How Effective was the American Recovery and Reinvestment Act of 2009?

Amit Swaroop Northwestern University

May 2010

I am extremely grateful to my thesis advisor Giorgio Primiceri for his guidance and mentorship. I could not have written this without his continued support. I am also very thankful to Chris Vickers for his assistance with STATA.

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Section I. Introduction

Harry Truman once said “It’s a recession when you neighbor loses his job; it’s a

depression when you lose yours.” Due to the financial crisis and horrible employment

numbers, on February 13, 2009, Congress passed the American Recovery and

Reinvestment Act of 2009, more commonly know as the 2009 fiscal stimulus. The act

calls for $787 billion to be spent in order to (1) create new jobs and save existing ones,

(2) spur economic activity and invest in long-term growth and (3) foster further

accountability and transparency in government spending. Of the $787 billion, $288

billion is to be spent on tax cuts and benefits for working families and businesses, $224

billion is apportioned for increasing funds for education and health care as well as

entitlement programs (such as unemployment benefits), and $275 billion is to be used for

federal contracts, grants, and loans. The act will offer financial aid directly to local

school districts, expand the Child Tax Credit, and finance a process to computerize health

records to reduce errors and save on health care costs. Additionally, the act will pump

money into infrastructure development, construction and repair of roads and bridges, and

the expansion of broadband and wireless services. Due to infrastructure improvements,

the act is expected to provide long term economic growth benefits. However, the main

purpose of the stimulus is to help the economy after the financial crisis by giving it a

much needed jumpstart. Unemployment numbers started going up and the US

government thought that some type of fiscal policy intervention was necessary. This was

thought to be even more needed when the economy did not recover after aggressive

monetary policy actions. After interest rates hit the zero bound, fiscal policy action was

deemed necessary.

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This paper attempts to examine whether or not the fiscal stimulus has been

effective thus far. We do this by stimulating the path of the US Economy

counterfactually without the stimulus. This allows us to evaluate the effectiveness of the

fiscal stimulus. We can, for example, look at whether unemployment numbers would

have been better or worse off without the stimulus. We conduct our tests within the

framework of vector autoregressions (VARs) so that we can account for any covariance

amongst variables. This paper’s results show that the effect of government spending

shocks– i.e. the difference between the actual path of government spending and the

expected path as of 2008, on unemployment, has been almost negligible so far.

This paper is organized into five parts including the introduction. The next

section is a literature review that looks at related papers and summarizes their results. We

also discuss the importance of this paper and how our findings add to this field of

research. The third section discusses the data and the fourth section the VAR and its

results. The final section summarizes our work and discusses consequences of our

findings.

Section II. Related Literature

Much has been written about fiscal stimuli and their effectiveness. However, the

question of how to evaluate a fiscal stimulus is tricky. There have been many different

ways of determining whether fiscal policy actions have benefited an economy. When

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evaluating the effect of government spending on the economy, one important thing that is

always looked at is the government spending multiplier. Thus, much literature is focused

on the government spending multiplier. Lawrence Christiano, Martin Eichenbaum, and

Sergio Rebelo in a 2009 paper argue “that the government-spending multiplier can be

much larger than one when the nominal interest rate does not respond to an increase in

government spending” (Christiano, 2009). They found that “it can be socially optimal to

substantially raise government spending in response to shocks that make the zero bound

on the nominal interest rate binding” (Christiano, 2009). This is an important finding

because it takes into account current economic conditions (i.e. the zero bound). We

know that the appropriate fiscal policy response will depend and vary based on the

monetary conditions.

There has also been a lot written about the effect of government spending.

Valerie Ramey in her 2009 paper investigates why it has been found that government

spending can either raise or lower consumption and real wages. She finds that the “main

difference is that the narrative approach shocks appear to capture the timing of the news

about future increases in government spending much better” (Ramey, 2009). Her

findings also “suggest that most components of consumption fall after a positive shock to

government spending” (Ramey, 2009). The government multiplier she finds range from

0.6 to 1.1. Her paper does not seem to discuss much about the unemployment rate and

the effect of government spending on it. We think that the unemployment rate is a good

way to measure how effective a stimulus is because the final result of economic policy is

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to increase economic activity, and thus employment. Thus our analysis differs in that the

effect on the unemployment rate is the most important variable of interest to us.

