Impact of financial leverage on the incidence and severity of product failures:

Evidence from product recalls

Omesh Kinia, Jaideep Shenoyb, Venkat Subramaniamc,*

aRobinson College of Business, Georgia State University, Atlanta, GA 30303

bSchool of Business, University of Connecticut, Storrs, CT 06269

cA.B. Freeman School of Business, Tulane University, New Orleans, LA, 70118

Latest draft: August 2016

Abstract

We study the impact of firms’ financial condition on their ability to produce safer products that result in fewer

recalls. Using a variety of tests, including two quasi-natural experiments that result in exogenous negative

industry cash flow shocks, we find that firms with higher financial leverage or distress likelihood have a greater

probability of a product recall. These firms also experience more frequent and severe recalls. Further, firms

with more debt due at the onset of the financial crisis experienced greater likelihood and frequency of recalls.

We conclude that a firm’s financial condition has real effects that impact product safety.

Keywords: Product failures, leverage, financial distress, product recalls.

We are grateful to David Denis (the Editor) and two anonymous referees for their comments and suggestions that have

improved the paper substantially. We also thank Viral Acharya, Kenneth Ahern, Heitor Almeida, Bernard Black, Patrick

Bolton, Valentin Dimitrov, Todd Gormley, Gregg Jarrell, Simi Kedia, Naveen Khanna, Evgeny Lyandres, Vojislav

Maksimovic, David Matsa, Gordon Phillips, Nagpurnanand Prabhala, Russ Robins, Sakya Sarkar, Paul Spindt, Rick

Swedloff, Shawn Thomas, Sheri Tice, and the seminar participants at the 2013 Conference on Empirical Legal Studies at

the University of Pennsylvania, Philadelphia, the 2013 Finance, Organizations and Markets Conference at the USC

Marshall School of Business, and the 2014 Summer Research Conference at the Indian School of Business for their helpful

comments.

* Corresponding author: Venkat Subramaniam, A.B. Freeman School of Business, Tulane University, New Orleans, LA

70118, USA. Tel: +1-504-865-5493; Fax: +1-504-862-8327.

** E-mail addresses: okini@gsu.edu (Omesh Kini), jaideep.shenoy@business.uconn.edu (Jaideep Shenoy),

vencat@tulane.edu (Venkat Subramaniam).

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Product failures can manifest themselves in many ways and, when severe, can result in the products being

recalled from the marketplace. The recalled products are oftentimes associated with harmful effects such as

injury, sickness, or death, and have received significant scrutiny from the media and various U.S. agencies in

charge of regulating product safety.1 Once a firm determines that a safety defect exists, federal regulations

require that the firm cease selling the product immediately and report the defect to the relevant regulating

agency.2 Existing studies have shown that recalls are costly and the total costs far exceed the costs of replacing

or repairing the defective products. These additional costs can include significant indirect penalties in the form

of lost goodwill, tarnished brand image, lost sales, and product liability lawsuits (e.g., Jarrell and Peltzman,

1985; Hoffer, Pruitt, and Reilly, 1988; Mitchell, 1989; and Barber and Darrough, 1996).

Theoretical research has implications for how the financial condition of a firm can affect its ability and

incentives to invest in initiatives such as product quality and safety enhancements (e.g., Beard, 1990;

Maksimovic and Titman, 1991; and Chevalier and Scharfstein, 1996). Maksimovic and Titman (1991) argue

that firms with high levels of debt face investment distortions because shareholders will find it optimal to cut

discretionary investments, such as those in quality, and increase current cash flows to avoid immediate

financial distress. This increase in current cash flows, however, comes at the expense of long-term benefits to

the firm, but is still optimal from the shareholders’ perspective since the future losses are shared with the

bondholders. The focus on short-term cash flow needs at the expense of discretionary long-term investments

can also arise directly due to financial constraints and the firms’ inability to invest, as seen in the model of

counter-cyclical markups in Chevalier and Scharfstein (1996). Bolton and Scharfstein (1990), Chevalier (1995),

and Phillips (1995) also argue that high leverage will constrain a firm, thereby leading to suboptimal investment.

We classify these theories under the rubric of financial constraint effects of leverage.

1 The U.S. agencies regulating safety for the different types of products are Consumer Product Safety Commission (CPSC),

Food and Drug Administration (FDA), and National Highway Traffic Safety Administration (NHTSA). 2 The Consumer Product Safety Act requires that firms report safety defects immediately to the CPSC. The Federal, Food,

Drug, and Cosmetic Act, which governs FDA related recalls, has similar provisions. Finally, automobiles firms are

required to report safety defects to the NHTSA within five business days of determining that a safety defect exists.

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However, not all theoretical work argues that high leverage leads to lower quality. Beard (1990)

develops a model that shows that shareholders of firms in poor financial condition actually have the incentive

to increase discretionary investments because, with limited liability, shareholders are using “someone else’s

money,” but to improve their own value. Yet another view is presented in Jensen (1986, 1993) who argues that

high leverage imposes discipline on managers and forces them to be more competitive in the product market,

providing them with greater incentives to invest in quality. These arguments, which we classify as the incentive

effects of leverage, indicate that high leverage can cause an increase in quality. Ultimately, however, whether

the financial condition of a firm impacts quality adversely or beneficially is an empirical question. We shed

light on these and related issues by examining the impact of the financial condition of a firm on the incidence,

frequency, and severity of the types of quality failures that lead to product recalls (henceforth recalls).

To conduct the analysis in this paper, we build a comprehensive database on recall campaigns

announced by publicly traded firms during the 2006–2010 period. Specifically, we hand collect data on

consumer recalls from the Consumer Product Safety Commission (CPSC), food, drug and medical device

recalls from the Food and Drug Administration (FDA), and automobile recalls from the National Highway

Traffic Safety Administration (NHTSA). Our final sample comprises of 3,508 recall events spread over 37 (97)

different two-digit (three-digit) SIC industries. As control firms, we include firms that do not have a recall over

our sample period but operate in the same three-digit SIC code as the recalling firms.

Our analysis of the determinants of recalls using probit regression models shows that the financial

position of a firm has a tangible effect on recall incidence. Consistent with the argument that firms in poor

financial condition suffer distortions in discretionary investments that negatively impact quality, we document

a significant positive relation between leverage/distress likelihood and the probability of a subsequent recall.

These results hold not only for the standard leverage measures used in the literature, but are also robust to other

measures including leverage net of cash holdings, debt due in three years, and residual leverage. Further, we

find consistent evidence that firms that experience larger adverse cash flow shocks exhibit a higher incidence

of recalls in the following year. These results suggest that poor financial condition adversely impacts product

quality and increases the propensity for recalls. In addition, we also find that recall incidence is higher if the

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firm operates in a more unionized industry, has a larger number of suppliers, or is bigger in size, while it is

lower if the firm is vertically integrated or invests more in research and development.

To more definitively conclude that the financial condition of a firm affects recall incidence, we

condition our tests on two exogenous negative shocks to industry cash flows – (i) sharp cuts in import tariffs

that increase competition for domestic firms, and (ii) input price shocks that result in exogenous cost increases

for firms’ inputs. If high leverage and likelihood of distress constrain firms from undertaking the investments

necessary to improve product quality, then such constraints should be exacerbated following the exogenous

shocks. That is, we should expect the impact of leverage and distress likelihood on recalls to be more

pronounced for firms affected by these shocks. As a third exogenous shock, we examine the level of debt that

is maturing at the onset of the 2008 financial crisis (Almeida, Campello, Laranjeira, and Weisbenner, 2012).

Since liquidity constraints were especially severe during the crisis, our prior results suggest that we should

expect firms with larger amounts of debt due at the onset of the crisis to cut back more on quality investments

and, therefore, experience greater incidence and higher frequency of subsequent recalls. In addition to the

above quasi-natural experiments, we treat financial leverage as endogenous and perform two-stage least

squares estimation to examine the impact of leverage on recalls. Further, to avoid any spurious

contemporaneous correlation, we use lagged values of the independent variables in all our tests. Using

additional tests we also directly rule out the possibility of reverse causality where recalls result in poor financial

health and not the other way around. All the results indicate that firms in poor financial condition are more

prone to producing lower quality products and, hence, are more likely to have recalls.

Our empirical analysis also shows that the constraints imposed by high leverage and distress likelihood

not only increase the likelihood of a recall, but also increase the likelihood that it is a more severe product

failure. Specifically, we conduct our recall severity analysis on FDA recalls because, unlike the CPSC and

NHTSA, the FDA provides us with an objective measure of severity of product failures based on the level of

potential harm caused by the product. Using ordered probit regressions, we find that firms that have high

leverage or distress likelihood and those that experience adverse cash flow shocks have an increased likelihood

of experiencing a more severe recall. Additionally, the effect of leverage/distress likelihood on severity of

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recalls is more pronounced for firms exposed to the two exogenous negative cash flow shocks compared to

firms not exposed to these shocks. These results further reinforce our prior evidence of a possible causal link

between financial condition and product failures.

We additionally examine the lag with which leverage may have an impact on the incidence of recalls.

In some instances, it is possible that although a product’s manufacturing is compromised because of constraints

imposed by high leverage or distress likelihood, the actual product failure and recall may not occur by the end

of the following year and may only happen a few years later. To address this possibility we perform additional

empirical analysis that can potentially capture the time lag between when financial condition affects product

quality and when the product failure is detected. Our results show that firms with higher three-year average

leverage and distress likelihood have a higher probability of experiencing a product failure, and those with

higher year-2005 leverage and distress likelihood have a higher frequency of recalls over our sample period

(2006-2010). In addition, when we examine the impact of the two-year (year t-2) and three-year (year t-3)

lagged leverage measures individually on recalls (year t), we find them to generally increase the incidence and

frequency of recalls. Notably, in all cases, the marginal effect associated with the one-year lagged measures of

financial condition is higher than the marginal effect associated with any of its longer lags or with the average

of lagged measures of the financial condition variable (years t-1, t-2, and t-3). These findings suggest that our

sample consists of two types of products – (i) where the quality failures are detected by the end of the next

year and (ii) where quality failures take longer to be detected – with the former outweighing the latter in

numbers. These results are also consistent with the view that the financial condition of a firm has a long-term

adverse impact on quality, but the shorter-term impact is generally more pronounced than the longer-term

impact. Finally, we do not find any evidence to suggest that firms report quality defects prior to capital raising

events. This finding suggests that the relation between financial condition and recalls is not a spurious one,

caused merely by the reporting of recalls triggered by due diligence efforts around capital raising events of

financially constrained firms.

Overall, our results are consistent with the premise that firms in poor financial condition either find it

optimal (from a shareholders’ perspective), or are forced due to financial constraints, to reduce discretionary

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investments in quality, thereby increasing the likelihood of recalls (Maksimovic and Titman, 1991; Bolton and

Scharfstein, 1990; and Chevalier and Scharfstein, 1996). Our results are, however, inconsistent with the notion

that poor financial condition increases the incentives of shareholders to make more discretionary investments

in product quality either due to limited liability (Beard, 1990) or because it imposes greater discipline on

managers (Jensen, 1986, 1993) forcing them to be more responsive to product markets.

Our paper makes the following contributions. First, to the best of our knowledge, no other paper has

examined the impact of financial leverage/likelihood of distress or cash flow shocks on product quality failures.

One advantage of studying recalls is that it is an agnostic measure of product failure governed by guidelines

from governmental agencies, instead of being a measure that is influenced by the data collector’s perception

of quality. More importantly, if the financial condition of a firm affects any quality attribute of a product

significantly, it will be reflected in the incidence of recalls. Thus, as researchers, we do not have to ex ante

focus on any one quality attribute that may be affected due to financial pressures.

Second, our study encompasses a broad cross-section of industries and that allows us to generalize the

impact of financial condition on quality outcomes. Existing studies confine themselves to a specific industry

to document the effects of leverage on a particular aspect of quality such as product shortfalls in the

supermarket industry (Matsa, 2011) and baggage mishandling and on-time performance in the airline industry

(Phillips and Sertsios, 2013). In fact, while we find there are substantially more recalls in some industries than

in others, our results hold in each subsample of these recall-intensive industries and also hold in the

complementary subsample of industries which excludes the recall-intensive industries. Third, while no single

test in our analysis may perfectly solve the identification issue, our employment of two exogenous negative

industry cash flow shocks, the impact of debt maturing at the onset of the financial crisis on recall incidence,

the 2SLS regression methodology, and reverse causality tests all together allow us to make a plausible causal

argument linking financial condition to product quality.

Fourth, we document that higher financial leverage and distress likelihood result in more frequent and

severe product failures – a result that reinforces the causal impact of leverage on product quality. Finally, our

analysis of the role of leverage and other firm- and industry-specific factors that affect recalls should be of

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interest to product safety watchdogs such as the CPSC, FDA, and NHTSA in their regulation of product safety

practices. Since these agencies have limited financial and human resources, fine-tuning their inspection

strategies based on these factors can be a more effective way to marshal their limited resources.

The remainder of the paper proceeds as follows. Section 2 provides the theoretical underpinnings for

the relation between the financial condition of the firm and the incidence of recalls. Section 3 describes our

data sources, sample selection criteria, and the salient characteristics of the sample. In Section 4, we present

our main tests that analyze the link between financial condition and propensity for a recall, and also the results

from several robustness tests. In Section 5, we present results from additional tests including alternative

explanations for our findings. Section 6 concludes the paper.

1. Theoretical considerations for recall incidence: Background and hypotheses

In this section, we provide background information on product recalls and theoretical justifications for why

the financial condition of a firm can influence the incidence of recalls.

1.1 Background information on product recalls

Recalls can be attributed to a variety of reasons including design flaws, manufacturing faults, product

contamination, poor packaging, inadequate warnings or instructions, insufficient inspection of raw materials

and parts from an increasingly global supply chain, etc. Industry studies find that most recalls are preventable

through adequate investments in the design, production, and maintenance process. For example, particulate

contamination and incorrect formulation, the two leading causes of recalls in the pharmaceutical industry, are

primarily due to insufficient maintenance of machinery and the use of antiquated manufacturing technology

(Taylor, 2011). In manufacturing industries, low investment in capacity resulting in over-utilization of plants,

and inadequate investment in labor through over-reliance on overtime pay and temporary workers are major

reasons for the recalls caused by manufacturing defects (Shah, Ball, and Netessine, 2016). In the food

processing industry, departing from industry quality standards in order to cut costs is an important determinant

of recalls. In fact, product risk mitigation experts emphasize the need for costly dedicated quality control

systems, investment in achieving certifications in specific world-class standards, and a process of continuous

expenditures on design, engineering, and manufacturing changes to maintain product reliability and safety

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(Connally, 2009). 3 Overall, these studies suggest that although there are discretionary investments and

expenditures in which firms can invest to mitigate the likelihood of recalls, the optimal type and amount of

these investments vary vastly from industry to industry, and it is often more than one type of investment in

most industries. So, in order to develop generalizable inferences across industries, our empirical analysis

focuses on the impact of financial condition of a firm on recalls, which is a direct outcome of compromised

investment in quality.

1.2 Financial condition and the propensity for a product recall

Maksimovic and Titman (1991) develop a model in which shareholders of highly levered firms have the

incentives to cut costs by reducing investments in quality. By doing so, they increase current cash flows to

shareholders at the expense of bondholders who are forced to share the adverse consequences of quality failures

in the future. The main intuition of their model derives from the debt overhang problem articulated in Jensen

and Meckling (1976) and Myers (1977). Specifically, Myers (1977) argues that in highly levered firms there

is underinvestment because the expected residual payoffs to shareholders is lower and, therefore, less likely to

compensate them for their investment. Thus, both the Maksimovic and Titman (1991) and Myers (1977)

models predict that shareholders of highly levered firms have the incentives to forego some investments in

quality even if they create value for the firm as a whole.

High leverage can affect investment in product quality directly from a financial constraints perspective

as well. Bolton and Scharfstein (1990) endogenize financial constraints and show that high leverage makes a

firm financially constrained and, therefore, inhibits the firm’s ability to compete. Chevalier (1995) in her

analysis of product prices in the supermarket industry following high-leverage transactions finds results

consistent with the view that liquidity constraints make firms myopic. She shows that firms “charge short-run

profit maximizing prices after the LBO” compared to the “inter-temporal profit maximizing prices prior to the

LBO.” Chevalier and Scharfstein (1996), using an intertemporal model with switching costs, argue that highly

levered firms may be financially constrained and, hence, forced to cut investment even if the firm has positive

3 See also “Product Recalls: 5 tips to keep your company out of the headlines,”

http://www.arenasolutions.com/blog/post/product-recalls-5-tips-to-keep-your-company-out-of-the-headlines/

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NPV projects. In his analysis of industries with large debt increases, Phillips (1995) finds that under certain

product market environments, financial constraints related to high leverage force a firm towards a low

investment policy either through reduced capital expenditures or plant closings. Matsa (2011) analyzes the

supermarket industry and finds that higher leverage is associated with greater product shortfalls. Phillips and

Sertsios (2013) study the airline industry and find that baggage handling and on-time performance deteriorate

when the airline is in financial distress. These findings are also consistent with prior reports of quality failures

in the food and airline industries that point to financial constraints as a reason for adulterated products and

inadequate maintenance and safety measures (Traub, 1988; Lawrence, 1998; and Acohido, 2000). These

arguments and results suggest that highly levered firms are more likely to have lower investments in quality,

and therefore, a higher propensity for recalls.

