The Role of Tax Planning Incentives in the Use of Earnouts in Taxable Acquisitions

Abstract

In an acquisition, an earnout is a component of transaction price that is contingent upon future events. Despite its usefulness to acquirers in mitigating valuation risk, using an earnout also has a potentially undesirable tax consequence for the acquirer because there is no immediate step-up in tax basis for the earnout portion of deal consideration until the resolution of associated contingencies. We thus hypothesize that acquiring firms with high marginal tax rates (MTRs) are less likely to use earnouts. We analyze a sample of taxable acquisitions by U.S. public companies, holding constant other non-tax determinants of earnout use from prior research, and we find results consistent with our prediction. We also find some evidence that strong tax incentives can offset the effect of target valuation uncertainty, suggesting that acquiring firms facing sufficiently high MTRs are willing to trade off mitigating valuation risk for a full, immediate step-up in tax basis. We contribute to the prior literature on determinants of earnout use as well as the role of tax planning incentives in firm choices within mergers and acquisitions.

1. Introduction

Mergers and acquisitions (M&A) transactions often involve complex tax issues that buyers and sellers must plan for (Bloomberg Tax & Accounting, 2018). In a survey of corporate tax practitioners by Bloomberg Tax & Accounting (2018), 60 percent of respondents note that they are “becoming involved in M&A transactions before preliminary agreements are struck to help optimize tax structuring”. As such, taxes are an important consideration in M&A deals (Hanlon & Heitzman, 2010). Prior research has shown that tax planning incentives influence a wide array of choices made by transacting parties in structuring and financing the acquisition (Dhaliwal et al., 2005; Erickson, 1998; Erickson & Wang, 2000).

In this paper, we examine whether the acquirer’s tax planning incentive is associated with earnout use in a taxable asset acquisition. An earnout, also referred to as contingent consideration, is a portion of total purchase price that is contingent upon events or performance milestones after closing, allowing the buyer to share the valuation risk with the seller by deferring the transfer of deal consideration until at least some of the valuation uncertainty is resolved (Bruner & Stiegler, 2001; Dahlen, 2024). Acquirers generally face at least some uncertainty concerning the value of the target at the time of the acquisition, and one way to mitigate such valuation uncertainty is to include an earnout agreement in the deal terms (Broughton, 2024; Kohers & Ang, 2000). Structuring the transaction price in this way requires the seller of the target firm to “earn” the full purchase price upon realization of its value to the acquirer.

Our study is motivated by the growing popularity of earnouts in M&A deals: nearly a third of M&A deals in 2023 included an earnout provision, even after excluding those in life sciences where earnouts have been commonly used (Broughton, 2024; RSM LLP, 2024). Earnouts also represent an economically meaningful portion of total transaction value, typically making up a third of the purchase price paid at closing (Broughton, 2024; Cain et al., 2011). When the economic environment for M&A deals is characterized by high uncertainty, earnout is an attractive tool for potential buyers to manage valuation risk because it bridges the valuation gap between buyers and sellers and protects the buyers from overpaying for a transaction (Broughton, 2024; Dahlen et al., 2024).

However, despite the increasing popularity of earnouts as well as the general importance of tax planning in M&A deals, extant research devotes little attention to the tax consequences of earnouts and how they might interact with an acquirer’s willingness to mitigate valuation risk through earnouts. One potentially adverse tax implication of earnout use in a taxable asset acquisition is that the step-up in the tax basis of acquired assets to fair market value is delayed and uncertain for the earnout portion of the purchase price until those amounts become fixed (Wellen, 2014). Furthermore, under the residual method, any future additions to tax basis for earnout payments are automatically allocated to goodwill and intangibles (i.e., Section 197 intangibles) that must be amortized over 15 years. This potentially denies the acquirer an opportunity to allocate more of the total purchase price to shorter-lived assets at the acquisition date (Lynch et al., 2019). Because the incremental depreciation deduction from stepping up the basis in acquired assets is a key tax advantage of a taxable asset acquisition structure (Erickson et al., 2020; Hayn, 1989), an acquirer facing a sufficiently high marginal tax rate1 might prefer an immediate step-up in basis for the full purchase price over using an earnout, in order to maximize the present value of such tax benefits.

We therefore hypothesize that no earnout use could be optimal for the acquiring firm when it faces a sufficiently high tax rate. An important implication of our prediction is that acquirers face a trade-off between mitigating valuation risk and optimizing tax savings. Applying the Scholes and Wolfson (1992) framework—in which an effective tax planning strategy requires a consideration of all costs—our study examines how the tax savings from forgoing an earnout might be weighed against the opportunity cost of shielding the acquiring firm and its shareholders from valuation risk.

Using data on taxable asset acquisitions by U.S. public companies between 1988 and 2018, we estimate the association between the acquirer’s use of earnout and the tercile rank of the acquiring firm’s estimated marginal tax rate, which acts as a proxy for the relative strength of its tax planning incentives. Marginal tax rate estimates are based on the simulation approaches from Blouin et al. (2010) and Graham (1996). We predict that, all else equal, acquiring firms facing higher marginal tax rates are less likely to use an earnout in a taxable acquisition transaction due to concerns over delayed and uncertain tax basis step-up and that such concerns are incremental to the acquirers’ preference for mitigating valuation uncertainty. Furthermore, we predict that strong tax planning motives have a moderating effect on the role of target valuation uncertainty for earnout use documented in the prior literature on earnouts.

Results are consistent with our predictions, with a negative and significant relation between the tercile rank of the acquirer’s marginal tax rate and the probability that the deal consideration includes an earnout in taxable transactions after controlling for some of the known determinants of earnout use from the prior literature (Bates et al., 2018; Cain et al., 2011; Kohers & Ang, 2000). We then interact the marginal tax rate variable with our proxies for information asymmetry and target firm valuation uncertainty, and we find some evidence consistent with strong tax planning motives reducing the probability of earnout use motivated by valuation uncertainty. Last, we conduct cross-sectional tests by separating debt-financed acquisitions from the rest of the sample, the results of which suggest that tax planning incentives are less important in the choice of earnout in acquisitions financed with debt that can act as a substitute tax shield.

Our study makes three contributions. First, we contribute to the literature on determinants of earnout use (Dahlen, 2024). Prior research has documented non-tax economic reasons for an acquirer’s use of earnout,2 such as high information asymmetry and target valuation uncertainty (Cain et al., 2011; Datar et al., 2001; Kohers & Ang, 2000), target manager retention (Cadman et al., 2014), and a lack of access to conventional sources of financing for acquisitions (Bates et al., 2018). Some studies have addressed the costs of using earnouts (Dahlen et al., 2024; Viarengo et al., 2018), but to our knowledge, our study is the first to empirically examine the cost of earnout use from a corporate tax planning standpoint and show that the acquirer’s tax planning incentives are an incremental factor after controlling for the non-tax factors listed above.

Second, we contribute to research on the impact of tax considerations on choices that firms make in M&A deals. In addition to the impact of taxation on M&A activity (Blouin et al., 2021; Coles et al., 2024), prior research shows that tax planning affects how acquirers structure their M&A deals. Erickson (1998) shows that acquiring firms with strong tax planning incentives are more likely to choose a debt-financed, taxable acquisition structure. A more recent study by Lynch et al. (2019) examines how tax planning and financial reporting incentives influence the acquirer’s allocation of the purchase price in a taxable asset acquisition that results in a step-up in tax basis to fair value. Greater allocation to shorter-lived depreciable assets (as opposed to goodwill and intangible assets) increases the present value of tax savings through higher depreciation in the first few years following the acquisition, though this strategy also imposes financial reporting costs by lowering book income. Consistent with their predictions, Lynch et al. (2019) report a greater allocation to shorter-lived depreciable assets when the acquirer has strong tax planning incentives, and such effect is moderated by the presence of strong financial reporting incentives. Our study shows that the tax benefits of depreciation on acquired assets can also motivate an acquirer’s choice of deal consideration, thus adding to existing knowledge on how a firm’s tax incentives influence various choices that it makes within a taxable acquisition structure (Dhaliwal et al., 2005).

Third, we contribute to the literature on the risks faced by firms engaged in M&A deals and how they manage such risks. Prior studies examined a wide variety of risks associated with M&A activity, such as political risk (X. Chen et al., 2024), interim risk or the risk of failed transactions (Heath & Mitchell, 2023), customer concentration (Cheng et al., 2022), financial distress and default risk (Bruyland & De Maeseneire, 2016; Clark & Ofek, 1994; Ghosh & Jain, 2000), and information asymmetry and valuation risk (Even-Tov et al., 2024). We contribute to this literature by showing how an acquirer’s tax planning incentives may influence its willingness to use earnouts to manage the valuation risk associated with M&A deals.