Robert Barro of Harvard University also finds the government multiplier to be

less than one. He estimates a spending multiplier of 0.4 the same year and 0.6 the next

year (i.e “if the government spends an extra $300 billion in each of 2009 and 2010, GDP

would be higher than otherwise by $120 billion in 2009 and $180 billion in 2010” (Barro,

2010)). He goes on to say that “since the multipliers are less than one, the heightened

government outlays reduce other parts of GDP such as personal consumer expenditure,

private domestic investment and net exports” (Barro, 2010). He claims that “the

available empirical evidence does not support the idea that spending multipliers typically

exceed one, and thus spending stimulus programs will likely raise GDP by less than the

increase in government spending” (Barro, 2009). That is why Barro has argued that the

current stimulus package was a major mistake. He is not alone in thinking this and our

paper attempts to shed light on whether this particular stimulus was the right one given

the circumstances.

Veena Jha finds that “a fiscal stimulus should be timely (as there is an urgent need

for action), large (because the drop in demand is large), lasting (as the recession will

likely last for some time), diversified (as there is uncertainty regarding which measures

will be most effective), collective (all countries that have the fiscal space should use it

given the severity and global nature of the downturn), and sustainable (to avoid debt

explosion in the long run and adverse effects in the short run)” (Jha, 2009). If these

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conditions are met, she argues that the fiscal stimulus can be effective. We find that the

current stimulus is lasting, diversified, collective, and sustainable. The question of

whether it has been large enough is hotly debated, with Paul Krugman at the forefront of

the argument that the stimulus was not large enough. The question of whether the

stimulus was timely can be debated because our findings show that it may be possible

that the stimulus is acting too slowly. If this is the case, it should have been implemented

earlier – before the recession got so bad.

In regard to the stimulus not being large enough, Paul Krugman has argued that

the “$787 billion stimulus is not nearly enough to fit the well over $2 trillion hole in the

economy” (Huffington Post). Krugman hypothesized that the economy is likely to stay

down for the next two years. He predicted that unemployment would probably peak

around 9%, though it could go into double digits if the crisis continued to spread around

the world. It is worth noting that the unemployment rate reached 10.1% in the fourth

quarter of 2009.

A paper written by Charles Freedman, Michael Kumhhof, Douglas Laxton, and

Jaewoo Lee finds based on their simulations that “either government investment

expenditure and/or targeted transfers would have sizable multiplier effects on the

economy” (Freedman, 2009). They also find that accommodative monetary policy is

important, a view shared by many others. Again, the concept of the size of the

government multiplier seems to be a great topic of discussion. The idea is that a stimulus

cannot be effective unless the government spending multiplier is large enough. This is

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because a stimulus has the effect of “crowding out” investment and lowering GDP and

economic output that way. Therefore, it is necessary that the multiplier be large enough

so that positive increases in government spending and consumption can outweigh the

negative effect due to the decrease in investment activity.

The purpose of this paper is to analyze the effectiveness of the current stimulus.

Relevant literature argues that under the proper conditions, a fiscal stimulus can certainly

be effective. However, previous literature has placed a great deal of emphasis on the

government spending multiplier – a metric that is difficult to predict and a metric that

notable economists have differing views on. We will instead of relying on the

government multiplier, determine the effectiveness of the fiscal stimulus by examining

the unemployment rate because we feel this is a good way to measure how well the

economy is doing. We think that determining how many people are out of work is the

best way to see how strong an economy is.

Our approach to assessing the effectiveness of the stimulus differs from that of the

above mentioned economists. By conducting a counterfactual experiment, we will be

able to look at predicted values and compare them to actual numbers, thus observing

whether or not the fiscal stimulus has been effective or not. Our experiment involves

shutting down government spending “shocks” and projecting what the unemployment

rate would be without these “shocks.”

