Corporate Lobbying and Procurement: The Allocation of Contracts under the American. Recovery and Reinvestment Act

Transcription

1 Corporate Lobbying and Procurement: The Allocation of Contracts under the American Recovery and Reinvestment Act VERY PRELIMINARY Joonkyu Choi University of Maryland Veronika Penciakova University of Maryland Felipe E. Saffie University of Maryland This Version: February 1, 2017 Abstract We use federal government procurement data and corporate lobby expenditure to identify the causal effect of lobby behavior in procurement allocation. We use the allocation of contracts under the American Recovery and Reinvestment Act (ARRA) of 2009 as a laboratory. In particular, we compare firms with the same propensity to lobby on ARRA related bills in 2009 and show that firms that actually lobbied on ARRA related bills win 5% more contracts and 50% larger contracts than firms that did not lobby on ARRA. To validate the identification strategy we show that all results vanish for non ARRA related contracts. Because procurement spending is key for the life cycle of firms in key industries, corporate lobbying by incumbents can distort the efficient allocation of resources in the economy. Key words: Misallocation, Lobbying, Procurement, Rent seeking, ARRA. 1

2 1 Introduction Public procurement is the buying of goods and services by government organizations. In the United States, the federal government procurement market is vast. Procurement by the federal government accounted for 3% of GDP and 12% of the total government expenditure between 2005 and Moreover, for some industries, the government is a powerful client that shapes the entry, growth, and decline of the average firm. For instance, Figure 1 shows the ratio of procurement expenditure to total industry output in 4-digit NAICS industries for the 20 industries with the largest procurement incidence. The average size of government contracts in these industries represents more than 12% the annual revenue of the average firm. These 20 industries also play an important role in the economy. They represent 8% of the gross output produced by private industries and 7% of the aggregate employment. Moreover, 67% of the total federal procurement spending occurs within these industries. Beside their direct impact on employment and production, six out of these 20 industries are among the 14 industries (out of 304) identified by the Bureau of Labor Statistics as high tech industries (Hecker (2005)), one of the steppingstones of productivity growth in the economy. Despite the importance of procurement for industry dynamics and the aggregate economy, the empirical macroeconomic literature has devoted little attention to the determinants of the allocation of these contracts across firms. A growing literature in firm dynamics suggests that firm-specific demand or customer capital can explain a large share of firm heterogeneity (Foster et al. (2016) and Hottman et al. (2016)). Recent empirical evidence indicates that public demand is also another important form of firm-specific demand, leading them to large subsequent growth (Ferraz et al. (2015)). Hence the allocation of public demand across firms can have a large impact on aggregate productivity and employment through the allocation of procurement contracts. Corporatelobby isakey channel forfirmstoaffect publicpolicy(kerr et al. (forthcoming)) and procurement allocation is not likely to be an exception. In fact, although only 3.5% of procurement winners lobby each year, they account for 60% of the total yearly procurement expenditure. There- 2

3 Apparel manu Areospace manu Civil engineering const Comms equip manu Computer systems design Control inst manu Electronic equip repair Engineering services Fabricated metals Facilities services Machinery manu R&D services Security services Ship manu Tech consulting Technical services Textile prod mills Transport & equip manu Vehicle body manu Waste management Procurement/Output Contract Value/Output Figure 1: Top 20 industries: procurement incidence fore, if lobbying has a causal effect on the allocation of procurement contracts it could potentially distort the efficient allocation of resources in the economy. This paper identifies and quantifies how corporate lobby affects the allocation of procurement spending. There are important threats to identification that need to be addressed in establishing a causal relationship between lobbying and procurement outcomes. First, lobbying is an endogenous choice made by firms. Second, observable (e.g. firm size) and unobserved characteristics (e.g. political connections) may drive the selection into lobby. Third, lobbying is a highly persistent activity. In fact, firms that lobby in a given year lobby in the following year with more than 90% probability. Therefore, it is challenging to associate lobbying expenditure to particular contracts. Fourth, firms that are actively engaged in the procurement market are naturally more likely to lobby for 3

