The impact of Leveraged Buyouts on targets’ innovation output
Era Merlika (13056913) July 2021, Amsterdam
Based on a sample of 325 U.S leveraged buyouts announced over the period from 1997 to 2019, this paper analyzes the causal effect of private equity backed leveraged buyouts on the target’s innovation output. The empirical analysis uses as an identification strategy a difference-in- difference methodology and a quasi-experiment of cancelled vs completed LBO transactions.
The results show that following the LBO, the innovation output as measured by the number of patent applications is lower for targets of completed LBOs than targets of cancelled LBOs.
Similarly, targets of completed LBO transactions experience lower levels of patent quality compared to their counterparts. These findings provide evidence in favor of the private equity short-termism argument.
Keywords: private equity, leveraged buyouts, corporate innovation, innovation output, patents University of Amsterdam, Amsterdam Business School
Msc Finance Corporate Finance Track Supervisor Dr. T. Tolga Caskurlu
Statement of Originality
This document is written by Student Era Merlika who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
It is with immense gratitude that I acknowledge the invaluable guidance and feedback of my Supervisor Dr. Tolga Caskurlu during the course of this research work.
I also would like to thank my family and friends for their continuous support and encouraging words.
1.Introduction ... 5
2. Literature review ... 9
Private equity firms and leveraged buyouts ... 9
Private equity funds’ performance ... 10
Private equity backed LBO target’s performance ... 11
Operating Performance ... 12
Leveraged buyouts and innovation ... 13
Hypothesis Development ... 16
3. Methodology ... 17
4. Data ... 19
Data Sources ... 19
Descriptive Statistics ... 23
5. Results ... 28
6.Robustness Checks ... 34
7. Conclusion ... 37
Limitations ... 37
Recommendations for further research ... 38
Appendix ... 39
References ... 41
Appendix A ... 44
Despite the broad literature analyzing private equity LBO targets and their operating performance, studies examining innovation of target companies following an LBO are very limited or focus their empirical analysis only on the first private equity waves. Considering the important role of innovation on firm long-term performance and the significant developments in the private equity industry in the last decades, this paper aims to answer the research question of whether private-equity-backed leveraged buyouts affect the target companies’ innovation output measured by the patenting activity.
Do private equity funds stifle the long-term value of their target companies? This question has long been linked to much controversy in previous literature, and the debate whether private equity firms prioritize short term profits over long term value creation is far from settled.
On the one hand, Jensen (1989) claims that private equity backed buyouts are forms of organizational structures which allow firms to reduce agency costs and improve operating performance. On the other hand, opponents of private equity such as (Rappaport 1990) suggest that the highly leveraged structure of the buyouts may reduce the target’s flexibility to adapt to new technology changes or shifts of consumer preferences and, as a result causing the company to lose competitiveness towards other market players. Additionally, the limited investment period of private equity firms may create pressure on the target’s management to undertake cost- cutting strategies, divest assets or give up valuable projects in order to maximize firm value prior to their exit.
Consistent with Jensen (1989), a vast array of papers have analyzed the impact of private equity leveraged buyouts on target’s operating performance, employment or sensitivity towards economic downturns and have shown that, with only a few exceptions, the impact of LBOs on target firms is positive. For instance, following an LBO transaction, target firms experience increased levels of total factor production (TFP), higher earnings, improved profitability, higher growth rates and lower financial constraints (Bergstrom, Grubb and Jonsson, 2007; Boucly, Sraer and Thesmar, 2011; Liu, 2017). On top of that, the empirical evidence shows that this increase in operating performance does not come at the cost of an excessive number of layoffs or divestitures (Kaplan, 1989b; Bergstrom, Grubb and Jonsson, 2007; Boucly, Sraer and Thesmar, 2011). In line with these findings, Davis et al. (2014) show that the net effect of private equity on jobs is negligible, and the buyouts result in more job reallocation and more productive plants.
6 Contrary to critics who suggest that private equity firms may aggravate financial crisis due to their highly leveraged buyouts, Bernstein, Lerner and Mezzanotti (2017) show that private equity backed firms experience lower decreases in investments and lower financial constraints during the financial crisis of 2008-2009 compared to their counterparts.
Based on the previous literature, there are two main possible channels through which leveraged buyouts may affect the target’s innovation. First, private equity backed LBOs may help reduce agency problems by improving the governance of the target company. According to Kaplan & Stromberg (2009), following an LBO target, companies have a smaller and more efficient board of directors, allowing for better monitoring of the target’s management.
Additionally, the large amount of leverage may act as a disciplinary tool and prevent the management from wasting cashflows in unproductive projects. The management will invest in profitable projects to ensure that the debt repayments will be made on time; otherwise, the company may risk going bankrupt. The second channel through which the private equity firms may affect the target’s innovation is by providing the target company with the financial and human capital needed to innovate. For instance, the expertise of private equity firms to raise funds will help relieve the target’s financial constraints, and consequently, the target companies may invest in more innovative projects. In addition to the funding, the target companies may be provided with the right human capital and expertise, increasing internal innovation.
On the flip side, private equity backed LBOs may also negatively affect the innovation of the target company. For instance, very high leverage levels may also reduce available cash flows that can be used to invest in innovation. Moreover, private equity firms may put too much pressure on the target’s management to invest in short-term projects that accelerate the increase in firm value before their exit. In this way, private equity firms would be able to liquidate their investment at a much higher price compared to the initial investment value.
Whether private equity backed transactions increase or decrease the innovation output of target companies remains an empirical question. However, establishing a causal relationship between the LBO transactions and the target’s innovation may be difficult due to possible selection biases regarding the private equity firm’s investment choices. For instance, private equity firms may select target companies that have scope for further improvements in their innovation strategy, and as a result, using simple regression analysis, the estimated effect would be overestimated.
