 16 20quarter

services consumption

0 4 8 12 16 20quarter

consumer durable purchases

0 4 8 12 16 20quarter

nonresidential investment

0 4 8 12 16 20quarter

residential investment

Effect of Defense Shocks with and without WWII(Blue: 1939 - 2008; green: 1947 – 2008)

0 4 8 12 16 20quarter

government spending

0 4 8 12 16 20quarter

total hours

0 4 8 12 16 20quarter

manufacturing wage

0 4 8 12 16 20quarter

3-month Tbill rate

0 4 8 12 16 20quarter

average marginal income tax

Effect of Defense Shocks with and without WWII(Blue: 1939 - 2008; green: 1947 – 2008)

0 4 8 12 16 20quarter

real baa bond rate

50 4 8 12 16 20

quarter

0 4 8 12 16 20quarter

Alternative Measure for Later Period

•The defense news variable has little predictive power for the post-Korean War Period.

•As an alternative, I use government spending shocks constructed using data from the Survey of Professional Forecasters.

1968:4 – 1981:2: Forecast nominal defense spending, GDP deflator

1981:3 – 2008:4: Forecast real federal spending

Shock(t) = Δ ln(gt) - Et-1Δ ln(gt)

VAR with Shock Based on Professional Forecasters:1968-2008

(red lines: 68%; green lines: 95%)-.

0 4 8 12 16 20quarter

government spending

.50 4 8 12 16 20

quarter

0 4 8 12 16 20quarter

total hours

0 4 8 12 16 20quarter

business wage

0 4 8 12 16 20quarter

3 month Tbill rate

0 4 8 12 16 20quarter

average marginal income tax rate

VAR with Shock Based on Professional Forecasters:1968-2008

(red lines: 68%; green lines: 95%)

0 4 8 12 16 20quarter

real baa bond rate

0 4 8 12 16 20quarter

Conclusions Standard VAR methods appear to identify shocks

that are anticipated. Anticipation effects are very important from the

view point of a neoclassical model. Both theory and empirical evidence suggests that

difference in standard VAR results and Ramey-Shapiro dates is due to faulty timing of VAR shocks.

Bottom Line: An increase in (nonproductive)

government spending lowers consumption and has a multiplier of unity or less.

Identifying Government Spending Shocks: It’s all in the timing By Valerie A. Ramey

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