economic performance and vulnerability to ecological...
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ECONOMIC PERFORMANCE, STRATEGIC POSITION, AND
VULNERABILITY TO ECOLOGICAL PRESSURE AMONG
INTERSTATE MOTOR CARRIERS
Jack A. Nickerson
Washington University in St. Louis
John M. Olin School of Business
Brian S. Silverman
University of Toronto
Rotman School of Management
Running head: Economic Performance, Strategic Position, and Failure in
U.S. Trucking [70 characters]
Mailing addresses etc.: Jack A. Nickerson, Washington University in St.
Louis, Olin School of Business, Campus Box 1133, One Brookings Drive, St.
Louis, MO 63130-4899, (314) 935-6374 [phone], (314) 935-6359 [fax].
Brian S. Silverman, University of Toronto, Rotman School of
Management, 105 St. George Street, Toronto, ON M5S 3E6, (416) 978-0305
[phone], (416) 978-4629 [fax].
ABSTRACT
We explore interactions between an organization’s strategic position,
economic performance, and vulnerability to ecological pressures. We posit that
(1) high profitability buffers an organization from density-driven competitive
pressure and (2) this effect is moderated by an organization’s strategic positioning
choices. Our empirical tests, relying on longitudinal data from the U.S. for-hire
trucking industry, provides evidence in support of these predictions.
1. INTRODUCTION
Firm survival and exit have been studied through at least two distinct
lenses: economics and organization theory. Economists typically focus on market
forces that enable efficient firms to drive out their inefficient rivals (Tirole 1988).
As competition in a market increases, usually due to reduced demand, the
pressure on less efficient firms to exit increases accordingly. Organizational
ecologists agree that populations of organizations expand and contract as
resources of various kinds become more or less abundant (Hannan and Freeman
1989). However, while they acknowledge that market competition influences this
process, organizational ecologists typically emphasize the role of socially driven
criteria, such as political or institutional ties, rather than organizational efficiency
in determining which firms survive and which exit (Carroll 1988).
Until recently, economic and ecological approaches to organizational
failure have remained separate and distinct. In their recent assessment of the state
of organizational ecology research, Amburgey and Rao (1996) point out that
“despite numerous ecological analyses of organizational death relying on diverse
populations, researchers’ understanding of dissolution...is limited by the dearth of
studies that treat financial performance as a predictor of mortality.” Similarly,
economic approaches to organization failure (e.g., Ghemawat and Nalebuff 1985;
Klepper 1996; Schary 1991) have tended to ignore ecological considerations.
Recent research has attempted to bridge the gap between economics and
organizational ecology. Ingram (this volume) incorporates concepts from agency
theory into ecological models to explain growth and survival of hotel chains.
Haveman (1992, 1993) explores the influence of banks’ financial performance on
organizational change, finding that poorer performance triggers organizational
change after controlling for conventional ecological factors. Silverman et al.
(1997) explore the effect of interstate motor carriers’ financial performance on
carrier mortality, finding that poorer financial performance significantly increases
the likelihood of failure independent of the effects of conventional ecological
factors.
Our study contributes to this recent research stream by further integrating
economic and ecological factors. We build upon Nickerson and Silverman (1997)
and Silverman et al. (1997) to explore interactions between an organization’s
strategic choices, economic performance, and vulnerability to ecological
pressures. In particular, we explore the underlying nature of the effect of
profitability on organization survival. We posit that an organization’s economic
performance affects its vulnerability to density-driven competitive pressure. We
further predict that the effect of economic performance operates differentially on
organizations depending on their strategy. High-performing organizations whose
strategies employ firm- and industry-specific resources enjoy differentially
greater survival benefits than high-performing organizations whose strategy relies
on more generic resources.
We test our hypotheses through an empirical study of the U.S. trucking
industry. Although this industry was essentially deregulated in 1980, unusually
strict reporting requirements remained in effect through 1995. We are
consequently able to include remarkably detailed financial and organizational
information for an important segment of the motor carrier population. We
interact measures of competitive pressure—notably population density—with a
measure of economic performance (profitability) to determine the degree to which
performance affects a firm’s vulnerability to competitive pressure. We also
examine the extent to which the effect of profitability on survival is moderated by
the degree to which a carrier targets a particular segment of the market: less-than-
truckload (LTL) freight, which relies more heavily on specific resources and
capabilities than does the truckload (TL) motor carriage segment (Nickerson and
Silverman 1997).
This paper proceeds as follows. In Section 2 we present theoretical
background and hypotheses. Section 3 briefly describes the for-hire U.S.
interstate trucking industry. Section 4 describes the data and specifies the model.
Section 5 presents empirical results. Section 6 discusses and concludes.
2. THEORY AND HYPOTHESES
While economic theory is admittedly opaque about the process by which
firms are selected out of a population, economists have no doubt as to the
fundamental mechanism that drives selection. The lack of (expected) profits is
the primary reason that a firm exits a market. In neoclassical economics, a firm is
conceived as a production function – a mechanism to produce a particular set of
goods. If the firm cannot produce goods at a cost sufficiently below the market
price such that it earns its risk-adjusted rate of return, then it will cease production
(Ghemawat and Nalebuff 1985; Deily 1988; Lieberman 1990). The implication
of economic theory, then, is that profitable economic performance is the primary
driver of organizational mortality.1
Organization theorists have acknowledged that variance in economic
performance is likely to generate different probabilities of organization failure. In
their examination of a sample of large U.S. corporations that declared bankruptcy
and a matched pair of surviving firms, Hambrick and D’Aveni (1988) find that
economic performance as measured by return on assets had a stronger and more
consistent impact on failure than any of their nonperformance measures. Barnett
and Carroll (1987) include market share, which they interpret as a proxy for
economic performance, in an analysis of telephone company mortality and find its
effect to be negative, although the models that include market share do not
estimate density-dependent selection.2 Finally, Silverman et al. (1997), find that
economic performance (as measured by return on sales or return on assets) has a
negative impact on mortality of large motor carriers in the deregulated U.S.
trucking industry, independent of density dependent mortality effects.
While this literature indicates that firms generating high profits will
exhibit lower mortality rates than low-profitability firms, we expect profitability
to be most beneficial to organizations under particularly hazardous competitive
conditions. Specifically, we propose that the survival advantage associated with
high profitability will increase with the intensity of density-driven competition.
Organizational ecology scholars have paid particular attention to the role
of population density in influencing rates of exit (Hannan and Carroll 1992;
Hannan and Freeman 1989). Although the density dependence model
traditionally assumes that all organizations in a population are equally susceptible
to density-driven competitive pressure, recent work has extended the model by
permitting each firm to experience competition differently, on the basis of
differences in organizational features.