7 In order to overcome these possible endogeneity issues, similarly to Seru (2014), this paper uses a difference in difference (DiD) methodology with a quasi-experiment where the
“treatment group” includes targets of successfully completed LBO transactions and the “control group” includes targets of cancelled LBO transactions. One of the main requirements for the cancelled transactions to be included in the control group is that their reason for cancellation should not be related to the innovation activity of these companies. Therefore, after identifying the reasons for the cancellations using target firms’ websites or well-known journals, only the target companies that meet the exogeneity requirement are included in the control group. As a result, the companies included in the analysis can be considered as if randomly assigned. The data sample used to conduct the empirical analysis consists of 375 U.S private equity backed LBO transactions announced over the period 1997 to 2019, out of which 271 are completed transactions, and 104 are cancelled transactions. The LBO targets included in the analysis are publicly listed before the announcement date and have at least one successfully filed patent application over the period five years before and five years after the announcement. The patent- based metrics for the target companies are obtained from the established patent database of Kogan et al. (2017), which is also referred to as KPSS.
The first empirical test analyzing the effect of LBO transactions on the number of patent applications show that the number of patent applications following the transaction is lower for targets of completed transactions compared to targets of cancelled transactions. Moreover, in additional tests where the main dependent variable is patent quality measured by the scaled number of patent citations, the results indicate that the patent quality of target companies drops after the leveraged buyout transaction. While the number of patent applications and the patent quality decrease following the LBO transaction, the economic value of patents is not impacted.
In order to verify the robustness of these results, the same analysis is repeated on a different event window of three years before and five years after the transaction announcement and the same results are obtained. In further robustness checks, the full sample is split into two subsamples covering the periods 1997 to 2007 and 2007 to 2019, respectively, and it is shown that the same results hold.
In contradiction to Lerner et al. (2011) and Amess et al. (2016), who find that private equity backed LBOs have a positive impact on target’s innovation, the results of this paper show evidence of a negative effect. However, it should be noted that these papers analyze the impact
8 of leveraged buyouts on target’s innovation only up to the period before the financial crisis of 2007-2009. Since then, almost two decades have elapsed, and the private equity firms' assets under management not only have grown impressively, reaching a total amount of $4.5 trillion in the first half of 2020 (Mckinsey 2021), but have also outperformed other asset classes. The growth and improved performance of private equity funds in the latest years seems to have come at the cost of increased competition and decreases in success persistence. Hence, one may suggest that private equity funds have faced much pressure to maintain their results, which may have affected their decision-making in target companies, including innovation activity.
This paper is related to several strands of literature. First, it provides a strong identification strategy which allows isolating the effect of private equity backed LBOs on target’s innovation levels, and it contributes to the private equity literature that analyzes target’s performance following the LBO (Acharya et al. (2012); Boucly, Sraer and Thesmar, (2011);
Davis et al. (2014); Liu (2017)). Second, this work also contributes to literature analyzing the effect of governance, capital structure and ownership on corporate innovation. For instance, Attanasov (2013) studies the effect on corporate innovation for firms incorporated in countries that have passed antitakeover laws vs firms incorporated in countries that have not, Seru (2014) analyzes the effect of conglomeration on innovation measures such as patents and citations, and Bernstein (2015) examines the effect of initial public offerings (IPO) on innovation activity.
The rest of the paper is organized as follows. Section II provides a broad overview of previous related work and theoretical frameworks, concluding with a description of the hypothesis development. Section III describes the empirical strategy, and Section IV presents the data, sample construction and summary statistics. Section V presents the main results, Section VI discusses the robustness checks, and Section VII concludes.
9 2. Literature review
This section will provide an overview of previous literature analyzing private equity transactions and corporate innovation. The section starts with a brief overview of private equity firms and private equity backed leveraged buyout transactions, followed by a literature review on private equity value generation at the fund and company level. Next, it summarizes the main empirical findings and theoretical frameworks linking private equity to corporate innovation and measurement metrics of corporate innovation. Finally, the section concludes by presenting the hypothesis development based on the reviewed literature.
Private equity firms and leveraged buyouts
Private equity firms, such as Kohlberg Kravis Roberts & Co. or The Carlyle Group, are organizations, usually in the form of partnerships or limited liability corporations, that raise equity capital through private equity funds of limited duration (Kaplan and Strömberg, 2009).
The equity capital in the funds is raised from the so-called limited partners (LPs) and is later employed to make investments in buyouts, growth capital or secondary buyouts. The funds are managed by the general partners (GPs), who play a key role in the decision-making process, including managing the fund size, debt usage, investment selection and distributing cash flows at the termination of the investments (Kaplan and Strömberg, 2009). General partners are usually paid based on a combination of fixed management fees and a performance-based fee linked to the fund’s net profits to ensure that their incentives align with those of the limited partners (Axelson, Strömberg and Weisbach, 2009).
Leveraged buyouts, the typical transactions conducted by private equity firms, are transactions in which target companies are acquired by funding the largest fraction of the acquisition price by debt, whereas only a small fraction is funded by equity capital. Unlike venture capital firms that invest in the early stages of innovative companies without subsequently acquiring control, private equity firms in leveraged buyouts usually target more mature companies and acquire majority control (Kaplan and Strömberg, 2009). This paper considers only private-equity-backed leveraged buyout transactions (LBOs), and hence in the later parts of this section, private equity and LBO will be used reciprocally to refer to these transactions.
10 During the first private equity wave beginning in the early 1980s, one of the main proponents of private equity, Jensen (1989), suggested that forms of corporate restructurings such as takeovers, leveraged buyouts, divisional buyouts or going private transactions would help solve the well-known problem of separation between ownership and control. The main features of private equity transactions involving performance-based managerial compensation, high leverage levels, and active monitoring would, in turn, induce the management of the target companies to act in the company’s best interest. Consequently, agency costs would be reduced, and the long-term value of the target company would be maximized.
In contrast to Jensen (1989) suggestions that private equity firms create value and generate returns by improving the target’s operations, governance, and financial structure, the opponents of private equity firms attribute fund returns to cost-cutting strategies, tax reductions and investment timing. For instance, Rappaport (1990) argues that LBOs cannot substitute public corporations as an organizational form mainly for two reasons. First, private equity backed LBOs usually involve turning the firm from public to private, making it harder to assess their performance, and hence fewer investors would be willing to invest in the private equity funds.
