Impact of financial leverage on the incidence and severity of product failures:
Evidence from product recalls
Omesh Kinia, Jaideep Shenoyb, Venkat Subramaniamc,*
a
Robinson College of Business, Georgia State University, Atlanta, GA 30303
b
School of Business, University of Connecticut, Storrs, CT 06269
c
A.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: [email protected] (Omesh Kini), [email protected] (Jaideep Shenoy),
[email protected] (Venkat Subramaniam).
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.
1
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
2
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
5
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
6
(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
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/
3
7
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
8
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 twodigit 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
9
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.
10
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 longterm 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 threedigit 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 crosssectional regression setting using probit regression models. Our main specification is:
Prob(RecallDumi,t  1)  F (β 0  β 1 Financial leverage/D istress likelihood i,t -1  β 2 Free cash flow shock i,t -1
 β 3Unionizationi,t -1  β 4 Number of suppliersi,t -1  β 5Vertical integration dummy i,t -1
 β 6 R & D intensityi,t -1  β 7 Total factor productivi tyi,t -1  β 8 Herfindahl index i,t -1  β 9 Sizei,t -1
 Year and/or industry dummies  ε
i, t
)
(1)
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), yearand 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
12
(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.
13
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.
14
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.
15
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:
16
Prob(Recall Dumi,t  1)  F (β 0  β1Shock dummy i,t -1  β 2 Financial leverage/D istress likelihood i,t -1
 β 3 Shock dummy i,t -1 * Financial leverage/D istress likelihood i,t -1  β 4 Free cash flow shocki,t -1
 β 5Unionizationi,t -1  β 6 Number of suppliersi,t -1  β 7Vertical integration dummy i,t -1  β 8 R & D intensityi,t -1
 β 9Total factor productivi tyi,t -1  β10 Herfindahl index i,t -1  β11Sizei,t -1
 Year and/or industry dummies  ε
i, t
)
(2)
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:
Prob(RecallDum  1) (Frequency of recall i,t )  F ( β 0  β1 Debt due at onset of crisisi,t -1
i,t
 β 2 Free cash flow shocki,t -1   Unionizationi,t -1   Number of suppliersi,t -1
3
4
 β 5Vertical integration dummy i,t -1  β 6 Herfindahl index i,t -1  β 7 R & D intensityi,t -1
 β 8Total factor productivi tyi,t -1  β 9 Sizei,t -1  Industry dummies  ε )
i, t
8
(3)
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.
17
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 industryyear 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.
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,
10
18
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.
19
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 fiveyear window to compute input price shocks are qualitatively similar, and are reported in Internet Appendix Table 4.
20
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
21
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.
22
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).
23
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.
24
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.
25
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
26
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.
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.
16
27
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
28
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 leveragerelated 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.
29
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.
30
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.
31
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.
32
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).
33
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).
34
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37
Table 1
Frequency of recall events and industries covered in recall sample
A: Frequency of recall events by type of recall
Year of recall
FDA recalls
2006
2007
2008
2009
2010
Total
552
453
504
496
439
2,444
Number of observations
CPSC recalls
NHTSA recalls
78
108
75
52
64
377
225
170
121
92
79
687
Total
855
731
700
640
582
3,508
B: Industries covered in recall sample at two-digit SIC level from highest to lowest frequency of recalls
Two-digit
Description of industry
Number of
SIC
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
79
Equipment
53
35
54
39
51
57
56
59
26
34
50
73
58
23
87
General Merchandise Stores
57
Industrial and Commercial Machinery and Computer Equipment
54
Food Stores
54
Miscellaneous Manufacturing Industries
46
Wholesale Trade-non-durable Goods
33
Home Furniture, Furnishings, and Equipment Stores
24
Apparel And Accessory Stores
20
Miscellaneous Retail
18
Paper and Allied Products
17
Fabricated Metal Products, Except Machinery and Transportation Equipment
17
Wholesale Trade-durable Goods
9
Business Services
9
Eating And Drinking Places
8
Apparel and Other Finished Products Made From Fabrics
6
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.