Our findings regarding the trade-off between tax planning and protecting against valuation uncertainty may also have practical implications for firms, managers, and investors. Prior research has shown that corporate governance and tax avoidance are negatively associated, consistent with the view that tax avoidance facilitates managerial opportunism (Atwood & Lewellen, 2019; R. Chen et al., 2022; Kim et al., 2011). These findings suggest that managers and shareholders disagree on the optimal level of tax avoidance.3 Applied to our setting, whereas a manager might want to take full advantage of a taxable asset acquisition structure by avoiding earnouts, shareholders might place greater value on managing valuation risk than on maximizing tax savings. When there is disagreement on whether the tax savings from forgoing earnouts are worth the incremental valuation risk, this trade-off becomes an important consideration in the choice of earnout use in risky M&A deals.

The remainder of the paper is organized as follows. Section 2 discusses the institutional background and the prior literature on earnouts and tax structure in M&A deals, followed by the development of our hypotheses. Section 3 describes our data and research design to test our hypotheses. We discuss our findings in Section 4, and we conclude in Section 5.

2. Background and Hypothesis Development

2.1. Earnouts

An earnout, also referred to as contingent consideration, is a component of the purchase price that is contingent upon events after the transaction closing date (Bruner & Stiegler, 2001). Earnout payments are frequently linked to the target firm’s achievement of financial performance goals over an agreed-upon earnout period. For example, the terms of an earnout agreement might stipulate that the acquiring firm make several annual cash payments to the seller based on a fixed percentage of future revenue or pre-tax earnings generated by the target, subject to a maximum payout amount. In other cases, earnout payments are linked to discrete milestones, such as regulatory approval of a new therapeutic under development.

From the acquirer’s perspective, there are several benefits to using an earnout. Prior studies on earnouts have documented that earnouts are commonly used in response to information asymmetry and uncertainty concerning the target’s valuation (Dahlen, 2024; Dahlen et al., 2024). Accordingly, earnouts are more likely to be included in deals involving privately held targets, targets in a high-tech or service-intensive industry, or targets in high-growth industries (Cain et al., 2011; Datar et al., 2001; Kohers & Ang, 2000). When information asymmetry and valuation uncertainty are at sufficiently high levels, including an earnout in the transaction price allows both parties to complete the transaction without coming to a full agreement about the target’s value at closing, effectively allowing the buyer to share some of the valuation risk with the seller. Furthermore, acquirers can use earnouts to address seller moral hazard and retain target managers when target manager effort following the transaction is deemed to be important (Cadman et al., 2014; Cain et al., 2011). Earnouts also provide an alternative source of financing for the acquisition when there is limited access to conventional external financing sources (Bates et al., 2018), and current accounting and reporting standards for earnouts allow managers to use earnouts to create “cookie jar reserves” for boosting post-M&A earnings (Ferguson et al., 2024).4

Despite these benefits, there are reasons that an acquirer might prefer not to include an earnout in the deal consideration. While earnout use is an effective way to address adverse selection and seller moral hazard problems, it also exposes the acquiring firm to potential legal disputes in the future regarding the target’s performance measurement or the buyer’s involvement in the target’s operations (Dahlen et al., 2024; Viarengo et al., 2018; Weinstein et al., 2018). For instance, integration of the target firm into the existing operations of the acquiring firm might raise issues regarding the realization of acquisition-related synergies and its impact on the target’s performance (Duffee & Vozar, 2019). As such, an earnout clause can be difficult to structure and implement. If the bidder is willing to tolerate some valuation risk, it may choose to avoid the added complexity by attempting to resolve the valuation uncertainty upfront through pre-closing negotiations.

In addition to valuation and legal considerations, there are unique tax issues involved in a transaction with an earnout in the deal consideration. While the role of acquirer’s tax planning incentives in the structure and financing of M&A transactions has been documented in prior studies (Dhaliwal et al., 2005; Erickson, 1998; Lynch et al., 2019), few academic studies in the tax literature have specifically examined earnout use.5 Before turning to the specific tax issues related to earnout use and our hypothesis development, we review and compare basic acquisition structures under U.S. tax law.

2.2. Tax Treatment of Acquisition Structures

Acquisitions of freestanding C corporations can be structured as either taxable or tax-free. This tax status largely depends on the form of consideration. Typically, if the consideration is mostly in cash (acquirer’s stock), the transaction is treated as taxable (tax-free). Both taxable and tax-free transactions can be further categorized into acquisitions of the target corporation’s assets and acquisitions of stock or controlling interest in the target corporation. In addition, parties can elect to treat a taxable stock acquisition as a taxable asset acquisition as provided under Section 338 of the Internal Revenue Code.

In short, under U.S. tax law, the major tax implications of an acquisition depend on (1) whether the transaction is taxable or tax-free, (2) whether the acquirer acquires the assets of the target or stock in the target, and (3), in the case of a taxable stock sale, whether the parties elect to treat it as a taxable asset sale under Section 338. One such tax implication that depends on the acquisition structure is whether the acquirer can adjust or “step up” its basis in the acquired assets to fair value. Under U.S. tax law, only taxable asset acquisitions and taxable stock acquisitions with a Section 338 election trigger a step-up in the tax basis of acquired assets. All else equal, a transaction structure that triggers this step-up is tax-advantageous because it results in a larger base from which additional depreciation can be taken to reduce current and future taxable income.

In practice, however, such step-ups are rare in acquisitions of freestanding C corporations but are common in acquisitions of S corporations and other flow-through entities as well as acquisitions of subsidiaries.6 The reason is that in taxable acquisitions of freestanding C corporations, the immediate tax cost of the step-up generally exceeds the present value of tax savings from incremental depreciation. When the target is a freestanding C corporation, the step-up attributable to an excess in the purchase price over the target firm’s basis in assets is first treated as a taxable gain at the target corporation level, before the remaining proceeds are distributed to shareholders and then taxed again at the shareholder level as capital gains. To avoid this double taxation, the transaction can be structured as a taxable stock acquisition without a Section 338 election. Because there is no step-up, the acquirer forgoes incremental tax savings from depreciation but avoids tax at the target corporation level and thus can offer a lower purchase price without affecting the net cash proceeds to target shareholders.

In other words, the acquirer’s net after-tax cost of the acquisition is lower when the transaction structure does not trigger tax at the target corporation level before any shareholder-level taxes. In contrast, such double taxation does not occur in acquisitions of S corporations and flow-through entities, in which the taxable gains simply pass through to the shareholders, or in acquisitions of subsidiaries, in which there are no shareholder-level taxes because the seller is normally the parent of the subsidiary rather than a shareholder of the parent firm.7

2.3. Tax Consequences of Earnouts

Provided that the transaction structure results in a step-up in tax basis,8 an important implication of using an earnout is that the acquirer will not receive an additional tax basis for the contingent portion of the purchase price until those amounts become fixed or paid to the sellers. Furthermore, when payments are made, the basis additions are allocated to goodwill and Section 197 intangibles following the residual method (Wellen, 2014). Consequently, the delay and uncertainty in tax basis step-up could render earnouts less attractive to an acquiring firm concerned with reducing its tax liability in the short term. Instead, it may be preferable to finalize and pay the full deal consideration at closing, because it not only provides an immediate depreciable tax basis to use against current and future taxable income, but also presents an opportunity to allocate a greater share of the aggregate purchase price to shorter-lived depreciable assets instead of goodwill (Lynch et al., 2019). As such, the acquiring firm can maximize the present value of tax savings from depreciation deduction.

To illustrate, suppose that Firm A is negotiating a deal to acquire Firm T. The sellers of Firm T demand USD 100 as the transaction price. Firm A believes that there is a possibility that Firm T is overvalued at USD 100, but it still wants to close the deal due to its potential upside. Thus, provided that the sellers are indifferent, Firm A is contemplating including an earnout agreement, whereby USD 80 would be paid upfront at closing and a maximum of USD 20 would be paid three years later depending on the resolution of contingencies stipulated in the earnout agreement, such as Firm T’s financial performance, realization of expected synergies, or achievement of specific milestones that influence Firm T’s value realization to Firm A.

In Appendix B, we calculate and compare the net present value (NPV) of incremental tax savings from depreciation that Firm A would receive under three scenarios: a base case where no earnout is used and Firm A pays USD 100 at closing, an earnout case where Firm A pays USD 80 at closing and pays the maximum USD 20 three years later, and another earnout case where Firm A pays USD 80 at closing but does not need to pay any earnout amount (e.g., failure of the target firm to meet performance targets). We assume that Firm A faces a tax rate of 35% (pre-TCJA top corporate statutory tax rate) in the year of the acquisition, and for simplicity, we assume that this tax rate is constant through time.9 We further assume that after allocating a fixed amount of USD 30 from the purchase price to non-depreciable net working capital, firm A is able to allocate half of the remaining initial purchase price to short-term assets with an average depreciable life of three years and the remaining half to goodwill and intangible assets amortizable over 15 years.10 This is possible because there is room for discretion in assigning fair market value to depreciable acquired assets in a taxable asset acquisition structure (Lynch et al., 2019). Furthermore, as previously noted, any basis additions due to future earnout payments are automatically added to goodwill and intangible assets following the residual method.

