Why Do Firms Hold Less Cash?

Daniel Cohen Bin Li

Naveen Jindal School of Management

University of Texas at Dallas

dcohen@utdallas.edu

(972) 883-4772

Naveen Jindal School of Management

University of Texas at Dallas

bin.li2@utdallas.edu

(972) 883-5836

Current version: April 16, 2013

(Preliminary. Please do not quote without permission. Comments welcome.)

Abstract

Recent empirical and anecdotal evidence suggest that U.S. firms hold a significant amount of cash on their balance sheets. Motivated by this observation, we seek to examine whether the firm’s customer base, in particular the amount of sales transactions between a firm and the U.S. government affects the amount of its cash holdings. Building on numerous research streams in the literature to date, we predict and find that firms that have the U.S. government as a major customer hold less amounts of cash. In addition, our evidence suggests that the firm’s suppliers take into account the relation between these firms and the U.S. government by providing less trade credit. Our focus on the U.S. government as a determinant of firm’s cash holdings has not been addressed before in the literature and therefore advances our understanding why firms might hold less cash rather than more.

JEL Classification: G14; G32; M4; M41

Keywords: Cash holdings; U.S. government; Supply chain; Marginal value of cash.

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1. Introduction

The amount of cash holdings of public U.S. firms are economically significant and have

been growing constantly over time. In particular, as of fiscal year 2011, the aggregate cash

holdings of firms included in Compustat amounted to $10.8 trillion that consists of 41% of the

overall market capitalization of these firms. Moreover, recent evidence in Bates, Kahle, and

Stulz (2009) suggests that the average cash-to-assets ratio more than doubles over their sample

period, from 10.6% in 1980 to 23.2% in 2006. Seeking to understand why firms hold large

amounts of cash has been the focus of academic research for a long time. To date, numerous

determinants have been found to affect the level of cash holdings, such as transaction costs,

adverse shocks, financial distress, repatriation tax, and agency problems.

In this paper we investigate and develop a new explanation for the observed level of cash

holdings as we examine whether firms that have the U.S. government as their major customer

affects cash holdings. To the extent that engaging in major sales transactions with the U.S.

government reduces future uncertainty with regards to future operating performance as well

other consequences such as exploitation from suppliers and repatriation of taxes, we predict that

it is more likely that these firms will have lower cash holdings as compared to other firms.

Our research design consists mainly of multiple regressions of cash holdings (defined as

natural logarithm of cash and cash equivalents over noncash assets) on prior determinants of cash

holdings and a measure that captures sales made to the U.S. government.1

1 Following prior literature, we use the terms “cash” and “cash and cash equivalents” interchangeably in this paper.

We identify whether

sales to the U.S. government can be classified as a transaction with a major customer by utilizing

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the data being disclosed following the new segment reporting requirements in SFAS 14 and 131

which identifies major customer sales greater or equal to 10% of total sales.2

Our results show that sales to the U.S. government increase the firms’ level of

profitability as well as the long-term persistence of profitability. In addition, we also find that the

standard deviation of profitability measured over a period of five years is lower for firms that

engage in substantial transactions with the U.S. government. Based on these findings we claim

that these firms tend to hold less cash and cash equivalents to cope with future potential adverse

shocks. The negative relation between sales to the U.S. government and corporate cash holdings

is also attributable to the exploitation from the firms’ suppliers as well as repatriation of taxes.

Specifically, our results suggest that suppliers extend less trade credit to firms with U.S.

government sales, and thus reduce the firms’ cash holdings, assuming that suppliers are informed

about the firms’ sales to major customers by having access to their segment disclosures.

Furthermore, we document that for firms with significant U.S. government sales, more cash

holdings further reduce the trade credit provided by their suppliers, suggesting that it is more

costly for these firms to hold large amounts of cash. Finally, our results suggest that firms with

U.S. government sales have lower marginal value of cash holdings and they tend to spend more

of their operating cash flows.

We make four contributions to the literature. First, we add to the extant literature on

corporate cash holdings that identifies the determinants and consequences of cash holdings for

2 FASB issued the Statement of Financial Accounting Standards No. 14 (SFAS 14) that requires disclosure of public firms’ major customers in 1976. Later SFAS 14 was amended by SFAS 30, and both SFAS 14 and 30 were suspended by SFAS 131 in 1997. SFAS 14 stipulates that “if 10 percent or more of the revenue of an enterprise is derived from sales to any single customer, that fact and the amount of revenue from each such customer shall be disclosed.” SFAS 131 reiterates that “if revenues from transactions with a single external customer amount to 10 percent or more of an enterprise’s revenues, the enterprise shall disclose that fact, the total amount of revenues from each such customer, and the identity of the segment or segments reporting the revenues.” Similar disclosure requirements are set by Regulation S-K of the SEC.

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U.S. firms. To date, previous research has shown that the main determinants of cash holdings

include transaction costs, adverse shocks, financial distress, repatriation tax, and agency

problems. Our study is the first to identify another important determinant of cash holdings – the

relation between the firm and the U.S. government. We utilize this characteristic by first

identifying whether the U.S. government indeed engages in business transactions with a

particular firm and subsequently measure the amount of sales made to this major customer. In

addition, our study expands the literature on supply chain. Prior research did not focus on

specific major customer characteristics and how this might affect the strategic interaction

between the firm and its suppliers. Given that the U.S. government consists of a major customer

for numerous firms it is economically important to examine how this might affect both the firm’s

own business strategy as well as the strategy employed by the firm’s suppliers which are

assumed to be aware of the existing relation between the firm and the U.S. government.

Our third contribution relates to the existing literature on political connections attributes.

Prior research focused on lobbying and campaign contributions and examined how these

activities affect firms’ corporate strategies and subsequently the firms’ performance and market

value. We add to this line of research by identifying an additional attribute of political

connections, resulting from the observation that the U.S. government can be identified as a major

customer for many firms. Our evidence is important as U.S. firms can be politically connected

through the firm-customer channel apart from the existing known channels such as lobbying and

campaign contributions. The implications of our findings are important as one can easily identify

and classify whether a firm is more likely to be politically connected by simply analyzing its

sales to major customers.

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Finally our study expands the existing literature on the consequences of detailed segment

disclosures along two streams. The first one relates to the lower value investors assign to cash

holdings of firms disclosing the U.S. government as their major customer. In addition, the

disclosure of sales to major customers affects the firm’s suppliers in their strategic interaction

with the firm. To date, most of the literature on segment disclosures focused on the costs and

valuation benefits of these specific disclosures without taking into account the specific attributes

of the information being actually provided at the segment level. We emphasize that one specific

piece of information, which relates to the sales made to major customers, is not only value

relevant to the firm’s investors but it also affects the behavior of its suppliers. Our study is the

first to identify and investigate this important and overlooked attribute. As such, our evidence is

also relevant to the ongoing debate on the costs and benefits of increased segment disclosures,

beyond the known arguments advanced so far in the literature relating mainly to competitive

costs and capital markets valuation benefits.

The rest of the paper is organized as follows. Section 2 reviews the literatures on

corporate cash holdings, supply chain, and political connections. Section 3 discusses our

empirical methodology, including our sample construction and estimation equations. Section 4

discusses our empirical evidence on cash holdings and trade credit. Section 5 concludes.

2. Literature review and hypothesis development

Our paper unites three streams of research that have previously been disparate, one on

corporate cash holdings, the second on customer-supplier relations and the other on firms'

political connections. We first discuss related research in each one of the related streams of

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research, and then we build on the existing body of evidence to state our research objectives and

develop our hypotheses.

2.1. Corporate Cash Holdings

Both anecdotal and recent empirical evidence suggests that the amount of cash holdings

of U.S. firms has increased significantly, especially in recent years (e.g., Bates et al., 2009). In a

recent study, Bates et al. (2009) summarize four theories explaining the determinants of firms'

cash holdings. These theories are referred to in prior research as the transaction cost motive, the

precautionary motive, the agency motive, and the tax motive.

Under the transaction cost motive, a firm incurs transaction costs associated with

converting a noncash asset into cash that is being used for payments (e.g., Mulligan, 1997).

Under this theory, firms that are larger will hold fewer amounts of cash as there are economies of

scale associated with these transaction costs. The second theory advanced in prior research is the

precautionary motive which suggests that firms hold cash to better cope with adverse shocks

when access to capital markets is costly (e.g., Han and Qiu, 2007). Consistent with this theory,

Opler, Pinkowitz, Stulz, and Williamson (1999) document that firms with cash flows that are

assumed to be riskier and have limited access to outside capital have larger amounts of cash

holdings. In addition, these authors provide evidence implying that firms facing a better

investment opportunities set have more cash holdings since negative shocks and financial

distress are more costly for them. In a related study, Bates et al. (2009) argue that higher cash

holdings are associated with riskier cash flows, lower inventories and accounts receivables and

higher R&D expenditures.

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The third theory advanced in the literature is referred to as the agency motive. Under this

view, entrenched managers would rather retain cash than increase payouts to shareholders when

the firm has poor investment opportunities (e.g., Jensen, 1986). In line with the agency motive,

prior research has shown that firms hold more cash in countries with greater agency problems

(Dittmar, Mahrt-Smith, and Servaes, 2003). In addition, cash holdings are valued less when

agency costs are regarded to be higher (e.g., Faulkender and Wang, 2006; Dittmar and Mahrt-

Smith, 2007; Pinkowitz, Stulz, and Williamson, 2006). The evidence in the literature to date

suggests that firms with larger cash holdings engage in more acquisitions and that these

acquisitions are value decreasing (e.g., Harford, 1999). However, in a recent study, Fresard

(2010) shows that large cash holdings lead to gains of future product market share at the expense

of industry rivals, concluding that industry competition is positively related to corporate cash

holdings suggesting a positive consequences for holding excess cash. Finally, Haushalter, Klasa,

and Maxwell (2007) document that if a firm shares a larger proportion of its growth

opportunities with rivals, it builds up its cash holdings to manage the predation risk.

The fourth and final theory advanced in prior research relates to the tax motive. Under

this view, U.S. firms that would incur negative tax consequences associated with repatriating

foreign earnings hold higher levels of cash. Foley, Hartzell, Titman, and Twite (2007) advance

and test this hypothesis and provide evidence which is consistent with multinational firms with

higher repatriation tax costs having higher cash holdings.

As summarized in Bates et al. (2009), while it seems that there is evidence in the

literature that suggests that the four theories are related to cash holdings, the exact effect is

however debatable. For example, Bates et al. (2009) conclude that the precautionary motive is

indeed important in explaining variation in cash holdings while they do not find support for the

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agency motive. In addition, Bates et al. (2009) findings imply that the average cash to assets ratio

of U.S. industrial firms has more than doubled during 1980 and 2006 and that this observed

increase is unrelated to the fact that firms have any foreign operations. In addition, Pinkowitz et

al. (2012) also casts doubt on the tax motive explanation by arguing that the cash holdings of

multinational firms cannot be explained by the tax treatment of earnings repatriations.