4 procurement contracts. To identify the causal relationship of lobbying on procurement contracts we take advantage of the American Recovery and Reinvestment Act (ARRA) of There are three reasons why the introduction of ARRA is a suitable laboratory to establish the causal effects of lobbying on procurement allocation. First, the swift nature of the introduction of the bill guarantees that no lobbying behavior in the prior years could have had been specifically targeted on winning ARRA procurement contracts. In fact, the Obama administration first took office in January of 2009, and the ARRA program was signed into law in the February Second, the richness of the data allows for matching lobby expenditure to ARRA related bills and also to identify what contracts where awarded under the ARRA program providing a path for overcoming the timing challenge induced by the persistence of lobby. Third, ARRA is representative of the contracting dynamics. In fact, ARRA contracts accounted for 6% of yearly contract value at their peak in 2010, and as shown in figure 2 the distribution of contracts across Product/service is similar to the distribution of non ARRA contracts in regular times. To resolve the three challenges related to selection into lobbying we first prune the sample by matching on a number of observable characteristics. These characteristics include firm size, firm industry, prior lobbying status, and the number and size of the contracts that the firms have previously won. After matching the sample, we obtain 901 treated firms matched with 412 control firms. We verify that the treated and matched control firms are similar in all observable dimensions. We find that firm that lobby on ARRA are 50% more likely to win ARRA contracts and win 5.3% more and 50% larger ARRA contracts. All results hold after controlling for industry specific trends, and the full list of variables used in the first-stage match. This matching approach may not fully capture all the unobserved heterogeneity. For example, even after matching, firms that lobby on ARRA could be more politically connected to the government in an unobserved fashion. In such cases, firms that lobby on ARRA should win more procurement contracts in general, and we might be miss-attributing this effect to lobby. However, we validate our identification strategy by showing that ARRA lobbying only has an effect on ARRA procurement outcomes and does not 4

5 Correlation Ln(Average spending on ARRA contracts) Ln(Average spending on non ARRA contracts) Figure 2: Product/service level procurement spending by ARRA and non-arra contracts, average have any significant impact on the corresponding non-arra outcomes. We are currently working on extending the results to evaluate if lobby related contracts are less efficient in stimulating local employment and local output, one of the key motivations for the implementation of ARRA. The paper is organized as follows. Section 2 reviews the related literature. Section 3 describes the goals and characteristics of the American Recovery and Reinvestment Act. Section 4 describes the construction of the data and present summary statistics. Section 5 discuss the identification strategy and the main results of the paper. Finally, Section 6 concludes the paper. 5

6 2 Literature Review This paper belongs to three strands of literature. First, literature on corporate lobbying has primarily focused on preferential tax treatment (Richter et al. (2009); Meade and Li (2015); Arayavechkit et al. (2014)) and trade policy (Gawande and Bandyopadhyay (2000); Bombardini (2008); Bombardini and Trebbi (2012)). Lobbying has also been found to increase the likelihood of receiving government relief (Duchin and Sosyura (2012); Blau et al. (2013); Adelino and Dinc (2014)) and to generate high returns when policies are enacted (Kang (2015)). With the exceptions of Brogaard et al. (2016) and Adelino and Dinc (2014), little has been done to explore how corporate lobbying affects the allocation of procurement contracts. Brogaard et al. (2016) identify the positive effect of corporate political connections on the probability of winning and subsequent size of procurement contracts by focusing on campaign contributions (PAC) in close elections. Our analysis differs in two main dimensions. First, we use corporate lobbying instead of campaign contributions. There are three advantages to using lobbying data. i) Lobby expenditure can be directly related to bills and issues while PAC contributions can onlyberelatedtopoliticiansandtheircommittees, ii)lobbydatacanbeusedatanannualfrequency while PAC data depends on the election frequency, and iii) corporate lobby expenditure is seven times larger than corporate campaign contributions. Second, our identification strategy relies on our ability to directly observe lobbying on ARRA and to identify ARRA-related procurement contracts. The main result in Adelino and Dinc (2014) find that financially distressed firms are more likely to lobby on ARRA. They also document a positive correlation between lobbying and the receipt of ARRA stimulus funds. Our identification strategy links ARRA-related lobbying to ARRA-related procurement contracts and corrects for selection bias, which allows us to establish a causal link between lobbying and the allocation of procurement contracts. Our analysis also considers a broader sample of both publicly-traded and privately-held firms. This increases the number of firm and contracts, and allows for a broaderlook at across thesize distribution. 1 Moreover, the ultimate goal 1 More loosely related is the study of (Goldman et al. (2013)) on how ties to the corporate board helps firms to win more contracts. 6

