Is there a relation between labor investment inefficiency and corporate tax avoidance?

Accepted Manuscript Is there a relation between labor investment inefficiency and corporate tax avoidance? Grantley Taylor, Ahmed Al-Hadi, Grant Richardson, Usamah Alfarhan, Khamis AlYahyaee PII:

S0264-9993(18)31359-2

DOI:

https://doi.org/10.1016/j.econmod.2019.01.006

Reference:

ECMODE 4807

To appear in:

Economic Modelling

Received Date: 21 September 2018 Revised Date:

22 December 2018

Accepted Date: 13 January 2019

Please cite this article as: Taylor, G., Al-Hadi, A., Richardson, G., Alfarhan, U., Al-Yahyaee, K., Is there a relation between labor investment inefficiency and corporate tax avoidance?, Economic Modelling (2019), doi: https://doi.org/10.1016/j.econmod.2019.01.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Is there a Relation between Labor Investment Inefficiency and Corporate Tax Avoidance?

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Grantley Taylor* School of Accounting, Curtin Business School Curtin University GPO Box U1987 Perth, Western Australia 6845, Australia Tel: +61-8-9266-3377 Fax: +61-8-9266-7196 E-mail: [email protected]

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Ahmed Al-Hadi College of Applied Science at Nizwa, Sultanat of Oman Tel: +968-98999651 E-mail: [email protected]

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School of Accounting, Curtin Business School Curtin University GPO Box U1987 Perth, Western Australia 6845, Australia Tel: +61-4-22020993 Fax: +61-8-9266-7196

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Grant Richardson Department of Accounting and Corporate Governance Faculty of Business and Economics Macquarie University Eastern Road, North Ryde, NSW, Australia 2109 Tel: +61-2-9850-7994 Fax: +61-2-9850-8497 E-mail: [email protected]

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Usamah Alfarhan Department of Economics and Finance, College of Economics and Political Science Sultan Qaboos University PO Box 20 Muscat 123, Sultanat of Oman Tel: +968-24141850 E-mail: [email protected] Khamis Al-Yahyaee Department of Economics and Finance, College of Economics and Political Science Sultan Qaboos University PO Box 20 Muscat 123, Sultanat of Oman Tel: +968-24141833 Email: [email protected] *Corresponding author.

ACCEPTED MANUSCRIPT Is there a Relation between Labor Investment Inefficiency and Corporate Tax Avoidance?

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Abstract: This paper investigates the relation between labor investment inefficiency and corporate tax avoidance. Employing a large sample of 61,542 U.S. firm-year observations over the 1962–2014 period, our regression results show that labor investment inefficiency is significantly positively related to tax avoidance. More specifically, we find that a one standard deviation of labor investment inefficiency leads to a 0.71% reduction in the accounting effective tax rate. Our findings are robust to endogeneity concerns, alternative proxy measures of tax avoidance and labor investment efficiency, and additional control variables pertaining to accounting quality and managerial ability. Taken together, our regression results show that labor investment inefficiency is an important determinant of corporate tax avoidance.

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JEL Classification: D22, G30; H26.

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Keywords: Firm behavior; labor investment inefficiency; corporate tax avoidance. 1. Introduction

A growing body of accounting and economics research argues that inefficiencies in a firm’s labor investment1, which represents a significant input into a firm’s production costs, are a consequence of broader market imperfections concerning firms’ operational, investing

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and financing activities (e.g. Pinnuck and Lillis 2007; Jung et al. 2014). Indeed, labor inefficiencies are reflective of the contracting and control environment that encapsulates

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agency costs, monitoring, information transparency and exchange (Pinnuck and Lillis 2007; Jung et al. 2014). We extend prior research by specifically investigating the relation between

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labor investment inefficiency and firms’ propensity to engage in tax avoidance.2 We are motivated to examine labor efficiency in this study because of its economic importance both as an input and as a cost component (Jung et al. 2014). Labor efficiency in a firm is a reflection of its broader organizational capabilities, resource endowment and investment efficiencies. In fact, labor efficiency is critical to a firm’s success because it can 1

Labor investment inefficiency is defined as the difference between the actual change in a firm’s labor force and the expected change based on fundamental economic factors. For further detail, see 3.3 in the section on research design. 2 We define corporate tax avoidance in this study as a broad spectrum of activities ranging from the exploitation of uncertainties or variability in the interpretation of the tax law to arrangements or schemes designed specifically to reduce taxable income which may be illegal including tax evasion (e.g. Hanlon and Heitzman 2010).

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ACCEPTED MANUSCRIPT affect its ability to effectively compete with its peers, to ensure that there is adequate transmission of information amongst stakeholders and to comply with regulations and legislation. Finally, labor is also important to be analyzed separately from other inputs (e.g.

firm’s competitive aspirations (Pfeffer 1996).

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capital) because it is increasingly considered to be one of the most important factors for a

Based on neoclassical theory tenets, labor inefficiencies will lead to sub-optimal profitability which may impact firms’ ability to continue as a going concern (Cameron et al.

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1991; Cameron 1994). The reason for this is that labor, unlike capital, is fundamentally paid for out firm’s operational cash flows, and not by means of debt or equity financing. If

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inefficient employment of labor reduces firms’ profits and their internally generated revenues such that firms’ fail to cover their current expenditure needs (i.e. payment of wages, interests and taxes), they will have an incentive to engage in cash-saving behavior which may include tax avoidance.

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We posit that less efficient investments in labor lead to increased levels of corporate tax avoidance. Labor inefficiencies involves reduced information transparency and exchange (Jung et al. 2014). Firms, in practice, that experience labor inefficiencies are subject to

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information asymmetry or agency problems, leading to situations involving moral hazard or

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adverse selection3. For instance, moral hazard problems relating to over-investment in labor could lead to inefficiencies in contracting and monitoring in a firm creating obscurity and lack of internal control that is conducive to tax aggressive planning and the opportunity of firm management to engage in rent-seeking behaviour. Under-investment in labor through 3

Two forms of capital market imperfections may arise in the form of information asymmetry and moral hazard, which could lead to investment inefficiencies (Biddle 2009). These imperfections may also affect firms hiring and firing policies. Moral hazard arises when information asymmetry allows firm managers to act in their own self-interest making monitoring costly if these problems are to be mitigated (Jensen 1986). Moral hazard may result in over-investment when managers possess and use their superior information to invest in projects that may not generate positive NPVs, but do so to empire-build. Moral hazard may also lead to under-investment when firm managers pass-up on profitable investments or projects with positive NPVs as they prefer to act in a risk-averse manner. Thus, moral hazard can result in a firm passing-up on profitable investments or making unprofitable investments for rent extraction purposes (Jensen 1986).

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ACCEPTED MANUSCRIPT information asymmetry between firm managers and investors may be manifested as overfiring or under-hiring that generate associated operational inefficiencies and a flow-on impact on firms’ cash flow (Jung et al. 2014). For instance, a conservative approach in terms of hiring and firing may, in turn, constrain the profitability of a firm’s operations, providing the

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incentive for managers to pursue tax avoidance activities to increase cash flows. In particular, inefficiencies around labor may impact the ability of the firm to effect adequate monitoring and control. As support for this assertion, Chyz et al. (2013) find that labor union power

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reduces tax planning aggression attributed to the increased monitoring by those labor unions. Further, Taylor and Richardson (2012) find that higher levels of cash and equity based

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compensation of directors incentivize managers to take risks leading to a higher degree of tax aggressiveness. Similarly, as labor inefficiencies increase rent seeking opportunities for managers, they are more likely to take on board risk and to pursue tax aggressive strategies. Joulfaian (2000) for instance, find that managers of firms that cheat on their personal tax

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returns have a greater likelihood of assisting the firm in which they manage to avoid taxes. Overall, there seems to be support for the premise that labor efficiencies matter for the tax planning of firms.

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We use the percentage change in firms’ number of employees (net hiring) to proxy for

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labor investment, and we create a measure of labor efficiency based on the absolute deviation of actual net hiring from expected net hiring. Our primary measure of labor efficiency is based on the model devised by Pinnuck and Lillis (2007) and the expected level of net hiring includes such variables as sales growth, liquidity, leverage and profitability which are typical determinants of firms’ hiring practices. Consequently, our measure of abnormal net hiring encapsulates the amount of net hiring not attributable to underlying economic factors in a

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ACCEPTED MANUSCRIPT firm (Pinnuck and Lillis 2007; Jung et al. 2014).4 Finally, the proxy measures of corporate tax avoidance and related control variables employed in this study all represent commonly used measures which are drawn from prior tax avoidance research (see Hanlon and Heitzman 2010).

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Our study differs from prior research by Francis et al. (2014) and Koester et al. (2016) who relate managerial ability by applying the Demerjian et al. (2012) model to corporate tax avoidance.5 They explore the effect of firm manager’s overall ability to convert resources

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(i.e. labor, capital and intangible assets) into revenues through an efficient frontier, linking various input mixes to output levels. However, we specifically focus our attention on a firm’s

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level of efficiency in labor employment, which is predicted by the overall economic environment. Differentiation between labor and other inputs is justified because capital inputs are normally fixed in the short-run of a firm’s operations and are less likely to affect its current levels of tax avoidance. Further, labor which is generally accepted to account for

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around two-thirds of the economic value added by a firm (e.g. Hamermesh 1993; Bernanke 2004), is normally paid out of current revenues (Jung et al. 2014) and therefore makes labor more closely aligned to tax avoidance than capital, which is financed externally over the

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long-term. Finally, the expertise needed to maximize revenue and effectively deploy a firm’s

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resources can also be distinguished from a manager’s efficiency in labor hiring and firing (Jung et al. 2014).

Using a large sample of 61,542 U.S. firm-year observations over the 1962–2014 period, our regression results show that labor investment inefficiency is significantly positively related to tax avoidance. More specifically, we find that a one standard deviation of labor investment inefficiency gives rise to a 0.71% reduction in the accounting effective tax rate.

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As part of our sensitivity analyses, we also employ an alternative measure of abnormal net hiring based on the industry median (see Jung et al. 2014). 5 We note that these studies provide conflicting results. In particular, Francis et al. (2014) find a negative relation between managerial ability and tax avoidance, while Koester et al. (2016) observe the opposite result.

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ACCEPTED MANUSCRIPT Our findings are robust to endogeneity concerns, alternative proxy measures of tax avoidance and labor investment inefficiency, and additional control variables relating to accounting quality and managerial ability. This study contributes to the extant literature in the following ways. First, it extends prior

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literature on corporate tax avoidance (see Hanlon and Heitzman 2010 for a survey of this literature). For instance, early research on tax avoidance assumes that labor investment efficiency is static (Hanlon and Heitzman 2010), whereas later research emphasizes that a

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firm manager’s access to and use of labor resources is likely to represent a key determinant of tax avoidance (e.g. Higgins et al. 2014). Second, this study provides robust empirical

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evidence which indicates for the first time that labor investment inefficiency is significantly positively related to tax avoidance. Our results therefore show that labor investment inefficiency is a key determinant of tax avoidance. Third, by analyzing a firm’s labor investment, this study also enhances our understanding of the relation between investment

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efficiency and tax avoidance generally (see Francis et al. 2014; Koester et al. 2016) which is likely to have flow-on effects in terms of financial reporting quality, profitability and firm value (e.g. Lambert et al. 2007; McNichols and Stubben 2008; Biddle et al. 2009). Fourth,

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this study also contributes to research that links agency theory to corporate tax avoidance

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(e.g. Crocker and Slemrod 2005; Desai and Dharmapala 2006) by analyzing the moral hazard and adverse selection framework, and how it leads to significant levels of tax avoidance in a firm. Finally, this study also provides important insights to tax authorities (e.g. the Internal Revenue Service (IRS)) and policymakers who seek to identify the circumstances where the risk of corporate tax avoidance is higher. This paper proceeds as follows. Section Two provides the literature review and theory relating to labor investment efficiency, and develops our hypothesis. Section Three describes

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ACCEPTED MANUSCRIPT the research design, and Section Four presents the empirical results. Finally, Section Five concludes the paper.