38
Table 2
Univariate comparisons between recalling firms and control firms
A: Leverage/financial distress variables
Variable name
Recall sample
N
Mean
Median
Book leverage
3,508
0.266
0.257
Market leverage
3,508
0.263
0.168
Altman Z
3,508
4.093
3.250
N
12,315
12,306
12,244
B: Control variables
Variable name
Control sample
Mean
Median
0.167
0.091
0.156
0.061
4.139
3.153
T (Z) Stat
3.77*** (34.1***)
2.54** (30.30***)
-0.11 (3.03**)
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. ZStat 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.
39
Table 3
Probit regressions: Impact of leverage and distress likelihood on recall incidence
Dep. Variable: Recall incidence
Book leverage
Market leverage
(1)
(2)
0.0622***
(0.002)
(3)
(4)
(5)
0.0596***
(0.009)
0.0566**
(0.028)
(7)
(8)
(9)
0.0579***
(0.001)
0.1205***
(0.000)
Altman Z
(6)
0.1279***
(0.000)
-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)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
2-digit SIC
2-digit SIC
3-digit SIC
3-digit SIC
3-digit SIC
Industry dummies
No
No
No
2-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.
40
Table 4
The impact of tariff cuts on the relation between recall incidence and financial leverage or distress likelihood
Dep. Variable: Recall incidence
Book leverage
Tariff cut * Book leverage
(1)
(2)
0.0923***
(0.001)
0.1430***
(0.009)
Market leverage
0.2330***
(0.000)
0.1826***
(0.001)
Tariff cut * Market leverage
Altman Z
0.0085
(0.680)
-0.1053***
(0.000)
0.0031***
(0.002)
0.0092***
(0.000)
-0.0918***
(0.000)
-0.5771***
(0.000)
-0.0033
(0.581)
-0.0775*
(0.099)
0.0005
(0.978)
-0.0904***
(0.000)
0.0029***
(0.004)
0.0076***
(0.000)
-0.0963***
(0.000)
-0.4590***
(0.000)
0.0029
(0.640)
-0.0819*
(0.082)
-0.0042***
(0.000)
-0.0038*
(0.080)
0.0650***
(0.000)
-0.0945***
(0.000)
0.0027***
(0.005)
0.0090***
(0.000)
-0.0963***
(0.000)
-0.6294***
(0.000)
-0.0034
(0.587)
-0.0901*
(0.061)
0.0974***
(0.000)
Yes
Yes
0.1016***
(0.000)
Yes
Yes
0.1008***
(0.000)
Yes
Yes
Tariff cut * Altman Z
Tariff cut
Free cash flow shock
Unionization
Number of suppliers
Vertical integration dummy
R&D intensity
Total factor productivity
Herfindahl index
Size
Year dummies
Industry dummies
(3)
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.
41
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)
Book leverage
(2)
(3)
0.0099***
(0.000)
0.1011*
(0.076)
Negative input price shock * Book leverage
Market leverage
0.1299***
(0.000)
-0.0109
(0.819)
Negative input price shock * Market leverage
Altman Z
Negative input price shock * Altman Z
Negative input price shock
-0.0539***
(0.000)
-0.0435***
(0.000)
0.0010**
(0.043)
0.0045***
(0.000)
-0.0435***
(0.000)
-0.3146***
(0.000)
-0.0089***
(0.003)
-0.0150
(0.523)
0.0511***
(0.000)
Yes
Yes
Free cash flow shock
Unionization
Number of suppliers
Vertical integration dummy
R&D intensity
Total factor productivity
Herfindahl index
Size
Year dummies
Industry dummies
-0.0449***
(0.001)
-0.0377***
(0.002)
0.0012**
(0.019)
0.0035***
(0.000)
-0.0449***
(0.000)
-0.2421***
(0.000)
-0.0046
(0.133)
-0.0170
(0.467)
0.0532***
(0.000)
Yes
Yes
-0.0025***
(0.000)
-0.0104***
(0.000)
-0.0253*
(0.055)
-0.0423***
(0.000)
0.0014***
(0.005)
0.0042***
(0.000)
-0.0452***
(0.000)
-0.3121***
(0.000)
-0.0070**
(0.024)
-0.0265
(0.265)
0.0529***
(0.000)
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.