For example, in a study of the effects of institutional linkages on day care
and nursery school mortality rates, Baum and Oliver (1991) generalize the density
dependence framework to account for the greater buffering against competitive
pressure experienced by organizations that have institutional linkages (as
compared to those without such links).3 To accomplish this, they model the
interaction between the presence of institutional linkages and population density.
Similarly, Miner et al. (1990) study the effects of alliance formation on survival
of Finnish newspaper publishers. While they do not test directly for the
relationship between a publisher’s alliances and the newspaper’s ability to
withstand density-driven competition, the results of their models indicate that
density exerts somewhat less severe competition on publishers with alliances than
on those without.
We contend that economic performance provides an alternate source of
buffering against competitive pressure (Porter 1985; Barney 1991; Silverman et
al. 1997). Profitable economic performance enhances an organization’s survival
in a number of ways. Current profits provide resources to offset future temporary
losses that might otherwise cause an organization to fail; strong economic
performance eliminates the need to draw down a firm’s accumulated resources.
Further, profits provide the wherewithal to undertake additional investments that
can improve organizational fitness without incurring the cost of accessing the
capital markets.4 This leads us to hypothesize:
H1: Strong economic performance reduces the effect of density-driven
competition on an organization’s mortality rate.
Economic theory provides three competing explanations for variation in
profits in the short-run (that is, at a given point in time). First, variation in
performance can be a manifestation of industry-wide entry barriers or strategic
group-wide mobility barriers (Porter 1980, 1985). Such barriers often rely on
strategic commitments by incumbent firms that deter entry (Ghemawat 1991).
Second, such variation can stem from unmeasured firm heterogeneity, such that
firms with highly valuable and costly-to-imitate assets, resources, and capabilities
are comparatively efficient and earn greater profits (Demsetz 1973; Barney
1991).5 Third, variation in economic performance can be driven by market niche
heterogeneity, such that firms that face unexpectedly few direct competitors (or
enjoy unexpectedly high demand) earn greater profits. These explanations offer
distinct implications for survival benefits associated with economic performance.
Profits driven by the first two explanations are likely to be sustainable, in that
heterogeneous resource profiles (Barney 1991; Dierickx and Cool 1989),
idiosyncratic industry-specific or firm-specific investments (Williamson 1985;
Nickerson 1997), or investments that serve as strategic commitments to fight new
entrants (Ghemawat 1991) create conditions that make entry costly and difficult,
and protect firms’ profits from competition. In this case, high profits should be
associated with long-run superior performance, including enhanced survival
chances. In contrast, high profits driven by the third explanation should attract
entry, which should increase competitive pressure, erode the high profits of the
incumbents to normal levels, and thus yield few long-term benefits to these firms
in terms of profits or survival (Tirole 1988).
The degree to which a firm invests in heterogeneous capabilities,
idiosyncratic assets, and strategic commitments is largely driven by its strategic
position. A firm’s choice of strategic position—which type of customers to
target—has far-reaching implications for the profile of assets and capabilities it
must assemble (Nickerson and Silverman 1997). This asset/capability profile in
turn forms the basis of a firm’s ability to sustain long-term performance. For
example, Pirrong (1993) notes that steamship carriers targeting unusual types of
freight invest in highly idiosyncratic ships to economically transport such freight.
One implication of this, we contend, is that profits accruing to the transport of
such freight are partially shielded from entry because of the idiosyncratic assets
involved and the long lead times in assembling such assets (lead time for
construction of a ship typically takes years). In contrast, high profits in ocean
transport activities that rely on generic or fungible assets are likely to quickly
attract entry, thus competing away long-term benefits. Conversely, low-
performing ocean carriers that rely on specialized ships can not easily divert their
assets to more profitable niches, while low-performing ocean carriers that rely on
generic ships are more likely to find profitable alternatives. Thus, we predict that:
H2: Economic performance has a greater effect on the mortality rates of
organizations whose strategic positions rely on investments in firm- or
industry-specific resources than on the mortality rates of those whose
strategies do not.
3. THE U.S. INTERSTATE TRUCKING INDUSTRY
The deregulated U.S. interstate for-hire trucking industry offers a
conducive setting in which to test our hypotheses.5 While the U.S. for-hire
trucking industry was born at the turn of this century, it remained something of a
curiosity until the First World War. Railroads (and incumbent motor carriers),
threatened by the dramatic increase in entry during the 1920s, lobbied intensely
for regulatory constraints on price and entry at both the intrastate and interstate
levels (Stigler 1971; Childs 1985). As a result, the U.S. interstate for-hire
trucking industry was placed under the regulatory supervision of the Interstate
Commerce Commission (ICC) in 1935. The ICC severely restricted entry of new
firms and expansion of existing motor carriers. At the same time, regional price
bureaus were established to set route- and freight-specific price floors for motor
carriage services, thus enabling motor carriers to earn significant rents, a portion
of which was extracted by unionized labor (Moore 1973; Rose 1987). This
arrangement persisted until the Carter administration pushed regulatory reform of
the industry through Congress in 1980. The reform process essentially
deregulated entry and price, which has led to tremendous increases in both entry
and exit of motor carriers, intense competition and severe downward pressure on
prices (Robyn 1984; Corsi et al. 1992). Whereas the number of ICC certified
carriers hovered around 16,000 between 1960 and 1975, by the end of 1991 some
47,890 ICC certified carriers were in operation.
For the purposes of this study, two features of trucking firms are salient.
First, for-hire motor carriage is generally divided into two types of carriage. The
first, known as less-than-truckload (LTL) carriage, involves the movement of
shipments of under 10,000 pounds (Roadway Express is a familiar example of
this type of transport). The second, known as truckload (TL) carriage, involves
the movement of shipments of 10,000 pounds or more directly from origin point
to destination point. These two types of carriage require significantly different
types of investment and organizational resources. LTL carriage typically uses a
hub-and-spoke system to efficiently consolidate and distribute freight from
multiple origin points to multiple destinations (see Figure 1). This network
frequently requires specialized investments in breakbulk facilities—large,
specially designed warehouses to allow rapid unloading, sorting, and reloading of
freight onto trucks. While breakbulk facilities can be redeployed for other uses
such as manufacturing, the idiosyncrasies of their construction have little value
outside of LTL carriage, which translates into a high degree of industry-specific
and site-specific investment. Such specialization consequently represents a
degree of commitment to fighting entry into a carrier’s LTL territory, as well as a
difficult-to-replicate resource within that territory. Such specialization also
implies that a carrier involved in LTL carriage can not easily “move” to a new
geographic area in response to poor performance in its current area.
