Financial Distress Costs

cost of financial distress

In Column (2), we exclude firms whose Herfindahl–Hirschman Index (HHI) of RE assets across states is below the median. In Supplementary Appendix Table IA.16, we restrict the sample to suppliers that operate in a single business segment. For these nondiversified suppliers, RE assets are more likely to be located in the county of the headquarters. In Column (1), our measure of RE assets follows Chaney, Sraer, and Thesmar (2012) and is based on the market value of RE in 1993 updated to year t, scaled by total assets, using the HP Index (Market RE).

cost of financial distress

The estimated difference in sales can be plausibly attributed to differences in financial distress across suppliers. Assessments of the trade-off theory have typically compared the present value of tax benefits to the present value of bankruptcy costs. We verify that this comparison overwhelmingly favors tax benefits, suggesting that firms are under-leveraged.

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Bankruptcy costs vary for different types of firms, but they typically include legal fees and, losses incurred from selling assets at distressed fire-sale prices, and the departure of valuable human capital. The way to measure bankruptcy cost is to multiply the probability of bankruptcy by the expected cost of bankruptcy. A company should consider the expected cost of bankruptcy when deciding how much debt to take on. Because this result is at odds with the results in Section 3.5, where we find that clients are more likely to switch when switching costs are low as proxied by geographical distance, we further explore this heterogeneity including the geographical dimension.

However, they apply Andrade and Kaplan’s 10–23% range of proportional losses to the firm’s current value, rather than to value near default from which it was originally estimated. To the extent that a firm is not close to defaulting, this method will overstate the present value of its expected bankruptcy costs. To evaluate a firm’s indirect costs of financial distress, we use the supplier’s leverage as our main measure of financial constraints and the change in sales to each client obtained from the Compustat Segment database. Since information about corporate RE assets is only available in Compustat until 1993, in our main estimations, we use the book value of PPE as a proxy for them. Moreover, the ratio of PPE to total assets is highly correlated with the ratio of the book value of corporate RE assets to total assets, with a correlation coefficient of 0.82. In our main tests, we use the headquarters location as a proxy for the location of a firm’s RE assets.

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In the regression for Column (1), Market Leverage corresponds to a dummy variable containing a one for firms with higher than median market leverage (the ratio of total debt to the market value of assets). In the regression for Column (2), we consider the Kaplan–Zingales (KZ) Index as a summary measure cost of financial distress of financial constraints (Kaplan and Zingales, 1997). In the regression for Column (3), we consider a market-based measure of financial constraints, the Distance to Default, based on Merton’s (1974) bond pricing model and estimated following the naïve approach proposed by Bharath and Shumway (2008).

  • First, we test the prediction that the negative effect of a supplier’s financial distress on a client’s purchases should be more pronounced when the supplier has a lower market share (calculated at the three-digit SIC code level).
  • If, instead, suppliers cut back production, we would expect to see a stronger effect in the sales of the low inventory group than in the high inventory group.
  • In particular, we analyze whether the segment sales data are representative of the total sales of the supplier firm.
  • During our 2000–15 sample period, the sum of reported sales represents, on average, 37% of the total sales (the median is 30%).

This identification strategy is similar to that commonly used in the banking literature to study the impact of bank liquidity shocks in which the comparison is across banks for the same borrower (Khwaja and Mian, 2008). Paravisini, Rappoport, and Schnabl (2020) argue that an additional identifying assumption of this approach is that changes in firms’ credit demand are equally spread across all banks that lend to the firm. In our case, this implies that changes in clients’ purchases are equally spread across all suppliers, which is more plausible when suppliers are from the same industry.

Supplementary data

However, this debt may still be implicitly backed by tangible assets (Rampini and Viswanathan, 2020). An alternative hypothesis in the case of more specific goods is that clients increase purchases to build up inventory for precautionary reasons, or even to bail out a strategic supplier because switching to another supplier is not feasible. PSA Group, the manufacturer of the brands Peugeot and Citroen, agreed to contribute to a rescue plan for the struggling supplier GM&S, which consisted of a purchasing commitment of €60 million (Reuters, July 19, 2017). In this section, we use insights from different theories to examine the heterogeneity of the indirect costs of financial distress. Like all crises, business failure is perceived as a problem only by those who are directly or indirectly the victims and must bear the consequences.