2. Theory and Hypothesis development

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We assess the relation between labor employment inefficiency and corporate tax avoidance using neoclassical theory of the firm as developed, amongst others, by pioneering economist Alfred Marshall in his magnum opus, Principles of Economics (1890) and later by

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Ronald Coase (1937). Efficient employment requires firms to hire inputs such that the marginal revenue product of each input is equal to its marginal cost. Prior research confirms

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that the productivity and compensation of labor are significant components of firms’ valueadded and total costs of production across industries and economies (Bentolila and Saint-Paul 2003). Ultimately, these studies show that firms’ have the fundamental objective of profit maximization which requires the production of a certain level of output at the lowest possible

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cost. This prompts firms to employ inputs, including labor and capital, efficiently. This is of particular importance since, to the extent that labor costs are variable, as opposed to other fixed costs associated with labor employment such as hiring and firing, they are financed by

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firms’ current revenues as opposed to external funding (Jung et al. 2014). According to

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Jensen (1993), firm managers tend to ignore excess capacity until investment operations deteriorate. During periods of economic downturn, they might revert to the option of “rightsizing” their firms through permanent layoffs to enhance efficiency (Cameron et al. 1991; Cameron 1994). Consequently, firms with higher labor investment efficiency are expected to perform better and are less likely to engage in tax avoidance as they have greater access to external financing when needed. Hence if internal funding sources fall short of current financing needs that include payment of wages, firms will be motivated to revert to less costly means of securing liquidity, including the possibility of engaging in tax avoidance.

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ACCEPTED MANUSCRIPT We posit that labor inefficiencies are likely to lead to increased corporate tax avoidance through a number of conduits or pathways that may involve poor information flow and reporting, resourcing and monitoring and through capital rationing. Each of these ‘channels’

follows:

2.1 Labor efficiency, information environment and tax avoidance

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through which labor inefficiencies may impact corporate tax avoidance are discussed as

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Prior research shows that a reduction in information asymmetry through the provision of higher quality financial reporting increases employment efficiency (e.g. Biddle and Hilary

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2006; McNichols and Stubben 2008). In practice, firms may experience suboptimal employment levels because of information asymmetry or agency problems that may also involve moral hazard or adverse selection (Jensen and Meckling 1976; Myers and Majluf 1984; Baker et al. 2003). Specifically, labor investment inefficiency could impact a firm’s

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propensity to engage in tax avoidance by affecting the flow of information to capital markets, creating informational asymmetry between firm managers and investors, and by increasing the level of uncertainty in expected future cash flows (Balakrishnan et al. 2012; Rego and

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Wilson 2012). Persistent information asymmetry between managers and input providers

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could potentially bring about ill-informed and inefficient operational and financing decisions that lead to lower-than-optimal profits, market values and, hence, reduced access to external funding (Jensen 1986; Bertrand and Mullainathan 2004; Richardson 2006). These events could have implications in terms of funding opportunities for firms providing impetus for firm managers to draw upon savings from tax avoidance activities (Lambert et al. 2007). At an extreme, managers can justify that the complexity and obfuscation associated with various market frictions around labor inefficiencies are necessary to mask poor performance and increased levels of risk. These activities then shield tax avoidance schemes and reduce

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ACCEPTED MANUSCRIPT the risk of detection by the Internal Revenue Service (IRS). Higher quality financial reporting reduces information asymmetry by assisting firm managers to make more efficient investment decisions (including decisions related to labor investment) which leads to improved financial performance (Jung et al. 2014). Deviations from a firm’s optimal

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employment level could exert pressure on its liquidity position and create an incentive for firm managers to engage in high levels of tax avoidance, particularly for firms already facing some degree of financial distress. 6

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2.2 Labor efficiency, monitoring and resourcing, and tax avoidance

An environment characterized by market frictions and labor investment inefficiencies may

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aid managerial rent extraction because firm managers are allowed to act in their own selfinterest to pursue private gains (Jensen and Meckling 1976). It is likely that labour inefficiencies facilitate managerial rent extraction and bad news hoarding for extended periods by providing masks and justification for such opportunistic behavior. The

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concealment of bad news around labor based outcomes and inefficiencies may be driven by a range of incentives such as compensation contracts, career concerns and empire building which can facilitate managerial opportunism and their incentive and opportunity to engage in

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tax avoidance. Accumulation of negative news around labor inefficiencies over an extended

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period of time can prevent management from taking corrective action or for investors to question managements’ strategies around improving operational efficiencies. Such agency costs can place additional financial constraints on a firm as managers may be reluctant to expose their rent extraction to outside finance providers by accessing external capital markets (Stein 2003). If this is the case, firm managers who are more reliant on internal financing may be motivated to engage in obscure tax avoidance activities to increase funding.

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Edwards et al. (2015) find that financially constrained firms (as defined by those experiencing increases in the cost of external funding, or an increase in the difficulty to access external funding) require additional internal sources of finance to fund existing operations and maintain solvency.

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ACCEPTED MANUSCRIPT Tax avoidance in a moral hazard setting may go unimpeded as monitoring is costly and is unlikely to constrain firm managers incentivized to engage in tax avoidance activities (Jung et al. 2014). Additionally, a lack of transparency and information exchange generated through labor inefficiencies may also make it difficult to maintain effective governance and internal

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control structures thereby opening up the potential for aggressive tax planning to take place. For instance, a lack of appropriately trained personnel in certain audit or governance committees’ roles may lead to internal control weaknesses and an increase in probability of

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avoidance activities (Desai and Dharmapala 2009).

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tax avoidance. It may also be difficult for a firm’s auditors to monitor obscure a tax

2.3 Labor efficiency, expected cash flows and tax avoidance

Labor investment inefficiencies can lead to capital rationing for a firm because outside investors (who have inferior information compared to firm managers) charge a higher cost of

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capital to those firms (Jung et al. 2014). The reason for this is that labor market efficiencies could obscure capital provider’s ability to understand a firm’s financial statements, and interpret the source and persistence of its earnings, cash flows and risks. An increase in cost

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of external financing makes firms more reliant on internal funds to finance their operations

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and investments (Myers and Majluf 1984; Amihud and Mendelson 1986; Diamond and Verrecchia 1991; Easley and O’Hara 2004). Labor inefficiencies are likely then to impact operational cash flows and hence increased cash tax savings via tax avoidance mechanisms may be necessary to meet any shortfall.

2.4 Labor efficiency and tax avoidance-summary The nexus between labor efficiency, firms’ information and internal control environment and financing capacity is important to our current discussion. Overall, we expect that labor

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ACCEPTED MANUSCRIPT investment inefficiency may adversely affect the transparency and flow of information amongst stakeholders, may impair the strength of a firms’ governance and internal control systems and ultimately is likely to impact the financial capacity of firms as labor is typically funded out of operational cash flows. In aggregate, increased obscurity, a weaker control

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environment and reduced expected cash flows stemming from labor inefficiencies provide firm management with the opportunity and capacity to engage in increased tax avoidance. Our schematic relation between labor efficiency and corporate tax avoidance is shown as

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Figure 1. The evidence provided is that labor investment efficiency is a potential determinant of operating and financing performance which impacts the efficient use of internally

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generated cash flows (see Edwards et al. 2015; Law and Mills 2015). Prior literature provides no or limited evidence of the consequences of labor inefficiencies in firms and the linkages we provide, depicted as Figure 1, provide evidence for the first time that such inefficiencies can drive firms’ propensity to engage in tax avoidance. We therefore develop the following

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(directional) hypothesis:

H1: Labor investment inefficiency is positively related to corporate tax avoidance.

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3. Research Design

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3.1. Sample selection and data source Our sample originally consisted of all firms merged from the Compustat and CRSP annual files over the 1962–2014 period. Initially, this gave rise to 238,702 firm-year observations (see Table 1, Panel A). However, the sample was then reduced to 81,192 firm-year observations after excluding missing observations (111,913) used to calculate the net hiring variable (NET_HIREit) in Eqn. (1) in addition to missing values for the control variables (45,597) employed in Eqn. (1). Finally, the sample was reduced to 61,542 firm-year

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ACCEPTED MANUSCRIPT observations after excluding missing values for the tax variables (19,650) used in Eqn. (2), which is our baseline regression model. Table 1 (Panel B) reports the distribution of the sample based on the absolute NET_HIREit variable. Finally, we note that all variables (except dummy variables) are winsorized at the 1st

our empirical results. [Insert Table 1 Here]

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3.2. Dependent variable

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and 99th percentiles in our study to reduce the likelihood of outliers significantly impacting

Our dependent variable is represented by corporate tax avoidance (TAX_AVOID). We

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employ the accounting effective tax rate (GAAP_ETRit), which has been used extensively in prior research (e.g. Rego 2003; Wilson 2009; Dyreng et al. 2010; Hoi et al. 2013), as our main proxy measure of tax avoidance.7 However, to improve the robustness of our empirical results, we also utilize other measures of tax avoidance such as the cash effective tax rate

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(CASH_ETRit), discretionary book-tax differences (BTD_DDit) and total unrecognized tax benefits (UTB_TOTALit) in our sensitivity analyses.8 GAAP_ETR it is computed in this study as total tax expense (comprising both current and

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deferred tax expense) scaled by pre-tax book income less special items.9 This particular tax

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avoidance proxy measure considers tax avoidance practices that affect a firm’s net income (Robinson et al. 2010) and is used to evaluate its overall tax burden and level of tax avoidance (e.g. Rego 2003; Wilson 2009; Dyreng et al. 2010; Hoi et al. 2013). Consistent

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We do not employ CASH_ETRit as a main proxy measure of tax avoidance in this study as cash taxes paid are often fragmented in nature. For example, it is possible that a firm reports nil or negligible amounts of cash taxes paid in some years followed by large absolute cash taxes paid upon IRS audit settlements in other years (Hanlon and Heitzman 2010). Nevertheless, CASH_ETRit together with several other proxy measures of tax avoidance (i.e. BTD_DDit and UTB_TOTALit) are employed in our sensitivity analyses later in this paper (see below). 8 In fact, Hanlon and Heitzman (2010) claim that the use of different proxy measures of tax avoidance circumvents any inherent limitations of any specific measure. 9 We also note that our measure of GAAP_ETRit is bounded between 0–1.

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ACCEPTED MANUSCRIPT with Dyreng et al. (2010), lower GAAP_ETR

it

values represent higher levels of tax

avoidance.

3.3. Independent variable

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To proxy for investments in labor, we follow prior research by Pinnuck and Lillis (2007) and Jung et al. (2014) and compute a firm’s net hiring (NET_HIREit) as reflected in the change in the number of its employees. We then calculate a firm’s labor investment

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inefficiency using abnormal net hiring (AB_NET_HIREit) which denotes our independent variable of interest. AB_NET_HIREit is measured as the abnormal change in the number of

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employees (Pinnuck and Lillis 2007). In particular, AB_NET_HIREit represents the difference between the actual change in a firm’s labor force and the expected change in labor as predicted by fundamental economic factors: abnormal net hiring = actual net hiring – expected net hiring (Jung et al. 2014). The ordinary least squares (OLS) regression model

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used to calculate a firm’s net hiring based on prior research by Pinnuck and Lillis (2007) is estimated as follows:

NET_HIREit = α0it + β1SALES_GROWTHit-1 + β2SALES_GROWTHit + β3∆ROAit +

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β4∆ROAit-1 + β5ROAit + β6RETURNit + β7SIZE_Rit-1 + β8QUICKit-1 + β9∆QUICKit-1 +

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β10∆QUICKit + β11LEVit-1 + β12LOSSBIN1it-1 + β13LOSSBIN2it-1 + β14LOSSBIN3it-1 + β15LOSSBIN4it-1 (1)

where:

NET_HIREit

+

=

β16LOSSBIN5it-1

the

percentage

+

change

INDUSTRYDUMMIES

in

the

number

of

+

εit,

employees;

SALES_GROWTH = the percentage change in sales revenue; ROAit = net income scaled by beginning of year total assets; RETURNit = the annual stock return for year t; SIZE_Rit-1 = the log of the market value of equity at the beginning of the year, ranked into percentiles; QUICKit-1 = the ratio of cash and short-term investments plus receivables to current

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ACCEPTED MANUSCRIPT liabilities; LEVit-1 = the ratio of long term debt to total assets at the beginning of the year; and LOSSBINit-1 = five separate loss bin variables to indicate each 0.005 interval of ROA from 0 to 0.025.10 All of the variables included in Eqn. (1) are defined in Appendix A Consistent with Pinnuck and Lillis (2007), we expect NET_HIREit to be positively related

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to sales growth, profitability, stock returns, firm size and liquidity, and negatively related to current year changes in profitability and reported losses. No sign predictions are made for change in liquidity or leverage (Pinnuck and Lillis 2007). AB_NET_HIREit is then measured

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as the absolute value of the regression residual from Eqn. (1). Table 2 reports the descriptive statistics of the variables included in Eqn. (2) (Panel A) and the regression results (Panel B).

