Document 10622194

Did the Stimulus Create Lasting Employment?

An analysis of the American Recovery and Reinvestment Act

Anthony Randazzo, Emily Ekins, and Katie Furtick

*

Reason Foundation

REVIEW DRAFT — February 11, 2013

Executive Summary

How many individuals hired through projects funded by the American Recovery and

Reinvestment Act (ARRA) kept their job after the stimulus funds were fully spent?

The primary goal of this paper is to examine the long-‐term effects of stimulus spending on the labor market, specifically to determine the duration of employment that resulted from Recovery Act spending.

We wanted to measure the nature of the employment that was created, specifically whether it primarily created sustainable jobs or temporary jobs. We are interested in how stimulus spending affects employment beyond its distribution cycle, i.e., does stimulus spending primarily create short-‐term jobs that disappear from the labor force after monies like funding from the Recovery Act are expended or does stimulus spending create employment that is retained over the long-‐term?

We approached our research questions using a multimethod survey, including a telephone-‐based establishment survey and web-‐based survey, discussing hiring practices with ARRA grant recipients directly in order to determine how many of the jobs created by the stimulus were short-‐term or long-‐term. By contacting a statistically representative sample of stimulus recipients we can estimate how many workers were let go after their Recovery Act projects were completed, how many were kept on, and how many organizations refrained from laying off workers as a result of Recovery Act money.

* This project would not have been possible without the dedicated assistance and talents of Kayla

Westbrook and Scott Piazza. We also greatly benefited from comments and advice from Jim Witte,

Dan Houser, Garrett Jones, Dan Rothschild, and Alex Schibuola. All errors are our own.

Reason Foundation – 2/11/13 |

This draft is for review purposes only and not for attribution/distribution. 1

Part 1—Introduction

How many individuals hired through projects funded by the American Recovery and

Reinvestment Act (ARRA) kept their job after the stimulus funds were fully spent?

Most studies of ARRA—also known as the Recovery Act or colloquially as the stimulus—have focused on estimating the aggregate number of jobs “created or saved” by the spending and tax provisions of the legislation. However, these estimates of the Recovery Act’s effects on the labor market generally fail to distinguish between a job created for two months and the job funded for two years.

The duration distinction is not only substantial for measuring changes in the labor market, but also to household balance sheets. Further, if duration effects were not factored in to a Recovery Act employment estimate, a worker hired six times on two month contracts may be counted as six jobs created, where as the same worker hired for one year would register as one job. Just counting up all the jobs at any given time does not capture the whole picture of the effects of the Recovery Act.

The primary goal of this paper is to examine the long-‐term effects of stimulus spending on the labor market, specifically to determine the duration of employment that resulted from Recovery Act spending. Whether this employment crowded out other private employment, was the proper role of government, or helped to slow the rate of employment decline in 2009 is not immediately relevant to our research questions (though these are important questions). Very narrowly, we are interested in how stimulus spending affects employment beyond its distribution cycle, i.e., does stimulus spending primarily create short-‐term jobs that disappear from the labor force after the stimulus funding is expended or does stimulus spending create employment that is retained over the long-‐term?

Another way to think about this approach is that we want to measure the nature of the employment that was created, specifically whether it primarily created sustainable jobs or temporary jobs.

We approached our research questions using a multimethod survey, including a telephone-‐based establishment survey and web-‐based survey, discussing hiring practices with ARRA grant recipients directly in order to determine how many of the jobs created by the stimulus were short-‐term or long-‐term. By contacting a statistically representative sample of stimulus recipients we can estimate how many workers were let go after their Recovery Act projects were completed, how many were kept on, and how many organizations refrained from laying off workers as a result of Recovery Act money.

A. Did the Stimulus “Work?”

By way of contrast, this paper does not aim to answer the question of whether the stimulus “worked.” Such claims are completely dependent on the metric being used to determine success or failure.



Many center-‐right commenters have declared the stimulus a failure because the

White House claimed that the Recovery Act would prevent the unemployment rate from passing 8 percent in 2009 (it peaked at 9.6), and that it would be down to 5.5 percent in the third quarter of 2012 (it was actually 8.3 percent in July 2012).

1

Center-‐left commenters counter that when this projection was made the economy was much worse than anticipated. They further claim that without the stimulus the economy would be much worse off and that therefore the Recovery Act was a success—though even the phrase “worse off” can be subjectively defined.

2

There have also been claims that the Recovery Act worked because its goal was to slow down the rate of job losses in 2009. And indeed, the rate slowed from an average of 772,000 jobs lost per month from December 2008 to February 2009, to about 260,000 jobs lost per month from August to September 2009.

3 However, it could be countered that since the monthly rate at which the economy was shedding jobs actually peaked in February 2009 and was declining even before Recovery Act money started to be distributed in April 2009, that stimulus spending just sped up a process already underway.

These conflicts over whether the stimulus “succeeded” or “failed” stem from differences not just in economic analysis, but primarily from the subjective thresholds for economic norms and goals psychologically determined by each analyst. For example, if the subjective benchmark for success is the economy reaching a point where more jobs are added each month than the growth rate of population and labor market participation, then the Recovery Act could be declared a failure for not reaching that milestone (as of this publication). Alternatively, if the subjective benchmark for success is the economy avoiding 20 percent unemployment, then the Recovery Act could be considered a success. And of course, neither of these benchmarks considers that either situation could unfold despite or aside from stimulus funds flowing from the Recovery Act.

It is also clear from observing anecdotal stories that there are conflicting views on the affects of the stimulus on employment in America—whether positive or negative.

In January 2010, Charleston, SC Mayor Joe Riley credited the Recovery Act with job creation in his city, highlighting $16 million received from the federal government to help “pay for affordable housing renovations, new police officers, a new community center and more.” 4

However, across the country Seattle was not as successful in turning Recovery Act funds into job opportunities. In August 2011, the Seattle Post Intelligencer reported that a $20 million grant designed to create 2,000 jobs was used to hire a mere 14 workers to weatherize three homes. This was a far cry from the 2,000 homes that were intended to be retrofitted.

5

During the course of our research we spoke with a Pennsylvania school official who said that money from the Recovery Act “prevented children in the school district

[from] suffering the consequences of the recession” by retaining teachers and



funding defined benefit payouts that would have been missed due to cuts from state budgets. The Recovery Act also paid the salary for a new full-‐time employee and new part-‐time employee. He added, though, that both of these employees had to be let go when Recovery Act funding was fully consumed.

Another interview we conducted was with a woman from Michigan who hired over one hundred new employees with Recovery Act money, only to have to lay them all off once the funding was fully consumed. She articulated that she wishes there was more stimulus money to hire back the workers because she “hates having to fire everyone when projects are over.”

If these anecdotal stories are a guide, then the stimulus did not create lasting employment. However, whether this reflected negatively on the stimulus depends on the benchmark of success or failure any given analyst might use when considering the Recovery Act. Therefore, we aim to make sure all of our assumptions and measures are clearly stated so as to not cloud the analysis.

B. Research Questions

As previously stated, the primary goal of this paper is to look at the duration of employment that was created by stimulus spending. More specifically, we set out to answer the following questions through an establishment survey:

(1) What percentage of organizations that received stimulus funding laid off

workers after finishing their stimulus-‐funded project?

(2) What was the net employment increase or decrease of organizations that received stimulus money from the start of the stimulus program up until nine

months after the end of the stimulus program?

(3) What proportion of workers hired with stimulus funds were full-‐time employees and what proportion were part-‐time employees?

In this paper we first review the literature on the Recovery Act and discuss alternative methodologies for reaching estimates on stimulus spending’s effects on the labor market. Then we outline our own methodology for identifying a sample of businesses to review and conduct a survey with. Finally, we present the findings from the survey and make some observations based on the data collected.

Part 2 — Selected Literature Review

Based on publicly available data at Recovery.gov, from the third quarter of 2009 through the second quarter of 2012, recipients of Recovery Act funding reported

5,803,469 full-‐time-‐equivalent (FTE) jobs were created using stimulus funds.

6 As noted previously, we argue that whether or not this number is accurate, it is not the



best reflection of the Recovery Act’s affects on the labor market since jobs of different durations have varying effects. Primary recipients report the number of jobs created on a quarterly basis, but do not report net created jobs. For instance, if the Recovery Act funded two million jobs for one day it would have a substantially different affect on household balance sheets than two million jobs created for several years.

We hypothesized that a more accurate way to measure how the Recovery Act affected the labor market would be to look at the longevity of employment for workers funded by the stimulus. Unfortunately, the recipient-‐reported numbers to

Recovery.gov do not include details on the duration of each job reportedly created.

Moreover, the recipient reported data is riddled with inconsistencies, as respondents were merely given a form to fill out with little adherence to norms with submitted data. The Congressional Budget Office has been vocal in its criticism of the unreliability of the recipient reported data for its inconsistent formatting.

7

Therefore we developed an alternative method of determining duration effects, an establishment survey based on direct interviews with a statistically significant sample of recipients of Recovery Act funds, asking about their hiring practices and ensuring the data collected was in consistent formatting.

While we are certainly not the first to look at the effects of the Recovery Act on the labor market, we are the first to use this methodology. Our approach runs counter to several other methodologies taken by other scholars considering similar Recovery

Act employment effect questions, each with their own strengths and weaknesses.

Here we consider five possible approaches that could be used to analyze the effect of the Recovery Act on employment: *

A. The Model-‐Based Approach and the Multiplier Critique

The non-‐partisan Congressional Budget Office (CBO) has issued quarterly reports on the stimulus since the fall of 2009, using a mix of economic models and historical data in its various estimates of the Recovery Act’s employment effects. Essentially, the CBO separates elements of the Recovery Act, then assigns a multiplier to each of these to estimate the law’s total affect on output, and then compared that result to estimates of how changes in output tend to affect the unemployment rate in general.

8 In order to do this, CBO assigned a multiplier ranging from 0.2 to 2.5 to general categories of the Recovery Act. But while somewhat straightforward, this model-‐based approach using multipliers has come under substantial criticism.

The Legacy of World War II — In order to develop estimates of how particular outlays affect the economy, many economists use data from the years during World

War II, the largest era of federal spending as a percentage of GDP in American history. But there are substantial differences between the economy during World

* This literature review is not intended to be comprehensive of all major papers written analyzing the

Recovery Act; rather we review the major approaches taken to analyzing the Recovery Act effects on the economy, providing some brief methodology examples, as a way of contextualizing our approach.