As shown in Appendix B, the net present value, NPV, of incremental tax savings from depreciation is higher under the base case compared to either of the scenarios in which the acquirer uses an earnout. Applying a 10% discount rate throughout the post-acquisition period, even if the maximum earnout is paid so that the total transaction price is the same whether an earnout is used or not, the NPV of tax savings is lower by USD 2.35 for the entire period and by USD 3.57 if one looks at the first three years only (in case the acquirer is focused on its short-term tax savings). The difference is driven by both the delay in basis additions as well as a smaller share of the aggregate purchase price allocated to short-term assets. In this case, based only on the net cost of acquisition, the acquirer would be better off not using an earnout ceteris paribus. The gap in the NPV of tax savings between the base case and the earnout case is wider when the earnout is not paid.

Thus, whether a high-tax rate acquirer decides to use an earnout depends on the acquirer’s perceived level of valuation uncertainty and risk. If the acquirer believes that the outcome is closer to the scenario in which maximum earnout is paid (i.e., low valuation risk), then using an earnout becomes less an attractive option. In an extreme case where the acquirer is fully confident that it will have to make earnout payments, the acquirer could be better off not using an earnout because of the opportunity cost of incremental tax savings from depreciation, and the higher the acquirer’s marginal tax rate, the higher the opportunity cost. In the opposite case where the acquirer believes that the outcome is closer to the scenario in which it does not have to make additional earnout payments (i.e., high valuation risk), then the benefit of earnout use outweighs the lost incremental tax savings from depreciation.

2.4. Hypothesis Development

The interaction between tax planning incentives and perceived valuation risk—and its effect on the acquirer’s choice of earnout use—can be viewed through the Scholes and Wolfson (1992) framework for effective tax planning. Under this framework, effective tax planning considers all parties, all taxes, and all costs relevant to the tax strategy employed to maximize the after-tax rate of return. The Scholes and Wolfson framework serves as a grand theory central to various streams of empirical tax research. Among the streams of the literature grounded in this framework is an empirical examination of whether taxes matter in the structure and financing of corporate M&A transactions (Dhaliwal et al., 2005; Erickson, 1998; Hayn, 1989). In developing our hypotheses, we apply and extend this middle-range theory on the role of taxation in corporate M&A to the setting of earnouts in corporate M&A deals.

Despite its usefulness for managing valuation risk, an earnout agreement may be disadvantageous to an acquirer seeking tax savings from the depreciation of acquired assets, as demonstrated by our illustrative example in Appendix B. We thus predict that an acquirer with sufficiently strong tax planning motives or high marginal tax rates may choose not to use an earnout, even at the opportunity cost of protecting its shareholders from the risk of overpayment for the acquisition. Conversely, an acquirer facing relatively low marginal tax rates or sufficiently high target valuation uncertainty might be more concerned about addressing valuation risk than it is about the adverse tax consequences and thus might be more open to using an earnout.

We also predict a trade-off exists between tax planning motives and valuation-related motives in the decision to use an earnout in an acquisition. All else equal, forgoing an earnout in favor of maximizing the tax basis step-up at the acquisition date may be a part of a prudent tax planning strategy, but the acquirer must be willing to bear the cost of valuation risk that could have been avoided by using earnouts. We formalize these predictions into hypotheses in the alternative form below:

Hypothesis 1a.

All else equal, an earnout is less likely to be included in transaction consideration when the acquiring firm faces a higher marginal tax rate.

Hypothesis 1b.

Strong tax planning incentives moderate the association between the likelihood of earnout use and target characteristics that indicate high valuation uncertainty.

Our hypotheses are not without tension, and it is possible that we do not find the predicted relation between the acquirer’s marginal tax rate and earnout use for two reasons. First, non-tax motives such as mitigating valuation uncertainty might dominate any tax planning consideration in the decision to use an earnout. Accordingly, the extent to which tax consequences matter after controlling for other determinants of earnout use is unclear ex ante. Second, while the delay and uncertainty in tax basis step-up are potential concerns for an acquirer, earnout use can also present opportunities to reduce future taxable income. For instance, the parties can negotiate the earnout contract such that future payments are tied to continued employment of key employees. In this case, the acquirer treats the earnout payments as a compensation expense rather than an additional purchase consideration,11 resulting in ordinary business deductions on the full amount of earnout payments instead of the additional tax basis to be amortized over time. Even if the earnout payment was classified as an additional purchase consideration, the acquirer may instead take advantage of an imputed interest deduction on a portion of the deferred payments (Wellen, 2014). By including an earnout provision in the deal terms—regardless of the accounting treatment of earnout payments as compensation or as purchase consideration—the acquiring firm may reduce future taxes without making other significant modifications to the underlying economics of the deal. As such, it is unclear whether and to what extent these potential tax benefits of earnout use offset concerns over the delayed and uncertain step-up in tax basis.

3. Data and Research Design

3.1. Sample

We collect data on all mergers and acquisitions completed by U.S. public companies from 1988 to 2018 from the SDC Platinum Mergers and Acquisitions (M&A) database. We restrict the sample to deals that are worth at least USD 1 million in transaction value as reported by SDC. Acquisition target firms are public, private, or subsidiaries of public companies. Following Bates et al. (2018), we restrict the sample to acquisitions in which the bidder owned less than 50% of the target on the date of the acquisition announcement and sought to own a majority of ownership interests, in order to exclude acquisitions of minority interests that are not relevant to this study.

Transactions flagged as spin-offs, split-offs, repurchases, and exchange offers are excluded from the sample since they typically do not involve a target entity that is separate from the acquirer.12 We also exclude transactions that are part of a recapitalization or restructuring plan, as the primary purpose of these transactions is likely related to special circumstances in which the choice of earnout is not a relevant decision, such as extreme financial distress and Chapter 11 bankruptcy by the target firm.

Since our predictions pertain to the choice of earnout in taxable acquisitions that result in a step-up in tax basis (either taxable asset sale or taxable stock sale with a Section 338 election to treat stock sale as deemed asset sale), we exclude tax-free reorganizations from the sample for the main analyses. While the SDC M&A database does not report the precise tax status of transactions, we infer the tax status from the percentage of deal consideration that is in the acquiring firm’s stock. For an acquisition to qualify as a tax-free reorganization, a substantial portion of the deal consideration must be in the acquiring firm’s stock. Therefore, following the approach by Lynch et al. (2019), we classify transactions with more than 80% of the deal consideration in the acquirer’s stock as tax-free and exclude them from the final sample for main analyses.13

We obtain estimates of marginal tax rates (MTRs) for acquiring firms from two different sources. First, we use the Marginal Tax Rates database accessible through Capital IQ Compustat North America (hereafter $MTRBCG$). The MTRs in this database are estimated according to the method proposed by Blouin et al. (2010). Additionally, we use the MTR simulated by Graham (1996), available by request on the author’s personal website14 (hereafter $MTRJG$).15

Other control variables for target and acquirer characteristics are constructed from Compustat Quarterly and CRSP daily stock returns data. We match the MTR, Compustat, and CRSP data to the SDC M&A database, and then exclude deals in which either party is a regulated public utility firm (SIC code 4800-4899) or a financial services firm (SIC code 6000-6999). The reason is that these industries are heavily regulated, and M&A activity involving firms in these industries is subject to specific rules and regulatory processes that would not apply elsewhere. For instance, the Federal Reserve evaluates and approves mergers involving banks, bank holding companies, and their subsidiaries.16

This procedure results in 18,119 unique taxable transactions, of which 16,482 (10,163) taxable transactions have corresponding $MTRBCG$ ($MTRJG$) data available. After removing transactions with missing data for other covariates, our final sample for regressions using $MTRBCG$ ($MTRJG$) consists of 16,101 (9,908) unique taxable acquisitions.

3.2. Research Design

To test H1a, we estimate the following two-way fixed effects model for the sample of taxable acquisitions17:

$$\begin{array}{ll}EARNOUT=& {\beta }_0+{\beta }_{1}MTRRANK+{\beta }_{2}HIGHFC+{\beta }_{3}TNONPUB+{\beta }_{4}TARGETSIGMA\\ & +{\beta }_{5}TARGETRD+{\beta }_{6}TARGETQ+{\beta }_{7}LNTVAL+TargetIndustryFE\\ & +YearFE+ϵ\end{array}$$

(1)

The dependent variable $EARNOUT$ is an indicator variable equal to one if the deal consideration contains an earnout as reported by SDC and zero otherwise. The regressor of interest, $MTRRANK$, is the tercile rank of the estimated marginal tax rate of the acquiring firm within the year of the acquisition announcement, zero being the lowest rank and two being the highest. If the deal is less likely to include an earnout due to concerns over a delayed step-up in basis, then we predict the coefficient on $MTRRANK$ (${\beta }_1$) to be negative. We use a tercile rank-transformed MTR for the ease of interpreting results from regressions that facilitate a comparison among low, medium, and high tax rate acquirers within each year. Our sensitivity tests indicate that the results are qualitatively robust to using continuous MTR variables instead of rank-transforming them.

