SERVICE REFUSALS, INFORMATION SHARING, AND …

The Pennsylvania State University

The Graduate School

Smeal College of Business

SERVICE REFUSALS, INFORMATION SHARING, AND

COMMITMENTS: EMPIRICAL ESSAYS IN FOR-HIRE TRUCKING

A Dissertation in

Business Administration

by

Frank Alexander Scott

©2016 Frank Alexander Scott

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

May 2016

ii

The dissertation of Frank Alexander Scott was reviewed and approved* by the following:

Terry P. Harrison

Professor of Supply Chain & Information Systems

Dissertation Co-Adviser

Co-Chair of Committee

Christopher W. Craighead

Professor of Supply Chain Management

Dissertation Co-Adviser

Co-Chair of Committee

Chris Parker

Assistant Professor of Supply Chain & Information Systems

Keith J. Crocker

Professor of Insurance and Risk Management

Department Chair of the Department of Risk Management

Nicholas C. Petruzzi

Professor of Supply Chain Management

Department Chair of the Department of Supply Chain & Information Systems

*Signatures are on file in the Graduate School.

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ABSTRACT

This dissertation analyzes several important transportation questions using empirical

methodologies. In the first essay, I theorize why truckload transportation carriers refuse service

(i.e., reject load offers) when their service is requested at previously-negotiated prices. Using a

large database of truckload transactions from a U.S.-based shipper, I find that carriers often do

not fully live up to their commitments. They are most likely to reject load offers when the

market is most in their favor and/or during spikes in a shipper’s demand on a lane. This suggests

that, when the market is favorable for carriers, shippers may be better off incentivizing carriers

for performance via flexible pricing mechanisms instead of using “strong-arm” tactics.

In the second essay, I study the value to shippers and carriers to share load information in

advance. This question has been posed by researchers for some time, but industry data has never

been used to answer the question. I use a highly-credible dataset from truckload spot auctions to

estimate the price increase associated with one, two, and three days of lead time. I also propose

a method to estimate market conditions in for-hire trucking in near-real-time using hedonic

regression techniques. Interestingly, market conditions are highly autocorrelated.

The third essay evaluates how shipper-enacted governance mechanisms – more explicit

contracts, output monitoring, and commitment – affect carrier performance and restrain

opportunism. Using a variety of datasets, most notably Electronic Data Interchange

transmissions, I find that more explicit contracts are more effective when market conditions are

unfavorable for carriers but less effective when market conditions are favorable for carriers.

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TABLE OF CONTENTS

List of Figures ................................................................................................................... …v

List of Tables .................................................................................................................... ...vi

Acknowledgements ........................................................................................................... ..vii

Chapter 1. INTRODUCTION .......................................................................................... …1

Chapter 2. SERVICE REFUSALS IN SUPPLY CHAINS: DRIVERS AND

DETERRENTS OF FREIGHT REJECTION .................................................................... ....4

Chapter 3. THE VALUE OF INFORMATION SHARING FOR TRUCKLOAD

SHIPPERS ........................................................................................................................ ..39

Chapter 4. OUR AGREEMENT ONLY GOES SO FAR: CONTRACT

EXPLICITNESS AND THE PAYOFF TO OPPORTUNISM ........................................... ..66

References ........................................................................................................................ 106

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LIST OF FIGURES

Figure 2-1. Rejection percentage and average spot price premium by month ..................... 19

Figure 2-2. Morgan Stanley sentiment index versus the spot premium index ..................... 21

Figure 3-1. Price premium decrease versus days of lead time, for winning bids ................. 57

Figure 3-2. The effect of temperature on spot prices. ...................................................... 59

Figure 3-3. Weekly price index for 2014 ........................................................................... 60

Figure 4-1. The ten indexes over 2014 ............................................................................... 82

Figure 4-2. The national spot price index versus the Morgan Stanley sentiment index. ...... 83

Figure 4-3. Marginal response rates by governance mode and market conditions.............. 98

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LIST OF TABLES

Table 2-1. Variable descriptions and summary statistics .................................................... ..24

Table 2-2. Main results ...................................................................................................... ..26

Table 2-3. The effect of interorganizational commitment .................................................. ..30

Table 2-4. Differential carrier response rates by volume conditions ................................... ..32

Table 2-5. Differential carrier response rates by market conditions .................................... ..32

Table 2-6. Robustness checks ............................................................................................ ..35

Table 3-1. Summary statistics............................................................................................ ..49

Table 3-2. Effect of lead time on spot market bid premium ............................................... ..56

Table 3-3. Day of week and bid day-of-week fixed effects ................................................ ..58

Table 3-4. Serial correlation of the weekly price index ...................................................... ..61

Table 3-5. Robustness checks ............................................................................................ ..65

Table 4-1. Control and treatment groups............................................................................ ..79

Table 4-2. Data description and summary statistics for price index analysis ...................... ..81

Table 4-3. Example of market status classification, using the “within Midwest” series ...... ..86

Table 4-4. Data tables........................................................................................................ ..87

Table 4-5. Summary statistics and correlations .................................................................. ..91

Table 4-6. Number and percentage of load offers by governance mode and market condition.