A contrary view of the impact of poor financial condition on quality investments may be seen in Beard

(1990) and Jensen (1986, 1993). Beard (1990) models the relation between discretionary investment and

shareholders’ financial position in the presence of limited liability. He shows that due to limited liability, the

investment expenditure of financially weak firms are implicitly subsidized by debtholders and, thus, it is

possible to observe shareholders of financially weak firms actually increasing their discretionary investments

since that provides an opportunity for the firm to emerge out of financial trouble in the future. Jensen (1986,

1993) arrives at similar conclusions, although for entirely different reasons. He argues that managers of highly

levered firms and LBO firms are more disciplined and are especially under pressure to be more attentive to

product market concerns. So, if product quality is an important variable of competition, we would expect these

firms to invest more in quality and reduce the incidence of product failures. Therefore, these latter theories

argue that the propensity for product failures is lower when leverage is high.

2. Data sources, sample selection, and salient characteristics

2.1 Data sources and sample selection

We collect data on product recall campaigns announced during the period January 2006 – December 2010

from three U.S. regulatory agencies that govern product quality and safety – FDA, CPSC, and NHTSA.

Specifically, we collect information on food, drug, and medical device recalls from the weekly enforcement

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reports published by the FDA. We collect information on consumer recalls from CPSC. The CPSC covers a

diverse range of industries such as children’s products, household appliances, heating and cooling equipment,

home furnishings, toys, nursery products, workshop hardware and tools, yard equipment among others. Finally,

we collect information on automobile recall campaigns from the NHTSA. Specifically, we collect information

on the manufacturer of the product, the product being recalled, the number of units recalled, the reason for

recall, and the recall date. Further, to be included in our recall sample, we require recalling firms to be publicly

traded because we need their financial information for our analysis.

Panel A of Table 1 provides a summary of our final sample. It comprises of 3,508 recall events during

the 2006–2010 period. Of these, 687 events are automobile recalls from NHTSA, 2,444 are food, drug, and

medical devices recalls from FDA, and 377 are consumer recalls from CPSC. Our sample consists of recalls

spanning a wide range of industries – specifically, covering industries in 97 three-digit SIC codes and 37 two-

digit SIC codes. Panel B of Table 1 presents the industry break-up of our sample of recalls based on two-digit

SIC codes sorted by the frequency of recalls. Measuring, Analyzing, and Controlling Instruments had the most

recalls (1,148), followed by Transportation Equipment (722), Chemical and Allied Products (672), and Food

and Kindred Products (194). Thus, these 4 two-digit SIC industries (encompassing 29 three-digit SIC

industries) constitute approximately 78% of our overall sample of recalls. Our inferences regarding the relation

between the propensity for a recall and the financial condition of a firm, however, remain unchanged when we

examine recalls in each of these 4 two-digit SIC industries as well as in all other industries separately. These

sub-sample results are reported in Internet Appendix Table 1.

2.2 Stock market reaction to product recalls

Our sample includes recalls with and without popular press coverage. We find that for recalls with news

coverage in publications covered by the Factiva database, the recall dates reported on the CPSC and FDA

websites closely match the date of the first news article in the media. Therefore, we use the recall dates

collected from CPSC and FDA as the event date for all CPSC and FDA recalls. For the NHTSA sample, we

find that the date of the first news article on the Factiva database was the date the safety issue was reported to

the NHTSA (report received date) in the vast majority of the cases. Hence, for the NHTSA sample we use the

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report received date as the event date. Using these dates as event date 0 for all recalls, we compute the market

model cumulative abnormal returns (CARs) and dollar abnormal returns over the (-2, +2), (-5, +5), and (-10,

+10) event day windows.4 We use the CRSP value-weighted market index for the market portfolio. For the

overall sample of all recalls, the (-2, +2), (-5, +5), and (-10, +10) event windows have mean CARs of -0.31%,

-0.63%, and -1.11%, respectively (all significant at the 1% level). These CARs amount to $46.94 million,

$126.58 million, and $256.45 million average abnormal decrease in market capitalization for the recalling

firms. These results suggest that recalls are significant adverse events in the life of a company.

2.3. Salient characteristics of product recall and control firms

Table 2 presents univariate comparisons between recalling and control firms on the basis of financial condition

and other control variables that can impact recalls. Detailed definitions of all these variables are presented in

the Appendix. We measure financial leverage based on book values (Book leverage) as well as market values

(Market leverage). In addition, to control for the possibility that some firms may be able to sustain high

leverage without any adverse effects because they have higher earnings, cash flows, or more liquid assets in

the form of working capital, we use the Altman Z-score (Altman Z) developed by Altman (1968) as a measure

of financial distress likelihood, where firms with lower financial leverage or higher profitability, cash flows,

or liquid assets have a higher Altman Z, i.e., lower likelihood of financial distress.

We use several control variables to control for factors that are plausibly related to the incidence of

recalls. These include Free cash flow shock, Unionization, Number of suppliers, Vertical integration dummy,

R&D intensity, Total factor productivity, Herfindahl index, and Size. To capture recent changes in the cash

flow of the firm, we define Free cash flow shock as the change in the free cash flow of a firm relative to its

mean free cash flow over the prior three years. The cash flows are normalized by total assets of the firm to

control for scale effects. We measure Unionization as the percentage of unionized employees in the firm’s

4 Our choices of event windows are slightly wider than those seen in event studies of other corporate events, but are

consistent with those used for product recalls by Jarrell and Peltzman (1985) and Hoffer, Pruitt, and Reilly (1988). The

reason is twofold. Some recalls are not covered in the popular press in detail, so dissemination of full information may

take longer, while other recalls may be preceded by a few days by news reports of adverse events related to the product

use and, consequently, the impending recall may have been effectively “leaked” to the market earlier than the event date.

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industry, and use this variable to proxy for the firm’s unionization rate. Number of Suppliers is the number of

key suppliers of the firm as identified in the Compustat segment tapes. Vertical integration dummy is an

indicator variable that equals 1 for vertically integrated firms and 0 for non-integrated firms. To capture long-

term investment in quality we compute R&D intensity, which is defined as R&D expenditures over total assets.

We use total factor productivity (Total factor productivity) as a measure of managerial ability. It is calculated

based on the approach outlined in Kovenock and Phillips (1997) and Faleye, Mehrotra, and Morck (2006). We

use the sales-based Herfindahl index of the firm’s three-digit SIC industry to control for industry competition.

Finally, we use the logarithm of market value of equity (Size) as a proxy for firm size.

In Table 2, the univariate statistics on the leverage/financial distress (control) variables for recalling

and control firms are presented in Panel A (Panel B). Control firms are all firms belonging to the same three-

digit SIC industries as the recalling firms, but have not had a recall in any of the years in our sample period.

We perform a t-test to test for the equality of means between the recalling and control firms where we use

robust standard errors that are clustered by firm. We also perform a Wilcoxon rank-sum test to test for the

equality of medians.

In Panel A, we observe that recalling firms have significantly higher Book leverage and Market

leverage than control firms. Specifically, the mean Book leverage (Market leverage) is 0.266 (0.263) for

recalling firms, while it is 0.167 (0.156) for control firms. The mean Altman Z is 4.09 (4.14) for recalling

(control) firms. Note that a lower value for Altman Z is associated with a higher likelihood of financial distress.

The difference in the mean Altman Z between the two groups of firms is, however, not statistically significant.

These univariate results are generally consistent with the notion that firms in poorer financial condition have

an increased propensity for recalls. Further, in Panel B, we find that recalling firms operate in more unionized

industries, have a larger number of suppliers, are bigger in size, have lower R&D intensity, exhibit smaller

total factor productivity, and are less likely to be vertically integrated.

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3. Multivariate analysis of product recall incidence – Main tests

3.1 The impact of leverage/financial distress on the incidence of product recalls: Basic results

We empirically investigate our hypotheses about the factors that impact the propensity for a recall in a cross-

sectional regression setting using probit regression models. Our main specification is:

(1) )ti,

1-ti,β

1-ti,β

1-ti,β

1-ti,β

1-ti,β

1-ti,β

1-ti,β

1-ti,β

1-ti,ββ()1

ti,

9876

543

210

εdummiesindustry and/or Year

Sizeindex Herfindahltyproductivi factor Totalintensity D&R

dummy nintegratio Vertical suppliersof NumberonUnionizati

shockflow cash Freelikelihood istressleverage/D FinancialFlDumProb(Recal

In the above model, the dependent variable, RecallDum is a dummy variable that takes the value 1 if

a recall takes place for firm i in year t. F() is a cumulative distribution function of a standard normal variable.

All independent variables are lagged by one year to help alleviate concerns regarding reverse causality. We

also separately address the issue of reverse causality in more detail in Section 5.3. We use three main proxies

for the leverage/distress likelihood variable in the estimated regressions – Book leverage, Market leverage, and

Altman Z. Note that we also control for the direct effect of cash flow shocks in all the estimated regressions.

The regression results are reported in Table 3. We include year dummies in Columns (1) – (3), year-

and two-digit SIC industry dummies in Columns (4) – (6), and year- and three-digit SIC industry dummies in

Columns (7) – (9). The table reports marginal effects with their p-values in parentheses. These p-values are

based on heteroskedasticity robust standard errors and are clustered by firm. The results reported in all nine

columns indicate that if the firm either has higher leverage or financial distress likelihood, then the likelihood

of a recall is greater. Specifically, we find that there is a significant positive relation between the probability

of a recall and leverage – both book and market leverage – at the 1% level of significance in all the estimated

regressions. Further, we find that the relation between the likelihood of a recall and Altman Z is negative and

significant at the 1% level in the estimated regressions. Note again that a higher value of Altman Z signifies a

lower probability of financial distress. The addition of industry dummies at either the two- or three-digit SIC

levels slightly reduce the marginal effect of Book leverage, but substantially increase the marginal effects of

both Market leverage and Altman Z. These results indicate that there is a positive correlation between leverage

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(or distress likelihood) and the propensity for recalls.5 These preliminary findings are consistent with the

theories that fall under the rubric of financial constraint effects of leverage, but inconsistent with the theories

that we classify under the incentive effects of leverage. While the above positive association can be causal in

nature, it can also be driven by the presence of a latent factor that is correlated with both leverage/distress

likelihood and the incidence of recalls, a possibility that we explore in detail in the next sub-section.

To assess the economic significance of the above results, we examine the change in implied probability

of a recall if the value of the leverage variable in question changes from its 25th to 75th percentile value, while

all the other independent variables take on their mean values. For purposes of brevity, we provide these

numbers only for models where we include industry dummies at the three-digit SIC level (Columns (7) – (9)

of Table 3). We find that as Book leverage changes from its 25th to 75th percentile value, the implied probability

of a recall goes up by 1.68% (Column (7)). This change in implied probability is evaluated relative to the

unconditional probability of a recall of 22.09% (3,296 recall observations from a total of 14,918 observations

in Column (7)). It amounts to a percentage increase of about 7.58% relative to the unconditional probability of

a recall (equal to 1.68/22.09), and results in about 250 additional recalls (0.0168*14,918). Similarly, when

Market leverage changes from its 25th to 75th percentile value, the implied probability of a recall goes up by

3.05% (Column (8)); this amounts to a percentage increase of about 13.81% and translates into about 455

additional recalls. Finally, when Altman Z changes from its 25th to 75th percentile value, the implied probability

of a recall goes down by –1.42% (Column (9)), which is equivalent to a percentage decrease of about 6.45%

and results in a decrease of about 211 recalls.

To provide additional perspective, an alternative, albeit simplistic, way to assess the economic

significance of these results is to use the estimated marginal effects associated with the leverage measures to

assess the impact of a 10% increase in leverage on the number of recalls. For example, the estimated marginal

effect associated with Market leverage is 0.1279 (Column (8)) and, therefore, a 10% increase in Market

5 As a robustness test we also match each recall firm with a non-recall firm that is in the same industry and is closest in

size to it. We then estimate conditional logit models on this size- and industry-matched sample and find that

leverage/financial distress likelihood is significantly positively related at the 1% level to the probability of product recalls.

These results are reported in Internet Appendix Table 2.

14

leverage will result in 191 additional recalls (0.1279*0.10*14,918; where 14,918 is the number of observations

used to estimate the model reported in Column (8)). Thus, it appears that these leverage/financial distress

variables not only have a significant statistical impact, but also have a significant economic impact on the

incidence of recalls.

We also examine the direct impact of cash flow shocks, i.e., the change in the free cash flow of a firm

relative to its mean free cash flow over the prior three years, on recall incidence. We find that there is a lower

propensity for a recall if the cash flow shock is higher, thereby indicating that better cash flow outcomes

alleviate financial constraints faced by firms and, thus, are less likely to restrict investments in quality. This

relation is significantly different from zero at the 1% level in all the reported models. With regard to the other

control variables, we find that a higher degree of unionization in the industry (Unionization) significantly

increases the propensity for a firm to have a recall.6 This result is consistent with the notion that unions offer

protection to workers and, as such, their incentives to enhance quality are diminished because their job risk is

lower. Alternatively, since unions negotiate employment terms including wages and benefits, the fixed claims

against the firm increase, and the firm may make some compromises on quality to manage its overall costs.

In addition, we find the incidence of recalls is higher if the Number of suppliers is greater. This

marginal effect is significant at the 1% level in all the reported models. This result is consistent with the idea

that it is more difficult and costly to coordinate with suppliers and monitor the quality of all its inputs if the

firm sources them from a larger number of suppliers, resulting in poorer quality products. Further, we document

a generally significant negative relation between the probability of a recall and the Vertical integration dummy.

This suggests that vertically integrated firms are able to reduce coordination costs across different layers in the

production process resulting in better quality products. Similarly, we find that the relation between the

probability of a recall and R&D intensity is negative in all the estimated regressions and significant at least at

the 10% level (with the exception of Column (2) where its p-value = 0.112). Thus, if R&D intensity can be

6 It is not surprising that the marginal effects corresponding to the independent variables that are constructed at the industry

level, e.g., Unionization, Vertical integration dummy, and Herfindahl index, get weaker with the inclusion of industry

dummies. This is more so if there is very little time-series variation in that industry variable.

15

construed as a proxy for innovation and/or long-term investments in quality, our results are consistent with the

notion that firms that make such investments are less likely to experience quality failures. In addition, we find

that there is generally a negative relation between the propensity to have a recall and Total factor productivity.

This relation is, however, significantly different from zero only in Columns (1) – (3). If higher total

productivity implies more efficient use of factors of production – labor and capital – in generating sales and,

thus, can be thought of as a proxy for managerial ability, our results are weakly consistent with the view that

we are less likely to observe recalls for firms with more efficient managers. We do not find a consistent relation

between the probability of a recall and the Herfindahl index.7 Finally, we find that larger firms are more likely

to have recalls. This result can be attributed to the fact that larger firms are likely to be more complex

organizations, which produce/sell in higher volumes and, thus, either due to greater coordination problems or

for purely mechanical reasons are more likely to experience quality failures.

3.2 Is the relation between firm leverage/financial distress and product recall incidence causal in

nature?

In this section, we perform additional tests to more definitively conclude that financial leverage and distress

likelihood affect the propensity for a recall. The results in Table 3 may be driven by financial leverage proxying

for some factors such as inefficient management or weak industry conditions that are correlated with low

product quality, and hence, also with recall incidence. We partly address this issue by including (i) control

firms that are from the same industry as the recalling firms but do not have recalls themselves, (ii) proxies for

managerial efficiency and other control variables, and (iii) industry and time dummies in our regressions. To

address the possibility that there may nevertheless be some missing latent factor that is correlated with both

leverage and recalls and not fully captured by any of the above mechanisms, we perform additional tests to

7 This result may be because two countervailing effects of competition are both present in the data, and generally offset

each other. Specifically, Chamberlin (1933) and Abbott (1955) argue that firms with greater market power have the ability

to reduce product quality to maximize their profits. Along similar lines, Pakdl and Aydin (2007) argue that firms operating

in more competitive industries face greater market pressures to make necessary investments in physical and labor capital

to reduce their chance of product quality failures. In contrast, Donnenfeld and Weber (1992) argue that dominant firms

invest more in quality as a way to differentiate their products, and Karaer and Erhun (2015) show that when quality is a

variable of competition, over-investing in quality is an entry deterrence tool to prevent an increase in competition.

16

establish the causal link between leverage and recalls. First, we identify three exogenous shocks that serve as

negative financial shocks either to all the firms in an industry or to a given subset of firms. We then examine

the impact of these shocks on the relation between financial condition and recall incidence. Second, we utilize

a 2SLS methodology to account for the possibility that some missing latent factor, e.g., managerial ability, can

impact both leverage and recall incidence.

3.2.1 Quasi-natural experiments

Recent research that examines the impact of financing on product market outcomes addresses endogeneity

concerns by considering shocks to either the competitive environment or financial market conditions (see, e.g.,

Fresard, 2010; Campello, 2003; Khanna and Tice, 2000; and Zingales, 1998). We consider three largely

exogenous shocks – (i) a significant reduction in tariffs, (ii) a large increase in industry input prices, and (iii)

the percentage of a firm’s debt maturing at the onset of the recent financial crisis.

