Non-compete Agreements and Payout Policy:

Evidence from a quasi-natural experiment.

December 2018

Abstract

In this paper, I study the impact of labor mobility and its associated risk on corporate payout policy.

I find a strong positive relation between non-compete agreement (NCA) enforcement and dividend

payout. To establish causality, I rely on both cross-sectional and time-series variation in state-level

NCA enforceability, namely NCA enforceability index and 3 major NCA law changes occurred

over the period 1992-2004. This finding is consistent with the argument that reduced labor mobility

decreases predatory risk associated with proprietary information loss through former employees

and thus increases propensity to payout dividend. Moreover, I rule out an alternative explanation

related to manager’s career concern and provide further support for the predatory explanation by

showing that the effect is stronger among firms that are more concerned with proprietary

information protection (i.e. firms with high R&D and key human capital risk).

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1 Introduction

Both existing theoretical and empirical studies have provided evidence that risks arising from

product market may substantially impact corporate financial policies. These risks, including

predatory behavior from rivals, entry of new players and product market fluidity can lead the firm

to make strategic choices regarding financial flexibility. Specifically, firms are more likely to

maintain higher degree of financial flexibility in an environment where product market threats are

more likely to materialize. Focusing on payout policy, it follows that lower dividend payout frees

up more cash flow which could be used to fend off predatory attacks from competitors or to deter

them from executing their moves ex ante.

In this paper, I study how dividend policy is affected by the risk that a firm’s proprietary

information is divulged to its competitors and ultimately be used to damage its competitive

position2. The type of information may include research and development, customer list and related

data and financial data which play a vital role in a firm’s competitive advantage and are critical

income-generating assets. Firms are likely to factor this risk in making payout decisions because

it poses substantial threats and thus adversely affects future stability, a central determinant of

payout policy. This is especially important for dividend because it represents a rigid commitment

to distribute cash to shareholders which is difficult to reverse once set (Brav et al. 2005).

Plenty of evidence has highlighted the important of proprietary information, intellectual property

and the associated risks. Shapiro and Hassett (2005) report that US intellectual property is worth

between $5 trillion and $5.5 trillion, equivalent to about 45 percent of U.S. GDP and greater than

the GDP of any other nation in the world. According to a survey sponsored by PWC and the US

2 Klasa et al. (2018) analyzes the same risk but focus on its relation to leverage.

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chamber of Commerce, 70 percent or more of the market value of a typical US firm may be

attributed to intellectual property. Thus, firms are exposed to substantial risks related to proprietary

information and intellectual property. A large portion of firms participated in this study report a

known or suspected incident of proprietary information loss. More importantly, the economic

damage originating from proprietary information and intellectual property losses is substantial. In

2001, the amount was reported to be between $54 and $59 billion dollar. In the same survey,

former employee is cited as the greatest risk factor associated with proprietary information and

intellectual property loss. In addition, Almeling et al. (2010) report that former employee is

responsible for the majority of legal cases related to trade secrets. Therefore, in this paper, I focus

on the risk of proprietary information loss though the channel of former employee.

The empirical challenge is to identify an exogenous shock to this risk. I overcome this challenge

by exploiting the cross-sectional heterogeneity in non-compete agreement (NCA) enforceability

as well as its time-series changes. To this end, I rely on the state-level NCA enforceability index

developed by Garmaise (2011) and the three major legal changes in NCA enforcement discussed

in his paper, namely Texas in 1994, Florida in 1996 and Louisiana in 2001. I find a strong positive

relation between NCA enforceability and dividend payout. In a cross-sectional setting, 2-point

increase in Enforceability index results in a 25% increase evaluated at the sample average dividend

payout measured by dividend scaled by sales. Following Garmaise (2011), I also examine the

effect of NCA enforcement on dividend payout conditional on the level of in-state competition.

NCA should matter the most for firms that face intense in-state competition because it is effective

only within a particular geographic scope3, therefore, the risk that in-state competitors will poach

employees and obtain proprietary information is higher for these firms. Consistent with this idea,

3 Beyond state boundaries, enforcement become considerably more difficult (Gibson 1999, Garmise 2011).

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I find that coefficient on the interaction term between NCA enforceability index and in-state

competition is positive and statistically significant. Next, I provide support for a causal relationship

between NCA enforceability and dividend payout using time-series analyses. I show that NCA

legal changes increase dividend payout by 35% for firms headquartered in affected state. I further

show that the increases in dividend payout of treated firms relative to control firms occur only after

the legal events, but not before. This test provides support for the parallel trend assumption

required in a standard different-in-differences analysis.

An alternative explanation of my findings concerns managerial incentive. Since strong NCA

enforcement limit manager’s outside employment opportunity, he will be more inclined to make

conservative choices (Colak and Korkeamaki 2017, Garmaise 2011). A risk averse manager could

waste firm’s liquid asset on diversifying, low-return projects and thus decrease shareholder value.

In this case, a higher dividend payout can act as a disciplinary mechanism as it (1) lowers the pool

of liquid asset that could be wasted and (2) makes external financing more likely thus scrutiny

imposed by the capital market. This competing hypothesis, therefore, have the same prediction as

the predatory hypothesis. To address this issue, I examine the effect of being a dominant firm in

an industry on the relation between NCA enforceability and dividend payout. If the predatory

hypothesis is true, the effect of NCA enforceability on dividend payout should be weaker among

dominant firms because they are much less susceptible to predatory behavior from competitors.

Specifically, these firms should have access to abundant resources and market power to fight off

any predatory attack. In contrast, if the alternative hypothesis holds, the effect of NCA

enforceability on dividend should be stronger among dominant firms because they are good

candidate for severe agency conflict. Consistent with the predatory hypothesis, I find that the

positive relation between NCA enforceability and dividend payout is weaker among dominant

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firms. In addition, I show that the effect is also stronger for firms who are more concern with

proprietary information protection, i.e. firms that high research and development, and high risk of

key human capital. These results further support the predatory explanation.

This paper makes two main contributions to the literature. First, it provides more support for the

notion that product market risks and corporate financial policies are closely linked. While most of

existing studies focus on risks arose from competition in general, this paper focuses on a specific

type of risk – the risk that proprietary information and intellectual property are loss to competitors

through former employee. The risk is critical in the present context due to the fact that value of

modern firm is becoming heavily concentrated on intangible assets. Thus, studying the impact of

this risk on corporate finance is extremely important. Second, this study contributes to the literature

of payout policy. Previous papers have established the important determinants of payout policy

such as agency conflicts, corporate lifecycle, etc. This paper provide evidence that labor mobility

and the associated risk may have a meaningful impact on dividend payout.

The remainder of the paper proceeds as follow. The next section provides a review of the literature

and develops the main hypotheses. Section 3 discusses data and variable construction. Section 4

presents the empirical strategy and results. Section 5 investigates the cross-sectional differences

of the effect. Finally, section 6 provides a conclusion.

2 Background and hypothesis development

2.1 Non-compete Agreement

Employees who engage in firms activities on a daily basis often acquire firm-specific knowledge

of a proprietary or confidential nature (Rajan and Zingales 2001). Therefore, employee mobility

is one of the major channels through which information is transferred between organizations. One

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notable real-world example is the Intel Corporation, the world largest producer of computer’s

processor. The company was founded when Fairchild Semiconductor International, Inc.’s former

managers left the firm with proprietary information about the microprocessor (Rajan and Zingales

2001). Naturally, firms would want to reduce the risk of proprietary information loss by restricting

labor mobility. The most common tool they use is the non-competes agreement.

NCA is relevant to risk of proprietary information loss and thus useful for my research question

for several reasons. First, Non-compete clauses itself are contractual provisions that prohibit

exiting employee from working for a competitor in a specified period of time. Notably, prior

research has shown that NCA is indeed an effective restricting tool as it limits employee’ outside

employment opportunities and imposes higher jobs-witching and unemployment costs (Marx et al

2009; Garmaise, 2011). However, its effectiveness is dependent on jurisdiction under which it will

be enforced. Under jurisdiction where enforcement is weak, non-compete clauses could appear in

employment contract but can easily be waived at the court (i.e. California). Second, NCA is

commonly used for upper-level management. Specifically, Garmise (2001) shows that about 70%

publicly traded firms have NCA as a part of the contract with top management. These individuals

usually have access to a great deal of firm’s proprietary information thus the risk of proprietary

information loss is greatly impacted by their mobility. Taken as a whole, NCA enforceability is

arguably an exogenous variation to the risk of proprietary information and intellectual property

loss through former employees.