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James Stock and Mark Watson in their March 2001 paper discuss something that

we find useful in determining the effectiveness of a fiscal stimulus – impulse response

functions. Stock and Watson write that “impulse responses trace out the response of

current and future values of each of the variables to a one unit increase in the current

value of one of the VAR errors, assuming that this error returns to zero in subsequent

periods and that all other errors are equal to zero” (Stock and Watson, 2001). This is

something of interest to our analysis. We too would essentially like to trace the response

of a government spending shock to the unemployment rate. Thus impulse response

functions, which are relevant within the framework of VARs, are something of great

importance to our research.

The technique we use is similar to that of Miguel Almunia, Agustin Benetrix,

Barry Eichengreen, Kevin O’Rourke, and Gisela Rua. Almunia et al use a VAR model

and rely on recursive ordering to identify shocks. They say that “since assumptions

regarding ordering are central to identification strategy, given the absence of more

structure, it is important to acknowledge that there is less than complete consensus on the

appropriate ordering when total government is the fiscal variable whose output effects we

wish to consider” (Almunia et al., 2009). In our analysis, we also choose an ordering

which is accepted by most, and order government spending in various places to see the

effect of various orderings. We end up focusing on the ordering that implies

unemployment initially drops due to an increase in government spending, and then

gradually goes up over time, since this was our original hypothesis.

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Section III. Data

In order to determine the effectiveness of the fiscal stimulus, it was necessary to

gather data on government spending, price levels, GDP, consumption, investment,

unemployment rate, and the federal funds rate. All of the data were obtained from the St.

Louis Federal Reserve Economic Database (FRED). We utilized a vector autoregression

(VAR) and traditionally VARs use quarterly real data. We did the same in order to be

consistent with other experiments. Our data dates from the third quarter of 1954, the first

year the federal funds rate was reported, to the first quarter of 2010.

For government spending, we used Real Government Consumption Expenditure

and Gross Investment. We used the natural log of it and a seasonally adjusted annual

rate. This metric includes goods and services bought by the federal government

including “military equipment, highways, and the services government workers provide”

(Mankiw, 2007).

The price level is measured by the GDP Deflator. We used the natural log of the

GDP Deflator and a seasonally adjusted rate. The GDP Deflator is a reflection of what is

happening to the overall level of prices and can be considered a proxy for inflation. It is

“the ratio of nominal GDP to real GDP” (Mankiw, 2007).

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We use real GDP from FRED and took the natural log and seasonally adjusted

rates as well. GDP refers to “the total expenditure on domestically produced goods and

services” (Mankiw, 2007).

For consumption, we used Real Personal Consumption Expenditures. The data

included seasonal adjustments and we took natural logs. Consumption is “goods and

services bought by households” and is divided into nondurable goods, durable goods, and

services (Mankiw, 2007).

For investment, we use Real Gross Private Domestic Investment which we take

the natural log of and is seasonally adjusted. It includes non residential investment,

residential investment, and changes in inventories. It is used as an indicator of the

forward looking productive capacity of an economy.

As for unemployment, we used the traditional measure for the unemployment rate

– the number of unemployed as a percent of the labor force. A person is considered

unemployed if “they do not have a job, have actively looked for work in the prior 4

weeks, and are currently available for work” (BLS). The “labor force measures are based

on the civilian noninstitutional population 16 years old and over” (BLS). The labor force

can be considered the employed plus the unemployed.

For the federal funds rate, we use the Effective Federal Funds rate. The federal

funds rate is “the interest rate on reserves that one bank loans to another” (Abel).

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These are all the variables of interest for the VAR we chose to run which is

described in depth below.

Section IV. VAR and Results

In order to determine the effectiveness of the fiscal stimulus, it was necessary to

develop a vector autoregression (VAR) model to be able to capture co-movements

between the variables. “A VAR is a n-equation, n variable linear model in which each

variable is in turn explained by its own lagged values, plus current and past values of the

remaining n-1 variables” (Stock and Watson, 2001).

As previously mentioned, the VAR we ran was from the third quarter of 1954 to

the first quarter of 2010, using data for government spending, price level, GDP,

consumption, investment, unemployment rate, and the federal funds rate. We used four

lags in our analysis.