The first two shocks will have an impact on the cash flows of firms in the industry, but the impact will

be felt disproportionately by firms with higher leverage. Specifically, if our earlier results are robust, these

firms will be forced to cut costs and substantially reduce product quality to avoid financial distress. Thus, we

expect the relation between leverage and recall incidence to be stronger for firms impacted by the adverse cash

flow shock relative to firms unaffected by the shock. If firms with high leverage/distress likelihood are not

actually faced with greater constraints that impact their product quality, then these exogenous shocks should

not have any incremental impact on the relation between leverage and likelihood of recalls. Our empirical

strategy to exploit these two exogenous shocks is to estimate the probit regression model detailed in Equation

(1), but with the addition of the shock variable and the interaction between the shock variable and the specific

financial leverage/distress likelihood variable. Specifically, we estimate the following probit model:

17

(2) )ti,

1-ti,11β

1-ti,β

1-ti,9β

1-ti,8β1-ti,β

1-ti,6β1-ti,β

1-ti,4β1-ti,1-ti,3β

1-ti,2β1-ti,1β0β()1ti,

10

75

εdummiesindustry and/or Year

Sizeindex Herfindahltyproductivi factor Total

intensity D&Rdummy nintegratio Vertical suppliersof NumberonUnionizati

shockflow cash Freelikelihood istressleverage/D Financial*dummy Shock

likelihood istressleverage/D Financialdummy ShockFDumlProb(Recal

Note that the shock dummy in the above equation is measured with a lag relative to the recall year. Given our

prior findings, we expect the coefficient on the interaction term Shock dummy*Financial leverage/Distress

likelihood to be positive (i.e., 3 > 0).8

In regards to the third shock, Almeida, Campello, Laranjeira, and Weisbenner (2012) argue that debt

maturing at the onset of the financial crisis can be considered to be a negative financial shock because of the

liquidity constrained environment during the crisis. More importantly for our purpose, the percentage of a

firm’s debt maturing at the onset of the financial crisis can be treated as exogenous if the crisis was

unanticipated when the maturity structure of the firm’s debt was established. We exploit this feature to test

whether the debt maturing at the onset of the financial crisis increases the subsequent propensity of a recall

(recall frequency). Specifically, we estimate the following probit (Poisson) model to test our prediction:

(3) )ti,

1-ti,

β1-ti,

β

1-ti,β

1-ti,β

1-ti,β

1-ti,41-ti,31-ti,β

1-ti,1β0β()ti,

98

765

2

εdummiesIndustry Sizetyproductivi factor Total

intensity D&Rindex Herfindahldummy nintegratio Vertical

suppliersof NumberonUnionizati shockflow cash Free

crisis of onset at due DebtFrecall of (Frequency 1)ti,

lDumProb(Recal

8 It may also be instructive to examine whether these adverse shocks have a direct impact on product quality and recall

incidence in firms exposed to these shocks, irrespective of firm leverage. Our probit regression specifications may not,

however, be the best setting to draw inferences about the main effects of the shocks. This is because the dummy variable

that represents shock industries in the probit regressions would not be able to discriminate very well between recalling

and control firms as both would have been exposed to the same industry shock. In order to examine the direct impact of

shocks on recall incidence we create a new dependent variable, which is the number of recalls in an industry. Then, using

a Poisson regression specification, we examine whether industries exposed to these shocks have a higher frequency of

recalls compared to the frequency of recalls in industries not exposed to these shocks. We find that this is indeed the case.

These results are presented in Internet Appendix Table 3.

18

If our prior results are robust, we expect RecallDum (Frequency of recalls) to be positively related to the

percentage of debt maturing at the onset of the financial crisis (i.e., 1 > 0).9

3.2.1.1 Significant cut in industry tariff rates

The first shock that we consider is a significant cut in industry tariff rates. A significant decrease in the tariff

rate will increase foreign competition and, as such, represent a cash flow shock to all domestic firms in the

industry. Fresard (2010) shows that import penetration, one measure of realized competition, goes up

significantly in the year in which tariffs are reduced and continues to exhibit an increase in the subsequent

years. In our sample, we further find that industry profitability decreases around tariff reductions, suggesting

that this indeed is an adverse cash flow shock to the domestic firms in the industry. Specifically, we find that

the average change in industry ROA for treatment industries, i.e., industries that experience tariff shocks, is –

1.96% (t-statistic = –1.55), the average change in industry ROA for control industries, i.e., industries that did

not experience tariff shocks and whose revenues are within 10% of the treatment industries, is 0.89% (t-statistic

= 1.07), and the difference-in-differences is –2.86% (t-statistic = –2.05; significant at the 5% level).

To measure significant reductions in the tariff rate, we follow an approach similar to that described in

Fresard (2010). Specifically, we first obtain U.S. import data from the United States International Trade

Commission website. In particular, we download the annual values for Calculated Duties and Imports by

Custom Value by the four-digit NAICS industry over the 1997 – 2010 period.10 The tariff rate for an industry-

year is computed as Calculated Duties divided by Imports by Custom Value. Next, we compute the annual

percentage change in the tariff rate for each industry-year observation. We then measure the median level of

the annual percentage change for each industry across all years. Finally, an annual percentage drop in tariff

rate for any industry-year is considered a significant tariff cut if it was at least 2.0 times the industry median

level (in alternative specifications we also use thresholds of 2.5 and 3.0 times).11 Furthermore, to ensure that

9 In the case of the Poisson model, F() is an exponential function of the independent variables. 10 Calculated Duties represents the estimated import duties collected which in turn are based on the applicable rate(s) of

duty as shown in the Harmonized Tariff schedule. Imports by Custom Value is the value of imports as appraised by the

U.S. Customs Service. (Source: http://dataweb.usitc.gov/). 11 We slightly modify our procedure in cases where the industry median level is a positive number as these instances

would fail to register any tariff reduction as a tariff cut. In particular, in industries where the industry median was positive,

19

large tariff cuts reflect permanent rather than transient changes in tariffs, we exclude tariff cuts if there was a

comparable large percentage increase in the tariff rate the following year. We create an indicator variable Tariff

cut set to 1 for an industry-year that recorded a significant drop in tariff rates, and is 0 otherwise. Overall, the

variable captures exogenous changes in product market competition and an associated cash flow shock to the

industry. In our sample of recalling and control firms, we find that 30.30% of the industry-year observations

and 19.13% of firm-year observations experience tariff cut shocks.

The results from the estimated probit regressions are reported in Table 4. Note that the sample size is

smaller in this table because information to compute tariff rates is only available for manufacturing industries.

In the interests of brevity, we only report models that include industry dummies as the results are qualitatively

similar even when we exclude them. The financial leverage/distress likelihood variable is Book Leverage in

Column (1), Market Leverage in Column (2), and Altman Z in Column (3). In the estimated probit regressions

in Columns (1) and (2), the marginal effect related to the interaction term between Tariff cut and either Book

leverage or Market leverage is significantly positive at the 1% level. For example, the marginal value on the

interaction term, Tariff cut * Book leverage is 0.1430 in Column (1), and is significant at the 1% level. The

marginal effect related to the interaction term between Tariff cut and Altman Z is significantly negative at the

10% level. We, thus, find that the impact of leverage/financial distress on the propensity for a recall is

significantly stronger when the firm is faced with an exogenous negative cash flow shock arising from a large

decrease in import tariffs. Thus, this difference-in-differences estimation procedure is consistent with the

causal impact of leverage/distress on the propensity for quality failures.

A caveat is in order here. To the extent that lowering import tariffs increases competition and that

industry competition can have its own direct impact on the incidence of recalls, we need to view the results

from this quasi-natural experiment with some caution. In footnote 7, however, we note that different studies

argue that a more competitive environment can lead to either more or less investment in product quality. In

addition, in all our base results that include industry dummies, we do not find a consistent significant relation

an annual percentage drop in tariff rate for an industry-year is considered a significant tariff cut if it was at least 2.0 times

the industry median level in absolute terms.

20

between the incidence of recalls and Herfindahl index. Thus, while we have no reason to believe that that the

above natural experiment results are biased in any given direction, we do note a potential limitation of this

experiment is that the concurrent increase in industry competition makes the results harder to interpret.

3.2.1.2 Negative input price shocks

The second shock that we examine relates to a large increase in input prices. As with significant reduction in

tariff rates, a sharp increase in input prices also represents a cash flow shock to firms in the recalling industry.

To compute significant input price increases, for each recalling industry in our sample, we identify the five

supplier industries that the recalling industry is most heavily reliant upon to manufacture its output. We use

the 2002 benchmark input-output tables of the U.S. economy published by the Bureau of Economic Analysis

to identify the supplier input-output (IO) industries. We then obtain supplier industry prices using the Producer

Price Index (PPI) constructed by the Bureau of Labor and Statistics. The PPI reflects price movements for the

net output of producers at the industry level. Monthly PPI data are adjusted for inflation by the Gross Domestic

Product deflator to obtain the real PPI (RPPI). Data at the six-digit NAICS level are matched to the 2002 IO

industries. When finer data is not available, four-digit NAICS level data are matched to IO industries.

For each recall and control observation in our sample we use the year and month of the fiscal year end

at which we measure leverage (year t-1 in Equation (2)) as our reference date. We obtain 60 months of monthly

RPPI ending in the reference date for the five supplier industries associated with each firm-year in our sample.

These five supplier monthly RPPI series are then aggregated into a weighted RPPI series where the weights

are the relative importance of each supplier industry to the downstream industry. The monthly weighted RPPI

are averaged into annual Weighted RPPI Series for each of the five years before the reference date. Our input

price shocks are based on the Weighted RPPI Series. In particular, we create an indicator variable Negative

input price shock dummy which is set to 1 if the geometric average growth rate over the three years before the

recall is 5% or greater, and 0 otherwise.12

12 As an alternative, we define a similar input price shock using a five-year window as well. The results using the five-

year window to compute input price shocks are qualitatively similar, and are reported in Internet Appendix Table 4.

21

We find that 4.50% of the industry-year observations and 6.39% of firm-year observations experience

input price shocks. To examine whether this shock is exogenous to industries affected by the shock, we analyze

the revenue growth for treatment industries (industries that experience input price shocks) relative to control

industries (industries that did not experience input price shocks and whose revenues were within 10% of the

treatment industries). We find that there is no significant difference in the revenue growth between these two

groups in the one-year (-2.10%; t-statistic = -0.46) and two-years (5.41%, t-statistic = 1.39) before the shock

year. This result suggests that the input price increase in our sample originates from a shock to supplier

industries and is not induced by increased demand in the downstream firms and, therefore, appears to be

exogenous to the treatment industry. We also examine the change in recalling industry profitability around

input price shocks. We find that the average change in industry ROA around input price shocks for the treatment

industries is –2.27% (t-statistic = –1.44), the average change in industry ROA for the control industries is 1.78%

(t-statistic = 1.41), and the difference-in-differences is –4.05% (t-statistic = –2.82; significant at the 1% level).

Thus, the decrease in industry profitability around input price shocks suggests that it is an adverse cash flow

shock to the firms in the industry.

The results from this analysis are reported in Table 5. The structure of this table is identical to that of

Table 4 with the exception that our focus is now on the impact of input price shocks, rather than tariff cuts, on

the relation between firm leverage/financial distress and the propensity for a recall. In the estimated probit

regressions in Columns (1), the marginal effect related to the interaction term between Negative input price

shock dummy and Book leverage is significantly positive at the 10% level. The marginal effect related to the

interaction term between Negative input price shock dummy and Market leverage in Column (2) is, however,

not significantly different from zero. Finally, the marginal effect related to the interaction term between

Negative input price shock and Altman Z in Column (3) is significantly negative at the 1% level. Here too we

generally find that the impact of leverage/distress likelihood on the propensity for a recall is significantly

22

stronger if the firm is faced with an exogenous negative cash flow shock. Thus, this estimation procedure is

also consistent with the causal impact of leverage/distress likelihood on the propensity for recalls.13

3.2.1.3 Percentage of debt maturing at the onset of the 2008 financial crisis

Almeida, Campello, Laranjeira, and Weisbenner (2012) provide the motivation for the third shock that we

exploit in our analysis. Specifically, we examine whether the percentage of a firm’s debt maturing at the onset

of the financial crisis is related to both the subsequent propensity and frequency of recalls. The results from

this analysis are reported in Table 6. This table presents probit regression models in Columns (1) – (3) with

the recall dummy as the dependent variable, and Poisson regression models in Columns (4) – (6) with the

frequency of recalls for the firm as the dependent variable.

The sample for these tests is firms that have a 2007 fiscal year-end months in September, October,

November, December, or January. We follow Almeida, Campello, Laranjeira, and Weisbenner (2012) by

defining percentage of a firm’s debt maturing during the financial crisis (Debt due at onset of crisis) as debt

due during the next year (Compustat item DD1) divided by the sum of debt due during the next year (Compustat

item DD1) plus long-term debt (Compustat item DLTT) for firms that have 2007 fiscal year-end months as

identified above. The dependent variable Rcldum08 is set to one if the firm has a recall in the period February

2008 to December 2008, and zero otherwise. In a similar vein, Rcldum08-09 (Rcldum08-10) is set to one if the

firm has a recall in the period February 2008 to December 2009 (February 2008 to December 2010), and zero

otherwise. The dependent variable #Recalls08 is the number of recall events for a recalling firm during the

period February 2008 to December 2008. Similarly, #Recalls08-09 (#Recalls08-10) is the number of recall

events for a recalling firm in the period February 2008 to December 2009 (February 2008 to December 2010).

These variables equal zero for control firms because they do not have any recalls. All independent variables

are measured as of the fiscal year end month in year 2007.

13 We conduct falsification tests for the industry tariff cuts and input price shocks by randomly assigning an industry

shock year to one of the non-shock years. If the relation between financial condition and recall incidence is not spurious,

then we should find the coefficient on the interaction term between financial condition and the reassigned industry shock

year dummy to be insignificant. As reported in Internet Appendix Table 5, we find that this is indeed the case, thereby

suggesting that our findings are not likely to be spurious.

23

In Columns (1) – (3) of Table 6, we find that the likelihood of a recall over all the three periods

described above is significantly positively related to Debt due at onset of crisis at the 5% level. Further, in

Columns (4) – (6), we find that the frequency of recalls over each of these three periods is also significantly

positively related to Debt due at onset of crisis at the 1% level. Thus, this setting provides yet another

identification strategy to help us separate the effects of leverage on recalls from alternative explanations based

on omitted characteristics such as firm quality or managerial ability, and suggests a causal effect of leverage

on recalls.

3.2.2 Instrumental variable approach

The relation between financial leverage and recall incidence documented in Table 3 can be due to a missing

latent factor that affects both leverage and the propensity for a recall. One approach to address this issue would

be to estimate firm fixed effects regressions, which would control for time invariant firm-specific factors that

affect both the financial position and the recall propensity of the firm. However, firm fixed effects regressions

are not possible in our setting because the data in this study do not comprise a panel since our control firms are

firms from the same industry as the recalling firms, but have not had a recall during the five-year sample period.

That is, recall firms do not enter the control group, and vice-versa, so there is no within-firm variation in the

recall/control status of the firms in the study.14

Another approach to address the possibility that both leverage and recall incidence are endogenously

determined by a missing latent factor is to use a 2SLS estimation framework. This approach would control for

any time varying latent factors that affect both the firm’s leverage and recall likelihood. In the 2SLS method,

we instrument for the specific leverage-related variable in the first stage and use a linear probability model in

the second stage to model the propensity for a recall. We employ the average market leverage of firms that are

geographically proximate to the given firm as an instrument in our 2SLS estimation. The use of local firm

14 If we classify the recalling firms as control firms in those years where they do not have a recall, we would have induced

some time variation in recall status within the set of recalling firms, thereby allowing for firm fixed effects estimation.

The power of these tests, however, is low since the sample size drops by almost 90% as all the original control firms drop

out in the fixed effects regression because these firms do not have a recall during the entire sample period, i.e., they never

change their recall status. Further, we find that there is a high degree of serial correlation in the key explanatory variables.

In such instances firm fixed effects are not recommended (Zhou, 2001).

24

leverage as an instrument is predicated on the fast growing literature that documents that there are geographic

patterns in a variety of corporate policies such as compensation plans (Kedia and Rajgopal, 2009), dividend

policies (Graham and Kumar, 2006), and financing practices (Loughran, 2008; Gao, Ng, and Wang, 2011). In

particular, the findings in Gao, Ng, and Wang (2011) suggest that non-economic factors such as local culture

and social interactions amongst executives also influence capital structure decisions of firms located in the

same geographical area. We, therefore, use Local firm leverage, the average market leverage of all firms

located within 250 kilometers of the headquarters of the given firm (excluding the firm and its industry rivals)

as an instrument for financial condition.15 We believe this instrument is likely to meet the exclusion restriction

as local leverage (especially given that it is based on firms from other industries) should not have a direct

impact on a firm’s product quality. Finally, we employ exactly-identified specifications because it is difficult

to find good instruments and the benefits of instrument validity tests are unclear (Roberts and Whited, 2011).

The results from our analysis are reported in Table 7. In this table, we present coefficients from OLS

regressions in Columns (1) – (3) and the coefficients from 2SLS regressions with market leverage of

geographically proximate firms as the instrument in Columns (4) – (6). We use Book Leverage, Market

leverage, and Altman Z as the measure of financial leverage/distress likelihood in the first, second, and third

columns in each of the two sets of regressions, respectively. We provide OLS regression results to illustrate

that our linear probability model results are similar is spirit to what we presented earlier in Table 3 using probit

regression models. Specifically, in these regressions, we find Book Leverage (Column (1)) and Market

Leverage (Column (2)) are significantly positively related, while Altman Z (Column (3)) is significantly

negatively related to the propensity for recalls (all at the 1% level).

In the 2SLS regression models, we find that our local leverage instrument is significant at the 1% level

in the first-stage regression and the F-statistic for relevance is highly significant in all three estimated models

(Columns (4) – (6)). Further, in all models, the Kleibergen-Paap rk LM test statistic rejects the null hypothesis

15 We also define local leverage based on all firms (excluding the firm and its industry rivals) headquartered in the same

metropolitan area that is geographically closest to where the firm is headquartered. We consider the top 50 populated

metropolitan areas in the United States based on the Population Estimates Program of the U.S. Census Bureau. We find

qualitatively similar results under this alternate definition. The results are provided in Internet Appendix Table 6.