2.2 The relation between NCA enforceability and payout policy

2.2.1 The predatory hypothesis

Bolton and Scharfstein (1990) examine a theoretical model where financially constrained firm are

susceptible to predatory attacks from rivals who want to make sure that the firm performs poorly.

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This increases the chance that financial constrains becomes binding thus leads to premature exit.

Alternatively, Chi and Su (2016) focus on underinvestment problem. They show that in a

competitive environment, financially strong rivals implement predatory attacks on financially

weak opponents to drive down their cashflow. This deficiency renders them unable to take on

investment opportunities and consequently end up losing these opportunities and market share to

their rivals. Empirical research such as Hoberg, Phillips, and Prabhala (2014) provides support for

this view by showing that product market threats increase cash holdings and decrease payout. Chi

and Su (2016) find that cash is more valuable in more competitive environment. Taken together,

the need for financial flexibility become more critical when product market threats are prevalent

because it not only allow firms to effectively respond to predatory behaviors but also act as a signal

to deter its rivals from attacking ex-ante.

In states where weak NCA enforcement allows employees to switch jobs and work for former

employers’ competitors, the risk that proprietary information is leaked following their transfer is

high. Competitors who have access to this information can launch predatory attacks against the

firm thus may cause severe damage to its competitive position. This risk destabilizes future

earnings which is the central determinant of payout policy (Brav et al. 2005). The argument is even

more applicable to dividend policy because dividend is perceived as a rigid commitment that

managers refrain from altering. Taken together, restriction to labor mobility such NCA and its

enforcement substantially decrease the risk of proprietary information and intellectual property

loss through former employee and thus increase the propensity of dividend payout. This discussion

predicts a positive relation between state-level NCA enforceability and dividend payout.

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2.2.2 The career-concern hypothesis.

Labor mobility can potentially affect managerial incentives. Reduced mobility limits manager’s

outside job options thus block him from diversifying his human capital. As the results, career

concerns may induce managers to act more conservatively than what would be optimal for the

shareholders of the firm. Garmaise (2011) and Colak and Korkeamaki (2017), among others, find

evidence of decreasing corporate policy risks in presence of restricted labor mobility. Given the

notion that “playing safe” can be value destroying (Jensen and Meckling 1976; Amihud and Lev

1981), certain disciplinary mechanism should be put in place to counter act this effect. In this case,

dividend suits the purpose for several reasons. First, high ongoing dividend payout is a

commitment that managers are refrain from altering (Brav et al. 2005). This raises the chance that

the firm must obtain external funding for future investment thus is subjected to additional

monitoring from the capital market (Easterbrook 1984). Second, paying out excess cash limit the

resources that could be wasted on low return, value-destroying projects by a risk averse manager

(Jensen 1986). Third, from the manager’s perspective, he has the incentive to pay dividend to

establish a reputation for fair treatment to shareholder (Porta et al. 2000; Shleifer and Vishny

1997), expecting to raise capital at favorable terms in the future and potentially gain support from

shareholders to secure his job. Taken together, this discussion also points to a positive relation

between NCA enforceability and dividend payout. In later test, I attempt to rule out this alternative

explanation by investigating the effect of being a dominant firm on this relation.

3 Sample construction and variables definition

3.1 Dependent variable

The data is from Compustat. To ensure that the result is robust to different measure of dividend

amount, I consider three alternatives including: div_to_s is the ratio of dividends (dvc) to sales

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(sale); div_to_e is the ratio of dividends (dvc) to sales (sale), and div_to_cf is the ratio of dividends

(dvc) to cash flow (i.e., net income (ni) plus depreciation (dp)). For div_to_e, and div_to_cf, the

variables are defined only when earnings and cash flow are positive. As the results, sample size

for these measures are smaller in comparison to that of div_to_s.

3.2 Independent variable of interest: Non-compete Agreement enforceability index

The state-level measure of Non-compete Agreement enforceability is from Garmaise (2011) who

scrutinized 12 questions related to the level of enforcement and generated an index. This procedure

assigns one point to each of the states when one of the twelve dimensions of enforcement exceeds

a given threshold. The index ranges from 0 for states such as California where there is virtually no

enforceability to 9 for states like Florida where extremely strong enforcement is in presence. Bird

and Knopf (2015) and Ertimur et al. (2018) adopted this procedure and extended the index from

1992-2004 to 1976-1991 and 1980-2013 respectively. I supplement Garmaise (2011)’s data using

these sources to cover the period from 1976 to 2013.

3.3 Control variables

In all regressions, I control for a set of firm characteristics that are commonly used in the dividend

payout research.

• Maturity: rte is retained earnings (re) scaled by the book value of assets (ta); te is

shareholder equity (ceq) scaled by the book value of total assets (ta); log_at is the natural

logarithm of the book value of total assets (ta); age is the natural logarithm of firm age.

• Risk: sd is the stock return volatility over the last 12 months.

• Profitability: roa is net income (ni) scaled by the book value of total assets (ta).

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• Investment Opportunities: sgr is the natural logarithm of sale growth rate (ln(salet/salet-1));

mb is the ratio of market to book value.

• Leverage: lev is the book value of leverage.

• Cash balance: cash_total is the total of cash and short-term investment (che) scaled by the

book value of assets (ta).

3.4 Summary statistics

I present descriptive statistics in Table 1. Panel A and B represent sample period 1992-2004 and

from 1976-2013, respectively. To mitigate the effect of outliers, all continuous variables are

winsorized at the 1st and 99th percentile of the distribution. I find large variation across most of the

dependent and independent variables. The median value for each dependent variable is 0,

suggesting that the data is truncated at zero. The mean firm-year dividend payout amounts are:

dividend-to-sales ratio (div_to_s) of 0.8%; dividend-to-earnings ratio (div_to_e) of 20.1%; and

dividend-to-cash flow ratio (div_to_cf) of 9.6%, which are consistent with prior reserach. For the

independent variables, the median firm-year values are: 13.7% for retained earnings over book

value of total equity (rte); 0.493 (in billions of US dollars) for equity (te); 11,2% for return on

assets (roa); 6.7% for the rate of sales growth (sgr); 5.36 for the natural logarithm of the book value

of total assets (log_at); and 8.5% for cash and short-term investments scaled by the book value of

total assets (cash_total). Overall, the summary statistics does not suggest any bias toward a specific

type of firm. Also, the large dispersion in both dependent and independent variables should

improve the power of my empirical tests.

[Insert Table 1]

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4 Methodology and empirical results

In this section, I describe the model specifications and present the empirical results. Since the

measures of corporate payouts are truncated at zero and one, I estimate the regression coefficients

using Tobit model following prior literature.

4.1 Cross-sectional analysis

4.1.1 Raw index

I begin by examining the effect of state-level NCA enforceability on dividend payout policy using

the following equation:

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 = α + β1 ∗ 𝑁𝐶𝐴𝑠,𝑡 + γ ∗ 𝑋𝑖,𝑡 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡 + 𝑌𝑒𝑎𝑟𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡 + ϵ𝑖,𝑡 (1)

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 = {𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡: 𝑖𝑓 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 > 0

0 ∶ 𝑖𝑓 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

where i and t indicate firms and years, and s indicates the states.

The dependent variable, Dividend, includes three different measures of dividend amount described

in the above section. The main variable of interest is the NCA enforcement index. If the predatory

hypothesis is true, the coefficient on NCA should be positive and statistically significant. X is a

vector of control variables widely used in payout policy research. All regression includes year

fixed effect and industry fixed effect, defined as SIC two-digit number. Following Bertrand and

Mullainathan (2003), I cluster standard error at the most aggregate level of the covariates (i.e.

state-level) to allow for within state correlation of the error terms. Since NCA is largely time-

invariant, I do not control for firm-fixed effect or state-fixed effect to make use of the cross-

sectional heterogeneity in NCA enforcement.

[Insert Table 2]

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Table 2 presents the results from estimating equation (1). All coefficient of NCA enforcement

index is positive and statistically significant at 1% level except for div_to_s (significant at 5%

level). The coefficient magnitudes are 0.001, 0.02 and 0.008 for dividend payout scaled by sales,

earnings, cash flow, respectively. This result show that state-level NCA enforcement significantly

increases firm’s dividend payout. More importantly, the result is also economically significant.