  • Our identification strategy relies on comparing the same client buying from different suppliers each year.
  • In the regression for Column (4), ΔHP is a dummy variable that takes a value of one if the change between years t – 1 and t in HP is lower than the 10th percentile of the distribution (–3.3%), and zero otherwise.
  • Other factors that can affect the probability of financial distress include the quality of a firm’s management and the company’s corporate governance structure.
  • Suppliers with high market share are likely to have more market power and bargaining power, which could allow them to impose higher switching costs on their clients (Klemperer, 1987).
  • These results suggest that the indirect costs of financial distress are driven by clients reducing purchases from distressed suppliers, rather than by suppliers cutting back their supply of products and/or services.

We estimate our baseline specifications for these periods and present the results in Supplementary Appendix Table IA.11. We find that the coefficients of the triple interaction are negative and significant, and more pronounced during these periods. The point at which financial distress costs become significant can be difficult to predict with precision. The Key Development data contain information for doubts on going concern from 2003 onward, and on firm defaults on debt obligations from 2006 onward. We therefore restrict the analyses of these two measures to the years in which these events are nonmissing in the Key Development data.

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In addition to this, finding further suggest that leverage, the level of intangible assets and changes in investment policy have positive while the size of the firm and Tobin’s Q have a significant negative impact on IFDC. Further, this paper argues that the level of tangible assets and liquid assets are statistically unimportant in observing the IFDC for PSX financially distressed firm-year observations. Optimal model selection along with panel data analysis technique is used to select the most optimal model to observe the findings. Financial distress is measure through Altman’s Z-score and firm-specific variables cover leverage, level of intangible assets, investment policy, tangible assets, firm’s size, level of liquid assets and Tobin’s Q of sample firms. Durable goods and manufactured goods typically require post-purchase client service and clients might be concerned that the supplier will get liquidated and will not be able to provide this service.

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Our client–supplier data allow us to include client-by-year fixed effects in Equation (1), which ensure that identification comes from the variation, within the same year, of shocks to real estate across the suppliers of a given client. Client-by-time fixed effects absorb all unobserved heterogeneity at the client level in each period. Thus, concerns that our results are driven by changes in demand that coincide with a decline in local HPs are mitigated.

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For this reason, in some regressions, we further interact the client-by-time fixed effects with supplier industry fixed effects to restrict the variation to suppliers within the same industry. To address the first question, we begin with a benchmark case that considers default (and formal bankruptcy) to be the triggering event for financial distress. That is, for the time being, we follow Andrade and Kaplan (1998), Graham (2000), Molina (2005), and Almeida and Philippon (2007), and ignore any financial distress costs that are incurred prior to bankruptcy. Given such a large discrepancy, it is perhaps unsurprising that our estimates of bankruptcy costs are not large enough to offset the tax benefits of debt, whereas those found by Almeida and Philippon (2007) are. To address this concern, we estimate firm-level (rather than client–supplier level) regressions similar to those for Table IV using the change in total sales as a dependent variable. Supplementary Appendix Table IA.13 reports the results for the full sample of Compustat firms.

Imbens and Wooldridge (2009) recommend focusing on the normalized difference, rather than on the t-statistic for the difference in averages because large samples mechanically lead to large t-statistics. Financial distress in companies requires management attention and might lead to reduced attention on the operations of the company.

The Costs of Financial Distress Across Industries

We thank Eva Steiner for sharing data on the market value of commercial real estate. For consistency with the analysis in Campello et al. (2022), we winsorize this variable at the 5% level. Lian and Ma (2021) show that most of the debt is based on the value of cash flows from the firm’s continuing operations (i.e. going-concern value)—cash flow-based lending (as opposed to asset-based lending).

With this set of main results established, we move to our second set of analyses. Rather than consider only bankruptcy costs experienced near default, we now permit firms to experience debt-related value losses prior to declaring bankruptcy. Here, we still allow firms to experience costs after declaring bankruptcy, but also penalize them for getting sufficiently close to default. Importantly, this allows for firms to enter financial distress when their going-concern value becomes questionable (such as losing key employees or risk-shifting), but well before entering bankruptcy.

cost of financial distress

The coefficients in Columns (1)–(3) are qualitatively similar to those in Table IV. The table also presents estimates of the indirect costs of financial distress for alternative scenarios using different values of the parameters. We find that the indirect costs of financial distress are larger in economic downturns, during an RE crisis, and for firms with a lower market share or selling durable and standardized goods.