25

that the equation is under-identified indicating that the chosen instruments are highly correlated with the

endogenous regressors. Our test for endogeneity indicates that the use of the 2SLS approach is appropriate.

The coefficients on all the leverage-related variables remain statistically significant (at least at the 5% level)

in the second stage regression and in the predicted direction. For example, in Column (4), the coefficient on

Book leverage is 2.1759, and is statistically significant at the 1% level. Thus, our instrumental variable

estimations provide support for a causal link between leverage/distress likelihood and recalls.

3.3. Robustness tests with alternative measures of leverage

In robustness tests, we use debt net of cash holdings, Book net debt and Market net debt, as additional proxies

for leverage (see, e.g., Opler, Pinkowitz, Stulz, and Williamson, 1999; and Bates, Kahle, and Stulz, 2009). To

the extent corporate cash holdings can be thought of as liquid assets that ease the financial pressure on firms,

a more tangible measure of leverage is the debt of the firm net of its cash holdings. Specifically, we define

Book net debt as book value of debt minus cash over book value of assets and Market net debt as book value

of debt minus cash over the sum of book value of debt and market value of equity. The results for these

robustness tests are presented in Internet Appendix Table 7. The measure of leverage is Book net debt (Market

net debt) in the first column (second column). We find a significant positive relation between the propensity

for a recall and both measures of net debt (significant at the 1% level in both cases). Thus, the results with net

debt are consistent with the earlier findings using our primary measures of leverage/distress likelihood.

Additionally, based on the findings in Almeida, Campello, Laranjeira, and Weisbenner (2012), we also

use market and book measures of leverage based on debt due in three years (Debt due in three years). If the

weak financial condition of a firm has an adverse impact on quality, then for firms with more short-term debt

there is even less flexibility to allow for long-term development of projects, and arguably more pressure to cut

corners on product quality to generate immediate cash flow to take care of its obligations. As with measures

of leverage based on total debt, we find a significant positive relation between the propensity for a recall and

short-term book and market leverage ratios (significant at the 1% level in both cases). These results are reported

in Internet Appendix Table 8.

26

As yet another proxy, we use excess leverage instead of raw leverage as the measure of financial

strength. This measure isolates the idiosyncratic leverage choice of firms net of other factors (such as size, age,

return on assets, asset collateral value, etc.) known to affect leverage. Excess leverage is estimated as the

residual from the model of the economic determinants of leverage in Kale and Shahrur (2007). Specifically,

the independent variables in the estimated models of leverage are Return on assets, Asset collateral value,

Ln(Assets), R&D intensity, Selling, general, and administrative expenses, Tobin’s Q, Herfindahl index,

Industry cash flow volatility, and Non-debt tax shield. Internet Appendix Table 9 provides a description of

these variables and the coefficient estimates from OLS regressions models that estimate the relation between

our leverage-related measures and these determinants of leverage. In Internet Appendix Table 10, we examine

whether excess leverage impacts the incidence of recalls using probit regression models. We find that Excess

book leverage and Excess market leverage are significantly positively related to the incidence of recalls at the

10% and 1% level, respectively, while Excess Altman Z is significantly negatively related to the incidence of

recalls at the 1% level. These results suggest that it is the firm’s idiosyncratic leverage choices that affect the

incidence of recalls rather than factors such as size, industry structure, profitability, growth opportunities,

industry, etc.

4. Additional tests

4.1 Impact of leverage/financial distress likelihood on the severity of product recalls

In this section, we examine the impact of leverage/financial distress likelihood on the severity of recalls using

ordered probit regression models. We limit this analysis to FDA recalls because neither CPSC nor NHTSA

classifies recalls by their severity. For example, some of the reasons provided in NHTSA recalls are risk of

injury, fire hazard, programming error, loss of steering control, airbag inflator problems, steering defect,

automatic transmission defect, etc. In the case of CPSC recalls, some of the reasons provided are burn hazard,

choking hazard, strangulation hazard, injury hazard, fire hazard, electrical shock hazard, violation of lead paint

standard, etc. We are, however, unable to come up with an objective and defensible classification scheme for

recall severity based purely on recall descriptions. The FDA sample is different because the agency itself

classifies recalls into one of three classes based on the severity of the product failure, where Class I recalls are

27

considered most severe, likely to “cause adverse health consequences or death,” Class II recalls likely to “cause

temporary or medically reversible adverse health consequences,” and Class III recalls being the least severe.16

The results from our analysis are reported in Table 8.

The key independent variables are Book leverage, Market leverage, and Altman Z in Models 1, 2, and

3, respectively. In Models 4 – 6 (Models 7 – 9), we additionally interact these variables with Tariff cut

(Negative input price shock). In general, we find that firms with high leverage/distress likelihood have an

increased probability of experiencing a more severe recall, and that this effect is significantly stronger for firms

exposed to the two exogenous negative cash flow shocks. For example, the coefficient associated with Book

leverage in Column (1) is 0.2115 (significant at the 5% level). Further, the coefficient associated with the

interaction of Book leverage with Tariff cut in Column (4) is 1.0184 (significant at the 1% level) and the

coefficient associated with the interaction of Book leverage with Negative input price shock in Column (7) is

1.0083 (significant at the 10% level). Thus, it appears that the constraints imposed by high leverage/distress

likelihood also increase the severity of a recall, thereby providing additional support for a possible causal link

between financial condition and quality failures.

4.2 The lag between cutting investments in quality and eventual product recall

In all the analysis reported thus far in the paper, we only use a lag of one-year between leverage/distress

likelihood and the recall. We are, thus, assuming that the impact of cutting discretionary investments in quality

results in recalls before the end of the next year. However, for certain products, several years can elapse

between when investments that affect quality are undertaken and when the problem with the product is

detected. The problem is that we do not have a reasonable way to determine the appropriate lag between the

reduction in quality and the eventual recall because it is likely to be specific to each product. We shed light on

this issue in two different ways.

16 In an attempt to validate FDA’s classification scheme, we examine the announcement-period wealth effects for FDA

recalls by severity of recall. We find that the CARs over the (-5, +5) day window are -2.06%, -0.89%, and -0.29% for

Class I, Class II, and Class III FDA recalls, respectively. Thus, it appears that the market’s perception of severity of a

FDA recall is consistent with FDA’s classification.

28

First, we examine the effect of different lags of our leverage/financial distress variables on the

propensity for a recall. The results from this analysis are reported in Internet Appendix Table 11. All the control

variables in the estimated probit regressions are the same as in Table 3. We focus on lagged values of Book

leverage, Market leverage, and Altman Z in Panels A, B, and C, respectively. In each panel, Column (1)

includes only the financial condition variable for year t-1 for reference purposes (the results reported in this

column are the same as those reported in the equivalent column in Table 3, Column (2) only includes the

average of the financial condition variable for the years t-1, t-2, and t-3, Column (3) includes individually the

lagged values of the financial condition variable for all three years, t-1, t-2, and t-3, Columns (4) and (5) include

the financial condition variable for the years t-2 and t-3, one at a time, respectively.

In this analysis, we find a significantly positive relation between the three-year average

leverage/financial distress likelihood and the incidence of recall. Specifically, the marginal effect associated

with the three-year average Book leverage, Market leverage, and Altman Z is significant at the 10%, 1%, and

1% level, respectively. These marginal effects, however, are smaller in magnitude than when we only include

the value of the financial condition variable in year t-1. When we include the lagged values of the

leverage/financial distress variables for years t-1, t-2, and t-3 separately (Column (3) in each panel), we find

that the marginal effect associated with the first lag is significantly positive at the 5% level for Book leverage,

significantly positive at the 1% level for Market leverage, and significantly negative at the 1% level for Altman

Z. These marginal effects either are larger or similar in magnitude to the case when we only included the first

lag of the financial condition variable (Column (1) in each panel). The marginal effects associated with the

longer lags of the financial condition variables, however, tend to be insignificant. Thus, for the average product,

the sensitivity of the likelihood of a recall to the three-year average value of the financial condition variable

appears to be driven by its first lag. Even when we re-estimate the probit regression models with only the

second lag (Column (4)) and the third lag (Column (5)) of the financial condition variable, we find that longer

lags of the financial condition variables have a weaker impact on recall incidence. These findings suggest two

possibilities that are not mutually exclusive: (i) our sample of recalls is predominantly made of products where

quality failures manifest and are detected within the end of the next year after production, and (ii) the financial

29

condition of a firm does have a long-term adverse impact on quality, but the shorter-term impact is generally

more pronounced than the longer-term impact. Our inferences are unchanged when we consider the frequency,

instead of the incidence, of recalls as the dependent variable (see Internet Appendix Table 12).

The second approach we employ is to collapse each firm in our sample into a single observation by

counting the frequency of recalls for the firm over our entire sample period. In this method, each independent

variable is measured based on firm characteristics at the beginning of our sample period (in year 2005). We

then estimate the relation between the frequency of recalls over our entire sample period and the leverage-

related variables measured at the beginning of our sample period using Poisson regression models. This

approach allows for leverage to impact recalls over a period of up to five years (the time length of our sample).

The results from this analysis are reported in Table 9. We find that Book leverage (Altman Z ) is significantly

positively (negatively) related to the frequency of recalls at the 5% level, and Market leverage is significantly

positively related to the frequency of recalls at the 1% level. Thus, if frequency of recalls can be viewed as

another dimension of product quality failures, perhaps indicative of a systemic deficiency in the firm, the

results reported in Table 9 (and those in Internet Appendix Table 12) reinforce our prior finding that weak

financial condition constrains discretionary investments and exacerbates product quality problems.

5.3 Alternative explanations for findings

5.3.1 The impact of leverage/financial distress on the incidence of product recalls: Reverse causality

tests

To partially address the issue of reverse causality, where the poor financial condition of a firm is a result of

recalls rather than a cause of it, we use lagged values for the leverage/financial distress variables in all the

regressions estimated in Table 3. Additionally, if leverage has a causal impact on the incidence of recalls, and

our results are not driven by reverse causality, then future leverage should be unrelated to the propensity for a

recall once we include lagged leverage in the regression specification. Thus, to further alleviate concerns about

reverse causality, we conduct falsification tests by also including lead leverage/financial distress (year t+1)

variables in all the regressions specifications reported in Table 3. The results from this analysis are reported in

Internet Appendix Table 13.

30

There are two main findings from this analysis. First, with the inclusion of the lead leverage/financial

distress variables, the marginal effects related to lagged leverage/financial distress variables systematically go

up relative to equivalent specifications in Table 3. For example, the marginal effects associated with Book

Leverage in Columns (1), (4), and (7) are 0.0622, 0.0596, and 0.0579, respectively in Table 3, while they are

0.0989, 0.0772, and 0.0790, respectively in Internet Appendix Table 13. Second, consistent with the argument

that our results are not driven by reverse causality, the marginal effect associated with Lead Book leverage in

Columns (1), (4), and (7) and Lead Market Leverage in Columns (2), (5), and (8) are all statistically

insignificant. The marginal effects associated with Lead Altman Z are significantly positive, i.e., smaller

likelihood of future financial distress is associated with greater propensity for recalls, and run counter to the

reverse causality argument. Overall, these falsifications tests do not support the reverse causality argument.

We also address the issue of reverse causality in another way, by estimating regressions where the lead

leverage/financial distress variable (year t+1) is the dependent variable and the recall dummy (in year t) is the

main independent variable. In these regressions, we control for three lagged values of the leverage/financial

distress variable (t-1, t-2, and t-3) and other determinants of leverage/financial distress used in Kale and

Shahrur (2007) (measured as of year t). In these tests, the marginal effect of the recall dummy (RecallDum) is

never significantly different from zero. Thus, it does not appear that recalls lead to higher leverage/financial

distress. For reasons of brevity, these results are not reported in the paper. They are, however, reported in

Internet Appendix Table 14.17

5.3.2 An alternative explanation for the timing of recalls

It is possible that for some products defects are detected over time (through accidents and other adverse

consumer experiences) and the stock market incorporates this information and the firm suffers financially. This

17 As another direct test of reverse causality, we also examine the change in leverage/financial distress subsequent to a

recall event using a propensity score matching approach. We examine whether leverage increases following a recall when

compared to other similar, but non-recalling firms. We use two different sets of control firms. For each recalling firm, we

match on all the attributes used in Table 3 (other than financial condition variables) to identify the best four matches or a

best match. We conduct the analysis on the full sample of all recalls, as well as on a sample of clean recall events (i.e.,

those recalls where there is no recall event for the firm in the year prior to or in the year after this recall). In none of the

tests do we find that the change in leverage/likelihood of financial distress is significantly greater for recalling firms

compared to control firms. These results are reported in Internet Appendix Table 15.

31

may in itself cause the firm to have high leverage or a higher likelihood of distress. When a recall ultimately

occurs it would be associated with lagged leverage even though it was not caused by it. In Section 5.3.1., we

explored the possibility of reverse causality and conducted multiple tests to rule it out as an alternative

explanation. In this section we directly explore another potential channel of reverse causality. If adverse events

that precede a recall cause a firm to become more constrained and these firms access external capital markets

as a means to ease their constraints, then it is possible that due diligence around capital raising may cause the

firm to undertake recalls. Specifically, the due diligence process during issuance may cause these firms to

either (i) identify the quality problem or (ii) acknowledge the quality problem when their potential liability is

unearthed by the legal counsel for the underwriter, thereby resulting in the filing of a formal recall. This

explanation predicts that highly levered/distressed firms that plan to have security issuances over the next few

months are more likely to report a recall.

We address this potential explanation for the timing of a recall by including a Capital raising dummy

in isolation as well as interacting it with our financial condition variables in the base probit regressions reported

in Table 3. The Capital raising dummy is an indicator variable set to 1 if the recalling firm had a capital raising

event during a period of six months after the recall date, and set to 0 otherwise. We choose a six month period

after the recall date because it is a conservative estimate of the time elapsed between the initiation of the

issuance process and when the issuance actually takes place. For control firms, it is set to 1 if there is a capital

raising event during a period of six months after the fiscal year end date, and set to 0 otherwise. Capital raising

events are identified based on all equity and debt offerings available on the SDC Platinum database. In the

interests of brevity, the results from these tests are reported in Internet Appendix Table 16. Contrary to the

predictions from this explanation for the timing of recalls, we find that (i) the marginal effect of the Capital

raising dummy is insignificant when the variable is included in our probit regressions in isolation and (ii) the

marginal effect of the interaction term between the Capital raising dummy and our financial condition variables

is also insignificant in all our estimated regressions. These results suggest that the relation between financial

condition and recalls is not a spurious one, caused merely by the reporting of recalls triggered by due diligence

efforts around capital raising events of financially constrained firms.

32

5. Summary and conclusions

We study the role of the financial condition of a firm in explaining the incidence of severe product failures that

result in recalls. We conduct our analysis on a sample of 3,508 recalls announced by public firms during the

period 2006 – 2010. Our results indicate that higher leverage, a greater likelihood of financial distress, and

more adverse cash flow shocks all impact product quality adversely. Using different types of negative

exogenous shocks, multiple measures of leverage and distress, and several other robustness tests, we establish

a plausible causal link between financial condition and quality failures. In addition, we find that the constraints

imposed by high financial leverage/distress likelihood increase the incidence, frequency, and severity of recalls.

In additional tests, we determine that our results are not the consequence of reverse causality where recalls

result in poor financial condition rather than being caused by it. Finally, we do not find any evidence to suggest

that firms report product quality failures prior to capital raising events by constrained firms.

Overall, our results support the hypothesis that firms in poor financial condition either find it optimal

(from a shareholders’ perspective), or are forced, to reduce discretionary investments in quality, thereby

increasing the likelihood of recalls. The results are inconsistent with the notion that poor financial condition

increases the incentives of shareholders to make more discretionary investments in product quality either due

to limited liability or because it imposes greater discipline on managers and, therefore, forces them to be more

responsive to the quality demands of the market. Our paper contributes to the broad literature that documents

that the financial condition of a firm has an impact on the firm’s product market performance. Phillips (1995),

Chevalier (1995), Chevalier and Scharfstein (1996), Kovenock and Phillips (1997), and Campello (2003) all

document that high leverage is a disadvantage for firms in the product market arena as rivals are able to

compete more aggressively, prey on these constrained firms, and take away market share and value. The results

in our paper show that leverage and distress likelihood may also be a disadvantage to the firm in a different

way – by adversely affecting the firm’s product quality. It is another distinct disadvantage of debt that firms

should consider when they make their capital structure choice.

33

Appendix

This appendix provides details on the construction of variables used in the paper.