For example, when NCA enforceability index increases by one standard deviation, which

approximates 2 points, div_to_s increases by 0.002 which is equivalent to a 25% change evaluated

at the sample mean of 0.008. Alternatively, when dividend payout is measured by div_to_e and

div_to_cf, the increases in dividend payout is predicted to be 18% and 15% respectively for the

same change in NCA enforceability index.

Although statistically and economically significant, the result from equation (1) can at best speak

to an association between NCA enforceability and dividend payout due to the presence of

endogeneity. It could be the case that NCA enforcement is correlated other state-specific factors

that affect dividend payout. In this case, the result might suffer from omitted variable bias. I

address this problem later in the time-series tests.

4.1.2 NCA enforceability, product market characteristic and dividend payout.

As argued in Garmise (2010), changes in NCA laws should have the greatest impact on firms with

substantial in-state competition because the effectiveness of NCA is limited by geographic scope

and enforceability is much easier under the same jurisdiction. For these firms, an increase in NCA

enforcement will significantly decrease the probability that executives or employees can leave the

firm to work for its rivals and thus decrease the risk of proprietary information leakage to a greater

extent. Following Garmise (2010), I propose the interaction between the non-competition

enforcement index and the degree of in-state competition as an alternative measure of the effect of

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enforceability. The coefficient of this interaction term should capture the effect of NCA

enforcement on disclosure conditional on the level of local competition.

I empirically implement the test using the following equation:

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 = α + β1 ∗ 𝑁𝐶𝐴𝑠,𝑡 + β2 ∗ 𝐼𝑛 − 𝑠𝑡𝑎𝑡𝑒 𝐶𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑜𝑛𝑖,𝑡

+ β3 ∗ 𝑁𝐶𝐴𝑠,𝑡 ∗ 𝐼𝑛 − 𝑠𝑡𝑎𝑡𝑒 𝐶𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑜𝑛𝑖,𝑡 + γ ∗ 𝑋𝑖,𝑡

+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡 + 𝑆𝑡𝑎𝑡𝑒𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡 + 𝑌𝑒𝑎𝑟𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡 + ϵ𝑖,𝑡 (2)

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 = {𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡: 𝑖𝑓 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 > 0

0 ∶ 𝑖𝑓 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

where i and t indicate firms and years, s indicates states.

In this specification, the level of in-state competition faced by a firm is defined as the portion of

total industry sales (excluding sales of the firm) produced by its competitors headquartered in the

same state. Similar to equation (1), I control for industry, state, year fixed effect and cluster

standard errors at state level. Unlike the previous test, I can include state fixed effect because the

focus of this test is on the interaction term. Also, the inclusion of state fixed effect alleviates the

concern that results may be driven by state-specific time-invariant factors such as legal aspects

unrelated to non-compete agreement, tax level and other economic conditions. I predict that the

coefficient of the interaction term will be positive and statistically significant.

[Insert Table 3]

The regressions results are presented in Table 3. Across all measure of dividend payout, I find that

the coefficients on NCA * In-state competition are positive and statistically significant at 1% level.

The magnitudes of coefficient are 0.004, 0.106 and 0.047 when the dependent variables are

div_to_s, div_to_e and div_to_cf respectively. In addition to being statistically significant, these

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results exhibit economic significance. A one-standard deviation increases in the interaction

between the enforcement index and the level of in-state competition increase payout ratio

(div_to_s) by 35% evaluated at the sample mean. These results support the notion that NCA

enforcement particularly reduce the risk of proprietary information leakage for firms that face

substantial local competition and thus increase the propensity to payout to a greater extent.

The effect of NCA enforceability on dividend policy also depends on product differentiation. For

firms with high degree of product differentiation, the economic damage resulting from proprietary

information leakage should be much larger than for their counterpart. The reason is that

competitive advantage of these firms lies in product differentiation. If proprietary knowledge

related to the product is divulged to competitors, this differentiation will diminish and thus entail

substantial economic loss. In contrast, firms whose products are similar to others should suffer less

from the same problem because their product-related knowledge is less valuable to competitors.

Taken together, highly product-differentiated firm should be more concerned about the risk of

proprietary information loss and thus the effect of NCA enforceability on payout policy should be

more pronounced among these firms. To test this conjecture, I take advantage of the total similarity

measure developed by (Hoberg and Phillips 2016). This measure captures the extent to which a

firm’s product description presented 10-K resembles that of other firms. Higher value of this

measure is equivalent to lower degree of product differentiation. I interact NCA enforceability

index with the similarity measure and evaluate the coefficient of the interaction term. If my

conjecture is true, this coefficient should be negative and statistically significant.

[Insert Table 4]

The results are presented in table 4. First, it is important to note that the coefficient of the product

similarity measure is negative and significant, which is similar to that of in-state competition.

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According to product differentiation theory, competition is stronger when there is a higher degree

of product similarity between competitor. Thus, this coefficient illustrates that firms pay less

dividend when faced by stronger competition. However, when it comes to the interaction term, the

coefficient of product similarity interaction has negative sign, as opposed to the positive sign of

in-state competition interaction. This finding highlights that although both product similarity and

in-state competition are measures of competition, they are conceptually different and thus impact

the effect of NCA enforceability in different directions. In two out of three dividend models, I find

that the coefficients of the interaction terms are negative and statistically significant at 5% level.

A concern with this test is that product similarity might be just another measure of competition

therefore, the results might be fragile when one includes in-state competition in the same

regression. I address this concern by including both in-state competition and total similarity

measure in one regression. I find that the coefficients of the interaction still have the predicted

signs and remain statistically significant.

4.2 Time-series analysis

In this section, I focus on the period from 1992-2004 covered in Garmise 2010 when 3 major

changes in NCA law occurred. First, in an untabulated test, I repeat the cross-sectional analysis

with this sub sample and find that the effect of NCA enforceability and dividend payout is even

stronger than in the main sample. This result is not surprising because substantial time-series

change in NCA enforcement concentrated in this period.

4.2.1 Increased Enforceability

Garmise discussed 3 incidences where the treatment of NCA substantially changed. First, in June

1994, in Light v. Centel Cellular Co. of Texas, the Texas Supreme Court imposed new

requirements for NCA to be enforced. Subsequence to the ruling, an employer is mandated to offer

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an employee specific consideration in exchange for the NCA. In addition, the court’s ruiling also

apply retroactively to all NCA signed previously in Texas. Taken together, NCA enforcement in

Texas decreased significantly and thus index score went from 5 before 1994 to 3 after. The second

NCA legal change occurred in Louisiana in 2001. The ruling in SWAT 24 Shreveport Bossier, Inc.

v. Bond mandated that employees could not be prohibited from joining a competing company in

which they held no equity interest. More importantly, this ruling had a stronger effect on managers’

mobility as the relevant employment opportunities for them would typically be other larger rival

firms. Similar to Texas, the ruling applied to all NCA previously signed. As the result, the

enforcement index in Louisiana dropped from 4 to 0 in 2001 and 2002. However, in 2003, the state

legislature changed the law, allowing NCA to prohibit employees from joining competitors in

which they have no ownership interest. The enforcement index, therefore, reversed back to 4.

Third, in 1996, the state legislature in Florida repealed its previous law governing NCA. The new

law significantly strengthened employers’ position and thus increased enforcement index from 7

to 9 after 1996. These regulatory changes effectively serve as exogenous shocks to labor mobility

within the affected states and thus allow for a diff-in-diff analysis.

I implement a difference-in-differences analysis using the following specification:

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 = α + β1 ∗ 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑑 𝐸𝑛𝑓𝑜𝑟𝑐𝑒𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑠,𝑡 + γ ∗ 𝑋𝑖,𝑡 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡

+ 𝑆𝑡𝑎𝑡𝑒𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡 + 𝑌𝑒𝑎𝑟𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡 + ϵ𝑖,𝑡 (3)

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 = {𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡: 𝑖𝑓 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 > 0

0 ∶ 𝑖𝑓 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

where i and t indicate firms and years, s indicates states.

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Following Garmise (2011), I create a variable Increased Enforceability, assuming that the legal

changes affected dividend policy following their occurrence. This variable is equal to 1 for firms

in Florida in 1997-2004, -1 for firms in Texas in 1995-2004 and for firms in Louisiana in 2001-

2003, and 0 otherwise. The inclusion of State Fixed Effect allows the specification to assign firms

in states without NCA legal change to act as control for firms in states where NCA law changed.

For a balanced panel, using state fixed effect and firm fixed effect should yield same result.