Secondly, private equity firms invest only for a limited duration in their target companies and afterwards, they exit the companies either by selling the company to other potential buyers or by listing the company on the stock exchange. The short investment period would, in turn, encourage the private equity firms to perform divestitures or cost-cutting strategies to increase the chances of profitably exiting the company at the termination of the investment period and consequently, this would hinder the prospects of the target company.
Private equity funds’ performance
Besides the controversy around the private equity effect on the target’s long-term performance, the fund’s performance has also opened a debate within the world of academics. As a result, much research has been done to study the performance of private equity funds and understand whether they outperform the capital markets. For instance, Kaplan and Schoar (2005) analyze the performance of private equity funds and capital inflows of private equity partnerships over the period from 1980 to 1997. Using data from Venture Economics, they find that private equity fund returns are approximately equal to the S&P 500. Even though they do not find evidence of
11 outperformance, in additional tests, their results show that there is persistence across subsequent funds of a partnership and that fund flows are positively related to past performance.
Contrary to Kaplan and Schoar (2005), another study by Phalippou and Gottschalg (2009) shows that part of the private equity funds performance in previous studies is driven by accounting valuation of investments and that instead the average returns after accounting for the fees is 3% below the S&P 500. Additionally, after adjusting for risk, the funds’
underperformance compared to S&P500 reaches up to 6%. However, using a dataset from Burgis from 1984 to 2008, Kaplan, Jenkinson, and Harris (2014) found that buyout funds have outperformed public markets by more than 3% annually. These results are similar to using Cambridge associates and Preqin datasets but substantially different from the Venture Economics datasets. The different results can be explained by the fact that many funds in Venture Economics stopped being updated around 2001 and still were kept in the database.
Private equity backed LBO target’s performance
The aforementioned positive evidence regarding private equity funds performance has opened another discussion among academics where a part of them assigns this outperformance to the so- called “cherry-picking theory” (Liu, 2017). Under this theory, private equity funds are specialized in targeting undervalued companies, acquiring them using huge amounts of debt and later on selling them for a much higher price (Kaplan and Stromberg, 2009). In line with the argument that private equity firms are able to distinguish targets with scope for improvements, (Dittmar, Li and Nain, 2012) show that firms who acquire companies that are initially bid by private equity firms instead of a strategic buyer seem to outperform their counterparts who acquire companies bid only from strategic buyers. Similarly, Liu (2017) uses a sample of LBO targets consisting of both successfully completed deals and withdrawn deals and finds evidence of an increase in market value compared to pre-announcement value for targets of withdrawn deals, confirming the expertise of PE firms to detect undervalued targets.
On the other hand, proponents of private equity suggest that private equity firms outperform the capital market by using financial, governance and operational engineering (Kaplan and Stromberg, 2009). Unlike their public counterparts, private equity firms endowed their target’s management with higher amounts of equity-linked compensation in the forms of options or stock plans (Jensen and Murphy, 1990). The effectiveness of the equity-linked
12 compensation in decreasing agency costs is further amplified by the illiquidity of the target’s stock. Since the target companies are turned private after the LBO, the management cannot vest their options until the private equity firm exits the company in an initial public offering (IPO), and as a result, the management has fewer incentives to prioritize short-term returns instead of long-term value (Kaplan and Stromberg, 2009). Another factor which allows private equity firms to create value in the invested company’s is leverage. According to Jensen (1986), high leverage may serve as a disciplinary tool for the target’s management in preventing them from wasting the company’s cash flows in unproductive projects, which typically tends to occur in large and mature companies. In order to meet strict schedules of debt repayments and interest, the management will instead employ the cash flows in projects that generate value. In addition to the disciplinary effect of leverage, high leverage creates value for the private equity funds investors since the interest payments are tax-deductible (Kaplan and Stromberg, 2009).
However, the effect of high leverage is two-fold as too much of it may increase the chances of entering into financial distress if future debt payments cannot be paid on time.
The next sub-section will review more in detail empirical evidence about the impact of private equity on the target company’s operating performance.
Empirical evidence on the operating performance of leveraged buyout targets is consistent with the view of Jensen (1989), who suggests that private equity firms improve the efficiency of their LBO targets and allow them to become more profitable. For instance, Kaplan (1989b), after studying a sample of U.S public-to-private LBOs from 1980 to 1986, shows that LBO targets’
operating income to sales ratio exceeds the industry average change by almost 20%. Similarly, also the cash flow ratio shows an increase of approximately 43% in the second year after the buyout. In additional tests, Kaplan (1989b) shows that this increase in operating performance does not come at the cost of higher layoffs. However, instead, it is achieved due to lower agency costs. In line with these findings, Lichtenberg and Siegel (1990) show that there is a significant increase in total factor production along with a decrease in compensation and employment of white-collar workers after analyzing a sample of leveraged buyouts during 1983-1986. Due to data limitations for U.S private equity firms, most of the previous literature on the period following the 1980s investigates European private equity backed LBOs. Harris, Siegel and
13 Wright (2005), studying a sample of U.K management buyouts, provide evidence of increased profitability following the buyout. Based on an event study of Swedish LBOs, Bergstrom, Grubb and Jonsson (2007) show that the operating performance of the target companies increases following the LBO. These findings are consistent with a later study on French leveraged buyout conducted by Boucly, Sraer and Thesmar (2011), who find that LBO targets have higher profitability, experience higher growth, have higher debt and higher capital expenditures compared to their peers. Likewise, Acharya et al. (2012), based on European LBO deal-level data of big private equity houses, provide evidence of a positive abnormal performance after accounting for leverage and sector returns.
Additionally, Acharya et al. (2012) prepared one of the first papers to attribute a part of the private equity deals performance to human capital and, more specifically, the general partner’s background. In a later study considering U.S leveraged buyouts from 1979 to 2012, Liu (2017) supports the positive impact of private equity firms on their target’s operating performance. The empirical results show that the target companies of completed LBOs experience an increase of 0.031 in both earnings and operating cash flows; meanwhile, the companies of the cancelled deals show no significant change for the previously mentioned variables.