2.2. Supplier-customer relations

A large body of research has developed to address how the relation between the firm's

stakeholders affects corporate strategies, such as investment policies and capital structure choices.

An important aspect of this relation is the interaction between the firm and its customers as well

as with its suppliers. Specifically, numerous studies (e.g., Titman and Wessels, 1988; Banerjee,

Dasgupta, and Kim, 2008) focused on cases in which a firm sells its products to numerous large

customers referring to such an interaction as a bilateral one. In such cases, the firm depends

largely on a few significant customers that affect its investments and capital structure, therefore

influencing the level of its cash holdings.

A few recent studies examine the profitability and valuation of firms that engage in

transactions with major customers. For example, Gosman, Kelly, Olsson, and Warfield (2004)

show that retail firms with major customers have higher profitability, increased profitability

persistence, and a higher market-to-book ratio, compared to other retail firms. Patatoukas (2012)

finds that concentrated customer base increases a firm’s profitability by reducing its operating

expenses and enhancing asset utilization. Pandit, Wasley, and Zach (2011) document that firms

experience an information externality at the time of their major customers’ quarterly earnings

announcements, because the information conveyed in such announcements can revise investors’

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expectations about the level of the firms’ expected future cash flows and/or the uncertainty

associated with future cash flows. Raman and Shahrur (2008) examine the determinants and

consequences of earnings management by firms in the context of their relationships with

suppliers and customers, and find that earnings management is used opportunistically to

influence the perception of suppliers/customers about the firm’s future prospects. Hui, Klasa, and

Yeung (2012) show that the importance of a firm’s economic performance to its suppliers and

customers leads to a demand from these stakeholders for the firm to report more conservatively.

In summary, firms’ strategies, such as investment policies, capital structure choices, and

disclosure decisions, are largely influenced by their relations with their major suppliers and

customers.

2.3. Political connections

The third and final stream in the literature we build upon while examining our research

objectives and developing our hypotheses is the important body of work that investigates how

firms’ political connections relate to their operating performance and value creation. Prior

literature usually measures firms’ political connections by focusing on lobbying expenditures,

campaign contributions, or private access to top government officials (e.g., Cooper, Gulen, and

Ovtchinnikov, 2010; Ramanna and Roychowdhury, 2010).

Recent evidence is consistent with the claim that politically connected firms are likely to

derive some benefits relative to firms that are not politically connected (e.g., Faccio, 2006 and

2010). Consistent with the above, related empirical evidence suggests that firms which are

politically connected receive certain government benefits and protection such as preferential

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access to capital or obtaining specific contracts (e.g., Shleifer and Vishny, 1994; Johnson and

Mitton, 2003; Cull and Xu, 2005; Khwaja and Mian, 2005; Faccio, 2010).

Adding to the studies discussed above, Cooper et al. (2010) find that firms making

political contributions have better future operational and stock return performances. Goldman,

Rocholl, and So (2009) show that the political connections of board members affect firm value.

Ramanna and Roychowdhury (2010) study the accrual choice of outsourcing firms with links to

U.S. congressional candidates when corporate outsourcing was a major campaign issue, and find

that politically connected firms with more extensive outsourcing activities have more income-

decreasing discretionary accruals. Belo, Gala, and Li (2012) document that government sales

affect industry-level cash flows and stock returns.

Gaining specific benefits from being politically connected involves certain costs, some of

them direct as well as non-direct/observable. Firms that are assumed to be politically connected

might be more scrutinized and monitored, especially by outside parties such as the media and

opposing political parties (e.g., Faccio, 2006).

2.4. Hypothesis Development

Based on the above discussion of prior research we develop and test three main

hypotheses. One of our main innovations compared to the literature to date is the use of firms’

sales to the U.S. government to proxy for their political connections. We believe that this

measure captures firms’ relation with the U.S. government from a new perspective, that is, firms’

political connections through the supply chain. In addition, this connection is different from

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traditional supplier-customer relations, since the customer is no longer an individual or a

business organization, but rather a government/federal entity.

As discussed previously, we know from the existing literature that a firm’s performance

and business strategy are affected by its major customers. However, it is unclear how a firm’s

connection with the U.S. government as a major customer will influence its capital structure

decisions as reflected in the amount of cash holdings. Prior research documents that politically

connected firms have better operating performance and higher stock value in the long term (e.g.,

Goldman et al., 2009; Cooper et al., 2010). On one hand, firms with government sales may have

higher and more persistent profitability so that they would hold less cash for transactions and

precautionary motives, e.g., the U.S. government should be less likely to dishonor promised

payables compared to individuals and/or business organizations. Compared with other firms,

firms with government sales should be less financially constrained and have better accesses to

external capital, so their demand for cash to cope with future adverse shocks would be also lower.

In addition, other related parties may exploit the potential of superior profitability of firms with

U.S. government sales, e.g., suppliers may provide them more negative contractual terms as

evident in less trade credit. Finally, more sales to the U.S. government indicate less foreign

earnings that have potential tax costs of repatriation, so firms with government sales should hold

less cash for the tax motive the literature has previously identified.

On the other hand, firms that have superior profitability often face potential competition

from industry rivals (e.g., Fresard, 2010; Haushalter et al., 2007), suggesting that firms with

government sales may have to hold more cash to deal with a potential increase in product market

competition. In addition, weak corporate governance has more negative effects on firms in less

competitive product markets, (e.g., Giroud and Mueller, 2011). Since firms with government

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sales should face lower product market competition than others, they would hold more excess

cash when their corporate governance is assumed to be weak. Therefore, how the U.S.

government sales impact corporate cash holdings is still an open empirical question. The two

competing predictions discussed above lead us to our first hypothesis:

H1. Compared with other firms, firms with U.S. government sales have less cash

holdings.

Our second hypothesis predicts that firms receive less trade credit from their suppliers

when the U.S. government is their major customer. Petersen and Rajan (1997) show that

suppliers extend more trade credit to financially constrained customers. Since firms with

government sales should have less financial constraints, their suppliers may take advantage of

their financial position by extending them less trade credit. On the other hand, those firms may

have higher buyer bargaining power so that they would receive more trade credit from suppliers

(e.g., Giannetti et al., 2011), because of their superior profitability. Assuming that suppliers are

aware of their transactions with the U.S. government that increase profitability, we predict that

U.S. government sales will influence the amounts of trade credit received from the suppliers.

Therefore, our second hypothesis states:

H2. Compared with other firms, firms with U.S. government sales receive less trade

credit from their suppliers.

It is also unclear how U.S. government sales affect the marginal value of corporate cash

holdings. According to Faulkender and Wang (2006), marginal value of cash increases with

financial constraints. If firms with U.S. government sales are less financially constrained, they

should have lower marginal value of cash. Faulkender and Wang (2006) also find that marginal

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value of cash decreases with the magnitude of cash holdings, so firms with U.S. government

sales may have higher marginal cash value when their cash holdings are very low. Thus, U.S.

government sales could have different effects on marginal value of cash holdings. Our final

hypothesis is stated below:

H3. Compared with other firms, firms with U.S. government sales have lower marginal

value of cash.

Next, we discuss the empirical methodology we employ to test our empirical predictions.

3. Sample selection and research design

3.1. Sample selection

We obtain our sample from the Compustat Segment Files that provide the types and

names of major customers of U.S. public firms along with the dollar amount of annual sales

generated from each major customer. The initial sample consists of all major customer

observations on the Compustat Segment Files between 1976 and 2011. Next, we require firm-

years with major customer information to have both financial statement data on the Compustat

annual database and stock return data on the CRSP monthly file. We remove observations

without enough information to calculate our primary explanatory variables that calibrate the

incidence and extent of a firm’s sales to the U.S. government (details are provided below).

Following prior literature on corporate cash holdings (e.g., Bates et al., 2009), we exclude

financial firms (SIC codes 6000-6999) and utilities (SIC codes 4900-4999), because their cash

holdings can be subject to capital requirements and/or regulations. We further require all the

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observations to have information of current and one-year lagged cash holdings, market value of

equity, total assets, total debt, and annual stock returns. Our final sample contains 61,387 firm-

year observations over 1976 – 2011, representing 9,437 distinct firms. Some analyses impose

additional data requirements that further reduce the sample size.

3.2. Research design

We construct two explanatory variables to calibrate firm i’s sales to the U.S. government

in year t. 3 The first variable (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 ) measures the incidence of sales to the U.S.

government. Specifically, 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 equals to one if firm i has the U.S. government as a

major customer in year t, and zero otherwise The second variable (𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) measures the

extent of U.S. government sales, calculated as the percentage of aggregate sales to the U.S.

government in firm i’s total sales in year t.4

Apart from the two primary explanatory variables, we also construct a variable to

measure the concentration of firm i’s sales to its major customers (𝐶𝐶) per

Patatoukas (2012).

𝐶𝐶𝑖𝑡 = ∑ (𝑆𝑎𝑙𝑒𝑠𝑖𝑗𝑡𝑆𝑎𝑙𝑒𝑠𝑖𝑡

)2𝐽𝑗=1 , where 𝑆𝑎𝑙𝑒𝑖𝑗𝑡 represents firm i’s sales to customer j in year t and 𝑆𝑎𝑙𝑒𝑖𝑡

represents firm i’s total sales in year t. 𝐶𝐶 ranges between 0 and 1, where higher values

correspond to more concentrated customer bases.

To test our first hypothesis (H1), we regress corporate cash holdings on U.S. government

sales and a set of control variables.

3 Sales to the U.S. government include sales to federal government, state government, and local government. More than 97% of firm-years with U.S. government sales have the federal government as a major customer. 4 We replace total sales in Compustat annual file with sum of sales to all major customers in Compustat segment file, when the former amount is lower than the latter one.