War II and the economy of 2009-‐2012. The CBO even admits the estimate vary widely.

9 And Harvard economist Robert Barro argues that the World War II data really show that when government spending has a multiplier above 1 that it means the government is crowding out private growth in GDP, rather that contributing to a healthier economy.

Inaccuracy of Static Multipliers — In large part, multipliers are simplistic expressions of economic conditions, a static measurement of dynamic market effects heavily dependent on a wide range of factors in any given time or place. The static measurement allows for easier economic forecasting and can serve a helpful role in trying to create expectations for the possible effects of any given policy. However, using them to estimate the actual effects of outlays or tax cuts after the policies have been implemented fails to account for the highly complex nature of market economies.

10 Since the private sector is not wholly predictable in the way it will respond to any given tax or spending policy adjustment, the CBO has had to change its multiplier estimates several times between 2009 and 2012.

11 These changes reflect how imprecise such estimates can be and how untrustworthy any estimate based on such a methodology can be.

Collectively, these concerns cast a large shadow over the accuracy of the economic model-‐based approach that measures inputs (the money spent) rather than by outputs (looking at the actual jobs created and what their nature has been) in determining the effects of the stimulus on employment.

B. The GDP Forecasting Approach and Trouble with Abstract Inputs

The White House Council of Economic Advisors (CEA) also employs economic models and multiplier calculations to estimate the effects of the Recovery Act on the labor market. However, CEA supplements this approach with an estimate of what would have happened to employment and GDP in absence of the Recovery Act. With this baseline estimate built from macroeconomic models for projecting future GDP growth based on the two decades preceding the stimulus the White House economists then compare their model to what actually happened after the stimulus began to distribute funds and suggests the difference is the effect of the Recovery

Act.

This approach has appeal in its simplicity, but the possible pitfalls are immediately apparent. The whole evaluation is based on abstract inputs being properly projected into an accurate estimate about the future. This approach has been famously undermined as the Chairwoman of the Council of Economic Advisors Christina

Romer used this method to argue in January 2009 that without stimulus spending, unemployment could top out at as high as eight percent, but with a stimulus unemployment would kept much lower than that maximum. She vastly underestimated how bad the labor market would get though, and even with the

Recovery Act unemployment grew past 10 percent in 2010 before rescinding. The

White House explains this estimation failure by claiming that they just didn’t know how bad the economy was at the time. However, this defense undermines their



whole approach if they are unable to properly project the path of GDP and employment. If the CEA economists did not fully understand the economy in 2009, then why would any other projection of economic trends be any more reliable?

C. James Feyrer and Bruce Sacerdote Changes in Employment Approach

In a 2011 paper, Dartmouth College professors James Feyrer and Bruce Sacerdote decided to step outside the methodological approaches that favored calculated inputs to focus directly on the output data of employment changes. Feyrer and

Sacerdote decided to contrast the changes in employment in the 50 states and at the county level to overall Recovery Act dollars that went to those states and counties.

Not only did they look at the aggregate effects, but they measured at a month-‐by-‐ month level to see the response rate that employment had to stimulus money becoming available.

12

The primarily critique of this method is that it does not account for spillover effects of stimulus spending beyond the geographic regions being measured. Jobs are not always tightly concentrated relative to places of residence, and companies that received Recovery Act dollars but spent the money outside of their geographic region (such as in another state or county) would not be measured by this approach.

D. Timothy Conley and Bill Dupor Highway Funding Approach

University of Western Ontario professor Timothy Conley and his American colleague Bill Dupor wrote in a 2011 paper that Recovery Act funding for highways could be used to measure the affect of the stimulus on private sector employment.

“We use the Generalized Method of Moments on a panel of states to estimate a linear model of employment growth as a function of state budget loss, ARRA aid and ancillary variables,” Conley and Dupor write.

13 This state-‐by-‐state analysis of changes in employment based on measuring “budget loss” allowed Conley and

Dupor to isolate how much stimulus funds were able to fill holes in state budgets and therefore keep state outlays at previous levels. This enabled an estimate of whether jobs were saved (by stimulus dollars maintaining norms) or lost during

Recovery Act spending.

However, the weakness of this approach is that it limited its measurement to one specific sector and then applied the findings in analyzing the professional services sector, goods-‐producing sector, municipal government sector, and assorted other services. This creates a problem of significance and raises the question of whether the methodology can be used for estimating the affect of the whole Recovery Act on employment duration. The study’s approach suggests the job creation range of the stimulus was between -‐35,920 and 2.17 million, which is a very large difference in upper and lower bound thresholds.

14



E. The Survey Approach and the Ability to Interview Businesses

Looking to determine whether Recovery Act funds helped to hire unemployed workers or just hire workers away from firms where they were already employed,

Garrett Jones and Daniel M. Rothschild chose a survey by mail approach to considering Recovery Act effects in a paper for the Mercatus Center.

15 Jones and

Rothschild mailed surveys to 7,994 organizations (including private firms, non-‐ profits, and political entities), asking them a series of questions about the status of their workers at the time of hiring.

The focus of this approach was an on-‐the-‐ground methodology, directly measuring the outputs created by the stimulus. Jones and Rothschild write, “Our goal was to survey organizations that were closest to the actual hiring decisions. Thus, we screened out, to the best of our ability, state governor’s offices, which were predominantly pass-‐through entities.” 16

As the authors note, the strong point of the survey was the ability to directly interview the small businesses that received Recovery Act funds and discuss their use of the money. Since the goal of the survey was not to determine aggregate employment created by the stimulus, the approach does not suffer from neglecting spillover effects or exogenous factors.

Part 3 — Survey Methodology

After reviewing the literature and various approaches, we felt confident that our multimethod survey approach would better represent the affect of Recovery Act dollars on the labor market, and more accurately estimate whether employers were able to retain workers hired with Recovery Act money or if they had to let them go after the funds were expended, than the methodologies already developed.

The papers relying on multipliers and economic models do not actually measure the outputs of the Recovery Act (who did companies actually hire with the money).

Instead they rely on comparing inputs (what was the money spent on) and comparing that to historical examples of similar spending. Our approach was to call companies directly that received Recovery Act grants or contracts and interview them about their hiring practices.

In some ways, our analysis approach was similar to the Feyrer and Sacerdote method. The survey questions we asked ARRA recipients were designed to measure employment levels before during and after stimulus spending, in order to estimate what the effects were. However, Feyrer and Sacerdote measured at the state and county level, missing out on how dollars travel (and the fact that some ARRA recipients were located in districts separate from where they completed their projects).

17 Instead, we measured at the last stop of each stimulus dollar on the



project it was distributed for. So if an ARRA recipient received a grant, but then contracted out to a vendor to actually do the project, we measured the employment levels of the vendor instead of the initial recipient or the county the recipients headquarters were located.

The Conley and Dupor paper’s primary weakness was the significance of their findings because the measurement was primarily on highway spending. In contrast, we started our project by collecting data on all grants and contracts distributed by the Recovery Act. We did narrow down our population to focus only on projects that had been completed and that were over $100,000. This was necessary because our research questions were focused on job retention after a project was over and because smaller projects rarely were hiring workers and we did not want to distort the findings. We also do not claim our findings reflect on all aspects of the stimulus, such as the many tax cut provisions, but rather our project is focused on estimating how stimulus outlays affect the labor market, in particular the duration of stimulus created jobs.

In this respect, we were pleasantly surprised to find the Jones and Rothschild paper had already taken a similar data collection approach as we were intending. While they were focused on whether workers hired came from other companies or off unemployment rolls, and we are more interested in if someone hired with stimulus money was able to keep their job, we benefited from their insights in developing a similar, multimethod survey project.

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A. Developing Our Data Source

The Recovery Act requires the direct recipients of stimulus money and any secondary recipients that receive pass-‐through money, to report “a variety of information each calendar quarter.” According to the CBO, the recipient reporting group “includes most grant and loan recipients, contractors, and subcontractors, but it excludes individual people.” These recipients submit information about “the amount of funding received and spent; the name, description, and completion status of the project or activity funded; the number of jobs funded; and, for investments in infrastructure, the purpose and cost of the investment.” 19

The recipient reported data is reported on Recovery.gov, the U.S. government’s official website for providing data related to Recovery Act spending.

20 We used this source to identify recipient names to contact for our survey. Data used for our analysis was the Cumulative National Summary of reported job totals from February

17, 2009 through June 30, 2012.

21

From this holistic list of 576,036 recipients of ARRA funds we narrowed down the data to just grant and contract recipients of at least $100,000, that were the final destination of funds (i.e. if an organization received a grant and then just contracted out all of the work, we called the contractors instead of the pass through organization), that had finished their Recovery Act project. We also excluded state and local executive offices.

22



If a primary ARRA recipient passed through some or all of their grant/contract, we identified that company and then separated out the dollar distribution so that there was no double counted money. For example, if a primary recipient received $1 million and contracted out $250,000 each to two sub vendors, we reduced the primary recipients’ total amount to $500,000. We then dropped all companies that had fallen below the $100,000 threshold, either because they were a sub vendor receiving less than $100,000 or because they were a primary recipient that passed through most or all of their grant/contract.

The remaining population after all of these filters was 85,953 companies. Many of these companies received more than one grant or contract, so we dropped the duplicate names (and the 1,909 observations that were missing a company name) and then divided the remaining population into four quartiles based on grant/contract award size, sorted smallest ($100,000) to largest ($4.88 billion).

From this we selected a random sample of 12,000 observations to call with our survey questions. A more detailed methodology for how we used the publicly available Recovery.gov report to select our sample is available in Appendix B.

B. Conducting Our Survey

We contracted with the Center for Social Science Research at George Mason

University to conduct our phone survey and record recipient responses.

23 The authors of this study sat in on many of the sessions, provided oversight to the calling staff, and conducted dozens of surveys themselves. Research assistants at Reason also participated in making phone calls to ARRA recipients.

The call center called Recovery Act recipients and asked the questions listed on our survey (a full text of the survey is available in Appendix A). The call center also recorded anecdotal comments made by respondents. If the recipient being surveyed agreed to answer the survey, the caller used an online survey tool to record answers from the recipient. If the recipient did not have time to answer our survey on the phone but was willing to answer our questions, we would email them a link to a survey using the same web tool.