......................................................................................................................................... ..94

Table 4-7. Average responses by governance form and market condition .......................... ..95

Table 4-8. Main results ...................................................................................................... ..96

Table 4-9. Wald tests of rejection rates by market condition and governance mode ........... ..99

Table 4-10. Robustness checks .......................................................................................... 101

Table 4-11. Regression using OLS and 2SLS .................................................................... 102

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ACKNOWLEDGEMENTS

I am indebted to a number of groups and individuals for support and advice through the

dissertation process. First, I thank my committee members, Terry Harrison, Chris Craighead,

Chris Parker, and Keith Crocker, for their insight and feedback. All have been generous with

their time and their help has improved this document immensely. I also thank Penn State and the

Department of Supply Chain & Information Systems for financial support for the last four years.

Gene Tyworth in particular has been very generous during my stay here.

I also thank my industry partner for their incredible generosity throughout this research.

While I cannot mention anyone in particular, this dissertation would not have been possible

without the shared data and industry insight. I am grateful.

I can never be thankful enough to my parents for all of the love and support they have

provided through the course of my life. They have always encouraged and emphasized the

importance of education and life experiences, and were supportive and encouraging as I tossed

around the unorthodox prospect of transitioning from a career in industry to one in academia.

Perhaps the only way to repay my debts, in some small way, is to provide as much love and

support to their grandchildren as they have shown me.

Finally and most importantly, I thank my wonderful family – Crystal, Wesley, and the as-

yet-unnamed little one on the way. Crystal unselfishly and uncomplainingly gave up a

comfortable life in Chicago to live amongst the undergraduates in State College. I am lucky to

have such a wonderful life-partner to experience this journey. And Wesley and the new one

provide more incentive to work hard – and way more joy and satisfaction – than any job ever

could.

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CHAPTER 1: INTRODUCTION

Firms who want truckloads of goods shipped through their supply chains (“shippers”) often hire

firms who specialize in transportation services (“carriers”) to move the loads. Because of the

cost and criticality of truckload services to a shipper, much time and effort is spent procuring and

negotiating competitive prices from carriers. On the flip side, pricing freight and deciding which

shipper’s demand to service as service requests arise in real-time are decisions of the utmost

importance for carriers in the highly competitive truckload (TL) transportation industry.

The size and importance of the TL sector to the global economy has led to a large

academic literature addressing many of these issues. One literature proposes strategies for a

shipper to generate competitive prices from carriers via procurement auctions (e.g., Moore,

Warmke, and Gorban 1991, Caplice and Sheffi 2003). During these auctions, a shipper projects

demand on origin-destination combinations (“lanes”) and invites carriers to submit their lowest

price on lanes. Shippers and carriers then agree to prices (“contract prices”) based on the auction

submissions for a fixed time period, typically a year (Caplice 2007). An important, often

implicit, assumption in this literature is that carriers fully live up to these prices once established.

Another large literature considers optimal decision-making for TL carriers in highly

uncertain, dynamic environments (e.g., Berbeglia, Cordeau, and Laporte 2010, Powell 1987,

Powell 1996). Because a shipper’s demand for service is usually derived demand – that is,

dependent on someone else’s demand and therefore not completely under a shipper’s control –

forecasting the demand for TL service is imperfect and often wildly inaccurate. In addition to

service requests from shippers at contract prices, carriers also can service spot loads. Because

there is no widely-used centralized market in TL transportation (Tsai, Regan, and Saphores

2009), the availabilty of spot loads is an additional element of randomness that a carrier must

consider. In the “pickup and delivery problem” (Berbeglia et al. 2010, p. 8), a carrier decides

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which service requests to accept or reject in real-time as requests for service arise randomly from

contracted shippers and shippers needing spot capacity.

Shippers contract with carriers at static prices, and it is typically assumed that carriers

perfectly live up to these prices. But, carriers strategically accept or reject load service requests.