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ln �𝐶𝑎𝑠ℎ𝑁𝐴

�𝑖,𝑡

= 𝑎0 + 𝑎1𝑆𝑎𝑙𝑒𝐺𝑜𝑣𝑖,𝑡 + 𝑎2𝐶𝐶𝑖,𝑡 + 𝑎3 ln(𝑁𝐴)𝑖,𝑡 + 𝑎4 �𝑀𝑉𝐸𝑁𝐴

�𝑖,𝑡

+ 𝑎5 �𝐹𝐶𝐹𝑁𝐴

�𝑖,𝑡

+ 𝑎6 �𝑁𝑊𝐶𝑁𝐴

�𝑖,𝑡

+ 𝑎7 �𝐶𝑎𝑝𝑒𝑥𝑁𝐴

�𝑖,𝑡

+ 𝑎8 �𝑅&𝐷𝑁𝐴

�𝑖,𝑡

+ 𝑎9 �𝐴𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛

𝑁𝐴�𝑖,𝑡

+ 𝑎10 �𝐷𝑒𝑏𝑡𝑇𝐴

�𝑖,𝑡

+ 𝑎11(𝐷𝑢𝑚_𝐷𝑖𝑣)𝑖,𝑡 + 𝑎12(𝑖𝑛𝑑𝐶𝐹𝑅𝐼𝑆𝐾)𝑖,𝑡

+ � 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑗48

𝑗=1+ � 𝑌𝑒𝑎𝑟𝑡

2011

𝑡=1976+ 𝜀𝑖,𝑡

(1)

where the subscript i, j, t stands for firm i, industry j, and year t. Following Dittmar and Mahrt-

Smith (2007) and Liu and Mauer (2011), ln (𝐶𝑎𝑠ℎ/𝑁𝐴) is defined as the natural logarithm of the

ratio of cash and cash equivalents over net assets, where net assets equal total assets minus cash

and cash equivalents. 𝑆𝑎𝑙𝑒𝐺𝑜𝑣 represents 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 and 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 that capture the

incidence and extent of a firm’s sales to the U.S. government, respectively. Our first hypothesis

(H1) predicts that the coefficient on 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 or 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 is significantly negative

(𝑎1 < 0 ) in equation (1), indicating that firms with U.S. government sales have less cash

holdings. We include Patatoukas’ (2012) customer-base concentration measure (𝐶𝐶) to control

for the diversification of a firm’s sales to its major customers. We expect that customer

concentration (𝐶𝐶) is positively related to corporate cash holdings due to the increased financial

risk from concentrated customer base.

Control variables are computed following previous studies (e.g., Dittmar and Mahrt-

Smith, 2007; Bates et al., 2009; Liu and Mauer, 2011). We use the natural logarithm of net assets

(ln (𝑁𝐴)) to measure firm size, because economy of scale reduces the demand for cash. We

measure investment opportunities and growth opportunities using the ratio of market value to net

assets (𝑀𝑉𝐸/𝑁𝐴) and the ratio of R&D to net assets (𝑅&𝐷/𝑁𝐴), respectively. Firms with better

investment opportunities or better growth opportunities tend to hold more cash, since it is costly

for these firms to be financially constrained. We also incorporate the ratio of cash flows to net

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assets (𝐹𝐶𝐹/𝑁𝐴) and the ratio of working capital to net assets (𝑁𝑊𝐶/𝑁𝐴) in the model, since

firms with higher cash flows accumulate more cash and firms with more working capital demand

less cash. Capital expenditures (𝐶𝑎𝑝𝑒𝑥/𝑁𝐴 ) may have opposite effects on corporate cash

holdings. If capital expenditures create assets that can be used as collateral, it could increase debt

capacity and reduce the demand for cash. On the other hand, capital expenditures could proxy for

financial distress costs and/or investment opportunities, in which case they would be positively

related to cash. Acquisition activity (𝐴𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛/𝑁𝐴) reflects the cash outflows associated with

acquisitions, so it is correlated with cash holdings in the same way as capital expenditures are.

Firms that do not pay dividends (𝐷𝑢𝑚_𝐷𝑖𝑣) and/or firms with higher industry cash flows risk

(𝑖𝑛𝑑𝐶𝐹𝑅𝐼𝑆𝐾) tend to hold more precautionary cash than other firms. In addition, firms will use

cash to reduce leverage, if debt is sufficiently constraining. Thus, firms with more debt (𝐷𝑒𝑏𝑡/

𝑁𝐴) will have more cash holdings. We also include industry fixed effects based on Fama and

French (1997) 48 industry classifications and year fixed effects to account for industry specific

and year specific effects on corporate cash holdings.

Next, we test our second hypothesis (H2) by estimating the following regressions:

ln(𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡)𝑖,𝑡= 𝑏0 + 𝑏1𝑆𝑎𝑙𝑒𝐺𝑜𝑣𝑖,𝑡 + 𝑏2𝐶𝐶𝑖,𝑡 + 𝑏3 ln(𝑇𝐴)𝑖,𝑡 + 𝑏4 ln(𝐴𝑔𝑒)𝑖,𝑡

+ 𝑏5ln (𝐴𝑔𝑒)𝑖,𝑡2 + 𝑏6 �𝐷𝑒𝑏𝑡𝑇𝐴

�𝑖,𝑡

+ 𝑏7𝐷𝑢𝑚_𝐷𝑖𝑣𝑖,𝑡 + 𝑏8𝐶𝑅𝑖,𝑡 + 𝑏9𝑅𝑂𝐴𝑖,𝑡

+ 𝑏10 �∆𝑆𝑎𝑙𝑒𝑠𝑇𝐴

�𝑖,𝑡

+ 𝑏11(𝐵/𝑀)𝑖,𝑡 + 𝑏12𝐿𝑖𝑞𝑢𝑖𝑑𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 + 𝑏13𝑂𝑝𝑒𝑟𝐶𝑦𝑐𝑙𝑒𝑖,𝑡

+ � 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑗48

𝑗=1+ � 𝑌𝑒𝑎𝑟𝑡

2011

𝑡=1976+ 𝜖𝑖,𝑡

(2)

where the subscript i, j, t stands for firm i, industry j, and year t. ln(𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡) is the trade

credit from firm i’s suppliers, measured as the natural logarithm of the ratio of accounts payable

to cost of goods sold (Petersen and Rajan, 1997). Our second hypothesis (H2) predicts that

16

suppliers take advantage of firms with U.S. government sales by providing them less trade credit,

so we predict that the coefficient on 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 or 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 will be significantly

negative (𝑏1 < 0) in equation (2).

We control for the variables that impact firms’ trade credit, following Petersen and Rajan

(1997) and Ma and Martin (2012). Rajan and Petersen (1997) find that large firms have higher

bargaining power to get more trade credit, so total assets (ln (𝑇𝐴)) will be positively related to

trade credit. They also find that firms will have lower demand for trade credit as firm becomes

more mature, so we include the natural logarithm of firm age plus one (ln (𝐴𝑔𝑒)) and its squared

value (ln (𝐴𝑔𝑒)2) to account for the non-linear relationship between firm age and demand for

trade credit. In addition, Petersen and Rajan (1997) argue that suppliers have an advantage over

other lenders to repossess and resell the inventory, but it becomes more costly for them to do so

once their customers have transformed the inputs into products. Thus, we include the liquidation

cost of inventory (𝐿𝑖𝑞𝑢𝑖𝑑𝑎𝑡𝑖𝑜𝑛) to partially account for the supply of trade credit. On the other

hand, we use debt in total assets (𝐷𝑒𝑏𝑡/𝑇𝐴), dividends payment (𝐷𝑢𝑚_𝐷𝑖𝑣), current ratio (𝐶𝑅),

profitability (𝑅𝑂𝐴), and operating cycle (𝑂𝑝𝑒𝑟𝐶𝑦𝑐𝑙𝑒) to control for the demand of trade credit,

and use change in sales (𝛥𝑆𝑎𝑙𝑒𝑠/𝑇𝐴) and the market-to-book ratio (𝐵/𝑀) to account for the

supply of trade credit, following Ma and Martin (2012). We also control for customer-base

concentration (𝐶𝐶), industry fixed effects, and year fixed effects in equation (2).

We test our third hypothesis (H3) by estimating the regression below, following

Faulkender and Wang (2005) and Dittmar and Mahrt-Smith (2007):

17

𝐸𝑥𝑅𝑒𝑡𝑖,𝑡 = 𝑐0 + 𝑐1∆𝐶𝑎𝑠ℎ𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐2𝑆𝑎𝑙𝑒𝐺𝑜𝑣𝑖,𝑡 ∗∆𝐶𝑎𝑠ℎ𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐3𝑆𝑎𝑙𝑒𝐺𝑜𝑣𝑖,𝑡

+ 𝑐4∆𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐5∆𝑁𝐴𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐6∆𝑅&𝐷𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐7∆𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐8∆𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐9𝐶𝑎𝑠ℎ𝑖,𝑡−1𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐10𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡 + 𝑐11𝑁𝑒𝑤𝐹𝑖𝑛𝑎𝑛𝑐𝑒𝑖,𝑡

𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐12𝐶𝑎𝑠ℎ𝑖,𝑡−1𝑀𝑉𝐸𝑖,𝑡−1

∗∆𝐶𝑎𝑠ℎ𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

+ 𝑐12𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡 ∗∆𝐶𝑎𝑠ℎ𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

+ � 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑗48

𝑗=1+ � 𝑌𝑒𝑎𝑟𝑡

2011

𝑡=1976+ 𝜇𝑖,𝑡

(3)

where the subscript i, j, t stands for firm i, industry j, and year t. ∆𝑋 indicates a change in 𝑋 from

year t-1 to t. Our dependent variable, 𝐸𝑥𝑅𝑒𝑡𝑖,𝑡, is the size and book-to-market adjusted excess

stock return from year t-1 to t per Fama and French (1993). 𝑀𝑉𝐸𝑖,𝑡 is market value of equity at

time t. 𝐶𝑎𝑠ℎ𝑖,𝑡 is cash and cash equivalents at time t. 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑖,𝑡 is earnings before

extraordinary items at time t. 𝑁𝐴𝑖,𝑡 is net assets at time t, calculated as total assets minus cash

and cash equivalents. 𝑅&𝐷𝑖𝑡 is research and development expenditures at time t. 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑖,𝑡 is

interest expenses from year t-1 to t. 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖𝑡 is common dividends from year t-1 to t.

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡 equals long term debt plus short term debt at time t. 𝑁𝑒𝑤𝐹𝑖𝑛𝑎𝑛𝑐𝑒𝑖𝑡 equals net new

equity issues plus net new debt issues from year t-1 to t. Our third hypothesis (H3) predicts that

firms with U.S. government sales have lower marginal value of cash holdings, so we predict that

the coefficient on the interaction between 𝑆𝑎𝑙𝑒𝐺𝑜𝑣𝑖,𝑡 and ∆𝐶𝑎𝑠ℎ𝑖,𝑡𝑀𝑉𝐸𝑖,𝑡−1

is negative (c2 < 0).

Finally, we test whether firms with U.S. government sales will save less cash out of

operating cash flows, because of their lower marginal value of cash holdings. This test provides

additional evidence to support our first and last hypotheses (H1 and H3) that firms with U.S.

government sales have lower cash holdings and lower marginal value of cash. Specifically, we

estimate the following regression that is adopted from Almeida et al. (2004).