42
Table 6
The impact of debt maturing at the onset of the financial crisis on recall propensity (number of recalls)
(1)
Rcldum08
(2)
Rcldum08-09
(3)
Rcldum08-10
Debt due at onset of crisis
(4)
#Recalls08
(5)
#Recalls08-09
(6)
#Recalls08-10
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)
Yes
Yes
Yes
Yes
Yes
Yes
Industry dummies
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.
43
Table 7
Impact of leverage and distress likelihood on recall incidence: Linear probability models and two-stage least squares estimations
(1)
(2)
(3)
(4)
Dep. Variable: Recall incidence
OLS
OLS
OLS
IV
Book leverage
Market leverage
0.0936***
(0.001)
(5)
IV
Instrument: Local leverage
(6)
IV
2.1759***
(0.000)
0.1798***
(0.000)
1.6200**
(0.041)
Altman Z
-0.0049***
(0.000)
-0.0498***
(0.000)
0.0013
(0.389)
0.0075***
(0.000)
-0.1228***
(0.000)
-0.2827***
(0.000)
-0.0373***
(0.000)
-0.0728
(0.220)
0.0756***
(0.000)
-0.4499***
(0.000)
-0.0485**
(0.038)
Free cash flow shock
-0.0540***
-0.0486***
0.0086
0.0145
0.0128
(0.000)
(0.000)
(0.682)
(0.679)
(0.729)
Unionization
0.0013
0.0010
-0.0033**
-0.0037
-0.0005
(0.384)
(0.511)
(0.042)
(0.365)
(0.855)
Number of suppliers
0.0078***
0.0070***
0.0060***
0.0007
0.0059
(0.000)
(0.000)
(0.009)
(0.934)
(0.235)
Vertical integration dummy
-0.1173***
-0.1234***
-0.1078***
-0.1833***
-0.1484***
(0.000)
(0.000)
(0.000)
(0.004)
(0.007)
R&D intensity
-0.2097***
-0.1666***
-0.0181
0.2243
-0.8753***
(0.000)
(0.000)
(0.803)
(0.318)
(0.005)
Total factor productivity
-0.0359***
-0.0348***
0.0108
-0.0154
-0.0348***
(0.000)
(0.000)
(0.376)
(0.177)
(0.000)
Herfindahl index
-0.0676
-0.0614
-0.1515***
-0.0547
-0.1542*
(0.260)
(0.294)
(0.006)
(0.474)
(0.086)
Size
0.0727***
0.0757***
0.0556***
0.0924***
0.0966***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Constant
-0.4420***
-0.4891***
-0.3276***
-0.5426***
0.4611
(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.