7 of this paper is to assess whether this political influence affected the real macroeconomic effects of stimulus spending. In particular, if ARRA contracts that are likely attributed to lobbying activities had a differential pass-trough to real local outcomes. Second, this paper also contributes to the empirical literature evaluating the effects of ARRA. Several studies have analyzed cross-state and cross-county data to evaluate the stimulative effect of ARRA. Some focus on the bill s provisions regarding aid to state governments(chodorow-reich et al. (2012); Wilson (2012)), while others emphasize the provisions for low income households and infrastructure spending (Feyrer (2011)). We focus on whether firms exert influence over the allocation of procurement-related stimulus spending through their lobbying activities. Third and more broadly, this paper also belong to the large literature investigating the mechanisms through which procurement spending is allocated to firms. The empirical literature on public procurement examines the factors that shape competitiveness and contractual terms(bajari and Tadelis (2001); Warren (2014); Bajari et al. (2008)), and how different designs of procurement processes affect their efficient allocation outcomes (see Sweeting and Bhattacharya (2015) for a review). Also, Liebman and Mahoney (2013) show that wasteful year-end fiscal spending leads to inefficient procurement allocation in general. In comparison to these studies, we examine lobbying as a determinant of procurement allocation. 3 The American Recovery and Reinvestment Act To assess the impact of political influence on the allocation of procurement contracts, we consider the passage of the American Recovery and Reinvestment Act of ARRA was passed in February 2009 in response to the Great Recession with a stated goal of stabilizing the economy and saving jobs by providing temporary relief programs, federal tax incentives, and investing in infrastructure, education, health care, and energy. Of the estimated $787 billion to be spent between 2009 and 2019, approximately $140 billion was allocated towards infrastructure investment, including in the health care and energy sectors. The majority of ARRA-related procurement 7

8 contracting occurred between 2009 and Of the 42,000 procurement contracts identified as ARRA-related, 92 percent were awarded during these three years. ARRA was first introduced in the Senate on January 6, 2009 and in the House (H.R. 1 of the 111th Congress) on January 26,2009. The bill passed the House on January 28, 2009 and was passed on to the Senate (S. 1 of the 111th Congress) which passed it on February 10, ARRA was then signed into law by President Obama on February 17, Its $787 billion price tag made ARRA the largest single spending bill in U.S. history and efforts were made to monitor the disbursement of stimulus spending. The legislation required that all awards funded by ARRA be reported on quarterly and that these reports be made public. We are interested in whether political influence, exerted through corporate lobbying, affects the allocation of procurement contracts. Our empirical strategy takes advantage of publicly available information to identify which procurement contracts are specifically associated with ARRA, and of publicly available information on lobbying to identify firms that specifically lobbied on ARRA. 4 Data Description 4.1 Construction of Data Our empirical analysis relies on several sources. Federal procurement data is obtained from USAspending.gov, a website mandated by the Federal Funding Accountability and Transparency Act of This site hosts data on the universe of federal procurement contracts awarded since 2000, with size above $2,500 ($25,000 prior to 2005). The data include detailed information on the contracts such as contract size, terms, awarding agency, the location of performance, and product/service type. Also, it provides with information on the recipient including business name, location, and employment. Among all the contracts awarded, we identify the ARRA supported contracts using the Recovery Report data from the Federal Procurement Data System (FPDS). Section 1512 of the ARRA 8