The time necessary to complete a survey took longer than initially anticipated. We faced two primary challenges:

(1) Companies that received stimulus money were not required to designate an individual or department to be responsible for managing the federal funds they received. As a result, survey callers had to navigate secretaries, answering menus, and confused staffers that they were incorrectly transferred to in search of the right person at each company to answer questions about ARRA related hiring practices.

(2) Companies that received stimulus money were often suspect of our callers, believing them to be surreptitiously from the government or a political action committee. We had to develop language for the callers to use explaining the



nature of our survey and the nonpartisan nature of both the Center for Social

Science Research and Reason Foundation.

We were only able to call about half of our sample (recipients were called at random) in the time arranged to work with the survey call center. We also were only able to generate a 4.7 response rate from this method.

We changed our approach and directed the call center to only ask ARRA recipients for an email address that we could send our survey to. After collecting email addresses we sent out the survey with unique tags to collect data. We waited 48 hours after emails were sent and then made follow up calls to those who had not responded. This alternative method proved faster and yielded a higher rate of response, with 14.3 completing surveys.

Part 4 — Survey Results and Findings

As expected, the results of our survey revealed a mixed story about the effects of the

Recovery Act on the labor market. Nearly a third of companies receiving an ARRA grant, 32 percent, hired no new workers at all. At the same time, 41 percent of ARRA grant recipients did see increases in their payrolls during their stimulus project, while 27 percent actually reduced their payrolls while in receipt of stimulus funds.

While our project was not intended to measure aggregate job creation our findings do conflict with the general story suggested by federal estimates of ARRA’s effects on the labor market, specifically that millions of jobs were created permanently for the economy. Our data show that retention rates for the stimulus as a whole were very low.

Overall, only 23.1 percent of ARRA grant recipients hired new workers and then retained 100 percent of those employees after their stimulus grant ran out. There was a small group of ARRA recipients, 3.36 percent, that hired workers with stimulus money and retained between 1-‐50 percent of those workers, and another small group, 2.94 percent, that hired workers with stimulus money and retained between 67-‐95 percent of those workers.

It is conceivable that these particular firms we surveyed would have incurred higher net job losses had it not been for the Recovery Act. However, in February 2009, three moths before the first stimulus dollars started to be handed out, the economy rate of job losses peaked and began to decline. It is not discernible whether the stimulus actually prevented further job losses, or if it rode a decline in the rate of job losses. As we noted in the introduction, the rate of job losses slowed from an average of 772,000 jobs lost per month in a three-‐month window between

December 2008 to February 2009, to about 260,000 jobs lost per month from

August to September 2009.

24 Further, if the stimulus did prevent some job losses, the rate of jobs being saved does little to address the job losses that had already



occurred. For these reasons, when trying to assess the effects of the Recovery Act on the labor market, we are focused on the jobs that were added by ARRA recipients and then the net retained jobs once the ARRA-‐funded project was completed.

Capturing data on hiring practices by recipients of Recovery Act funds did present challenges beyond the expected difficulties of conducting an establishment survey.

Federal systems for tracking stimulus money were not well developed from the beginning of the Recovery Act’s implementation, leading to imperfect records and incorrect data. For instance, 157 companies that we called claimed they had received no money from the federal government, despite the name being listed in the Recovery.gov database of self-‐reported of ARRA recipients.

More challenging to analyzing our results are two unforeseen challenges in collecting the data that has the potential to distort our results. First, during a survey call with an ARRA recipient from a local government office, the director of the agency reported that he had hired 20 workers with stimulus funds. When we asked how many he laid off from that total, he reported two were let go after the project was completed. He was quick to add, though, that this number was a little skewed— all 18 other workers hired with Recovery Act funds left the project by their own choice in the weeks before the stimulus money ran out. The workers knew they were going to be laid off once the project was completed and they left to start looking for other jobs before being laid off.

Because our goal is to ascertain job retention statistics, the real effect of the stimulus in this particular scenario is that the Recovery Act created 20 short-‐term jobs that were then terminated when the project was complete, creating no net long-‐term jobs from stimulus money. Therefore, any reported figures on workers laid off may underreport the true percentage of workers that were let go after their project was complete, having not secured stable employment as the result of the stimulus.

Second, as mentioned in the previous section, our callers were often met with skepticism from the ARRA recipients. Some respondents were convinced we were from the government, even though we informed them the survey was being conducted by a nonpartisan think-‐tank in partnership with a nonpartisan research center at a public university and pointed them to confirming sources online. As a result these callers might have the psychological temptation to over sell the answers to the survey questions about the effects of the stimulus, particularly those who answered that they would prefer to receive government grants in the future.

Therefore, any results found that reflect badly on the Recovery Act might be worse, and are not likely overstate anything negative about the stimulus.

Below we present several headline findings along with detailed numbers. In

Appendix C we offer numerous data tables with cross tabs contrasting the results.

All raw data is available from Reason Foundation for academic analysis.



A. 59% of ARRA Recipients Either Hired No Full-‐time Workers or Reduced

Payrolls to Complete Their Recovery Act Project

Companies did not act uniformly with regards to hiring full-‐time workers with money received from the Recovery Act. Some hired new workers for their projects, some kept their staff levels the same, and some even let workers go with the stimulus money coming in. Between March 2009 and the quarter when ARRA recipients completed their stimulus project:

•

27 percent experienced net full-‐time job loss.

•

32 percent experienced no net full-‐time job increase.

•

41 percent experienced net full-‐time jobs added.

Among companies with net job loses, each company lost an average of 32 full-‐time jobs (mean) or 6 full-‐time jobs (median). Among companies with net job increases gained an average of 25 full-‐time jobs (mean) or 8 full-‐time jobs (median). These numbers are shown in Figure 1:

Figure 1: Measures of Full-‐time Job Creation

Net Job Increases

41%

Net Job Losses

27%

No Job Changes

32%

B. 29% of ARRA Recipients Retained Some Full-‐time Workers After

Completing Their Recovery Act Project

Among the 41 percent of organizations who added full-‐time jobs on net, 64 percent on average kept some of their stimulus workers on staff. Another way to look at this data is, of these job-‐adding companies:

•

55 percent kept 100 percent of their stimulus workers.

•

30 percent kept none of their stimulus workers.

•

8 percent kept 1-‐50 percent of their workers.

•

7 percent kept 67-‐95 percent of their workers.



When measured against all ARRA Recipients, after stimulus money rant out:

•

23.1 percent kept 100 percent of their stimulus workers.

•

12.6 percent kept none of their stimulus workers.

•

3.4 percent kept 1-‐50 percent of their workers.

•

2.9 percent kept 67-‐95 percent of their workers.

Combining data of those that did not hire workers or saw a decline in payrolls, of all

ARRA recipients:

•

29 percent retained at least some of their full-‐time stimulus-‐hired employees.

•

13 percent retained none of their full-‐time stimulus-‐hired employees.

•

27 percent hired no additional workers and experienced net job losses.

•

31 percent hired no additional workers.

These numbers are shown in Figure 2:

Figure 2: Job Retention Break Down

Added + Did

Not Retain Jobs

13%

Added +

Retained Some

Jobs

29%

Net Job Losses

27%

No Job Changes

31%

C. 79% of ARRA Recipients Either Hired No Part-‐time Workers or Reduced

Payrolls to Complete Their Recovery Act Project

Companies did not act uniformly with regards to hiring part-‐time workers with money received from the Recovery Act. Some hired new workers for their projects, some kept their staff levels the same, and some even let workers go with the stimulus money coming in. Between March 2009 and the quarter when ARRA recipients completed their stimulus project:

•

70 percent experienced net full-‐time job loss.

•

10 percent experienced no net full-‐time job increase.

•

20 percent experienced net full-‐time jobs added.



Among companies with net job loses, each company lost an average of 10 part-‐time jobs (mean) or 2 part-‐time jobs (median). Among companies with net job increases gained an average of 14 part-‐time jobs (mean) or 5 part-‐time jobs (median). These numbers are shown in Figure 3:

Figure 3: Measures of Part-‐time Job Creation

Net Job Increases

20%

No Job Changes

10%

Net Job Losses

70%

D. 14% of ARRA Recipients Retained Some Part-‐time Workers After

Completing Their Recovery Act Project

Among the 20 percent of organizations who added part-‐time jobs on net, 59 percent on average kept some of their stimulus workers on staff. Another way to look at this data is, of these part-‐time job-‐adding companies:

•


•


•

16 percent kept some, but not all of their workers.

When measured against all ARRA Recipients hiring part-‐time workers, after stimulus money rant out:

•


•


•

14 percent kept some but not all of their workers



E. ARRA Recipients Primarily Hired Full-‐Time Workers

ARRA recipients reported primarily hiring full time workers with stimulus funds.

This corresponds with the actual aggregate job data collected from the survey. This is represented in Tables 1 and 2:

Table 1: Type of Worker Hired by Company

Company Hiring Type

All Full-‐Time

Mostly Full-‐Time, Some Part-‐Time

Equally Full-‐Time and Part-‐Time

Mostly Part-‐Time, Some Full-‐Time

All Part-‐Time

Total Number % of Total

98

51

16

11

23

47

25

8

5

11

Table 2: Share of Workers Hired with Stimulus Funds

Net Jobs Added

Full-‐Time

Part-‐Time

Total


1286

568

1854

69%

31%

100%

F. Full-‐time Jobs that Were Retained Cost Approximately $311,054 Per Job

Of the 42 percent of firms which added net jobs between March 2009 and when the

ARRA-‐funded project finished, each full-‐time job added cost approximately

$176,240.33. Among the 29 percent of firms that added net jobs and retained at least some of the net job increase as of the fall of 2012, each job cost approximately

$311,054.

G. 65% of ARRA Recipients Received Federal Grant Money in Last Five Years

Recovery Act funds were divided into several categories and then designated for particular types of projects. Private sector firms then bid for grants or contracts to perform the tasks required to complete the stimulus projects. Requesting the government money meant navigating the process of applying for a federal grant, something that can be daunting and time consuming. This barrier to entry means that some stimulus money likely went to recipients who were the best at navigating the grant request process, not necessarily the best company for the job. We asked reach recipient whether the ARRA money they received was their first federal grant at least in the past five years.

Of the companies we talked with, 309 answered our question, and 65 percent said it was not their first grant, and that they were familiar with how to request federal money before the stimulus. Of the remaining recipients we talked to, 25 percent said



it was their first grant, while 10 percent could not recall if they had received a grant in the past five years or simply did not know.