44
Table 8
Impact of leverage and distress likelihood on severity of product recalls: Ordered probit estimations
Dep. Variable: Recall severity
Book leverage
(1)
(2)
(3)
0.2115**
(0.029)
Market leverage
(4)
(5)
(6)
-0.0333
(0.775)
0.6926***
(0.000)
Altman Z
(7)
-0.0171***
(0.000)
-0.0975
(0.242)
1.0184***
(0.000)
Tariff cut * Book leverage
Tariff cut * Market leverage
-0.0434
(0.596)
0.6085***
(0.000)
-0.0115***
(0.000)
0.2015***
(0.004)
-0.0130***
(0.000)
1.0867***
(0.003)
Tariff cut * Altman Z
-0.0168*
(0.062)
Negative input price shock
-1.0991***
(0.000)
Negative input price shock * Book
leverage
-0.9107***
(0.000)
0.2900
(0.496)
Negative input price shock *
Altman Z
Unionization
Number of suppliers
Vertical integration dummy
R&D intensity
Total factor productivity
-0.5465***
(0.000)
1.0083*
(0.069)
Negative input price shock *
Market leverage
Free cash flow shock
(9)
0.0502**
(0.011)
0.6182***
(0.000)
Tariff cut
(8)
-0.5993***
(0.000)
0.0090**
(0.036)
0.0157***
(0.000)
-0.4278***
(0.001)
-2.5676***
(0.000)
0.0522**
-0.5672***
(0.000)
0.0073*
(0.092)
0.0134***
(0.000)
-0.4600***
(0.000)
-2.3021***
(0.000)
0.0686***
-0.5657***
(0.000)
0.0091**
(0.033)
0.0145***
(0.000)
-0.4410***
(0.001)
-2.6630***
(0.000)
0.0593**
-0.6285***
(0.000)
0.0073
(0.106)
0.0088**
(0.013)
-0.3808***
(0.008)
-2.8657***
(0.000)
0.0392
45
-0.5661***
(0.000)
0.0053
(0.249)
0.0074**
(0.036)
-0.4121***
(0.004)
-2.4931***
(0.000)
0.0588**
-0.5912***
(0.000)
0.0080*
(0.074)
0.0063*
(0.073)
-0.3940***
(0.006)
-2.9060***
(0.000)
0.0479*
-0.5932***
(0.000)
0.0100**
(0.023)
0.0117***
(0.000)
-0.3782***
(0.004)
-2.6825***
(0.000)
0.0389
-0.5600***
(0.000)
0.0084*
(0.061)
0.0103***
(0.000)
-0.4018***
(0.002)
-2.4141***
(0.000)
0.0552**
-0.0812***
(0.000)
-0.5594***
(0.000)
0.0100**
(0.022)
0.0112***
(0.000)
-0.3898***
(0.003)
-2.7032***
(0.000)
0.0501*
(0.037)
(0.007)
(0.019)
(0.156)
(0.036)
(0.087)
(0.125)
(0.033)
(0.052)
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)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Industry dummies
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 threedigit 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.
Herfindahl index
46
Table 9
The impact of financial leverage and distress likelihood on number of recalls: Poisson models
(1)
#Recalls
Dep. Variable: Number of recalls (2006-2010)
Book leverage
(2)
#Recalls
(3)
#Recalls
0.9441**
(0.047)
Market leverage
2.1103***
(0.000)
Altman Z
Free cash flow shock
-0.3132
(0.581)
0.0163
(0.281)
0.0326***
(0.000)
-1.7339***
(0.000)
-3.5500*
(0.060)
-0.0206
(0.843)
-3.8529***
(0.000)
0.7931***
(0.000)
-4.3933***
(0.000)
Yes
0.713
Unionization
Number of suppliers
Vertical integration dummy
R&D intensity
Total factor productivity
Herfindahl index
Size
Constant
Industry dummies
Pseudo R-squared
-0.4458
(0.439)
0.0124
(0.425)
0.0214***
(0.001)
-1.7308***
(0.000)
-2.4823
(0.182)
0.0562
(0.588)
-3.7925***
(0.000)
0.8524***
(0.000)
-5.2571***
(0.000)
Yes
0.713
-0.0357**
(0.017)
-0.4609
(0.449)
0.0178
(0.236)
0.0357***
(0.000)
-1.7984***
(0.000)
-3.5865*
(0.055)
0.0097
(0.929)
-3.9454***
(0.000)
0.7898***
(0.000)
-3.9967***
(0.000)
Yes
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.
47
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
48
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
49
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
Book leverage
Control variables
Observations
Chg_Prob
SIC=38
SIC=28
SIC=20
SIC=37
0.1554***
(0.001)
Yes
2,058
7.41%
0.0827**
(0.010)
Yes
2,645
9.41%
0.4424***
(0.001)
Yes
446
29.21%
0.7665***
(0.000)
Yes
1,044
19.12%
SIC=38
SIC=28
SIC=20
SIC=37
0.4348***
(0.000)
Yes
2,058
12.20%
0.3039***
(0.000)
Yes
2,645
19.09%
0.4756***
(0.002)
Yes
446
30.61%
0.9923***
(0.000)
Yes
1,044
56.26%
SIC=38
SIC=28
SIC=20
SIC=37
All other SIC
codes
0.0130*
(0.082)
Yes
8,846
4.22%
Dependent Variable: RecallDum
Market leverage
Control variables
Observations
Chg_Prob
All other SIC
codes
0.0200***
(0.007)
Yes
8,846
6.21%
Dependent Variable: RecallDum
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.