PLACE FIGURE 1 ABOUT HERE
Second, different logistics in LTL hauls as compared to TL hauls require
different degrees of coordination by motor carriers. At its most basic level, TL
carriage requires little more than a truck and a telephone: a dispatcher gets a call
from shipper X requesting carriage of freight from point A to point B, and she
dispatches a truck and driver to undertake the haul. The driver need not interact
with any other co-workers to complete the assignment. For LTL carriage,
however, a truck not only carries shipper X’s freight, but also carries freight from
many other shippers with origin points near point C to destinations possibly quite
distant from point D. The hub-and-spoke nature of LTL carriage requires the
timely coordination of truck arrivals and departures at breakbulk facilities. The
late arrival or departure of a truck into or out of a breakbulk facility can cause a
costly ripple effect throughout the entire LTL network. The ability to manage
logistical coordination activities is therefore an organizational capability of
significantly greater competitive importance for LTL than for TL activities.
The differences between TL and LTL carriage correspond to different
strategic positions, at least as measured along a single dimension, which are
supported by different resource profiles.6 The physical and organizational
resources employed in TL carriage are relatively generic compared to those
resources employed by LTL carriage. Thus, according to H2 we expect that high
performing organizations concentrating on LTL carriage will enjoy greater
survival benefits than high performing organizations concentrating on TL
carriage, and that low-performing organizations concentrating on LTL carriage
will incur higher survival penalties than low-performing TL-oriented carriers.
4. RESEARCH METHODS
Data Description
We tested our hypotheses using data describing characteristics of all large
interstate motor carriers operating in the United States between 1977 and 1989.7
Thanks to the reporting demands placed on motor carriers by the Interstate
Commerce Commission, the data available for this segment of the trucking
industry is unusually detailed. The ICC required large motor carriers—private
and public—to file detailed annual reports, known as Form Ms, from as early as
1944 through 1995. The Form M includes a comprehensive income statement,
balance sheet, and description of a number of operational and organizational
characteristics. We used the Form Ms to compile event histories for all large
motor carriers that operated in the United States at any time between 1977 and
1989. This covers the entire period of deregulation until relaxation of some
reporting requirements in 1990.
In 1977, 2669 of the carriers in the sample were already in operation. The
life histories for these organizations are left-censored. Additional information
provided by the ICC enabled us to identify the founding dates for virtually all left-
censored carriers. By the end of 1989, entry and exit led to a population of 1588
large carriers. As described below, exit is defined as the failure of a firm or the
closure of a subsidiary. Changes in ownership are not included as failures,
because the organization itself continued to operate. This is consistent with
common practice in organizational mortality studies (Baum 1996).
During the 1977-1989 period, the number of small carriers rose from
16,606 to 42,700, reflecting the end of regulatory restrictions on entry.
Limitations of the data
The data used in this study has several limitations that constrain
interpretation of empirical results. Below we describe in detail two significant
limitations, addressing their implications and describing our methods for
minimizing their effect.
Size Bias. Since 1980, the ICC has only required interstate carriers with
annual revenues of $1 million or more to file comprehensive Form Ms.
Fortunately, the ICC’s Annual Report to Congress provides information on the
total motor carrier population, regardless of carrier revenue, for each year in our
sample. We are therefore able to add small carrier population density to our data,
partially ameliorating the revenue cutoff. However, we can not derive life
histories for each small carrier. As a result, we can examine the effect of small
carrier density on failure rates of large carriers, but we are unable to directly
investigate organizational mortality of small carriers.
Given this omission, we can not generalize our results to the small carrier
population. However, we find no reason ex ante to expect that inclusion of small
carriers would yield different results in our models. Silverman et al. (1997)
estimate ecological models with this data using various revenue floors to partially
evaluate the effect of omitting small carriers. Their results are essentially
identical across a wide range of revenue cutoffs, indicating that the size-bias
problem is not severe.
Exit Measurement. There are two potential problems with measuring exit
in this sample: 1) counting as an exit instances where a carrier falls below the $1
million revenue floor but continues to operate, and 2) aggregating two types of
exit—failure and acquisition—into a single category.
It is possible that firms that exit our database in fact continue to operate
with revenues slightly below the $1 million floor. To reduce this problem, we
categorize as failed only those carriers that disappear from the ICC’s Form M
population and never return (through 1989). In addition, we artificially
introduced a revenue floor of $1.2 million and found that of the 591 firms that
exited by permanently dropping below this floor, only 10 of the firms did not also
drop below the $1 million threshold.8
The size bias in our data adds one further wrinkle to our measurement of
mortality. Until 1980 the ICC used a revenue cutoff of $500,000 rather than $1
million to classify Class 2 carriers. The change in disclosure requirements
threatens to bias our results by artificially creating a spike in exits in 1980.
Following Zingales (1994), who tested several levels between $500,000 and $1.5
million to determine an appropriate cutoff, we set the sales floor for 1977-1979 at
$1 million.
Second, and potentially more problematic, is the challenge of
distinguishing between exit by dissolution and exit by acquisition. Prior research
has demonstrated that different processes operate on dissolution than on
divestiture/acquisition (Mitchell 1994). As Boyer (1993) notes, however, merger
activity has been low in the post-deregulation for-hire trucking industry—firms
have generally eschewed mergers in favor of purchasing the assets of bankrupt
carriers. This is due to “the problem of unfunded pension liabilities that followed
the writing down of the value of certificates after deregulation: an operating
trucking firm often has a negative net worth while a bankrupt carrier has positive
scrap value” (Boyer 1993: 485). Further, in those acquisitions that have taken
place, the acquirer has frequently maintained an arm’s length relationship with its
acquisition (at least in a legal sense), particularly when the acquirer is unionized
and the acquiree is not. In these cases, the acquired company continues to report
to the ICC as a separate entity, and remains in our data sample as a continuing
firm.
To check the prevalence of exit through merger, we searched through one
year of Traffic World, a weekly trade journal, for announcements of failures,
mergers, or acquisitions. Failure announcements outnumbered acquisition
announcements by more than two to one (19 vs. 8). We then checked our
database to determine whether these firms stopped filing Form Ms. All but one of
the failed firms disappeared from our sample; all but one of the acquired firms
continued to file their own Form Ms. In sum, 18 of the 19 exits from our database
that we checked were failures. Therefore, we assume that large carriers that exit
our sample do so due to failure rather than merger.
Independent variables
Table 1 summarizes definitions and predictions for all independent and
control variables used in this study. The independent variables of interest in this
study are all interaction terms. We first describe the relevant stand-alone
variables and their associated main effects.
Profitability: Firms’ economic performance typically is parameterized in
terms of expected profits and past profits.9 In an ideal world, Tobin’s Q would be
the most appropriate measure of expected profits for our study, since it most
directly measures expected profitability. The vast majority of firms in our sample
are private, which precludes calculation of Tobin’s Q values.10 We instead rely
on a measure of the prior year’s profitability—return-on-sales (ROS) in the prior
year—as a second-best proxy for our expected profits.11 Silverman et al. (1997)
find that increasing ROS reduces a carrier’s likelihood of exit. As described
below, we include a carrier’s debt/equity ratio as a proxy to control for past
profits.