Overall, the previous empirical studies provide evidence on the positive impact of the private equity backed LBOs on the targets operating performance. In most of these studies, it is also shown that the increase in operating performance arises due to more efficient employment of resources rather than divestitures, increases in layoffs or wage reductions (Kaplan, 1989b;
Bergstrom, Grubb and Jonsson, 2007; Boucly, Sraer and Thesmar, 2011). The findings of the private equity impact on jobs can also be confirmed by Davis et al. (2014), who show that the net effect of private equity on jobs is very small, and buyouts seem to result in more job reallocation and more productive plants.
Leveraged buyouts and innovation
Up to this point, the literature review has shown that after the LBO, target companies undergo a series of changes involving financial structure, governance, and operating performance. Even though many papers provide evidence favoring private equity firms as value enhancers, there is still an ongoing debate whether private equity firms prioritize short-term profits instead of long-
14 term value investments. Considering that innovation is one of the main drivers of a firm’s profitability and competitive advantage, allowing firms to grow and create value over the long term, studying the effect of private equity firms on target’s innovation would serve as a good measure to analyze the impact of private equity on target’s long-term investments. Hence, this subsection will summarize the main theoretical frameworks and empirical findings that link private equity firms and leveraged buyouts to corporate innovation.
According to Bertoni (2017), there are two main theoretical frameworks that may explain the channels through which private equity backed LBOs will impact corporate innovation:
agency theory and strategic entrepreneurship. The first theory relies on the previously discussed argument of Jensen (1989) that private equity backed LBOs may help reduce agency costs in their target companies by improving governance and increasing leverage. In the absence of effective governance and monitoring from shareholders, management would otherwise waste funds by investing in value-destroying projects (Attanasov,2013) at the expense of long-term value-generating projects. High leverage and the threat of a possible takeover can also help discipline the management and increase innovation. For instance, based on panel data of U.S firms located in states that pass antitakeover laws vs states that do not, Attanasov (2013) shows that companies incorporated in states that pass antitakeover laws experience a lower level of innovation as measured by the number of patents and citations. However, this effect seems lower for firms with institutional shareholders, higher leverage, and higher product market competition.
Similarly, Bernstein (2015) shows that firms’ overall level and quality of innovation decreases following an initial public offering (IPO) and the subsequent decrease is explained by agency problems arising between management and shareholders.
Despite the overall positive effect of leverage in disciplining management and as a result fostering innovation, the effect may also go the other way around and lead to management myopia. For example, Bertoni et al. (2015) show that due to high levels of leverage, LBO target’s management may become more reluctant to use capital in innovative projects as the company becomes more dependent on internal capital. Consistent with this argument, Long and Ravenscraft (1993) in a study analyzing the R&D intensity of LBO targets, find that the R&D expenditures after an LBO decrease by 40% compared to the pre-LBO level. Their results show that the decrease in R&D level does not come at the expense of lower operating performance.
However, in further tests, they do find evidence that debt can significantly impact the level of
15 R&D. Additionally, Long and Ravenscraft (1993) emphasize that this effect does not pose concerns for the performance of typical low growth and low-technological intensive targets for whom R&D and innovation are not critical to be profitable.
The second theory explaining the private equity impact on corporate innovation relies on resources and competencies that the private equity firms supply their targets with following the LBO transactions (Bertoni, 2017). According to previous literature, the ability of companies to innovate is highly sensitive to financial constraints. For example, Aghion et al. (2005) suggest that companies facing tight financial constraints may decide to cut on long-run innovative investments due to the fear of facing liquidity shock’s which negatively affect those innovative investments. Similarly, due to asymmetrical information on the quality of R&D and lack of good collateral, many innovative companies may face issues in raising the needed capital to invest in long term innovative projects compared to companies that invest in tangible investments (Brown, Martinsson and Petersen, 2012). In line with the resource-based theory, Engel and Stiebale (2014) show that private equity backed firms experience higher levels of investment and less financial constraints compared to their non-PE backed counterparts. Besides helping firms become less financially constrained, private equity firms usually employ highly skilled management and specialists, which allows them to improve the target’s operating performance and investment strategies (Kaplan and Stromberg, 2009).
With only a few exceptions, empirical findings of private equity backed LBOs do not favor the short-termism argument and instead show that the target companies experience higher levels of innovation as measured by patenting activity and related measures. Most of these papers measure technological innovation of the LBO targets using the number of patent applications and number of forward citations instead of R&D expenses because they better capture innovation output quality. Hall, Trajtenberg and Jaffe (2005) suggest that citations are good measures of a patent’s economic value by showing that an additional patent citation corresponds to an increase of 3% in a firm’s market value.
For instance, Popov and Roosenboom (2009), based on panel data of 18 European countries from 1991 to 2004, show that the number of patents increases due to the risk capital of private equity firms. Using an identification strategy that exploits variations in investment laws, they find empirical evidence that private equity represents almost 12% of the aggregate industrial innovation. In another study focused on U.S LBO targets, Lerner et al. (2011) show that target
16 companies experience a higher number of patent citations and a more concentrated innovation activity towards core areas after a private equity LBO. However, these results must be taken with caution as their empirical analysis fails at establishing a causal relationship between LBO transactions and patenting activity due to the lack of a proper identification strategy. Similarly, Amess et al. (2016), based on a sample of 407 UK based LBO targets, provides evidence that LBO targets experience higher patent applications and improved patent quality. Interestingly, in a study based on European manufacturing firms that become targets of private equity backed LBOs, Ughetto (2016) finds that different investors affect corporate innovation in different ways depending on their objectives, risk propensity, investment decisions and expected returns.
This section presents the hypothesis developed based on the previously reviewed literature to answer the main research question of this paper: Do private equity backed LBOs impact target’s innovation following the transaction?
According to previous literature, private equity firms are equipped with the right skills, knowledge and resources needed to reduce inefficiencies and improve the operating performance of their target companies (Kaplan and Stromberg, 2009; Boucly, Sraer and Thesmar, 2011;
Acharya et al. 2012; Liu, 2017). In line with the positive impact of private equity on target’s operating performance as shown in the previously mentioned empirical papers and the findings of Amess (2016), who finds a positive impact on patenting activity, the first hypothesis of the paper is constructed as below:
Hypothesis 1: Private equity backed LBO targets experience an increase in the number of patent applications following the transaction.
