The cash-holding link within the supply chain

Abstract

Using a customer–supplier matched sample of US-listed firms from 1980 to 2016, we study the corporate cash-holding relationship between suppliers and their major customers. The key findings suggest that the cash-holding levels of suppliers are positively affected by those of their major customers, consistent with the liquidity argument. The effects are more pronounced when the major customers are in more favourable financial conditions and when they are considered more important to their suppliers. Our results are robust to various endogeneity problems and additional tests. Taken together, these results bring forth an important corporate cash-holding link within the supply chain.

Appendices

Appendix A: Variable measurement

1.1 Control variables

Consistent with existing studies in corporate cash holdings (Opler et al. 1999; Dittmar et al. 2003; Ozkan and Ozkan 2004; Bates et al. 2009; Venkiteshwaran 2011; Itzkowitz 2013; Jiang and Lie 2016; Lin and Chiu 2017; Nguyen 2019; Amin and Williamson 2021), we control for several important supplier characteristics (S_Control) which jointly determine these firms’ levels of cash holdings. The relations between these variables and firms’ levels of cash holdings are as follows.

1.1.1 Growth opportunities (S_GO)

This variable is constructed by taking the sum of the market value of equity or market capitalization and the book value of total debt scaled by the book value of total assets. According to the precautionary motive argument, firms’ growth opportunities have a positive impact on their levels of cash holdings as high-growth firms are likely to experience more significant costs associated with adverse cash flow shocks and financial distress (Opler et al. 1999; Bates et al. 2009). High-growth firms, due to greater information asymmetries, are more likely to give up positive NPV investments when there is a cash shortage and external financing is costly. As a result, they need to hold more cash to grasp growth opportunities more effectively.

1.1.2 Firm size (S_FS)

This is the natural logarithm of the book value of total assets measured in 2010 US dollars (Opler et al. 1999; Bates et al. 2009; Jiang and Lie 2016). The finance literature suggests a negative link between firm size and cash holdings for several reasons. First, in the spirit of the transaction cost motive argument, large firms should hold less cash as the result of economies of scale, i.e., because they tend to incur lower transaction costs when converting their non-cash financial assets into cash (Miller and Orr 1966). Second, since these firms are less subject to financial distress and information asymmetries, they usually get greater access to external capital markets and there is hence a less prominent need for them to hold cash, as suggested by the precautionary motive argument (Almeida et al. 2004). Finally, according to Sufi (2009), large firms are likely to secure greater access to bank lines of credit, which is a close alternative to cash. These factors together make it less necessary for them to hoard cash.

1.1.3 Book leverage (S_BL)

This is the ratio of the sum of long-term and short-term debt to total assets. As leverage is seen as an alternative to cash holdings, there should be an inverse link between this variable and cash holdings (Opler et al. 1999; Bates et al. 2009; Jiang and Lie 2016).

1.1.4 Cash flows (S_CF)

This is operating income before depreciation minus interest expenses, income taxes and total dividends paid, scaled by the book value of total assets. Firms’ cash flows and cash holdings may be positively related to each other for two reasons. First, the pecking order view suggests that firms with greater cash flows may be in a better position to accumulate more cash (Myers 1984; Myers and Majluf 1984). Second, these firms tend to have more growth opportunities and should therefore hold more cash (Opler et al. 1999; Bates et al. 2009; Jiang and Lie 2016).

1.1.5 Cash flow volatility (S_CFV)

This variable is constructed by taking the standard deviation of cash flow (CF) over a five-year period (from year t-6 to year t−1) (Ghaly et al. 2015; Jiang and Lie 2016). The precautionary motive argument suggests a positive link between the variable and cash holdings, since firms with riskier cash flows should accumulate more cash to better cope with potential adverse cash flow shocks in the presence of costly external financing (Opler et al. 1999; Bates et al. 2009).

1.1.6 Capital expenditures (S_CAPEX)

This is capital expenditures scaled by the book value of total assets. Its impact on cash holdings needs to be empirically resolved for there are opposing predictions on how this variable may influence cash holdings. In particular, the pecking order view suggests a negative relation between CAPEX and cash holdings as firms which are making significant investments in assets may experience temporary falls in their cash balances (Riddick and Whited 2009). Also, if capital expenditure results in a rise in fixed assets, which can be used as high-quality collateral, firms’ debt capacities may be improved, and they may have less need to hoard cash. In contrast, the trade-off view suggests a positive link between CAPEX and cash holdings as firms which are making significant CAPEX may be those with a lot of growth opportunities and hence the need to hold more cash (Opler et al. 1999; Bates et al. 2009; Jiang and Lie 2016).

1.1.7 Net working capital (S_NWC)

This variable is constructed by taking the difference between working capital and cash and cash equivalents scaled by the book value of total assets (Opler et al. 1999; Bates et al. 2009; Jiang and Lie 2016). According to Bates et al. (2009), since net working capital consists of highly liquid assets which can be seen as close substitutes for cash, such as accounts receivables and inventories, firms with more NWC may hold less cash.

1.1.8 Research and development expenses (S_R&D)

This is research and development expenses scaled by total sales (Opler et al. 1999; Bates et al. 2009; Jiang and Lie 2016). The trade-off view tends to take these expenses as a close proxy for growth opportunities, which potentially lead to financial distress costs and adverse cash flow shocks. This suggests a positive link between R&D and cash holdings. Brown and Petersen (2011) find empirical evidence consistent with this view which shows that, in the presence of financing frictions, firms tend to depend greatly on cash to smooth their R&D expenses as adjustments in these expenses, such as the wages of highly skilled technology workers, are costly. On the contrary, the financing hierarchy or pecking order view suggests that these expenses are negatively related to cash holdings. The reason is that firms incurring large amounts of these expenses may temporarily see a fall in cash.