However, if the data is unbalanced with respect to state-level grouping as in this case, state fixed

effect will help us obtain the average effect on firms in one state versus the others while firm fixed

effect assign more weight on some states than the others and thus might not return the effect that

we want. The coefficient β1 is the diff-in-diff estimate which captures the average effect of changes

NCA enforcement on the treatment group relative to the control group. I predict that this coefficient

is positive and statistically significant.

[Insert Table 5]

In table 5, I find that the coefficient on Increased Enforceability is positive and statistically

significant at 1% level. The estimated coefficients are 0.003, 0.112 and 0.034 when the dependent

variable are div_to_s, div_to_e and div_to_cf, respectively. The magnitude of the effect implies

strong economic significant. For example, firms that experienced an improvement in NCA

enforcement increase their dividend payout (div_to_s) by 50% evaluated at the sample mean.

These results show that tightened NCA law increase the propensity to pay dividend.

A limitation to the method mentioned above is that it cannot precisely gauge the timing of the

effect. It could be the case that legislative events may result from firms’ lobbying activities or may

be anticipated by managers. As such, firms may start to change their payout policy before the law

change. To address this problem, I follow Bertrand and Mullainathan (2003)’s specification that

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includes pre and post dummies. This specification allows me to pin point the timing of the effect

and rule out this concern. I replaced the Increased Enforceability in the previous model with 3

dummy variables for 2 years pre-law (pre_2), 1 year pre-law (pre_1), during the year of law change

(pre_0) and two interaction terms of Increased Enforceability with 1 year post-law (post_1) and 2

year or more post-law (post_2) dummies. I used the interaction for post period to account for the

fact that law changes in the 3 states have opposite signs (stronger enforceability in Florida, weaker

enforceability in Texas and Louisiana).

[Insert Table 6]

Table 6 demonstrated that the change in payout policy only occur after the law change. Across all

measure of dividend payout, the coefficients on pre_2, pre_1 and pre_0 are indistinguishable from

zero whereas the interaction between post_1, post_2 and Increased Enforceability are positive and

statistically significant.

4.2.2 Texas

In this test, I shift my focus on the most significant law change, which is Texas in 1994. During

this year, the Texas Supreme Court added a new requirement that made it substantially more

difficult to enforce noncompete agreements in the state. This change is much more profound

compare to the changes in the other two states. In Louisiana, the change was temporary and it is

possible that firms could have anticipated a reinstatement of noncompete agreements by the state

legislature. Besides, the change in Florida applied only to prospective contracts. Moreover, the

state’s enforcement level was already very high, so the incremental effect of changes in

enforcement was probably limited. As a result, it is appropriate to focus on the change in Texas in

a separate test. I conduct a diff-in-diff analysis with the treated group that includes firms

headquarter in Texas and post treatment period being after 1994.

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𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 = α + β1 ∗ 𝑇𝑒𝑥𝑎𝑠 ∗ 𝑃𝑜𝑠𝑡 + γ ∗ 𝑋𝑖,𝑡 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡

+ 𝑆𝑡𝑎𝑡𝑒𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡 + 𝑌𝑒𝑎𝑟𝐹𝑖𝑥𝑒𝑑𝐸𝑓𝑓𝑒𝑐𝑡 + ϵ𝑖,𝑡 (3)

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 = {𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡: 𝑖𝑓 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 > 0

0 ∶ 𝑖𝑓 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

In this model, the individual coefficients of Texas and post are omitted due to the inclusion of state

and year fixed effect. The coefficient of the interaction term Texas * Post is expected to be negative

and significant. The sign of this coefficient should be opposite to that of Increased Enforceability

in previous tests because NCA legal change in Texas significantly decreases NCA enforceability.

[Insert Figure 1]

[Insert Table 7]

Figure 1 and Table 7 shows the results for Texas law change in 1994. Figure 1 graphs the average

payout ratio (div_to_e) by treated and control group against the event year timeline. After 1994, it

is visually clear that the level of dividend payout of firms headquartered in Texas is consistently

below that of non-Texas firms. The multivariate results presented in table 5 speaks to the same

conclusion as the coefficient of the interaction Texas x Post is negative and statistically significant.

The magnitude of the coefficients are -0.003, -0.105 and -0.024 for div_to_s, div_to_e and

div_to_cf respectively. These coefficients represent a 50%, 57%, 28% drop in these dividend

measure for firms headquartered in Texas.

Although both the graph and difference-in-differences estimates show that the Texas effect is

strong, the result with Increased enforceability variable in the previous section is not driven by

firms headquartered in Texas. In an untabuled test, I find the same positive and statistically effect

of increased enforceability on dividend amount even when all Texas firms are excluded.

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4.2.3 Robustness check

For robustness check, I extend the time-series analysis to sample periods beyond 1992-2004. To

this end, I apply 2 different procedures. First, I focus on the sample from 1992 to 2013. This period

allows me to capture 10 clean changes in NCA enforcement, instead of 3, because during this

period, states that changed non-compete index, changed just once. As the result, I was able to

construct the increased enforceability similar to Garmise's (-1,0,1). Also, it is important to note

that these 10 changes capture most significant and the majority of time-series changes in the data.

Consistent with the previous section, the coefficient on the new increased enforceability variable

is positive and statistically significant. For the period from 1976 to 2013, it gets more complicated

because there are states where NCA enforcement index changed multiple times (increased twice,

decreased then increased, etc). Therefore, I added +2 and -2 to account for the magnitude of

changes relative to the starting point of each state. For example, Idaho changed from 5 to 6 after

1991 and then 6 to 7 after 2008. I put 1 for period from 1992 to 2008 and 2 for period from 2009

to 2013 and 0 otherwise. Basically, I am measuring the changes relative to the starting point of

Idaho which is 5. Using this method I end up with an increase enforceability variable including

value of (-2,-1,0,1,2). Similarly, I find positive and statistically significant relation between

increased NCA enforcement and dividend payout.

Additionally, in order to show that the time-series results are robust to different estimation methods

and model specifications, I repeat all the regressions in OLS. Following Betrand and Mulainathan

2004, I control for firm and year fixed effect and cluster standard errors at state-level to allow for

within-in state dependence. This robustness check confirms my results with Tobit model (see

Table 8). In general, the coefficients remain of the same sign and statistically significant as in Tobit

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model although magnitude is smaller. This is not surprising because OLS does not take into

account the censored/truncated aspect of dividend data.

[Insert Table 8]

5 Heterogeneity of the effect

In this section, I provide more support for the predatory hypothesis by further examining cross-

sectional heterogeneity of the effect.

5.1 Predation or Career-concern?

As discussed above, increased NCA enforceability reduced predatory risk and thus can potentially

increase the propensity of payout. However, if a firm is the dominant force in its industry, it would

be less concerned about predatory attack because it has the resources and market power to defend

itself. Therefore, if the predatory explanation for the relation between NCA enforceability and

payout policy is true, the effect should be weaker among dominating firms. On the other hand, if

managerial incentives is the channel through which NCA enforceability increases payout then the

effect should be stronger among dominant firms because these firms usually have characteristics

that are associated with agency problem, namely they have limited grow opportunities but

abundant of resources that can be wasted by managers. I follow Grullon and Michaely (2007) to

define a dominant as a dummy indicator for firm who has largest market value of equity in an

industry, defined as SIC 4-digit number, in year (t). I then use a triple interaction to estimate the

effect of being a dominant firm on the conditional effect of NCA enforceability on dividend

payout. If the coefficient of the tripple interaction term is negative and statistically significant, the

predatory hypothesis is supported.

[Insert Table 9]

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Panel A of Table 9 compares dominant and non-dominant firms. It is evident that dominant firms

are older, more profitable, less volatile and hoard significantly more cash. This is consistent with

Jensen (1986) who argues that “firms or divisions of larger firms that have stable business histories

and substantial free cash flow” are most susceptible to agency cost of free cash flow. Panel B

provides support for the predatory explanation. Being a dominant firm mitigate the effect of NCA

enforceability on dividend amount. The coefficient of the triple interaction is negative and

statistically significant at 1% level for div_to_s and di_to_e and at 10% level for div_to_cf. The

magnitudes of the coefficients are -0.004, -0.084 and -0.028 for div_to_s, div_to_e and div_to_cf

respectively. Focusing on div_to_s, the coefficient of the triple interaction is -0.004 while the

coefficient of NCA*In-state competition is 0.005 This suggests that the positive conditional effect

of NCA enforceability on dividend payout is almost completely negated among dominant firms.