We follow the approach by Cain et al. (2011) and Bates et al. (2018) to control for other determinants of earnout use. First, we control whether the acquiring firm is financially constrained, as Bates et al. (2018) show that acquirers with limited access to capital are more likely to use earnouts to finance their acquisitions. Thus, we include $HIGHFC$, which is an indicator variable equal to one if the acquirer’s WW Index,18 a measure of firm financial constraint proposed by Whited and Wu (2006), is above the median in the year of the acquisition announcement and zero otherwise.

Next, we control for target characteristics that are likely indicative of relatively high levels of information asymmetry and valuation uncertainty.19 As documented in the prior literature, earnouts are more commonly used when the target is privately held (Datar et al., 2001; Kohers & Ang, 2000). Since privately held firms are generally subject to less comprehensive financial reporting requirements compared to public companies, greater information asymmetry is likely to be present in acquisitions of private firms (Officer et al., 2009). In addition, financial performance information about subsidiaries of either private or public firms is less transparent as financial statements are reported on a consolidated basis. Thus, we include $TNONPUB$ as an indicator variable equal to one if the target is either private or a subsidiary as reported by SDC and zero otherwise.

As many of the target firms in the sample are private or subsidiaries of publicly traded firms, standalone financial or valuation data to measure individual growth potential or valuation risk are not available. As an alternative, following the approach used by prior studies on earnouts (Bates et al., 2018; Cain et al., 2011), we use certain characteristics of the industry in which the target firm operates based on financial and valuation data for public firms in the same two-digit SIC industry. Specifically, we control for volatility in the industry’s daily value-weighted returns ($TARGETSIGMA$), industry median R&D-to-sales ratio ($TARGETRD$), and industry median Tobin’s Q ratio ($TARGETQ$). These control variables are intended to act as proxies for target firm characteristics that are likely to motivate the acquirer to include an earnout as a hedge against valuation risk and are thus expected to exhibit positive coefficients.20 Lastly, we control for transaction size using the log of the transaction value as reported by SDC ($LNTVAL$), since earnouts are more likely to be included in acquisitions of smaller growth-stage companies. Appendix A contains detailed descriptions of all variables used in this study.

In addition to the acquirer’s MTR, the seller’s MTR could also influence whether a deal includes an earnout. For example, a seller facing a high tax rate might prefer an earnout because it allows the seller to defer taxable gains to future periods in which the seller expects a lower tax rate, as the proceeds would be eligible for an installment sale treatment for tax purposes. Although we acknowledge the possibility that the seller’s MTR is an omitted variable, a priori, we do not expect it to be a confounding variable that, if omitted, would bias our inferences about the relation between the acquirer’s MTR and the likelihood that a deal includes an earnout. Nonetheless, we caution the reader to interpret our findings with the caveat that we are limited in our ability to test the potential effect of the interaction between the acquirer’s and seller’s tax incentives due to data limitations, as a substantial portion of our sample of taxable asset acquisitions involves private sellers.21

We test H1b by estimating a modified form of Equation (1) to include interaction terms between $MTRRANK$ and the aforementioned proxies for target characteristics indicative of valuation uncertainty. If the acquiring firm’s tax planning motives have an offsetting effect on the likelihood of earnout use in response to these target characteristics, then we expect the coefficients on the interaction terms to be in the opposite direction of the main effect of those target characteristics indicative of valuation uncertainty. In all regressions, we include target industry and year fixed effects to account for unobservable time-invariant target industry characteristics as well as macroeconomic trends that may correlate with overall acquisition activity, acquirer tax status, and popularity of earnout use.

3.3. Descriptive Statistics

Table 1. Frequency of earnout use by year and by industry.

Panel A: Earnout Use by Year
Year	Transactions in Sample	Transactions with Earnouts	Percentage with Earnouts
1988	225	11	4.9%
1989	253	14	5.5%
1990	235	20	8.5%
1991	238	25	10.5%
1992	338	29	8.6%
1993	458	44	9.6%
1994	589	47	8.0%
1995	680	43	6.3%
1996	877	50	5.7%
1997	997	66	6.6%
1998	1110	87	7.8%
1999	909	66	7.3%
2000	758	71	9.4%
2001	635	56	8.8%
2002	708	86	12.1%
2003	687	82	11.9%
2004	802	103	12.8%
2005	831	123	14.8%
2006	868	110	12.7%
2007	858	114	13.3%
2008	593	92	15.5%
2009	417	72	17.3%
2010	548	82	15.0%
2011	565	94	16.6%
2012	631	97	15.4%
2013	526	69	13.1%
2014	614	84	13.7%
2015	396	71	17.9%
2016	272	54	19.9%
2017	229	39	17.0%
2018	272	51	18.8%
Total	18,119	2052	11.3%
Panel B: Top 10 Industries with the Greatest Number of Transactions with Earnouts
Industry Description (Two-Digit SIC)	Transactions in Sample	Transactions with Earnouts	Percentage of Transactions with Earnouts
Business Services (73)	3651	541	14.8%
Chemicals and Allied Products (28)	1211	221	18.2%
Measuring, Photographic, Medical, and Optical Goods and Clocks (38)	1259	218	17.3%
Electronic and Other Electrical Equipment and Components (36)	1326	171	12.9%
Engineering, Accounting, Research, and Management Services (87)	723	163	22.5%
Health Services (80)	761	81	10.6%
Wholesale Trade—Durable Goods (50)	549	79	14.4%
Industrial and Commercial Machinery and Computer Equipment (35)	961	72	7.5%
Communications (48)	1039	37	3.6%
Printing, Publishing and Allied Industries (27)	238	28	11.8%

This table reports trends in earnout use by year and by industry based on the final sample constructed from data on acquisitions between 1988 and 2018 from the SDC M&A database. Panel A reports the number and percentage of transactions that include earnouts each year. Panel B reports the number and percentage of transactions that include earnouts for each industry, which is defined at the two-digit SIC level.

reports summary statistics on trends in earnout use. Panel A shows an overall upward trend in the frequency of earnout use during the sample period, which is consistent with statistics reported by Bates et al. (2018). Panel B reports the top ten target firm industries in the number of transactions with earnouts over the sample period. Remarkably, transactions with earnouts involving target firms in the top five industries account for nearly two thirds of all transactions with earnouts in our sample. This result suggests that earnout use is concentrated in certain industries, many of which are technology- and R&D-intensive. As such, the summary statistics in Table 1 appear consistent with findings from the prior literature indicating that earnout use is more common in technology- and service-driven industries (Datar et al., 2001).

Table 2. Descriptive statistics.

Panel A: Sample Means of Variables by EARNOUT
	EARNOUT = 0				EARNOUT = 1		Difference (2)–(4)
Variable	Obs (1)		Mean (2)		Obs (3)	Mean (4)	Mean (5)	T-Stat (6)
MTRBCG	14,676		0.311		1806	0.283	0.028	15.270
MTRJG	9248		0.311		915	0.297	0.014	4.325
FC	16,067		0.874		2052	4.719	−3.845	−2.391
TNONPUB	16,067		0.863		2052	0.988	−0.125	−16.316
TARGETSIGMA	15,687		0.013		2016	0.013	0.000	0.055
TARGETRD	16,067		0.034		2052	0.055	−0.021	−18.161
TARGETQ	16,067		1.709		2052	1.850	−0.142	−12.718
TVAL	16,067		328.398		2052	139.442	188.956	6.565
LNTVAL	16,067		3.905		2052	3.480	0.425	9.754
Panel B: Descriptive Statistics of MTR Variables by Rank
				N	Mean	Median		Standard Deviation
MTR by Rank: MTRBCG
0				5361	0.235	0.273		0.092
1				5371	0.338	0.340		0.008
2				5369	0.349	0.350		0.007
All				16,101	0.307	0.339		0.074
MTR by Rank: MTRJG
0				2725	0.207	0.237		0.128
1				4513	0.344	0.350		0.023
2				2670	0.355	0.351		0.009
All				9908	0.310	0.350		0.094
Panel C: Correlation Matrix
	EARNOUT	MTRBCG	MTRJG	FC	TNONPUB	TARGETSIGMA	TARGETRD	TARGETQ	TVAL
EARNOUT	1.000	−0.072 ***	−0.245 ***	0.040 ***	0.121 ***	0.008	0.098 ***	0.056 ***	−0.056 ***
MTRBCG	−0.082 ***	1.000	0.439 ***	−0.155 ***	−0.103 ***	−0.056 ***	0.010	0.038 ***	0.340 ***
MTRJG	−0.031 ***	0.670 ***	1.000	−0.074 ***	−0.036 ***	0.047 ***	0.061 ***	0.007	0.235 ***
FC	0.021 **	−0.031 ***	−0.031 ***	1.000	0.126 ***	−0.040 ***	−0.078 ***	−0.019**	−0.214 ***
TNONPUB	0.128 ***	−0.048 ***	−0.043 ***	0.044 ***	1.000	−0.047 ***	−0.047 ***	−0.014	−0.349 ***
TARGETSIGMA	−0.009	−0.124 ***	−0.017 *	−0.012 *	−0.028 ***	1.000	0.102 ***	−0.138 ***	0.051 ***
TARGETRD	0.120 ***	−0.171 ***	−0.033 ***	−0.032 ***	−0.044 ***	0.071 ***	1.000	0.572 ***	0.038 ***
TARGETQ	0.037 ***	−0.101 ***	−0.09 ***	−0.015 **	0.004	−0.040 ***	0.358 ***	1.000	0.020 **
TVAL	−0.033 ***	0.050 ***	0.026 ***	−0.046 ***	−0.203 ***	−0.002	0.030 ***	0.015 **	1.000

This table reports some descriptive statistics for the data used in our regressions. In Panel A, we report sample means of variables from mergers and acquisitions included in the final sample (excluding tax-free reorganizations), divided into two groups of observations based on whether the acquisition includes an earnout. Refer to Appendix A for a full description of each variable. All continuous variables are winsorized at the 1st and 99th percentile. The difference in the means of variables between the two groups is reported in Column 5, with the corresponding t-statistics in Column 6. In Panel B, we report the mean, median, and standard deviation of each MTR estimate by tercile rank. Panel C presents the Pearson (Spearman) correlation coefficients of key variables below (above) the diagonal. *, **, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively.