VAR is a methodology developed by Christopher Sims. The problem with

simultaneous equation models was that some variables are treated as endogenous and

other as exogenous. In order to estimate such models, it is necessary to ensure that the

equations in the system are identified – either exactly or over-identified. “This

identification is often achieved by assuming that some of the predetermined variables are

present only in some equations” (Gujarati, 1995).

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Sims believed that if there really was simultaneity amongst variables, they should

all be treated the same, without any pre-existing assumptions about which variables are

endogenous and which are exogenous. It is for this reason he developed VARs – which

essentially assume that all variables within the model are endogenous. VARs are then

estimated using ordinary least squares (OLS).

A VAR model looks like this:

yt =c+Φ1 yt-1 +Φ2 yt-2 +… +Φp yt-p +εt

yt is the nx1 vector of the endogenous variables and their lags, c is the vector of

constants, Φ i is the coefficient matrix and εt is the residual vector. For the coefficients to

be unbiased and consistent, the VAR must satisfy the following two equations below,

with Ω an (n x n) symmetric positive matrix (Gordon and Krenn, 2010).

E(εt) = 0 for all t

E(εt ετ) = Ω for t = τ 0 otherwise

There are three types of VAR models – reduced form VARs, recursive VARs, and

structural VARs.

Reduced form VARs express “each variable as a linear function of its own past

values, the past values of all other variables being considered, and a serially uncorrelated

error term” (Stock and Watson, 2001). Each equation is then estimated by OLS. The

error terms can be considered the “surprise” or “shocks” to the variables. It is important

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to note that if the variables are correlated with one another, (which is often the case) the

error terms will also be correlated across the equations.

Since all variables in a VAR are endogenous, the ordering of the variables makes

a difference. The ordering of variables implies assumptions about how long it takes

variables to react to a shock. For example, if a variable is ordered first, it implies that all

other variables react in the same period to a shock from that first variable. On the other

hand, if a variable is ordered last, it implies that all other variables react with a one period

delay to a shock from that variable which is ordered last. As per Hamilton, we choose to

order the variables in our experiment as such: Price Level, GDP, Consumption,

Investment, Unemployment Rate, and Federal Funds Rate. We then include our final

variable, Government Spending, in each way possible (i.e. ordered one through seven).

This allows us to see the effect of government spending shocks in every possible

scenario. We then determine which ordering is of most interest to our analysis, and

choose to focus solely on that ordering.

Structural VARs require “identifying assumptions to allow correlations to be

interpreted casually” (Stock and Watson, 2001). Structural VARs are important in our

analysis because they allow us to impose identification about the error terms so we can

look at individual shocks, uncorrelated from other variable shocks. The ability to look at

uncorrelated individual shocks is very important. This is because only if shocks are

uncorrelated and separated, can we shut down a specific shock, and look at how a

variable would react.

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So the question becomes, what type of identification should we impose? We

chose to utilize a Cholesky Decompostion. This is a method used to orthogonalize the

error term in a VAR, so that the resulting impulse response functions can be interpreted

easily. Assume the SVAR takes the form:

Ã(IK - A1 - A2L2 - … ApL

p)yt = Bet

where à is a lower triangular matrix with ones on the diagonal and B is a diagonal

matrix.

Using a Cholesky Decomposition allows us to be able to isolate separate

“shocks”, so that we can shut down specific shocks (in our case government spending)

and see how a variable would react (in our case the unemployment rate).

The method of performing our analysis is as follows: first we estimated a VAR

with four lags. From our VAR, we obtained the residuals or error terms. Then, using the

Cholesky Decomposition, we constructed structural shocks from the residuals. We then

shut down the government spending shocks for time periods 2009Q1 and onwards, and

estimated the path of unemployment. This gave us a counterfactual simulation of the

path of unemployment, had the fiscal stimulus not existed.

Individual coefficients in estimated VAR models are generally hard to interpret,

so impulse response functions (IRFs) are often used. “Impulse response functions trace

out the responses of current and future values of each of the variables to a one unit

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increase in the current value of one of the VAR errors” (Stock and Watson, 2001). IRFs

allow us to capture how an economy reacts to shocks. The IRFs we generated can be

found in Figures 1 through 7. Figure 1 indicates government spending ordered first,

Figure 2 indicates government spending order second, and so forth.