18

(∆𝐶𝑎𝑠ℎ𝑁𝐴

)𝑖,𝑡 = 𝑑0 + 𝑑1 �𝐶𝐹𝑂𝑁𝐴

�𝑖,𝑡

+ 𝑑2𝑆𝑎𝑙𝑒𝐺𝑜𝑣𝑖,𝑡 × �𝐶𝐹𝑂𝑁𝐴

�𝑖,𝑡

+ 𝑑3𝑆𝑎𝑙𝑒𝐺𝑜𝑣𝑖,𝑡 + 𝑑4𝐶𝐶𝑖,𝑡

+ 𝑑5𝑇𝑜𝑏𝑖𝑛′𝑠𝑄𝑖,𝑡−1 + 𝑑6 ln(𝑇𝐴)𝑖,𝑡 + 𝑑7 �𝐶𝑎𝑝𝑒𝑥𝑁𝐴

�𝑖,𝑡

+ 𝑑8 �𝐴𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛

𝑁𝐴�𝑖,𝑡

+ � 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑗48

𝑗=1+ � 𝑌𝑒𝑎𝑟𝑡

2011

𝑡=1976+ 𝜏𝑖,𝑡

(4)

where the subscript i, j, t stands for firm i, industry j, and year t, ∆𝐶𝑎𝑠ℎ is change in change

holdings from year t-1 to t, 𝐶𝐹𝑂 is operating cash flows from cash flow statement, 𝑇𝑜𝑏𝑖𝑛’𝑠𝑄

equals market value of assets over book value of assets, 𝑙𝑛(𝑇𝐴) is the natural logarithm of total

assets, 𝐶𝑎𝑝𝑒𝑥 denotes capital expenditures, and 𝐴𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛 denotes acquisition expenditures.

We predict that firms with U.S. government sales tend to spend more operating cash flows,

suggesting that they have lower sensitivity of cash holdings (∆𝐶𝑎𝑠ℎ𝑁𝐴

) to operating cash flows (𝐶𝐹𝑂𝑁𝐴

)

(𝑑2 < 0).

4. Empirical results

4.1. Summary statistics

Table 1 presents summary statistics for our main variables of interest. The government-

sales sample includes firm-years with significant sales to the U.S. government, and the non-

government-sales sample includes firm-years without material U.S. government sales. Number

of observations varies depending on the data availability. Panel A of Table 1 shows that 19% of

firm-years (that is, 11,469/(11,469+49,918)) have significant sales to the U.S. government,

which comprise 31% of their total sales (𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣). The two samples do not have significant

differences in mean values of total assets (𝑇𝐴), net assets (𝑁𝐴), and debt-to-asset ratio (𝐷𝑒𝑏𝑡/

𝑇𝐴), implying that they have similar firm sizes and capital structures. The government-sales

19

sample has lower cash holdings (𝐶𝑎𝑠ℎ and 𝐶𝑎𝑠ℎ/𝑁𝐴) and trade credit (𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡) compared

to the non-government-sales sample (at 0.01 level), consistent with our predictions. Compared to

the non-government-sales sample, the government-sales sample has more concentrated customer

base (𝐶𝐶), more sales (𝑆𝑎𝑙𝑒𝑠), higher profitability (𝑅𝑂𝐴), and higher stock returns (𝑅𝑒𝑡) (at the

0.05 level or better). These results also confirm Patatoukas’ (2012) findings that customer base

concentration is positively related to firm performance. In addition, the government-sales sample

has lower earnings volatility (𝜎(𝑅𝑂𝐴)) and cash flow volatility (𝜎(𝐹𝐶𝐹/𝑁𝐴)) compared to the

non-government-sales sample, indicating that sales to the U.S. government increase persistence

of profitability. We also find that firms with U.S. government sales are more likely to pay

dividends or repurchase stocks (𝑃𝑎𝑦𝑜𝑢𝑡 ) and less likely to have credit ratings (𝑅𝑎𝑡𝑖𝑛𝑔 ),

compared to other firms. Also, they have weaker corporate governance (𝐺𝑖𝑛𝑑𝑒𝑥 ) and less

taxable foreign income (𝐹𝑜𝑟𝑒𝑖𝑔𝑛𝑇𝑎𝑥).

Panel B of Table 1 reports correlation coefficients between the main variables. Our main

focus is the associations of government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 or 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 ) with cash

holdings (𝐶𝑎𝑠ℎ/𝑁𝐴) and trade credit (𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡 ). The correlation coefficients between

𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 and 𝐶𝑎𝑠ℎ/𝑁𝐴 are at least 0.060 in magnitude (Pearson: -0.060, Spearman: -

0.069). Similarly, 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 is negatively associated with 𝐶𝑎𝑠ℎ/𝑁𝐴 (Pearson: -0.041,

Spearman: -0.071). This finding suggests that firms with government sales have less cash

holdings relative to other firms, consistent with our first hypothesis (H1). In addition, U.S.

government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 and 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) are negatively correlated with trade credit

(𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡), e.g., Pearson correlation between 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 and 𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡 is -0.106

(significant at the .01 level). Panel B shows that firms with U.S. government sales have more

concentrated customer base (𝐶𝐶), higher excess stock returns (𝐸𝑥𝑅𝑒𝑡), less volatile cash flows

20

( 𝜎(𝐹𝐶𝐹/𝑁𝐴) ), weaker corporate governance (𝐺𝑖𝑛𝑑𝑒𝑥 ), and less taxable foreign income

(𝐹𝑜𝑟𝑒𝑖𝑔𝑛𝑇𝑎𝑥) compared to other firms. The results in Panel B are consistent with those in Panel

A.

Table 2 reports the Fama and French (1997) 48 industry profile for our samples. Forty-

one industries have the U.S. government as their major customer (i.e., % of sales higher than 10%

of total sales). Number of firms with U.S. government sales varies across industries. More than

78% of firms have material sales to the U.S. government in the “Aircraft” and “Defense”

industries, while those in the “Tobacco”, “Precious Metals”, and “Mines” industries do not have

any significant government sales. On average, firms with U.S. government sales have lower cash

holdings (lower trade credit) in 29 (32) out of 41 industries, which have major sales to the U.S.

government, suggesting that U.S. government sales have negative impacts on corporate cash

holdings and trade credit within each industry. Since both corporate cash holdings and trade

credit vary across industries, it is crucial to control for industry effects in the multiple regressions

of cash holdings or trade credit on government sales.

4.2. Government sales and cash holdings (tests of H1)

Our first hypothesis (H1) predicts that firms with U.S. government sales have lower cash

holdings compared to other firms. As discussed previously, we regress corporate cash holdings

on government sales to test this prediction. Table 3 reports the results of estimating Equation (1).

We focus on the coefficients on government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 or 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) where the

industry and year fixed effects are both considered. The coefficients on 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 and

𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 are -0.113 (t = -2.78) and -0.585 (t = -5.41) in columns (2) and (4), respectively.

21

Consistent with our first hypothesis (H1), this evidence suggests that sales to the U.S.

government reduce corporate cash holdings. Ceteris paribus, the ratio of cash holdings to net

assets is 0.893 lower (= 𝑒𝑥𝑝(−0.113)) for firms that have the U.S. government as a major

customer ( 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 = 1 ) compared to other firms ( 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 = 0 ). For the

government-sales sample, the coefficient on government sale percentage (𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) is -

0.750 (t-value = -4.32) in column (6), implying that corporate cash holdings is impacted by both

incidence and extent of government sales. If a firm were to increases its sales to the U.S.

government from 25% to 75%, its ratio of cash holdings to net assets (𝐶𝑎𝑠ℎ/𝑁𝐴) will decrease

by 0.687 (= 𝑒𝑥𝑝(−0.750 × 50%)).

The coefficients on the customer concentration (𝐶𝐶) are significantly positive in Table 3,

except for column (2). This result implies that, as a firm’s customer base becomes more

concentrated, the firm will have lower bargaining power in firm-customer relation, and thus will

hold more cash to satisfy the demand from its major customers. In other words, firms with

customer base concentration will keep more cash holdings given the transaction motive advanced

in the literature. Consistent with previous research (e.g., Bates et al., 2009), Table 3 also shows

that firms hold more cash when they have smaller size (𝑙𝑛(𝑁𝐴)), lower leverage (𝐷𝑒𝑏𝑡/𝑇𝐴),

more growth opportunities (𝑀𝑉𝐸/𝑁𝐴 ), more free cash flows ( 𝐹𝐶𝐹/𝑁𝐴 ), more capital

expenditures and R&D expenditures (𝐶𝑎𝑝𝑒𝑥/𝑁𝐴 and 𝑅&𝐷/𝑁𝐴), less working capital (𝑁𝑊𝐶/

𝑁𝐴), less dividend payments (𝐷𝑢𝑚_𝐷𝑖𝑣). In summary, Table 3 provides evidence to support our

first hypothesis (H1).

22

4.3. Why firms with government sales hold less cash?

Bates et al. (2009) study the determinants of corporate cash holdings, and find that firms

hold cash for the transaction motive, the precautionary motive, the agency motive, and/or the tax

motive. In this section, we use two methods to examine the impacts of different motives on the

relation between corporate cash holdings and U.S. government sales. First, we identify situations

where firms have extremely high or low demands for cash (e.g., high or low transaction motive

to hold cash), and then examine whether the association between cash holdings and government

sales varies under different situations. Second, we include the interaction of government sales

(𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) with a proxy for one of the numerous motives in Equation (1), and test whether

the coefficients on the interaction is significantly different from zero.

Panel A of table 4 reports regression results for the subsamples where firms have high or

low motives to hold cash. Specifically, we use trade credit (𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡), volatility of free cash

flows (𝜎(𝐹𝐶𝐹/𝑁𝐴)), the governance index (𝐺𝑖𝑛𝑑𝑒𝑥) developed in Gompers et al. (2003), and

incidence of taxable foreign income (𝐹𝑜𝑟𝑒𝑖𝑔𝑛𝑇𝑎𝑥 ) to measure the transaction motive, the

precautionary motive, the agency motive, and the tax motive, respectively. Using the top and

bottom terciles of each measure to identify subsamples with high and low demand for cash

(except for 𝐹𝑜𝑟𝑒𝑖𝑔𝑛𝑇𝑎𝑥 that is an indicator variable), respectively, we investigate whether the

coefficient on government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) in Equation (1) varies across subsamples with

high or low motives to hold cash.

Under the transaction motive it has been argued that firms hold cash to address potential

transaction costs associated with converting a noncash asset into cash which is used for payments.

Panel A of table 4 shows that the coefficients on government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) is more

23

negative for firms with high trade credit (coeff. = -0.211, t-value = -3.41) compared with firms

with low trade credit (coeff. = -0.061, t-value = -1.16), suggesting that firms with U.S.

government sales hold less cash when they receive more trade credit from suppliers. As the

amounts of trade credit increase, the impact of trade credit on the firms’ cash holdings will be

more significant. If suppliers provide trade credit conditional on the firms’ sales to major

customers, having the U.S. government as a major customer will affect the firms’ cash holdings

and trade credit received from their suppliers. In other words, U.S. government sales could

impact cash holdings by affecting the amounts of trade credit that firms receive from suppliers.