H. ARRA Recipients were Overwhelmingly from the Construction Industry

The American Recovery and Reinvestment Act allocated money to various categories of projects, including education, transportation, and energy, among others. However, the companies that did projects across these various categories were not necessarily companies in the same field. For instance, a school improvement project would have spent money with a company in the construction field. Similarly, an energy project might have required hiring a technology company.

Therefore, in order to ascertain what sectors were primary recipients of ARRA funds, it was important for the company to self identify their field. This information allows us to cross tab industry classification to determine if particular industries were more likely to retain workers.

We found that recipients of Recovery Act grants were primarily construction companies (40.7 percent), education institutions (15.4 percent), or non-‐ construction companies in the housing industry (8 percent). Table 3 lists the top eight categories:

Table 3: ARRA Recipients by Industry Classification

Industry Classification

Construction

Education

Housing Industry

Medical Services

Government

Technology

Non-‐Profit

Other

Total

Source: Reason Foundation Survey


132

50

26

24

22

10

8

52

324

40.7%

15.4%

8.0%

7.4%

6.8%

3.1%

2.5%

16.1%

100%

I. 88% of ARRA Recipients Not Required to Retain Workers

The vast majority of recipients, 88 percent, said they were not required to keep employees for a set amount of time as a condition of receiving ARRA money. Two percent of companies claimed they were required to keep workers for a set amount of time in order to receive an ARRA grant, while 10 percent did not know if such a

term applied to their completion of a grant.



J. ARRA Recipient Opinions of Stimulus Appropriateness Primarily Favorable

We asked ARRA recipients several questions about their personal opinions about the money that they received. The answers were provided by the company manager or human resources individual identified by the company as being best able to answer questions about the use of Recovery Act funds, but these answers do not necessarily reflect the official view of the recipient companies.

Of the 296 individuals who answered our questions about the use of ARRA money:

•

65 percent said the project they completed would not have been possible without Recovery Act funds.

•

25 percent said they would have done the project even without the

Recovery Act funds.

•

10 percent were unsure if they would have done the project without the

Recovery Act funds.

A high percentage of ARRA recipients believed the stimulus money was properly managed:

•

84 percent of individuals claimed that Recovery Act money granted to their company went to a valuable project.

•

8 percent believed the funds should have been used for some other project.

•

7 percent were unsure of whether the money they received was put to the best use possible.

In slight contrast, only 61 percent said they would request more funding if Congress passed a second stimulus similar to the Recovery Act, 29 percent said they would not ask for money again, and 9 percent were unsure if they would ask for more money if possible in the future.

Finally, we asked whether, in the personal opinion of the respondent, they thought the Recovery Act had helped the economy. Of the 299 who answered:

•

62 percent thought ARRA had helped the economy.

•

17 percent thought ARRA had hurt the economy.

•

21 percent thought ARRA had no significant effects on the economy.

K. No Link Between Hiring Practices and Industry

Statistical tests fail to detect a statistically significant relationship between industry classification and net hiring during ARRA.

* However, there is some indication that with a larger sample size, we may find construction ARRA recipients were slightly

*

Fisher exact tests fail to reject null.



more likely than other industries to hire new workers only to return to previous levels after completing the project. See Tables 4 and 5:

Table 4: Industry Percentage Share of Changes in Labor Market (Full Time)


Construction

Education


Technology

Other

Total

Note: * Sample size too small

Net Increase in FT Jobs

%

41

20

7*

3*

30

100%

No Change in FT Jobs

%

36

17

5*

2*

40

100%

Table 5: Industry Percentage Share of Retained Workers

Net Decrease in FT Jobs

%

43

11*

12*

0*

35

100%


Construction

Education


Technology

Other

Total

Note: * Sample size too small

Retained

36

20

7*

3*

34

100%

Returned

62*

19*

10*

0*

10*

100%

No Increase

39

14

8*

1*

38

100%

L. ARRA Recipients Had Limited Interaction with Federal Officials

We asked ARRA recipients about their interaction with federal officials, both during the grant application process and in follow up experiences. Of the 296 recipients who answered our question, 41 percent said they had no direct contact with federal officials. Of those who did have contact with federal officials related to their ARRA grant, 57 percent said the officials were easy to work with, 14 percent said officials were difficult to work with, and 29 percent were neutral on the ease of their ability to work with federal officials.

When asked whether the federal government had contacted the company to discuss the Recovery Act’s affect on hiring, 57 percent of recipients said they had received no follow up contact while 35 percent said they had heard from federal officials to discuss the effects of ARRA. A small number, 8 percent, were unsure if the government had contacted their company after the distribution of the ARRA grant.

In contrast, 74 percent of ARRA recipients said the federal government did give them detailed specifications on the use of ARRA monies, 24 percent said they



received no detailed specifications, and two percent were unsure if their company had received directions specifying the use of ARRA funds.

Overall, 81 percent of ARRA recipients said they appreciated the government’s largely hands off approach to managing the use of Recovery Act money, while 19

percent said they would have preferred more direction from the federal government.

Cross Tabbed Data Tables

For more detailed results of cross-‐tabulated data, see Appendix C . We take our measured changes in full-‐time and part-‐time employees, the number of workers laid off or shifted into part-‐time labor, and opinions on the value of the Recovery Act itself and cross these with the industry the companies represented, the geographic region of the country the companies were doing projects in, the size of the company, the amount of stimulus money they received, and whether they had received money from a federal grant or contract in the past.

Part 5 — Conclusion

The politics of analyzing the Recovery Act are about as complex as the politics of the stimulus project itself. For defenders of the Recovery Act, the narrative shifted at some point from supporting stimulus spending as the means of jump-‐starting a recovery to preventing things from getting worse than they were. For opponents of the Recovery Act, the narrative has generally remained a stalwart claim that stimulus spending doesn’t create jobs and didn’t help the economy.

Our research shows that the stimulus did create some employment, but that most of the jobs created or saved eventually disappeared once the stimulus money was spent. The short-‐term spending provisions ultimately created a short-‐term boost in employment, but only 23 percent of grant or contract recipients kept their worker hired while completing an ARRA project.

If the goal of the stimulus was to create temporary employment, in many cases it was successful. If the goal of the stimulus was to create lasting employment, its effects were lackluster. Only 41 percent of companies receiving a Recovery Act grant added net jobs to their payroll with the stimulus money. And of the companies that added jobs 45 percent laid off some or all of their workers after the stimulus project was completed.

These finding is hardly surprising. Standard Keynesian theory suggests that government spending should increase when the economy is stagnating to fill the consumption gap. Stimulus spending is only supposed to hire short-‐term labor, the classic example being a project paying workers to dig a ditch, and then paying them



to fill the ditch back in. This theory suggests that with enough stimulus spending, the government can bridge the gap between a recession and recovery.

However, the unemployment rate at the end of 2012 was 7.8 percent—or 14.4 percent if you count those who have technically dropped out of the labor force but still want work.

25 Economic growth has remained weak through 2012, with average annual GDP of a mere 1.6 percent. The economy and labor market on the other side of the Recovery Act are not strong, and the low level of job retention from stimulus spending has not helped.

This analysis assumes, though, that a larger stimulus would not have a different outcome. It also does not fully take into account ways in which stimulus spending might have crowded out private investment. It is consistent, though, with the answers to our main research questions. Our first primary research question was:

(1) What percentage of organizations that received stimulus funding laid off workers after finishing their stimulus-‐funded project?

The direct answer to this question is 13 percent of ARRA recipients laid off all their workers once Recovery Act money dried up. However, we found that only 41 percent of ARRA grant and contract recipients hired workers in the first place, and

30 percent of those firms released all of their workers hired during the stimulus. A small 15 percent of companies that hired workers retained some, but not all of their staff. Our second primary research question was:

(2) What was the net employment increase or decrease of organizations that received stimulus money from the start of the stimulus program up until nine months after the end of the stimulus program?

The direct answer to this question is a 37 increase in net employment, comprehensive of all ARRA recipients. This number primarily reflects the retention of a large number of employees at a few of the 29 percent of ARRA recipients that retained some or all of their workers. Our third primary research question was:

(3) What proportion of workers hired with stimulus funds were full-‐time employees and what proportion were part-‐time employees?

The direct answer to this question is, of companies that hired workers 69 percent were full-‐time employees, and 31 percent were part-‐time employees.

It could be argued that our methodology does not account for the retention of workers that firms avoided laying off. Some employers may have used Recovery Act funds to keep already hired workers, others used money to hire new workers, and still others received money as self-‐employed contractors and increased the profitability of their already stable businesses. Therefore, our data on a company might reflect no jobs added or lost even though the stimulus dollars prevented the recipient from laying off workers.



However, even if a company was able to delay firing a worker due to becoming an

ARRA recipient, once the stimulus money was spent the company could be back in a position of having to let the worker go. This may be reflected in the 27 percent of

ARRA recipients that saw a net job decrease from the beginning to the end of the stimulus. On the other hand, ARRA money could have prevented a company from firing a worker and provided enough funding so that by the end of the stimulus project, the company was on sounder financial footing. To the degree that this company did not have to let that worker go, this data may be reflected in the 31 percent of companies that neither gained or lost workers from the start to the end of the Recovery Act.

Another critique is that we don’t fully measure job creation since some job hires might have already been planned and some employees were hired away from other companies (yielding net zero change in aggregate unemployment). Further, demand shifts towards businesses that received Recovery Act funding could have led to employment decreases at other businesses resulting little change in overall labor statistics.

However, whether an employee came from another firm or just filled an already open position was not the question our paper has sought to address. The Jones and

Rothschild paper for Mercatus Center took this question on. This paper, in contrast, looks at the duration of employment at firms that received Recovery Act money, whether or not the jobs were created for already established positions. This critique does suggest that it is possible our data overestimates the number of jobs that were retained after stimulus money was all spent.

Ultimately, the results of our survey also show why the story of the Recovery Act is so complex. Of ARRA recipients, 62 percent believe the stimulus helped the economy. Further, when the money received is able to generate a positive result close to home, the political ramifications of stimulus spending—i.e. arguments about federal outlays crowding out the private sector and requiring increased tax revenues—can be crowded out themselves.