9 requires that recipients of ARRA resources report certain information such as the amount of recovery funds received and a list of projects for which funds will be used. Also, the government agencies are required to review the Recovery Report posted on the FPDS website every day to ensure that all entries are accurate. We match the USAspending.gov data and Recovery Report to identify ARRA associated contracts among all procurement contracts. The federal procurement data are used to construct several outcome variables of interest, including: a dummy equal to one if the contract is associated with ARRA (DARRA), total number of ARRA contracts (N arra), total first-year value of ARRA contracts(v arra), total number of non- ARRA contracts (N nonarra), and total first-year value of non-arra contracts (V nonarra). It is also used to construct key control variables: log average first-year value of new contracts in the previous three years (MP3) and log total number of new contracts won in the previous three years (NP3), employment (EMP), and 2-digit industry (NAICS). Because our employment data is derived from reports filed by contracting firms, which is not as reliable as employment from administrative records, rather than use a continuous measure of employment, we place firms into four bins. The first bin contains firms with less than 50 employees, the second with 50 to 249 employees, the third with 250 to 999 employees and the fourth with 1,000 or more employees. Lobbying data is obtained from the Center for Responsive Politics (CRP). The Lobbying Disclosure Act of 1995 requires the disclosure of lobbying activity when expenditure exceeds $3,000 during a quarter. In addition to the total amount of expenditure, disclosures also report which issue areas and bills were targeted in lobbying efforts. The data also include name of organizations or firms on behalf of which lobbying is done. We use this information and a probabilistic name matching algorithm to link lobby data to federal procurement data. In our empirical analysis we use two variables derived from the lobbying data. The first is a time-varying dummy equal to one if a firm lobbied on any issue in the previous three years (LP3) and the second is a time-invariant dummy equal to one if a firm lobbied on ARRA in 2009 (FARRA). We identify a firm as lobbying on ARRA if duringthe 111th Congress it lobbied on any of the House or Senate versions of ARRA, or related bills (H.R.1, H.R.861, H.R.679, H.R.598, S.1, 9

10 S.336, and S.350). Lobbying on these bills is identified in the cleaned bill-level data available from CRP and through a string search for the American Recovery and Reinvestment Act and ARRA in the raw disclosure data Descriptive Statistics The raw data shows that the American Recovery and Reconstruction Act attracted significant attention from lobbying firms. In fact, in 2009, approximately $3.5 billion were spent on lobbying and ARRA related lobby accounted for around 10% of the total expenditure. A significant fraction of the entities trying to shape the ARRA related bills came from firms that are active in the procurement market. In fact, among the 2,100 entities (e.g. foreign governments, associations of firms, associations of consumers, etc...) 860 (41%) are identified in the procurement data. When we consider lobby expenditure, the dominance of procurement related firms is striking; the lobby done by these 860 firms represents two thirds of all ARRA-related lobbying expenditure. Nevertheless, lobbying on ARRA related bills does not guarantee a firm to be granted an ARRA related contract. For instance, among the contractors that lobbied on ARRA, approximately 25% were awarded at least one ARRA contract between 2009 and Figure 3 compares the size distribution of the average ARRA and non ARRA contract awarded to firms, where size is measured as the first year dollar value of a contract. To be clear, if a firm has two ARRA contracts and three non ARRA contracts, the average first year value of the first two contracts is used to build the distribution of ARRA contracts and the average of the other three for the non ARRA contracts. Every firm in the procurement database with active contracts is used to generate this figure. Interestingly, the average ARRA contract awarded to firms is on average larger than the average non ARRA contract awarded to firms. In fact, the average size of an ARRA contract is nearly $1.6 million compared to $250,000 for non-arra contracts. Figure 4 divides the aforementioned size distribution of ARRA contracts between firms that 2 Our results are robust to a stricter definition wherein the dummy is only equal to one if the firm lobbied on H.R.1 or S.1 during the 111th Congress 10

11 Density log(real first year contract value) ARRA Not ARRA Notes: This figure shows distribution of log mean first-year contract value for ARRA and non-arra contracts at the firm level between 2009 and The figure excludes observations with zero contract value. Figure 3: Distribution of average value of ARRA versus non-arra contracts ( ) lobbied on ARRA and contractors that did not lobby specifically on ARRA. Figure 4 suggests that large ARRA contracts are more likely to be awarded to firms that lobbied on ARRA. In fact, the average ARRA contract size to non-lobbying firms is $1.3 million compared to $8.6 million to lobbying firms. There is a similar fraction of lobbying and non-lobbying firms receiving small contracts. But, non-lobbying firms are more likely to receive medium-sized contracts, whereas lobbying firms are more likely to receive large contracts. Perhaps most striking is the fact that firms that lobby on ARRA account for less than one percent of the total number of firms awarded ARRA contracts. Yet, these firms were awarded 36% of ARRA contracts and 30% of the total first-year value of all ARRA contracts. 11