Panel A reports a univariate comparison of the sample means of selected variables between taxable acquisitions with and without earnouts and the corresponding t-statistics. On average, the acquiring firm’s marginal tax rates are higher in acquisitions without earnouts than in acquisitions with earnouts, and the differences are statistically significant for both estimates of marginal tax rates. The average WW Index of acquiring firms is higher in deals with earnouts, consistent with the findings of Bates et al. (2018) indicating that financially constrained bidders tend to use earnouts more than less constrained bidders. A total of 98.8% of deals with earnouts are acquisitions of nonpublic targets, compared to 86.3% of deals without earnouts. As documented in prior studies, information asymmetry is likely greater in acquisitions of private companies and subsidiaries, and thus the target’s private or public status has been found to be an important determinant of earnout use. Significant differences in $TARGETRD$ and $TARGETQ$ also appear in line with the information- and valuation-based explanation for earnout use. Transactions with earnouts are on average smaller in terms of deal value, which is expected considering that valuation uncertainty could be higher for smaller growth-stage firms.

Panel B of Table 2 reports means of MTR estimates by year and by tercile rank. The gap between the top rates of rank 0 and rank 1 is much larger than that between rank 1 and rank 2. The majority of acquirers in our sample have MTRs close to the pre-TCJA top statutory corporate tax rate.

Panel C reports the correlation matrix, with the Pearson (Spearman) correlation coefficients below (above) the diagonal. Both measures of MTR are negatively correlated with the likelihood of earnout use, consistent with our predictions. The two measures of MTR we use in our study—$MTRBCG$ and $MTRJG$—are moderately positively correlated, which is not surprising given the difference in how each is estimated. Other correlation coefficients are in line with expectations based on findings from the prior literature. The acquirer’s financial constraint is negatively correlated with $EARNOUT$, while three of the four proxies for target valuation uncertainty are positively correlated with $EARNOUT$. The industry-based proxies for valuation uncertainty ($TARGETSIGMA$, $TARGETRD$, and $TARGETQ$) are positively correlated except for the correlation between $TARGETSIGMA$ and $TARGETQ$, which is negative. We expect each of these variables to capture different sources of valuation uncertainty, as $TARGETSIGMA$ measures volatility whereas $TARGETQ$ measures the target’s future growth potential. Finally, the acquirer’s financial constraint is negatively correlated with the acquirer’s MTR, which is expected given that less profitable firms tend to be more financially constrained and such firms may have loss carryforwards that reduce their tax rates.

4. Results

4.1. Acquirer Marginal Tax Rate and Earnout Use

Table 3. Acquirer firm MTR and earnout use.

Panel A: Tercile Rank of MTRBCG as Regressor of Interest
	EARNOUT
	(1)	(2)		(3)
MTRRANK	−0.039 ***	−0.030 ***		−0.030 ***
	(0.005)	(0.005)		(0.005)
HIGHFC		0.018 ***		0.018 ***
		(0.006)		(0.006)
TNONPUB		0.107 ***		0.104 ***
		(0.009)		(0.010)
TARGETSIGMA		−0.035		−1.067
		(0.800)		(0.888)
TARGETRD		0.815 ***		0.434 ***
		(0.122)		(0.097)
TARGETQ		0.007		0.009
		(0.011)		(0.010)
LNTVAL		−0.000		−0.001
		(0.002)		(0.002)
Target industry FE	No	No		Yes
Year FE	No	No		Yes
Observations	16,101	16,101		16,101
Adjusted R-squared	0.011	0.038		0.062
F-test: valuation uncertainty proxies	Not applicable	41.30		32.59
Joint significance level	Not applicable	0.00		0.00
Panel B: Tercile Rank of MTRJG as Regressor of Interest
	EARNOUT
	(1)	(2)		(3)
MTRRANK	−0.012 ***	−0.010 ***		−0.009 **
	(0.004)	(0.003)		(0.004)
HIGHFC		0.021 ***		0.021 ***
		(0.007)		(0.007)
TNONPUB		0.096 ***		0.092 ***
		(0.010)		(0.010)
TARGETSIGMA		0.083		−1.019
		(0.994)		(0.937)
TARGETRD		0.704 ***		0.308 **
		(0.147)		(0.132)
TARGETQ		0.006		0.016
		(0.015)		(0.014)
LNTVAL		−0.003 *		−0.006 **
		(0.002)		(0.003)
Target industry FE	No	No		Yes
Year FE	No	No		Yes
Observations	9908	9908		9908
Adjusted R-squared	0.001	0.032		0.056
F-test: valuation uncertainty proxies	Not applicable	24.09		20.74
Joint significance level	Not applicable	0.00		0.00
Panel C: Results Using Standardized Continuous Variables
	EARNOUT
	(1)		(2)
MTRBCG	−0.029 ***
	(0.005)
MTRJG			−0.009 **
			(0.004)
FC	0.004		0.004 *
	(0.004)		(0.002)
TNONPUB	0.109 ***		0.098 ***
	(0.009)		(0.009)
TARGETSIGMA	−0.002		0.000
	(0.005)		(0.006)
TARGETRD	0.035 ***		0.034 ***
	(0.006)		(0.007)
TARGETQ	0.003		0.002
	(0.005)		(0.007)
TVAL	−0.003 **		−0.006 ***
	(0.001)		(0.001)
Observations	16,101		9908
Adjusted R-squared	0.039		0.030
F-test: valuation uncertainty proxies	41.86		30.44
Joint significance level	0.00		0.00

This table reports the estimates of coefficients from Equation (1). In Panel A, the independent variable MTRRANK is the tercile rank of MTRBCG of the acquirer in the year of the acquisition announcement. In Panel B, MTRRANK is the tercile rank of MTRJG of the acquirer in the year of the acquisition announcement. In Panel C, we present results after standardizing all continuous variables to have a mean of 0 and a standard deviation of 1. Results are presented with and without controls and fixed effects. Standard errors are clustered by acquirer and by year. The corresponding robust standard errors are in parentheses, and *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively. In each column, we also report the F-statistic and its significance level from the joint test of the significance of valuation uncertainty proxies: TNONPUB, TARGETSIGMA, TARGETRD, and TARGETQ.

reports the results of the main analysis to test H1a. We separately report results using tercile-ranked MTRBCG and MTRJG as our measure of acquirer’s tax status in Panel A and Panel B, respectively. In both panels, we report results of estimating Equation (1) both with and without fixed effects. We estimate the model without fixed effects to check for over-controlling from including target industry fixed effects, as our proxies for target valuation uncertainty significantly vary across industries.

In all specifications, after controlling for other known determinants of earnout use from the prior literature such as target valuation uncertainty and acquirer financial constraint, the coefficient on $MTRRANK$ is negative and significant, consistent with our prediction that a high-tax rate acquirer is less likely to use an earnout compared to a low-tax rate acquirer. Our results indicate that, between transactions by a top-tercile MTR firm and those by a bottom-tercile MTR firm, the likelihood of earnout use declines by up to 6 percentage points or approximately 55 percent relative to the unconditional average frequency of earnout use in our sample (11 percent). In other words, acquirers facing a high MTR are about half as likely to use an earnout compared to acquirers facing a low MTR,22 holding constant other relevant transaction characteristics.

We also present results using standardized continuous variables in Panel C. We standardize each continuous variable (MTRBCG, MTRJG, FC, TARGETSIGMA, TARGETRD, TARGETQ, and TVAL) to have a mean of zero and standard deviation of 1. A one-standard deviation increase in the acquirer’s MTR is associated with a 0.9 to 2.9 percentage point lower likelihood of earnout use, which is greater than the effect of a one-standard deviation change in acquirer financial constraint (WW Index) and, based on the specification in Column 1, comparable to the effect of a one-standard deviation change in the target industry’s median R&D-to-sales ratio (TARGETRD).