Despite this clear mismatch in assumptions, very little research analyzes how uncertain contract

carriers affect transportation costs for shippers. This research fills much of this void.

Specifically, I address the following questions:

1) Do carriers reject a significant number of service requests? If yes, when and why are

they likely to do so?

2) When contract carriers reject service requests, do shippers typically pay a high

penalty? How much higher are spot prices than contract prices?

3) What cause spot prices to be high or low? Can a “market price” be inferred from a

large amount of spot prices and, if so, how can it be calculated?

4) How well do shipper governance mechanisms – the commitment of future business,

performance monitoring, and more explicit contracts – ensure better carrier

performance?

5) Do governance mechanisms perform differentially as the spot market changes?

I adopt an empirical lens in this dissertation. My primary reason is due to the lack of

concrete evidence in the extant research. I use several large industry datasets and perform

rigorous econometric analyses to address the research questions. This dissertation proceeds as

follows. Chapter 2 addresses the first two questions – do carriers often reject service requests,

when and why do they do so, and are spot prices punitive to shippers. Chapter 3 addresses

questions 2 and 3 – what causes high or low spot prices, and can a market status be inferred from

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a large collection of spot prices. Finally, chapter 4 examines how governance mechanisms

ensure good performance from carriers and how these mechanisms perform as prices in the spot

market change. Each chapter is self-contained, with suggestions for future research at the end.

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CHAPTER 2: SERVICE REFUSALS IN SUPPLY CHAINS: DRIVERS

AND DETERRENTS OF FREIGHT REJECTION

Contracts in the for-hire trucking industry are unusual in that, while they establish prices for

different services, there is typically no legally-binding obligation or penalty for either party to

offer or accept a load. When a load is rejected by all contract carriers, shippers must turn to the

spot market, which can significantly increase supply chain costs. Because these transactions

occur between private parties, data on load acceptances/rejections and contract/spot prices have

not been available to academic researchers, leaving the freight rejection problem largely

unexplored. We are able to examine this problem using a detailed transactional dataset of a large

national shipper. We estimate that spot prices for truckload services average about 62% higher

than contract rates. We find key operational and economic factors to be drivers of freight

rejection and the shipper-carrier relationship to be a deterrent to freight rejection. We also find

that primary and secondary carriers respond differently to these operational and economic

factors. We discuss how these insights could be used by a shipper to get better performance and

lower cost from their carrier base.

2.1 Introduction

Trucking has been described as “the lifeblood of the US economy” (Costello 2014). Hauling

70% of all domestic tonnage in the United States and with revenues valued at $681.7 billion, it

touches every industry that makes or ships goods (Corridore 2014). For-hire truckload

transportation is one of the largest trucking subsectors, with annual revenues of $298.6 billion in

2012 (Corridore 2014). For-hire truckload transportation consists of manufacturers and

distributors (i.e., shippers) who hire service providers (i.e., carriers) or third-party intermediaries

(i.e., brokers) to haul loads, which occupy a full trailer, from one point to another. With a large

number of shippers, carriers, and brokers and a homogeneous product, truckload transportation is

best characterized as a perfectly competitive market.

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Carriers continuously decide whether to accept or reject potential loads as their trucks

move from one location to another. This problem is known as the dynamic pickup and delivery

problem, and it has been an active research area for decades (e.g., Powell 1987; Berbeglia et al.

2010). A related area of research is the procurement of contract rates for shippers from carriers

(Moore et al. 1991; Caplice and Sheffi 2003; Caplice and Sheffi 2005). Rates are most

commonly established for origin-destination combinations (i.e., lanes) via annual reverse

auctions, where a shipper invites carriers to bid competitively on the right to haul their freight on

a given lane. The standard agreements between shippers and carriers are highly flexible, best

described as relational contracts (Baker et al. 2002). In these agreements, the winning carrier

(“primary carrier”) gets the first-right-of-refusal for loads on a lane at the bid price based on the

outcome of the auction. The shipper generally holds the primary carrier accountable for some

level of service (e.g., percentage of freight accepted) (Caplice 2007). Interestingly, the

enforcement of these agreements are not legal sanctions but the promise of future business –

poor performance can result in a loss of business for a carrier. As a Senior Vice President at a

carrier describes it: “the ‘rate agreements’ and ‘load commitments’ for the most part have no

contractual obligation or penalties on either party” (Taylor 2011, p. 2).