Population Density: The traditional density dependence model proposes
that organizational failure rates are affected by two forces—legitimation and
competition. Each of these increases with population density, but legitimation
increases at a decreasing rate while competition increases at an increasing rate.
As a result, organization failure rates exhibit a U-shaped curve as a function of
population density. Empirical estimation of density dependence typically
includes a count of the population and the square of this count, and finds a
negative and positive coefficient, respectively.
Silverman et al. (1997) found separate effects of the density of large
carriers and of small carriers on large carrier mortality rates. Specifically, they
hypothesized and found that organizational failure rates for large motor carriers
increase with large motor carrier density.12 They also hypothesized and found
that organizational failure rates of large motor carriers first decrease, and then
increase, as small motor carrier density increases. We therefore include separate
measures for large carrier and small carrier density. Large Density is a count of
the number of large motor carriers in the population at time t. Small Density is a
count of the number of small motor carriers in the population at time t. Large
Density2 and Small Density2 are the squares of Large Density and Small Density,
respectively.
LTL vs. TL freight: LTL_Prop is the proportion of a carriers total
revenue that is derived from LTL carriage. We have no theoretical expectations
for the main effect of a carrier’s dedication to LTL carriage on its likelihood of
exit.
Interactions between profitability and density: To test Hypothesis 1 we
need information on the interaction between each carrier’s economic performance
and the density-driven competition that it faces. ROS*Large Density is the
product of ROS and Large Density. ROS*Small Density2 is the product of ROS
and Small Density2. Hypothesis 1 proposes that higher economic performance
will buffer an organization from the effects of increasing competition in the
population, thus reducing its likelihood of failure. We therefore expect the
coefficients for each of these terms to be negative. We interact ROS with these
two density terms, and not with the others, because these are the density terms for
which Silverman et al. (1997) hypothesized and found competitive effects.
(Sensitivity tests involving other ROS-Density interactions yielded similar results
and are available from the authors).
Interactions between profitability and LTL_Prop: To test Hypothesis 2 we
need information on the interaction between each carrier’s performance and the
degree to which it targets LTL carriage, which we argue requires greater
investment in rare capabilities and idiosyncratic assets that signify the ability and
commitment to fight new entry. ROS*LTL_Prop is the product of ROS and
LTL_Prop. Hypothesis 2 proposes that the effect of economic performance on an
organization’s mortality rates is differentially greater for organizations that
employ firm-specific or industry-specific resources and assets. As described
above, LTL carriage requires firms to invest in tangible and organizational
resources that are firm-specific and industry-specific whereas TL carriage
requires no such investment. We therefore expect the coefficient for this
interaction term to be negative.
PLACE TABLE 1 ABOUT HERE
Control Variables
Many other factors may influence the fates of large motor carriers.
Accordingly, in addition to the terms described above, the analysis controls for a
variety of additional carrier characteristics and industry-specific and
macroeconomic environmental factors.
Organization Characteristics. The U.S. trucking industry underwent severe
environmental changes sparked by deregulation in 1980. Following Silverman et
al. (1997), we employ three age clocks to account for the associated effects.
AgeAtD is an age clock that freezes in 1980, the year of deregulation. For the
years 1981 through 1989, AgeAtD retains its value from 1980. The clock is set to
0 for carriers that were not born before 1980. AgePostD/Inc and AgePostD/Ent
are age clocks for firms existing in 1980 and for firms born after 1980,
respectively. Each begins with the first year of a carrier’s existence after 1980.
Separate clocks are constructed for incumbents and for entrants to test for
different effects of age on the two types of firms. LnRev, the log of revenue, is
included to control for effects of carrier size. Leverage, measured as the ratio of
debt to total assets (debt + equity), is included to control for effects of a carrier’s
past profits. This is consistent with Bourgeois (1981), Singh (1986), and
Hambrick and D’Aveni (1988), and is based on the argument that a firm’s profits
or losses over time are reflected in the firm’s capital structure.13 In addition,
LTL*Leverage, is included to control for LTL-specific effects of carrier capital
structure. Silverman et al. (1997) find that both of these terms raise a carrier’s
mortality rate. Union is a categorical variable set equal to 1 if a carrier
contributed to a union pension fund and 0 otherwise. Finally, we included a Left-
Censored variable, set equal to 1 for carriers founded before 1977 and 0
otherwise, to examine whether left-censored carriers had systematically different
survival rates.
Environment Characteristics. We also included several measures to
control for factors influencing the carrying capacity of the for-hire trucking
industry. Ecological research suggests that prior foundings and failures can
influence organizational mortality. To control for these effects, we include
Births and Deaths in year t-1. The trucking industry’s fortunes largely mirror the
business cycle. To control for general macroeconomic conditions, we include the
change in GDP in year t. We also include Future GDP—the change in GDP in
year t+1 to capture the effect of economic expectations on carrier survival.
Lastly, we include a categorical variable, Dereg, set equal to 1 for years 1980
through 1989 and 0 otherwise, to control for greater likelihood of mortality after
deregulation in 1980.
Interactions between organization and environment characteristics:
LTL*Large Density and LTL*Small Density2 are the products of LTL_Prop
and Large Density and Small Density2, respectively. We interact LTL_Prop with
these two density terms to control for differential competitive effects of density
on firms focusing on different strategies. We include these measures to control
for the possibility that the coefficients for our independent variables –
ROS*Large Density, ROS*Small Density2, and ROS*LTL_Prop – are biased by
unobserved differences in the effect of density-driven competition on LTL-
oriented carriers as compared to TL-oriented carriers.
Correlations and descriptive statistics for all independent variables are
presented in Table 2. The correlations are generally small to moderate in
magnitude, with the exception of the density measures and AgePostD/Inc. Such
levels of multicollinearity generally do not raise problems for our estimation. To
the extent that they do, they result in less precise parameter estimates but do not
bias parameter estimates (Kennedy 1992).
PLACE TABLE 2 ABOUT HERE
Specification of the Model
This study estimated the exit rate of large motor carriers as h(t), the
instantaneous rate of exit. We modeled the hazard rate as an exponential model
(performed in STATA) according to the following specification:
h(t) = exp{βXt}
where Xt = a vector composed of the independent variables that appear in Table 1.
To incorporate time variation in the covariates, we used a multiple-spells
construction of this model. Each carrier’s life history is disaggregated into one-
year observations in which the carrier is at risk of failure. Each of these spells is
treated as right-censored unless the carrier fails.
4. RESULTS
Table 3 reports maximum-likelihood estimates for the analysis of large
motor carrier failure rates. Model 1 provides a baseline model that includes basic
ecological variables and control variables. The baseline indicates a liability of
smallness and a liability of age for motor carriers—larger carriers and younger
carriers are less likely to exit than their smaller or older counterparts.