12 Density log(real first year contract value) Lobby Non Lobby Notes: This figure shows distribution of log mean first-year contract value for ARRA contracts awarded to firms that lobbied and did not lobby on ARRA at the firm level between 2009 and The figure excludes observations with zero contract value. Figure 4: Distribution of average value of ARRA contracts ( ) 5 Empirical Analysis 5.1 Pooled OLS Specification Our aim is to assess whether lobbying is causally linked to procurement allocation. We take advantage of detailed disclosures that identify lobbying related to the American Recovery and Reinvestment Act, along with ARRA s transparency provisions, which allow us to identify procurement contracts related to the stimulus package. ARRA contracting began in 2009 and we therefore restrict our analysis to 2009 onward. Our empirical approach is cross-sectional in nature 12

13 and compares outcomes of firms that lobbied on ARRA versus those that did not. We estimate the following regression: d=6 Y it =α st +βfarra i + γ d (D t FARRA i )+δ 1 MP3 it + d=1 (1) δ 2 NP3 it +δ 3 EMP it +δ 4 LP3 it +ε it where Y it is our outcome variable of interest and α st captures industry-year fixed effects. MP3 measures the average first-year value of contracts awarded in the previous three years and NP3 measures the total number of new contracts awarded in the previous three years. Both control for the fact that firms may be awarded more and larger ARRA and/or non-arra contracts simply because they have experience in handling such contracting volume and size. EM P controls for the correlation between firm size and federal contracting. And LP 3 controls for the possibility that corporate lobbying of any kind, rather than targeted lobbying on ARRA influences outcomes. The coefficient on β captures the average effect of lobbying on ARRA in 2009 on outcomes over the sample peeriod (FARRA), while the coefficients on the interaction between year dummies (D t ) and the time-invariant ARRA dummy (F ARRA) captures the extra (if any) effect that lobbying has on outcomes in 2010 through Therefore, the total effect of lobbying on ARRA in 2009 on, for instance, the total number of ARRA contracts awarded in 2010 is the sum of β and γ 1. Our working hypothesis is that corporate lobbying influences procurement allocation, we would expect the coefficient of β and possibly (though not necessarily) the coefficients of γ t to be positive and significant for outcome variables associated with ARRA contracting, and insignificant for outcome variables not directly associated with ARRA. Our preliminary results from pooled OLS regressions are reported in Table 1. Consistent with our expectations, we find that lobbying on ARRA is positively associated with obtaining an ARRA contract (column 1), the number of ARRA contracts (column 2) and the total first-year value of these contracts (column 3). Moreover, it appears that on top of its 13

14 average affect, lobbying on ARRA further increased the chances, number and (marginally) the size of ARRA contracts awarded in This is consistent with the fact that the number of contracts awarded rose between 2009 and 2010, and suggests that those firms who lobbied on ARRA in 2009 disproportionately benefited from this increase. The fact that the interaction term turns negative and at times significant in later years is intuitive, and suggests that influence of lobbying dies out over time. However, inconsistent with our expectations, is the fact that lobbying on ARRA is also positively correlated with the number of non-arra contracts awarded in the post-2009 period, and somewhat un-intuitively is negatively correlated with the total first-year value of non-arra contracts. 14

15 Table 1: Corporate lobbying and procurement allocation (pooled OLS regression) (1) (2) (3) (4) (5) DARRA N ARRA V ARRA N nonarra V nonarra FARRA (0.0120) (0.0183) (0.176) (0.0333) (0.0871) D 2010 FARRA (0.0187) (0.0308) (0.265) (0.0471) (0.121) D 2011 FARRA (0.0164) (0.0262) (0.231) (0.0489) (0.127) D 2012 FARRA (0.0155) (0.0208) (0.213) (0.0511) (0.127) D 2013 FARRA (0.0161) (0.0223) (0.213) (0.0537) (0.127) D 2014 FARRA (0.0166) (0.0225) (0.217) (0.0571) (0.134) D 2015 FARRA (0.0163) (0.0221) (0.222) (0.0538) (0.131) Ind-Year FE Yes Yes Yes Yes Yes Full Controls Yes Yes Yes Yes Yes Obs. 615, , , , ,988 R-sq Notes: The dependent variable in the first column is a dummy whether the firm was awarded any ARRA contract; in the second column is the number of ARRA contracts awarded; and in the third column is the total first-year value of ARRA contracts. The fourth column is the number of non-arra contracts awarded and the last column is the total first-year value of non-arra contracts. We only report the coefficients of variables of interest, namely the time-invariant dummy FARRA, which is equal to one if the firm lobbies on ARRA in 2009, and its interaction with time dummies. In all regressions we also control for industry-year fixed effects and for firm-level employment, lobbying in the previous three years, and the average value and total number of new contracts awarded in the previous three years. Standard errors are robust. ***, **, and * indicates significance at the 1%, 5%, and 10% levels,respectively. One important concern regarding the full sample used in our analysis thus far is selection bias. Ideally, we would like to know the counter-factual award of ARRA contracts to firms that lobbied on ARRA if they had instead chosen not to lobby. Since such a counter-factual is unobservable, 15