Consistent with Lerner et al. (2011) and Amess et al. (2016), who show that the patent quality of target companies improves after an LBO transaction, I expect that the same results will hold for the two different measures of innovation importance employed in the analysis. The first measure is the Citations Weighted measure, and it is estimated as the number of forwarding citations in a given year scaled by the number of total patent applications in the same year ( Seru, 2014) and the second measure, the Economic Value measure it is a pre-constructed variable from
17 Kogan et al. (2017) which also incorporates market reactions to the patent activity. In addition to the patent quality captured by the citations weighted measure, the economic value measure also incorporates the target’s future growth prospects.
Hypothesis 2a: Private equity backed LBO targets will experience an increase in the Citations Weighted measure following the transaction.
Hypothesis 2b: Private equity backed LBO targets will experience an increase in the Economic Value measure following the transaction.
This paper’s empirical strategy aims to establish a causal relationship between the private equity backed leveraged buyouts and the target’s corporate innovation following the transaction. The primary data that will be used for the analysis consists of a sample of announced leveraged buyout transactions over the period starting from 1997 to 2019 of targets with at least one successfully filed patent application over the period five years prior to or after the announcement of the transaction and their corresponding patenting activity before and after the announcement.
In order to analyze the impact of the private equity backed LBOs on the target’s innovation level, a quasi-experiment together with a difference-in-difference methodology similar to Seru (2014) and Liu (2017) was used. The estimation of the effect arising due to the completion of the deal calls for a comparison between the corporate innovation level after the takeover and the innovation level if the takeover had not taken place, generally referred to as the counterfactual.
In their identification strategy, Seru (2014) and Liu (2017) overcome this issue by making use of a sample consisting of both completed and withdrawn takeovers, where completed deals are assigned to the treatment group, and the withdrawn deals are assigned to the control group. The control group is assumed to be randomly assigned since the withdrawn deals’ targets included in the final sample have failed due to reasons that are exogenous to the causal effect under consideration.
In addition to the quasi-experiment, a difference-in-difference specification allows to get rid of the bias that would arise due to pre-deal characteristics differences between the treatment and control groups (Stock and Watson, 2020). As a result, the coefficient of interest would
18 estimate the real effect caused by the treatment, in this case, the corporate innovation change caused by the private equity backed leveraged buyout. If a simple OLS regression had been used instead, the result would have suffered possible endogeneity issues such as omitted variable bias or reverse causality. For example, it may be the case that the LBOs would affect the corporate innovation activity of the target or the other way around in which firms’ innovation level would affect the likelihood of being targeted by private equity firms. To overcome these endogeneity issues and establish a causal relationship, the research analysis will be based on the following specification:
Yit = α + β1Completedit + β2Postit + β3 (PE-Completedit * Postit) +τt + γi + Xit’δ + εit (1)
Where Yit stands for one of the three corporate innovation measures of firm i in a given year t, more specifically: the number of patent applications, citation weighted patents and the measure of patent’s economic value. Completed is a dummy variable that equals one if the firm has been a target of a successful PE LBO deal and zero if the deal was withdrawn. Post is a dummy variable that equals one on the deal announcement date and onwards and equals zero in the years prior to the announcement. The coefficient β3 of the interaction term of Completed and Post is the coefficient of interest representing the effect of the leveraged buyouts on targets’
corporate innovation measures. Based on the main hypothesis of this paper that suggests an increase in the target’s number of patent applications after being taken over in a leveraged buyout, the expected sign of the coefficient β3 should be positive.
X is a vector of pre-deal characteristics of the targets, which according to previous innovation literature, influence the possibility of the firm being acquired or to influence its future performance, including corporate innovation output. Similarly to Seru (2014), Bernstein (2014), and Chemmanur et al. (2014), this set of control variables will consist of: Size, Return on Assets (ROA), Leverage Ratio, Cashflow Ratio, Market to Book ratio (generally referred to as Tobin’s Q), R&D expenses scaled by total assets, Age, Age Squared, Herfindahl Index based on sales and Herfindahl Index Squared. The detailed construction of these variables can be found in Table 1 in the Appendix. In addition to the usage of panel data that allows for reducing omitted variable bias, the equation is also incorporated with year, industry, and firm fixed effects. In the equation above τt are year fixed effects that control for time-variant characteristics but are
19 constant across firms, and γi are industry fixed effects that control for time-invariant characteristics that vary across different industries. In order to check the robustness of the results, all the regressions are repeated with firm fixed effects instead of industry fixed effects, and in that case, the variable Completed is omitted to prevent multicollinearity. For the same reason, the regressions are either run with year and industry fixed effects or with year and firm fixed effects.
Finally, standard errors (εit) clustered at the firm level are used in the regressions allowing for heteroskedastic and autocorrelated standard errors across different years for the same firm but treating the standard errors as uncorrelated across the different firms. If the simple heteroskedasticity-robust only standard errors were instead used, the standard errors would have been underestimated due to possible serial correlation, and as a result, the t-statistics of the variable of interest would have been overestimated (Bertrand, Duflo, and Mullainathan 2004).
4. Data Data Sources
Data regarding the leveraged buyout transactions, patenting activity, and accounting information are used to perform the research analysis.
Leveraged Buyout Transactions
The data collection process of this paper starts by obtaining the leveraged buyout transactions data from Capital I.Q. First, in Capital I.Q. are screened all merger and acquisition transactions referred to as “Leveraged Buyout” or “Platform” with a positive market capitalization 1-day before the deal announcement date. By adding this requirement, the selected transactions will include only companies with publicly listed shares before the takeover occurs. Secondly, the transactions are screened based on the transaction status, which falls into the following categories: “Closed,” “Effective,” “Successful,” “Cancelled,” and “Withdrawn.” Additionally, the list of the selected transactions is augmented with information about the target company, the acquirer, the company identifiers, and the transaction variables, which will further be used to prepare the final sample. Before exporting the dataset obtained from Capital IQ to Stata, the dataset was cleaned to remove any unwanted punctuation signs or symbols. Next, from the dataset are removed all transaction in which the target company is not incorporated in the United States, all transactions where the buyer is not a financial buyer, all transactions which do not
20 involve a private equity sponsor, and all transactions where the stake acquired was not a majority stake. Finally, only the leveraged buyout deals announced over the period starting from 1997 until 2019 were kept in the sample.