This result provides strong support for the predatory hypothesis instead of the career concern

hypothesis.

5.2 R&D Intensity and Key Human Capital risk

If the relation between NCA enforceability and dividend payout indeed materializes through

predatory risk channel, it should be more pronounced for firms who are more concerned with

losing proprietary information to competitors. To test this prediction, I used R&D intensity and

key human capital risk to proxy for firm’s exposure to this risk. For firms that invest intensively

in R&D, knowledge generated from this process is substantial and so as the risk of information

spillover through former employee. I define high R&D when its R&D is among the fifth quintile

in a given year. Key employees are likely to have access to a great deal of proprietary information

thus their departure to rival company would expose the firm to greater risk of proprietary

information loss. I rely on the data provided by Israelsen and Yonker (2017) who utilize the U.S.

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Securities and Exchange Commission (SEC) filings disclosures of key man life insurance. Key

human capital is an indicator variable which takes the value of 1 if a firm discloses that it carries

key man insurance and 0 if otherwise. I repeat the triple interaction analysis with these 2 variables

and predict that the coefficient on the triple interaction will be positive and statistically significant.

[Insert Table 10]

The results are presented in table 10. In the key human capital analysis, the sample size is smaller

due to availability of this measure. Consistent with the prediction, the coefficients of the triple

interactions are positive and statistically significant. Focusing on the high R&D analysis, the

magnitudes of the coefficients are 0.004, 0.112 and 0.052 for div_to_s, div_to_e and div_to_cf

respectively. According to these coefficients, the conditional effect of NCA enforceability on

dividend almost doubles in magnitude among high R&D firms. I find similar effect with key

human capital risk although statistical significance is lower. Overall, these results provide further

support for the predatory hypothesis.

6 Conclusion

In this paper, I study the impact of exogenous variation in NCA enforceability and dividend policy.

I find that firm pay more dividend when this risk is reduced by strong NCA enforcement. This

relation can be explained by the predatory risk that arises from proprietary information divulgence

to competitors. Specifically, this risk can destabilize a firm’s future prospect and therefore decrease

the propensity to payout. I rule out the alternative explanation that is based on managerial career-

concern by showing that the effect is weaker among dominant firms. The additional tests provide

supports for the notion that the effect of NCA enforceability on dividend payout is strongest for

firms who are most concerned with proprietary information leakage. Taken together, corporate

payout policy is partially shaped by risks associated with labor mobility.

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Appendix: Variable Construction

Dependent variables

div_to_s The ratio of dividends (dvc) to sales.

div_to_e The ratio of dividends (dvc) to sales (sale).

div_to_cf The ratio of dividends (dvc) to cash flow (net income (ni) plus depreciation (dp)).

Independent variables of interest

NCA enforceability

index

Is from Garmaise (2011), Bird and Knopf (2015) and Ertimur et al. (2018).

In-state competition The portion of industry sales (exclude the firm’s sales) produced by competitors

headquartered in the same state.

Increased

Enforceability

An indicator that is equal to -1 for firms headquartered in Texas in 1995-2004 and

Louisiana in 2002-2003, 1 for firms headquartered in Florida in 1997-2004, and

zero otherwise.

Control variables

rte Retained earnings (re) scaled by the book value of assets (ta).

te Shareholder equity (ceq) scaled by the book value of total assets (ta).

roa Net income (ni) scaled by the book value of total assets (ta).

sgr The natural logarithm of sale growth rate (ln(salet/salet-1)).

log_at The natural logarithm of the book value of total assets (ta).

cash_total The ratio of cash and short-term investment (che) to total assets (ta).

mb The market to book ratio.

sd Stock return volatility over the last 12 months.

lev The book value of leverage.

age The natural logarithm of firm age.

Other variables

pre_1 An indicator that is equal to 1 for firms in treated states one year prior to NCA

enforcement changes, and zero otherwise.

pre_0 An indicator that is equal to 1 for firms in treated states in the year of NCA

enforcement changes, and zero otherwise.

post_1 An indicator that is equal to 1 for firms in treated states one year after NCA

enforcement changes, and zero otherwise.

post_2 An indicator that is equal to 1 for firms in treated states two or more years after

enforcement changes, and zero otherwise.

texas An indicator that is equal to 1 for firms headquartered in Texas.

post An indicator that is equal to 1 for years after 1994.

dominant An indicator that is equal to 1 for firms with the largest market value in a given year.

tnic3tsimm The total similarity measure is from Hoberg and Phillips (2016)

high_rd An indicator that is equal to 1 for firms with R&D in the 5th quintile in a given year.

keyhumancapital An indicator that is equal to 1 if a firm discloses that it carries key man insurance

and 0 if otherwise.

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Appendix: Non-Compete Agreement Enforceability Index by State (1992-2004)

Alabama 5 235 Missouri 7 684

Alaska 3 17 Montana 2 22

Arizona 3 619 Nebraska 4 143

Arkansas 5 219 Nevada 5 381

California 0 5,619 New

Hampshire 2 213

Colorado 2 1023 New Jersey 4 1952

Connecticut 3 854 New Mexico 2 31

Delaware 6 108 New York 3 3,225

DC 7 72 North

Carolina 4 780

Florida 1992-1996

7

1865

North Dakota 0 19

Florida 1996-2004

9 Ohio 5 1501

Georgia 5 1129 Oklahoma 1 332

Hawaii 3 42 Oregon 6 434

Idaho 6 88 Pennsylvania 6 1722

Illinois 5 1785 Rhode Island 3 142

Indiana 5 443 South

Carolina 5 189

Iowa 6 206 South Dakota 5 47

Kansas 6 218 Tennessee 7 563

Kentucky 6 207 Texas 1992-1994

5

3,994 Louisiana 1992-2001,

2003-2004 4

222

Texas 1994-2004

3

Louisiana 2001-2003

0 Utah 6 348

Maine 4 41 Vermont 5 57

Maryland 5 605 Virginia 3 958

Massachusetts 6 2088 Washington 5 549

Michigan 5 922 West

Virginia 2 37

Minnesota 5 1411 Wisconsin 3 705

Mississippi 4 77 Wyoming 4 9

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Table 1: Summary Statistics

This table shows the summary statistics for the overall sample and annual number of observations.

Panel A shows the sample from 1976-2013. Panel B shows the sample from 1992-2004 where all

major changes in NCA law are concentrated. Div_to_s(t) is the ratio of dividends (data 34) to sales

(data 1). Re(t) is retained earnings (data 131) scaled by the book value of assets (data 89). Te(t) is

the shareholders’ equity (data 135) scaled by the book value of assets (data 89). Roa(t) is net

income (data 32) scaled by the book value of assets (data 89). Sgr(t) is the logarithmic sales growth

computed as log (data 1t/data 1t1). Log_at(t) is the natural logarithm of the book value of assets

(data 89) in billions of $US. Cash_total(t) is the cash balance (data 61) scaled by the book value

of assets (data 89). The NCA enforceability index is from Garmaise (2011), Bird and Knopf (2015)

and Ertimur et al. (2018). The index ranges between 0 (weakest enforceability) and 9 (strongest

enforceability).