The precautionary motive claims that firms with riskier cash flows and poorer access to external

capital hold more cash. A comparison of the coefficients on 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 in columns (3) and

(4) indicates that U.S. government sales are negatively associated with corporate cash holdings

when the uncertainty associated with cash flows (𝜎(𝐹𝐶𝐹/𝑁𝐴)) is high. On the contrary, U.S.

government sales do not affect cash holdings for firms with low cash-flow uncertainty.

Next, the agency motive reveals that entrenched managers would rather retain cash than

increase payouts to shareholders when the firm has poor investment opportunities (e.g., Jensen,

1986). The coefficient on 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 is negative and significant at 0.20 level (coeff. = -0.145,

t = -1.38) for observations with strong corporate governance, but the coefficient is insignificant

for observations with weak corporate governance. This result indicates that the association

between corporate cash holdings and government sales may be affected by corporate governance.

Finally, the tax motive suggests that U.S. firms hold more cash when they would incur negative

tax consequences associated with repatriating foreign income (Foley et al., 2007). Panel A shows

that the coefficients on 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 are both reliably negative in columns (7) and (8). A

24

comparison of the coefficients indicates that the negative relation becomes larger in magnitude

(coeff. = -0.147, t = -2.63) for the firm-years with taxable foreign income.

Finally, we investigate whether the cross-sectional variation in the relation between

corporate cash holdings and U.S. government sales is statistically significant under each motive.

We introduce the measures of the four motives into Equation (1) as both main effects and

interactions with government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜). The parameter estimations are reported in

Panel B of Table 4. The coefficients on the interactions of 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 with 𝜎(𝐹𝐶𝐹/𝑁𝐴) and

𝐺𝑖𝑛𝑑𝑒𝑥 are statistically insignificant at the 10% level, while those on the interactions of

𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 with 𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡 and 𝐹𝑜𝑟𝑒𝑖𝑔𝑛𝑇𝑎𝑥 are both reliably negative (coeff. = -0.395

and t = -2.70; coeff. = -0.152 and t = -2.23), respectively. Consistent with Panel A, Panel B

shows that firms with U.S. government sales hold less cash when they have more trade credit or

taxable foreign income. Taken together, results in Table 4 suggest that the negative relation

between corporate cash holdings and U.S. government sales is attributable to both the transaction

motive and the tax motive.

4.4. Government sales and trade credit (test of H2)

Our second hypothesis (H2) predicts that suppliers provide less trade credit to firms with

U.S. government sales. We examine the relation between trade credit and U.S. government sales

by estimating Equation (2). Table 5 reports the parameter estimates. We focus on the coefficients

on government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 or 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 ) in multiple regressions where both

industry and year fixed effects are included. In columns (2) and (4), the coefficients on

𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 and 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 are both significantly negative (coeff. = -0.107, t = -5.08; coeff.

25

= -0.304, t = -5.48), indicating that a firm that has the U.S. government as its major customer

seem to obtain less trade credit from its suppliers. Since firms with U.S. government sales have

higher and more persistent profitability, their suppliers will exploit their superior profitability by

cutting their trade credit. For the government-sales sample, the percentage of government sales

in total sales (𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) has a negative but insignificant correlation with trade credit (coeff.

= -0.082, t = -0.90).

The coefficients on customer concentration (𝐶𝐶) are insignificant at the 10% level in

regressions that include all control variables, different from the coefficients on U.S. government

sales. Consistent with prior research (e.g., Petersen and Rajan, 1997; Ma and Martin, 2012), we

find that suppliers provide more trade credit to smaller (𝑙𝑛(𝑇𝐴)), younger (𝑙𝑛(𝐴𝑔𝑒)), less

profitable (𝑅𝑂𝐴), and non-dividend-paying (𝐷𝑢𝑚_𝐷𝑖𝑣) firms. In addition, firms will receive

more trade credit from their suppliers when they have higher leverage (𝐷𝑒𝑏𝑡/𝑇𝐴), a higher

current ratio (𝐶𝑅), higher sales growth (𝛥𝑆𝑎𝑙𝑒𝑠/𝑇𝐴), a lower book-to-market ratio (𝐵/𝑀), and

longer operating cycles (𝑂𝑝𝑒𝑟𝐶𝑦𝑐𝑙𝑒). In summary, the results in Table 5 support our second

hypothesis (H2) that, compared with other firms, firms with U.S. government sales receive less

trade credit from their suppliers.

4.5. The impact of cash holdings on the relation between trade credit and government sales

In this section, we examine how cash holdings affect the association between trade credit

and U.S. government sales. Specifically, we include cash holdings in Equation (2) as both the

main effect and an interaction term with government sales. The estimations are reports in Table 6.

We focus on the regressions that include all the control variables. All the coefficients on cash

26

holdings (ln (𝐶𝑎𝑠ℎ/𝑁𝐴)) are significantly positive (at the 1% level) in columns (2), (4), and (6),

suggesting that suppliers tend to provide less trade credit customers who hold more cash. The

coefficients on the interactions of government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 and 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) with

cash holdings (ln (𝐶𝑎𝑠ℎ/𝑁𝐴)) are both significantly negative (coeff. = -0.038, t = -4.27; coeff. =

-0.078, t = -3.61) in columns (2) and (4). This result indicates that, for two firms with the same

amounts of cash holdings, the one with government sales obtains less trade credit from their

suppliers than the other. For the observations that already have government sales, trade credit is

not significantly correlated with the percentage of government sales in total sales. Taken together,

Table 6 suggests that firms that have the U.S. government as a major customer hold less cash,

because their suppliers appear to take advantage of them by providing less trade credit.

4.6. Government sales and value of cash (tests of H3)

Our third hypothesis (H3) predicts that firms with U.S. government sales have lower

marginal value of cash. The results in the previous section show that, keeping cash holdings

constant, firms with U.S. government sales obtain less trade credit from their suppliers compared

with other firms. Ceteris paribus, it should be more costly for firms with government sales to

hold an additional dollar of cash relative to other firms, since suppliers may put higher

transaction costs on their cash holdings. Thus, the marginal value of holding an additional dollar

should be lower for firms with U.S. government sales, resulting in lower cash holdings.

Table 7 presents the parameter estimates of Equation (3). We focus on the coefficients on

the interactions of U.S. government sales with change in cash (∆𝐶𝑎𝑠ℎ/𝑀𝑉𝐸) in regressions that

include all control variables. The coefficients on 𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 × ∆𝐶𝑎𝑠ℎ/𝑀𝑉𝐸 and

27

𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 × ∆𝐶𝑎𝑠ℎ/𝑀𝑉𝐸 are -0.203 (t = -3.97) and -0.509 (t = -4.28) in columns (2) and (4),

respectively. The coefficient on 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 × ∆𝐶𝑎𝑠ℎ/𝑀𝑉𝐸 is also negative and marginally

significant (at the 20% level) for firm-years with U.S. government sales in column (6). The

coefficients on control variables are consistent with those in prior research (e.g., Dittmar and

Marht-Smith, 2007). In summary, the results in Table 7 provide evidence in support our third

hypothesis (H3).

4.7. The impact of government sales on the sensitivity of cash holdings to operating cash flows

In this section, we examine whether government sales affect the portion of operating cash

flows that a firm spends. Our previous results show that firms with government sales have less

cash holdings and lower value of cash, since their suppliers tend to exploit their superior

profitability by providing less trade credit. Thus, we predict that, compared with other firms,

firms with U.S. government sales will save a smaller portion of its operating cash flows, as

measured by the sensitivity of cash holdings to operating cash flows as described in Almeida et

al. (2007).

Table 8 presents the parameter estimates of Equation (4). We focus on the interactions

between operating cash flows ( 𝐶𝐹𝑂/𝑁𝐴 ) and U.S. government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 or

𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 ) in the regressions that include all control variables. The coefficients on the

interactions are -0.070 (t = -1.86) and -0.113 (t = -1.55), respectively. For every dollar of

operating cash flows, firms without government sales save 27.5 cents, while firms with

government sales save 7 cents less. Consistent with prior research (e.g., Almeida et al., 2007),

firms save more operating cash flows when they have more investment opportunities

28

(𝐿𝑎𝑔𝑔𝑒𝑑 𝑇𝑜𝑏𝑖𝑛’𝑠 𝑄), larger size (𝑙𝑛(𝑇𝐴)), less capital expenditures (𝐶𝑎𝑝𝑒𝑥/𝑁𝐴), and less

acquisition expenditures (𝐴𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛/𝑁𝐴). In addition, firms with more concentrated customer

base (𝐶𝐶) have lower sensitivity of cash holdings to operating cash flows. In summary, Table 8

provides further support for our previous findings.

5. Conclusions

In this paper, we investigate whether firms’ relation with the U.S. government as a major

customer affects their cash holdings and the marginal value of cash. Prior research has examined

how firms’ political connections, proxied by lobby contributions, campaign expenditures, or

private access to top politicians, influence their operating performance and stock value. Our

study is the first study to document how firms’ relation with the U.S. government as a major

customer impacts their corporate finance policies in term of holding liquid assets, specifically,

cash and cash equivalents.

First, we document that firms that have the U.S. government as a major customer hold

less cash and cash equivalents, compared with other firms. Using the framework proposed by

Bates et al. (2009), we attempt to explain why firms with U.S. government sales have lower cash

holdings, and find that this phenomenon is attributable to both lower trade credits provided by

suppliers and more repatriation taxes. Furthermore, we show that firms that have the U.S.

government as a major customer have lower value of a marginal dollar of cash holdings. Finally,

our evidence suggests that these firms spend more cash out of cash flows from operations than

other firms, consistent with our main prediction.

29

Our study contributes to the extant literature on the determinants of firm’s cash holdings.

Specifically, we identify an unexplored important determinant that explains the level of firm’s

cash holdings by focusing on the characteristics of the firm’s customer base, in particular one

major customer – the U.S. government. We do so by utilizing specific segment disclosures

included in public firms filings. Although much of the evidence to date examines why firms hold

more cash, our evidence provides both a statistical and economically plausible explanation for

holding less cash.

30

Appendix: Variable Definitions

Variable Definition 𝛥𝑋 A change in 𝑋 from year t-1 to t, and (Compustat codes in parentheses).

Dum_SaleGov A dummy variable equals to one if the firm has the U.S. government as a major customer,

including federal government, state government, and local government; and zero otherwise (from Compustat segment files).

Pct_SaleGov Percentage of U.S. government sales in total sales (from Compustat segment files).

CC Customer-base concentration (Patatoukas, 2012); 𝐶𝐶𝑖𝑡 = ∑ (𝑆𝑎𝑙𝑒𝑠𝑖𝑗𝑡𝑆𝑎𝑙𝑒𝑠𝑖𝑡

)2𝐽𝑗=1 , where 𝑆𝑎𝑙𝑒𝑖𝑗𝑡

represents firm i’s sales to customer j in year t and 𝑆𝑎𝑙𝑒𝑖𝑡 represents firm i’s total sales in year t.

TA Book value of total assets (𝐴𝑇).