In order to measure corporate innovation levels, this paper uses an updated patent dataset from Kogan et al. (2017), which contains data for patents granted by the United States Patent and Trademark Office (USPTO) over the period from 1926 to 2019. Furthermore, they also provide a matching table that links the patents granted to their filing company and the corresponding CRSP identifier (permno). This linking table makes it easier to match the leverage buyout targets to their patent activity data. Kogan et al. (2017) construct this dataset using data for granted patents obtained from Google Patents and NBER. Google Patents database was considered over USPTO Bulk Data Storage System (BDSS), as it contains more information about citations and patent classifications. Hence, all the individual patents granted in their dataset are supplemented with information regarding application date, grant date, number of citations, and company name.
Before merging this dataset to the leveraged buyout transactions, I construct the main innovation measures used to test the main research question: the patent count measure and citations weighted measure. The patent count variable is constructed as the natural logarithm of a firm’s patent applications in a given year. The patent count is based on the number of applications rather than the number of grants since the applications can be considered more closely related to innovation (Chemmanur et al., 2014). The second measure of innovation, Citations weighted, is calculated as the natural logarithm of the ratio between the total number of citations in a given year for all patents of a firm and the total number of the patent applications that occurred in the same year (Seru, 2014). According to Hall, Jaffe, and Trajtenberg (2005), patent citations provide for good measure of firm innovation levels as they significantly affect firm market value. In addition to the citations weighted measure, a pre-constructed variable called Xi Nominal (ξ) from the dataset of Kogan et al. (2017) measuring the economic value of patenting activity in a given year is also used as an innovation measure. Since this measure also incorporates the market reaction to the patent applications, it can capture both the patent quality and the expected growth in the innovation activity of a given firm.
21 Accounting Data
All the accounting data necessary to construct the variables controlling for pre-deal characteristics of the LBO target companies were collected from the Compustat North America database, accessible via WRDS. The dataset obtained, which includes all listed Compustat firms and covers the period 1970-2021, was then checked for duplicates, and companies not located in the United States were removed. In addition to the accounting data, to estimate the target companies’ age, the Field-Ritter (Field and Karpoff, 2002) ( Loughran and Ritter, 2004) dataset containing the founding years was used.
The first step of the sample construction is to merge the LBO dataset with the control variables.
The merge of the control variables with the LBO transactions is done using the WRDS Compustat-Capital IQ linking table that matches the target’s Capital I.Q. Company ID to the firm identifier gvkey. Another linking table is used to match the gvkey to permno to merge the patent data with the LBO transactions based on the permno. Starting with a number of 3351 LBO transactions that meet the criteria to be analyzed, the resulting sample consists of 375 target firms that can be matched to their patenting activity. To correctly measure the impact of the takeover on the patenting activity of the target, the firms included in the final sample are required to have at least one successful patent application in the period five years before the transaction and five years after. As shown in Table 1, the previous requirement results in a sample of 220 transactions, out of which 151 are completed transactions, and 69 are cancelled transactions. The significant decrease in the number of companies that can be matched to patenting activity can be explained in part because many companies decide not to file their innovation activity with the patenting office to protect their trade secrets or branding of intellectual property (Lerner, 2011).
Table A1 and Figure 1, found in the Appendix, show the distribution of the full sample number of the announced transactions over time, split into deals that materialize and deals that do not. The graphical evidence is in line with the cyclical nature of the private equity-backed LBO transactions. From the graph, it can be noticed that the number of announced transactions peaks during periods of booming market conditions and significantly drops during financial downturns such as the burst of the Dot-com bubble in 2000 and The Financial Crisis of 2007- 2009.
22 Table 1. Sample Size. This table provides an overview of the number of transactions that are considered in the empirical analysis of this paper. The first column presents the number of transactions that are matched to their innovation activity and occur over the period 1997 until 2019, totalling to a number of 375 transactions. This number can be split into 104 cancelled deals and 271 closed deals. The second column presents the number of transactions that have at least one patent application over the five years before or five years after relative to the deal announcement year. The total number of companies with at least one patent application in the event window is 220 and consists of 69 cancelled deals and 151 closed deals.
Sample At least one patent application over the event window
Total number of transactions 375 220
Number of cancelled deals 104 69
Number of closed deals 271 151
Additionally, it can be observed that the number of cancelled deals also has a higher concentration during the period prior to and during the Financial Crisis. This increase in the cancelled deals’ number can be explained by the large number of total LBO announcements in the period leading to the crisis, out of which some transactions do not materialize. The increase in cancelled deals during the financial crisis can be explained by the difficulties of the private equity firms to raise funding and as a result preventing the deals to go through. This argument is in line with Kaplan and Stromberg (2009) who show that the number of leveraged buyout transactions follows the same patterns as the private equity fundraising volumes.
The sample construction part is finalized by determining and grouping the reasons behind the cancelled transactions. For each of the 69 cancelled transactions remaining in the final sample, the reason for the cancellation is researched using different sources, including firm websites, renowned news agencies, and newspapers such as CNBC, Reuters, The Wall Street Journal, Financial Times, etc. According to Seru (2014) and Liu (2017), the assignment in the control group of the cancelled transactions can be considered as randomly assigned only if the reasons for cancellation are not related to the causal effect that is being analyzed. Following this strategy, in Table 2, the cancelled deals are placed either in the exogenous or endogenous sample based on the cancellation reasons. Due to the removal of a high number of 45 deals withdrawn for reasons which may indeed bias the impact of the leveraged buyout on patenting activity, the final number of cancelled transactions for the exogenous samples is 24.