Given static rates, highly flexible contracts, and carriers that strategically accept or reject

freight, shippers are often faced with a “freight rejection problem.” That is, when a shipper

needs service on a lane, what happens when it is offered to a contract carrier? What factors

affect freight rejection and thus transportation costs? Despite the ubiquity of the problem, the

causes of freight rejection has received little research attention.

We are able to provide insight into the issue of freight rejection because we have been

granted access to a two-year transactional dataset from our industry partner, a large national

shipper. We estimate when and why a shipper is required to utilize the spot market to secure

transportation service. In doing so, we make several contributions to the extant research. We

quantify the incremental expense due to freight rejections and show that it can be extremely high.

Specifically, based on an analysis using tens of thousands of spot transactions over a two-year

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period, we observe that truckload spot prices are, on average, about 62% higher than their

corresponding contract rates. It is remarkable how widely average spot prices can vary

throughout the year; at times, spot prices are comparable to contract rates, while at other times

spot prices average more than double their respective contract rates.

We analyze the drivers of and deterrents to freight rejection by carriers. Carriers are

influenced by three primary factors: (1) operational effects, such as upswings and uncertainty in

demand; (2) economic effects, such as when the transportation market is tight (i.e., demand

exceeds supply); and (3) relationships, such as the volume of business transacted between the

two parties. We find general support for our conjectures and thus contribute to a better

understanding of freight rejection and its consequences.

In further analysis, we investigate whether the level of interorganizational commitment

between shippers and carriers elicits different behavior. Primary carriers are influenced

relatively more by operational factors, whereas carriers that are offered a load only after the

primary carrier has rejected it (“secondary carriers”) are influenced more strongly by economic

factors. Moreover, secondary carriers are not influenced by relationship factors, such as the

value of business transacted between the two parties.

A final contribution of our paper is that it informs models that include transportation

costs. Operations management and transportation models typically assume either a

transportation cost that is linear in the number of units shipped or with a fixed charge and linear

component (e.g., Hirsch and Dantzig 1954; Roberti et al. 2014). Our data and results suggest

that in some cases the underlying transportation costs for a shipper are non-linear in volume due

to contract carrier rejections and the necessity to use the spot market.

2.2 Transportation Industry Background

There are three important areas that pertain to our study. First, we discuss the ownership and

contract structures that exist in the transportation industry, focusing particularly on the most

common: for-hire truckload contracts. Second, we briefly review the extensive research that has

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been done on the dynamic pickup and delivery problem. Finally, we review the scant literature

that discusses carrier performance from a shipper’s perspective.

2.2.1 Shipper-Carrier Contracts

In truckload transportation, there are four primary contract types: private fleets, dedicated fleets,

contract freight moves, and spot market freight moves. In terms of asset ownership, private

fleets are owned by shippers whereas the assets are owned by carriers in the last three.

Private fleets are observed frequently in industry, with a total value of services almost

equivalent to that of for-hire services, $292.0 billion versus $298.6 billion, respectively

(Corridore 2014). When choosing a private fleet, the shipper sacrifices cost efficiencies (for

example, Caplice (2007) estimates that the percentage of empty miles for private fleets average

about 24%, whereas for-hire fleets average between 6% and 12%) for service and the residual

right of control over the assets (Baker and Hubbard 2003). The residual right of control gives the

shipper authority to direct the truck assets as they see best; when they do not own the asset, they

must negotiate with carriers. Having a private fleet, however, does not preclude a shipper from

utilizing for-hire carriers. On the contrary, shippers, such as Wal-Mart and Sysco, who own

large private fleets are also large buyers of for-hire transportation services (Caplice 2007).

Two hybrid contract structures are common in the industry, dedicated fleets and for-hire

contract freight. Dedicated fleets are owned and operated by a carrier, but the shipper has full

control over how the assets are used. In essence, it is a private fleet for the shipper that takes

advantage of a carrier’s operating expertise.

For-hire “contract” freight is the structure under which most carriers operate (Corridore

2014). For-hire fleets are advantageous from a cost point-of-view because they can aggregate

supply and demand across a variety of shippers and lanes, referred to as economies of integration

(Keeler 1989) and economies of scope (Caplice and Sheffi 2003, Özener, Ergun, and Savelsbergh

2011).