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PANEL A

1976-2013 mean sd p5 p25 p50 p75 p95 count

div_to_s 0.008 0.017 0.000 0.000 0.000 0.010 0.041 89900

div_to_e 0.228 0.469 0.000 0.000 0.000 0.297 0.864 62229

div_to_cf 0.108 0.189 0.000 0.000 0.000 0.163 0.428 70829

NCA 3.895 2.142 0.000 3.000 4.000 5.000 7.000 89900

Increased Enforceability -0.040 0.648 -2.000 0.000 0.000 0.000 1.000 89900

log_at 5.206 2.112 1.908 3.639 5.089 6.668 8.904 89900

rte -0.216 1.459 -2.646 -0.122 0.181 0.383 0.672 89900

te 0.473 0.261 0.039 0.329 0.485 0.657 0.856 89900

roa 0.083 0.178 -0.246 0.051 0.117 0.174 0.275 89900

sgr 0.069 0.296 -0.379 -0.034 0.069 0.177 0.507 89900

cash_total 0.149 0.182 0.004 0.023 0.075 0.205 0.568 89900

mb 1.766 1.300 0.772 1.036 1.341 1.948 4.298 89900

sd 0.143 0.086 0.052 0.085 0.120 0.174 0.311 89900

lev 0.239 0.209 0.000 0.058 0.208 0.358 0.636 89900

age 2.578 0.680 1.386 2.079 2.565 3.091 3.689 89900

1992-2004 PANEL B

div_to_s 0.006 0.016 0.000 0.000 0.000 0.005 0.035 38760

div_to_e 0.183 0.429 0.000 0.000 0.000 0.225 0.771 25770

div_to_cf 0.085 0.173 0.000 0.000 0.000 0.116 0.380 29761

NCA 3.924 2.194 0.000 3.000 4.000 5.000 7.000 38760

Increased Enforceability -0.070 0.749 -2.000 0.000 0.000 0.000 1.000 38760

log_at 5.189 2.043 2.020 3.682 5.079 6.574 8.806 38760

rte -0.268 1.461 -2.799 -0.199 0.136 0.352 0.666 38760

te 0.476 0.269 0.032 0.323 0.489 0.672 0.866 38760

roa 0.075 0.190 -0.299 0.045 0.114 0.172 0.274 38760

sgr 0.081 0.312 -0.391 -0.029 0.074 0.194 0.571 38760

cash_total 0.151 0.190 0.003 0.019 0.069 0.211 0.588 38760

mb 1.919 1.461 0.780 1.076 1.423 2.130 5.019 38760

sd 0.156 0.096 0.053 0.089 0.131 0.193 0.350 38760

lev 0.235 0.211 0.000 0.047 0.204 0.360 0.632 38760

age 2.542 0.704 1.386 1.946 2.485 3.135 3.689 38760

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Table 2: The effect of state-level enforceability on payout policy.

This table reports the results for dividend amount models, focusing on the effect of NCA

enforcement. The sample covers the period from 1976-2013. Dividend amount is scaled by sales,

earnings and cashflow. The NCA enforceability index is from Garmaise (2011), Bird and Knopf

(2015) and Ertimur et al. (2018), ranging between 0 (least restrictive) and 9 (most restrictive). All

continuous variables are winsorized at 1st and 99th percentiles. I include industry, year, state

dummy variables to control for unobserved characteristics. Statistical significance is calculated

based on state-level clustered standard error to allow for correlation of errors within state following

Bertrand and Mullainathan (2003) .***, **, and * indicate statistical significance at the 1%, 5%

and 10% levels, respectively.

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Tobit Tobit Tobit

Dependent Variable = div_to_s div_to_e div_to_cf

NCA 0.001** 0.020*** 0.008***

(2.39) (3.04) (2.68)

log_at 0.003*** 0.043*** 0.019***

(6.06) (4.47) (4.44)

rte 0.014*** 0.327*** 0.165***

(6.56) (5.64) (6.78)

te 0.002 -0.202** -0.098***

(0.58) (-2.54) (-2.81)

roa 0.061*** -0.315* 0.069

(8.51) (-1.65) (0.94)

sgr -0.018*** -0.502*** -0.207***

(-17.13) (-15.89) (-12.16)

cash_total 0.011** -0.197** -0.004

(2.45) (-2.28) (-0.09)

mb 0.003*** 0.030*** 0.022***

(6.37) (3.40) (5.95)

lev -0.143*** -3.080*** -1.481***

(-19.14) (-29.77) (-28.43)

sd -0.006* -0.184*** -0.151***

(-1.79) (-3.08) (-5.87)

age 0.009*** 0.224*** 0.102***

(8.16) (10.16) (9.94)

_cons -0.035*** -0.439** -0.240***

(-3.51) (-2.56) (-3.42)

Year FE Y Y Y

Industry FE Y Y Y

Cluster SE State State State

N 89900 62229 70829

pseudo R2 -0.60 0.18 0.41

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Table 3: NCA enforcement, in-state competition and dividend payout.

This table reports the results for dividend amount models, focusing on the effect of NCA

conditional on the degree of instate-competition. The level of in-state competition faced by a firm

is defined as the portion of total industry sales (excluding sales of the firm) produced by its

competitors headquartered in the same state. Dividend amount is scaled by sales, earnings and

cashflow. The NCA enforceability index is from Garmaise (2011), Bird and Knopf (2015) and

Ertimur et al. (2018), ranging between 0 (least restrictive) and 9 (most restrictive). All continuous

variables are winsorized at 1st and 99th percentiles. I include industry, year, state dummy variables

to control for unobserved characteristics. Statistical significance is calculated based on state-level

clustered standard error to allow for correlation of errors within state following Bertrand and

Mullainathan (2003) .***, **, and * indicate statistical significance at the 1%, 5% and 10% levels,

respectively.

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Tobit Tobit Tobit

Dependent Variable = div_to_s div_to_e div_to_cf

In-state competition -0.019*** -0.427*** -0.188***

(-8.85) (-7.42) (-7.85)

NCA x In-state competition 0.004*** 0.106*** 0.047***

(5.49) (6.14) (7.14)

log_at 0.003*** 0.045*** 0.020***

(6.37) (4.94) (4.92)

rte 0.013*** 0.313*** 0.156***

(6.49) (5.61) (6.75)

te 0.004 -0.177** -0.083**

(1.00) (-2.20) (-2.30)

roa 0.059*** -0.382** 0.042

(8.49) (-2.02) (0.59)

sgr -0.018*** -0.489*** -0.201***

(-16.14) (-15.28) (-11.96)

cash_total 0.013*** -0.141* 0.022

(3.10) (-1.75) (0.58)

mb 0.003*** 0.034*** 0.024***

(6.74) (3.73) (6.21)

lev -0.139*** -2.985*** -1.435***

(-18.98) (-27.01) (-26.78)

sd -0.005 -0.176*** -0.145***

(-1.53) (-2.87) (-5.34)

age 0.009*** 0.220*** 0.099***

(8.27) (10.79) (10.43)

_cons -0.005 0.065 0.116**

(-0.68) (0.58) (2.37)

Year FE Y Y Y

Industry FE Y Y Y

State FE Y Y Y

Cluster SE State State State

N 89900 62229 70829

pseudo R2 -0.62 0.19 0.42

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Table 4: NCA enforcement, product similarity and dividend payout.

This table reports the results for dividend amount models, focusing on the effect of NCA

enforcement conditional on product similarity. The total similarity measure is from (Hoberg and

Phillips 2016). Dividend amount is scaled by sales, earnings and cashflow. The NCA

enforceability index is from Garmaise (2011), Bird and Knopf (2015) and Ertimur et al. (2018),

ranging between 0 (least restrictive) and 9 (most restrictive). All continuous variables are

winsorized at 1st and 99th percentiles. I include industry, year, state dummy variables to control for

unobserved characteristics. Statistical significance is calculated based on state-level clustered

standard error to allow for correlation of errors within state following Bertrand and Mullainathan

(2003) .***, **, and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.

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Product Differentiation Both

Dependent Variables div_to_s div_to_e div_to_cf div_to_s div_to_e div_to_cf

NCA 0.001 0.043 0.010 0.001 0.041 0.009

(0.64) (1.34) (0.72) (0.54) (1.25) (0.60)

tnic3tsimm -0.001*** -0.018* -0.010*** -0.001** -0.015* -0.008**

(-2.65) (-1.88) (-2.60) (-2.51) (-1.71) (-2.46)

NCA * tnic3tsimm -0.0002 -0.005** -0.002** -0.0002** -0.006*** -0.002***

(-1.63) (-2.35) (-2.13) (-2.01) (-2.81) (-2.58)

In-state competition -0.015*** -0.345*** -0.167***

(-4.38) (-3.82) (-4.34)