23 Table 2. Cancellation reasons. This table shows the categories into which the cancellation reasons are classified in order to determine their exogeneity. The reasons that are formatted in bold are considered endogenous cancellation reasons. The transactions withdrawn due to endogenous reasons may bias the results of the quasi-experiment and hence are not included in the exogenous cancelled deals subsample. After removing the cancelled deals withdrawn due to endogenous reasons, the exogenous cancelled deals sample consists of 24 companies with at least one patent application over the five years before or five years after relative to the deal announcement year
Total number of cancelled deals with at least one patent application over the event
Unfavourable shocks in financial markets 3
Rejected by target's shareholders as deemed not in the best interest 9
No consensus reached over the price 11
Negotiations failed for reasons unrelated to price or innovation activity 3
Outbid by another buyer 15
Target experienced an improved performance following the deal announcement 1
Rejected by authorities/government 1
Hostile bid 1
Cancellation reason ambiguous 25
One of the main assumptions that need to hold for the resulting analysis to establish a causal relationship regarding the LBO impact on corporate innovation after the takeover is that the companies that were successfully taken over and the companies that did not do not substantially differ in terms of innovation level after controlling for unobservable factors. In Table 3 are presented summary statistics for the main innovation measures and main control variables in the years before the deal announcement. The average value of the patent count variable for the completed transaction companies is approximately 0.847, while for the cancelled transaction companies, 0.762, and amounting to a difference of 0.085, which is not very large. Similarly, also the other variables seem to not differ substantially between the completed and cancelled transactions. A comprehensive presentation of descriptive statistics for all the variables used in the analysis can be found in Table A2 in the Appendix.
24 Table 3. Summary statistics. This table provides summary statistics for the main innovation measures and three main pre-transaction characteristics. The variables Size, Leverage Ratio and Return on Assets (ROA) are estimated with data collected over the five years before the announcement of the transactions and are winsorized at a 0.5% level to control for possible outliers. Patent Count, Citations Count and Citations Weighted are estimated only for firms with at least one patent application over the five years before or five years after the deal announcement year and are estimated with data covering the period before the transaction announcement. Panel A presents the summary statistics of the variables mentioned above for the completed transaction targets, and Panel B presents the summary statistics for the cancelled transaction targets.
Panel A. Completed Transactions
Mean Median SD 25th Percentile 75th Percentile N
Size 4.719 4.770 1.798 3.518 6.065 4707
Leverage Ratio 0.220 0.185 0.203 0.027 0.349 4691
Return on Assets 0.109 0.126 0.126 0.062 0.189 4685
Patent Count 0.847 0.693 1.000 0 1.386 2498
Citations Count 1.995 1.242 2.208 0 3.807 2498
Citations Weighted 1.409 0.693 1.549 0 2.799 2498
Panel B. Cancelled Transactions
Mean Median SD 25th Percentile 75th Percentile N
Size 4.923 4.909 1.878 3.614 6.126 1886
Leverage Ratio 0.228 0.192 0.208 0.028 0.353 1880
Return on Assets 0.078 0.105 0.140 0.030 0.167 1862
Patent Count 0.762 0 1.045 0 1.099 1198
Citations Count 1.784 0 2.265 0 3.738 1198
Citations Weighted 1.237 0 1.569 0 2.639 1198
However, in order to verify whether the pre-transaction characteristics are able to predict the likelihood of the successful completion of a LBO transaction, a conditional logit regression as below is run:
Successit = α + β1Patent Countit+ β2Citations Weightedit + β3Size it+ β3Return on Assets yit+ β3Leverage Ratioit + τt + Xit’δ + εit (2)
Where Success is a dummy variable that equals one for completed deal targets on the event year and equals zero otherwise. Patent Count and Citations Weighted are the two measures of innovation. Size, Return on Assets, and Leverage ratio denote other factors which may affect the
25 probability of being successfully acquired in a PE-backed LBO. X is a vector containing some control variables, τt are year fixed effects, and εit are standard errors.
Table 4: Probability of deal succeeding. This table presents the results from the conditional logit regression augmented with year fixed effects testing the probability of the deal succeeding based on the main pre-transaction characteristics of the target companies. The dependent variable is a dummy variable that equals one if the deal is successfully completed and zero otherwise.
Patent Count, Citations Weighted and R&D/Sales measure the pre-announcement level of innovation and the other remaining variables are added to control for pre-announcement financial characteristics of the target companies. This regression is run using observations in the period prior to the deal's announcement for both the completed deals and cancelled deals. Standard errors in parentheses are bootstrapped and ***, **, * denote significance levels at 1%, 5% and 10%, respectively.
Operating Cash -4.292**
Return on Assets -1.520
Leverage Ratio -0.041
Patent Count -0.482
Citations Weighted -0.213
Year Fixed Effects YES
Bootstrap SE YES
The resulting coefficients for this equation are presented in Table 4. The results of this conditional logit regressions can confirm that the treatment versus the control group does not significantly differ in terms of variables affecting the likelihood of being successfully taken over in a leveraged buyout. It can be noted that the coefficients in neither of the innovation measures
26 are significant and hence the difference in the pre-transaction innovation measures between the closed and cancelled deals’ targets shown in Table 3 is insignificant.
Figure 1. The Number of Patent Applications. This graph shows the distribution of the average number of patent applications over the event window for both Closed and Cancelled transactions. In order to be included in this graph, the patent applications have to be filed within the period covering five years prior to the announcement and five years after the announcement.
The patent application measure presented in the graph is estimated as the natural logarithm of patent applications. The solid line represents patent applications for completed transactions, while the dotted line represents patent applications for cancelled transactions.
27 Figure 2. The Citations Weighted Measure Distribution. This graph shows the distribution of the Citations Weighted measure over the event window for both Closed and Cancelled transactions. In order to be included in this graph, the patent applications and the citations have to be filed within the period covering five years prior to the announcement and five years after the announcement. Citations weighted measure is calculated as the total number of citations in a given year for all patents of a firm divided by the number of the total number of patent applications that occurred in the same year. The solid line represents Citations Weighted for completed transactions, while the dotted line represents Citations Weighted for cancelled transactions.
In Figure 1 and Figure 2 is plotted the natural logarithm of the number of patent applications and the natural logarithm of the citations weighted measures respectively over the event window.
What can be noticed is that in both graphs, the treatment and control groups seem to follow similar trends regarding the number of patent applications and citations weighted measure.
However, after the transaction announcement, the treatment group seems to exhibit an increase in the innovation measures in line with the two hypotheses of this paper. Based on the graphical evidence, it can be inferred that the leveraged buyouts positively affect the innovation activity of the target firms. Nevertheless, this evidence may just arise due to spurious correlation, and
28 hence, in the following section, this will be tested using the difference in difference methodology outlined in the previous section.