16 we are forced to rely on a control group of firms that did not lobby on the stimulus bill. Since lobbying is an endogenous choice, and correlated with factors such as firm size that also affect the allocation of procurement contracts, using the sample of all firms that did not lobby on ARRA is in fact not the appropriate counter-factual. We address this selection bias by using a standard propensity score matching approach. 5.2 Matching For the results reported in the next section, we restrict ourselves to a sample of firms that lobbied on ARRA in 2009 (treatment group) and a sample that is observationally similar to the treated group but that did not lobby (control group). In the first stage, we focus on the crosssection of firms in 2009 since this is the year in which treatment status is determined. We estimate a logit model to predict whether a firm lobbies on ARRA as a function of all the firm characteristics used in the pooled regression, including employment (EM P), industry (N AICS), lobbying status over the previous three years (LP3), number of new contracts awarded in the previous three year (NP3), and the mean value of those contracts (MP3). Formally, we estimate: FARRA i = λ s +η 1 MP3 it +η 2 NP3 it +η 3 EMP it +η 4 LP3 it +ε it (2) We then use the resulting propensity scores to construct a nearest-neighbor matched sample of firms. Because we allow for matching with replacement, our second stage regressions will employ frequency weights. Once we identify our control group in 2009, we track the group from that year onward in our second stage regressions. With this approach we eliminate from our sample procurement contractors that are observationally very different from those firms that lobbied on ARRA in Before turning to our matched sample estimation results, it s helpful to review the results from our first stage. The results in table 2 confirm that baseline regressions suffered from selection bias. 16

17 In particular, in our full sample, while the average treated firm falls into the employment bin with between 250 and 999 employees, the average control group firm has between 50 and 250 employees. The control group also has very few firms with past lobbying experience, whereas those firms that lobbied on ARRA almost universally have lobbied in the previous three years. Treated firms have also been awarded substantially more and larger contracts in the past. As the last column in table 2 indicates, our nearest neighbor matching virtually eliminates this selection bias. Table 2: First-stage post-estimation) Mean Variable Sample Treated Control Bias EM P Unmatched Matched LP 3 Unmatched Matched M P 3 Unmatched Matched N P 3 Unmatched Matched Notes: The table reports the comparison of means between treated and untreated samples in the unmatched and matched sample for all variables used in the first-stage regressions. 5.3 Regression Results with Matched Sample The results from our second stage regressions, reported in table 3 show that lobbying is indeed influential in shaping the allocation of procurement contracts. After controlling by selection into lobby by restricting the analysis to a smaller sample of firms that are similar in size, past lobbying and experience in federal procurement, we still find that firms that lobbied on ARRA in 2009 are on average significantly more likely to win ARRA contracts. Moreover, the magnitudes are economically significant. In fact, when the regression is evaluated at the mean, the regression results imply that firms that lobbied on ARRA related bills won 5% more and 50% larger ARRAcontracts than firms that did not lobby on ARRA. 17