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In terms of the descriptive statistics, Table 2 (Panel A) shows that the mean and median values of the variables included in the regression model (NET_HIREit, SALES_GROWTHit, SALES_GROWTHit-1, ∆ROAit, ∆ROAit-1, ROAit, RETURNit, SIZEit-1, QUICKit-1, ∆QUICKit-1, ∆QUICKit and LEVit-1) are reasonably consistent with the prior research by Jung et al.

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(2014). For example, NET_HIREit has a mean (median) of 0.059 (0.022), which is highly consistent with the mean (median) of the Jung et al. (2014) study of 0.0586 (0.0204). Finally, concerning the regression results (see Table 2, Panel B), we find that the

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regression coefficients for SALES_GROWTHit, SALES_GROWTHit-1, ∆ROAit, ∆ROAit-1,

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ROAit, RETURNit, SIZEit-1, QUICKit-1, ∆QUICKit-1, ∆QUICKit, LEVit-1, LOSSBIN1it-1, LOSSBIN2it-1, LOSSBIN3it-1 and LOSSBIN5it-1 are significant (p < 0.10 or better) with predicted signs (where appropriate) in the regression model. [Insert Table 2 Here]

3.4. Control variables Consistent with prior research (e.g. Rego 2003; Gupta and Newberry 1997; Chen et al. 2010; Cheng et al. 2012; McGuire et al. 2012), we include several control variables in our 10

For example, LOSSBIN1 is equal to 1 if the ROA ranges from 0.005 to 0 and LOSSBIN2 is equal to 1 if ROA is between 0.005 and 0.010. LOSSBIN3, LOSSBIN4 and LOSSBIN5 are similarly defined. Our regression model also includes industry fixed effects (INDUSTRYDUMMIES).

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ACCEPTED MANUSCRIPT regression model to control for other effects on tax avoidance such as firm size (SIZEit), the market-to-book ratio (MTBit), leverage (LEVit), cash balances (CASHit), return on equity (ROEit), net operating loss carry forwards (NOLit and ∆NOLit), foreign income (FOR_INCit), capital intensity (CAP_INTit), intangible assets (INTANGit), income related to the equity

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method of accounting (EQINCit), research and development expenditure (RNDit) and sales growth (SALES_GROWTHit).

SIZEit is included as a control variable in our regression model because large firms usually

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benefit from economies of scale in tax planning (Rego 2003). We also incorporate MTBit in our regression model to control for a firm’s growth opportunities (Chen et al. 2010). LEVit is

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included in our regression model because highly-levered firms have greater incentive to engage in tax avoidance due to the tax shield offered by corporate debt (Gupta and Newberry 1997). CASHit is incorporated as a control variable in our regression model to account for a firm’s cash needs which could necessitate tax avoidance (e.g. tax deferral strategies)

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(McGuire et al. 2012). ROEit is included in our regression model as a control variable because it captures a firm’s financial performance (Cheng et al. 2012). NOLit and ∆NOLit are included in our regression model to proxy for the need for a firm to reduce its corporate tax

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burdens (McGuire et al. 2012). We incorporate FOR_INCit in our regression model because a

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firm with extensive foreign operations has the advantage of being able to shift income between high- and low-tax jurisdictions (Rego 2003). We also include CAP_INTit in our regression model as a control variable since a capital intensive firm is affected more by the differential treatment of depreciation expenses for tax and financial reporting purposes than other firms (Gupta and Newberry 1997). INTANGit, EQINCit and RNDit are incorporated in our regression model to control for the differential book and tax treatments of intangible assets, consolidated earnings accounted for using the equity method and R&D expenditure (Gupta and Newberry 1997; Chen et al. 2010; McGuire et al. 2012). SALES_GROWTHit is

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ACCEPTED MANUSCRIPT included in our regression model as an additional control for a firm’s growth opportunities given that a rapidly growing firm in terms of sales is likely to invest more in tax planning strategies (McGuire et al. 2012). Finally, we also incorporate dummy variables in our regression model to control for industry (INDUSTRYDUMMIES) and year (YEARDUMMIES)

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fixed effects.

3.5. Baseline regression model

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We empirically test the relation between labor investment inefficiency and corporate tax avoidance using OLS regression analysis with industry and year fixed effects. Our baseline

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regression model is estimated as follows:

TAX_AVOIDit = α0it + β1AB_NET_HIREit + β2SIZEit + β3MTBit + β4LEVit + β5CASHit + β6ROEit + β7NOLit + β8∆NOLit + β9FOR_INCit + β10CAP_INTit + β11INTANGit + β12EQINCit + + β13RNDit + β14SALES_GROWTHit + INDUSTRYDUMMIES + (2)

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YEARDUMMIES + εit,

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where: GAAP_ETRit is employed as our main proxy measure of TAX_AVOIDit. We employ the accounting effective tax rate (GAAP_ETR , ), which has been used extensively in previous studies (e.g. Rego 2003; Wilson 2009; Dyreng et al. 2010; Hoi et al. 2013), as our key proxy estimation of tax avoidance. Furthermore, discussing recent studies of (Graham et al. 2014; Armstrong et al. 2012; Robinson et al. 2010), Edwards et al. (2016) suggest that managers who engage in tax avoidance, usually tend to focus on strategies that reduces financial statement expense GAAP_ETR , is at the first interest, with a direct impact on earnings, not cash (cash taxes paid) GAAP_ETR , is computed as total tax expense (consisting of current and deferred tax expenditure) scaled by pre-tax book income minus special items. This particular tax avoidance proxy looks at tax avoidance measures that have effect on firms’ net income (Robinson et al. 2010) and is used to evaluate its overall tax burden and level of tax avoidance (e.g. Rego 2003; Wilson 2009; Dyreng et al. 2010; Hoi et al. 2013). Lower GAAP_ETR , values denote higher amounts of tax avoidance (Dyreng et al., 2010). The key variable of interest in our regression model is our proxy measure of abnormal labor investment (AB_NET_HIREit). We also include a number of control variables in our regression model (SIZEit, MTBit, LEVit, CASHit, ROEit, NOLit, ∆NOLit, FOR_INCit,

15

ACCEPTED MANUSCRIPT CAP_INTit, INTANGit, EQINCit, RNDit, SALES_GROWTHit, INDUSTRYDUMMIES and YEARDUMMIES). All of the variables included in Eqn. (2) are defined in Appendix A.

4. Empirical results

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4.1. Descriptive statistics

Table 3 reports the descriptive statistics of the variables included in our baseline regression model (Eqn. 2). We find that the mean (median) value of our dependent variable

SC

(GAAP_ETRit) is 0.336 (0.370). The mean (median) value of our GAAP_ETRit estimate is slightly higher than that of Dyreng et al. (2010), i.e. 0.309 (0.337), but similar to that of

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McGuire et al. (2012), i.e. 0.355 (0.367). The mean (median) of our absolute net hiring variable (AB_NET_HIREit) is 0.129 (0.074), which is similar to that of Jung et al. (2014) i.e. 0.127 (0.070). Finally, the mean (median) values of our control variables are largely consistent with those in prior tax avoidance studies (e.g. Rego 2003; Gupta and Newberry

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1997; Chen et al. 2010; Cheng et al. 2012; McGuire et al. 2012). [Insert Table 3 Here]

4.2. Correlation results

negatively

correlated

with

AB_NET_HIREit,

NET_HIRE_OVit

and

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significantly

EP

The Pearson correlation results are presented in Table 4. We observe that GAAP_ETRit is

NET_HIRE_UNit

(p < 0.01), providing some preliminary support for H1 that labor

investment inefficiency is positively related to corporate tax avoidance. Further, several of the control variables (SIZEit, MTBit, CASHit, ROEit, NOLit, FOR_INCit, CAP_INTit, INTANGit, EQINCit, RNDit and SALES_GROWTHit) are significantly correlated with GAAP_ETRit (p < 0.01), as predicted. Overall, the correlations between the main variables are generally in the expected direction, providing strong support for the validity of our key constructs and measures.

16

ACCEPTED MANUSCRIPT [Insert Table 4 Here] 4.3. Regression results Table 5 reports our regression results using GAAP_ETRit as our main proxy measure of tax avoidance, namely AB_NET_HIREit.. We find that labor investment inefficiency is

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significantly negatively related to a firm’s accounting effective tax rate (p < 0.01). Consequently, H1 is supported by our empirical results. In terms of economic significance we find that, on average, a one-standard deviation increase in AB_NET_HIREit leads to a

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decrease in the GAAP_ETRit of our sample firms by around 0.71%. Finally, we also find that some of our control variables (SIZEit, MTBit, LEVit, CASHit, ROEit, NOLit, FOR_INCit,

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CAP_INTit, INTANGit, EQINCit, RNDit, SALES_GROWTHit) are statistically significant in our regression model with predicted signs, where appropriate (p < 0.01), which is consistent with the results of prior studies (e.g. Rego 2003; Gupta and Newberry 1997; Chen et al. 2010; Cheng et al. 2012; McGuire et al. 2012).

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We also expand on the Pinnuck and Lillis (2007) labor model by following prior research by Jung et al. (2014) and compute separate measures of over-investment (NET_HIRE_OVit) and under-investment (NET_HIRE_UNit) in labor by generating subsamples based on the

EP

sign of abnormal net hiring. In particular, NET_HIRE_OVit is calculated where actual net

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hiring is greater than the expected value of net hiring, which is captured where residual values > 0; and NET_HIRE_UNit is computed where actual net hiring is less than the expected value of net hiring, which is captured where residual values are < 0 (see Jung et al. 2014). For

over-investment

and

under-investment

in

labor,

Table

5

indicates

that

NET_HIRE_OVit and NET_HIRE_UNit are significantly negatively related to GAAP_ETRit. (p < 0.01). Hence, both over-investment and under-investment in labor contribute to higher levels of corporate tax avoidance. Finally, we find that a number of the control variables

17

ACCEPTED MANUSCRIPT (SIZEit, MTBit, LEVit, CASHit, ROEit, NOLit, ∆NOLit, FOR_INCit, CAP_INTit, INTANGit, EQINCit, RNDit, SALES_GROWTHit) are statistically significant in these other regression models with predicted signs, where appropriate (p < 0.10 or better). [Insert Table 5 Here]

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Consistent with Jung et al. (2014), we further decompose over- and under-investment in terms of whether the expected level of NET_HIREit from Eqn. (1) is either positive or negative (i.e. whether the economic fundamentals suggest that a firm’s labor force should

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expand or contract). In particular, we generate the following four subsamples in our study (Jung et al. 2014):

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(1) Over-hiring: over-investment when expected net hiring is positive.

(2) Under-firing: over-investment when expected net hiring is negative. (3) Under-hiring: under-investment when expected net hiring is positive. (4) Over-firing: under-investment when expected net hiring is negative.

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We then estimate Eqn. (2) separately for each subsample and report these additional results in Table 6. We find that AB_NET_HIREit is significantly negatively related to GAAP_ETRit in each of the regression models (p < 0.05 or better). Hence, our inferences

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about the relation between labor investment efficiency generally, as well as over-investment

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and under-investment specifically, are sustained in periods of either expected expansion or contraction. Therefore, H1 is again supported by our empirical results. We also find that some of the control variables (SIZEit, MTBit, LEVit, CASHit, ROEit, NOLit, FOR_INCit, CAP_INTit, INTANGit, EQINCit, RNDit, SALES_GROWTHit) are statistically significant in these regression models with predicted signs, where appropriate (p < 0.10 or better). Taken together, these regression results indicate that corporate tax avoidance is not merely related to either higher or lower levels of hiring and firing as such, but rather with net hiring which is closer to the level justified by a firm’s economic fundamentals.