Consider that while conducting our survey a Recovery Act recipient responded to our prompt by saying he normally did not take time to answer surveys, but that he wanted to express the positive effect stimulus dollars had on his school district. He reported that the Recovery Act grant allowed him to hire one full-‐time employee and one part-‐time employee. In addition, the stimulus money was used to retain other employees at the school, as state budget deficits had led to education cuts. He argued that without the stimulus funds to hire these workers, “the children in the school district would’ve suffered the consequences of the recession.” Once the stimulus money ran out the school had to lay off the two newly hired staff, but the recipient was still positive.

In contrast, at the start of another call a Recovery Act recipient responded to our request they take the survey by responding: “I actually have a meeting in five minutes, but I’d love to bitch about the stimulus. So I’ll be late.” The recipient



explained for the next 20 minutes that while the government had offered him a grant, they had not paid him the roughly $900,000 he was owed. He further complained that the government placed so many restrictions on the project that it was repeatedly delayed, even though the project would have been completed without the government’s money.

These two stories represent the dichotomy of responses to the Recovery Act. Some recipients had very positive responses to the stimulus. Others simply hired short-‐ term labor and then had to lay off the workers once the project they were working on was completed. This brings us back to the reality that depending on where the benchmark for success is set, the stimulus could be considered a success or failure.

About the Authors

Anthony Randazzo is director of economic research at Reason Foundation, based in New York City.

Emily Ekins is director of polling at Reason Foundation, based in Boston.

Katie Furtick is a policy analyst at Reason Foundation, based in Washington D.C.

Contact: anthony.randazzo@reason.org



Appendix A — Full Survey Text

After identifying recipients of Recovery Act funds, we selected a randomized sample and used the following text for a telephonic establishment survey:

1. Public Records indicate that your organization received funds through the Recovery

Act. Can you confirm that a Recovery Act project has been completed?

•

Yes, completed

• No, not completed

•

Don't know if completed

•

Did Not Receive ARRA Funds

2a. How many total employees does your organization employ full-time as of today?

[OPEN]

2b. Is this number exact or a best guess? [Exact Number / Best Guess]

3a. How many total employees does your organization employ part-time as of today?

[OPEN]


4a. As of (quarter after end of ARRA project listed in excel document) how many total employees did your organization employ full-time? [OPEN]


5a. As of (quarter after end of ARRA project listed in excel document) how many total employees did your organization employ part-time? [OPEN]


6a. As of March 2009 how many total employees did your organization employ fulltime? [OPEN]


7a. As of March 2009 how many total employees did your organization employ parttime? [OPEN]


8a. Since March 2009, how many total workers has your organization laid off? [OPEN]


9a. Since March 2009, how many total workers has your organization shifted from fulltime to part-time? [OPEN]




10a. Since March 2009, how many total workers voluntarily left your organization because ARRA funds ran out? [OPEN]


11. How would you characterize the type of workers your organization hired with stimulus money? Where they…

A.

All full-time

B.

Mostly full-time, some part-time/contractor

C.

Equally full-time and part-time/contractor

D.

Mostly part-time/contractor, some full-time

E.

All part-time/contractor

12a. How would you classify the industry in which your organization operates?

•

Construction

•

Education

•

Medical services

•

Technology

•

Other (please specify)

12b. Other industry [OPEN]

13. Was this the first federal contract or grant your organization received in the past five years? [Yes / No / Don’t know]

14a. What percentage of your organization’s annual revenue came from the Recovery Act funded project? [OPEN] %


15a. How many previously laid-off workers did your organization rehire as a result of

Recovery Act-funded projects? [OPEN]


16a. How many entirely new workers did your organization hire as a result of Recovery

Act funds? [OPEN]


17a. How many workers did your organization avoid laying off as a result of Recovery

Act funds? [OPEN]


18a. After the project was completed, what percentage of the workers hired for the

Recovery-funded projects were laid-off? [OPEN] %




19a. After the project was completed, what percentage of the workers hired for the

Recovery-funded projects voluntarily left your organization because ARRA funds ran out? [OPEN] %


20. Was your organization required to retain employees for a set amount of time as a condition of receiving the Recovery Act contract or grant? [Yes / No / Don’t know]

21. In your opinion, would the Recovery Act-funded project have been completed without the Recovery Act contract or grant? [Yes / No / Don’t know]

22. If Congress were to authorize a second Recovery Act, would you request new funding to hire more workers than are currently on your payroll? [Yes / No / Don’t know]

23. In your opinion, would you say the money your organization received from the

Recovery Act was used for the right project or should the money have been used for a different project?

• Right Project

• Different Project

•

Don’t Know

24. In your opinion, do you think the Recovery Act helped the economy in the long-term or hurt the economy in the long-term, or had no significant impact?

• Helped the economy

• Hurt the economy

•

No significant impact

25. Would you say the federal officials who distributed the money were:

• Easy to work with

•

Difficult to work with

•

Neither particularly easy, nor particularly difficult to work with

• I did not personally work with federal officials

26. Did the federal government give you detailed specifications for how to use the

Recovery Act contract or grant? [Yes / No / Don’t Know]

27. Has anyone in the federal government contacted your company to discuss the impact of the Recovery Act contract or grant on your organization’s hiring?

[Yes / No / Don’t Know]



Appendix B — Recovery Act Data Cleaning Methodology

Data used for our analysis was collected from Recovery.gov, the U.S. government’s official website that provides access to data related to Recovery Act spending.

26 The data collected was the cumulative national summary of reported job totals from

February 17, 2009 through June 30, 2012.

27

(1) Data Collection

The way that the reporting is completed is by primary and sub recipients of

Recovery Act funds reporting through the FederalReporting.gov web interface, and then Recovery.gov providing public data access to those reports. The reports are published quarterly and submitted by recipients detailing how the Recovery funds have been spent and the status of their projects—including the number of jobs created or saved as a result of Recovery Act funds. The most recent report available at the time this project was started was updated through the second quarter of 2012, and contained data for 576,036 Recovery Act recipients across the United States.

Each observation in the data set also had 98 variables describing the individual observation. These variables were used to narrow down the total population to a population of interest, which we eventually randomly sampled 12,000 Recovery Act recipients for our telephonic survey. Table 1 below lists a summary of the main variables, and their descriptions, which were used in narrowing down our population of interest to sample from.

Table 1: Selected List of Variables Used

Variable Name award_key recipient_role award_type award_amount local_amount project_status recipient_name

Source: Recovery.org

Description

A derived field that identifies an award.

Indicates the type of recipient: Prime Recipient (P), Sub Recipient (S),

Prime Vendor (PV), Sub Vendor (SV)

Type of award: Contracts, Grants, and Loans

The amount of the award as issued by the Federal agency to the Prime recipient. The field is left blank for sub-‐recipients and vendors.

The amount of the award accrued to each recipient by recipient role.

Evaluation of the completion status of the project, activity, or contract:

Not started, Less than 50% complete, More than 50% complete, or

Complete

The name of the recipient.

These variables will be referred to throughout the methodology outlined in this paper. The most recent data set on Recovery Act recipients was downloaded in July

2012 from Recovery.gov.

28 The following describes our methodology by which we arrived at our final sample population of 12,000 Recovery Act recipients.



(2) Cleaning and Summarizing the Full Population

The full dataset with all 576,036 observations was read into Stata, statistical data management software. This software allowed us to strengthen our results by reducing variability in the sampling population. The sampling population was limited to only those observations that met the following criteria:

-‐ Received at least $100,000 in Recovery Act funds;

-‐ Received Recovery Act funds in the form of a contract or grant;

-‐ Project status indicated “complete”;

-‐ Present local amount of at least $100,000;

-‐ Present recipient name;

-‐ Recipient was the final destination for Recovery Act funds (i.e. A primary recipient without a vendor or sub-‐recipient to pass money through to, a vendor, or a sub-‐recipient) and;

-‐ Recipient was not a state executive or city office (we included schools).

From the total population of 576,036 approximately 26,683 observations were dropped which indicated an award amount of less than $100,000. We assigned the

$100,000 floor to the award amount so as to only survey recipients who received what we defined as a substantial amount of Recovery Act funds. Recipients who received funds in the form of a loan indicated that the funding was awarded with the stipulation that it had to be paid back to the Federal government, which may have affected the way the funds were spent. We dropped recipients who received funds in the form of a loan and limited the sample population to those who received funding in the form of a contract or grant.

Those whose project status was anything other than “complete” (i.e. Less than 50%,

More than 50%, or Not Started) were dropped. Limiting observations to only those with completed projects allowed us to measure the final effect that stimulus money had on the various surveyed organizations. However, observations that had a missing field for project status were kept because they were a sub-‐recipient or sub-‐ vendor of a prime recipient or vendor. Table 2 below shows the total population of recipients by award type and project status. The project statuses and award type in light blue indicate the observations that were dropped out of the total population.

Table 2: Number of Recovery Act Recipients by Award Type and Project Status

Award Type

Project Status Contract Grant Loan Total

Blank 29,672 440,383 4,260 474,315

Completed 21,214 40,244 767 62,225

Less than 50% 2,055 5,604 399 8,058

More than 50% 4,090 23,609 379 28,078

Not Started 1,016 2024 309 3,349

Total 58,047 511,864 6,114 576,025

Source: Recovery.gov, Reason Foundation



(3) Recovery Act Funds Awarded to Each Recipient Role

Table 3 below shows the number of observations by recipient role. The recipient role is a variable in the data set that indicates what kind of recipient the observations represented. The four types of recipient roles that are entered by reporting primary or sub recipients are primary recipient, sub recipient, primary vendor, or sub vendor.

Table 3: Observations by Recipient Status

Recipient Role

Primary Recipient 101,710

Primary Vendor

Sub-‐Recipient

Sub-‐Vendor

Total

122,761

174,535

177,028

576,034


Only primary recipients (P) entered an award amount when reporting, and other recipient roles -‐ sub-‐recipient (S), primary vendor (PV), and sub-‐vendor (SV) -‐ associated with the award key were left missing. Since our criteria for the sample population included the recipient receive funding of at least $100,000 and being the final destination for the funding, we had to come up with a way to determine the specific amount of funding that was left to each recipient per award key (unique identifier assigned to a primary recipient of Recovery Act funds and their vendors or sub-‐recipients).

For example, if a primary recipient gave out any part of the total award to a sub-‐ recipient or to a primary vendor we wanted to account for the amount (if any) that was left to the primary recipient. In some cases an award was given to a primary recipient, with which part was passed through to sub-‐recipients. If the sub-‐recipient then chose a sub-‐vendor to hire for project completion, part or the entire award that was given to the sub-‐recipient was then passed through again to the sub-‐vendor.