Density effects are consistent with Silverman et al. (1997). Large Density
has a competitive effect; while Large Density2 has a negative coefficient, the
downward-sloping portion of the density curve falls almost entirely outside the
range of observed values in our data. This suggests that the second-order effect
of Large Density moderates the rate at which mortality increases with Large
Density. Small carrier density exhibits a U-shaped curve, suggesting a
mutualistic and then competitive effect from small carrier density. However,
much of the downward sloping portion of this curve falls outside the range of
observed values in our data. Thus, while mutualism may operate, the competitive
effect on large carrier mortality appears to quickly swamp any mutualistic effect
between the populations as small carrier density increases.
Future GDP has a significant negative effect on failure while GDP is
insignificant, suggesting that economic expectations play a more significant role
than current business conditions on failure rates of large motor carriers.
LTL_PROP is insignificant. Leverage is positive and significant, which suggests
that mortality increases with lower past profits. LTL*Leverage is insignificant.
Union is positive and significant, which suggests that unionized firms are more
likely to fail.
PLACE TABLE 3 ABOUT HERE
Model 2 adds the ROS measure. ROS has a significant negative effect on
failure rates—the better a carrier’s economic performance, the less likely it will
fail. A likelihood ratio test (Χ2[1]= 10.98, p < 0.01) indicates that Model 2
improves significantly over Model 1. In addition, we note that aside from a
modest increase in significance for LTL*Leverage and decline in significance for
Union, none of the variables is affected by the addition of ROS.
Model 3, which improves significantly over Model 2 (Χ2[2] = 9.22, p <
0.01), adds the interaction terms between ROS*Large Density and ROS*Small
Density2. H1 predicted that strong economic performance would reduce the
effect of density-driven competition on a carrier’s failure rate. Consistent with
H1, ROS*Large Density and ROS*Small Density2 both have significant negative
coefficients. Inclusion of these variables reverses the sign of ROS to positive.
However, the overall relationship between ROS and failure throughout the
observed range of data remains negative.14 All other independent density
variables retain their magnitude, sign, and significance. Thus, carriers with
higher levels of economic performance are less affected by the competitive effects
of population density than their low-profit counterparts. How much less? When
large carrier density and small carrier density are both held at their means, an
increase in a carrier’s ROS from its mean value to 1 standard deviation above this
corresponds to a decrease in its failure rate of almost 5%.
More interesting is the effect of carrier ROS throughout the range of
population densities observed in our sample. Figure 2 plots large carrier mortality
rates as a function of large carrier competition for three different levels of carrier
ROS. For low levels of density, ROS has little differentiating effect on carrier
mortality. As density increases, however, the moderating effect of ROS on the
density-failure rate relationship increases as well. Figure 3 replicates this plot for
the effects of small carrier density. Just as with large carrier density, the
buffering effects of ROS are increasingly evident as population density
increases.15
PLACE FIGURES 2 AND 3 ABOUT HERE
Model 4 adds only the interaction term ROS*LTL_Prop to model 2. This
model improves significantly over Model 2 (Χ2[1] = 5.96, P< 0.01). Applying H2
to the for-hire interstate trucking industry, we predicted that economic
performance would affect mortality rates of carriers targeting LTL freight more
than those of carriers targeting TL freight. Consistent with this hypothesis, the
coefficient for ROS*LTL_Prop is significant and negative. In this model, ROS
falls to insignificance, suggesting that the effect of ROS on a carrier’s failure rates
is entirely dependent on the degree to which that carrier targets LTL freight.
Figure 4 plots the large carrier mortality rates over the observed range of ROS in
this population. A firm that is entirely dedicated to truckload carriage receives a
relatively slight buffering effect from increased ROS, whereas a firm that has
invested in serving the LTL market sees dramatic improvement in its mortality
rate. Alternatively, the survival prospects of a firm that is dedicated to TL
carriage are only mildly reduced by poor economic performance, whereas those
of an LTL carrier are dramatically harmed.
PLACE FIGURE 4 ABOUT HERE
Model 5 includes all three interaction terms. In addition, Model 5
includes the interaction terms LTL*Large Density and LTL*Small Density2 to
control for spurious interaction effects. This model offers a significant
improvement over Models 3 (Χ2[3] = 14.88, P < 0.01) and 4 (Χ2[4] = 18.18, P <
0.01)). The interaction terms related to our hypotheses retain essentially the same
signs, magnitudes, and significance. We interpret these results as evidence of
the robustness of our findings, and as further support for our hypotheses: ROS
buffers a firm from the increasing competitive intensity associated with density—
strong economic performance reduces the likelihood that a firm will exit as
density increases. In turn, the salutary effects of economic performance on a
carrier’s survival chances are moderated by the degree to which these profits are
protected by market imperfections, be they mobility barriers, firm-specific
resources, or entry-deterring commitments.
5. DISCUSSION AND CONCLUSION
Competition is central to both ecological and economic approaches to
organizational failure. This study was prompted by the assumption that
investigating organizational failure through an integrative ecological and
economic framework would generate more insight into the dynamics of
organizational failure than would either theory in isolation. Until recently,
organizational ecologists have been silent on the role of economic factors in
organizational failure. Similarly, economists have ignored those factors, which
are at the heart of ecological approaches to organizational failure. Our paper has
attempted to address this gap by investigating (1) the effect of an organization’s
economic performance on its susceptibility to density-related competitive
pressures and (2) the effect to which the buffering effects of economic
performance on organizational mortality are conditional on an organization’s
strategic choices.
The results of our analysis suggest that economic performance moderates
density-related competitive pressures and thus reduces the likelihood of
organizational mortality in predictable ways. Profitability was found to have a
significant negative effect on failure rates after controlling for the level of
competition. Indeed, the benefits of high profitability increase with increasing
population density. Perhaps more interesting is the result that some benefits of
economic performance vary by firm strategy. LTL carriers reap more sustainable
effects—for good or ill—from economic performance than do TL carriers. We
interpret this result as an indication that the underlying source of economic
performance—whether driven by investments in sticky and difficult-to-replicate
assets and capabilities (as in LTL) or by fungible and generic assets and
capabilities (as in TL)—determines the long-term effects of performance on
organizational mortality. These results suggest that an organization's risk of
failure depends critically on the interaction between its past economic
performance, its strategy, and the magnitude of density-related competitive
pressures.
The deregulated trucking industry offered a unique test bed for exploring
these interactions. Trucking is an industry that was long regulated, with a very
large number of organizations in the regulated population. Consequently, the
winds of change blew particularly fiercely when the industry was deregulated. It
is possible that the interaction between profitability and density may not be a
general characteristic of other populations. A better understanding of the
generalizability of these results is conditional on their replication in other
industries.