18

ACCEPTED MANUSCRIPT [Insert Table 6 Here] 4.4. Instrumental variables (2SLS) regression analysis It is possible that our main regression results (see Table 5) could be affected by endogeneity (e.g. simultaneity and/or reverse causality), so we make use of instrumental

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variables (2SLS) regression analysis (e.g. Wooldridge 2010) to corroborate our main regression results. Three instrumental variables (IVs) were selected in this study to capture the endogenous variable (AB_NET_HIREit) as follows: (1) the mean number of employees in

SC

a SIC (2-digit code) industry and year (IND_EMPit); (2) the mean net labor hiring in a SIC (2-digit code) industry and year (IND_NET_HIREit); and (3) the log of the number of

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employees that are union members in a SIC (2-digit code) industry and year (IND_UNIONit) (Jung et al. 2014). The first-stage regression model used to predict labor investment inefficiency is estimated as follows:

16CONTROLSit

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AB_NET_HIREit = α0it + β1IND_EMPit + β2IND_NET_HIREit + β3IND_UNIONit + β4+ INDUSTRYDUMMIES + YEARDUMMIES + εit,

(3)

EP

where: IND_EMPit = the median number of employees for a SIC (2-digit code) industry i in

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year t; IND_NET_HIREit = the median net hiring variable for a SIC (2-digit code) industry i in year t; IND_UNIONit = the median union membership of employees in for a SIC (2-digit code) industry i in year t; and CONTROLSit = a vector of control variables from Eqn. (2). All of the variables included in Eqn. (3) are defined in Appendix A. For these IVs to be valid, they should be correlated with the endogenous regressor (AB_NET_HIREit) in our regression model, but not correlated with our dependent variable (GAAP_ETRit) (Wooldridge 2010). This essentially means that the instruments should (1) not be affected by the dependent variable, (2) not affect the dependent variable except

19

ACCEPTED MANUSCRIPT through the endogenous variables, and (3) not be correlated with omitted variables in the model. Theoretically, it appears that our choice of instruments is reasonable. In particular, we expect that the industry number of employees, industry net labor hiring and industry union membership to be correlated with labor investment efficiency because these factors determine

are not necessarily correlated with corporate tax avoidance.

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how firms are able to efficiently employ labor resources for operational purposes, but they

Prior literature shows that industries tend to cluster in certain geographic areas (Baptista

SC

and Swann, 1998). Labor through their norms and values within firms tend to be spatially sticky (Rutten et al., 2010). If industries cluster and labour investment efficiency is spatially

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sticky, then it follows that the labour investment of firms in an industry might be similar. Therefore, we expect all (IND_UNIONit, IND_EMPit and IND_NET_HIREit) instruments to highly correlate with labour investment efficiency. Therefore we following pervious literature (e.g., Jha and Cox 2015; Jung et al. 2014; Hasan et al. 2015) by using SIC (2-digit code)

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industry classification based on the premise that labor is spatially sticky. We include industry-level unionization rates in a given industry (IND_UNIONit) as a measure of a powerful organized labor cohort (see e.g., Rosen 1969). Jung et al. (2014) examine the

EP

potential moderating role of labor unions on the association between accounting quality and

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labor investment efficiency. They find that accounting quality has a larger effect on labor investment efficiency for firms in industries with high levels of unionization, which is consistent with larger adjustment costs when employees are unionized. This effect of unionization is particularly strong for firms in strong financial health. They interpret that this results is consistent with these firms maintaining increased levels of information asymmetry, as they have the most to lose in bargaining with unions by revealing their true position (Hilary 2006). Also it is unlikely that firms labour investment efficiency will not be effected

20

ACCEPTED MANUSCRIPT by

number

of

employees

or

labour

investment

efficiency

(IND_EMPit

and

IND_NET_HIREit) in particular industry. In addition to above theoretical justification for choice of instrumental variables, our instruments also pass the statistical tests for strength, validity and appropriateness. The results

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of the first-stage regression model are presented in Table 7 (Panel A). Consistent with our expectations, we find that IND_EMPit, IND_NET_HIREit and IND_UNIONit are significantly related to AB_NET_HIREit, NET_HIRE_OVit and/or NET_HIRE_UNit in the regression

SC

models (p < 0.10 or better). We also test the suitability of our IVs by computing several postestimation tests (see Table 7, Panel B). First, we compute the under-identification test and

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find that the Anderson LM statistic is significant (p < 0.01) in all of the regression models, so our IVs are relevant. Second, we calculate the weak identification test and observe that the Cragg-Donald Wald F statistic for each regression model is above the Stock-Yogo (2005) critical value of 19.93 (based on a 10% maximal IV size), so weak IVs does not represent a

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major concern with our regression estimates. Third, for the exclusion restriction test, we compute the over-identification test and find that the Hansen J-statistic is not significant, which shows that our IVs are not over-identified and are satisfactory. Finally, we compute the

EP

Hausman (1978) test for endogeneity and find that it rejects the exogeneity of the IVs (p <

AC C

0.09 or better), indicating that the 2SLS regression estimates are preferable to the OLS regression estimates. In general, we conclude that IND_EMPit, IND_NET_HIREit and IND_UNIONit are plausible IVs that enhance the validity of inferences in the second-stage regression models.

For the second-stage regression models reported in Table 7 (Panel C), we find that the labor

investment

efficiency

proxies

(AB_NET_HIREit,

NET_HIRE_OVit

and/or

NET_HIRE_UNit) are all significantly negatively related to GAAP_ETR (p < 0.05 or better), showing that firms with a greater propensity for labor investment inefficiency exhibit higher

21

ACCEPTED MANUSCRIPT levels of tax avoidance. Consequently, our original inferences (see Table 5) remain unchanged, and H1 is once more supported by our empirical results. [Insert Table 7 Here]

4.5.1. Alternative proxy measures of corporate tax avoidance

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4.5. Sensitivity analyses

To improve the robustness of our empirical results reported in Table 5, we also test the relation between labor investment inefficiency and tax avoidance using several other proxy

SC

measures of tax avoidance such as CASH_ETRit, BTD_DDit and UTB_TOTALit.

CASH_ETRit is calculated as the cash tax paid (as disclosed in the cash flow statement)

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scaled over pre-tax accounting profit. This variable measures the proportion of cash tax paid in a particular year relative to a firm’s profit. In accordance with Dyreng et al. (2010), lower CASH_ETRit values represent higher levels of tax avoidance.

BTD_DDit is computed as the book-to-tax difference residual utilizing the method

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developed by Desai and Dharmapala (2006). We follow Desai and Dharmapala (2006) and estimate BTD_DDit as a residual obtained from a regression of permanent book-tax differences which is equal to µ i + εit from a fixed effects regression model of BTit = β1TAit + µ i As per Desai and Dharmapala (2006), higher values of BTD_DDit denote higher levels

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of tax avoidance.

EP

+ εit.

UTB_TOTALit is calculated as the total unrecognized tax benefits (UTBs) that a firm accrues as per FIN48 Accounting for Uncertainty in Income Taxes.11 UTBs signify expected future disallowed tax benefits and are recorded as a liability in a firm’s financial statement tax footnotes. It has been shown that UTB estimates can proxy for a firm’s tax avoidance because firm managers encounter significant uncertainty about the derivation of its ultimate

11

FIN48 Accounting for Uncertainty in Income Taxes, effective for fiscal years beginning after December 15, 2006, is classified as Accounting Standards Codification (ASC) 740-10-25 under the FASB’s new codification for U.S. GAAP. FIN48 was introduced by the FASB to provide financial statement users with information about the uncertainties a firm faces in computing its income tax liability estimates (FASB 2006).

22

ACCEPTED MANUSCRIPT tax position (see Rego and Wilson 2012; Lisowsky et al. 2013). UTB_TOTALit is computed at the end of the year scaled by beginning-of-year total assets. Consistent with Rego and Wilson (2012), higher values of UTB_TOTALit represent higher levels of tax avoidance. Table 8 presents our regression results based on our alternative proxy measures of

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corporate tax avoidance. First, we observe that AB_NET_HIREit and NET_HIRE_UNit are both significantly negatively related to CASH_ETRit (p < 0.05 or better). Second, we find that AB_NET_HIREit, NET_HIRE_OVit and NET_HIRE_UNit are all significantly positively

SC

related to BTD_DDit (p < 0.10 or better). Finally, we document that AB_NET_HIREit, NET_HIRE_OVit and NET_HIRE_UNit are all significantly positively related to

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UTB_TOTALit (p < 0.10 or better). Overall, these results show that firms with a greater propensity for labor investment inefficiency exhibit higher levels of tax avoidance and are qualitatively similar to our main regression results (see Table 5). Hence, our inferences remain unaffected and H1 is again supported by our empirical results.

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[Insert Table 8 Here] 4.5.2. Alternative proxy measures of abnormal net hiring

Our next sensitivity analysis considers an alternative proxy measure of abnormal net

EP

hiring, consistent with prior research by Cella (2010) and Jung et al. (2014). In particular, we

AC C

employ the median level of abnormal net hiring in a firm’s industry for the specific year in question (AB_NET_HIRE_INDit). Thus, the more a firm’s net hiring deviates from its industry peers, the larger the proxy measure of abnormal net hiring (Jung et al. 2014). We also extend this sensitivity analyses to include over-investment in labor where a firm’s net hiring is higher than the industry median (NET_HIRE_IND_OVit) and under-investment in labor where a firm’s net hiring is lower than the industry median (NET_HIRE_IND_UNit). We report the regression results for the alternative proxy measures of abnormal net hiring in Table 9. We find that the labor investment efficiency proxies (AB_NET_HIRE_IND_it,

23

ACCEPTED MANUSCRIPT NET_HIRE_IND_OVit and/or NET_HIRE_IND_UNit) are all significantly negatively related to GAAP_ETR (p < 0.01). Therefore, our initial inferences (see Table 5) remain unchanged and H1 is once more supported by our empirical results.

4.5.3. Controlling for accounting quality and managerial ability

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[Insert Table 9 Here]

Our final sensitivity analyses includes additional control variables relating to accounting quality and managerial ability in our base regression model, which were excluded because

SC

their data requirements lead to significant sample attrition. In particular, we also consider accounting quality and managerial ability because they are likely to be correlated with a

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firm’s investment policies (see Jung et al. 2014). Accounting quality is captured in this sensitivity analysis in terms of the Kothari et al. (2005) measure of performance-matched discretionary accruals (AQ_KOTHARIit), while managerial ability is captured by the Demerjian et al. (2012) measure of managerial ability score (M_ABILITY_SCOREit) and

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rank (M_ABILITY_RANKit).

Table 10 presents our regression results for the additional control variables. We observe that our AB_NET_HIREit, NET_HIRE_OVit and NET_HIRE_UNit labor efficiency proxies

EP

are all significantly negatively related to tax avoidance (p < 0.01) after controlling for

AC C

accounting quality and managerial ability. We also find that AQ_KOTHARIit is significantly negatively related to tax avoidance (p < 0.05 or better) for AB_NET_HIREit and NET_HIRE_UNit. Interestingly, we also find that M_ABILITY_SCOREit is significantly positively related to tax avoidance (p < 0.01), which is consistent with prior research by Koester et al. (2016). Overall, these results show that firms with a greater propensity for labor investment inefficiency display higher levels of tax avoidance even after controlling for accounting quality and managerial ability. They are also qualitatively similar to our main

24

ACCEPTED MANUSCRIPT regression results (see Table 5). Thus, our inferences remain unaffected and H1 is again supported by our empirical results.

4.5.3. DID analysis

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As our starting point, we initially perform a DID analysis (e.g. Roberts and Whited 2013) to examine the potential issue of endogeneity in our study. We make use of the introduction of the IRS repurchase regulation (IRS 2007-48) on May 31, 2007 as an exogenous shock-

SC

event regarding the tax avoidance which is likely to have negatively affected the ability of firms to use tax havens to reduce group tax payable post this specific event (Cen et al. 2017).

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IRS 2007-48 was brought in as a consequence of IBM’s use of a tax haven subsidiary on May 29, 2007 to buy back shares as part of its $12.5 billion stock repurchase arrangement. The IBM subsidiary firm repurchased shares from public shareholders to pay its U.S. parent and in doing so was able to avoid paying $1.6 billion in U.S. income taxes (Hoehne 2007;

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Johnston 2007). The IRS stated that it would disallow schemes of that nature under section 367(b) of the U.S. Tax Code beginning May 31, 2007 (IRS 2007). In fact, It is interesting to note that in the 2007 sample year, we find that IBM disclosed a total of 11 tax haven

EP

subsidiaries in its annual report, however this dropped to 7 tax haven subsidiaries in the 2014

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year, which signifies an estimated 36.36% decrease in the utilization of tax haven subsidiaries by IBM after the IRS 2007-48 event. For these reasons, we expect this exogenous event to impact the association between tax avoidance and the labor investment efficiency. To undertake the DID analysis based on the IRS 2007-48 event, we initially divide our sample into two sub-period groups by constructing a dummy variable denoted by IRS_REG, which is coded as 1 if the sample observations are from the (> or equal 2008) period, and 0 otherwise (< 2007 ). The IRS_REG dummy variable differentiates the effects of tax haven utilization on the labor investment inefficiency before the IRS 2007-48 event (i.e. non-

25

ACCEPTED MANUSCRIPT treatment group) and after the IRS 2007-48 event (i.e. treatment group). We then construct an interaction term between labor investment inefficiency and IRS_REG (labor investment inefficiency *IRS_REG)12 and estimate the following extended regression model:

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TAX_AVOIDit = α0it + β1AB_NET_HIREit + β2 IRS_REG + β3AB_NET_HIREit

*

IRS_REG it + β4SIZEit + β5MTBit + β6LEVit + β7CASHit + β8ROEit + β9NOLit + β10∆NOLit + β11FOR_INCit + β12CAP_INTit + β13INTANGit + β14EQINCit + + β15RNDit + (4)

SC

β16SALES_GROWTHit + INDUSTRYDUMMIES + YEARDUMMIES + εit,

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where: IRS_REG = a dummy variable coded as 1 if the sample observations are from the >2008 period, and 0 otherwise (< or equal 2007); and Labour Investment Efficiency * IRS_REG = an interaction term computed as Labor investment Efficiency (AB_NET_HIREit, NET_HIRE_OVit, NET_HIRE_UNit) multiplied by IRS_REG.