Because contracts between shippers and carriers in the contract freight segment specify

prices but do not require load offers or acceptances, these agreements cause tension in the

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relationship when there are market imbalances on either the supply or demand side. For

instance, in a case study with Hershey, a large confectionary company based in the United States,

Zsidisin et al. (2007, p. 15) state that “carriers must be allocated enough freight during periods of

slack transportation capacity to maintain their long-term commitment during periods of

constrained capacity.” J.B. Hunt Transport, one of the largest truckload carriers in the U.S.,

implicitly acknowledged this tension in a 2005 earnings release (J. B. Hunt Transport, Inc. 2005,

p. 3): “the number of loads moving under spot quotes increased significantly from earlier in the

year, which indicates that capacity availability in the truckload market remains extremely

fragile. Current spot activity in October remains at third-quarter levels indicating continued

tightness in the market. Despite this significant increase in spot demand, we continue to honor

our base customer commitments [emphasis added].” In an industry report, Taylor (2011, p. 3)

articulates the tension succinctly: “generally, there are no volume guarantees, nor financial

penalties, so essentially when load acceptance rates fall, a lot of yelling and hollering is what

happens. The same occurs when loose capacity emerges [emphasis added].”

The pure “buy” contract is the spot market. When the spot market is used, a shipper

searches for short-term capacity for a load or series of loads, with no further commitments on

either side. Brokers, who act as middlemen in the for-hire trucking market, are often used as a

source of spot capacity because they specialize in maintaining contacts with a large number of

carriers. Not much is known publically about the size and magnitude of the spot market.

Caplice (2007) estimates it at between 5% and 10% of the overall market, and Kirkeby (2013)

concurs with an estimate of “less than 10%” of most carriers’ business. Tsai et al. (2011, p. 925)

acknowledge the dearth of public information on spot prices: “reliable truckload spot price data

can be challenging” and they find no published papers “that analyzed industry data.” Lindsey et

al. (2015a) fill some of this gap with an analysis of observed spot rates from a large broker.

Their study attempts to explain the underlying costs of a spot carrier. Scott (2015) studied a

year’s worth of private spot transactions and estimated the value of lead time for carriers. Our

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study differs from previous studies because we address how changes in prevailing spot prices

affect carrier decision-making.

2.2.2 The Pickup/Delivery Problem

The carrier’s dynamic pickup and delivery problem has been thoroughly studied for decades

(Powell 1987). Berbeglia et al. (2010) surveys the literature for this class of problems. In

accordance with the contracts discussed in Section 2.1.1, a carrier faces random demand with

short lead-times as its trucks move from delivery to delivery. When a truck becomes empty after

a delivery, the carrier must decide whether to relocate the empty truck (referred to as

“deadhead”) from one location to another in search of more profitable freight or wait for offers to

appear in the current location. Faced with a highly uncertain environment, the carrier decides

which freight is most profitable to accept and which s/he should reject.

2.2.3 Freight Rejections from a Shipper’s Perspective

Freight rejections from the shipper’s perspective has received little research attention. Zsidisin

et al. (2007) is the closest published study that we found pertaining to our topic. Through a case

study with Hershey, the authors explore the impact that the strength of the relationship between a

shipper and carrier has on carrier performance, as measured by on-time deliveries and percentage

of freight rejected. Freight rejections are a major problem for Hershey because they delay

shipments, which can result in missed delivery appointments and therefore cause stockouts at the

retailer level. Furthermore, the search for transportation capacity becomes labor-intensive.

Zsidisin et al. (2007) do not specifically study the impact of spot freight moves, but do claim that

“arms’-length” carriers have higher freight rates than “partnership” carriers during periods of

tight transportation capacity, but lower rates during loose markets. Their primary findings are

that carriers with whom Hershey invests more in the relationship, via frequent communications,

high-level inter-firm interactions, and the willingness to allocate freight at above-market rates

perform better from a freight rejection standpoint.

In related research, Lindsey et al. (2015b) consider how a broker dynamically sources

capacity in an online environment. In their scenario, they include the likelihood of a carrier to

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accept freight at an offered price. Caplice (2007) does not specifically study freight rejections,

but mentions that 74% of loads were accepted in their dataset. Furthermore, Master’s theses by

Harding (2005), Kafarski and Caruso (2012), and Kim (2013) discuss the issues of carrier

capacity and freight rejection. In an analysis of two shippers’ data, Harding (2005) reports that

13% and 20% of loads were rejected. Harding hypothesizes that rejections and volume are

related to one another and provides correlational evidence of this assertion. Kim (2013)

examines a large dataset where 19.8% of loads were rejected at least once and finds that volume

volatility on a lane increases freight rejection, but geography, distance, and price are not

significant factors.