50
Internet Appendix Table 2
Effect of leverage and financial distress likelihood on recall incidence: Conditional logit estimations
Dep. Variable: Recall incidence
(1)
Book leverage
(2)
(3)
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.
51
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)
Tariff cut
0.1224***
(0.000)
0.1296**
(0.019)
0.1140**
(0.019)
Industry book leverage
0.6239***
(0.001)
0.6617***
(0.000)
2.1731**
(0.016)
Industry cash flow shock
Constant
(6)
(7)
(8)
0.6904***
(0.004)
0.6829***
(0.004)
0.5260***
(0.004)
0.6610***
(0.007)
0.5686***
(0.001)
1.8784**
(0.018)
0.6820**
(0.012)
0.5674***
(0.001)
1.9319**
(0.028)
-0.1260
(0.774)
0.1087*
(0.065)
Negative input price shock
Ln(industry revenues)
(5)
0.6635***
(0.000)
2.2724**
(0.016)
-0.1829
(0.696)
-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.
52
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)
0.0754*
(0.088)
Negative input price shock * Book leverage
Market leverage
0.1429***
(0.000)
-0.0165
(0.681)
Negative input price shock * Market leverage
Altman Z
Negative input price shock * Altman Z
Negative input price shock
-0.0568***
(0.000)
-0.0442***
(0.000)
0.0034
(0.885)
0.0010**
(0.034)
0.0044***
(0.000)
-0.0424***
(0.000)
-0.3249***
(0.000)
-0.0090***
(0.003)
0.0506***
(0.000)
Free cash flow shock
Herfindahl index
Unionization
Number of suppliers
Vertical integration dummy
R&D intensity
Total factor productivity
Size
-0.0482***
(0.000)
-0.0366***
(0.002)
0.0027
(0.911)
0.0009*
(0.061)
0.0035***
(0.000)
-0.0446***
(0.000)
-0.2522***
(0.000)
-0.0051*
(0.098)
0.0530***
(0.000)
-0.0027***
(0.000)
-0.0070***
(0.000)
-0.0372***
(0.000)
-0.0423***
(0.001)
-0.0066
(0.784)
0.0011**
(0.025)
0.0042***
(0.000)
-0.0447***
(0.000)
-0.3290***
(0.000)
-0.0073**
(0.019)
0.0527***
(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.
53
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
Book leverage
Tariff cut_R * Book leverage
(1)
0.1118**
(0.047)
0.0074
(0.917)
Market leverage
(2)
0.2364***
(0.000)
0.0623
(0.379)
Tariff cut_R * Market leverage
Altman Z
0.0216
(0.374)
Yes
Yes
0.0098
(0.656)
Yes
Yes
-0.0048***
(0.003)
0.0010
(0.656)
0.0456**
(0.029)
Yes
Yes
0.620
9,927
(1)
0.0099***
(0.000)
0.0177
(0.720)
0.625
9,927
(2)
0.624
10,048
(3)
Tariff cut_R * Altman Z
Tariff cut_R
Control variables
Industry and year dummies
Pseudo R-squared
Observations
Dep. Variable: Recall incidence
Book leverage
Negative input price shock_R * Book leverage
Market leverage
0.1382***
(0.000)
-0.0299
(0.553)
Negative input price shock_R * Market leverage
Altman Z
Negative input price shock_R * Altman Z
Negative input price shock_R
Control variables
Industry and year dummies
(3)
-0.0150
(0.231)
Yes
Yes
-0.0076
(0.521)
Yes
Yes
-0.0010***
(0.000)
-0.0011
(0.249)
-0.0077
(0.486)
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.
54
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.
55
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.