The regression results for the DID analysis are presented in Table 11. Consistent with the

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baseline regression results (see Table 5), we find that the regression coefficient for Labor investment Efficiency (AB_NET_HIREit, NET_HIRE_OVit,) is negatively and significantly

EP

associated with the Tax Avoidance (proxied by GAAP_ETRit) across all regression models (p < 0.05 or better), showing that the negative Labor investment Efficiency and Tax Avoidance

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association existed in the pre (< or equal 2007) IRS_REG period. The regression coefficient for IRS_REG is negatively and significantly associated with the tax avoidance (proxied by GAAP_ETRit) across all regression models (p < 0.01), as expected. The IRS 2007-48 event increases the GAAP_ETRit given that it is likely to reduce firm-level tax, financing and systematic risk (Cen et al. 2017). The regression coefficient for the interaction term Labor investment Efficiency (AB_NET_HIREit, NET_HIRE_OVit, NET_HIRE_UNit) * IRS_REG 12

In essence, the coefficient on the interaction term denotes the DID. This shows the effect of the treatment group (Labor Investment Efficiency) on the outcome (Tax avoidance measure) by comparing the mean change in the outcome variable for the treatment firms before and after the exogenous event (IRS_REG) to the mean change in the outcome variable for the control firms before and after that exogenous event.

26

ACCEPTED MANUSCRIPT is negatively and significantly associated with the Tax Avoidance (proxied by GAAP_ETRit) across all regression models (p < 0.05 or better). While the IRS repurchase regulation (IRS 2007-48) closed a major international tax loophole, the positive association between Labor investment Efficiency and the Tax Avoidance is magnified in the post (>2008) IRS_REG

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period. A possible reason for this outcome is that tax havens retain a significant proportion of firms’ earnings offshore which may be unavailable for immediate use in the U.S. and tax havens require coordination of both internal (e.g. transfer pricing) and external transactions

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(e.g. new IRS rules), so there is sustained uncertainty for investors about the availability of

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funding following the implementation of the IRS repurchase regulation.13

4.5.4. Propensity Score Matching (PSM)

We also employ PSM to address potential endogeneity identification concerns in our baseline regression results (see Table 5) related to functional form misspecification which

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could produce biased regression estimates (Shipman et al. 2017). We follow the method advanced by Shipman et al. (2017) and compute a logistic regression model based on the same set of control variables as our baseline regression model in Eqn.(2). We also construct a

EP

dummy variable for each model of labor investment efficiency (AB_NET_HIREit,

AC C

NET_HIRE_OVit, and NET_HIRE_UNit) where AB_NET_HIREit _HIGH (coded as 1 if AB_NET_HIREit is higher than the mean value of AB_NET_HIREit, and 0 otherwise), and we calculate this approach for NET_HIRE_OVit, and NET_HIRE_UNit, which is used as the dependent variable in the logistic regression model to calculate the propensity scores (Shipman et al. 2017). Thereafter, using the predicted propensity scores from the logistic regression, we match on a one-to-one basis by industry and year the observations in the treatment firms (i.e. firm-year observations with AB_NET_HIRE_HIGH equal to 1) to the 13

As a robustness check of our DID regression results (see Table 5), we drop US multinational firms from our sample that do not utilize any tax haven subsidiaries in the pre (2006–2007) or post (2008–2014) IRS_REG periods. Our untabulated results show that they provide qualitatively similar results to those reported in Table 5.

27

ACCEPTED MANUSCRIPT control firms (i.e. firm-year observations with AB_NET_HIRE_HIGH equal to 0.14 We then combine the matched pairs into pooled samples and perform our OLS regression analysis. The regression results for the PSM procedure are presented in Table 12. The first-stage regression models (see Table 12, Panel A) show that the control variables

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are significantly associated with labor investment efficiency (AB_NET_HIRE_HIGH, NET_HIRE_OV_HIGH and NET_HIRE_Un_HIGH (p < 0.10 or better with predicted signs). We test the quality of our matching process by computing covariates for all of the variables in

SC

the logistic regression models (un-tabulated). We achieve covariate balance for the OCF, BSEG, DISP, SPREAD, FSIZE, BETA, BTM, FBIAS and GAAP_ETR variables (p > 0.10),

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but not for the ACC and LEV variables. However, to ensure comparability between the first and second stage regression models, we include the ACC and LEV variables in all regression models (e.g. Shipman et al. 2017).

For the second-stage regression models, Table 12 reports that the regression coefficient for

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Labor Investment Efficiency is negatively and significantly associated with tax avoidance (proxied by GAAP_ETRit) across all regression models (p < 0.10 or better), which in supports H1 that labor investment inefficiencies lead to a higher tax avoidance for U.S. firms.

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Un-tabulated results we provide consistent evidence by using the radius (caliper) approach

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with replacement to perform the matching process (see e.g. Dehejia and Wahba 2002). [Insert Table 12 Here]

4.5.5 The Inverse Mills Ratio Method (IMR) In Section 4.5.4, we show that our results are consistent with main regression analysis (see Table 5,) even after using PSM which tests for selection bias. In this section, we will test 14

This approach of matching uses not only the nearest neighbor within each caliper, but all of the comparison members within the caliper (Dehejia and Wahba 2002). The benefit of this approach is that it uses only as many comparison firms that are available within the caliper and therefore allows for the use of more (fewer) firms when good matches are (not) available, but avoids the risk of bad matches. We impose a caliper distance of 0.05 in this study (Dehejia and Wahba 2002).

28

ACCEPTED MANUSCRIPT the sample selection method using IMR to test selection bias based on unobservables by estimating a bias correction term in the first stage through the choice model and adding it in the second-stage outcome regression (Tucker 2011). We use the traditional Heckman (1977) test by inputting the Ohlson (1980) multi-factor model in first stage regression, and then add

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the corrected term into the second stage regression. We can see in Table 13, that the effects or adding the Ohlson (1989) factor is nontrivial and significant. We notice that the coefficients of labour investment efficiencies in the first and second stage are different. Coefficients of

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Rho in all three models indicate that the correlation coefficient between error terms in the first stage regression are significant. The Wald test also indicates the correlation is significant

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in all three models. 5. Conclusion

This paper examines the relation between labor investment inefficiency and corporate tax avoidance. Our regression results indicate that labor investment inefficiency is significantly

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positively related to tax avoidance. More specifically, we find that a one standard deviation of labor investment inefficiency leads to a 0.71% reduction in the accounting effective tax rate. Our findings are robust to endogeneity concerns, alternative proxy measures of tax avoidance

EP

and labor investment efficiency, and additional control variables pertaining to accounting

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quality and managerial ability.

This study makes several notable contributions. First, it extends prior literature on corporate tax avoidance. For instance, early research on tax avoidance assumes that labor investment efficiency is static (see Hanlon and Heitzman 2010), while later research stresses that a firm manager’s access to and use of labor resources is likely to denote a key determinant of tax avoidance (e.g. Higgins et al. 2014). Second, this study provides robust empirical evidence which indicates for the first time that labor investment inefficiency is significantly positively related to tax avoidance. Thus, our results show that labor investment

29

ACCEPTED MANUSCRIPT inefficiency is a key determinant of corporate tax avoidance. Third, by examining a firm’s labor investment, this study also improves our understanding of the relation between investment efficiency and tax avoidance generally (see Francis et al. 2014; Koester et al. 2016) which is likely to have flow-on effects in terms of financial reporting quality,

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profitability and firm value (e.g. Lambert et al. 2007; McNichols and Stubben 2008; Biddle et al. 2009). Fourth, this study also contributes to research that links agency theory to corporate tax avoidance (e.g. Crocker and Slemrod 2005; Desai and Dharmapala 2006) by taking into

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account the moral hazard and adverse selection framework, and how it results in substantial levels of tax avoidance in a firm. Finally, this study also furnishes important insights to tax

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authorities (e.g. Internal Revenue Service (IRS)) and policymakers who attempt to identify the circumstances where the risk of corporate tax avoidance is higher. These findings have flow-on implications in terms of understanding the broad determinants of tax avoidance, and the risk areas that the IRS could include as part of their risk reviews. In particular, labor

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inefficiencies are reflective of broader contracting and control environment that encapsulates

References

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agency costs, monitoring, information transparency and exchange.

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ACCEPTED MANUSCRIPT Appendix A. Variable definitions

=

Eqn. (2) variables: GAAP_ETRit

=

AB_NET_HIREit

=

NET_HIRE_OVit NET_HIRE_UNit SIZEit MTBit

= = = =

LEVit

=

CASHit ROEit NOLit

= = =

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LOSSBINit-1

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= = =

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∆QUICKit-1 ∆QUICKit LEVit-1

Percentage change in the number of employees from year t-1 to year t for firm i. Percentage change in sales (total revenue) in year t for firm i Return on assets (net income / lag(total asset)) in year t for firm i Change in return on assets in year t for firm i Total stock return during fiscal year t for firm i Natural log of market value at the end of fiscal year t -1 for firm i Percentile rank of SIZEit-1 Quick ratio ((cash + accounts receivable) /current liabilities)) at the end of year t-1 for firm i Percentage change in the quick ratio at the end of year t -1 for firm i Percentage change in the quick ratio in year t for firm i Leverage for firm i, measured as the sum of debt in current liabilities and total long-term debt at the end of year t 1, scaled by year t-1 total assets There are five separate loss bins to indicate each 0.005 interval of ROA from 0 to 0.025 in period t for firm i. For example, LOSSBIN1 is equal to 1 if ROA ranges from 0.005 to 0. LOSSBIN2 is equal to 1 if ROA is between 0.005 and 0.010. LOSSBIN3, LOSSBIN4 and LOSSBIN5 are defined similarly Total tax expenses scaled by pre-tax book income less special items for firm i in year t. GAAP_ETRit is set as missing when the denominator is zero or negative. We truncate GAAP_ETRit to the range [0, 1] Regression residual value (εit) obtained from the following regression model: NET_HIREit = α0it + β1SALES_GROWTHit-1 + β2SALES_GROWTHit + β3∆ ROAit + β4 ∆ROAit-1 + β5 ROAit + β6 RETURNit + β7SIZE_Rit-1 + β8QUICKit-1 + β9∆QUICKit-1 + β10 ∆QUICKit + β11LEVit-1 + β12LOSSBIN1it-1 + LOSSBIN2it-1 + LOSSBIN3it-1 + LOSSBIN4it-1 + LOSSBIN5it-1 + εit Residual value (εit) for firm i in year t for AB_NET_HIREit > 0 Residual value (εit) for firm i in year t for AB_NET_HIREit < 0 The natural logarithm of total assets for firm i in year t Market-to-book ratio for firm i at the beginning of year t, measured as the market value of equity scaled by the book value of equity Sum of debt in current liabilities and total long-term debt for firm i in year t, scaled by total assets Cash and marketable securities for firm i in year t, scaled by total assets Operating income for firm i for year t, scaled by the book value of equity A dummy variable coded as one if loss carry forward or firm i is positive as of the beginning of the year t, and zero otherwise Change in loss carried forward for firm i in year t, scaled by lagged assets Foreign income for firm i in year t, scaled by total assets. Missing values are set to zero Property, plant and equipment for firm i in year t, scaled by total assets. Total intangible assets for firm i in year t, scaled by total assets Equity income in earnings for firm i in year t, scaled by total assets Research and development expenditure for firm i in year t, scaled by total assets. Missing values are set to zero Changes in sales scaled by lagged sales for firm i in year t Dummy variables (standard industry classification (SIC) 2-digit codes) to control for industry fixed effects Dummy variables to control for year fixed effects