1.1.9 Dividend payout (S_PAYOUT)

This variable is defined to be 1 for firms which pay dividends and 0 otherwise (Opler et al. 1999; Bates et al. 2009; Jiang and Lie 2016). There are also opposing predictions on the link between this variable and cash holdings. According to Fazzari et al. (1988), financially unconstrained firms, which may also be cash-rich, are more likely to pay dividends than financially constrained firms. However, Almeida et al. (2004) and Bates et al. (2009) find that dividend-paying firms tend to hold less cash as they are seen by investors as less risky and therefore receive greater access to external capital markets.

1.1.9.1 Credit rating (S_RATED)

This is a dummy variable which has a value of 1 if a firm has a non-missing S&P credit rating on long-term debt, and 0 otherwise. Credit ratings, by definition, incorporate credit suppliers’ opinions on firms’ capacities to meet their financial obligations (Whited 1992; Crouhy et al. 2001; Almeida et al. 2004). It is therefore expected that firms with a credit rating may gain greater access to external capital markets and hence have a less prominent need to hoard cash.

1.1.9.2 Firm age (S_LNAGE)

This variable is constructed by taking the natural logarithm of the current fiscal year minus the first year listed in the Compustat database. Mature firms are likely to have more stable cash flows and hence not need to hold much cash (Opler et al. 1999; Dittmar and Duchin 2010; Pinkowitz et al. 2013).

In addition to the above supplier controls, we follow studies in supply chain finance (e.g., Bauer et al. 2018; Chu et al. 2019) to control for their major customers’ main characteristics (C_Control). These customer controls are growth opportunities (C_GO), firm size (C_FS), book leverage (C_BL), cash flows (C_CF), cash flow volatility (C_CFV), capital expenditure (C_CAPEX), net working capital (C_NWC), research and development expenditure (C_R&D), dividend payout (C_PAYOUT), credit rating (C_RATED), and firm age (C_LNAGE), all of which may influence suppliers’ cash-holding decisions.

Appendix B: Variable definitions

Variable	Definition
Measures of cash holdings
CASH1	The ratio of cash and cash equivalents to the book value of total assets
CASH2	The ratio of cash and cash equivalents to net assets, where net assets are equal to total assets minus cash and cash equivalents
CASH3	The natural logarithm of one plus the ratio of cash and cash equivalents to total assets
CASH4	The natural logarithm of one plus the ratio of cash and cash equivalents to net assets, where net assets are equal to total assets minus cash and cash equivalents
CASH5	The natural logarithm of one plus the ratio of cash and cash equivalents to total sales
ΔCH	Change in firm’s cash-holding level from year t−1 to year t
Deviation	Deviation from firm’s target level of cash holdings, defined as the difference between their actual level of cash holdings in year t−1 and their target level of cash holdings in year t. Target levels of cash holdings are estimated using Model (1)
Deficit	A dummy variable equal to 1 if firm has a cash deficit i.e., their actual cash-holding level in year t−1 is less than their target level of cash holdings in the same year, and 0 otherwise
Firm characteristics
BL	Book leverage, which is the ratio of the sum of long-term and short-term debt to the book value of total assets
CF	Cash flow, which is equal to operating income before depreciation minus interest expenses, income taxes, and total dividends paid, all scaled by total assets
CFV	Cash flow volatility, which is the standard deviation of cash flow (CF) over a five-year period (from year t−6 to year t−1) (Ghaly et al. 2015; Jiang and Lie 2016). We require the sample firms to have at least four non-missing cash flow values to calculate this standard deviation
CAPEX	Capital expenditures scaled by total assets
CC	Customer concentration, which is calculated based on the Herfindahl–Hirschman Index (Irvine et al. 2016; Lian 2017), as follows: $CC = \mathop \sum \limits_{j = 1}^{n} \left( {\frac{{Sales \,to \,major\, customer_{i,j,t} }}{{Total \,sales_{i,t} }}} \right)^{2}$ where i represents supplier firm i; j represents major customer firm j; and n is the total number of major customers disclosed by a supplier in year t. To calculate this index, we keep only major customers which are classified as “COMPANY” in the Compustat Segment database (Irvine et al. 2016). We keep non-identifier customers as the calculation of the concentration index does not require customers’ identifiers. By definition, CC varies from 0 to 1, taking the value 1 if a supplier has only one customer and supplies all of its outputs to that customer
FS	Firm size, which is the natural log of total assets measured in 2010 US dollars
GO	Growth opportunities, which is the ratio of the market value of total assets to the book value of total assets, where the market value of total assets is the share price times the number of shares outstanding plus long-term and short-term debt
NWC	Net working capital, which is working capital minus cash and cash equivalents, all scaled by total assets
R&D	Research and development expenses scaled by total sales
PAYOUT	Common dividends paid, which is a dummy variable equal to 1 if a firm’s cash dividends paid to common shareholders are greater than 0, and 0 otherwise
LNAGE	Firm age, which is equal to the natural logarithm of the current fiscal year minus the first year the firm is listed in the Compustat database
RATED	Credit rating, which has a value of 1 if the firm has a non-missing S&P credit rating on long-term debt, and 0 otherwise