I. Variables for determinants of recall incidence

a. Book (Market) leverage

Book leverage is the sum of the long-term debt and debt in current liabilities (Compustat item DLTT +

Compustat item DLC) divided by total assets (Compustat item AT) for the year prior to the year of

announcement. For Market leverage, instead of dividing the numerator by AT we divide it by the sum

of the book value of debt (Compustat item DLTT + Compustat item DLC) and market value of equity

(Compustat item CSHO x Compustat item PRCC_F) for the year prior to the year of announcement.

b. Altman Z

Altman Z is the Altman Z-score score developed in Altman (1968). It is calculated as 3.3*(Compustat

item EBIT/Compustat item AT) + (Compustat item REVT/Compustat item AT) + 1.4*(Compustat item

RE/Compustat item AT) + 1.2*([Compustat item ACT – Compustat item LCT]/Compustat item AT) +

0.6*([Compustat item CSHO x Compustat item PRCC_F ]/Compustat item LT). All Compustat items

are measured for the year prior to year of announcement.

c. Book (Market) net debt

Book net debt is the sum of the long-term debt and debt in current liabilities (Compustat item DLTT +

Compustat item DLC) minus cash and short-term investments (Compustat item CHE) divided by total

assets (Compustat item AT) for the year prior to the year of announcement. For Market net debt, instead

of dividing the numerator by AT we divide it by the sum of the book value of debt (Compustat item

DLTT + Compustat item DLC) and market value of equity (Compustat item CSHO x Compustat item

PRCC_F) for the year prior to the year of announcement.

d. Book (Market) debt due in three years

Debt due in three years is debt in current liabilities (Compustat item DLC) plus long-term debt due in

one year (Compustat item DD1) plus debt maturing in second year (Compustat item DD2) plus debt

maturing in third year (Compustat item DD3). Book debt due in three years is the debt due in three

years divided by total assets (Compustat item AT) for the year prior to the year of announcement. For

Market debt due in three years, we divide debt due in three years by the sum of the book value of debt

(Compustat item DLTT + Compustat item DLC) and market value of equity (Compustat item CSHO x

Compustat item PRCC_F) for the year prior to the year of announcement.

e. Debt due at onset of crisis

Debt due at onset of crisis is the percentage of a firm’s debt that is due during the financial crisis as

defined by Almeida, Campello, Laranjeira, and Weisbenner (2012). It is computed as of the end of

fiscal year 2007 for firms that have 2007 fiscal year-end months in September, October, November,

December, or January. We follow Almeida, Campello, Laranjeira, and Weisbenner (2012) by defining

this percentage as debt due during the next year (Compustat item DD1) divided by the sum of debt due

during the next year (Compustat item DD1) plus long-term debt that matures in more than one year

(Compustat item DLTT).

f. Free cash flow shock

Free cash flow is calculated as income before extraordinary items (Compustat IB) plus depreciation

(Compustat DP) minus change in net working capital (based on Compustat INVT, RECT, ACO, and

LCO) minus capital expenditures (Compustat CAPX) divided by market value of assets (Compustat

DLTT + DLC + CSHO x PRCC_F). Free cash flow shock is calculated as Free cash flow for year t-1

minus the average value of Free cash flow over the previous three years (year t-4 through t-2).

34

g. Herfindahl index

The Compustat sales-based Herfindahl index for the primary three-digit SIC industry of the recalling

(control) firm for the year prior to the year of recall announcement.

h. Unionization

The industry rates of unionization are obtained from Union Stats website available at

http://www.unionstats.com. This database reports industry unionization rates for 3-digit Census

Industry Classification (CIC) industries. We determine which 4-digit SIC codes correspond to 3-digit

CIC codes and hence are able to assign industry unionization rates to our sample firms. When we are

unable to assign a four-digit SIC industry we impute the unionization rates at the three-digit SIC

industry level. Unionization is the rate of unionization for the primary industry of the recalling (control)

firm in our sample.

i. Number of suppliers

Number of Suppliers is the number of key suppliers of the firm as identified in the Compustat segment

tapes. FASB requires that firms report the names of customers that account for at least 10% of their

sales and this information is available on the Compustat database. We use this Compustat data to

identify the suppliers for all firms in the Compustat database. Using this data, we then generate the

number of suppliers for our sample firms for the year prior to the year of announcement. While this

database does not allow us to capture all the suppliers for a given customer firm, we believe that it is

a reasonable proxy in the sense there is likely to be a monotonic relation between our proxy for the

number of suppliers and the true number of suppliers.

j. Vertical integration dummy

Vertical integration dummy is an indicator variable that is set to 1 if any segment of the firm belongs

to an industry that sources 5% or more of its inputs from another industry in which the firm also has a

segment. Segment level information is obtained from Compustat segment tapes. To identify vertical

relatedness between sample industries, we use the 2002 benchmark input-output tables of the U.S.

economy published by the Bureau of Economic Analysis.

k. R&D intensity

It is measured as the ratio of the research & development expenditure (Compustat item XRD) to total

assets (Compustat item AT). All Compustat items are measured for the year prior to year of recall

announcement.

l. Total factor productivity

To calculate total factor productivity, we follow Kovenock and Phillips (1997) and Faleye, Mehrotra,

and Morck (2006) and assume a Cobb-Douglas production function. Thus, for each two-digit SIC

industry group, we regress the natural logarithm of firm sales (Compustat item REVT) on the natural

logarithm of number of employees (Compustat item EMP) and the natural logarithm of net property,

plant, and equipment (Compustat data item PPENT). Total factor productivity is measured as the

residual from this regression for the primary two-digit SIC industry group of the firm.

m. Size

It is the natural logarithm of the market value of equity for the recalling firm (control firm).

35

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38

Table 1

Frequency of recall events and industries covered in recall sample

A: Frequency of recall events by type of recall

Number of observations

Year of recall FDA recalls CPSC recalls NHTSA recalls Total

2006 552 78 225 855

2007 453 108 170 731

2008 504 75 121 700

2009 496 52 92 640

2010 439 64 79 582

Total 2,444 377 687 3,508

B: Industries covered in recall sample at two-digit SIC level from highest to lowest frequency of recalls

Two-digit

SIC

Description of industry Number of

recalls

38 Measuring, Analyzing, and Controlling Instruments 1,148

37 Transportation Equipment 722

28 Chemicals and Allied Products 672

20 Food And Kindred Products 194

36 Electronic & Other Electrical Equipment and Components, Except Computer

Equipment

79

53 General Merchandise Stores 57

35 Industrial and Commercial Machinery and Computer Equipment 54

54 Food Stores 54

39 Miscellaneous Manufacturing Industries 46

51 Wholesale Trade-non-durable Goods 33

57 Home Furniture, Furnishings, and Equipment Stores 24

56 Apparel And Accessory Stores 20

59 Miscellaneous Retail 18

26 Paper and Allied Products 17

34 Fabricated Metal Products, Except Machinery and Transportation Equipment 17

50 Wholesale Trade-durable Goods 9

73 Business Services 9

58 Eating And Drinking Places 8

23 Apparel and Other Finished Products Made From Fabrics 6

87 Engineering, Accounting, Research, Management, and Related Services 6

Other industries 315

This table presents the frequency of recall events and industries covered in our recall sample by public firms during our

sample period of 2006 – 2010. Panel A reports recalls covered by the Food and Drug Administration (FDA), the Consumer

Product Safety Commission (CPSC), and the National Highway Traffic Safety Administration (NHTSA). Panel B presents

the different two-digit SIC industries covered in our recall sample and the number of recalls under each two-digit SIC

industry. The industries are ranked from highest to lowest number of recalls.

39

Table 2

Univariate comparisons between recalling firms and control firms

A: Leverage/financial distress variables

Variable name Recall sample Control sample

N Mean Median N Mean Median T (Z) Stat

Book leverage 3,508 0.266 0.257 12,315 0.167 0.091 3.77*** (34.1***)

Market leverage 3,508 0.263 0.168 12,306 0.156 0.061 2.54** (30.30***)

Altman Z 3,508 4.093 3.250 12,244 4.139 3.153 -0.11 (3.03**)

B: Control variables

Variable name Recall sample Control sample

N Mean Median N Mean Median T (Z) Stat

Free cash flow shock 3,492 0.001 0.005 12,249 0.015 0.001 -1.61 (4.88***)

Unionization 3,508 9.869 5.225 12,321 5.859 3.300 2.77*** (21.78***)

Number of suppliers 3,508 12.275 3.000 12,321 0.532 0.000 4.49*** (75.45***)

Vertical integration dummy 3,473 0.023 0.000 12,011 0.049 0.000 -3.27*** (-6.45***)

R&D intensity 3,508 0.045 0.038 12,315 0.098 0.030 -11.75*** (0.94)

Total factor productivity 3,486 -0.180 -0.221 12,105 -0.013 0.037 -3.05*** (-19.42***)

Herfindahl index 3,508 0.150 0.079 12,320 0.146 0.097 0.24 (-1.92*)

Size 3,508 9.555 9.879 12,306 5.727 5.642 17.96*** (72.90***)

This table presents the univariate comparisons between recalling firms and control firms. The sample period is 2006 – 2010 and contains

recalls covered by the Consumer Product Safety Commission (CPSC), National Highway Traffic Safety Administration (NHTSA), and Food

and Drug Administration (FDA). Control firms are firms that belong to the same three-digit SIC industry as the recalling firm provided they

did not have a recall during 2006 – 2010. Book (Market) leverage is the book (market) value of debt divided by total assets. Altman Z is the

Altman Z-score score used in Altman (1968) and is calculated as 3.3*EBIT/Total assets + Sales/Total assets + 1.4*Retained earnings/Total

assets + 1.2*Working capital/Total assets + 0.6*Market value of equity/Total Liabilities. Free cash flow is calculated as income before

extraordinary items plus depreciation minus change in net working capital minus capital expenditures divided by market value of assets.

Free cash flow shock is calculated as Free cash flow minus the average value of Free cash flow over the previous three years. Herfindahl

Index is the sales-based Herfindahl index of the three-digit SIC industry of the firm. Unionization is the percentage of employees in the

industry that are unionized. Number of Suppliers is the number of key suppliers of the firm as identified in the Compustat segment tapes.

Vertical integration dummy is an indicator variable that is set to 1 if any two segments of the firm share a vertical relation of 5% or more,

and 0 otherwise based on the benchmark input-output tables of the U.S. economy. R&D intensity is the research & development expenditure

(XRD) divided by book value of assets (AT). Total factor productivity is calculated as the residual from a regression of logarithm of firm

sales on the logarithm of number of employees and logarithm of property, plant, and equipment where regressions are run by two-digit SIC

industry and year. Size is the logarithm of the market value of equity for the recalling firm (control firm). T-Stat provides the t-statistic from

a t-test for the equality in means between recalling and control firms where the standard errors are robust and clustered at the firm level. Z-

Stat provides the z-statistic for the equality of medians between the recalling and control firms and is based on a Wilcoxon rank-sum test

for the equality of medians. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

40

Table 3

Probit regressions: Impact of leverage and distress likelihood on recall incidence

Dep. Variable: Recall incidence (1) (2) (3) (4) (5) (6) (7) (8) (9)

Book leverage 0.0622*** 0.0596*** 0.0579***

(0.002) (0.009) (0.001)

Market leverage 0.0566** 0.1205*** 0.1279***

(0.028) (0.000) (0.000)

Altman Z -0.0013*** -0.0028*** -0.0033***

(0.003) (0.000) (0.000)

Free cash flow shock -0.0508*** -0.0491*** -0.0507*** -0.0422*** -0.0356** -0.0411*** -0.0409*** -0.0329*** -0.0389***

(0.000) (0.000) (0.000) (0.005) (0.011) (0.005) (0.003) (0.006) (0.002)

Unionization 0.0034*** 0.0033*** 0.0036*** 0.0023** 0.0020** 0.0025*** 0.0011 0.0010 0.0014*

(0.000) (0.001) (0.000) (0.012) (0.029) (0.008) (0.181) (0.189) (0.087)

Number of suppliers 0.0069*** 0.0066*** 0.0069*** 0.0045*** 0.0038*** 0.0044*** 0.0043*** 0.0035*** 0.0041***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Vertical integration dummy -0.1242*** -0.1246*** -0.1263*** -0.0399** -0.0415*** -0.0422** -0.0170 -0.0180 -0.0175

(0.000) (0.000) (0.000) (0.015) (0.006) (0.011) (0.293) (0.199) (0.286)

R&D intensity -0.1127* -0.0970 -0.1258* -0.2542*** -0.2060*** -0.2776*** -0.3180*** -0.2616*** -0.3396***

(0.089) (0.112) (0.079) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Total factor productivity -0.0200*** -0.0202*** -0.0215*** -0.0048 -0.0026 -0.0045 -0.0016 0.0004 -0.0007

(0.000) (0.000) (0.000) (0.393) (0.647) (0.437) (0.732) (0.933) (0.878)

Herfindahl index -0.0366* -0.0345* -0.0378* -0.0147 -0.0133 -0.0213 0.1227 0.1125 0.1110

(0.069) (0.086) (0.067) (0.744) (0.763) (0.643) (0.102) (0.125) (0.144)

Size 0.0794*** 0.0806*** 0.0806*** 0.0462*** 0.0481*** 0.0482*** 0.0401*** 0.0413*** 0.0420***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry dummies No No No 2-digit SIC 2-digit SIC 2-digit SIC 3-digit SIC 3-digit SIC 3-digit SIC

Pseudo R-squared 0.466 0.465 0.465 0.641 0.645 0.642 0.706 0.712 0.710

Observations 15,070 15,070 15,017 15,039 15,039 14,986 14,918 14,918 14,865

This table presents the recall incidence estimation results for recall events by public firms during our sample period of 2006 – 2010. The dependent variable is RecallDum which is

set to one for firms in the recall sample, and zero for control firms. Control firms are firms that belong to the same three-digit SIC industry as the recalling firm provided they did

not have a recall during 2006 – 2010. Refer to the Appendix for details on the construction of our variables. Columns (1) – (3) contain estimation results when we include calendar

year dummies. Columns (4) – (6) ((7) – (9)) contain estimation results when we include two-digit SIC (three-digit SIC) industry dummies and calendar year dummies. Marginal

effects are reported in the table and reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***, ** and * indicate

significance at 1%, 5%, and 10%, respectively.

41

Table 4

The impact of tariff cuts on the relation between recall incidence and financial leverage or distress likelihood

Dep. Variable: Recall incidence (1) (2) (3)

Book leverage 0.0923***

(0.001)

Tariff cut * Book leverage 0.1430***

(0.009)

Market leverage 0.2330***

(0.000)

Tariff cut * Market leverage 0.1826***

(0.001)

Altman Z -0.0042***

(0.000)

Tariff cut * Altman Z -0.0038*

(0.080)

Tariff cut 0.0085 0.0005 0.0650***

(0.680) (0.978) (0.000)

Free cash flow shock -0.1053*** -0.0904*** -0.0945***

(0.000) (0.000) (0.000)

Unionization 0.0031*** 0.0029*** 0.0027***

(0.002) (0.004) (0.005)

Number of suppliers 0.0092*** 0.0076*** 0.0090***

(0.000) (0.000) (0.000)

Vertical integration dummy -0.0918*** -0.0963*** -0.0963***

(0.000) (0.000) (0.000)

R&D intensity -0.5771*** -0.4590*** -0.6294***

(0.000) (0.000) (0.000)

Total factor productivity -0.0033 0.0029 -0.0034

(0.581) (0.640) (0.587)

Herfindahl index -0.0775* -0.0819* -0.0901*

(0.099) (0.082) (0.061)

Size 0.0974*** 0.1016*** 0.1008***

(0.000) (0.000) (0.000)

Year dummies Yes Yes Yes

Industry dummies Yes Yes Yes

Pseudo R-squared 0.624 0.629 0.623

Observations 10,073 10,073 10,048

This table presents the impact of tariff cuts on the relation between recall incidence and financial leverage or distress likelihood over

the sample period of 2006 – 2010. The dependent variable is RecallDum which is set to one for firms in the recall sample, and zero for

control firms. Control firms are firms that belong to the same three-digit SIC industry as the recalling firm provided they did not have a

recall during 2006 – 2010. Tariff cut is a dummy variable that is set to 1 if the annual percentage drop in the tariff rate of the recalling

industry was 2.0 times the industry median level and set to 0 otherwise. This definition is consistent with Fresard (2010). For all other

variables, refer to the Appendix for details on their construction. Columns (1) – (3) contain estimation results when we include two-digit

SIC industry dummies and calendar year dummies along with our explanatory variables. Marginal effects are reported in the table and

reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***, ** and *

indicate significance at 1%, 5%, and 10%, respectively.

42

Table 5

The impact of negative input price shocks on the relation between recall incidence and financial leverage or distress

likelihood

Dep. Variable: Recall incidence (1) (2) (3)

Book leverage 0.0099***

(0.000)

Negative input price shock * Book leverage 0.1011*

(0.076)

Market leverage 0.1299***

(0.000)

Negative input price shock * Market leverage -0.0109

(0.819)

Altman Z -0.0025***

(0.000)

Negative input price shock * Altman Z -0.0104***

(0.000)

Negative input price shock -0.0539*** -0.0449*** -0.0253*

(0.000) (0.001) (0.055)

Free cash flow shock -0.0435*** -0.0377*** -0.0423***

(0.000) (0.002) (0.000)

Unionization 0.0010** 0.0012** 0.0014***

(0.043) (0.019) (0.005)

Number of suppliers 0.0045*** 0.0035*** 0.0042***

(0.000) (0.000) (0.000)

Vertical integration dummy -0.0435*** -0.0449*** -0.0452***

(0.000) (0.000) (0.000)

R&D intensity -0.3146*** -0.2421*** -0.3121***

(0.000) (0.000) (0.000)

Total factor productivity -0.0089*** -0.0046 -0.0070**

(0.003) (0.133) (0.024)

Herfindahl index -0.0150 -0.0170 -0.0265

(0.523) (0.467) (0.265)

Size 0.0511*** 0.0532*** 0.0529***

(0.000) (0.000) (0.000)

Year dummies Yes Yes Yes

Industry dummies Yes Yes Yes

Pseudo R-squared 0.640 0.646 0.643

Observations 14,614 14,614 14,561

This table presents the impact of negative input price shocks on the relation between recall incidence and financial leverage/distress

over the sample period of 2006 – 2010. The dependent variable is RecallDum which is set to one for firms in the recall sample, and zero

for control firms. Control firms are firms that belong to the same three-digit SIC industry as the recalling firm provided they did not

have a recall during 2006 – 2010. Negative input price shock is a dummy variable that is set to 1 if the geometric average growth rate in

input prices for the recalling industry over the three years before the recall was 5% or more, and set to 0 otherwise. For all other variables,

refer to the Appendix for details on their construction. Columns (1) – (3) contain estimation results when we include two-digit SIC

industry dummies and calendar year dummies along with our explanatory variables. Marginal effects are reported in the table and

reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***, ** and *

indicate significance at 1%, 5%, and 10%, respectively.