The variable local_amount indicated the award amount given to a sub-‐recipient, primary vendor, or the amount left to the primary recipient depending on the observation’s recipient role. However, we found anomalies in the data that was originally downloaded from Recovery.gov.

In some cases the sub-‐vendor local_amount indicated the same amount as the sub-‐ recipient’s local_amount . In others, the local_amount for sub-‐vendors was zero when it should have indicated a clear amount. Also, in many cases, the local_amount for the primary recipient was left blank.

In order to consistently find the final amount (if any) that was left over to the primary recipient we subtracted the sum of payments to sub-‐recipients and primary vendors of the primary recipient from the award amount first received by the primary recipient. Sub-‐vendor observations that indicated a local_amount received



were not counted in this calculation because it would have double-‐counted the amount awarded to the sub-‐recipient. To complete the calculation of the local amount for primary recipients we first generated the variable SPV_loc_amt .

SPV_loc_amt = the sum of the local amount given to the sub-‐recipient(s) + the primary vendor(s) by award key.

Another variable, sum_loc_award , was generated by individual award key – the unique identifier linking a primary recipient with any of their vendors, sub-‐ recipients, or sub-‐vendors. Sum_loc_award indicated the sum of the local_amount by recipient role. This variable was useful in that we could now keep track of the sum of what each sub-‐recipient, primary vendor, and sub-‐vendor received from the primary recipient by their award key.

Sum_loc_award = the total local_amount given by recipient role, by award key.

Some observations in the full population data set that we sampled from showed that they were not attached to a primary recipient. This was due to the fact that their primary recipient could have been dropped for a number of reasons; either they were designated as a loan and then sub-‐recipient or vendor was designated as a different award type, or because the primary recipient had a project status as anything less than complete, and the sub-‐recipient’s project status was blank. We controlled for this by confirming with the recipient that the project they were working on with Recovery Act funds was actually complete during the survey process.

After making primary recipient local_amounts equal to [ award_amount -‐∑(PV+S local_amount )] and the local_amount equal to the award amount in the case that there was only one sub-‐recipient or primary vendor for each award key(meaning that the full primary recipient award amount would have gone to the sub-‐recipient or vendor) we dropped the observations that were not relevant for our sample purposes.

For reference, those observations that were first dropped were those that were not marked complete, those with award amounts less than $100,000, and award types designated as loans. At this point in the data cleaning process, there were still

358,115 observations with missing local_amount , which had to be dropped. A summary of the number of observations by recipient role is shown in Table 4 below.

Table 4: Observations by Recipient Role and Local Amount

Recipient Role

P

PV

S

SV

Local Amount Present

No

0

Yes Total

39,864 39,864

73,257 41,385 114,642

130,367 41,902 172,269

154,491 22,144 176,635



Total 358,115 145,295 503,410


It was necessary to drop observations with missing local_amounts due to questions tied to this amount in our survey process.

At this point the data set had been reduced to only observations with a primary award amount that was greater than $100,000, that were designated as either contracts or grants, whose project status was left blank or indicated as completed, and those observations that did not have a missing local_amount . The primary recipient roles that showed a local_amount and no “primary only” flag reflect the award amount that was offered to the primary recipient minus the sum of the total amount then given to any sub-‐recipients and primary vendors.

The next step was to drop all observations that had a local_amount that was less than $100,000 and were not primary-‐only recipients.

Table 5 below shows a summary of the data after the preliminary narrowing down of the full data set had been completed. The “1” under the first column (local amount less than $100,000 flag) indicates observations that had a local_amount of less than $100,000, the “0” indicates observations for which the local_amount was greater than or equal to $100,000. The column indicating the sum of local amounts shows the sum of all of the local award amounts that are less than $100,000, and those that are greater than or equal to $100,000, not including the primary recipient amount in the local_amount column. This reflects all primary-‐only primary recipients + sub-‐recipients + sub-‐vendors + primary vendors. The sum of local amounts of observations that total less than $100,000 out of the total sum of local amounts is less than 1 percent of the total local amount (see sum percentage column). But, 40.8 percent out of the total number of observations are those that have local amounts less than $100,000. Therefore, out of 145,295 observations,

59,342 were dropped.

Table 5: Preliminary Dataset Summary by Local Amount

Local Amount Less than $100,000 Flag

0

1

Total

Sum of Local

Amount

$ 158,218,757,726

$ 1,550,858,918

Sum

Percentage

99%

1%

$159,769,616,644 100%


Frequency of

Observations

85,953

59,342

145,295

Frequency

Percentage

59%

41%

100%

(4) Final Steps to Narrowing the Sampling Population

We dropped any observations that were missing recipient names. Again due to the construction of our survey, the recipient’s name was a necessary field to reference when surveying recipients; 1,909 observations were dropped.



We dropped all of the observations that were primary recipients but not prime-‐only recipients. By doing this we avoided the potential issue of sampling more than one recipient with the same award key. For example, we would not want to include a primary recipient in our sample as well as (one of) their sub-‐recipients or vendors.

This ensured that we measured the affect of Recovery Act funding on employment from the last known destination of Recovery Act funds; 11,277 observations were dropped.

The next step of narrowing the Recovery Act recipient population was to indicate and drop any duplicate recipient names. We assigned a random number between one and zero to each observation then sorted the dataset from smallest to largest on that random number. We then ran a report that flagged duplicate observations of recipient names. The random assignment of a number to each variable ensured that when any duplicate observations were found, they were found in a random order as the program ran down the list of observations. We dropped 34,931 observations that were shown to be duplicates of recipient names. Dropping duplicate recipient names eliminated the possibility of calling a company or office more than once. Also, we made sure to drop the observations that were not the most recent award to a specific company, if they were given awards on more than one date.

We flagged and dropped recipient names that indicated that the observation was a governor’s office, executive state office, or city office. The purpose of eliminating those observations from the total population prior to sampling was due to lack of specificity of where awarded funds would be disbursed from those city or state executive offices. These observations were then dropped (2,807), leaving the number of observations in the data set at 35,029.

Also, to break down the sample even further and to ensure that each observation had the total sum of the awards that a recipient name had received in Recovery Act awards, we dropped observations that were missing the sum of all of their company’s Recovery Act awards. We found 4,399 observations were missing the sum of their Recovery Act funding and were dropped from the sample, leaving the final population to sample from at 30,630.

(5) Creating a Random Sample of the Population

The dataset’s total local award amount was added and then divided by four to divide the sampling population into quartiles by award amount. This exercise was completed to keep a consistent ratio of award sizes when sampling. For example, a small proportion of observations with very high award amounts would be less likely to be chosen in the random sample. Sampling by quartile of award amount alleviated the issue of not having a true representation of the full population. The total award amount was $53,475,241,984 and divided by four, each quartile equaled

$13,368,810,496. The award amounts were then sorted by smallest award amount

($100,000) to largest ($4.88 billion). A running total was executed until each quartile summed to approximately $13,368,810,496 and divided as such.



The percent of observations in each quartile out of the total observations was applied to the targeted sample size of 12,000 observations. Table 6 below shows the breakdown of the four quartiles, their ratios to the total population, and the number of observations to sample from each quartile in order to keep a consistent ratio of award amounts per number of observations.

Table 6: Summary of Sampling Distribution

Quartile

Total

1

2

3

4

Frequency of

Observations

27,001

3,156

455

18

30,630


Percent of Total

Observations

88.2%

10.3%

1.5%

0.1%

100%

Number Per Quartile to

Include in Sample

10,578

1,236

178

7

11,999

The number of observations that we randomly sampled from each quartile was rounded to a whole number of observations. In Stata, each observation was again assigned a random number between zero and one and the data was sorted by that random number from smallest to largest. By quartile, the appropriate number of observations was randomly sampled to create a sample dataset of recipients of

12,000 observations.

B. Descriptive Statistics of the Sample Population

The following section explores the geography of the final sample population of

12,000 observations that were the basis for our survey. Table 7 below shows the frequency of observations, percent of total observations, and sum of local amounts by recipient role.

Table 7: Summary of Sample by Recipient Role

Recipient Role Frequency Percent of Total Observations Sum of Local Amount

P

PV

S

4,017

3,053

4,930


33.48% $ 5,929,436,458

25.44% $ 4,537,012,644

41.08% $ 6,616,267,012

In our final sample there were no sub-‐vendor recipient roles. This occurred due to the nature of how local amounts were reported by sub-‐recipients, and to avoid double-‐counting of Recovery Act awards (mentioned above).

To assure that the sample quartiles were in fact the same approximate ratio of local amount award sizes as the total population, we separated the sample into four quartiles weighted by the local amount awarded to each recipient. The local amount by quartile was summed and each quartile does in fact add up to approximately the



same total, $4.27 billion. Table 8 below shows the descriptive statistics of the sample by quartile.

Table 8: Descriptive Statistics of Sample by Quartile

Quartile Observations Mean

Standard

Deviation

Min Max

3

4

1

2

6639 $ 643,296 $ 158,249 $ 419,949 $ 983,635

3249 $ 1,314,636 $ 233,654 $ 983,683 $ 1,781,710

1890 $ 2,259,617 $ 336,200 $ 1,782,717 $ 2,961,440

222 $ 19,200,000 $ 38,100,000 $ 2,965,713 $ 281,000,000


Shown differently, Figure 1 below shows the percentage of observations in each quartile in the sample.

Figure 1: Percentage of Observations in Sample by Quartile


After discerning what our sample looked like, and proving that our sample was representative of the total population of Recovery Act recipients, we contracted with Odesk to provide phone numbers for each of the recipients in our sample.

C. Merging Survey Data with Original Data

We contracted with the Center for Social Science Research at George Mason

University to conduct the phone survey and record recipient responses. The authors of this study sat in on many of the sessions, provided oversight to the calling staff, and conducted dozens of surveys themselves.

The call center called Recovery Act recipients and asked the questions listed on our survey. They also recorded anecdotal comments made by respondents. We also used

Reason Foundation offices to conduct the same survey to increase the speed of data collection and to gather more detailed anecdotal stories when possible.



The recipient names in our sample were randomized before any phone calls were made.

