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