43

Table 6

The impact of debt maturing at the onset of the financial crisis on recall propensity (number of recalls)

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

Rcldum08 Rcldum08-09 Rcldum08-10 #Recalls08 #Recalls08-09 #Recalls08-10

Debt due at onset of crisis 0.3848** 0.3707** 0.3616** 0.2533** 0.2467*** 0.2529***

(0.045) (0.035) (0.036) (0.036) (0.009) (0.007)

Free cash flow shock -0.1256 -0.3639** -0.3257** 0.5129*** 0.4062*** 0.4061***

(0.375) (0.030) (0.038) (0.000) (0.000) (0.000)

Unionization -0.0130 -0.0103 -0.0115 -0.0270*** -0.0298*** -0.0295***

(0.244) (0.351) (0.294) (0.000) (0.000) (0.000)

Number of suppliers 0.0413*** 0.0398*** 0.0406*** 0.0077 0.0032 0.0038

(0.000) (0.000) (0.000) (0.129) (0.333) (0.217)

Vertical integration dummy -0.7115*** -0.6500*** -0.6461*** -1.7190*** -1.3706*** -1.3756***

(0.001) (0.007) (0.007) (0.000) (0.000) (0.000)

R&D intensity -2.1080* -2.4112** -2.5141*** -8.3217*** -6.3090*** -6.3273***

(0.054) (0.011) (0.008) (0.000) (0.000) (0.000)

Total factor productivity -0.0751 -0.0922 -0.1075* 0.0571 -0.0251 -0.0218

(0.322) (0.146) (0.086) (0.273) (0.594) (0.642)

Herfindahl index -2.0110** -0.9672 -0.9846 -5.6028*** -6.4051*** -6.1778***

(0.019) (0.226) (0.216) (0.000) (0.000) (0.000)

Size 0.5224*** 0.4641*** 0.4549*** 0.7236*** 0.6843*** 0.6845***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Constant -4.2733*** -4.1432*** -4.0546*** -3.1418*** -1.7127** -1.8383***

(0.000) (0.000) (0.000) (0.000) (0.014) (0.006)

Industry dummies Yes Yes Yes Yes Yes Yes

Pseudo R-squared 0.728 0.719 0.717 0.868 0.870 0.869

Observations 1,726 2,206 2,256 2,502 2,502 2,502

This table presents probit regression models in Columns (1) – (3) with the recall dummy as the dependent variable, and Poisson

regression models in Columns (4) – (6) with the frequency of recalls for the firm as the dependent variable. The sample for these tests

comprises of firms that have a 2007 fiscal year-end months in September, October, November, December, or January. We follow

Almeida, Campello, Laranjeira, and Weisbenner (2012) by defining percentage of a firm’s debt due at the onset of the financial crisis

(Debt due at onset of crisis) as debt due during the next year (Compustat item DD1) divided by the sum of debt due during the next year

(Compustat item DD1) plus long-term debt that matures in more than one year (Compustat item DLTT) for firms that have 2007 fiscal

year-end months as identified above. The dependent variable Rcldum08 is set to one if the firm has a recall in the period February 2008

to December 2008, and zero otherwise. The dependent variable Rcldum08-09 (Rcldum08-10) is set to one if the firm has a recall in the

period February 2008 to December 2009 (February 2008 to December 2010), and zero otherwise. The dependent variable #Recalls08

is the number of recall events for a recalling firm during the period February 2008 to December 2008. Finally, the dependent variable

#Recalls08-09 (#Recalls08-10) is the number of recall events for a recalling firm in the period February 2008 to December 2009

(February 2008 to December 2010). All independent variables are measured as of the fiscal year end in 2007. We include two-digit SIC

industry dummies in all the regressions reported in the table. Refer to the Appendix for details on the construction of our variables.

Reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***, ** and *

indicate significance at 1%, 5%, and 10%, respectively.

44

Table 7

Impact of leverage and distress likelihood on recall incidence: Linear probability models and two-stage least squares estimations

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

Dep. Variable: Recall incidence OLS OLS OLS IV IV IV

Instrument: Local leverage

Book leverage 0.0936*** 2.1759***

(0.001) (0.000)

Market leverage 0.1798*** 1.6200**

(0.000) (0.041)

Altman Z -0.0049*** -0.0485**

(0.000) (0.038)

Free cash flow shock -0.0540*** -0.0486*** -0.0498*** 0.0086 0.0145 0.0128

(0.000) (0.000) (0.000) (0.682) (0.679) (0.729)

Unionization 0.0013 0.0010 0.0013 -0.0033** -0.0037 -0.0005

(0.384) (0.511) (0.389) (0.042) (0.365) (0.855)

Number of suppliers 0.0078*** 0.0070*** 0.0075*** 0.0060*** 0.0007 0.0059

(0.000) (0.000) (0.000) (0.009) (0.934) (0.235)

Vertical integration dummy -0.1173*** -0.1234*** -0.1228*** -0.1078*** -0.1833*** -0.1484***

(0.000) (0.000) (0.000) (0.000) (0.004) (0.007)

R&D intensity -0.2097*** -0.1666*** -0.2827*** -0.0181 0.2243 -0.8753***

(0.000) (0.000) (0.000) (0.803) (0.318) (0.005)

Total factor productivity -0.0359*** -0.0348*** -0.0373*** 0.0108 -0.0154 -0.0348***

(0.000) (0.000) (0.000) (0.376) (0.177) (0.000)

Herfindahl index -0.0676 -0.0614 -0.0728 -0.1515*** -0.0547 -0.1542*

(0.260) (0.294) (0.220) (0.006) (0.474) (0.086)

Size 0.0727*** 0.0757*** 0.0756*** 0.0556*** 0.0924*** 0.0966***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Constant -0.4420*** -0.4891*** -0.4499*** -0.3276*** -0.5426*** 0.4611

(0.000) (0.000) (0.000) (0.000) (0.000) (0.230)

Instrument in first stage 0.1041*** 0.1399*** -4.6492***

(0.000) (0.000) (0.001)

Industry and year dummies Yes Yes Yes Yes Yes Yes

Observations 15,225 15,225 15,172 12,424 12,424 12,375

First stage F-statistic n/a n/a n/a 58.16*** 6.097** 5.99**

Kleibergen-Paap rk LM statistic n/a n/a n/a 24.22*** 13.10*** 10.85***

Endogeneity test n/a n/a n/a 54.52*** 4.936** 5.019**

This table presents the recall incidence estimation results for recalls during our sample period of 2006 – 2010. The dependent variable is set to one for firms in the recall sample, and

zero for control firms. Columns (1) - (3) contain linear probability model estimation results. Columns (4) – (6) contain results from the 2nd stage of the two-stage least squares estimation

using market leverage of local firms (excluding the firm and its industry rivals) as an instrumental variable. All estimations include two-digit SIC industry dummies and year dummies.

Reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

45

Table 8

Impact of leverage and distress likelihood on severity of product recalls: Ordered probit estimations

Dep. Variable: Recall severity (1) (2) (3) (4) (5) (6) (7) (8) (9)

Book leverage 0.2115** -0.0333 0.0502**

(0.029) (0.775) (0.011)

Market leverage 0.6926*** 0.6182*** 0.6085***

(0.000) (0.000) (0.000)

Altman Z -0.0171*** -0.0115*** -0.0130***

(0.000) (0.000) (0.000)

Tariff cut -0.0975 -0.0434 0.2015***

(0.242) (0.596) (0.004)

Tariff cut * Book leverage 1.0184***

(0.000)

Tariff cut * Market leverage 1.0867***

(0.003)

Tariff cut * Altman Z -0.0168*

(0.062)

Negative input price shock -1.0991*** -0.9107*** -0.5465***

(0.000) (0.000) (0.000)

Negative input price shock * Book

leverage 1.0083*

(0.069)

Negative input price shock *

Market leverage 0.2900

(0.496)

Negative input price shock *

Altman Z -0.0812***

(0.000)

Free cash flow shock -0.5993*** -0.5672*** -0.5657*** -0.6285*** -0.5661*** -0.5912*** -0.5932*** -0.5600*** -0.5594***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Unionization 0.0090** 0.0073* 0.0091** 0.0073 0.0053 0.0080* 0.0100** 0.0084* 0.0100**

(0.036) (0.092) (0.033) (0.106) (0.249) (0.074) (0.023) (0.061) (0.022)

Number of suppliers 0.0157*** 0.0134*** 0.0145*** 0.0088** 0.0074** 0.0063* 0.0117*** 0.0103*** 0.0112***

(0.000) (0.000) (0.000) (0.013) (0.036) (0.073) (0.000) (0.000) (0.000)

Vertical integration dummy -0.4278*** -0.4600*** -0.4410*** -0.3808*** -0.4121*** -0.3940*** -0.3782*** -0.4018*** -0.3898***

(0.001) (0.000) (0.001) (0.008) (0.004) (0.006) (0.004) (0.002) (0.003)

R&D intensity -2.5676*** -2.3021*** -2.6630*** -2.8657*** -2.4931*** -2.9060*** -2.6825*** -2.4141*** -2.7032***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Total factor productivity 0.0522** 0.0686*** 0.0593** 0.0392 0.0588** 0.0479* 0.0389 0.0552** 0.0501*

46

(0.037) (0.007) (0.019) (0.156) (0.036) (0.087) (0.125) (0.033) (0.052)

Herfindahl index 0.8332*** 0.7986*** 0.7899*** 1.0187*** 0.9576*** 0.9555*** 0.9983*** 0.9594*** 0.9502***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Size 0.3648*** 0.3761*** 0.3681*** 0.3789*** 0.3915*** 0.3830*** 0.3645*** 0.3739*** 0.3678***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes

Pseudo R-squared 0.460 0.462 0.461 0.402 0.405 0.402 0.461 0.462 0.461

Observations 11,309 11,309 11,266 7,102 7,102 7,085 10,906 10,906 10,863

This table presents the ordered probit estimation results for FDA recalls during 2006 – 2010. The dependent variable is Recall severity which is set to 3 for Class 1 recalls (the most

severe form of recalls), 2 for Class 2 recalls, 1 for Class 3 recalls (the least severe recalls), and 0 for control firms in our sample. Control firms are firms that belong to the same three-

digit SIC industry as the recalling firm provided they did not have a recall during 2006 – 2010. Tariff cut is a dummy variable that is set to 1 if the annual percentage drop in the tariff

rate of the recalling industry was 2.0 times the industry median level and set to 0 otherwise. Negative input price shock is a dummy variable that is set to 1 if the geometric average

growth rate in input prices for the recalling industry over the three years before the recall was 5% or more and set to 0 otherwise. All estimations include two-digit SIC industry dummies

and year dummies. Reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***, ** and * indicate significance at 1%, 5%,

and 10%, respectively.

47

Table 9

The impact of financial leverage and distress likelihood on number of recalls: Poisson models

(1) (2) (3)

Dep. Variable: Number of recalls (2006-2010) #Recalls #Recalls #Recalls

Book leverage 0.9441**

(0.047)

Market leverage 2.1103***

(0.000)

Altman Z -0.0357**

(0.017)

Free cash flow shock -0.3132 -0.4458 -0.4609

(0.581) (0.439) (0.449)

Unionization 0.0163 0.0124 0.0178

(0.281) (0.425) (0.236)

Number of suppliers 0.0326*** 0.0214*** 0.0357***

(0.000) (0.001) (0.000)

Vertical integration dummy -1.7339*** -1.7308*** -1.7984***

(0.000) (0.000) (0.000)

R&D intensity -3.5500* -2.4823 -3.5865*

(0.060) (0.182) (0.055)

Total factor productivity -0.0206 0.0562 0.0097

(0.843) (0.588) (0.929)

Herfindahl index -3.8529*** -3.7925*** -3.9454***

(0.000) (0.000) (0.000)

Size 0.7931*** 0.8524*** 0.7898***

(0.000) (0.000) (0.000)

Constant -4.3933*** -5.2571*** -3.9967***

(0.000) (0.000) (0.000)

Industry dummies Yes Yes Yes

Pseudo R-squared 0.713 0.713 0.713

Observations 4,014 4,014 4,007

This table presents the Poisson model estimation results for the number of recall events for our sample firms during 2006 -

2010. The dependent variable is #Recalls which is defined as the number of recall events for a recalling firm during the

sample period 2006 – 2010. For all control firms this variable is defined as zero. All independent variables are measured at

the beginning of our sample period (in year 2005). Refer to the Appendix for details on the construction of our variables.

Columns (1) – (3) contain estimation results when we include two-digit SIC industry dummies along with our explanatory

variables. Reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by

firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

48

Impact of financial leverage on the incidence and severity of product failures: Evidence from product

recalls

Internet Appendix

Table of Contents

Internet Appendix Table 1: Probit regressions examining the impact of leverage on recall incidence: Two-digit

SIC industry subsamples with highest frequency of recalls

Internet Appendix Table 2: Effect of leverage and financial distress likelihood on recall incidence: Conditional

logit estimations

Internet Appendix Table 3: Poisson regressions: The main effect of tariff cut and input price shocks on the

frequency of recalls in an industry

Internet Appendix Table 4: The impact of negative input price shocks on the relation between recall incidence and

financial leverage or distress likelihood (using a five-year window to define shocks)

Internet Appendix Table 5: Falsification tests to examine the robustness of the impact of the exogenous shocks on

the relation between financial condition of the firm and the incidence of recalls

Internet Appendix Table 6: Impact of leverage and distress likelihood on recall incidence: Two-stage least squares

estimations using local leverage in metropolitan area as instrument

Internet Appendix Table 7: Probit regressions: The effect of leverage measures net of cash (Net debt) on recall

incidence

Internet Appendix Table 8: Probit regressions: The effect of short term debt (Debt due in three years) on recall

incidence

Internet Appendix Table 9: OLS regressions: Estimation of excess leverage and excess Altman Z score

Internet Appendix Table 10: Probit regressions: The effect of excess leverage (Excess leverage) on recall

incidence

Internet Appendix Table 11: Probit regressions: The impact of financial leverage and distress likelihood on recall

incidence – Use of lagged leverage/distress likelihood

Internet Appendix Table 12: Poisson regressions: The impact of financial leverage and distress likelihood on

frequency of recalls – Use of lagged leverage/distress likelihood

Internet Appendix Table 13: Probit regressions: Test for reverse causality by inclusion of lead leverage and Altman

Z-Score

Internet Appendix Table 14: OLS regressions: The relation between recall incidence and financial leverage – Test

for reverse causality

49

Internet Appendix Table 15: Propensity score matching to detect change in leverage/distress likelihood following

recalls: Test for reverse causality

Internet Appendix Table 16: Capital raising events and recall incidence

50

Internet Appendix Table 1

Probit regressions examining the impact of leverage on recall incidence: Two-digit SIC industry subsamples with

highest frequency of recalls

Dependent Variable: RecallDum

SIC=38

SIC=28

SIC=20

SIC=37

All other SIC

codes

Book leverage 0.1554*** 0.0827** 0.4424*** 0.7665*** 0.0130*

(0.001) (0.010) (0.001) (0.000) (0.082)

Control variables Yes Yes Yes Yes Yes

Observations 2,058 2,645 446 1,044 8,846

Chg_Prob 7.41% 9.41% 29.21% 19.12% 4.22%

Dependent Variable: RecallDum

SIC=38

SIC=28

SIC=20

SIC=37

All other SIC

codes

Market leverage 0.4348*** 0.3039*** 0.4756*** 0.9923*** 0.0200***

(0.000) (0.000) (0.002) (0.000) (0.007)

Control variables Yes Yes Yes Yes Yes

Observations 2,058 2,645 446 1,044 8,846

Chg_Prob 12.20% 19.09% 30.61% 56.26% 6.21%

Dependent Variable: RecallDum

SIC=38

SIC=28

SIC=20

SIC=37

All other SIC

codes

Altman Z -0.0048*** -0.0045*** -0.0212*** -0.0103* -0.0017***

(0.000) (0.004) (0.000) (0.078) (0.000)

Control variables Yes Yes Yes Yes Yes

Observations 2,056 2,641 446 1,044 8,799

Chg_Prob -4.64% -10.15% -16.95% -1.73% -9.42%

The dependent variable is RecallDum which is set to one for recalling firms and zero for control firms. Measuring,

Analyzing, and Controlling Instruments industry is SIC=38, Transportation Equipment industry is SIC=37, Chemical

and Allied Products industry is SIC=28, and Food and Kindred Products industry is SIC=20. Marginal effects are reported

in the table and reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are

clustered by firm. Chg_Prob is calculated as the difference between Prob (P75) and Prob (P25) divided by the

unconditional probability of a product recall in the sample. Prob (P25) and Prob (P75) give the probability of a recall

when leverage (Altman Z) is kept at the 25th percentile and 75th percentile respectively, while all control variables are

kept at their respective sample means. Estimations for SIC codes 38, 28, 20, and 37 contain calendar year dummies while

estimations for all other SIC codes contain two-digit SIC industry dummies and calendar year dummies. ***, ** and *

indicate significance at 1%, 5%, and 10%, respectively.