The study suffers from a number of other limitations as well. Return-on-
sales is a proxy for expected profitability. ROS is a measure of past accounting
profits, although is likely to be strongly correlated with accounting profits. Thus,
our analysis does not directly evaluate expected profitability. Our empirical
analysis is limited by the lack of complete event histories for small carriers (size
bias) and by the threshold limits for large carriers (exit measurement), although,
as described above and in Silverman et al. (1997), sensitivity analyses suggest
these biases are minimal. Also, carrier strategy is parameterized by only one
variable: LTL_Prop. Although LTL_Prop is arguably one of the most important
dimensions of a carrier’s strategy, Nickerson and Silverman (1997) argue that
carriers may differentiate along other dimensions (notably service efficiency and
reliability supported by specific investments) as well. These additional strategic
dimensions may further complicate the relationship between economic
performance, strategy, and competitive pressure.
Finally, while we have explored the effect of profitability on mortality, we
have not explored the determinants of profitability. Recent research has
highlighted the effect of localized competition, rather than population- or
industry-wide competition, on organizational failure rates (e.g., Baum and Mezias
1992; Baum and Singh 1994). It is plausible that localized competition affects a
firm’s profitability—indeed, this is consistent with several above-described
economic explanations of variance in economic performance. Further elaboration
of our model to include localized competition may generate insight into the
relationships among localized competition, profitability, and motor carrier failure.
Limitations notwithstanding, this study is among the first to explore
interactions between organizational and competitive influences on firm
performance. As such, it responds to recent calls in the strategy and organization
theory literature for research to understand the “reciprocal interactions…between
the market environment and firm capabilities” (Henderson and Mitchell 1997).
While this study does not directly unpack organizational capabilities, it presents a
theoretical and methodological step toward this ultimate goal.
ACKNOWLEDGMENT
We gratefully acknowledge the comments and suggestions of Nick
Argyres, Joel Baum, Lyda Bigelow, Paul Ingram and Joanne Oxley. Research for
this paper was supported by the Alfred P. Sloan Foundation and the Connaught
Foundation.
NOTES
1 Although several theoretical and empirical studies of exit have
highlighted the relationship between capacity and exit – elaborating conditions
under which firms or plants with greater capacity will exit before their smaller
counterparts – these studies have assumed (and often found) a positive
relationship between cost and exit (Baden-Fuller 1989; Deily 1988; Ghemawat
and Nalebuff 1985; Reynolds 1988; Schary 1991; Whinston 1988).
2 Mitchell (1994) compares models predicting market share with those
predicting mortality, finding that new entrants in the medical imaging industry
face a trade-off between rapid increases in market share and the likelihood of
mortality. This may suggest a more complex relationship between market share
and failure.
3 Baum and Oliver (1991) identify two institutional linkages in their study
of child care service organizations: linkages to government agencies (via
Purchase of Service Agreements) and linkages to community organizations (via
Site-Sharing Arrangements). Such “legitimating linkages to well established
societal institutions” (p. 189) buffer an organization from failure by increasing its
ability to obtain resources (e.g., customers; financial capital; etc.). Baum and
Oliver attempt to disentangle the direct economic effects of these linkages from
the institutional effects, finding indirect evidence that the latter exists
independently of the former.
4 Firms with few profitable prospects will be unable to access capital
markets and thus will be unable to weather temporary losses. Firms with
profitable prospects but temporary cash-flow constraints may benefit from
accessing capital markets, albeit at a cost. (For instance, accessing capital
markets incurs transaction costs as well as opportunity costs from disclosing
strategic information.) Firms with profitable prospects and current profitability
can take advantage of opportunities by investing past profits without the cost of
accessing capital markets.
5 Bogner et al. (this volume) identify several commonalities underlying
resource-based and strategic group-based explanations.
6 “For-hire” trucking refers to motor carriage provided by stand-alone
transportation firms. It is distinguished from “private” trucking, which refers to
in-house provision of motor carriage. Since 1935, regulations have strictly
prohibited private trucking fleets from carrying any freight other than that
belonging to their parent firms.
7 Nickerson and Silverman (1997) propose that carriers match resource
profiles to the customer segments they serve. They test their hypotheses
empirically through a cross-sectional analysis of the for-hire trucking industry.
Emphasis on LTL freight carriage proved to be one of the most significant
dimensions along which carrier strategy differed. Other dimensions included
service efficiency and reliability supported by specific investments. In this paper
we investigate the long-run effects of the LTL vs. TL strategy/resource profile
choice.
8 The ICC categorizes motor carriers as Class 1, which have revenues
exceeding $5 million, Class 2, which have revenues exceeding $1 million, and
Class 3, which encompasses all other motor carriers. A carrier must have
approximately 10 trucks to generate more than $1 million in annual revenue. We
refer to Class 1 and 2 firms as “large carriers” and Class 3 firms as “small
carriers.”
9 We also checked the Yellow Pages for three cities—San Francisco, St.
Louis, and Denver—for firms that exited our database in 1985. None of the
exiting firms headquartered in those cities showed up in the Yellow Pages for
1994. In contrast, most of the trucking firms headquartered in those cities that
remained in our sample through 1989 did appear in the 1994 Yellow Pages.
While an ideal check would be to examine the Yellow Pages for each year after
1985 to gauge the speed with which these exiting firms ceased operations, this
check provides crude evidence that firms that fall below the $1 million threshold
do not persist for long.
10 Many of the economic studies described earlier parameterize economic
performance by cost rather than profits. This is consistent with the homogeneous
product market assumption of neoclassical economic theory. However, the
trucking industry includes heterogeneous services and strategic positions (Smith
et al. 1990). We therefore parameterize performance by profit rather than by cost,
and note that future work might entail more sophisticated cost measurement (e.g.,
Deily et al. 1997).
11 Alternatively, we could employ a measure of equity per unit of activity.
However, equity-based measures are problematic for two reasons. The market
value of equity includes both expected profits and liquidation value. Because
equity includes the value of both opportunities, the sign on its coefficient is not
clear ex ante (Schary 1991). In addition, most carriers in our sample are private,
so that reported equity does not reflect market valuation.
12 Return-on-sales is a signal of, albeit imperfectly correlated with,
expected profits. It is superior to return-on-equity because of the prevalence of
unfunded pension liabilities in this industry (Boyer 1993). The need to cover
such unfunded liabilites is likely to lead to a higher level of retained earnings and
hence higher reported equity, ceteris paribus. Thus, the beneficial effects of a
large amount of equity may be confounded with the unobserved detrimental
effects of an unfunded pension liability.
13 The motor carrier population in Silverman et al. (1997) and in this study
consists of an organization form that existed for more than 40 years at the time the
studies begin. Legitimation effects are likely to be negligible at this stage in a
population’s existence. It is therefore not surprising that population density
demonstrates competitive effects only.