NCA * In-state competition 0.004*** 0.076*** 0.042***

(3.36) (3.29) (4.18)

log_at 0.003*** 0.039*** 0.019*** 0.003*** 0.040*** 0.019***

(5.60) (3.65) (3.85) (5.83) (3.93) (4.13)

rte 0.010*** 0.215*** 0.115*** 0.010*** 0.214*** 0.114***

(4.55) (4.07) (4.96) (4.58) (4.08) (4.98)

te -0.005 -0.340*** -0.148*** -0.004 -0.338*** -0.147***

(-0.72) (-3.26) (-2.98) (-0.70) (-3.20) (-2.94)

roa 0.063*** -0.051 0.162 0.063*** -0.049 0.162

(6.23) (-0.21) (1.49) (6.23) (-0.20) (1.49)

sgr -0.023*** -0.523*** -0.232*** -0.023*** -0.522*** -0.232***

(-11.91) (-11.72) (-10.56) (-11.82) (-11.67) (-10.50)

cash_total 0.018*** 0.050 0.093 0.019*** 0.065 0.099*

(3.13) (0.40) (1.57) (3.36) (0.55) (1.79)

mb 0.004*** 0.036*** 0.027*** 0.004*** 0.036*** 0.027***

(6.96) (3.11) (5.05) (7.02) (3.18) (5.13)

sd -0.154*** -2.966*** -1.466*** -0.154*** -2.964*** -1.467***

(-16.44) (-17.55) (-19.83) (-16.43) (-17.47) (-19.77)

lev -0.009 -0.314*** -0.176*** -0.009 -0.313*** -0.177***

(-1.64) (-3.44) (-4.40) (-1.61) (-3.32) (-4.27)

age 0.012*** 0.243*** 0.114*** 0.011*** 0.241*** 0.113***

(7.99) (8.69) (9.27) (8.06) (8.74) (9.41)

_cons -0.027* -0.428** -0.117 -0.027* -0.430** -0.117

(-1.95) (-2.10) (-1.23) (-1.95) (-2.08) (-1.22)

Year FE Y Y Y Y Y Y

Industry FE Y Y Y Y Y Y

State FE Y Y Y Y Y Y

Cluster SE State State State State State State

N 47280 31320 36015 47280 31320 36015

pseudo R2 -1.03 0.18 0.35 -1.03 0.18 0.35

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Table 5: The effect of increased enforceability on payout policy

This table reports the results for dividend amount models, focusing on the effect of time-series

changes in NCA law. The sample covers the period from 1990-2004. Dividend amount is scaled

by sales, earnings and cashflow. Following Garmaise (2011), I define Increased Enforceability as

state-level change in NCA enforceability. This variable takes the value of -1 in Texas during 1995-

2004 or Louisiana during 2002-2003, the value of 1 in Florida during 1997-2004 and zero

otherwise. All continuous variables are winsorized at 1st and 99th percentiles. I include industry,

year, state dummy variables to control for unobserved characteristics. Statistical significance is

calculated based on state-level clustered standard error to allow for correlation of errors within

state following Bertrand and Mullainathan (2003) .***, **, and * indicate statistical significance

at the 1%, 5% and 10% levels, respectively.

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Tobit Tobit Tobit

Dependent Variable = div_to_s div_to_e div_to_cf

Increased Enforceability 0.003*** 0.112*** 0.034***

(7.07) (9.43) (3.72)

log_at 0.003*** 0.060*** 0.025***

(5.78) (4.87) (4.37)

rte 0.018*** 0.549*** 0.270***

(4.57) (6.06) (6.74)

te -0.002 -0.308*** -0.148***

(-0.33) (-2.64) (-2.67)

roa 0.041*** -0.671*** -0.124*

(5.15) (-4.38) (-1.69)

sgr -0.018*** -0.402*** -0.175***

(-11.10) (-9.18) (-9.28)

cash_total 0.008 -0.186* -0.018

(1.35) (-1.65) (-0.34)

mb 0.002*** 0.020* 0.018***

(5.20) (1.81) (3.67)

lev -0.136*** -2.906*** -1.379***

(-11.97) (-13.99) (-14.56)

sd -0.011** -0.309*** -0.167***

(-2.31) (-3.60) (-4.14)

age 0.009*** 0.231*** 0.099***

(7.59) (10.66) (9.82)

_cons -0.029*** -0.252 -0.178***

(-2.93) (-1.55) (-2.65)

Year FE Y Y Y

Industry FE Y Y Y

State FE Y Y Y

Cluster SE State State State

N 38760 25770 29761

pseudo R2 -0.96 0.24 0.47

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Table 6: The timing of effect of state-level law change on payout policy.

This table reports the results for dividend amount models, focusing on the timing of the effect of

NCA law change. The sample covers the period from 1990-2004. Dividend amount is scaled by

sales, earnings and cashflow. This specification is adopted from Bertrand and Mullainathan (2003)

who includes pre_x and post_x dummies to examine the timing effect of exogenous change in Anti-

takeover law. I defined pre_2 equals to 1 for firms in Texas, Louisiana and Florida 2 years before

the respective law changes and zero otherwise. Pre_1, pre_0, post_1 and post_2 are constructed

similarly. I interact post_1 and post_2 with increased enforceability to account for the difference

in law changes in the 3 states (NCA enforceability became more restrictive in Florida while less

restrictive in Texas and Lousiana). All continuous variables are winsorized at 1st and 99th

percentiles. I include industry, year, state dummy variables to control for unobserved

characteristics. Statistical significance is calculated based on state-level clustered standard error to

allow for correlation of errors within state following Bertrand and Mullainathan (2003) .***, **,

and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.

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Tobit Tobit Tobit

Dependent Variable = div_to_s div_to_e div_to_cf

pre_1 -0.000 0.013 0.006

(-0.72) (0.88) (1.07)

pre_0 0.001 0.036 0.004

(0.37) (0.59) (0.21)

post_1 x Increased Enforceability 0.002*** 0.058*** 0.020**

(3.08) (4.10) (2.44)

post_2 x Increased Enforceability 0.002*** 0.047*** 0.014***

(10.33) (12.19) (5.47)

log_at 0.003*** 0.060*** 0.025***

(5.78) (4.87) (4.37)

rte 0.018*** 0.549*** 0.270***

(4.57) (6.06) (6.74)

te -0.002 -0.309*** -0.148***

(-0.34) (-2.64) (-2.67)

roa 0.041*** -0.670*** -0.124*

(5.14) (-4.35) (-1.68)

sgr -0.018*** -0.402*** -0.176***

(-11.13) (-9.21) (-9.30)

cash_total 0.008 -0.186* -0.018

(1.35) (-1.65) (-0.34)

mb 0.002*** 0.020* 0.018***

(5.19) (1.81) (3.67)

lev -0.136*** -2.907*** -1.380***

(-11.98) (-14.01) (-14.57)

sd -0.011** -0.310*** -0.167***

(-2.31) (-3.61) (-4.14)

age 0.009*** 0.231*** 0.099***

(7.60) (10.67) (9.83)

_cons -0.043*** -3.258*** -0.190***

(-5.67) (-13.60) (-3.34)

Year FE Y Y Y

Industry FE Y Y Y

State FE Y Y Y

Cluster SE State State State

N 38760 25770 29761

pseudo R2 -0.96 0.24 0.47

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Figure 1: The change in dividend amount after NCA law change in Texas in 1994

This graph shows the change in average dividend amount of firms headquartered in Texas versus

their counterpart. The law change occurred in June 1994. The zero-point marks the end of 1994.

The red (blue) line represent average dividend amount paid by Texas (non-Texas) firms.

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Table 7: Texas law change in 1994 and dividend policy

This table reports the results for dividend amount models, focusing on the effect of NCA law change

in Texas in 1994. The sample covers the period from 1990-2004. Dividend amount is scaled by

sales, earnings and cashflow. This specification employs standard difference-in-differences. I

defined Texas equals to 1 for firms in Texas and zero otherwise, Post is equal to 1 after 1994. All

continuous variables are winsorized at 1st and 99th percentiles. I include industry, year, state dummy

variables to control for unobserved characteristics. Statistical significance is calculated based on

state-level clustered standard error to allow for correlation of errors within state following Bertrand

and Mullainathan (2003) .***, **, and * indicate statistical significance at the 1%, 5% and 10%

levels, respectively.

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Tobit Tobit Tobit

div_to_s div_to_e div_to_cf

Texas * post -0.003*** -0.105*** -0.024***

(-4.75) (-5.75) (-3.30)

log_at 0.003*** 0.060*** 0.025***

(5.78) (4.87) (4.37)

rte 0.018*** 0.549*** 0.270***

(4.57) (6.06) (6.74)

te -0.002 -0.308*** -0.148***

(-0.33) (-2.63) (-2.67)

roa 0.041*** -0.668*** -0.123*

(5.15) (-4.33) (-1.68)

sgr -0.018*** -0.401*** -0.175***

(-11.09) (-9.16) (-9.29)

cash_total 0.008 -0.187* -0.018

(1.35) (-1.65) (-0.34)

mb 0.002*** 0.020* 0.018***

(5.18) (1.79) (3.67)

sd -0.136*** -2.907*** -1.380***

(-11.99) (-14.01) (-14.58)

lev -0.011** -0.310*** -0.167***

(-2.32) (-3.62) (-4.15)

age 0.009*** 0.231*** 0.099***

(7.60) (10.68) (9.83)

constant -0.043*** -3.261*** -0.190***

(-5.72) (-13.55) (-3.33)

Year FE Y Y Y

Industry FE Y Y Y

State FE Y Y Y

Cluster SE State State State

N 38760 25770 29761

pseudo R2 -0.96 0.24 0.47

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Table 8: These tables report the robustness check for results in table 5, 6, 7. I used OLS regression.