51

Internet Appendix Table 2

Effect of leverage and financial distress likelihood on recall incidence: Conditional logit estimations

Dep. Variable: Recall incidence (1) (2) (3)

Book leverage 1.4914***

(0.000)

Market leverage 3.2279***

(0.000)

Altman Z -0.0693***

(0.000)

Free cash flow shock -1.3047** -1.0723 -1.1825*

(0.050) (0.115) (0.067)

Herfindahl index -19.9116*** -16.0767*** -21.3761***

(0.000) (0.000) (0.000)

Unionization -0.0505*** -0.0479*** -0.0454***

(0.000) (0.000) (0.000)

Number of suppliers 0.1175*** 0.0936*** 0.1169***

(0.000) (0.000) (0.000)

Vertical integration dummy 0.1203 0.2496 -0.0021

(0.677) (0.320) (0.994)

R&D intensity -9.9033*** -8.2035*** -9.4171***

(0.000) (0.000) (0.000)

Total factor productivity -0.0846 0.0221 -0.1014

(0.289) (0.789) (0.207)

Size 2.0201*** 2.0692*** 1.9989***

(0.000) (0.000) (0.000)

Observations 6,861 6,861 6,861

Pseudo R-squared 0.594 0.607 0.596

This table presents conditional logit estimation results for recall events by public firms during our sample period of 2006

– 2010. The dependent variable is RecallDum which is set to one for firms in the recall sample, and zero for control firms.

Each recall observation is matched to a control firm that is closest in size to the recalling firm and belongs to the same

three-digit SIC industry as the recalling firm. We match at the two-digit SIC level if there is no control firm available at

the three-digit SIC level. Refer to the Appendix for details on the construction of our variables. Marginal effects are

reported in the table and reported p-values in the parentheses are based on heteroskedasticity robust standard errors and

are clustered by firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

52

Internet Appendix Table 3

The main effect of tariff cut shocks and input price shocks on the frequency of recalls in an industry

Dep. Variable: #IndRecalls/year (1) (2) (3) (4) (5) (6) (7) (8)

Tariff cut 0.1224*** 0.1296** 0.1140** 0.1087*

(0.000) (0.019) (0.019) (0.065)

Negative input price shock 0.6904*** 0.6829*** 0.6610*** 0.6820**

(0.004) (0.004) (0.007) (0.012)

Ln(industry revenues) 0.6239*** 0.6617*** 0.6635*** 0.5260*** 0.5686*** 0.5674***

(0.001) (0.000) (0.000) (0.004) (0.001) (0.001)

Industry book leverage 2.1731** 2.2724** 1.8784** 1.9319**

(0.016) (0.016) (0.018) (0.028)

Industry cash flow shock -0.1829 -0.1260

(0.696) (0.774)

Constant -17.4645*** -21.8115*** -22.4277*** -22.4287*** 1.0582*** -4.6134** -5.4995*** -5.4779***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.021) (0.004) (0.004)

Industry and year dummies Yes Yes Yes Yes Yes Yes Yes Yes

Observations 239 239 239 239 359 359 359 359

Pseudo R-Squared 0.926 0.931 0.931 0.931 0.919 0.922 0.923 0.923

This table presents the Poisson model estimation results for the number of recall events per year for our sample industries during 2006 - 2010. The dependent variable is

#IndRecalls/year which is defined as the number of recall events for an industry during year t. Tariff cut is a dummy variable that is set to 1 if the annual percentage drop

in the tariff rate of the recalling industry was 2.0 times the industry median level and set to 0 otherwise. Negative input price shock is a dummy variable that is set to 1 if

the geometric average growth rate in input prices for the recalling industry over the three years before the recall was 5% or more and set to 0 otherwise. The tariff cut shock

and input price shock dummies are measures at time t-1. All columns contain two-digit SIC industry and calendar year dummies along with our explanatory variables.

Reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***, ** and * indicate significance at 1%, 5%, and

10%, respectively.

53

Internet Appendix Table 4

The impact of negative input price shocks on the relation between recall incidence and financial leverage or distress

likelihood (using a five-year window to define shocks)

Dep. Variable: Recall incidence (1) (2) (3)

Book leverage 0.0102***

(0.000)

Negative input price shock * Book leverage 0.0754*

(0.088)

Market leverage 0.1429***

(0.000)

Negative input price shock * Market leverage -0.0165

(0.681)

Altman Z -0.0027***

(0.000)

Negative input price shock * Altman Z -0.0070***

(0.000)

Negative input price shock -0.0568*** -0.0482*** -0.0372***

(0.000) (0.000) (0.000)

Free cash flow shock -0.0442*** -0.0366*** -0.0423***

(0.000) (0.002) (0.001)

Herfindahl index 0.0034 0.0027 -0.0066

(0.885) (0.911) (0.784)

Unionization 0.0010** 0.0009* 0.0011**

(0.034) (0.061) (0.025)

Number of suppliers 0.0044*** 0.0035*** 0.0042***

(0.000) (0.000) (0.000)

Vertical integration dummy -0.0424*** -0.0446*** -0.0447***

(0.000) (0.000) (0.000)

R&D intensity -0.3249*** -0.2522*** -0.3290***

(0.000) (0.000) (0.000)

Total factor productivity -0.0090*** -0.0051* -0.0073**

(0.003) (0.098) (0.019)

Size 0.0506*** 0.0530*** 0.0527***

(0.000) (0.000) (0.000)

Observations 14,599 14,599 14,546

Pseudo R2 0.643 0.649 0.645

This table presents the impact of negative input price shocks on the relation between recall incidence and financial

leverage/distress over the sample period of 2006 – 2010. The dependent variable is RecallDum which is set to one for

firms in the recall sample, and zero for control firms. Control firms are firms that belong to the same three-digit SIC

industry as the recalling firm provided they did not have a recall during 2006 – 2010. Negative input price shock is a

dummy variable that is set to 1 if the geometric average growth rate in input prices for the recalling industry over the

five years before the recall was 5% or more and set to 0 otherwise. For all other variables, refer to the Appendix for

details on their construction. Columns (1) – (3) contain estimation results when we include two-digit SIC industry

dummies and calendar year dummies along with our explanatory variables. Marginal effects are reported in the table and

reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***,

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

54

Internet Appendix Table 5

Falsification tests to examine the robustness of the impact of the exogenous shocks on the relation between financial

condition of the firm and the incidence of recalls

Dep. Variable: Recall incidence (1) (2) (3)

Book leverage 0.1118**

(0.047)

Tariff cut_R * Book leverage 0.0074

(0.917)

Market leverage 0.2364***

(0.000)

Tariff cut_R * Market leverage 0.0623

(0.379)

Altman Z -0.0048***

(0.003)

Tariff cut_R * Altman Z 0.0010

(0.656)

Tariff cut_R 0.0216 0.0098 0.0456**

(0.374) (0.656) (0.029)

Control variables Yes Yes Yes

Industry and year dummies Yes Yes Yes

Pseudo R-squared 0.620 0.625 0.624

Observations 9,927 9,927 10,048

Dep. Variable: Recall incidence (1) (2) (3)

Book leverage 0.0099***

(0.000)

Negative input price shock_R * Book leverage 0.0177

(0.720)

Market leverage 0.1382***

(0.000)

Negative input price shock_R * Market leverage -0.0299

(0.553)

Altman Z -0.0010***

(0.000)

Negative input price shock_R * Altman Z -0.0011

(0.249)

Negative input price shock_R -0.0150 -0.0076 -0.0077

(0.231) (0.521) (0.486)

Control variables Yes Yes Yes

Industry and year dummies Yes Yes Yes

Pseudo R-squared 0.639 0.644 0.639

Observations 14,614 14,614 14,561

This table presents the impact of randomized tariff cuts (input price shocks) on the relation between recall incidence and

financial leverage or distress likelihood over the sample period of 2006 – 2010. The dependent variable is RecallDum

which is set to one for firms in the recall sample, and zero for control firms. For each industry-year where Tariff cut

(Negative input price shock) was equal to 1, we randomly assign a year during 2005-2009 for which we code the industry

as falsely experiencing a shock. Tariff cut_R (Negative input price shock_R) is a dummy variable that is set to 1 to these

randomly assigned industry-year periods. For all other variables, refer to the Appendix for details on their construction.

We include two-digit SIC industry dummies and calendar year dummies in all estimations. Marginal effects are reported

in the table and reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are

clustered by firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

55

Internet Appendix Table 6

Impact of leverage and distress likelihood on recall incidence: Two-stage least squares estimations using local

leverage in the same metropolitan area as instrument

Dep. Variable: Recall incidence (1) (2) (3)

Instrument: Local leverage (same metro area)

Book leverage 1.2534***

(0.000)

Market leverage 0.9467*

(0.058)

Altman Z -0.0447*

(0.087)

Free cash flow shock -0.0181 -0.0142 0.0075

(0.144) (0.536) (0.850)

Herfindahl index -0.1041*** -0.0485 -0.1452

(0.005) (0.432) (0.116)

Unionization -0.0010 -0.0013 -0.0003

(0.324) (0.660) (0.913)

Number of suppliers 0.0097*** 0.0065 0.0066

(0.000) (0.187) (0.207)

Vertical integration dummy -0.1028*** -0.1470*** -0.1443***

(0.000) (0.001) (0.008)

R&D intensity -0.1142*** 0.0293 -0.8258**

(0.004) (0.839) (0.017)

Total factor productivity -0.0079 -0.0228*** -0.0346***

(0.251) (0.009) (0.000)

Size 0.0581*** 0.0796*** 0.0939***

(0.000) (0.000) (0.000)

Constant -0.3193*** -0.4451*** 0.4008

(0.000) (0.000) (0.348)

Instrument in first stage 0.1761*** 0.2332*** -4.9390***

(0.000) (0.000) (0.009)

Industry and year dummies 2sic, Year 2sic, Year 2sic, Year

Observations 12,424 12,424 12,375

First stage F-statistic 35.62*** 4.13** 4.09**

Kleibergen-Paap rk LM statistic 50.64*** 18.59*** 6.90***

Endogeneity test 31.50*** 2.99* 3.39*

The dependent variable is set to one for firms in the recall sample, and zero for control firms. Columns (1) – (3) contain

results from the 2nd stage of the two-stage least squares estimation using market leverage of local firms (excluding the

firm and its industry rivals) in the same metropolitan area of the firm as the instrument. We include two-digit SIC industry

dummies and calendar year dummies in all estimations. Reported p-values in the parentheses are based on

heteroskedasticity robust standard errors and are clustered by firm. ***, ** and * indicate significance at 1%, 5%, and

10%, respectively.

56

Internet Appendix Table 7

Probit regressions examining the impact of leverage on recall incidence: Using leverage measures net of cash

Dep. Variable: Recall incidence (1) (2)

Book net debt 0.0908***

(0.000)

Market net debt 0.0966***

(0.000)

Free cash flow shock -0.0294** -0.0329**

(0.044) (0.018)

Unionization 0.0019** 0.0018**

(0.034) (0.044)

Number of suppliers 0.0043*** 0.0039***

(0.000) (0.000)

Vertical integration dummy -0.0388** -0.0378***

(0.013) (0.010)

R&D intensity -0.1476*** -0.1827***

(0.002) (0.000)

Total factor productivity -0.0024 -0.0027

(0.674) (0.616)

Herfindahl index -0.0265 -0.0155

(0.558) (0.720)

Size 0.0440*** 0.0435***

(0.000) (0.000)

Year dummies Yes Yes

Industry dummies Yes Yes

Pseudo R-squared 0.650 0.648

Observations 14,962 14,962

This table presents the recall incidence estimation results. The dependent variable is RecallDum which is set to one

for firms in the recall sample, and zero for control firms. Control firms are firms that belong to the same three-digit

SIC industry as the recalling firm provided they did not have a recall during 2006 – 2010. Refer to the Appendix for

details on the construction of our variables. Book net debt is book value of debt minus cash over book value of assets

and Market net debt as book value of debt minus cash over the sum of book value of debt and market value of equity.

All estimations include two-digit SIC industry dummies and year dummies. Marginal effects are reported in the table

and reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by

firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

57

Internet Appendix Table 8

Probit regressions: The effect of short term debt on recall incidence

Dep. Variable: Recall incidence (1) (2)

Short term debt (book) 0.1023***

(0.004)

Short term debt (market) 0.1723***

(0.000)

Free cash flow shock -0.0485** -0.0439**

(0.010) (0.015)

Herfindahl index -0.0562 -0.0546

(0.299) (0.308)

Unionization 0.0026** 0.0024**

(0.020) (0.031)

Number of suppliers 0.0056*** 0.0049***

(0.000) (0.000)

Vertical integration dummy -0.0459** -0.0465**

(0.024) (0.021)

R&D intensity -0.2894*** -0.2603***

(0.000) (0.000)

Total factor productivity -0.0049 -0.0030

(0.460) (0.648)

Size 0.0541*** 0.0563***

(0.000) (0.000)

Industry and year dummies Yes Yes

Observations 13293 13293

Pseudo R-squared 0.649 0.652

This table presents the recall incidence estimation results for recall events by public firms during our sample period

of 2006 – 2010. The dependent variable is RecallDum which is set to one for firms in the recall sample, and zero for

control firms. Control firms are firms that belong to the same three-digit SIC industry as the recalling firm provided

they did not have a recall during 2006 – 2010. Refer to the Appendix for details on the construction of our variables.

Short term debt (book) is the debt due in the next three fiscal years divided by book value of assets. Short term debt

(market) is the debt due in the next three fiscal years divided by the market value of assets. All estimations contain

two-digit SIC industry dummies and calendar year dummies. Marginal effects are reported in the table and reported

p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***, **

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

58

Internet Appendix Table 9

Estimation of excess leverage and excess Altman Z score

(1) (2) (3)

Dep. variable Book leverage Market leverage Altman Z

Return on assets -0.1995*** -0.2735*** 11.6720***

(0.000) (0.000) (0.000)

Asset collateral value 0.2236*** 0.2186*** -4.2950***

(0.000) (0.000) (0.000)

Non-debt tax shield -24.3063* 7.1598 95.8906

(0.065) (0.622) (0.678)

Ln (Assets) 0.0225*** 0.0210*** -0.4156***

(0.000) (0.000) (0.000)

R&D intensity -0.1573*** -0.2693*** -12.2139***

(0.000) (0.000) (0.000)

Industry cash flow volatility -0.5280* -1.0088*** 12.6078

(0.097) (0.002) (0.146)

Selling, general, and administrative expenses -0.0099* -0.0253*** 0.2809

(0.069) (0.000) (0.243)

Tobin’s Q -0.0044** -0.0352*** 2.3330***

(0.044) (0.000) (0.000)

Herfindahl index 0.0375* 0.0111 -2.0907***

(0.062) (0.595) (0.000)

Constant -0.0015 0.1508 5.3878***

(0.985) (0.360) (0.000)

Year dummies Yes Yes Yes

Industry dummies Yes Yes Yes

R-squared 0.269 0.419 0.397

Observations 22,205 22,195 22,139

This table presents the first stage estimation results used to compute excess leverage and excess Altman Z score used in

Internet Appendix Table 10. The sample comprises of all Compustat firms during our sample period of 2006 – 2010. The

dependent variable is Book leverage in column 1, Market leverage in column 2, and Altman Z in column 3. Return on

assets is operating income (Compustat OIBDP) divided by total assets (Compustat AT). Asset collateral value is net

property, plant, and equipment (Compustat PPENT) divided by total assets (Compustat AT). Ln(Assets) is the natural

logarithm of total assets (Compustat AT). R&D intensity is research and development expenditures (Compustat XRD)

divided by total assets (Compustat AT). Selling, general, and administrative expenses is the selling, general, and

administrative expenses (Compustat XSGA) divided by total revenues (Compustat SALE). Tobin’s Q is book value of

assets (Compustat AT) plus market value of common equity (Compustat PRCC_F times CSHO) minus the book value of

common equity (Compustat CEQ) divided by book value of total assets (Compustat AT). Herfindahl index is the sales

based Herfindahl index of firm’s primary three digit SIC industry. Industry cash flow volatility is the average standard

deviation of quarterly operating income (Compustat OIBDPQ) divided by total assets (ATQ) measured using the past

five years of quarterly data for firms operating in the same two-digit SIC code. Non-debt tax shield is investment tax

credits (Compustat ITCB) divided by total assets (Compustat AT). Columns (1) – (3) include two-digit SIC industry

dummies and calendar year dummies. Reported p-values in the parentheses are based on heteroskedasticity robust

standard errors and are clustered by firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

59

Internet Appendix Table 10

Impact of excess leverage on recall incidence: Probit estimations

Dep. Variable: Recall incidence (1) (2) (3)

Excess book leverage 0.2149*

(0.072)

Excess market leverage 0.1867***

(0.003)

Excess Altman Z -0.0051***

(0.000)

Free cash flow shock -0.0398*** -0.0346** -0.0338**

(0.010) (0.015) (0.014)

Unionization 0.0008 0.0021** 0.0022**

(0.390) (0.032) (0.024)

Number of suppliers 0.0043*** 0.0042*** 0.0041***

(0.000) (0.000) (0.000)

Vertical integration dummy -0.0474*** -0.0442*** -0.0441***

(0.008) (0.008) (0.007)

R&D intensity -0.2712*** -0.1970*** -0.3115***

(0.000) (0.000) (0.000)

Total factor productivity -0.0031 -0.0011 -0.0014

(0.681) (0.871) (0.818)

Herfindahl index -0.0238 -0.0195 -0.0272

(0.639) (0.679) (0.561)

Size 0.0496*** 0.0476*** 0.0502***

(0.000) (0.000) (0.000)

Year dummies Yes Yes Yes

Industry dummies Yes Yes Yes

Observations 14,904 14,749 14,749

Pseudo R-squared 0.640 0.639 0.641

This table presents the recall incidence estimation results for recall events by public firms during our sample period of

2006 – 2010. The dependent variable is RecallDum which is set to one for firms in the recall sample, and zero for control

firms. Control firms are firms that belong to the same three-digit SIC industry as the recalling firm provided they did not

have a recall during 2006 – 2010. Refer to the Appendix for details on the construction of our variables. Excess book

leverage and Excess market leverage are residuals from regressions of book and market leverage on a set of independent

variables shown to be determinants of leverage. Excess Altman Z is the residual from a regression of Altman Z on

determinants of leverage as above. See Internet Appendix Table 9 for further details. Columns (1) – (3) contain probit

estimation results when we include two-digit SIC industry dummies and calendar year dummies along with our

explanatory variables. Marginal effects are reported in the table and reported p-values in the parentheses are based on

heteroskedasticity robust standard errors and are clustered by firm. ***, ** and * indicate significance at 1%, 5%, and

10%, respectively.