The IRFs generated from our VAR with government spending ordered first

through last all have a similar shape. When looking at the effect on the unemployment

rate of a government spending “shock”, all the IRFs initially show a slight increase in

unemployment (in the first 2 periods), followed by a sharp decrease in unemployment

(around period 3). This sharp decrease is followed by a gradual increase in

unemployment over the next 10 to 15 periods. However, it is important to note that these

IRFs are not significant. This should be taken into account when viewing our results.

For the purposes of our analysis, the IRF of most interest includes government

spending ordered last. Looking at Figure 7, it is clear that this IRF depicts what we

expect the results to be – an initial increase in the rate of unemployment, followed by a

sharp decrease, with the rate slowly going up over time. This is consistent with our initial

hypothesis so we chose to focus on this IRF and this ordering of variables when

conducting our analysis.

The process of conducting our counterfactual analysis was complicated. The first

step was estimating the VAR. With these results, we found the residuals created by our

estimates. We then used the fact that we imposed a type of identification – via the

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Cholesky Decomposition. We used matrix algebra to multiply the inverse of the lower

triangular matrix (gamma) by the residuals. This created our set of “shocks.” The graph

of the government spending shocks for 2009Q1 through 2010Q1 can be found in Figure

8. We now had isolated the individual shocks – a major step in our experiment. Since

we ordered government spending last, the last entry in the 7x1 matrix of shocks referred

to the “government spending shock.” We then changed that shock to 0 in order to

simulate what the economy would look like without the government spending shock. We

then re-estimated the residuals by multiplying the “gamma” matrix by our new shocks.

We then summed this result with an unconditional forecast of unemployment. This

created our “counterfactual estimates” for the unemployment rate. We repeated this

process for each relevant year, beginning with 2009Q1, the first quarter when the fiscal

stimulus was announced. This is the process of the counterfactual analysis that we

conducted.

The results from our counterfactual analysis can be found in Figure 9. Our results

show that the fiscal stimulus had nearly no effect on the unemployment rate from 2009Q1

to 2010Q1. The counterfactual line we have created through our experiment is nearly

identical to the actual data.

So does this mean that the fiscal stimulus had no effect on the economy or

unemployment rate? Did we not need it in order to prevent the recession from

worsening? Not necessarily. This could be the case for a number of reasons. One is that

there is a large degree of uncertainty in our analysis. It is not easy to pin down solely the

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effect of government spending on unemployment since in macroeconomics, every

variable affects another. Second, our findings show that the fiscal stimulus has not been

effective only under the identification we have imposed. However, based on our IRFs,

the identification we imposed (with government spending ordered last, implying that all

variables react to government spending with a one period delay), is the best type of

identification we can impose. Third, it is important to note that our analysis describes the

effect of fiscal policy shocks on the unemployment rate, not fiscal policy in general. This

means that our analysis picks up innovations from standard fiscal policy. It captures any

unconventional government spending or deviations from what is expected. For example,

during a recession, government spending is expected to go up. Our experiment attempts

to capture the effect of an extra government spending. This is important because our

analysis does not conclude that fiscal policy in general is ineffective. Fourth, only about

50% of the funds from the American Recovery and Reinvestment Act have thus far been

allocated. A breakdown of the allocated funds can be found in Table 1. Since only 50%

of the $787 billion has been dispersed so far, it might be too early to try and capture the

effect of the stimulus. Additionally, government spending is thought to be a sluggish

variable which reacts very slowly to changes. So the effects of the fiscal stimulus may be

taking longer than expected, and it might be too early to assess its effectiveness. If this is

the case, we just need to redo our analysis over the next few quarters as more funds are

dispersed.