18 Although the control group from the matched sample closely follows the observables of the treated group, there is still room for an unobserved factor that makes firms both, lobby more intensively in ARRA and win more contracts. For this reason, we evaluate again the relative success of the treated sample with respect to non ARRA contracts. In contrast to the pooled regression approach, once we correct for selection, we find that lobbying on ARRA has not impact on the number or size of non-arra contracts awarded. Because, it is unlikely that the unobserved factor affects differently ARRA and non ARRA contracts the relationship uncovered in this section between lobbying and procurement is likely to be causal. Because the interaction terms for 2010 and 2011 are positive, we can conclude that the marginal effect of ARRA-related lobbying on ARRA contracting outcomes rises over this period, although not significantly so. Both joint and total significance tests yield significant results for the 2010 and 2011 interaction terms. The fact that the interaction terms turn negative beginning in 2012 signals that the positive effect of lobbying begins to wane in the post-2012 period. In fact, the total significance of F ARRA and interaction terms in 2012 through 2015 is consistently insignificant. This is natural given that ARRA related contracts are practically very scarce at the end of the sample. Summarizing, after correcting for selection bias by employing nearest neighbor matching, we find a strong positive effect on the probability, number, and size of ARRA-contracts awarded to firms engaged in corporate lobbying on ARRA and the non-causal correlation between ARRA lobbying and non ARRA contracting vanishes. 18

19 Table 3: Corporate lobbying and procurement allocation (Second-stage regression) (1) (2) (3) (4) (5) DARRA N ARRA V ARRA N nonarra V nonarra FARRA (0.0147) (0.0199) (0.206) (0.0380) (0.126) D 2010 FARRA (0.0257) (0.0381) (0.350) (0.0569) (0.159) D 2011 FARRA (0.0210) (0.0340) (0.282) (0.0581) (0.162) D 2012 FARRA (0.0218) (0.0258) (0.280) (0.0613) (0.166) D 2013 FARRA (0.0220) (0.0263) (0.275) (0.0638) (0.173) D 2014 FARRA (0.0229) (0.0276) (0.283) (0.0764) (0.190) D 2015 FARRA (0.0238) (0.0266) (0.298) (0.0665) (0.193) Ind-Year FE Yes Yes Yes Yes Yes Full Controls Yes Yes Yes Yes Yes Freq. Weighted Yes Yes Yes Yes Yes Obs. 5,929 5,929 5,929 5, R-sq Notes: The table reports results for the matched sample of firms obtained from nearest neighbor matching. The dependent variable in the first column is a dummy whether the firm was awarded any ARRA contract; in the second column is the number of ARRA contracts awarded; and in the third column is the total first-year value of ARRA contracts. The fourth column is the number of non-arra contracts awarded and the last column is the total firstyear value of non-arra contracts. We only report the coefficients of variables of interest, namely the time-invariant dummy FARRA, which is equal to one if the firm lobbies on ARRA in 2009, and its interaction with time dummies. In all regressions we also control for industry-year fixed effects and for firm-level employment, lobbying in the previous three years, and the average value and total number of new contracts awarded in the previous three years. Standard errors are robust. ***, **, and * indicates significance at the 1%, 5%, and 10% levels,respectively. 19

20 6 Conclusion By matching data on firm lobbying and federal procurement contracts, we investigate how firm lobbying activities impact the allocation of procurement spending across firms. We take advantage of the American Reinvestment and Recovery Act of 2009 as our laboratory, which enables us to directly match lobbying on ARRA to the procurement contracts supported by ARRA. In addition, we match the lobbying firms on several characteristics to account for selection into lobbying status. Our main finding is that lobby on ARRA raises the likelihood of receiving ARRA procurement contracts by 50%, and increase the number of contracts awarded by 5%. When we weight the contracts by their size, we find that lobbying on ARRA increases winning 50% more value-weighted contracts. Although there may remain unobserved heterogeneity even after matching, we find that lobbying on ARRA impacts only on ARRA contract outcomes and does not have any significant effects on non-arra contract outcomes. We interpret this result as providing additional evidence of a causal relationship. Having established that lobbying activity affects the allocation of ARRA contracting the next natural step is to evaluate if the lobby induced distortion affected the effectiveness of the policy. In particular, an explicit goal for ARRA was to accelerate job creation and stimulate the economy. We are currently working on estimating if counties were most contracts were awarded to lobbying firms had the same return in terms of employment growth and economic activity by dollar than ARRA contracts awarded to non lobbying firms in the same product class. We will explore a Dif on Dif approach where the treatment will be given by the prevalence of lobby related contracts using our matched sample to control for selection. We also plan to examine the firm level effects of the policy matching procurement and lobby data to NETS database. We expect that the results from this part of the paper will shed light on how the policy influence of firms can determine the effectiveness of fiscal stimulation. 20