14 Alternate measures for past profits might include retained earnings or
changes in working capital. We also estimated models that included ln(Equity) as
a measure of retained earnings. Our results (available upon request) were not
sensitive to inclusion of this measure.
15 In other words, βROS*ROS + βROS*Large Density* ROS*Large Density +
βROS*Small Density2* ROS*Small Density2 yields a negative hazard rate over the
observed range of densities for any positive value of ROS. Negative values of
ROS lead to a positive hazard rate.
16 We note that large carrier density decreases, and small carrier density
increases, monotonically with time. To control for the possibility that our
ROS*Large Density and ROS*Small Density2 results are driven by unobserved
time varying covariates, we also estimated the above models with two additional
control variables: CLOCK (a clock beginning in 1980) and ROS*CLOCK (an
interaction term between ROS and CLOCK). The coefficients for ROS*Large
Density and ROS*Small Density2 are unaffected by inclusion of these variables.
(ROS*CLOCK is significant and negative, indicating that the effect of ROS on
survival grew stronger over time; CLOCK is insignificant.) We thank Paul
Ingram for suggesting this sensitivity analysis.
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Table 1: Definition and Predictions for Independent and Control Variables
* See Silverman et al. (1997) for additional discussion of predictions for control variables.
Variable Definition Prediction Independent variables ROS*Large Density product of ROS and Large Density - (H1)
ROS*Small Density2 product of ROS and Small Density2 - (H1)
ROS*LTL_Prop product of ROS and LTL - (H2)
Control variables* ROS net income/revenue for carrier i for year t-1 -
Large Density # of large motor carriers (sales >= $1 million) operating in the U.S. at end of year t
+
Large Density2 square of Large Density / 1000 -
Small Density # of small motor carriers (sales < $1 million) operating in the U.S. at end of year t / 1000
-
Small Density2 square of Small Density / 1000000 +
LTL_Prop proportion of carrier i’s revenue in year t-1 derived from LTL freight carriage
LTL*Large Density product of LTL_Prop and Large Density
LTL*Small Density2 product of LTL_Prop and Small Density2
LnRev natural log of revenue for carrier i for year t-1 -
AgeAtD for 1977-1980, age of carrier i; for 1981-1989, age of carrier i in 1980 (0 if carrier born after 1980)
+
AgePostD/Inc and AgePostD/Ent
number of years carrier i has had interstate motor carriage operating certification post-1980 in year t
+
Leverage debt/(debt+equity) for carrier i at end of year t-1 +
Leverage*LTL product of leverage and LTL +
Births (Deaths) # of carriers entering (exiting) the large motor carrier data set for year t-1 / 1000
GDP % change in U.S. GDP between t-1 and t (year-end) -
Future GDP % change in U.S. GDP between t and t+1 (year-end) -
Union 1 if carrier i contributes to union pension plan; 0 otherwise
+
Dereg 1 for years 1980-1989; 0 otherwise +
Left Censor 1 if carrier i existed before 1977; 0 otherwise
Tab
le 2
: Mea
ns, S
tand
ard
Dev
iatio
ns, a
nd C
orre
latio
n M
atri
x fo
r V
aria
bles
Exit
Larg
e D
ensi
ty
Larg
e D
ensi
ty2
Smal
l D
ensi
ty
Smal
l D
ensi
ty2
LTL
Prop
RO
S
RO
S*
Larg
e D
ensi
ty
RO
S*
Smal
l D
ensi
ty2
RO
S*
LTL
Prop
LTLP
rop
*Lar
ge
Den
sity
LTLP
rop
* Sm
all
Den
sity
2
LnR
ev
Mea
n
0.
075
2382
5837
24
.826
705
0.34
7 0.
027
62.7
18
.8 0
.008
84
0.9
226
15.8
78St
d. D
evia
tion
Exit
0.26
4
405
18
56
9.4
36
49
6
-
-
0.37
6
0.08
2
188.
9
60.5
0.0
37
926.
3
353
1.35
3
Larg
e D
ensi
ty-.0
05--
Larg
e D
ensi
ty2
-.008
.997
--Sm
all D
ensi
ty.0
05-.9
80-.9
81--
Smal
l Den
sity
2.0
02-.9
88-.9
80.9
91--
LTL
Prop
.0
37
.103
.1
02
-.098
-.
099
--
RO
S -.0
89
.009
.0
11
.006
.0
05
-.020
--
RO
S*La
rge
Den
sity
.069
.073
-.050
-.051
-.014
.986
--R
OS*
Smal
l Den
sity
2
-.217
-.2
14
.221
.2
23
-.045
.7
68
.654
--
R
OS*
LTL_
Prop
.044
.0
48
-.027
-.0
26
.262
.6
06
.613
.3
97
--
LT
L Pr
op*L
arge
Den
s
.243
.2
43
-.235
-.2
36 .9
76-.0
19-.0
02-.0
73 .2
62--
LTL
Prop
*Sm
all D
ens2
-.375
-.3
75
.384
.3
81
.720
-.0
17
-.037
.0
62
.188
.5
53
--
Ln
Rev
-.065
-.148
-.148
.160
.156
.178
.045
.044
.1
02
.110
.1
48
.214
--
Age
AtD
.0
36
.129
.1
28
-.126
-.1
28
.222
-.0
05
-.002
-.0
28
.042
.2
33
.107
.2
03
Age
Post
D/In
c .0
10-.8
94-.8
92.9
07.9
04-.0
78 .0
16-.0
41 .2
28-.0
13-.2
19 .3
61 .1
60A
gePo
stD
/Ent
-.006
-.234
-.225
.221
.237
-.052
-.028
-.035
-.020
-.043
-.046
.075
.191
Levr
ge
.100
.0
75
.074
-.0
83
-.081
-.0
37-.3
33-.3
35-.3
03-.2
70-.0
29-.0
57 .0
24Le
vrge
*LTL
_Pro
p
.182
.1
08
.107
-.1
08
-.108
.8
48
-.144
-.1
44
-.143
.0
14
.834
.5
87
.136
B
irths
-.013
.414
.440
-.405
-.366
.006
.039
.0
74
-.070
.0
76
.125
-.1
47
-.062
D
eath
s-.0
41-.1
45-.1
22 .1
73 .1
87.0
06.0
95
.111
.0
79
.108
-.0
05
.062
.0
88
GD
P .0
26
-.123
-.1
23
.118
.1
30
-.020
-.0
31-.0
47 .0
16-.0
34-.0
38 .0
50-.0
08Fu
ture
GD
P .0
13
-.123
-.1
60
.117
.0
77-.0
33-.0
76-.1
04-.0
19-.0
98-.0
60 .0
41-.0
32U
nion
-.011
-.0
08
.002
.0
09
.203
-.0
87-.0
89-.0
58-.0
28 .2
01 .1
35 .2
19D
ereg
-.637
-.674
.681
.609
-.055
.026
-.012
.140
-.011
-.152
.254
.138
Left
Cen
sor
-.007
.1
91
.194
-.1
88
-.185
.1
89
-.013
-.0
00
-.055
.0
23
.199
.0
91
.090
W
here
| ρ
| > .0
21, a
cor
rela
tion
is si
gnifi
cant
at p
< .0
5; w
here
| ρ
| > .0
29, a
cor
rela
tion
is si
gnifi
cant
at p
< .0
1
Tab
le 2
: Mea
ns, S
tand
ard
Dev
iatio
ns, a
nd C
orre
latio
n M
atri
x fo
r V
aria
bles
(c
ontin
ued)
Age
AtD
Age
Post
D/In
c
Age
Post
D/E
nt
Levr
ge
Levr
ge*
LTL
Birt
hs
Dea
ths
GD
P
Futu
re
GD
P
Uni
on
Der
eg
Left
Cen
sor
Mea
n
19.2
062.