If the fiscal stimulus is actually not working, what might be some of the reasons

why? One might be Ricardian Equivalence. The Keynesian view of cutting taxes and

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running a budget deficit is that consumers react by spending more, and thus help the

economy grow. However, Ricardian Equivalence assumes that consumers are more

forward looking than this. “The general principle [of Ricardian Equivalence] is that

government debt is equivalent to future taxes, and if consumers are sufficiently forward-

looking, future taxes are equivalent to current taxes” (Mankiw, 2007). Therefore,

running up a government deficit is the same as financing it by taxes, and consumers react

not by increasing their spending due to tax cuts, but rather, by curbing spending. If

Ricardian Equivalence holds, the ineffectiveness of the fiscal stimulus, in which $288

billion of the $787 billion are tax rebates, is not surprising. Consumers just see this as a

temporary increase in their net wealth, which will be erased with higher taxes in the

future. They therefore choose to save the money rather than spend it. This could be a

reason why we found that the economy would have been the same without the stimulus.

Another reason the stimulus may not be working is because small businesses did

not receive enough money. Small businesses received $730 million from the American

Recovery and Reinvestment Act, mostly to eliminate fees for loans on small businesses

and for new loans to them so that they can meet existing debt payments. Small

businesses are extremely important to the US Economy. According to the US Small

Business Administration, small firms represent 99.7% of all employer firms and employ

over half of all private sector jobs. Therefore, perhaps the fiscal stimulus should have

focused on getting small businesses more money, especially if increasing employment

was the main goal of the stimulus. The Obama Administration may have underestimated

the large effect that small business has on employment.

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The American Recovery and Reinvestment Act might also not be working as well

as expected because $787 billion was not a large enough stimulus. Many economists,

most notably Paul Krugman, have argued that the stimulus was too small. Krugman also

claims that “a fair bit of the bill is not really stimulus,” (Huffington Post) adding that only

about $650 billion would actually result in increased consumer spending. Additionally,

there is talk of implementing another stimulus which only re-enforces the point that $787

billion may not have been enough.

Finally, as discussed earlier, there is much debate about the government spending

multiplier. It is very possible that the multiplier is lower than one, and for this reason,

increased government spending is not worth the added cost. Though our analysis has not

focused on the government spending multiplier, its effect is important, and our analysis

may support the findings of economists who believe the spending multiplier is less than

one.

Section V. Conclusion

This paper examines the effectiveness of the fiscal stimulus on the 2008-2009

recession. By utilizing a VAR, we were able to simulate the path of the US economy,

counterfactually. Our findings show that an increase in government spending has

essentially had no effect on unemployment.

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Our testing is done within a 7 variable, 4 lag framework and we are able to

account for correlations between variables by use of a VAR. Our VAR model is run

between 1954Q1 and 2010Q1. Our counterfactual simulations are run with data up until

2008Q4 since the stimulus was announced in February of 2009. We conducted our

experiment by finding the residuals estimated by the VAR, imposing a Cholesky

Decomposition identification method and thus isolating each shock, setting the

government spending shock equal to zero, re-estimating the error terms, and finally

forecasting unemployment numbers using new government spending numbers implied by

our analysis. This resulted in a counterfactual simulation of unemployment without the

fiscal stimulus.

This paper sheds light on whether or not fiscal policy shocks are effective. We

have found that this is a very difficult question to answer – though our findings suggest

that in the case of the 2008-2009 recession, the shocks were not effective. We have come

up with many reasons why the stimulus may not be effective. Ricardian Equivalence

might be present, with consumers holding their spending off because they realize that this

increase in government spending needs to be financed with higher taxes in the future.

The government multiplier might be less than one, as argued by many notable

economists. This would result in the increased cost of government spending not being

worth it in the long run. Very importantly, a problem could be that the fiscal stimulus

was not large enough, as argued by Paul Krugman. Finally, given that only a little over

50% of the funds from the American Recovery and Reinvestment Act have been

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dispersed so far, it simply might be too early to try and determine the effectiveness of the

current fiscal stimulus.

However, it is important to note that our analysis has been done under a certain

identification scheme and our results are highly dependent on that. Our identification

assumes that the six variables in our dataset respond with a one period delay to

government spending shocks and that government spending responds in the same period.

Given our IRFs, this is the scenario of most importance to us, but it is still worth noting

that our findings rely on this assumption.

Our findings present us with some very interesting questions moving forward.