21 References Adelino, Manuel and Serdar Dinc, Corporate distress and lobbying: evidence from the Stimulus Act, Journal of Financial Economics, 2014, 114 (2), andd Sacerdote, Bruce Feyrer James, Did the Stimulus Stimulate? Real Time Estimates of the Effects of the American Recovery and Reinvestment Act, Working Paper 16759, National Bureau of Economic Research Arayavechkit, Tanida, Felipe Saffie, and Minchul Shin, Capital-based corporate tax benefits: endogenous misallocation through lobbying, Working Paper. Bajari, Patrick and Steven Tadelis, Incentives versus Transaction Costs: a Theory of Procurement Contracts, The RAND Journal of Economics, 2001, 32 (3), , Robert McMillan, and Steven Tadelis, Auctions versus Negotiations in Procurement: an Empirical Analysis, The Journal of Law, Economics and Organization, 2008, 25 (2), Blau, Benjamin, Tyler Brough, and Diana Thomas, Corporate lobbying, political connections and the bailout of banks, Journal of Banking and Finance, 2013, 37 (8), Bombardini, Matilde, Firm Heterogeneity and Lobby Participation, Journal of International Economics, 2008, 75 (2), and Francesco Trebbi, Competition and political organization: Together or alone in lobbying for trade policy?, Journal of International Economics, 2012, 87 (1), Brogaard, Jonathan, Matthew Denes, and Ran Duchin, Political Influence and Government Investment: Evidence from Contract-Level Data, Working Paper. Chodorow-Reich, Gabriel, Laura Feiveson, Zachary Liscow, and William Woolston, Does State Fiscal Relief during Recessions Increase Employment? Evidence from the American Recovery and Reinvestment Act, American Economic Journal: Economic Policy, 2012, 4 (3), Duchin, Ran and Denis Sosyura, The politics of government investment, Journal of Financial Economics, 2012, 106 (1), Ferraz, Claudio, Frederico Finan, and Dimitri Szereman, Procuring firm growth: the effect of governmenet purchases on firm dynamics, Working Paper 21219, National Bureau of Economic Research Foster, Lucia, John Haltiwanger, and Chad Syverson, The slow growth of new plants: Learning about demand?, Economia, 2016, 83 (329), Gawande, Kishore and Usree Bandyopadhyay, Is Protection for Sale? Evidence on the Grossman-Helpman Theory of Endogenous Protection, The Review of Economics and Statistics, 2000, 82 (1), Goldman, Eitan, Jorg Rocholl, and Jongil So, Politically Connected Boards of Directors and The Allocation of Procurement Contracts, Review of Finance, 2013, 17 (5). 21

22 Hecker, Daniel E., High-technology employment: a NAICS-basd update, Monthly Lab. Review, 2005, 128 (8), 57. Hottman, Colin, Stephen Redding, and David Weinstein, Quantifying the sources of firm heterogeneity, Quarterly Journal of Economics, 2016, 131 (3), Kang, Karam, Policy Influence and Private Returns from Lobbying in the Energy Sector, Review of Economic Studies, 2015, 83 (1), Kerr, William, William Lincoln, and Prachi Mishra, The dynamics of firm lobbying, American Economic Journal: Economic Policy, forthcoming. Liebman, Jeffrey and Neale Mahoney, Do Expiring Budgets Lead to Wasteful Year-End Spending? Evidence from Federal Procurement, Working Paper 19481, National Bureau of Economic Research Meade, Janet and Shihong Li, Strategic Corporate Tax Lobbying, The Journal of the American Taxation Association, 2015, 37 (2), Richter, Brian, Krislet Samphantharak, and Jeffrey Timmons, Lobbying and Taxes, American Journal of Political Science, 2009, 53 (4), Sweeting, Andrew and Vivek Bhattacharya, Selective Entry and Auction Design, International Journal of Industrial Organization, 2015, 43. Warren, Patrick, Contracting officer workload, incomplete contracting, and contractual terms, The RAND Journal of Economics, 2014, 45 (2), Wilson, Daniel J., Fiscal Spending Jobs Multiplier: Evidence from the 2009 American Recovery and Reinvestment Act, American Economic Journal: Economic Policy, 2012, 4 (3),