In all columns, the coefficients on $TNONPUB$ and $TARGETRD$ are both positive and significant, consistent with valuation-related motives for earnout use documented in the prior literature. A joint test of significance on the set of variables proxying for valuation uncertainty ($TNONPUB$, $TARGETRD$, $TARGETQ$, and $TARGETSIGMA$) strongly rejects the null of no significant relation between those variables and $EARNOUT$. The coefficient estimates on $HIGHFC$ are positive and significant, which is consistent with financially constrained acquirers using earnouts as an alternative source of financing for the acquisition when access to conventional external sources of capital is limited (Bates et al., 2018).

Finally, these results suggest that over-controlling by using fixed effects is not a significant problem, and as such, results with fixed effects are used for the remainder of this paper. Untabulated results show that our inferences are unchanged when estimating the model without fixed effects.

4.2. Trade-Off Between Tax Planning Motives and Valuation-Related Motives for Earnout Use

In order to examine whether tax planning motives negatively affect the choice of earnout use based on target characteristics associated with valuation uncertainty, we estimate a modified version of Equation (1) by adding interaction terms between $MTRRANK$ and the four proxies for target valuation uncertainty: the target’s nonpublic status, target industry volatility, target industry median R&D intensity, and target industry median Tobin’s Q ratio.

We present the results in

Table 4. Interaction between acquirer MTR and valuation uncertainty.

	EARNOUT
	(1)	(2)
MTRRANK	0.011	0.013
	(0.016)	(0.015)
HIGHFC	0.017 ***	0.021 ***
	(0.006)	(0.007)
TNONPUB	0.167 ***	0.119 ***
	(0.020)	(0.015)
TARGETSIGMA	−0.840	−0.394
	(0.999)	(1.076)
TARGETRD	0.928 ***	0.585 ***
	(0.239)	(0.199)
TARGETQ	0.013	0.014
	(0.020)	(0.020)
LNTVAL	−0.001	−0.006 **
	(0.002)	(0.003)
MTRRANK $\times$TNONPUB	−0.029 ***	−0.016 **
	(0.006)	(0.007)
MTRRANK $\times$TARGETSIGMA	−0.147	−0.327
	(0.622)	(0.544)
MTRRANK $\times$TARGETRD	−0.249 **	−0.176 *
	(0.107)	(0.090)
MTRRANK $\times$TARGETQ	−0.002	0.002
	(0.009)	(0.008)
Measure of MTR used	MTRBCG	MTRJG
Target industry FE	Yes	Yes
Year FE	Yes	Yes
Observations	16,101	9908
Adjusted R−squared	0.063	0.057
F-test: interaction terms	9.21	2.19
Joint significance level	0.00	0.09

This table reports the results of estimating a modified version of Equation (1) by adding interaction terms between MTRRANK and proxies for valuation uncertainty (TNONPUB, TARGETSIGMA, TARGETRD, and TARGETQ). In Column 1, the independent variable MTRRANK is the tercile rank of MTRBCG of the acquirer in the year of the acquisition announcement. In Column 2, MTRRANK is the tercile rank of MTRJG of the acquirer in the year of the acquisition announcement. Both specifications include target industry fixed effects (defined at the two-digit SIC code) and year fixed effects. Standard errors are clustered by acquirer and by year. The corresponding robust standard errors are in parentheses, and *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively. In each column, we also report the F-statistic and its significance level (p value) from the joint test of significance of the interaction terms.

. Columns 1 and 2 show results from identical specifications except that $MTRRANK$ is based on the tercile rank of $MTRBCG$ in Column 1 and $MTRJG$ in Column 2. The main effect of nonpublic status ($TNONPUB$) and industry median R&D intensity ($TARGETRD$) continue to be positive and significant in both columns, consistent with estimates reported in Table 3. In both columns, the coefficient on the interaction term $MTRRANK\times TNONPUB$ is negative and significant. Similarly, in both columns, the coefficient estimates on the interaction term $MTRRANK\times TARGETRD$ are also negative and significant. We also report the F-statistic from a joint test of significance of the interaction terms, which rejects the null of no significant association. Overall, the results in Table 4 provide some evidence consistent with our predictions in H1b that the acquiring firm’s tax planning motives have an offsetting effect on information asymmetry and valuation uncertainty as a determinant of earnout use. In other words, a high-tax acquirer is less likely to use an earnout to mitigate valuation uncertainty and risk compared to a low-tax acquirer, even when they face similar levels of valuation uncertainty and risk.

The estimate of the main effect of $MTRRANK$ is insignificant in both columns. Taken together with the coefficients on the interaction terms with proxies for valuation uncertainty, the direct interpretation is that the relation between the acquirer’s marginal tax rate and the acquirer’s preference for an earnout depends on the extent of valuation uncertainty. Specifically, when valuation uncertainty is low, the marginal tax rate becomes less important of a factor in the acquirer’s decision to include an earnout. One possible explanation is that lower target valuation uncertainty is associated with lower uncertainty surrounding the amount and timing of those future contingent payments and the resulting additions to tax basis, in which case the acquirer might be less concerned about the tax planning implications of using an earnout, namely, the delay and uncertainty in future tax basis additions from the contingent payments.

The prior literature on earnout contracts provides some support for this explanation. For instance, Cadman et al. (2014) show that the fair value of the earnout is closer to the maximum amount payable under the earnout contract as target industry R&D intensity decreases. This implies that earnout contracts tend to involve more easily attainable performance targets or milestones when acquirers are less concerned about valuation uncertainty. Furthermore, earnout contracts tend to cover a shorter post-acquisition period (i.e., the acquirer makes earnout payments over a shorter number of years) when valuation uncertainty is expected to be resolved sooner (Cain et al., 2011).

Thus, when valuation uncertainty is lower, acquirers are content to structure the earnout contract such that (1) there is less uncertainty surrounding the contingent payments and (2) the contingent payments (and the resulting tax basis additions) are made sooner. The acquirer can still be relatively confident that its basis in the acquired assets will indeed step up shortly after the acquisition date, which at least partially mitigates concerns over the implications of earnout use on the present value of expected tax savings from incremental depreciation. Hence, it is possible that acquirers are less concerned about the tax planning implications of using an earnout when the target exhibits relatively low valuation uncertainty. The converse is that when valuation uncertainty is high, earnout payments (and the resulting tax basis additions) are more uncertain and made over a longer post-acquisition period, which might accentuate concerns over the potential forgone tax savings from depreciation.

Overall, our main results in Table 3 and Table 4 have some important theoretical and practical implications. First, our results highlight one disadvantage of an earnout contract for the acquirer. Despite its usefulness in the presence of high target valuation uncertainty, an earnout contract is not costless for an acquiring firm with more salient tax planning objectives. Second, we demonstrate how the “all costs” aspect of the Scholes and Wolfson (1992) framework applies to the use of earnout contracts in corporate M&A transactions. Specifically, the acquirer’s decision rule in this context involves weighing potential tax savings from stepping up the basis in acquired assets against the cost of assuming valuation risk that an earnout contract can mitigate. Therefore, the non-tax business costs that an effective tax planner must consider under the Scholes and Wolfson framework may include the cost of unaddressed valuation risks in M&A transactions.

Finally, the trade-off we observe between tax planning and mitigating valuation uncertainty implies the acquirer’s manager may be willing to take on more valuation risks in favor of additional tax benefits. This implication should be of particular interest to investors and regulators who might not share the same belief but are not directly involved in negotiating specific terms of a transaction. Prior research on corporate governance and tax avoidance has shown that investors view tax avoidance as facilitating managerial opportunism (R. Chen et al., 2022; Kim et al., 2011), suggesting that investors and managers are not always aligned on the optimal level of tax avoidance. These investors may place less value on the tax implications of earnout use and more value on protecting shareholders from the risk of overpayment.

4.3. Effect of Substitute Tax Shields

As an additional test, we investigate whether the use of debt to finance the acquisition acts as a substitute tax shield that mitigates the acquirer’s concern regarding the delayed and uncertain step-up in basis in the decision to use an earnout. Extant studies have documented the importance of the acquiring firm’s tax incentives in its choice of acquisition structure and financing method. Erickson (1998) shows that the acquirer’s tax rate is positively associated with the likelihood of debt-financed taxable acquisition. As an extension to Erickson (1998), Dhaliwal et al. (2005) analyze a sample of taxable acquisitions and found that acquirers are less likely to fund their acquisitions with debt when foreign tax credit limitations reduce the marginal tax benefit from interest deductions. Overall, prior evidence from tax research suggests that acquirers with high marginal tax rates are incentivized to use debt to fund taxable acquisitions, and this preference is attributable to the marginal tax benefits of interest deductions. We build on this stream of knowledge and investigate whether the tax benefits of a debt-financed acquisition are sufficient to offset the acquirer’s concerns related to the delayed step-up in the context of the acquirer’s decision to use an earnout.

We extract debt-financed acquisitions from the sample based on the source of funds for each transaction as reported by the SDC M&A database. Then, we re-estimate Equation (1) separately for the debt-financed subsample and for the remainder of taxable acquisitions, the results of which appear in

Table 5. Debt-financed acquisitions.