This section discusses the quasi-experiment results combined with a difference in difference methodology to establish a causal relationship between the private equity-backed leveraged buyout takeovers on the targets’ innovation levels. First, I discuss the empirical results of testing the impact of LBO transactions on the patent application measure, followed by the results involving the two different measures of patent quality.
In Table 5 are shown the results of the difference in difference equation testing the first hypothesis of the paper, which suggests an increase in the number of patent applications following the LBO takeover. According to previous literature, the patent application number can be a better measure of corporate innovation when compared to the R&D expenses since they contain more information regarding the innovation output of the company and can be more easily accessible (Griliches,1990). The analysis starts by first estimating the main equation without including the standard control variables for the full sample, including as a control group both deals that failed due to exogenous and endogenous reasons.
The results of this equation are shown in Column (I), and it can be observed that the coefficient on the Post variable is significantly negative at a 5% significance level, meaning that the number of patent applications decreases for all the companies in the period after the transaction. As shown in Column (2), the same equation is repeated for the subsample, including only deals withdrawn due to exogenous reasons, and in this case, none of the coefficients is significant. In Column (3) and Column (4), these equations are augmented by the standard control variables, and the resulting coefficients are all insignificant. These results align with Lerner (2011) that finds no impact of the private equity-backed LBOs on the target’s number of patent applications.
However, in Column (5) and Column (6), instead of the industry fixed effects, firm fixed effects are used together with the year fixed effects to control unobservable characteristics. After the inclusion of firm fixed effects, the variable Completed is omitted from the regression to prevent collinearity issues. Now the model seems to capture a higher amount of data variability, explained by the significant increase in the value of the adjusted R squared. The variable of
29 interest, the interaction term of the Completed and Post variable, has a significantly negative coefficient for both the full sample and the subsample, where for the latter, the coefficient is significant at a significance level of 1%. What can be noticed is that the coefficient on the Post
The Effect of LBO Transactions on Patent Applications Measure
This table presents the resulting coefficients of the difference in difference regression, analyzing the impact of the private equity backed LBO deals on patent applications number of the target company over 1997-2019. The sample used to run this regression covers observations over the event window of 5 years before and 5 years after the transaction's announcement. Full Sample includes the full sample of companies with at least one patent application over the event window, including deals that failed for endogenous reasons. Sub Sample instead includes only deals that failed due to exogenous reasons. Completed is a dummy variable that equals one if the LBO transaction was closed and zero if the deal was cancelled. Post is a dummy variable that equals zero before the announcement day and equals one on the deal announcement day and onwards.
All regressions are augmented by control variables, except for the regressions of Column (1) and Column (2). In Column (1)-(4), year and industry fixed effects are included, while in Column (5) and Column (6), year and firm fixed effects are used. In Column (5) and Column (6) the variable Completed is omitted from the regressions in order to prevent multicollinearity with firm fixed effects. In parenthesis are shown the standard errors clustered at the firm level and ***, **, * denote significance level at 1%, 5% and 10%, respectively.
(1) (2) (3) (4) (5) (6)
Sub Sample Completed x Post -0.123 -0.306 -0.163 -0.284 -0.446** -0.690***
(-0.62) (-1.33) (-0.67) (-1.09) (-2.11) (-2.66)
Completed 0.074 -0.232 -0.152 0.132
(0.39) (-0.70) (-0.68) (0.43)
Post -0.231** -0.062 0.104 0.253 0.475*** 0.791***
(-2.06) (-0.38) (0.72) (1.39) (3.00) (4.26)
Observations 1,496 1,124 766 592 766 592
Adjusted R-squared 0.360 0.324 0.466 0.533 0.694 0.727
Year Fixed Effects YES YES YES YES YES YES
Industry Fixed Effects YES YES YES YES NO NO
Firm Fixed Effects NO NO NO NO YES YES
Standard Control Variables
NO NO YES YES YES YES
variable is both positive and significant, showing that the number of patent applications has increased in the period following the LBO transaction when considering all the firms. When considering all the firms, including completed and withdrawn firms, there is an increase of 115%
30 in the number of patent applications; however, the interaction term coefficient is -0.690 showing that the number of patent applications decreases after a completed LBO transaction. Hence, these results withdraw the first hypothesis, which suggests a positive sign of the interaction term, and it can be inferred that the private equity-backed LBOs have a negative impact on the number of patent applications. These results are in line with previous literature against the private equity impact on the long-run performance of the target companies (Viviani, Giorgino and Steri, 2008;
Table 6 presents the empirical results of testing the impact of the LBO deals on the second innovation measure, the Citations Weighted measure. This variable is estimated as the natural logarithm of the number of patent citations in a given year scaled by the number of patent applications in the same year. In previous literature, patent citations have been referred to as a measure of patent scientific importance (Jaffe and Trajtenberg, 2002; Chemmanur et al., 2014), and hence, by making use of this variable, it will be tested whether the patent quality of the target firm will be affected as a result of the deal. In columns (1) and (2) is run the main difference in difference baseline equation, but the control variables are not included. In this case, the coefficient for the Post variable is -0.293, and it is significant at a 5% level, meaning that the citations weighted measure decreases for all the firms in the period after the deal announcement.
After the inclusion of the control variables, all the coefficients remain insignificant and show no effect on this innovation measure. However, when the firm fixed effects are augmented instead of the industry fixed effects, the coefficient of interest becomes negative and significant at the 5% level for both the full sample and the sub-sample. The coefficient of the interaction term in the sub-sample equals -0.754. When compared to the coefficient of the Post variable, which is a 0.734 significant coefficient, it shows that meanwhile, the citations weighted measure increased significantly for all the firms, the LBO target companies experienced lower values of this measure. This finding contradicts the second hypothesis of the paper that predicts a positive coefficient on the interaction term, suggesting that the patent quality should increase after private-equity-backed LBO. At the same time, the resulting coefficients contradict Lerner (2011) and Amess (2016), who find that the quality of the patents as measured by weighted citations increases after the completion of private-equity-backed LBO transactions.