56
Internet Appendix Table 8
Probit regressions: The effect of short term debt on recall incidence
Dep. Variable: Recall incidence
(1)
Short term debt (book)
(2)
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.
57
Internet Appendix Table 9
Estimation of excess leverage and excess Altman Z score
Dep. variable
(1)
Book leverage
Return on assets
(2)
Market leverage
(3)
Altman Z
-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.
58
Internet Appendix Table 10
Impact of excess leverage on recall incidence: Probit estimations
Dep. Variable: Recall incidence
Excess book leverage
(1)
(2)
(3)
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.
59
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.
60
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)
Book leverage (t-1)
(7)
(8)
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)
Free cash flow shock
(9)
-0.3005**
(0.017)
Yes
Yes
-0.4118***
(0.002)
Yes
Yes
-0.2949**
(0.019)
Yes
Yes
-0.1298
(0.270)
Yes
Yes
-0.3130***
(0.002)
Yes
Yes
-0.1150
(0.274)
Yes
Yes
-0.2594**
(0.032)
Yes
Yes
-0.2993***
(0.004)
Yes
Yes
-0.0071*
(0.050)
-0.2643**
(0.012)
Yes
Yes
Control variables
Industry and year
dummies
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.
61
Internet Appendix Table 13
Probit regressions: Test for reverse causality by inclusion of lead leverage and Altman Z-Score
Dep. Variable: Recall incidence
Book leverage
Lead Book leverage
Market leverage
Lead Market leverage
(1)
(2)
0.0989***
(0.007)
-0.0129
(0.658)
(3)
(4)
(5)
0.0772**
(0.016)
0.0151
(0.435)
0.1212**
(0.032)
-0.0488
(0.308)
(7)
(8)
(9)
0.0790***
(0.002)
0.0048
(0.760)
0.1298***
(0.000)
0.0389
(0.177)
Altman Z
(6)
0.1425***
(0.000)
0.0306
(0.207)
-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)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
2-digit SIC
2-digit SIC
3-digit SIC
3-digit SIC
3-digit SIC
Industry dummies
No
No
No
2-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.
62
Internet Appendix Table 14
The relation between recall incidence and financial leverage: Test for reverse causality
Dep. Variable
RecallDum
Book leverage (t-1)
Book leverage (t-2)
Book leverage (t-3)
(1)
Book leverage (t+1)
0.0102
(0.187)
0.9298***
(0.000)
0.0423
(0.415)
-0.0123
(0.771)
Market leverage (t-1)
(2)
Market leverage (t+1)
0.0109
(0.112)
(3)
Altman Z (t+1)
-0.5339
(0.189)
0.6175***
(0.000)
0.1131***
(0.000)
0.0920***
(0.001)
Market leverage (t-2)
Market leverage (t-3)
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.
63
Internet Appendix Table 15
Propensity score matching: Test for reverse causality
A: Clean recall events
N
Treated
Change in book leverage
Change in market leverage
Change in Altman Z-Score
B: All recall events
233
233
236
0.0038
0.0307
-0.2260
Treated
Control
Difference
Four matches
0.0130
-0.0092
0.0439
-0.0132
-1.2264
1.0004
Control
T-stat
-0.93
-0.93
2.95***
Treated
0.0037
0.0307
-0.2260
Control
Difference
One match
-0.0024
0.0061
0.0702
-0.039**
-1.2813
0.4781**
T-stat
0.48
-2.13
2.21
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.
64
Internet Appendix Table 16
Capital raising events and recall incidence
Dep. Variable: Recall incidence
Capital raising dummy
Book leverage
Market leverage
(1)
(2)
(3)
(4)
(5)
(6)
0.0098
(0.298)
0.0570**
(0.013)
0.0072
(0.421)
0.0107
(0.275)
0.0119
(0.308)
0.0583**
(0.022)
-0.0007
(0.944)
0.0120
(0.298)
0.1176***
(0.000)
Altman Z
0.1112***
(0.000)
-0.0027***
(0.000)
Capital raising dummy x Book leverage
-0.0026***
(0.001)
-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.
65