NA Net assets, calculated as total assets minus cash (𝐴𝑇 − 𝐶𝐻𝐸 ). Ln(NA) is the natural logarithm of total assets.

Age Firm age, calculated as the number of years the firm has Compustat data. Ln(Age) is the natural logarithm of firm age plus one, and Ln(Age)2 is the squared value of Ln(Age).

Sales Total sales, if total sales (𝑆𝑎𝑙𝑒𝑖𝑡 ) is less than aggregate sales from major customers (∑ 𝑆𝑎𝑙𝑒𝑖𝑗𝑡

𝐽𝑗=1 ), then the measure equals the aggregate sales.

Cash Cash and cash equivalents (𝐶𝐻𝐸).

TradeCredit Trade credit, calculated as accounts payable to cost of goods sold (𝐴𝑃/𝐶𝑂𝐺𝑆).

MVE Market value of equity at the fiscal year end (𝑃𝑅𝐶𝐶_𝐹 × 𝐶𝑆𝐻𝑂).

FCF Free cash flows (𝑂𝐼𝐵𝐷𝑃 − 𝑋𝐼𝑁𝑇 − 𝑇𝑋𝑇 − 𝐷𝑉𝐶).

NWC Net working capital (𝐴𝐶𝑇 − 𝐿𝐶𝑇 − 𝐶𝐻𝐸).

Capex Capital expenditure (CAPX).

R&D Research and development expenditure (𝑋𝑅𝐷). Missing values are set to zero.

Acquisition Acquisition expenditure (𝐴𝑄𝐶).

Debt Total Debt (𝐷𝐿𝑇𝑇 + 𝐷𝐿𝐶).

Dum_Div A dummy variable equals to one if a firm paid common stock dividends (𝐷𝑉𝐶), and zero

otherwise.

Dividend Common dividend (𝐷𝑉𝐶).

ROA Return on asset (𝐼𝐵𝑡/[(𝐴𝑇𝑡 + 𝐴𝑇𝑡−1)/2]).

CFO Operating cash flows (OANCF).

31

Tobin’s Q Tobin’s Q, which equals market value of assets deflated by book value of assets ([𝑃𝑅𝐶𝐶_𝐹 × 𝐶𝑆𝐻𝑂 + 𝐿𝑇]/𝐴𝑇).

σ(FCF/NA) Standard deviation of free cash flow to net assets ratio (FCF/NA) over 5 years (from year t-4 to t).

σ(ROA) Standard deviation of return on asset ratio (ROA) over 5 years (from year t-4 to t).

indCFRISK Industry cash flow risk, calculated as the mean of σ(FCF/NA) for firms in the same industry (Fama and French 48 industry classification).

CR Ratio of non-cash current assets to total assets ((𝐴𝐶𝑇 − 𝐶𝐻𝐸)/𝐴𝑇).

B/M Book-to-market ratio at the fiscal year end ((𝐶𝐸𝑄 + 𝑇𝑋𝐷𝐼𝑇𝐶)/(𝑃𝑅𝐶𝐶_𝐹 × 𝐶𝑆𝐻𝑂))

Liquidation Liquidation cost, calculated as the ratio of finished goods to total inventory (𝐼𝑁𝑉𝐹𝐺/𝐼𝑁𝑉𝑇).

OperCycle Operating cycle, calculated as the natural logarithm of one plus the sum of day’s accounts receivable ( 365 × (𝐼𝑁𝑉𝑇𝑡 + 𝐼𝑁𝑉𝑇𝑡−1)/(𝐶𝑂𝐺𝑆 × 2) ) and day’s inventory ( 365 ×𝑅𝐸𝐶𝑇/𝑆𝐴𝐿𝐸).

Ret Annual stock returns (from CRSP monthly file).

ExRet Excess stock returns, adjusted by Fama and French (1993) size and book-to-market matched portfolio returns from year t-1 to t (e.g., Faulkender and Wang, 2006; Dittmar and Mahrt-Smith, 2007).

Earnings Earnings before extraordinary items (𝐼𝐵 + 𝑋𝐼𝑁𝑇 + 𝑇𝑋𝐷𝐼 + 𝐼𝑇𝐶𝐼).

Interest Interest expense (𝑋𝐼𝑁𝑇).

Payout A dummy variable equals to one if a firm has common dividend or stock repurchases (Skinner, 2008), and zero otherwise.

Rating A dummy variable equals to one if a firm a debt rating, and zero otherwise (from Compustat credit ratings database). The value is set to missing for firms without positive debt.

NewFinance New finance from year t-1 to t = net new equity issues (𝑆𝑆𝑇𝐾 + 𝑃𝑅𝑆𝑇𝐾𝐶) + net new debt issues (𝐷𝐿𝑇𝐼𝑆 + 𝐷𝐿𝑇𝑅).

Leverage Leverage ((𝐷𝐿𝑇𝑇 + 𝐷𝐿𝐶)/(𝐷𝐿𝑇𝑇 + 𝐷𝐿𝐶 + 𝑃𝑅𝐶𝐶_𝐹 × 𝐶𝑆𝐻𝑂)).

Gindex Gompers, Ishii, and Metrick (2003) index.

ForeignTax A dummy variable equals to one if a firm has taxable foreign income ((𝑃𝐼𝐹𝑂 − 𝑇𝑋𝐹𝑂) >0), and zero otherwise.

32

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Table 1. Summary statistics

This table represents the summary statistics of the main variables for the government-sale sample and the non-government-sale sample, respectively. Panel A reports descriptive statistics, and Panel B reports correlation coefficients. The sample includes all firm-years that have major customer data available in the Compustat segment database during the period 1976-2011. The sample excludes firm-years without sufficient accounting data in Compustat annual file or without sufficient stock return data in CRSP monthly file. The sample also excludes firm-years in the financial or utility industries. All variable are defined in the Appendix, and all continuous variables are winsorized at the 1st and 99th percentiles. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A. Descriptive statistics

Variable Government Sales No Government Sales Mean

Difference N Mean Median Std. Dev. N Mean Median Std.

Dev.

Pct_SaleGov 11,469 30.8% 23.1% 25.0% Cash 11,469 98.0 7.9 362.0 49,918 131.1 10.7 429.3 -33.1 *** TA 11,469 1,210.6 86.8 3,874.5 49,918 1,260.1 117.1 4,075.4 -49.5 NA 11,469 1,103.2 72.5 3,528.3 49,918 1,113.3 90.1 3,651.6 -10.1 Sales 11,469 1,329.9 108.4 3,859.4 49,918 1,146.6 118.5 3,575.5 183.3 *** Age 11,469 18.7 16.0 12.3 49,918 16.0 12.0 11.9 2.7 *** CC 11,469 0.199 0.120 0.211 49,918 0.142 0.068 0.185 0.057 *** Cash/NA 11,469 0.273 0.083 0.631 49,918 0.395 0.105 0.827 -0.122 *** ROA 11,469 -0.006 0.041 0.178 49,918 -0.032 0.029 0.213 0.026 *** Ret 11,469 0.176 0.063 0.660 49,918 0.159 0.019 0.742 0.017 ** Payout 11,469 0.524 1.000 0.499 49,918 0.468 0.000 0.499 0.056 *** Debt/TA 11,469 0.226 0.193 0.192 49,918 0.224 0.189 0.210 0.002 σ(ROA) 10,893 0.070 0.039 0.084 46,524 0.096 0.056 0.108 -0.026 *** σ(FCF/NA) 10,893 0.099 0.037 0.224 46,480 0.153 0.054 0.305 -0.054 *** TradeCredit 11,467 0.122 0.096 0.141 49,886 0.193 0.115 0.280 -0.071 *** Rating 10,398 0.199 0.000 0.399 42,550 0.208 0.000 0.406 -0.009 ** Gindex 1,720 9.364 9.000 2.823 8,432 8.917 9.000 2.619 0.447 *** ForeignTax 11,469 0.188 0.000 0.391 49,918 0.240 0.000 0.427 -0.052 ***

36

Table 1 (cont’d)

Panel B. Correlation coefficients (below diagonal: Pearson; above diagonal: Spearman)

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9)

(1) Dum_SaleGov 0.994 0.097 0.022 -0.069 -0.137 -0.103 0.051 -0.049

*** *** ** *** *** *** *** *** (2) Pct_SaleGov 0.743 0.134 0.024 -0.071 -0.148 -0.102 0.048 -0.060

*** *** ** *** *** *** *** *** (3) CC 0.116 0.338 0.004 0.165 -0.026 0.161 -0.024 -0.051

*** *** *** *** *** ** *** (4) ExRet 0.001 0.007 0.019 0.055 0.043 -0.087 0.033 0.094

*** *** *** *** ** *** (5) Cash/NA -0.060 -0.041 0.217 0.058 0.066 0.499 -0.152 0.048

*** *** *** *** *** *** *** *** (6) TradeCredit -0.106 -0.091 0.042 0.009 0.053 0.089 -0.007 0.160

*** *** *** ** *** *** *** (7) σ(FCF/NA) -0.072 -0.048 0.213 0.011 0.555 0.158 -0.178 -0.131

*** *** *** ** *** *** *** *** (8) Gindex 0.063 0.032 -0.036 0.002 -0.133 -0.015 -0.103 0.111

*** *** *** *** *** *** (9) ForeignTax -0.048 -0.075 -0.077 0.035 -0.059 -0.046 -0.131 0.110 *** *** *** *** *** *** *** ***

Table 2. Industry profile

This table compares the means of cash holdings (𝐶𝑎𝑠ℎ/𝑁𝐴) or trade credit (𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡) in each Fama-French 48 industry between the government-sale sample and non-government-sale sample, respectively. The variables are defined in the Appendix and winsorized at the 1st and 99th percentiles.