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= = = = = = = =

= =

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∆NOLit FOR_INCit

Description

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Variable Eqn. (1) variables: NET_HIREit SALES_GROWTH ROAit ∆ROAit RETURNit SIZEit-1 SIZE_Rit-1 QUICKit-1

CAP_INTit INTANGit EQINCit RNDit

= = = =

SALES_GROWTHit INDUSTRYDUMMIES

= =

YEARDUMMIES Eqn. (3) variables: IND_EMPit IND_NET_HIREit IND_UNIONit

= = = =

The median number of employees for SIC (2-digit code) industry i in year t The median net hiring variable for SIC (2-digit code) industry i in year t The median union membership of employees in for SIC (2-digit code) industry i in year t Additional variables for sensitivity analyses: Defined as income taxes paid scaled by (cash from operation activities + income CASH_ETRit

34

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=

AB_NET_HIRE_INDit

=

NET_HIRE_IND_OVit NET_HIRE_IND_UNit AQ_KOTHARIit

= = =

M_ABILITY_SCOREit M_ABILITY_RANKit

= =

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UTB_TOTALit

tax paid - extraordinary items and discontinued operations (cash flow item) Desai and Dharmapala (2006) discretionary book-tax difference for firm i in year t. BTD_DDit is equal to μ + ε , from the following firm fixed-effect regression: BT = β TA + μ + ε , where BTit is the Manzon-Plesko (2002) book-tax difference measure (described below); TAit is the Dechow et al. (1995) total accruals measure for firm i in year t, scaled by the lagged value of total assets; μ is the average value of the residual for firm i over the sample period; and εit is the deviation of the residual in year t from firm i’s average residual. BTit is defined as (US domestic financial income – US domestic taxable income – income taxes (state) – income taxes (other) – equity in earnings)/lagged total assets = (PIDOMit – TXFEDit/statutory tax rate – TXSit – TXOit – ESUB) it/ATt-1. Firms with zero or negative taxable income are assumed to have attenuated incentives (at the margin) to engage in tax avoidance activity. We follow prior literature (e.g. Desai and Dharmapala 2006) and include only firm-years with positive TXFEDit Total unrecognized tax benefits at the end of year t scaled by total assets at the beginning of year t Median level of abnormal net hiring in a firm’s SIC (2-digit code) industry i in year t AB_NET_HIRE_INDit > 0 AB_NET_HIRE_INDit < 0 Performance matched discretionary accruals computed in accordance with the Kothari et al. (2005) model Managerial ability score computed in accordance with the Demerjian et al. (2012) model Managerial ability rank computed in accordance with the Demerjian et al. (2012) model

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=

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BTD_DDit

Obscure Information Environmen

Weak Internal Control Environment

Tax avoidance

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Labor Inefficency

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Figure 1: Schematic relation between Labor Inefficiency and Corporate Tax Avoidance

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ACCEPTED MANUSCRIPT Table 1. Sample description

Less: missing values to compute the control variables for Eqn. (1) Total firm-year observations for Eqn. (1) Less: firm-year observations with missing tax data for Eqn. (2) Total observations for baseline regression model – see Eqn. (2)

N 1901 2000 1997 1899 1696 1643 1641 1707 1722 1757 1811 1805 1825 1948 2024 1978 1920 1843 1794 1823 1861 1887 1836 1819 1840 521 81,192

Percent 2.34 2.46 2.46 2.34 2.09 2.02 2.02 2.10 2.12 2.16 2.23 2.22 2.25 2.40 2.49 2.44 2.36 2.27 2.21 2.25 2.29 2.32 2.26 2.24 2.27 0.64 100

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Panel B: Number of observations and relative frequency for NET_HIREit Year N Percent Year 1962 1 0.00 1989 1963 149 0.18 1990 1964 112 0.14 1991 1965 179 0.22 1992 1966 243 0.30 1993 1967 546 0.67 1994 1968 626 0.77 1995 1969 666 0.82 1996 1970 771 0.95 1997 1971 1079 1.33 1998 1972 1247 1.54 1999 1973 1286 1.58 2000 1974 1322 1.63 2001 1975 1883 2.32 2002 1976 2180 2.68 2003 1977 2112 2.60 2004 1978 2008 2.47 2005 1979 1969 2.43 2006 1980 1878 2.31 2007 1981 1813 2.23 2008 1982 1763 2.17 2009 1983 1767 2.18 2010 1984 1820 2.24 2011 1985 1788 2.20 2012 1986 1808 2.23 2013 1987 1844 2.27 2014 1988 1834 2.26 Total

238702 (111913) 126789 (45597) 81192 (19650) 61542

RI PT

Panel A: Sample selection Total number of firm-year observations from Compustat and CRSP merged over the 1962–2014 period Less: missing value for variables to compute NET_HIREit – see Eqn. (1)

36

ACCEPTED MANUSCRIPT Table 2. Estimating the expected level of NET_HIREit

Mean 0.059 0.129 0.150 –0.004 –0.004 0.028 0.015 4.855 1.902 0.082 0.078 0.253

S.D. 0.275 0.360 0.388 0.125 0.124 0.159 0.043 2.308 7.245 0.570 0.565 0.232

Panel B: Regression results

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Coef. –0.039** 0.036*** 0.345*** –0.236*** –0.037*** 0.191*** 0.501*** 0.000*** 0.004*** 0.023*** –0.024*** –0.029*** –0.016* –0.015* –0.024*** –0.005 –0.033*** Yes

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Intercept SALES_GROWTHit SALES_GROWTHit-1 ∆ROAit ∆ROAit-1 ROAit RETURNit SIZE_Rit-1 QUICKit-1 ∆QUICKit-1 ∆QUICKit LEVit-1 LOSSBIN1it-1 LOSSBIN2it-1 LOSSBIN3it-1 LOSSBIN4it-1 LOSSBIN5it-1 IND Dummies

Median 0.022 0.089 0.097 –0.001 –0.001 0.053 0.013 4.703 1.213 –0.009 –0.010 0.219

75th 0.118 0.202 0.217 0.025 0.025 0.097 0.036 6.445 1.913 0.191 0.186 0.368

t–stat. (–2.37) (15.81) (140.34) (–28.49) (–4.89) (30.47) (24.40) (8.85) (9.87) (14.52) (–15.46) (–7.03) (–1.78) (–1.74) (–2.62) (–0.58) (–3.37) Yes

24.6% 81192

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Adj. R-sq. N Variables are defined in Appendix A.

25th –0.048 –0.010 –0.002 –0.034 –0.034 0.008 –0.009 3.122 0.813 –0.181 –0.182 0.067

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N 81192 81192 81192 81192 81192 81192 81192 81192 81192 81192 81192 81192

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Panel A: Descriptive statistics Variable NET_HIREit SALES_GROWTHit SALES_GROWTHit-1 ∆ROAit ∆ROAit-1 ROAit RETURNit SIZE_Rit-1 QUICKit-1 ∆QUICKit-1 ∆QUICKit LEVit-1

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ACCEPTED MANUSCRIPT Table 3. Descriptive statistics Median 0.370 0.074 0.279 0.276 4.917 1.583 0.218 0.080 0.201 0.000 0.000 0.000 0.292 0.017 0.000 0.000 0.098

25th 0.258 0.034 0.110 0.125 3.576 0.924 0.070 0.030 0.071 0.000 0.000 0.000 0.159 0.000 0.000 0.000 0.004

75th 0.445 0.147 0.753 0.647 6.504 2.745 0.360 0.202 0.326 1.000 0.000 0.000 0.478 0.105 0.000 0.032 0.208

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S.D. 0.166 0.191 2.803 2.283 2.088 3.047 0.223 0.205 0.457 0.477 18.648 0.021 0.253 0.162 0.005 0.069 0.348

SC

Mean 0.336 0.129 1.185 0.925 5.115 2.356 0.248 0.158 0.169 0.349 9.298 0.007 0.348 0.092 0.001 0.032 0.138

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Variable N GAAP_ETRit 61,542 AB_NET_HIREit 61,542 NET_HIRE_OVit 61,542 NET_HIRE_UNit 61,542 SIZEit 61,542 MTBit 61,542 LEVit 61,452 CASHit 61,452 ROEit 61,542 NOLit 61,542 ∆NOLit 61,542 FOR_INCit 61,542 CAP_INTit 61,542 INTANGit 61,542 EQINCit 61,542 RNDit 61,542 SALES_GROWTHit 61,542 Variables are defined in Appendix A.

38

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Table 4. Pearson correlation results 2.

3.

4.

5.

6.

7.

8.

9.

– 0.054*** –0.963*** 0.397*** 0.001 0.049*** –0.034*** 0.002 –0.009** –0.015*** 0.071*** 0.080*** 0.071*** 0.020*** –0.006 0.357***

– 0.000 –0.166*** 0.005 0.052*** 0.019*** –0.003 –0.001 0.023*** 0.001 0.095*** 0.045*** –0.010*** 0.019*** 0.067***

– –0.411*** –0.007 –0.017*** 0.063*** –0.005 0.003 0.028*** –0.044*** –0.053*** –0.031*** –0.021*** 0.080*** –0.222***

– 0.005 0.081*** –0.127*** 0.009** 0.021*** –0.071*** 0.133*** 0.038*** 0.042*** 0.050*** –0.061*** 0.395***

– 0.007* 0.002 0.000 –0.006 0.001 0.002 0.008* 0.000 –0.001 0.000 –0.006

– 0.093*** –0.121*** –0.017*** 0.010** –0.014*** 0.413*** 0.231*** 0.006 0.095*** 0.041***

– –0.008* 0.054*** 0.125*** 0.008** –0.039*** 0.116*** –0.020*** 0.462*** –0.023***

– –0.006 –0.001 0.004 –0.028*** –0.063*** 0.004 –0.039*** 0.006

10.

11.

12.

13.

14.

15.

16.

17.

– 0.002 0.031*** 0.019*** –0.008** 0.115***

– –0.015*** 0.015*** –0.017*** 0.084***

– –0.006 0.020*** 0.065***

– –0.011*** 0.022***

– –0.011***

–

RI PT

1. – –0.127*** –0.017*** –0.062*** 0.025*** –0.164*** –0.009 –0.205*** 0.278*** –0.394*** –0.003 –0.078*** 0.066*** –0.085*** 0.036*** –0.305*** –0.019***

– 0.035*** 0.023*** –0.109*** 0.047*** –0.028*** 0.027*** –0.017***

SC

GAAP_ETRit AB_NET_HIREit NET_HIRE_OVit NET_HIRE_UNit SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit

M AN U

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

– –0.024*** 0.002 0.010** –0.008* 0.053*** –0.015***

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Variables are defined in Appendix A. N = 61,542. *, **, *** correspond to 1%, 5% and 10% levels of significance, respectively (p–values are based on two–tailed tests).

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ACCEPTED MANUSCRIPT Table 5. Regression results – The effect of net hiring on corporate tax avoidance (GAAP_ETRit)

0.007*** –0.002*** –0.036*** –0.025*** 0.052*** –0.073*** 0.000 –0.140*** –0.018*** 0.047*** –0.977*** –0.317*** 0.017*** 0.388*** Yes Yes

(9.81) (–5.34) (–7.18) (–4.85) (22.00) (–27.62) (1.42) (–3.13) (–3.20) (6.65) (–4.67) (–17.15) (7.15) (18.01)

–0.022***

(–4.80)

0.008*** –0.002*** –0.028*** –0.038*** 0.048*** –0.074*** 0.000* –0.182*** –0.023*** 0.046*** –0.858*** –0.275*** 0.031*** 0.397*** Yes Yes

(8.81) (–3.79) (–4.70) (–5.65) (16.39) (–22.64) (1.89) (–3.06) (–3.23) (5.47) (–3.10) (–12.11) (7.73) (15.40)

Model 3 Under-investment in labor Negative abnormal net hiring subsample (NET_HIRE_UNit) Coef. t–stat.

