RDP 2016-03: Why Do Companies Hold Cash? 7. Extensions and Robustness Tests

7.1 The Cash Flow Sensitivity of Corporate Cash

In this section we further examine the role of agency costs and financing frictions in determining corporate cash holdings by extending the literature that looks at the ‘cash flow sensitivity’ of cash (Almeida, Campello and Weisbach 2004). Almeida et al focus primarily on the role of financing frictions. They develop a model to show that financially constrained companies have a positive propensity to save their cash flows due to their restricted access to external capital markets, while unconstrained companies do not. They test their model by examining how US public companies adjust their cash holdings in response to shocks to cash flows (referred to as the cash flow sensitivity of cash).

We apply their framework to the Australian data and broaden their analysis to incorporate agency costs by comparing the cash flow sensitivity of cash for private companies to that of public companies. Public companies may be more likely than private companies to accumulate cash out of cash flows during good times because they face higher agency costs. Similar to before, in the presence of agency costs and financing frictions, it is unclear whether the cash holdings of public or private companies should be more sensitive to shocks to cash flows. But, the finding that the cash holdings of public companies are relatively more (less) sensitive to cash flows provides evidence in support of agency costs (financing frictions).

To examine the cash flow sensitivity of cash we make a slight adjustment to the company-level model estimated in Equation (1). In particular, rather than look at the correlation between the level of cash (to assets) and cash flows, we now look at the correlation between the change in cash holdings and cash flows (Almeida et al 2004; Bao, Chan and Zhang 2012). Moreover, we estimate the model separately for private companies (j = PRIV) and unlisted public companies (j = PUB):

We are particularly interested in differences in the estimated coefficient on the variable CASHFLOW (β). If companies respond to positive cash flow shocks by raising the rate at which they save cash out of cash flows then we would expect the coefficient estimate to be positive (β > 0). Furthermore, if agency costs matter, then public companies should have a significantly higher propensity to accumulate cash out of cash flows (β^PUB > β^PRIV). Alternatively, if financing frictions matter, the sensitivity to cash flow should be greater for private companies (β^PRIV > β^PUB).

The results presented in Table 6 shows that both private and unlisted public companies have a positive propensity to save cash out of cash flows. This suggests that both financing frictions and agency costs could motivate companies to accumulate cash out of earnings in good times.

Moreover, we find that the coefficient on cash flow is nearly twice as large for public companies as for private companies. And a nested regression that combines the two samples reveals that this difference is statistically significant. This provides further evidence in favour of the agency costs hypothesis.

7.2 Comparing Private and Public Company Cash Holdings Using Propensity Score Matching

To control for potential sample selection issues, we also employ matching techniques to examine differences in the level of cash ratios across public and private companies.

In an ideal world, these selection concerns would be overcome by designing an experiment that assigns companies randomly to being either public or private. But, the choice to go public is confounded with a number of company characteristics that are likely to be correlated with the level of cash holdings (Brav 2009). As such, we implement propensity score matching to compare the cash holdings of public and private companies after matching them on observable company characteristics.

Specifically, we use ‘nearest neighbour’ matching that uses an average of the cash ratio from the ‘nearest’ private companies to impute an estimated counterfactual cash ratio for each public company. The set of nearest private companies is obtained by estimating the ‘distance’ between pairs of observations with regard to a set of company characteristics. A public company's nearest private company(s) is obtained using a weighted function of the covariates for each observation. In this application the Mahalanobis distance is used, where the weights are based on the inverse of the covariates' variance-covariance matrix. The difference between observed cash holdings and the imputed counterfactual level of cash holdings for each public company then yields an estimate of the selection effect.

For robustness, we use two sets of company characteristics to perform a match: the first uses all of the variables in Equation (2); the second uses SIZE as defined in Equation (2) and industry × year fixed effects. The results are provided in Table 7.

Model (1) is based on the first set of company characteristics and suggests that the average treatment effect (ATE) – the level of cash holdings one would have observed had all companies been public – is 4.6, meaning that the cash ratio would have been 4.6 percentage points higher if all companies were public. Model (2) is based on the second set of company characteristics and suggests the ATE is 2.5 percentage points.

In sum, the evidence from this exercise supports the idea that public companies hold higher levels of cash compared to otherwise similar private companies. This again suggests that agency costs affect corporate cash holdings, with the effects being statistically significant and similar in economic magnitude to the estimates presented earlier.