Code Industry Name Government Sales No Government Sales % of

Gov. Sales

Mean Difference

N Cash/NA

Trade Credit N Cash/

NA Trade Credit Cash/

NA Trade Credit

1 Agriculture 7 0.205 0.084 207 0.250 0.142 3.3% -0.045 -0.058 2 Food 66 0.059 0.074 1,080 0.110 0.095 5.8% -0.051 -0.022 3 Candy & Soda 1 0.009 0.114 153 0.168 0.126 0.6% -0.160 -0.011 4 Beer & Liqor 10 0.201 0.138 170 0.104 0.178 5.6% 0.097 -0.039 5 Tobacco 0 0.000 0.000 70 0.243 0.121 0.0% -0.243 -0.121 6 Recreation 37 0.102 0.101 766 0.213 0.126 4.6% -0.111 -0.024 7 Entertainment 24 0.034 0.095 512 0.243 0.282 4.5% -0.210 -0.187

8 Printing & Publishing 30 0.309 0.210 284 0.145 0.172 9.6% 0.164 0.038

37

9 Consumer Goods 104 0.124 0.096 1,342 0.211 0.147 7.2% -0.087 -0.051 10 Apparel 61 0.185 0.083 1,253 0.194 0.101 4.6% -0.009 -0.018 11 Healthcare 1,000 0.134 0.076 369 0.479 0.114 73.0% -0.345 -0.038 12 Medical Equipment 285 0.420 0.136 1,858 0.599 0.207 13.3% -0.179 -0.071 13 Pharmaceutical 269 2.171 0.167 3,322 1.425 0.263 7.5% 0.746 -0.096 14 Chemicals 150 0.390 0.178 1,133 0.218 0.149 11.7% 0.172 0.029 15 Rubber & Plastic 78 0.095 0.118 865 0.135 0.111 8.3% -0.040 0.007 16 Textiles 44 0.120 0.108 601 0.068 0.083 6.8% 0.051 0.025 17 Building Materials 272 0.127 0.125 1,378 0.139 0.098 16.5% -0.012 0.027 18 Construction 324 0.181 0.128 530 0.187 0.106 37.9% -0.006 0.022 19 Steel 180 0.133 0.099 1,087 0.095 0.113 14.2% 0.038 -0.014 20 Fabricated 94 0.093 0.099 387 0.118 0.107 19.5% -0.025 -0.008 21 Machinery 470 0.196 0.129 2,440 0.229 0.138 16.2% -0.033 -0.009

22 Electrical Equipment 464 0.273 0.126 1,026 0.229 0.151 31.1% 0.044 -0.025

23 Automobiles 221 0.081 0.115 1,106 0.115 0.115 16.7% -0.034 -0.001 24 Aircraft 529 0.126 0.110 149 0.066 0.113 78.0% 0.060 -0.003

25 Shipping and Railroad 101 0.238 0.106 104 0.157 0.121 49.3% 0.081 -0.015

26 Defense 191 0.109 0.102 49 0.368 0.132 79.6% -0.259 -0.030 27 Precious Metals 0 0.000 0.000 292 0.219 0.174 0.0% -0.219 -0.174 28 Mines 0 0.000 0.000 335 0.132 0.168 0.0% -0.132 -0.168

29 Industrial Metal Mining 3 0.164 0.113 169 0.107 0.127 1.7% 0.058 -0.014

30 Petroleum & Natural Gas 95 0.115 0.218 3,909 0.138 0.531 2.4% -0.023 -0.313

32 Communication 103 0.147 0.244 1,038 0.268 0.204 9.0% -0.120 0.041 33 Personal Services 169 0.275 0.105 304 0.322 0.175 35.7% -0.047 -0.070 34 Business Service 1,673 0.327 0.122 6,015 0.666 0.210 21.8% -0.339 -0.088 35 Computers 1,021 0.294 0.147 2,830 0.594 0.188 26.5% -0.300 -0.042

36 Electronic Equipment 1,786 0.257 0.126 4,801 0.533 0.169 27.1% -0.276 -0.043

37 Measuring and Control Equipment 751 0.287 0.113 1,397 0.496 0.152 35.0% -0.210 -0.040

38 Business Supplies 99 0.112 0.113 807 0.095 0.146 10.9% 0.017 -0.033

39 Shipping Containers 57 0.029 0.096 223 0.061 0.101 20.4% -0.032 -0.005

40 Transportation 234 0.183 0.068 1,563 0.096 0.111 13.0% 0.087 -0.043 41 Wholesale 285 0.076 0.128 2,673 0.123 0.138 9.6% -0.047 -0.009 42 Retail 117 0.127 0.105 763 0.179 0.135 13.3% -0.051 -0.030

43 Retaurant, Hotels, and Motels 7 0.054 0.093 179 0.176 0.081 3.8% -0.123 0.012

48 Other 57 0.186 0.321 379 0.262 0.251 13.1% -0.077 0.070

38

Table 3. The impact of government sales on corporate cash holdings

This table presents parameter estimations of Equation (1). The dependent variable is the natural logarithm of cash holdings (𝐶𝑎𝑠ℎ) scaled by net assets (𝑁𝐴). The main independent variable is an indicator of government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) or the percentage of government sales in total sales (𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣). Standard errors are clustered at both firm and year levels (Petersen, 2009). Industry fixed effect is based on Fama-French 48 industries. t-statistics are in parentheses below parameter estimates. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. All variable are defined in the Appendix, and all continuous variables are winsorized at the 1st and 99th percentiles.

Variable Predicted sign

All firm-years All firm-years Gov. sale firm-years (1) (2) (3) (4) (5) (6)

Dum_SaleGov - -0.265*** -0.113*** (-4.94) (-2.78) Pct_SaleGov - -1.134*** -0.585*** -1.358*** -0.750***

(-8.53) (-5.41) (-5.91) (-4.32) CC + 1.621*** 0.021 1.883*** 0.175*** 1.782*** 0.350*

(14.62) (0.31) (16.63) (2.81) (7.35) (1.75) ln(NA) - -0.085*** -0.084*** -0.110***

(-9.69) (-9.67) (-6.32) MVE/NA + 0.013** 0.013** 0.010*

(2.10) (2.10) (1.80) FCF/NA + 0.196*** 0.191*** 0.106

(4.77) (4.68) (1.16) NWC/NA - -0.952*** -0.940*** -0.866***

(-16.58) (-16.20) (-6.67) Capex/NA + 1.238*** 1.222*** 1.723***

(7.47) (7.42) (5.17) R&D/NA + 1.471*** 1.447*** 1.213***

(17.46) (17.16) (8.31) Acquisition/NA + -0.238 -0.221 -0.560*

(-1.57) (-1.48) (-1.66) Debt/TA + -3.205*** -3.199*** -3.174***

(-30.49) (-30.58) (-17.69) Dum_Div - -0.062* -0.059* -0.012

(-1.82) (-1.73) (-0.19) indCFRISK + -0.003 -0.003 -0.000

(-0.21) (-0.21) (-0.00) Intercept -2.588*** -2.612*** -2.463*** (-43.75) (-46.53) (-39.98) Industry Effect No Yes No Yes No Yes Year Effect No Yes No Yes No Yes

N 57,399 57,399 57,399 57,399 10,787 10,787 Adj. R2 2.8% 40.8% 3.4% 40.9% 2.0% 33.8%

39

Table 4. Cross-sectional variation in the relation between corporate cash holdings and government sales

Panel A reports the parameter estimations of Equation (1) for subsamples based on different cash holding motives. In Panel B, we include each proxy of a cash-holding motive in Equation (1) as both the main effect and an interaction with the government sale indicator (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣). Standard errors are clustered at both firm and year levels (Petersen, 2009). Industry fixed effect is based on Fama-French 48 industries. t-statistics are in parentheses below parameter estimates. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. All variable are defined in the Appendix, and all continuous variables are winsorized at the 1st and 99th percentiles.

Panel A. Parameter estimations of Equation (1) in subsamples

Variable Predicted sign

(1) Transaction Motive (2) Precautionary Motive (3) Agency Motive (4) Tax Motive Low

TradeCredit High

TradeCredit Low σ(FCF/NA)

High σ(FCF/NA) Good

Gindex Bad

Gindex No Foreign Tax

Foreign Tax

Dum_SaleGov - -0.061 -0.211*** 0.043 -0.122** -0.145 0.048 -0.082* -0.147***

(-1.16) (-3.41) (0.80) (-2.18) (-1.38) (0.48) (-1.78) (-2.63) CC + 0.061 0.017 -0.003 0.172** -0.033 0.043 0.050 0.066

(0.55) (0.18) (-0.03) (2.52) (-0.14) (0.16) (0.71) (0.58) ln(NA) - -0.172*** -0.031** -0.061*** -0.025 -0.206*** -0.182*** -0.110*** -0.090***

(-12.33) (-2.34) (-3.40) (-1.57) (-7.72) (-5.06) (-10.31) (-6.26) MVE/NA + 0.004 0.029*** 0.329*** 0.008* 0.099*** 0.129*** 0.010* 0.095***

(1.29) (4.50) (11.85) (1.89) (5.71) (3.94) (1.96) (6.61) FCF/NA + 0.143** 0.177*** 0.122 0.058* 0.471*** 1.151** 0.075* 0.702***

(2.37) (3.85) (0.22) (1.76) (2.61) (2.46) (1.82) (6.34) NWC/NA - -1.155*** -0.719*** -1.594*** -0.436*** -1.347*** -1.379*** -0.899*** -1.077***

(-13.33) (-9.97) (-11.26) (-7.09) (-6.38) (-5.62) (-13.84) (-8.81) Capex/NA + 0.723** 1.411*** -1.687*** 1.972*** -0.972* -2.281** 1.350*** 0.330

(2.24) (6.48) (-5.24) (11.11) (-1.65) (-2.30) (8.14) (0.97) R&D/NA + 1.027*** 1.539*** 4.022*** 1.231*** 1.223*** 1.701** 1.292*** 1.937***

(9.65) (15.81) (8.28) (17.00) (4.57) (2.20) (15.54) (11.74) Acquisition/NA + -0.784*** 0.307* -1.067*** 0.047 -0.616* -1.613*** -0.055 -0.787***

(-3.16) (1.87) (-4.18) (0.25) (-1.84) (-3.80) (-0.34) (-4.08) Debt/TA + -3.164*** -2.943*** -2.249*** -2.962*** -2.361*** -1.387*** -3.231*** -2.544***

(-20.59) (-25.46) (-14.53) (-21.12) (-9.35) (-5.01) (-28.95) (-15.61) Dum_Div - 0.009 -0.171*** -0.003 0.277*** -0.164* -0.194* -0.029 -0.157***

(0.17) (-2.73) (-0.06) (4.04) (-1.93) (-1.95) (-0.71) (-3.57) indCFRISK + -0.009 0.012 0.009 -0.003 0.000 -0.036* 0.009 -0.015

(-0.49) (0.59) (0.52) (-0.22) (0.02) (-1.72) (0.63) (-1.35) Industry Effect Yes Yes Yes Yes Yes Yes Yes Yes

40

Year Effect Yes Yes Yes Yes Yes Yes Yes Yes N 19,389 18,777 17,533 18,157 3,996 2,700 44,335 13,064 Adj. R2 42.8% 41.9% 28.5% 44.0% 54.3% 44.0% 40.3% 49.5%

Panel B. Interactions of government sales with cash holding motives

Variable Predicted sign

All firm-years (1) (2) (3) (4)

Dum_SaleGov - -0.067 -0.097** -0.394* -0.072

(-1.45) (-2.27) (-1.72) (-1.59) Dum_SaleGov × TradeCredit - -0.395*** (-2.70) TradeCredit - -0.157*** (-2.72) Dum_SaleGov × σ(FCF/NA) - -0.068 (-0.68) σ(FCF/NA) + 0.592*** (12.24) Dum_SaleGov × Gindex ? 0.033 (1.46) Gindex ? -0.010 (-0.79) Dum_SaleGov × ForeignTax - -0.152**

(-2.23) ForeignTax + 0.337***

(11.12) Controls Yes Yes Yes Yes Industry Effect Yes Yes Yes Yes Year Effect Yes Yes Yes Yes

N 57,370 53,718 9,251 57,399 Adj. R2 40.9% 41.2% 52.2% 41.3%

41

Table 5. The impact of government sales on trade credit

This table presents parameter estimations of Equation (2). The dependent variable is the natural logarithm of trade credit (𝑇𝑟𝑎𝑑𝑒𝐶𝑟𝑒𝑑𝑖𝑡 ). The main independent variable is an indicator of government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣) or the percentage of government sales in total sales (𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣). Standard errors are clustered at both firm and year levels (Petersen, 2009). Industry fixed effect is based on Fama-French 48 industries. t-statistics are in parentheses below parameter estimates. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. All variable are defined in the Appendix, and all continuous variables are winsorized at the 1st and 99th percentiles.