–0.108*** 0.006*** –0.002*** –0.052*** –0.024*** 0.052*** –0.069*** 0.000 –0.137*** –0.032*** 0.024*** –1.089*** –0.354*** 0.019*** 0.412*** Yes Yes

(–11.70) (8.83) (–5.18) (–8.05) (–3.77) (17.84) (–22.76) (0.17) (–2.79) (–4.67) (2.58) (–4.70) (–16.13) (5.71) (17.65)

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(AB_NET_HIREit) Coef. t–stat. –0.037*** (–8.79)

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Variables AB_NET_HIREit NET_HIRE_OVit NET_HIRE_UNit SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit Intercept IND Dummies YEAR Dummies

Model 2 Over-investment in labor Positive abnormal net hiring subsample (NET_HIRE_OVit) Coef. t–stat.

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Model 1 Abnormal net hiring

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EP

TE D

Adj. R–sq. 0.312 0.325 0.307 N 61542 24471 37071 Variables are defined in Appendix A. t–statistics are in parentheses, with standard errors clustered by firm. *, **, *** correspond to 1%, 5% and 10% levels of significance, respectively (p–values are based on two–tailed tests).

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Table 6. Regression results – The effects of over- and under hiring (and firing) on corporate tax avoidance (GAAP_ETRit)

RI PT

Model 3 Under–Hiring Coef. t–stat. –0.043*** (–2.91) 0.015*** (8.74) –0.001** (–1.98) –0.015 (–1.14) –0.026** (–2.12) 0.021*** (4.91) –0.100*** (–15.07) 0.000* (1.69) 0.381** (2.13) –0.048*** (–3.06) 0.041* (1.78) –0.090 (–0.17) –0.254*** (–7.29) 0.070*** (5.72) 0.404*** (6.86) Yes Yes

SC

M AN U

Model 2 Under–Firing Coef. t–stat. –0.051*** (–2.81) 0.014*** (9.89) –0.000 (–0.59) –0.074*** (–5.48) –0.016 (–1.00) 0.037*** (8.20) –0.094*** (–16.24) 0.000 (0.39) 0.210 (1.45) 0.012 (0.74) 0.041* (1.68) –1.033** (–1.96) –0.328*** (–7.91) 0.053*** (4.08) 0.412*** (8.69) Yes Yes

TE D

Variables AB_NET_HIREit SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit Intercept IND Dummies YEAR Dummies

Model 1 Over–Hiring Coef. t–stat. –0.011** (–1.96) 0.004*** (4.37) –0.002*** (–4.66) –0.028*** (–4.46) –0.039*** (–5.42) 0.062*** (16.68) –0.056*** (–16.07) 0.000 (0.99) –0.290*** (–4.92) –0.019*** (–2.63) 0.046*** (5.36) –1.096*** (–3.75) –0.256*** (–9.45) 0.002 (0.40) 0.441*** (19.68) Yes Yes

Model 4 Over–Firing Coef. –0.087*** 0.004*** –0.003*** –0.042*** –0.030*** 0.057*** –0.055*** –0.000 –0.205*** –0.043*** 0.012 –1.105*** –0.331*** –0.001 0.419*** Yes Yes

t–stat. (–7.74) (5.04) (–6.25) (–6.31) (–4.51) (16.26) (–17.09) (–0.34) (–4.16) (–6.18) (1.28) (–4.65) (–14.28) (–0.17) (17.27)

AC C

EP

Adj. R–sq. 0.300 0.282 0.345 0.316 N 18933 7449 5538 29622 Variables are defined in Appendix A. t–statistics are in parentheses, with standard errors clustered by firm. *, **, *** correspond to 1%, 5% and 10% levels of significance, respectively (p–values are based on two–tailed tests).

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Table 7. Regression Results – Instrumental variables (2SLS) regression analysis for the effect of net hiring on corporate tax avoidance (GAAP_ETRit) Panel A: first-stage regression model

CONTROLSit Intercept IND Dummies YEAR Dummies

Yes Yes Yes Yes

Panel C: second-stage regression model

Variables Labor investment efficiency proxies CONTROLS

Coef. 0.0019*** 1.010*** –0.0046

t–stat. (3.45) (10.01) (–1.56)

Yes Yes Yes Yes

Model 3 NET_HIRE_UNit Coef. –0.0001 0.3765*** –0.0009 Yes

t–stat. (–0.58) (12.35) (–0.07)

Yes Yes Yes

NET_HIRE_OVit

NET_HIRE_UNit

137.382 0.00

84.689 0.00

145.638 0.00

55.845 22.3

35.275 22.3

52.766 22.3

0.385 0.8249

0.182 0.9131

2.998 0.2234

8.898 0.010

4.689 0.030

2.827 0.090

Model 1 AB_NET_HIREit

Model 2 NET_HIRE_OVit

Model 3 NET_HIRE_UNit

TE D

AB_NET_HIREit

EP AC C

Panel B: post-estimation tests Description 1. Under-identification test Anderson LM statistic p-value 2. Weak identification test Cragg-Donald Wald F statistic Stock-Yogo (2005) critical value 3. Over-identifying restrictions test Hansen J–statistic p–value 4. Hausman (1978) test Hausman statistic Chi-square p–value

t–stat. (3.04) (12.78) (–1.78)

RI PT

Coef. 0.0009*** 0.6702*** –0.003*

M AN U

Variables IND_EMPit IND_NET_HIREit IND_UNIONit

Model 2 NET_HIRE_OVit

SC

Model 1 AB_NET_HIREit

Coef. –0.187*** Yes

t–stat. (–3.41)

Coef. –0.1119** Yes

t–stat. (2.42)

Coef. –0.0039*** Yes

t–stat. (–6.23)

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Yes Yes Yes

Yes Yes Yes

Yes Yes

RI PT

Intercept IND Dummies YEAR Dummies

AC C

EP

TE D

M AN U

SC

Adj. R–sq. 0.359 0.214 0.360 N 24646 10057 14589 Variables are defined in Appendix A. t–statistics are in parentheses, with standard errors clustered by firm. *, **, *** correspond to 1%, 5% and 10% levels of significance, respectively (p–values are based on two–tailed tests).

43

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Table 8. Regression results – The effect of net hiring on additional measures of corporate tax avoidance (CASH_ETRit, BTD_DDit and UTB_TOTALit)

(6.32) (–3.92) (–9.87) (4.60) (16.99) (–27.86) (1.76) (6.61) (–15.81) (3.11) (3.64) (–13.76) (8.78) (12.63)

0.206 32580

Model 7 AB_NET_HIREit Coef. 0.092**

t–stat. (2.40)

(4.41) (–3.57) (–6.55) (0.11) (12.49) (–21.50) (1.40) (5.23) (–13.06) (1.45) (2.50) (–7.81) (7.11) (8.13)

Model 8 NET_HIRE_OVit UTB_TOTALit Coef. t–stat.

0.076**

t–stat.

–0.105*** 0.006*** –0.001*** –0.074*** 0.045*** 0.045*** –0.075*** 0.000 0.275*** –0.126*** –0.014 0.814*** –0.395*** 0.037*** 0.315*** Yes Yes

(–8.73) (6.48) (–2.69) (–9.45) (4.89) (13.52) (–23.13) (1.07) (5.19) (–14.01) (–1.39) (2.81) (–14.33) (7.69) (11.58)

(1.42)

0.209 13178

Table 8 (continued)

Variables AB_NET_HIREit NET_HIRE_OVit NET_HIRE_UNit

0.004*** –0.002*** –0.049*** 0.001 0.049*** –0.082*** 0.000 0.314*** –0.114*** 0.014 0.864** –0.234*** 0.041*** 0.336*** Yes Yes

RI PT

0.005*** –0.001*** –0.060*** 0.032*** 0.048*** –0.079*** 0.000* 0.305*** –0.107*** 0.017*** 0.877*** –0.318*** 0.032*** 0.313*** Yes Yes

Model 4 AB_NET_HIREit

Coef.

TE D

0.010

Model 3 NET_HIRE_UNit

Coef. 0.003**

0.001*** 0.000 0.002 –0.001 –0.000 0.001 0.000 0.070*** –0.013*** –0.010*** 0.115 0.075*** –0.003*** 0.004 Yes Yes

SC

t–stat. (–2.19)

EP

Adj. R–sq. N

Coef. –0.015**

AC C

Variables AB_NET_HIREit NET_HIRE_OVit NET_HIRE_UNit SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit Intercept IND Dummies YEAR Dummies

Model 2 NET_HIRE_OVit CASH_ETRit Coef. t–stat.

M AN U

Model 1 AB_NET_HIREit

0.213 19402

0.192 8329

t–stat. (2.12)

(5.16) (0.24) (0.87) (–0.50) (–0.03) (0.94) (0.77) (5.56) (–6.17) (–4.03) (1.36) (6.26) (–3.61) (0.82)

Model 5 NET_HIRE_OVit BTD_DDit Coef. t–stat. 0.002*

(1.77)

0.001*** 0.000 0.002 –0.003 0.006** 0.001 0.000 0.063*** –0.011*** –0.006*** 0.050 0.058*** –0.002 0.004 Yes Yes

(5.27) (0.43) (1.02) (–1.56) (2.02) (1.05) (0.89) (5.46) (–5.55) (–3.27) (0.55) (7.56) (–1.20) (0.68)

0.241 3237

Model 6 NET_HIRE_UNit Coef.

t–stat.

0.008** 0.002*** 0.000 0.001 0.002 –0.001 0.001 –0.000 0.068*** –0.012*** –0.010*** 0.122 0.069*** –0.005*** 0.001 Yes Yes

(2.23) (5.31) (0.05) (0.32) (0.75) (–0.67) (0.96) (–0.14) (4.51) (–5.38) (–3.76) (1.44) (5.43) (–3.60) (0.12)

0.181 5092

Model 9 NET_HIRE_UNit Coef.

t–stat.

0.036*

(1.74)

(2.06)

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–0.003 0.002 0.013 –0.013 –0.022 –0.025* 0.000 0.078 0.011 –0.012 –2.87 0.226 –0.091*** 0.024 Yes Yes

(–0.79) (0.54) (0.35) (–0.33) (–1.15) (–1.91) (–0.32) (0.49) (0.34) (–0.33) (–0.94) (1.58) (–2.98) (0.45)

–0.001 0.000*** 0.019 –0.035** 0.004 –0.005 –0.000** 0.114* –0.010 –0.017 0.119 0.080 –0.006 –0.009 Yes Yes

(–0.82) (8.32) (1.5) (–2.31) (0.58) (–1.47) (–2.02) (1.89) (–0.69) (–0.64) (0.31) (1.63) (–0.62) (–0.28)

RI PT

(–1.17) (–1.67) (1.04) (0.70) (0.35) (–2.08) (–1.03) (1.48) (–1.27) (–1.47) (–0.41) –1.49 (–1.46) (–0.39)

SC

–0.004 –0.003* 0.033 0.048 0.005 –0.013** –0.000 0.209 –0.027 –0.040 –0.542 0.167 –0.053 –0.010 Yes Yes

M AN U

SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit Intercept IND Dummies YEAR Dummies

AC C

EP

TE D

Adj. R–sq. 0.002 0.014 0.004 N 13457 5681 8141 Variables are defined in Appendix A. t–statistics are in parentheses, with standard errors clustered by firm. *, **, *** correspond to 1%, 5% and 10% levels of significance, respectively (p–values are based on two– tailed tests).

45

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Table 9. Regression results – The effect of additional measures of abnormal net hiring (AB_NET_HIRE_INDit, NET_HIRE_IND_OVit and NET_HIRE_IND_UNit) on corporate tax avoidance (GAAP_ETRit)

–0.0001*** 0.007*** –0.002*** –0.035*** –0.040*** 0.054*** –0.069*** 0.000 –0.198*** –0.032*** 0.038*** –0.927*** –0.292*** 0.002 0.369*** Yes Yes

SC

(13.48) (–4.69) (–9.10) (–6.40) (26.43) (–32.83) (0.47) (–2.33) (–4.00) (7.31) (–4.31) (–19.24) (4.64) (19.92)

M AN U

0.008*** –0.001*** –0.040*** –0.029*** 0.052*** –0.080*** 0.000 –0.101** –0.021*** 0.048*** –0.801*** –0.306*** 0.009*** 0.372*** Yes Yes

RI PT

Model 2 NET_HIRE_IND_OVit Coef. t–stat.

TE D

Variables AB_NET_HIREit NET_HIRE_OVit NET_HIRE_UNit SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit Intercept IND Dummies YEAR Dummies

Model 1 AB_NET_HIRE_INDit Coef. t–stat. –0.000*** (–6.22)

Model 3 NET_HIRE_IND_UNit Coef. t–stat.