	EARNOUT
	(1)	(2)	(3)	(4)
MTRRANK	−0.011 *	−0.034 ***	−0.002	−0.011 ***
	(0.006)	(0.005)	(0.006)	(0.003)
HIGHFC	0.000	0.021 ***	0.006	0.025 ***
	(0.010)	(0.007)	(0.015)	(0.008)
TNONPUB	0.069 ***	0.115 ***	0.062 ***	0.105 ***
	(0.007)	(0.011)	(0.007)	(0.012)
TARGETSIGMA	−1.166	−0.857	0.387	−1.194
	(1.499)	(1.018)	(1.967)	(1.107)
TARGETRD	−0.221	0.539 ***	−0.207	0.446 ***
	(0.224)	(0.095)	(0.260)	(0.121)
TARGETQ	0.030	0.007	0.004	0.016
	(0.019)	(0.011)	(0.020)	(0.014)
LNTVAL	−0.017 ***	0.003	−0.019 ***	−0.003
	(0.004)	(0.002)	(0.005)	(0.002)
Difference in coefficient on MTRRANK	0.023 ***		0.009
	(9.25)		(1.93)
Financed with new debt	Yes	No	Yes	No
Measure of MTR used	MTRBCG	MTRBCG	MTRJG	MTRJG
Target industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	2751	13,350	1864	8044
Adjusted R-squared	0.061	0.063	0.053	0.061

This table reports the results of re-estimating Equation (1) after carving out transactions that are at least partially funded with new debt as reported by the SDC M&A database. Columns 1 and 3 show the results for debt-financed acquisitions, and Columns 2 and 4 report the results for all remaining taxable acquisitions. We also report the difference in coefficients on MTRRANK between two subsamples and its corresponding test statistics. In Columns 1 and 2, the independent variable MTRRANK is the tercile rank of MTRBCG of the acquirer in the year of the acquisition announcement. In Columns 3 and 4, MTRRANK is the tercile rank of MTRJG of the acquirer in the year of the acquisition announcement. Both specifications include target industry fixed effects defined at the two-digit SIC code and acquisition announcement year fixed effects. Standard errors are clustered by year of the acquisition announcement. The corresponding robust standard errors are in parentheses, except for the difference in the coefficient on MTRRANK for which the corresponding chi-square statistics are reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

. Consistent with Table 3, the regressor of interest $MTRRANK$ is based on both $MTRBCG$ (Columns 1 and 2) and $MTRJG$ (Columns 3 and 4). In Columns 1 and 3, which report the results for the debt-financed subsample, the coefficient estimates on $MTRRANK$ are lower in magnitude compared to the estimates for the rest of the taxable acquisitions reported in Columns 2 and 4. The difference is statistically significant for regressions using $MTRBCG$ but not for regressions using $MTRJG$. The results as a whole offer some evidence that tax implications are less important in the choice of earnout when the acquirer uses debt to finance the acquisition, consistent with the idea that the availability of a substitute tax shield in the form of debt removes the acquiring firm’s concerns over the delay and uncertainty in its tax basis step-up due to its choice of earnout use.

4.4. Sensitivity Tests

We perform the following sensitivity tests to check the robustness of our inferences. First, we refine our method of identifying transactions that involve a step-up in the tax basis of acquired assets. In our main analyses, as described above, we only exclude those transactions that are likely to be tax-free reorganizations based on the percentage of the acquirer’s stock in the deal consideration. However, this approach still potentially leaves in the sample taxable stock acquisitions with no Section 338 election. Our predictions do not apply to these transactions because they do not trigger a step-up in the tax basis of the acquired assets.

To better isolate transactions that trigger a step-up in basis in acquired assets, we use the form of transaction reported by SDC M&A database. Specifically, we can separate transactions reported as Acquisitions of Majority Interest (i.e., stock acquisitions) from those reported as Acquisitions of Assets. After removing tax-free reorganizations as described in Section 3, we use this form of transaction to identify a subsample of likely taxable transactions that are reported as acquisitions of assets. Although this results in a much smaller sample of transactions that trigger a step-up, one potential downside is that this removes all taxable stock acquisitions, including those in which parties elect Section 338 that also result in a step-up.

Table 6. Sensitivity tests using an alternative sample of taxable acquisitions.

	EARNOUT
	(1)	(2)	(3)	(4)
MTRRANK	−0.029 ***	−0.028	−0.011 ***	0.001
	(0.005)	(0.019)	(0.004)	(0.018)
HIGHFC	0.022 ***	0.021 ***	0.027 ***	0.027 ***
	(0.007)	(0.007)	(0.008)	(0.008)
TARGETSIGMA	−0.239	−0.087	−0.114	0.403
	(1.085)	(1.393)	(1.095)	(1.549)
TARGETRD	0.427 ***	1.191 ***	0.257	0.577 **
	(0.123)	(0.296)	(0.163)	(0.258)
TARGETQ	0.006	−0.008	0.019	0.021
	(0.011)	(0.015)	(0.016)	(0.022)
LNTVAL	−0.002	−0.002	−0.007 **	−0.008 **
	(0.002)	(0.002)	(0.003)	(0.003)
MTRRANK $\times$TARGETRD		−0.389 ***		−0.196
		(0.134)		(0.124)
MTRRANK $\times$TARGETSIGMA		−0.122		−0.254
		(0.693)		(0.569)
MTRRANK $\times$TARGETQ		0.008		−0.001
		(0.009)		(0.009)
Measure of MTR used	MTRBCG	MTRBCG	MTRJG	MTRJG
Target industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	10,692	10,692	6481	6481
Adjusted R-squared	0.050	0.051	0.049	0.049

This table reports the results of replicating Table 3 (Columns 1 and 2) and Table 4 (Columns 3 and 4) using an alternative sample of taxable asset acquisitions as a sensitivity check. Instead of simply removing tax-free reorganizations based on the portion of purchase consideration in the acquirer’s stock, we further refine the sample by only selecting those transactions reported as Acquisitions of Assets per the SDC M&A database. Robust standard errors are in parentheses, and *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively. In Columns 1 and 3, the independent variable MTRRANK is the tercile rank of MTRBCG of the acquirer in the year of the acquisition announcement. In Columns 2 and 4, MTRRANK is the tercile rank of MTRJG of the acquirer in the year of the acquisition announcement. All specifications include target industry fixed effects defined at the two-digit SIC code and year fixed effects.

reports the results of repeating our main regressions from Table 3 and 4 on this alternative sample. We drop $TNONPUB$ because 99.5% of this refined sample involves nonpublic targets. This high percentage is consistent with our expectation that taxable asset acquisitions of freestanding public corporations are rare.23 Overall, there is a significant reduction in sample size, likely due to the elimination of taxable stock acquisitions that may or may not trigger tax basis step-up depending on whether Section 338 is elected. The coefficient on $MTRRANK$ is negative and significant in both Columns 1 and 2, and the magnitudes are consistent with our results in Table 3. Results continue to be consistent with our prediction in H1a that high marginal tax rate acquirers prefer not to use an earnout.

In Columns 3 and 4, the main effect of $MTRRANK$ continues to be insignificant at conventional significance levels. The coefficients on the interaction term between $MTRRANK$ and $TARGETRD$ are negative in both columns, consistent with our main results in Table 4, but this is statistically significant only in Column 3. One possible explanation for the loss of statistical significance is the reduction in sample size and, specifically, the elimination of taxable stock acquisitions with a Section 338 election that would have also been subject to the hypothesized effect of tax status on the acquirer’s choice of earnout use.

Our second sensitivity test uses an alternative proxy for the tax status of the acquirer. A recent study by Christensen et al. (2022) reports that the majority of firms that are able to attain low effective tax rates do so because of large net operating loss carryforwards (NOL). Thus, the presence of large NOL is likely an important determinant of a firm’s tax status and the relative strength of its tax planning motives (Graham, 1996; Shevlin, 1990). In lieu of marginal tax rate estimates, we follow the approach proposed by Christensen et al. (2022) and estimate our models with $DEFICIT$ as the independent variable, which is a binary variable equal to one if the acquirer had accumulated deficit (i.e., negative value for Compustat variable RE) prior to the year of the acquisition announcement. This variable proxies for whether a firm has large NOL.24 To the extent that the existence of large NOL is associated with low marginal tax rates, we expect the coefficient on $DEFICIT$ to be positive.

We report the results using $DEFICIT$ in

Table 7. Sensitivity tests using proxy for large NOL.