60

Internet Appendix Table 11

The impact of financial leverage and distress likelihood on recall incidence: Use of lagged leverage/distress likelihood

A: Use of lagged Book Leverage

Dep. Variable: Recall incidence (1) (2) (3) (4) (5)

Book leverage (t-1) 0.0596*** 0.0699**

(0.009) (0.025)

Book leverage (avg. t-3 through t-1) 0.0495*

(0.051)

Book leverage (t-2) 0.0182 0.0427**

(0.571) (0.048)

Book leverage (t-3) -0.0375 0.0224

(0.190) (0.288)

Free cash flow shock -0.0422*** -0.0419*** -0.0373** -0.0442*** -0.0409***

(0.005) (0.006) (0.012) (0.003) (0.007)

Other control variables Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes

Industry dummies Yes Yes Yes Yes Yes

Pseudo R-squared 0.641 0.639 0.640 0.640 0.638

Observations 15,039 14,854 14,854 15,021 14,856

B: Use of lagged Market Leverage

Dep. Variable: Recall incidence (1) (2) (3) (4) (5)

Market leverage (t-1) 0.1205*** 0.1732***

(0.000) (0.000)

Market leverage (avg. t-3 through t-1) 0.1298***

(0.000)

Market leverage (t-2) 0.0339 0.1012***

(0.396) (0.000)

Market leverage (t-3) -0.0939** 0.0761**

(0.017) (0.016)

Free cash flow shock -0.0356** -0.0447* -0.0206 -0.0451** -0.0445

(0.011) (0.091) (0.372) (0.025) (0.102)

Control variables Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes

Industry dummies Yes Yes Yes Yes Yes

Pseudo R-squared 0.645 0.64 0.643 0.641 0.638

Observations 15,039 13,945 13,945 14,549 13,948

C: Use of lagged Altman Z

Dep. Variable: Recall incidence (1) (2) (3) (4) (5)

Altman Z (t-1) -0.0028*** -0.0028***

(0.000) (0.000)

Altman Z (avg. t-3 through t-1) -0.0021***

(0.005)

Altman Z (t-2) -0.0003 -0.0017***

(0.486) (0.005)

Altman Z (t-3) 0.0006 -0.0008

(0.171) (0.117)

Free cash flow shock -0.0411*** -0.0431*** -0.0349** -0.0454*** -0.0417***

(0.005) (0.004) (0.023) (0.002) (0.005)

Control variables Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes

Industry dummies Yes Yes Yes Yes Yes

Pseudo R-squared 0.642 0.640 0.641 0.640 0.639

Observations 14,986 14,736 14,736 14,923 14,783

This table presents the recall incidence estimation results using probit regression models for recall events by public firms during our sample

period of 2006 – 2010. The dependent variable is RecallDum which is set to one for firms in the recall sample, and zero for control firms.

The average leverage and Altman Z-Score measures are calculated as the average over years t-3 through t-1. Refer to the Appendix for

details on the construction of our variables. All estimations include two-digit SIC industry dummies and year dummies. The other control

variables are the same as in Table 3. Marginal effects are reported in the table and reported p-values in the parentheses are based on

heteroskedasticity robust standard errors and are clustered by firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

61

Internet Appendix Table 12

The impact of financial leverage and distress likelihood on frequency of recalls: Use of lagged leverage/distress likelihood

Dep. Variable:

#Recalls/year (1) (2) (3) (4) (5) (6) (7) (8) (9)

Book leverage (t-1) 1.1790***

(0.004)

Book leverage (t-2) 1.0860***

(0.005)

Book leverage (t-3) 0.8727**

(0.017)

Market leverage (t-1) 2.4005***

(0.000)

Market leverage (t-2) 2.0104***

(0.000)

Market leverage (t-3) 1.8952***

(0.000)

Altman Z (t-1) -0.0330**

(0.015)

Altman Z (t-2) -0.0163***

(0.000)

Altman Z (t-3) -0.0071*

(0.050)

Free cash flow shock -0.3005** -0.4118*** -0.2949** -0.1298 -0.3130*** -0.1150 -0.2594** -0.2993*** -0.2643**

(0.017) (0.002) (0.019) (0.270) (0.002) (0.274) (0.032) (0.004) (0.012)

Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry and year

dummies

Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 12,670 12,652 12,493 12,670 12,190 11,594 12,617 12,554 12,420

Pseudo R-Squared 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63

This table presents the Poisson model estimation results for the number of recall events per year for our sample firms during 2006 - 2010. The dependent variable is #Recalls/year

which is defined as the number of recall events for a recalling firm during year t. For all control firms this variable is defined as zero. The leverage and Altman Z-Score measures

are calculated over years t-3 through t-1. Refer to the Appendix for details on the construction of our variables. All columns contain two-digit SIC industry dummies and

calendar year dummies along with our explanatory variables. Reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by

firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

62

Internet Appendix Table 13

Probit regressions: Test for reverse causality by inclusion of lead leverage and Altman Z-Score

Dep. Variable: Recall incidence (1) (2) (3) (4) (5) (6) (7) (8) (9)

Book leverage 0.0989*** 0.0772** 0.0790***

(0.007) (0.016) (0.002)

Lead Book leverage -0.0129 0.0151 0.0048

(0.658) (0.435) (0.760)

Market leverage 0.1212** 0.1298*** 0.1425***

(0.032) (0.000) (0.000)

Lead Market leverage -0.0488 0.0389 0.0306

(0.308) (0.177) (0.207)

Altman Z -0.0025* -0.0040*** -0.0047***

(0.069) (0.000) (0.000)

Lead Altman Z 0.0015*** 0.0009*** 0.0008***

(0.000) (0.001) (0.000)

Free cash flow shock -0.0380** -0.0336 -0.0371* -0.0364** -0.0286 -0.0347* -0.0374** -0.0287* -0.0353**

(0.035) (0.125) (0.100) (0.049) (0.104) (0.061) (0.034) (0.076) (0.040)

Unionization 0.0036*** 0.0034*** 0.0039*** 0.0025** 0.0020* 0.0027** 0.0010 0.0009 0.0014

(0.000) (0.009) (0.002) (0.038) (0.083) (0.023) (0.354) (0.368) (0.175)

Number of suppliers 0.0075*** 0.0071*** 0.0076*** 0.0053*** 0.0044*** 0.0053*** 0.0047*** 0.0038*** 0.0045***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Vertical integration dummy -0.1480*** -0.1485*** -0.1497*** -0.0484** -0.0507*** -0.0509** -0.0146 -0.0168 -0.0147

(0.000) (0.000) (0.000) (0.023) (0.010) (0.021) (0.492) (0.360) (0.502)

R&D intensity -0.1481* -0.1319* -0.1531** -0.3378*** -0.2661*** -0.3571*** -0.4143*** -0.3386*** -0.4346***

(0.063) (0.071) (0.030) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Total factor productivity -0.0261*** -0.0265** -0.0287** -0.0063 -0.0034 -0.0071 -0.0010 0.0016 -0.0002

(0.000) (0.026) (0.015) (0.384) (0.641) (0.347) (0.871) (0.783) (0.977)

Herfindahl index -0.0220 -0.0206 -0.0235 -0.0059 -0.0039 -0.0140 -0.1195 -0.1145 -0.1066

(0.330) (0.785) (0.757) (0.917) (0.944) (0.810) (0.168) (0.160) (0.228)

Size 0.0900*** 0.0917*** 0.0908*** 0.0566*** 0.0592*** 0.0585*** 0.0504*** 0.0522*** 0.0523***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry dummies No No No 2-digit SIC 2-digit SIC 2-digit SIC 3-digit SIC 3-digit SIC 3-digit SIC

Pseudo R-squared 0.462 0.463 0.462 0.645 0.649 0.645 0.717 0.723 0.720

Observations 13,263 13,236 13,185 13,232 13,205 13,154 12,930 12,904 12,853

This table presents the recall incidence estimation results for recall events by public firms during our sample period of 2006 – 2010. The dependent variable is RecallDum which

is set to one for firms in the recall sample, and zero for control firms. Control firms are firms that belong to the same three-digit SIC industry as the recalling firm provided they

did not have a recall during 2006 – 2010. Refer to the Appendix for details on the construction of our variables. Columns (1) – (3) contain estimation results when we include

calendar year dummies. Columns (4) – (6) ((7) – (9)) contain estimation results when we include two-digit SIC (three-digit SIC) industry dummies and calendar year dummies.

Marginal effects are reported in the table and reported p-values in the parentheses are based on heteroskedasticity robust standard errors and are clustered by firm. ***, ** and *

indicate significance at 1%, 5%, and 10%, respectively.

63

Internet Appendix Table 14

The relation between recall incidence and financial leverage: Test for reverse causality

(1) (2) (3)

Dep. Variable Book leverage (t+1) Market leverage (t+1) Altman Z (t+1)

RecallDum 0.0102 0.0109 -0.5339

(0.187) (0.112) (0.189)

Book leverage (t-1) 0.9298***

(0.000)

Book leverage (t-2) 0.0423

(0.415)

Book leverage (t-3) -0.0123

(0.771)

Market leverage (t-1) 0.6175***

(0.000)

Market leverage (t-2) 0.1131***

(0.000)

Market leverage (t-3) 0.0920***

(0.001)

Altman Z (t-1) 0.5785***

(0.000)

Altman Z (t-2) 0.1071***

(0.009)

Altman Z (t-3) 0.1362***

(0.003)

Ln(assets) -0.0003 0.0029** 0.0053

(0.894) (0.011) (0.955)

Industry Cash flow volatility 0.4777 -0.3440 -11.7830

(0.536) (0.365) (0.683)

Return on assets -0.1584*** -0.0595*** 14.6723***

(0.001) (0.000) (0.000)

Asset collateral value -0.0042 0.0585*** -0.2084

(0.898) (0.000) (0.827)

Non-debt tax shield -9.7390 -11.8415 149.9926*

(0.126) (0.220) (0.077)

R&D intensity -0.0753 -0.0643** -1.9631

(0.321) (0.015) (0.675)

Selling, general, and administrative expenses 0.0078 -0.0003 -1.8073**

(0.520) (0.941) (0.021)

Tobin’s Q 0.0076* -0.0053*** -0.2892

(0.090) (0.000) (0.309)

Herfindahl index 0.0546 -0.0053 -2.9191

(0.427) (0.777) (0.246)

Constant -0.0934*** 0.0023 0.7656

(0.000) (1.000) (0.493)

Industry and year dummies Yes Yes Yes

Pseudo R-squared 0.421 0.680 0.259

Observations 13,577 12,790 13,465

This table presents results where the dependent variable is the lead value of Book leverage, Market leverage, or Altman

Z-Score in year t+1. RecallDum is equal to one for firms with a recall event in year t, and zero for control firms. Refer to

the Appendix for details on the construction of our variables. We include two-digit SIC industry dummies and calendar

year dummies in all estimations. Reported p-values in the parentheses are based on heteroskedasticity robust standard

errors and are clustered by firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.

64

Internet Appendix Table 15

Propensity score matching: Test for reverse causality A: Clean recall events N Treated Control Difference T-stat Treated Control Difference T-stat

Four matches One match

Change in book leverage 233 0.0038 0.0130 -0.0092 -0.93 0.0037 -0.0024 0.0061 0.48

Change in market leverage 233 0.0307 0.0439 -0.0132 -0.93 0.0307 0.0702 -0.039** -2.13

Change in Altman Z-Score 236 -0.2260 -1.2264 1.0004 2.95*** -0.2260 -1.2813 0.4781** 2.21

B: All recall events Treated Control Difference T-stat Treated Control Difference T-stat

Four matches One match

Change in book leverage 2,785 0.0086 0.0161 -0.0075 -0.63 0.0086 0.0121 -0.0035 -0.25

Change in market leverage 2,781 0.0256 0.0247 0.0010 0.08 0.0256 0.0172 0.0084 0.60

Change in Altman Z-Score 2,785 -0.4447 -0.8128 0.3681 0.57 -0.4447 -0.7078 0.2630 0.37

This table presents the results for change in leverage/financial distress around recall events during our sample period of 2006 – 2010. The treated group contains recalling firms

covered by the Consumer Product Safety Commission (CPSC), National Highway Traffic Safety Administration (NHTSA), and Food and Drug Administration (FDA). For each

recalling firm, we find matching control firms based on all attributes in Table 3 other than the financial condition variables. Results based on the four closest matching control

firms as well as the closest matching control firm are reported. The changes in financial condition variables are measured as the difference between the policy variable for year

t+1 and the value of the performance variable for year t-1 where year t is the recall announcement year. In Panel A, we consider only recalls that are uncontaminated by

preceding or subsequent recalls. In Panel B, we consider all recalls in our sample. Results are based on propensity score matching implemented through PSMATCH2 command

of STATA, the column Treated (Control) reports the change in the performance variable for the treated (control) group. The column Difference represents the average treatment

effect of the treated (ATT). The T-stat and Z-stat measure the statistical significance of ATT. ***, ** and * indicate significance at 1%, 5%, and 10% respectively.

65

Internet Appendix Table 16

Capital raising events and recall incidence

Dep. Variable: Recall incidence (1) (2) (3) (4) (5) (6)

Capital raising dummy 0.0098 0.0072 0.0107 0.0119 -0.0007 0.0120

(0.298) (0.421) (0.275) (0.308) (0.944) (0.298)

Book leverage 0.0570** 0.0583**

(0.013) (0.022)

Market leverage 0.1176*** 0.1112***

(0.000) (0.000)

Altman Z -0.0027*** -0.0026***

(0.000) (0.001)

Capital raising dummy x Book leverage -0.0075

(0.851)

Capital raising dummy x Market leverage 0.0352

(0.344)

Capital raising dummy x Altman Z -0.0003

(0.764)

Free cash flow shock -0.0396*** -0.0337** -0.0384*** -0.0397*** -0.0338** -0.0384***

(0.006) (0.014) (0.007) (0.006) (0.014) (0.007)

Herfindahl index -0.0143 -0.0130 -0.0208 -0.0144 -0.0120 -0.0208

(0.750) (0.769) (0.650) (0.748) (0.787) (0.650)

Unionization 0.0022** 0.0019** 0.0024** 0.0022** 0.0019** 0.0024**

(0.018) (0.038) (0.012) (0.018) (0.039) (0.012)

Number of suppliers 0.0044*** 0.0037*** 0.0043*** 0.0044*** 0.0036*** 0.0043***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Vertical integration dummy -0.0399** -0.0414*** -0.0421** -0.0399** -0.0413*** -0.0421**

(0.015) (0.006) (0.012) (0.015) (0.006) (0.012)

R&D intensity -0.2593*** -0.2110*** -0.2824*** -0.2593*** -0.2095*** -0.2829***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Total factor productivity -0.0045 -0.0024 -0.0042 -0.0045 -0.0026 -0.0043

(0.420) (0.665) (0.468) (0.423) (0.641) (0.466)

Size 0.0461*** 0.0480*** 0.0481*** 0.0460*** 0.0481*** 0.0481***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Industry and year dummies Yes Yes Yes Yes Yes Yes

Observations 15,039 15,039 14,986 15,039 15,039 14,986

Pseudo R-Squared 0.641 0.645 0.642 0.641 0.645 0.642

This table presents the recall incidence estimation results for recall events by public firms during our sample period of 2006 – 2010. The

dependent variable is RecallDum which is set to one for firms in the recall sample, and zero for control firms. Control firms are firms that

belong to the same three-digit SIC industry as the recalling firm provided they did not have a recall during 2006 – 2010. Capital raising

dummy is an indicator variable set to 1 if the recalling firm had a capital raising event during a period of six months after the recall date,

and set to 0 otherwise. For control firms, it is set to 1 if the there was a capital raising event during a period of six months after the fiscal

year end date, and set to 0 otherwise. Capital raising events are identified based on equity and debt offerings available on the SDC Platinum

database. Refer to the Appendix for details on the construction of our variables. All estimations include two-digit SIC industry dummies

and calendar year dummies. Marginal effects are reported in the table and reported p-values in the parentheses are based on

heteroskedasticity robust standard errors and are clustered by firm. ***, ** and * indicate significance at 1%, 5%, and 10%, respectively.