(–6.39)

(9.79) (–5.19) (–7.74) (–8.18) (23.06) (–26.31) (1.06) (–4.53) (–5.90) (5.73) (–4.80) (–16.97) (0.89) (14.77)

–0.114*** 0.011*** –0.001* –0.050*** –0.016* 0.041*** –0.089*** –0.000 0.103 –0.022** 0.043*** –0.533* –0.341*** –0.003 0.412*** Yes Yes

(–11.79) (13.09) (–1.91) (–6.45) (–1.89) (14.93) (–24.43) (–0.48) (1.25) (–2.48) (3.34) (–1.78) (–14.44) (–0.82) (20.92)

AC C

EP

Adj. R–sq. 0.321 0.325 0.319 N 61542 54713 21897 Variables are defined in Appendix A. t–statistics are in parentheses, with standard errors clustered by firm. *, **, *** correspond to 1%, 5% and 10% levels of significance, respectively (p–values are based on two–tailed tests).

46

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Table 10. Regression results – The effect of net hiring and additional control variables (AQ_KOTHARIit, M_ABILITY_SCOREit and M_ABILITY_RANKit) on corporate tax avoidance (GAAP_ETRit)

AQ_KOTHARIit M_ABILITY_SCOREit M_ABILITY_RANKit CONTROLS Intercept IND Dummies YEAR Dummies

–0.016*** 0.085*** 0.001 Yes Yes Yes Yes

t–stat. (–8.02) (–3.03) (4.72) (0.15)

RI PT

Coef. –0.039***

Coef. –0.020*** –0.004 0.104*** 0.004 Yes Yes Yes Yes

M AN U

Variables Labor investment efficiency proxies

Model 2 NET_HIRE_OVit

SC

Model 1 AB_NET_HIREit

t–stat. (3.75) (–0.59) (4.18) (0.34)

Model 3 NET_HIRE_UNit Coef. –0.113*** –0.017** 0.065*** 0.004 Yes Yes Yes Yes

t–stat. (–10.69) (–2.11) (3.21) (0.35)

AC C

EP

TE D

Adj. R–sq. 0.259 0.253 0.273 N 32580 13178 19402 Variables are defined in Appendix A. t–statistics are in parentheses, with standard errors clustered by firm. *, **, *** correspond to 1%, 5% and 10% levels of significance, respectively (p–values are based on two–tailed tests).

47

ACCEPTED MANUSCRIPT Table 11. Difference in Difference Regression results – IRS_REG

(38.51) (-4.60) (-9.28) (-10.44) (17.72) (-34.14) (-32.34) (3.36) (4.71) (3.26) (1.97) (-29.95) (-10.21) (15.71)

-0.0026

(-0.37)

-0.0313***

-0.0005*** -0.0009***

(-2.72) (-2.73)

0.0168*** (30.84) -0.0006** (-2.52) -0.0254*** (-5.80) -0.0318*** (-8.35) 0.0145*** (13.71) -0.0614*** (-28.00) -0.1033*** (-22.31) 0.1030** (2.38) 0.0193*** (3.89) 0.0058* (1.87) 0.5584** (2.26) -0.2254*** (-21.55) -0.0000*** (-9.09) 0.1680*** (7.77) YES YES 0.28 24471.00

M AN U

0.0153*** -0.0007*** -0.0315*** -0.0302*** 0.0151*** -0.0513*** -0.1228*** 0.1003*** 0.0174*** 0.0114*** 0.3398** -0.2554*** -0.0000*** 0.2356***

t–stat. (-5.38) (-9.02) (-4.03)

RI PT

Abnormal net hiring Coef. -0.0016*** -0.0397*** -0.0019***

Negative abnormal net hiring subsample (NET_HIRE_UNit) Under-investment in labor Coef. t–stat.

YES YES .246 61,542

(-5.30)

-0.0015*** (-2.89) -0.0055*** (-4.83) 0.0134*** (21.04) -0.0009*** (-3.79) -0.0415*** (-8.10) -0.0275*** (-6.31) 0.0155*** (10.87) -0.0413*** (-19.93) -0.1462*** (-23.65) 0.0793* (1.91) 0.0148*** (2.62) 0.0255*** (3.75) 0.1025 (0.43) -0.3004*** (-20.53) -0.0000*** (-7.14) 0.2815*** (14.06) YES YES 0.229 37071

AC C

EP

TE D

Variables AB_NET_HIREit IRS_REG AB_NET_HIREit * IRS_REG NET_HIRE_OVit NET_HIRE_OVit * IRS_REG NET_HIRE_UNit NET_HIRE_Unit * IRS_REG SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit Intercept IND Dummies YEAR Dummies Adj. R–sq. N

Positive abnormal net hiring subsample (NET_HIRE_OVit) Over-investment in labor Coef. t–stat.

SC

(AB_NET_HIREit)

48

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Table 12: Propensity Score Matching (PSM)

First Stage

Coef.

Nearest Neighbour t–stat. Coef. -0.0025***

t–stat. (-8.27)

0.0321*** (10.44) 0.0097*** (7.32) -0.3413*** (-14.08) 0.7695*** (22.88) 0.0762*** (11.78) -0.1208*** (-6.85) 0.3642*** (10.45) -1.8777*** (-4.51) -0.2262*** (-7.78) 0.0228** (2.40) 7.8387*** (4.30) 0.2761*** (3.68) 0.0002*** (10.32) 2.0015*** (13.59) Yes Yes 0.07 168365

0.0188*** -0.0003 -0.0228*** -0.0356*** 0.0146*** -0.0723*** -0.0995*** 0.1424*** 0.0128** 0.0077* 0.4832* -0.2198*** -0.0000*** 0.1486*** Yes Yes 0.288 23510

M AN U

(38.16) (-4.90) (-8.64) (-10.54) (17.28) (-35.31) (-32.29) (2.96) (4.20) (2.92) (1.79) (-29.52) (-6.35) (13.48) Yes Yes 0.250 60063

TE D

0.0156*** -0.0008*** -0.0303*** -0.0311*** 0.0148*** -0.0544*** -0.1228*** 0.0914*** 0.0161*** 0.0116*** 0.3257* -0.2538*** -0.0000*** 0.2054***

(-4.25)

(30.02) (-1.19) (-4.70) (-8.85) (13.21) (-29.51) (-21.63) (2.77) (2.36) (1.88) (1.67) (-20.55) (-4.42) (6.76)

0.0566*** (18.12) 0.0117*** (8.61) -0.3156*** (-12.67) 0.7519*** (22.36) 0.0786*** (11.90) -0.1395*** (-7.70) 0.3821*** (10.87) -2.0669*** (-4.88) -0.2288*** (-7.71) 0.0181** (2.53) 5.5451*** (2.99) 0.3818*** (5.03) 0.0002*** (10.66) 1.8559*** (12.79) Yes Yes 0.099 168365

-0.0016* 0.0117*** -0.0019*** -0.0384*** -0.0692*** 0.0121*** -0.0297*** -0.1031*** 0.3153*** -0.0118 0.0393*** 0.1499 -0.5746*** 0.0000 0.2018*** Yes Yes 0.225 5957

(-1.87) (6.51) (-3.45) (-3.12) (-2.93) (3.63) (-6.48) (-2.83) (3.57) (-0.92) (2.65) (0.28) (-12.21) (0.59) (3.77)

EP

-0.1356*** (-50.87) -0.0014 (-1.33) -0.3157*** (-14.70) 0.8785*** (32.79) 0.0498*** (9.66) -0.1226*** (-8.87) 0.2662*** (9.72) -1.8806*** (-5.97) 0.0158 (0.65) 0.1870*** (7.06) 10.1283*** (6.96) 0.0810 (1.36) 0.0005*** (37.53) 1.1033*** (9.73) Yes Yes 0.074 168365

SC

-0.0033***

Model 3 Under-investment in labor (Second Stage) Negative abnormal net hiring First Stage subsample (NET_HIRE_UNit) Nearest Neighbour Coef. t–stat. Coef. t–stat.

AC C

Variables AB_NET_HIREit NET_HIRE_OVit NET_HIRE_UNit SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit Intercept IND Dummies YEAR Dummies Adj. R–sq. N

Second Stage (AB_NET_HIREit)

Model 2 Over-investment in labor (Second Stage)Positive abnormal net hiring First Stage subsample (NET_HIRE_OVit) Nearest Neighbour Coef. t–stat. Coef. t–stat.

RI PT

Model 1 Abnormal net hiring

49

ACCEPTED MANUSCRIPT Table 13: The Inverse Mills Ratio Method Model 1

Model 2 Over-investment in labor ETR

Abnormal net hiring

Intercept Intercept

N Rho Sigma Lambda Wald Test

(-8.52)

-0.0182***

(43.82) 0.0200*** (-5.92) -0.0008*** (-5.71) -0.0112*** (-12.36) -0.0406*** (18.09) 0.0127*** (-36.53) -0.0783*** (-26.13) -0.1004*** (0.54) 0.0159 (-1.76) -0.0057 (2.41) 0.0012 (0.99) 0.3996 (-25.30) -0.2091*** (-9.57) -0.0000*** (18.19) 0.1799*** Heckman Test (Lambada) ETR (-6.70) 0.00 -0.0061***

-0.0007*** 0.0160*** -0.0010*** -0.0300*** -0.0332*** 0.0149*** -0.0528*** -0.1417*** 0.0318 -0.0091 0.0134*** -0.1359 -0.2835*** -0.0000*** 0.2826***

(-2.83) (22.89) (-4.91) (-5.79) (-7.41) (12.87) (-21.36) (-20.70) (0.67) (-1.62) (3.08) (-0.55) (-19.30) (-6.72) (16.46)

(5.39) (5.51) (-4.92) (3.08) (10.15) (-21.94) (2.92) (10.13) (8.05) (-1.72) (2.75) (5.48) (7.87) (-2.61) (31.52)

-0.0067*** 0.0159*** 0.0129*** -0.2831*** 0.0964*** 0.1225*** -0.4921*** 0.0372 8.6728*** 0.5078*** 0.2141*** 0.2298 0.0380 0.0001*** -0.0000*** 0.8734***

(-3.81) (3.31) (7.31) (-7.07) (2.62) (13.72) (-22.70) (0.74) (19.76) (13.24) (5.79) (0.11) (0.35) (7.24) (-3.18) (24.97)

(-1.98)

-0.0529*

(-1.78)

(34.37) (-3.54) (-2.60) (-9.83) (12.59) (-30.62) (-16.79) (0.31) (-1.20) (0.54) (1.58) (-17.14) (-9.37) (9.61)

RI PT

0.0190*** -0.0009*** -0.0188*** -0.0374*** 0.0137*** -0.0651*** -0.1181*** 0.0186 -0.0063* 0.0047** 0.1759 -0.2369*** -0.0000*** 0.2293***

(-2.91)

SC

-0.0006***

(5.37) (8.86) (-7.44) (3.36) (16.77) (-31.69) (2.45) (22.05) (15.41) (1.73) (1.96) (4.61) (12.38) (-4.70) (41.16)

-0.0552**

(-2.55)

0.0254*** 0.0112*** -0.1763*** 0.1197*** 0.0906*** -0.5394*** 0.1495*** 5.0272*** 0.2806*** -0.0294* 6.5423*** 0.5521*** 0.0002*** -0.0000*** 1.0470*** athrho -0.0605** lnsigma

EP

TE D

0.0180*** 0.0117*** -0.1977*** 0.0886*** 0.1050*** -0.5137*** 0.0874** 7.2328*** 0.3879*** 0.0284* 3.0714* 0.3382*** 0.0002*** -0.0000*** 0.9782***

AC C

AB_NET_HIREit NET_HIRE_OVit NET_HIRE_UNit SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit Olson_Dis Intercept

-0.0027***

M AN U

AB_NET_HIRE_INDit NET_HIRE_IND_OVit NET_HIRE_IND_UNit SIZEit MTBit LEVit CASHit ROEit NOLit ∆NOLit FOR_INCit CAP_INTit INTANGit EQINCit RNDit SALES_GROWTHit Intercept

Model 3 Under-investment in labor

(-1.8844*** 579.98) 59930 -.0551 (.0210) .15192 (0.000) -.0083 (0.003) 5.90 (0.015)

(-3.66)

-1.8980*** (-400.68) 27671 -.0604 (0.030) .14986 (0.000) -.00905 (0.004) 3.35 (0.060)

-1.8800*** (-421.07) 32259 -.0528 (0.030) .1525 (0.000) -.0080 (0.004) 2.91 -0.09

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AC C

EP

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ACCEPTED MANUSCRIPT

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