From a policy standpoint, does it make sense to allow government spending shocks? Our

preliminary findings seem to suggest that fiscal policy shocks were not necessary to

prevent a recession. If this is the case, we should propose a fiscal policy plan and stick to

it, because government spending shocks have no effect in reducing unemployment.

These findings are a big step forward in determining whether or not the government

should blindly follow a fiscal policy plan. For now, we find that they should stick to the

original plan and not deviate from proposed policy.

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Department, 2009. Geneva: International Labour Organization, 2009. Print.

Swaroop 23

Mankiw, Gregory. Macroeconomics. 6th ed. New York: Worth, 2007. Print.

Mishkin, Frederic S. The Economics of Money, Banking, and Financial Markets. 8th ed.

Boston: Pearson Education, 2007. Print.

"Paul Krugman: Stimulus Too Small, Second Package Likely." The Huffington Post 17

Feb. 2009. Print.

Ramey, Valerie A. "Identifying Government Spending Shocks: It's All in the Timing."

Diss. University of California, San Diego, 2009. Print.

"Series: Unemployed." Economic Reserach: Federal Reserve Bank of St. Louis. Federal

Reserve Bank of St. Louis. Web. 8 Mar. 2010.

<http://research.stlouisfed.org/fred2/series/UNEMPLOY>.

"Small Business Administration: Frequently Asked Questions." U.S. Small Business

Administration-Your Small Business Resource. Web. 16 May 2010.

<http://www.sba.gov/advo/stats/sbfaq.pdf>.

Stock, James H., and Mark W. Watson. Vectorautoregressions. 2001. Print.

"Total Budgeted* Government Spending Expenditure GDP ? CHARTS ? Deficit Debt."

USgovernmentspending.com. Web. 8 Mar. 2010.

<http://www.usgovernmentspending.com/>.

Swaroop 24

Figures

Figure 1: Government Spending Shock on Unemployment Rate (G ordered first)

Figure 2: Government Spending Shock on Unemployment Rate (G ordered second)

-.3

-.2

-.1

0

.1

0 5 10 15 20

varbasic, G, U

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

-.3

-.2

-.1

0

.1

0 5 10 15 20

varbasic, G, U

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

Swaroop 25

Figure 3: Government Spending Shock on Unemployment Rate (G ordered third)

Figure 4: Government Spending Shock on Unemployment Rate (G ordered fourth)

-.3

-.2

-.1

0

.1

0 5 10 15 20

varbasic, G, U

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

-.3

-.2

-.1

0

.1

0 5 10 15 20

varbasic, G, U

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

Swaroop 26

Figure 5: Government Spending Shock on Unemployment Rate (G ordered fifth)

Figure 6: Government Spending Shock on Unemployment Rate (G ordered sixth)

-.3

-.2

-.1

0

.1

0 5 10 15 20

varbasic, G, U

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

-.3

-.2

-.1

0

.1

0 5 10 15 20

varbasic, G, U

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

Swaroop 27

Figure 7: Government Spending Shock on Unemployment Rate (G ordered seventh)

Figure 8: Government Spending Shocks

-.2

-.1

0

.1

0 5 10 15 20

varbasic, G, U

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

Year

Shocks

Shocks

Shocks -0.39585978 2.8435011 2.42E+00 2.508679 -0.09854267

2009Q1 2009Q2 2009Q3 2009Q4 2010Q1

Swaroop 28

Figure 9: Counterfactual Simulation of Unemployment without the Fiscal Stimulus

0

2

4

6

8

10

12

Year

Unemployment Rate

Actual

Predicted

Actual 7.7 8.9 9.4 10.1 9.7

Predicted 7.7 8.9 9.4 10.1 9.7

2009Q1 2009Q2 2009Q3 2009Q4 2010Q1

Swaroop 29

Tables

Table 1

The American Recovery and Reinvestment Act of 2009 distributes the $787 billion as follows:

Category Total Recovery Act Funds Funds Paid Out

Tax Benefits $288B $162.7B (56%)

Contracts, Grants, Loans $275B $102B (37%)

Entitlements $224B $127B (57%)

Updated: 05/07/2010