Variable Predicted Sign

All firm-years All firm-years Gov. sale firm-years (1) (2) (3) (4) (5) (6)

Dum_SaleGov - -0.147*** -0.107*** (-6.51) (-5.80) Pct_SaleGov - -0.505*** -0.304*** -0.319*** -0.082

(-8.39) (-5.48) (-2.96) (-0.90) CC ? 0.174*** -0.030 0.284*** 0.032 0.077 -0.124

(3.79) (-0.76) (5.81) (0.80) (0.57) (-1.05) ln(TA) + 0.041*** 0.041*** 0.036***

(6.96) (6.94) (3.61) ln(Age) - -0.142** -0.146** -0.262*

(-2.18) (-2.25) (-1.65) ln(Age)2 ? 0.015 0.016 0.025

(1.23) (1.27) (0.85) Debt/TA + 0.095*** 0.099*** 0.185**

(2.71) (2.85) (2.41) Dum_Div - -0.098*** -0.098*** -0.106***

(-5.52) (-5.49) (-3.36) CR - -0.371*** -0.369*** -0.157*

(-8.51) (-8.52) (-1.85) ROA - -0.679*** -0.674*** -0.910***

(-15.52) (-15.53) (-9.76) ΔSales/TA + 0.213*** 0.212*** 0.217***

(12.60) (12.51) (6.20) B/M - -0.125*** -0.124*** -0.120***

(-10.99) (-11.03) (-6.69) Liquidation + 0.130*** 0.126*** 0.124*

(3.56) (3.41) (1.69) OperCycle + 0.554*** 0.551*** 0.466***

(30.58) (30.53) (12.17) Intercept -2.144*** -2.161*** -2.187*** (-120.05) (-124.94) (-79.87) Industry Effect No Yes No Yes No Yes

Year Effect No Yes No Yes No Yes N 35,247 35,247 35,247 35,247 6,464 6,464 Adj. R2 0.8% 29.2% 1.1% 29.2% 1.1% 28.7%

42

Table 6. The impact of cash holdings on the relation between government sales and trade credit

In this table, we include the natural logarithm of cash holdings (𝑙𝑛(𝐶𝑎𝑠ℎ/𝑁𝐴)) into Equation (2) as both the main effect and an interaction with the indicator of government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣). Standard errors are clustered at both firm and year levels (Petersen, 2009). Industry fixed effect is based on Fama-French 48 industries. t-statistics are in parentheses below parameter estimates. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. All variable are defined in the Appendix, and all continuous variables are winsorized at the 1st and 99th percentiles.

Variable Predicted Sign

All firm-years All firm-years Gov. sale firm-years (1) (2) (3) (4) (5) (6)

Dum_SaleGov - -0.296*** -0.199*** (-8.40) (-6.82) Dum_SaleGov × ln(Cash/NA) - -0.063*** -0.038*** (-6.21) (-4.27) Pct_SaleGov - -0.801*** -0.505*** -0.422*** -0.185

(-7.76) (-5.83) (-2.69) (-1.57) Pct_SaleGov × ln(Cash/NA) - -0.130*** -0.078*** -0.030 -0.028

(-5.01) (-3.61) (-0.76) (-0.95) ln(Cash/NA) - 0.038*** -0.021*** 0.033*** -0.024*** -0.018 -0.040***

(6.91) (-4.60) (5.85) (-5.30) (-1.21) (-3.11) CC ? 0.122*** -0.029 0.227*** 0.035 0.115 -0.099

(2.67) (-0.74) (4.43) (0.87) (0.84) (-0.85) ln(TA) + 0.038*** 0.038*** 0.030***

(6.43) (6.44) (2.94) ln(Age) - -0.130** -0.131** -0.242

(-2.01) (-2.04) (-1.53) ln(Age)2 ? 0.013 0.013 0.022

(1.06) (1.06) (0.76) Debt/TA + 0.002 0.003 0.026

(0.05) (0.09) (0.34) Dum_Div - -0.103*** -0.102*** -0.113***

(-5.85) (-5.82) (-3.62) CR - -0.495*** -0.494*** -0.385***

(-10.91) (-10.97) (-4.48) ROA - -0.682*** -0.678*** -0.904***

(-15.69) (-15.72) (-9.72) ΔSales/TA + 0.213*** 0.212*** 0.208***

(12.31) (12.27) (5.91) B/M - -0.133*** -0.132*** -0.130***

(-11.19) (-11.24) (-7.15) Liquidation + 0.135*** 0.131*** 0.132*

(3.74) (3.57) (1.80) OperCycle + 0.558*** 0.557*** 0.473***

(31.55) (31.57) (12.86) Intercept -2.050*** -2.078*** -2.230*** (-80.36) (-79.31) (-46.14) Industry Effect No Yes No Yes No Yes Year Effect No Yes No Yes No Yes N 35,247 35,247 35,247 35,247 6,464 6,464 Adj. R2 1.7% 29.6% 1.9% 29.6% 1.6% 29.6%

43

Table 7. The impact of government sales on the value of cash

This table represents the parameter estimations of Equation (3). The dependent variable is excess stock returns (𝐸𝑥𝑅𝑒𝑡). The main variable of interest is the interaction of change in cash holdings (𝛥𝐶𝑎𝑠ℎ/𝑀𝑉𝐸) with government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 or 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣). Standard errors are clustered at both firm and year levels (Petersen, 2009). Industry fixed effect is based on Fama-French 48 industries. t-statistics are in parentheses below parameter estimates. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. All variable are defined in the Appendix, and all continuous variables are winsorized at the 1st and 99th percentiles.

Variable Predicted Sign

Excess Returns (1) (2) (3) (4) (5) (6)

ΔCash/MVE + 1.079*** 1.746*** 1.062*** 1.740*** 0.862*** 1.434***

(16.17) (15.05) (16.03) (15.20) (7.91) (12.12) Dum_SaleGov × ΔCash/MVE - -0.277*** -0.203***

(-3.87) (-3.97) Dum_SaleGov ? -0.003 0.015 (-0.19) (1.19) Pct_SaleGov × ΔCash/MVE - -0.575*** -0.509*** -0.185 -0.296

(-3.36) (-4.28) (-0.77) (-1.57) Pct_SaleGov ? -0.004 0.064* -0.041 0.025

(-0.09) (1.91) (-0.79) (0.58) CC + 0.066* -0.043* 0.068* -0.056** 0.133** 0.025

(1.96) (-1.95) (1.74) (-2.38) (1.99) (0.44) ΔEarnings/MVE + 0.463*** 0.464*** 0.492***

(13.25) (13.27) (10.81) ΔNA/MVE + 0.222*** 0.221*** 0.220***

(12.53) (12.58) (8.23) ΔR&D/MVE + 0.744** 0.745** 0.635***

(2.51) (2.51) (2.61) ΔInterest/MVE - -1.464*** -1.465*** -1.256**

(-5.49) (-5.51) (-2.34) ΔDividend/MVE + 2.644*** 2.644*** 1.907**

(6.59) (6.60) (2.55) lagged Cash/MVE + 0.367*** 0.368*** 0.330***

(5.66) (5.70) (5.00) Leverage - -0.565*** -0.566*** -0.558***

(-12.20) (-12.22) (-12.30) New Finance/MVE + 0.037 0.038 -0.037

(1.21) (1.24) (-1.10) Lagged Cash/MVE × ΔCash/MVE - -0.635*** -0.633*** -0.407***

(-6.25) (-6.24) (-3.64) Leverage × ΔCash/MVE - -1.335*** -1.345*** -1.028***

(-11.05) (-11.01) (-6.53) Intercept 0.002 0.002 -0.002 (0.16) (0.12) (-0.14) Industry effect No Yes No Yes No Yes Year effect No Yes No Yes No Yes N 60,951 60,951 60,951 60,951 11,412 11,412 Adj. R2 5.9% 22.6% 5.9% 22.6% 4.4% 21.2%

44

Table 8. The impact of government sales on the sensitivity of cash holdings to operating cash flows

This table represents the parameter estimation of Equation (4). The dependent variable is change in cash holdings scaled by net assets (𝛥𝐶𝑎𝑠ℎ/𝑁𝐴). The main variable of interest is the interaction of operating cash flows (𝐶𝐹𝑂/𝑁𝐴) with government sales (𝐷𝑢𝑚_𝑆𝑎𝑙𝑒𝐺𝑜𝑣 or 𝑃𝑐𝑡_𝑆𝑎𝑙𝑒𝐺𝑜𝑣). Standard errors are clustered at both firm and year levels (Petersen, 2009). Industry fixed effect is based on Fama-French 48 industries. t-statistics are in parentheses below parameter estimates. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. All variable are defined in the Appendix, and all continuous variables are winsorized at the 1st and 99th percentiles.

Variable Predicted sign

ΔCash/NA (1) (2) (3) (4)

CFO/NA + 0.229*** 0.275*** 0.225*** 0.271***

(6.82) (9.91) (6.75) (9.80) Dum_SaleGov × CFO/NA - -0.089** -0.070* (-2.22) (-1.86) Dum_SaleGov ? -0.003 0.010* (-0.57) (1.92) Pct_SaleGov × CFO/NA - -0.151* -0.113

(-1.92) (-1.55) Pct_SaleGov ? -0.033** 0.010

(-2.09) (0.79) CC + 0.077*** 0.046*** 0.084*** 0.046***

(4.27) (3.03) (4.15) (2.77) Lagged Tobin's Q + 0.045*** 0.045***

(16.36) (16.54) ln(TA) + 0.005*** 0.005***

(4.27) (4.27) Capex/NA - -0.100** -0.101**

(-2.13) (-2.15) Aquisition/NA - -0.418*** -0.418***

(-12.29) (-12.21) Intercept -0.005 -0.005 (-0.74) (-0.78) Industry Effect No Yes No Yes Year Effect No Yes No Yes

N 47,115 47,115 47,115 47,115 Adj. R2 8.4% 15.3% 8.4% 15.2%