OLS OLS OLS OLS OLS OLS OLS OLS OLS

div_to_s div_to_e div_to_cf div_to_s div_to_e div_to_cf div_to_s div_to_e div_to_cf

Inreased

Enforceability 0.001*** 0.033*** 0.011**

(5.50) (4.05) (2.54)

pre_1 0.000 0.007 0.001

(0.03) (0.64) (0.19)

pre_0 0.000 0.022 -0.002

(0.21) (0.80) (-0.35)

Post_1 x

Increased

Enforceability

0.0003*** 0.013* 0.004

(3.24) (1.96) (1.00)

Post_2 x

Increased

Enforceability

0.001*** 0.012** 0.005***

(16.22) (2.38) (6.74)

Texas * post -0.001*** -0.038*** -0.007*

(-3.80) (-4.14) (-1.92)

Controls Y Y Y Y Y Y Y Y Y

Firm FE Y Y Y Y Y Y Y Y Y

Year FE Y Y Y Y Y Y Y Y Y

Cluster SE State State State State State State State State State

N 38760 25770 29761 38760 25770 29761 38760 25770 29761

R2 0.80 0.47 0.61 0.80 0.47 0.61 0.79 0.49 0.61

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Table 9: The effect of being a dominant firm

This table show the effect of being a dominant firm on the relation between NCA enforceability

on payout policy. Panel A reports the characteristic of dominant firms and non-dominant firms.

Panel B provides the regression results. I follow Grullon and Michaely (2007) to define dominant

firm as the one that has largest market value of equity in a given industry in year (t). The NCA

enforceability index is from Garmaise (2011), Bird and Knopf (2015) and Ertimur et al. (2018),

ranging between 0 (least restrictive) and 9 (most restrictive). All continuous variables are

winsorized at 1st and 99th percentiles. I include industry, year, state dummy variables to control for

unobserved characteristics. Statistical significance is calculated based on state-level clustered

standard error to allow for correlation of errors within state following Bertrand and Mullainathan

(2003) .***, **, and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.

PANEL A:

Dominant Non-dominant Difference

mkv 9837.352 1152.165 8685.187***

at 5247.729 1026.996 4220.733***

rte 0.284 -0.286 0.57***

te 0.455 0.475 -0.02***

roa 0.155 0.073 0.082***

sgr 0.089 0.066 0.023***

che 389.413 94.517 294.896***

mb 1.873 1.751 0.122***

lev 0.236 0.239 -0.003***

sd 0.102 0.148 -0.046***

age_raw 24.12 15.493 8.627***

N 11096 78804

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PANEL B:

Tobit Tobit Tobit

Dependen variable = div_to_s div_to_e div_to_cf

In-state competition -0.020*** -0.473*** -0.201***

(-8.58) (-6.45) (-6.11)

NCA * In-state competition 0.005*** 0.121*** 0.052***

(6.22) (6.00) (6.30)

NCA * In-state competition * dominant -0.004*** -0.084*** -0.028*

(-2.90) (-3.18) (-1.70)

log_at 0.003*** 0.045*** 0.019***

(5.88) (4.76) (4.38)

rte 0.013*** 0.312*** 0.156***

(6.49) (5.60) (6.74)

te 0.004 -0.176** -0.084**

(0.97) (-2.18) (-2.31)

roa 0.059*** -0.383** 0.040

(8.47) (-2.03) (0.57)

sgr -0.018*** -0.488*** -0.201***

(-16.21) (-15.24) (-12.04)

cash_total 0.013*** -0.141* 0.023

(3.14) (-1.75) (0.60)

mb 0.003*** 0.034*** 0.024***

(6.61) (3.71) (6.06)

sd -0.138*** -2.983*** -1.430***

(-19.08) (-26.92) (-26.76)

lev -0.005 -0.175*** -0.144***

(-1.51) (-2.89) (-5.35)

age 0.009*** 0.220*** 0.099***

(8.24) (10.84) (10.46)

_cons -0.005 0.042 0.118**

(-0.57) (0.38) (2.47)

Year FE Y Y Y

Industry FE Y Y Y

State FE Y Y Y

Cluster SE State State State

N 89900 62229 70829

pseudo R2 -0.62 0.19 0.42

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Table 10: The effect of R&D intensity and key human capital risk.

This table reports the results for the analysis of the heterogeneity of the effect. Dividend amount

is scaled by sales, earnings and cashflow. High R&D is an indicator taking the value of 1 when a

firm’s R&D is among the top quartile in a given year. Key human capital risk data is from Israelsen

and Yonker (2017) who utilize the U.S. Securities and Exchange Commission (SEC) filings

disclosures of key man life insurance. The NCA enforceability index is from Garmaise (2011),

Bird and Knopf (2015) and Ertimur et al. (2018), ranging between 0 (least restrictive) and 9 (most

restrictive). All continuous variables are winsorized at 1st and 99th percentiles. I include industry,

year, state dummy variables to control for unobserved characteristics. Statistical significance is

calculated based on state-level clustered standard error to allow for correlation of errors within

state following Bertrand and Mullainathan (2003) .***, **, and * indicate statistical significance

at the 1%, 5% and 10% levels, respectively.

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Tobit Tobit Tobit Tobit Tobit Tobit

Dependen variable = div_to_s div_to_e div_to_cf div_to_s div_to_e div_to_cf

NCA

* In-state competition

0.003*** 0.080*** 0.033*** 0.006*** 0.104** 0.052**

(3.96) (4.58) (5.37) (2.74) (2.19) (2.46)

NCA

* In-state competition

* high_rd

0.004** 0.112*** 0.052***

(2.41) (3.02) (3.49)

NCA

* In-state competition

* keyhumancapital

0.006* 0.187* 0.082*

(1.65) (1.68) (1.96)

log_at 0.003*** 0.049*** 0.022*** 0.003*** 0.049*** 0.022***

(6.84) (5.43) (5.58) (6.84) (5.43) (5.58)

rte 0.013*** 0.304*** 0.151*** 0.013*** 0.304*** 0.151***

(6.38) (5.51) (6.61) (6.38) (5.51) (6.61)

te 0.005 -0.140* -0.064* 0.005 -0.140* -0.064*

(1.39) (-1.77) (-1.82) (1.39) (-1.77) (-1.82)

Roa 0.057*** -0.429** 0.015 0.057*** -0.429** 0.015

(8.25) (-2.32) (0.22) (8.25) (-2.32) (0.22)

Sgr -0.018*** -0.489*** -0.202*** -0.018*** -0.489*** -0.202***

(-15.93) (-15.37) (-11.95) (-15.93) (-15.37) (-11.95)

cash_total 0.014*** -0.109 0.040 0.014*** -0.109 0.040

(3.59) (-1.34) (1.06) (3.59) (-1.34) (1.06)

Mb 0.003*** 0.039*** 0.027*** 0.003*** 0.039*** 0.027***

(7.37) (4.51) (7.14) (7.37) (4.51) (7.14)

Sd -0.137*** -2.933*** -1.407*** -0.137*** -2.933*** -1.407***

(-19.07) (-26.92) (-27.20) (-19.07) (-26.92) (-27.20)

Lev -0.005 -0.167*** -0.141*** -0.005 -0.167*** -0.141***

(-1.43) (-2.76) (-5.30) (-1.43) (-2.76) (-5.30)

Age 0.009*** 0.216*** 0.097*** 0.009*** 0.216*** 0.097***

(8.37) (10.87) (10.65) (8.37) (10.87) (10.65)

_cons -0.005 0.050 0.116** -0.005 0.050 0.116**

(-0.62) (0.43) (2.20) (-0.62) (0.43) (2.20)

Year FE Y Y Y Y Y Y

Industry FE Y Y Y Y Y Y

State FE Y Y Y Y Y Y

Cluster SE State State State State State State

N 89900 62229 70829 30596 20064 23143

pseudo R2 -0.62 0.19 0.43 -1.06 0.20 0.37

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