918
0.16
80.
560
0.19
1.3
09.1
73
2.82
92.
583
.502
.738
0.82
1St
d. D
evia
tion
Exit
13.6
51
3.
045
0.
990
ns
ity
si
ty
ns
ity
eA
tD
--
0.25
7
0.24
2
.736
.74
2.16
1
2.09
9
.500
.822
0.38
3
Larg
e D
eLa
rge
Den
sity
2
Smal
l Den
Smal
l Den
sity
2
LTL
Prop
RO
SR
OS*
Larg
e D
e
RO
S*Sm
all D
ensi
ty2
RO
S*LT
L_Pr
opLT
L Pr
op*L
arge
Den
sLT
L Pr
op*S
mal
l Den
s2
LnR
ev
Ag
Age
Post
D/In
c-.0
21--
Age
Post
D/E
nt
-.239
-.163
--Le
vrge
-.078
-.108
.047
--Le
vrge
*LTL
_Pro
p.1
57-.1
00-.0
25.3
17--
Birt
hs
.048
-.3
44
-.062
.0
06
.048
--
Dea
ths
.003
.1
28
.067
-.0
40
-.017
.0
92
--
GD
P -.0
38
.160
.0
31
-.008
-.0
11
.244
-.4
57
--
Fu
ture
GD
P-.0
33 .1
22-.0
22-.0
00-.0
24
-.056
-.4
96
.229
--
U
nion
.2
31
.002
.0
10
.012
.1
92 .0
12-.0
22 .0
21 .0
16--
Der
eg
-.067
.5
81
.084
-.0
56
-.069
-.5
45 .4
53-.3
13 .0
35-.0
41--
Left
Cen
sor
.287
-.096
-.224
-.039
.154
.136
.0
07
-.020
-.0
54
.143
-.1
35
--
W
here
| ρ
| > .0
21, a
cor
rela
tion
is si
gnifi
cant
at p
< .0
5; w
here
| ρ
| > .0
29, a
cor
rela
tion
is si
gnifi
cant
at p
< .0
1
Table 3: Exponential Estimation of Large Carrier Failure Rates **p<0.01; * p<0.05; + p<0.10
Variable Model 1 Model 2 Model 3 Model 4 Model 5 Large Density 0.057 **
(0.009) 0.057 ** (0.010)
0.057 ** (0.010)
0.058 ** (0.010)
0.058 ** (0.010)
Large Density2 -0.010 ** (0.002)
-0.010 ** (0.002)
-0.010 ** (0.002)
-0.010 ** (0.002)
-0.010 ** (0.002)
Small Density -1.316 ** (0.200)
-1.318 ** (0.201)
-1.300 ** (0.202)
-1.328 ** (0.203)
-1.335 ** (0.208)
Small Density2 0.029 ** (0.003)
0.029 ** (0.004)
0.029 ** (0.004)
0.029 ** (0.004)
0.029 ** (0.004)
LTL_Prop -0.062 (0.277)
-0.082 (0.275)
-0.105 (0.275)
0.088 (0.290)
7.653 (4.831)
ROS -1.337 ** (0.402)
35.518 * (17.589)
-0.778 (0.585)
46.283 ** 11.872)
ROS*Large Density -0.012 * (0.006)
-0.016 ** (0.004)
ROS*Small Density2 -0.011 * (0.005)
-0.143 ** (0.003)
ROS*LTL_Prop -2.417 * (0.971)
-4.347 ** (1.006)
LTL*Large Density 0.003 (0.002)
LTL*Small Density2 0.020 (0.013)
LnRev -0.232 ** (0.026)
-0.223 ** (0.026)
-0.217 ** (0.026)
-0.224 ** (0.026)
-0.210 ** (0.026)
AgeAtD 0.013 ** (0.003)
0.013 ** (0.003)
0.013 ** (0.003)
0.013 ** (0.003)
0.012 ** (0.003)
AgePostD/Inc 0.817 + (0.457)
0.820 + (0.463)
0.786 + (0.457)
0.832 + (0.469)
0.824 + (0.477)
AgePostD/Ent 1.016 + (0.532)
1.017 + (0.538)
0.972 + (0.534)
1.024 + (0.547)
0.998 + (0.558)
Leverage 1.233 ** (0.223)
1.069 ** (0.227)
1.052 ** (0.225)
1.143 ** (0.237)
1.129 ** (0.231)
Leverage*LTL_Prop 0.628 (0.391)
0.658 + (0.389)
0.707 + (0.390)
0.409 (0.409)
0.231 (0.409)
Births 0.199 * (0.086)
0.192 * (0.087)
0.178 * (0.087)
0.202 * (0.087)
0.188 * (0.087)
Deaths -7.593 ** (1.851)
-7.439 ** (1.846)
-7.383 ** (1.835)
7.372 ** (1.848)
-7.292 ** (1.832)
GDP -0.018 (0.045)
-0.019 (0.045)
-0.019 (0.045)
-0.019 (0.046)
-0.020 (0.046)
Future GDP -0.105 ** (0.032)
-0.108 ** (0.032)
-0.106 ** (0.032)
-0.110 ** (0.032)
-0.111 **
(0.032) Union 0.206 **
(0.080) 0.182 * (0.080)
0.172 * (0.080)
0.185 * (0.080)
0.164 * (0.080)
Dereg 2.371 ** (0.640)
2.354 ** (0.637)
2.255 ** (0.634)
2.361 ** (0.638)
2.262 ** (0.634)
Left Censored -0.117 (0.107)
-0.121 (0.107)
-0.109 (0.108)
-0.129 (0.107)
-0.122 (0.108)
Constant -69.433 ** (10.706)
-70.098 ** (10.789)
-69.830 ** (10.760)
-70.996 ** (10.923)
-69.221 ** (11.439)
Log Likelihood -2180.56 -2175.07 -2170.36 -2172.09 -2162.92