We were only able to call 62.4 percent of our sample (recipients were called at random) in the time arranged to work with the call center. Therefore, we changed our approach and contracted again with the call center, this time directing them to call the second half of the sample and ask ARRA recipients just for an email address that we could send our survey to. After collecting email addresses we sent out the survey with unique tags to collect data. We waited 48 hours after emails were sent and then made follow up calls to those who had not responded.

Survey data was compiled from these three sources: Center for Social Science

Research phone calls, internal Reason Foundation calls, and email surveys. We were careful to keep track of which data came from which source before merging the data with our original sample.

Overall, 86.9 percent of respondents provided us with complete answers to our survey questions about their full-‐time employment levels before and after receiving

Recovery Act funds and 77.9 percent of respondents provided us with complete answers to our survey questions about their part-‐time employment levels.



Appendix C — Cross Tabbed Data Tables

There are a number of ways of looking at the data we collected when cross-‐ tabulated. In the following we examine changes in full-‐time and part-‐time employees, the number of workers laid off or shifted into part-‐time labor, and opinions on the value of the Recovery Act itself. We measure these categories relative to the industry the companies represented, the geographic region of the country the companies were doing projects in, the size of the company, the amount of stimulus money they received, and whether they had received money from a federal grant or contract in the past.

Cross Tab Contents

A. By Sector/Type of Organization

(1) Change in Full-‐Time Employees

(2) Change in Part-‐Time Employees

(3) Number of Workers Laid Off & Number of Workers Shifted From Full-‐ to Part-‐Time

(4) Opinion of Whether or Not the Stimulus Helped or Hurt the Economy

B. By Geographic Region





C. By Company Size





D. By Tier of Stimulus Funds Awarded





E. By Whether or Not Company Received Federal Funding the in Past





F. General Descriptive Statistics



A. By Sector/Type of Organization

(1) Change in Full-‐Time Employees

Change in Full-‐Time Employees by Industry

From March 2009 to Project End

Min Max Mean Std. Dev. Net

800

600

400

200

0

-‐200

-‐400

-‐600

-‐800

-‐1000

Size of Company by Last Reported Number of Employees

Percent of Company Net Full-‐Time Employment

Change by Industry


Net Loss No Change Net Gain

40% 47%

36%

50%

30%

22%

50%

42%

52%

42%

31%

36%

23%

33%

45% 57%

17% 31% 20% 31%

29%

17%

41%

17%

25% 22%

33% 27% 28% 27%




(2) Change in Part-‐Time Employees

Change in Part-‐Time Employees by Industry



500

400

300

200

100

0

-‐100

-‐200


Percent of Company Net Part-‐Time Employment

Change by Industry


Net Loss No Change

4%

Net Gain

19% 20%

29% 26%

22% 20%

50%

75%

71% 74%

55% 100%

70%

67% 74%

96%

40%

25% 22%

10%

6% 4%

10% 10%





Cumulative Reported Company Layoffs by Industry

Since March 2009

2500

2000

1500

1000

500

0

Type of Industry

Percent of Companies Who Laid Off Workers vs.

Those Who Did Not by Industry


Had Layoffs No Layoffs

41%

62% 67% 60% 59%

85%

100%

47%

100%

41%

59%

38% 33% 40% 41%

15%

53%

59%

Type of Insudtry



Number of Workers Laid Off & Number of Workers Shifted From Full-‐ to Part-‐Time

Continued…

Total Reported Employee Shifts from Full-‐ to Part-‐

Time by Industry


40

30

20

10

0

80

70

60

50


Percent of Companies Shifted Workers from Full-‐ to Part-‐Time

After Project End

Had Shifts No Shifts

89% 88% 83%

50%

82%

96%

11% 12% 17%

50%

18%

4%

81%

98%

68%

19%

32%

Type of Insudtry



(4) Opinion of Whether or Not the Stimulus Helped or Hurt the Economy

Opinion of Effectiveness of Stimulus by Industry


Hurt Not Signiuicant Helped

33%

55%

66%

21%

80%

50%

68%

100%

74%

60%

24%

11%

21% 23%

46%

38%

20%

13%

27%

5%

19%

8%

22%

19%




B. By Geographic Region

(1) Change in Full-‐Time Employees

Change in Full-‐Time Employees by Region



1400

1200

1000

800

600

400

200

0

-‐200

-‐400

South Northeast Midwest

U.S. Region

West


Change by Region



37%

43%

46%

43% 41%

35%

19%

37%

37%

33%

28%

South

38%

17%

Northeast Midwest

U.S. Region

21%

West

26%

Total




Change in Part-‐Time Employees by Region



200

150

100

50

0

-‐50

-‐100

-‐150

-‐200


U.S. Region

West


Change by Region



21% 22% 22% 20% 21%

73%

58%

66% 70% 69%

6%

South

19%

Northeast

12%

Midwest

U.S. Region

10%

West

10%

Total



(3) Number of Workers Laid Off & Number of Workers Shifted from Full-‐ to Part-‐Time

Cumulative Reported Company Layoffs by Region


1000

900

800

700

600

500

400

300

200

100

0


U.S. Region

West


Those Who Did Not by Region



43%

37%

50%

43% 43%

57%

63%

50%

57% 57%


U.S. Region

West Total




Continued…


Time by Geographic Region


80

70

60

50

40

30

20

10

0

South Northeast

U.S. Region

Midwest West

Percent of Companies Shifted Workers from Full-‐ to Part-‐Time by Geographic Region



55%

72%

67%

71% 68%

28%

South

45%

33%

Northeast Midwest

U.S. Region

29%

West

32%

Total




Opinion of Effectiveness of Stimulus by Region



57%

66% 66% 63% 62%

22%

21%

South

20%

14%

Northeast

21%

13%

Midwest

U.S. Region

21%

West

21%

17%

Total



C. By Company Size


Change in Full-‐Time Employees by Company Size


Min. Max. Mean Std. Dev. Net Change

800

600

400

200

0

-‐200

-‐400

-‐600

-‐800

-‐1000

1 to 25 101 to 500 26 to 100 500 +



Change by Company Size



31%

19%

47%

3%

42%

3%

47% 22%

28%

31%

4%

36%

21% 39%

27%

1 to 25 101 to 500 26 to 100 500 + Total





Change in Part-‐Time Employees by Company

Size


Min. Max. Mean Std. Dev. Net Change

400

300

200

100

0

-‐100

-‐200

1 to 25 101 to 500 26 to 100 500 +



Change by Company Size



19%

27%

20%

50%

40%

100%

72% 70%

50%

33%

9% 10%

1 to 25 101 to 500 26 to 100 500 + Total





1200

Cumulative Reported Company Layoffs by Company

Size


1000

800

600

400

200

0

1 to 25 101 to 500 26 to 100

Company Size

500 +


Those Who Did Not by Company Size



29%

37% 35%

41%

50%

50%

63% 65%

71%

59%

1 to 25 101 to 500 26 to 100

Company Size

500 + Total




Continued…


Time by Company Size


70

60

50

40

30

20

10

0

1 to 25 101 to 500 26 to 100

Company Size

500 +

Percent of Companies Shifted Workers from Full-‐ to

Part-‐Time by Company Size



57%

62%

57%

68%

82%

43%

38%

43%

32%

18%

1 to 25 101 to 500 26 to 100 500 +

Company Size by Last Reported Number of Employees

Total




Opinion of Effectiveness of Stimulus by Company

Size



57%

49% 50%

69%

16%

16%

1 to 25

29%

24%

19%

101 to 500

22%

26 to 100

Company Size

20%

500 +

60%

22%

19%

Total



D. By Tier of Stimulus Funds Awarded


200

0

-‐200

-‐400

-‐600

-‐800

-‐1000

-‐1200

600

Change in Full-‐Time Employees by Amount of

Stimulus Funds Awarded



400

Tier 1 ($419K to $970K)

Tier 2 ($985K to $1.766M)

Tier 3 ($1.802M to $2.940M)

Tier 4 ($2.961M to $189.9M)

Award Amount by Tier


Change by Amount of Stimulus Funds Awarded



43%

38%

42%

64%

26%

34%

29%

18%

22%

37%

29%

18%

Tier 1 ($419K to $970K)

Tier 2 ($985K to $1.766M)

Tier 3

($1.802M to

$2.940M)


Tier 4

($2.961M to

$189.9M)

42%

31%

27%

Total




100

0

-‐100

-‐200

600

500

400

300

200

Change in Part-‐Time Employees by Amount of

Stimulus Funds Awarded


Min Max Mean Std. Dev.

700

Net

Tier 1 ($419K to

$970K)

Tier 2 ($985K to

$1.766M)

Tier 3 ($1.802M to $2.940M)

Tier 4 ($2.961M to $189.9M)



Change by Amount of Stimulus Funds Awarded


Net Loss

12%

No Change Net Gain

25%

21% 20%

78%

81%

65% 70% 70%

22%

10%

Tier 1 ($419K to $970K)

7% 9%

Tier 2 ($985K Tier 3 ($1.802M to $2.940M)

Tier 4 ($2.961M


10%

Total




Cumulative Reported Company Layoffs by Award

Amount


1800

1600

1400

1200

1000

800

600

400

200

0

Tier 1 ($419K to

$970K)

Tier 2 ($985K to

$1.766M)

Tier 3 ($1.802M to

$2.940M)


Tier 4 ($2.961M to

$189.9M)


Those Who Did Not by Award Amount



39%

43%

46%

33%

41%

61%

57%

54%

67%

59%

Tier 1 ($419K to

$970K)

Tier 2 ($985K to

$1.766M)

Tier 3 ($1.802M to

$2.940M)

Tier 4 ($2.961M to

$189.9M)


Total




Continued…


Time by Award Amount


140

120

100

80

60

40

20

0

Tier 1 ($419K to

$970K)

Tier 2 ($985K to

$1.766M)

Tier 3 ($1.802M to

$2.940M)


Tier 4 ($2.961M to

$189.9M)

Percent of Companies Shifted Workers from Full-‐ to Part-‐Time by Award Amount



64%

77%

69%

75%

68%

36%

23%

31%

25%

Tier 1 ($419K to

$970K)

Tier 2 ($985K to

$1.766M)

Tier 3 ($1.802M to

$2.940M)

Tier 4 ($2.961M to

$189.9M)


32%

Total




Opinion of Effectiveness of Stimulus by Award

Amount



61%

55%

64%

71%

60%

21%

25%

18%

14%

18% 20% 18%

Tier 1 ($419K to

$970K)

Tier 2 ($985K to

$1.766M)

Tier 3 ($1.802M to

$2.940M)

Tier 4 ($2.961M to

$189.9M)

Award Amount Tier

22%

19%

Total



E. By Whether or Not Company Received Federal Funding the in Past


200

0

-‐200

-‐400

-‐600

-‐800

-‐1000

600

Change in Full-‐Time Employees by Prior

Federal Funding


Min Max Mean Std. Dev.