	EARNOUT
	(1)	(2)	(3)	(4)
DEFICIT	0.035 ***	−0.010	0.030 ***	−0.028
	(0.008)	(0.027)	(0.010)	(0.061)
HIGHFC	0.022 ***	0.021 ***	0.025 ***	0.025 ***
	(0.006)	(0.006)	(0.008)	(0.008)
TNONPUB	0.105 ***	0.095 ***	0.066 ***	0.046
	(0.009)	(0.009)	(0.022)	(0.030)
TARGETSIGMA	−0.855	−0.780	−0.005	−0.176
	(0.945)	(0.896)	(1.145)	(1.095)
TARGETRD	0.447 ***	0.205 *	0.411 ***	0.137
	(0.084)	(0.108)	(0.113)	(0.143)
TARGETQ	0.009	0.012	0.008	0.016
	(0.010)	(0.013)	(0.011)	(0.016)
LNTVAL	−0.007 ***	−0.007 ***	−0.008 ***	−0.007 **
	(0.002)	(0.002)	(0.003)	(0.003)
DEFICIT $\times$TNONPUB		0.039 ***		0.061
		(0.012)		(0.036)
DEFICIT $\times$TARGETRD		0.669 ***		0.740 ***
		(0.172)		(0.238)
DEFICIT $\times$TARGETSIGMA		−0.298		0.313
		(0.990)		(1.094)
DEFICIT $\times$TARGETQ		−0.007		−0.020
		(0.015)		(0.022)
Target industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	17,288	17,288	11,403	11,403
Adjusted R-squared	0.063	0.065	0.051	0.053

This table reports tests of H1a and H1b using an alternative measure of tax status as a sensitivity check. In lieu of MTRRANK, we use DEFICIT, a binary variable equal to 1 if prior to the year of acquisition, the acquirer had accumulated deficit (i.e., negative retained earnings) and 0 otherwise (Christensen et al., 2022). In Columns 1 and 2, we define our sample of taxable acquisitions by excluding deals where at least 80% of the consideration is in the acquirer’s stock, consistent with the sample used in our main analyses in Table 3 and Table 4. In Columns 3 and 4, we define our sample of taxable acquisitions as those reported as Acquisitions of Assets per the SDC M&A database, consistent with the sample used in our sensitivity test in Table 6. All specifications include target industry fixed effects defined at the two-digit SIC code and year fixed effects. Standard errors clustered by acquirer and by year are in parentheses, and *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

. In Columns 1 and 2, we define our sample of taxable acquisitions by excluding deals where at least 80% of the consideration is in the acquirer’s stock, consistent with the sample used in our main analyses reported in Table 3 and Table 4. In Columns 3 and 4, we define our sample of taxable acquisitions as those reported as Acquisitions of Assets per the SDC M&A database, consistent with the sample used in our first sensitivity test reported in Table 6. Consistent with our predictions in H1a, the coefficients on $DEFICIT$ in Columns 1 and 3 are positive and significant. A firm that does not have a large NOL likely faces a comparatively higher tax rate and thus is less likely to use an earnout due to its tax implications concerning the tax basis step-up. In addition, the results reported in Columns 2 and 4 provide some evidence consistent with the offsetting effect of tax planning motives on valuation risk and uncertainty. We caution, however, that although we separately control for the acquirer’s financial constraint using $HIGHFC$, we cannot rule out the possibility that a positive coefficient on $DEFICIT$ partially captures the effect of financially constrained bidders having a preference for earnout use (Bates et al., 2018) that is not fully captured by $HIGHFC$.

In an additional sensitivity test reported in

Table 8. Sensitivity tests using continuous MTR.

Panel A: Original Sample
	EARNOUT
	(1)	(2)	(3)	(4)
MTRBCG	−0.365 ***		−0.174
	(0.069)		(0.170)
MTRJG		−0.090 **		−0.048
		(0.043)		(0.235)
HIGHFC	0.018 ***	0.019 ***	0.018 ***	0.018 **
	(0.006)	(0.007)	(0.006)	(0.007)
TNONPUB	0.106 ***	0.092 ***	0.204 ***	0.140 ***
	(0.010)	(0.010)	(0.028)	(0.032)
TARGETSIGMA	−1.187	−1.130	−2.196	−3.270 *
	(0.911)	(0.941)	(2.161)	(1.791)
TARGETRD	0.409 ***	0.309 **	1.208 ***	1.232 ***
	(0.099)	(0.131)	(0.365)	(0.419)
TARGETQ	0.008	0.016	−0.020	−0.001
	(0.010)	(0.014)	(0.022)	(0.032)
LNTVAL	−0.002	−0.006**	−0.002	−0.006 **
	(0.002)	(0.003)	(0.002)	(0.003)
MTRBCG × TNONPUB			−0.307 ***
			(0.077)
MTRBCG × TARGETRD			−2.680 **
			(1.197)
MTRBCG × TARGETSIGMA			3.238
			(7.251)
MTRBCG × TARGETQ			0.100
			(0.088)
MTRJG × TNONPUB				−0.146
				(0.088)
MTRJG × TARGETRD				−3.137 **
				(1.278)
MTRJG × TARGETSIGMA				7.109
				(6.449)
MTRJG $\times$TARGETQ				0.065
				(0.106)
Target industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	16,101	9908	16,101	9908
Adjusted R-squared	0.062	0.056	0.063	0.058
Panel B: Alternative Sample from Table 6
	EARNOUT
	(1)	(2)	(3)	(4)
MTRBCG	−0.338 ***		−0.474 **
	(0.081)		(0.228)
MTRJG		−0.047		−0.123
		(0.045)		(0.229)
HIGHFC	0.022 ***	0.026 ***	0.022 ***	0.024 ***
	(0.007)	(0.008)	(0.007)	(0.008)
TARGETSIGMA	−0.254	−0.223	−0.041	−2.846
	(1.091)	(1.089)	(2.281)	(2.428)
TARGETRD	0.401 ***	0.256	1.418 **	1.396 ***
	(0.127)	(0.161)	(0.551)	(0.351)
TARGETQ	0.005	0.019	−0.046	0.006
	(0.011)	(0.016)	(0.030)	(0.033)
LNTVAL	−0.003	−0.007 **	−0.003	−0.007 **
	(0.002)	(0.003)	(0.002)	(0.003)
MTRBCG × TARGETRD			−3.417*
			(1.774)
MTRBCG × TARGETSIGMA			−1.117
			(7.440)
MTRBCG × TARGETQ			0.180
			(0.115)
MTRJG × TARGETRD				−3.882 ***
				(1.209)
MTRJG × TARGETSIGMA				8.807
				(7.922)
MTRJG $\times$TARGETQ				0.057
				(0.117)
Target industry FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Observations	10,692	6481	10,692	6481
Adjusted R-squared	0.050	0.048	0.051	0.051

This table reports the results of sensitivity tests using continuous MTR variables instead of rank-transforming them. Panel A shows results from replicating Table 3 (Panel A Column 3, Panel B Column 3) and Table 4 using a continuous MTR variable as a sensitivity check. Panel B shows results from replicating Table 6 using a continuous MTR variable but with the alternative sample of taxable asset acquisitions as described in Table 6. The corresponding robust standard errors are in parentheses, and *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively. All specifications include target industry fixed effects defined at the two-digit SIC code and year fixed effects. Standard errors are clustered by acquirer and by year.

, we use the continuous MTR variable rather than the tercile-ranked MTR. Panel A reports results based on the same sample from our main analyses in Table 3 and Table 4, and Panel B reports results based on the alternative sample as reported in Table 6. The results continue to show a negative relation between the MTR variable and the likelihood that the deal includes an earnout, and thus our inferences are largely robust to these alternative measures of acquirer tax status.

5. Conclusions

In this study, we examine the role of tax planning incentives in the acquirers’ choice of earnout use in taxable corporate acquisitions. Although firms can benefit from using earnouts, the U.S. tax code does not allow acquirers in taxable acquisitions to receive an additional tax basis in the acquired assets until the contingency is resolved and the earnout payments are made. Because of this potentially adverse tax consequence, we predict and find that taxable transactions are less likely to include an earnout when the acquirer faces a high marginal tax rate. Furthermore, the results of our analysis suggest that tax planning motives have a moderating effect on information asymmetry and target valuation uncertainty that motivate acquirers to use earnouts. An important implication of our findings is that some high-tax acquirers are willing to leave some valuation risk unmanaged, which becomes potentially problematic insofar as their shareholders and regulators do not hold the same belief on whether the incremental tax avoidance from tax basis step-up is worth the extra valuation risk.

This study contributes to prior literature in two ways. First, while extant research on determinants of earnout use has largely focused on non-tax motives for earnout use, to our knowledge, this paper is the first to explore acquirers’ use of earnouts from a corporate tax planning standpoint. More broadly, it contributes to an understanding of the costs of earnout use and a trade-off against its benefits. Second, this paper adds to tax research on mergers and acquisitions. In particular, this paper extends prior studies on corporate income tax-motivated firm choices within a taxable acquisition structure (Dhaliwal et al., 2005; Erickson & Wang, 2007; Lynch et al., 2019). Future research might examine whether acquirers manage post-acquisition earnings downwards to avoid making earnout payments altogether and simultaneously preclude any tax uncertainty about delayed addition to basis.

We acknowledge two limitations of our study. First, the vast majority of our sample consists of acquisitions of private targets or subsidiaries, which limits our ability to accurately measure target characteristics as covariates. We follow prior studies and measure proxies for target valuation uncertainty at the industry level (Bates et al., 2018; Cain et al., 2011). Second, we are unable to rule out the possibility that both the acquirer’s choice of earnout and marginal tax rates are endogenously determined, limiting our ability to draw causal inferences.