400

Net

Yes No Don't Know

Received Federal Funding Previously (Y/N)

Percent of Company Net Full-‐Time

Employment Change by Prior Federal

Funding



35%

40%

48%

39%

32%

37% 20%

34%

32%

23%

32%

26%

Yes No Don't Know

Received Federal Funding Previously (Y/N

Total




600

Change in Part-‐Time Employees by Prior Federal

Funding



500

400

300

200

100

0

-‐100

-‐200

Yes No Don't Know

Received Federal Funding Previously (Y/N)


Change by Prior Federal Funding


24%

Net Loss No Change

20%

Net Gain

19% 21%

69%

70% 74% 70%

6%

Yes

11%

7% 9%

No Don't Know

Received Federal Funding Previously (Y/N

Total




Cumulative Reported Company Layoffs by Past

Fed. Funding


2500

2000

1500

1000

500

0

Yes No Don’t Know

Received Federal Money Previously (Y/N)


Those Who Did Not by Prior Fed. Funding



54%

51% 50% 52%

46%

49% 50% 48%

Yes No Don't Know Total

Recieved Federal Funding Previously (Y/N)




Continued…


Time by Indication of Prior Fed. Funding


70

60

50

40

30

20

10

0

Yes No Don’t Know

Recieved Federal Funding Previously (Y/N)

Percent of Companies Shifted Workers from Full-‐ to

Part-‐Time by Prior Federal Funding



88%

86% 87% 86%

12%

Yes

14% 13%

No Don't Know

Previously Recieved Federal Funding (Y/N)

14%

Total




Opinion of Effectiveness of Stimulus by Prior Fed.

Funding



58%

54%

68%

60%

11%

23%

36% 21%

21%

Yes

19%

11%

No Don't Know

Previously Recieved Federal Funding (Y/N)

Total



F. General Comparison of Descriptive Statistics

Comparison of Average Number of Layoffs by Category +

Standard Deviation of the Mean

Mean Std. Dev.

160

140

120

100

80

60

40

20

0

160

140

120

100

80

60

40

20

0

Comparison of Average Number of Shifts FT to PT by Category

+ Standard Deviation of the Mean

Mean Std. Dev.



Citations & Endnotes

1 Christina Romer and Jared Bernstein, “The Job Impact of the American Recovery and Reinvestment

Plan,” January 9, 2009, http://otrans.3cdn.net/45593e8ecbd339d074_l3m6bt1te.pdf

2 Barack Obama, interview with KIRO 7 , December 13, 2011, http://www.kirotv.com/news/news/obama-‐washington-‐state-‐budget-‐crisis-‐huge-‐problem/nF2c7/

3 Bureau of Labor Statistics, Labor Force Statistics from the Current Population Survey, Employment

Level, Table LNS12000000

4 http://www.charlestonbusiness.com/news/32667-‐jobs-‐a-‐focus-‐in-‐rileys-‐state-‐of-‐the-‐city-‐address;

Similar stories can be found at: http://www.post-‐gazette.com/stories/news/education/school-‐ districts-‐bracing-‐for-‐end-‐of-‐stimulus-‐funds-‐284643/ ; http://www.pressandguide.com/articles/2011/05/09/news/doc4dc81321c64e8757890451.txt

5 Vanessa Ho, “Seattle’s ‘green jobs’ program a bust,” Seattlepi.com

, August 15, 2011, http://www.seattlepi.com/local/article/Seattle-‐s-‐green-‐jobs-‐program-‐a-‐bust-‐2031902.php

;

Amy Oliver, “Government Weatherization: an exercise in Soviet style efficiency,” Townhall , August 20,

2011, http://finance.townhall.com/columnists/amyoliver/2011/08/20/government_ weatherization_an_exercise_in_soviet_style_efficiency/page/full/

6 Jobs Summary”, Recovery.gov, February 2013, http://www.recovery.gov/Transparency/RecoveryData/Pages/JobSummary.aspx?qtr=2011Q2

7 “Estimated Impact of the American Recovery and Reinvestment Act on Employment and Economic

Output as of September 2009,” Congressional Budget Office, November 2009, http://www.cbo.gov/sites/default/files/cbofiles/ftpdocs/106xx/doc10682/11-‐30-‐arra.pdf


Output as of September 2009,” Congressional Budget Office, November 2009, http://www.cbo.gov/sites/default/files/cbofiles/ftpdocs/106xx/doc10682/11-‐30-‐arra.pdf


Output From April 2010 Through June 2010,” Congressional Budget Office, August 24, 2010 http://www.cbo.gov/publication/21671

10 Friedrich A. Hayek, “The Use of Knowledge in Society,” American Economic Review , XXXV, No. 4,

Sep. 1945, http://www.econlib.org/library/Essays/hykKnw1.html

11 For instance, in the CBO’s in August 2012 report it showed transfer payments to state and local governments for infrastructure spending are now estimated to have a 2.2 to 0.4 affect on output instead of an affect between 2.5 and 1 as was previously estimated. Overall the average multiplier has shrunk from 1.54 to 1.43 at the high end and 0.5 to 0.27 at the low end.

“Estimated Impact of the American Recovery and Reinvestment Act on Employment and Economic

Output From April 2010 Through June 2010,” Congressional Budget Office, August 24, 2010 http://www.cbo.gov/publication/21671

12 James Feyrer and Bruce Sacerdote, ”Did the Stimulus Stimulate? Real Time Estimates of the Effects of the American Recovery and Reinvestment Act,” NBER Working Paper http://papers.nber.org/tmp/37561-‐w16759.pdf

13 Timothy Conley and Bill Dupor, “The American Recovery and Reinvestment Act: Public Sector Jobs

Saved, Private Sector Jobs Forestalled,” May 17, 2011, http://web.econ.ohio-‐ state.edu/dupor/arra10_may11.pdf

14 Timothy Conley and Bill Dupor, “The American Recovery and Reinvestment Act: Public Sector Jobs

Saved, Private Sector Jobs Forestalled,” May 17, 2011, http://web.econ.ohio-‐ state.edu/dupor/arra10_may11.pdf

15 Garett Jones and Daniel M. Rothschild, “Did Stimulus Dollars Hire the Unemployed?” Mercatus

Center Working Paper, August 30, 2011, http://mercatus.org/publication/did-‐stimulus-‐dollars-‐hire-‐ unemployed

16 Garett Jones and Daniel M. Rothschild, “Did Stimulus Dollars Hire the Unemployed?” Mercatus

Center Working Paper, August 30, 2011, http://mercatus.org/publication/did-‐stimulus-‐dollars-‐hire-‐ unemployed



17 We recognize that nearly any study that tries to estimate the impact of a large program like the

Recovery Act will face endogenous factors impacting economic activity, such as monetary policy and weather, which have impacted the labor market in ways we will never be able to compare to alternative scenarios. All studies are able to control for some endogenous factors to a degree, but never will be able to perfectly measure the impact of stimulus spending. This does not negate the process, but is important to re-‐emphasize this point to keep all results in context.

18 Jones and Rothschild actually wrote two papers at the same time, one based on in person interviews conducted primarily by Rothschild traveling the country and collecting anecdotal information on how businesses used the stimulus funds. This paper provided substantial insights from its flexible approach, though the limits of travel also reduced the paper’s ability to represent the whole of the stimulus. http://mercatus.org/publication/no-‐such-‐thing-‐shovel-‐ready


Output from January 2011 Through March 2011,” Congressional Budget Office, May 2011, http://www.cbo.gov/sites/default/files/cbofiles/ftpdocs/121xx/doc12185/05-‐25-‐arra.pdf

20 Recovery.gov, http://recovery.gov/Transparency/RecoveryData/Pages/RecipientSearch.aspx

, accessed August 6, 2012

21 Recovery.gov, http://recovery.gov/FAQ/Pages/DownloadCenter.aspx, accessed August 6, 2012

22 The distinction between a federal grant and federal contract is narrow. Technically, a grant is

“financial assistance awarded by a federal agency to a recipient to carry out a public project or service authorized by a law of the United States,” while a contract is “an award made directly to an independent recipient (not a state or government) by a federal agency.” Recovery.gov, http://www.recovery.gov/Opportunities/Pages/Grants.aspx

, accessed January 31, 2013

23 The Center for Social Science Research at George Mason University is independent from academic departments at GMU and conducts surveys on a daily basis for a wide range of non-‐profit and for-‐ profit clients.

24 Bureau of Labor Statistics, Labor Force Statistics from the Current Population Survey, Employment

Level, Table LNS12000000

25 Bureau of Labor Statistics, Table A-‐15; U-‐3 and U-‐6 measures of unemployment

26 Recovery.gov, http://recovery.gov/Transparency/RecoveryData/Pages/RecipientSearch.aspx

, accessed August 6, 2012





Document 10622194

Did the Stimulus Create Lasting Employment?

An analysis of the American Recovery and Reinvestment Act

Anthony Randazzo, Emily Ekins, and Katie Furtick

Reason Foundation

REVIEW DRAFT — February 11, 2013

Executive Summary

Part 1—Introduction

Part 2 — Selected Literature Review

Part 3 — Survey Methodology

Part 4 — Survey Results and Findings

Part 5 — Conclusion

About the Authors

Appendix A — Full Survey Text

Appendix B — Recovery Act Data Cleaning Methodology

Appendix C — Cross Tabbed Data Tables

Citations & Endnotes

Related documents

Products

Support

Document 10622194

Did the Stimulus Create Lasting Employment?

An analysis of the American Recovery and Reinvestment Act

Anthony Randazzo, Emily Ekins, and Katie Furtick

Reason Foundation

REVIEW DRAFT — February 11, 2013

Executive Summary

Part 1—Introduction

Part 2 — Selected Literature Review

Part 3 — Survey Methodology

Part 4 — Survey Results and Findings

Part 5 — Conclusion

About the Authors

Appendix A — Full Survey Text

Appendix B — Recovery Act Data Cleaning Methodology

Appendix C — Cross Tabbed Data Tables

Citations & Endnotes

Related documents

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