Anecdotal evidence of this problem can be found in weekly survey reports from Morgan

Stanley, a large investment bank. For example, in a report on January 21st, 2015, a carrier stated:

“Extraordinarily tight market. We turn down 40% of what's offered to us each week” (Greene et

al. 2015, p. 5). In a report on October 7th

, 2015, a carrier reported: “Market is still very tight.

We're turning down 25% of what's offered to us and anticipate even higher demand in Q4”

(Vecchio et al. 2015, p. 18). It seems evident that contract carrier freight rejection is a

widespread issue for shippers in the for-hire trucking industry.

2.3 Freight Profitability from a Carrier’s Perspective

We consider the salient costs that a carrier should consider when accepting or rejecting freight.

Caplice and Sheffi (2005) discuss a model with respect to carrier considerations during a

truckload auction. While the underlying costs of the model we propose are similar, the

individual components of our model differ.

The situation we consider is thus: when a shipment is required on a lane, the shipper’s

transportation management system (TMS) offers it to the primary carrier at previously negotiated

rates. The primary carrier has a rough time window (e.g., about 90 minutes) to decide whether to

accept or reject the shipment. If the load is rejected by the primary carrier, the TMS offers it to

secondary carriers, also at previously negotiated prices, generally in order of cheapest to most

expensive (although this need not always be the case). This process continues through

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contracted carriers until either a) a carrier accepts the load, or b) all contracted carriers reject the

load, in which case the load is offered to the spot market. Spot market capacity and prices are

procured using a first-price, sealed-bid auction (Milgrom 1989) in which numerous carriers and

brokers are invited to participate; the lowest bid at the close of the auction wins the right to haul

the load. The carrier that accepts the load for either the contract or spot price picks up the freight

and delivers it to the destination at the specified price.

A carrier must evaluate whether it is in their interest to haul a given load. Previous

research (e.g., Powell 1996) has recognized that a carrier faces four major costs: (1) the direct

cost to haul a load from one point to another; (2) the relocation cost associated with assigning a

particular truck to a particular load; (3) the opportunity cost due to the truck being exclusively

assigned to a particular load for the duration of the delivery; and (4) the network cost associated

with moving a truck from one region to another, where each region may be differentially

desirable due to “next load” possibilities (Özener, Ergun, and Savelsbergh 2011). Furthermore, a

carrier must consider the revenue for each load and the goodwill cost associated with a possible

load rejection – that is, the “yelling and hollering” discussed in Section 2.1.1. Given these

considerations, we propose the following carrier profit function:

* min( )i i i

i Ir dc oc rc nc gw

(1)

where I is the set of trucks that could possibly service the load. The revenue r is decided upon

during the annual auction and is generally viewed as non-negotiable ex post. The direct cost, dc,

is the cost incurred to haul the load from the origin to the destination. The opportunity cost, oci,

is the lost profit from not being able to serve other freight by committing truck i to the accepted

load. The cost incurred when deadheading truck i to the pickup location and after the delivery to

its next pickup location is the relocation cost, rci. The network cost, nci, which may be positive

or negative, is the value associated with removing a truck from the origin region to the

destination region. For example, some regions may be more or less profitable, on average, for a

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carrier to have a truck located in, based on the availability of follow-on loads. Finally, the

goodwill in the relationship, gw, derives from living up to these informal agreements.

Equation (1) states that, if a truck is available and the load is profitable, a carrier will

assign the profit-maximizing truck. If the set I is empty (i.e., the carrier has no trucks available)

or if the load is unprofitable (considering goodwill), the carrier will reject the load. There are

three general categories of factors that influence a contract carrier’s decision to accept or reject a

load: (1) operational factors; (2) economic factors; and (3) relationship factors. Below we

discuss how each factor affects a carrier’s decision, using equation (1) as a framework for

discussion.

2.3.1 Operational Drivers

Operational drivers that affect a contract carrier’s decision to accept or reject a load include

empty repositioning cost, the value of having assets in particular regions due to varying

profitability in different regions, and the certainty of freight (Figliozzi et al. 2007; Powell 1987;

Powell 1996; Yang et al. 2004). For every load that is accepted by a carrier, a truck has to be

repositioned inbound to pick it up. A carrier who assigns the most profitable truck to pick up a

load will see decreasing profitability in volume, ceteris paribus. In solving the dynamic pickup

and delivery problem, a carrier that plans based on an average volume from a shipper will

experience higher relocation costs on loads that are offered above the average volume.

Furthermore, for a carrier with a “balanced network” – that is, where freight flows into

and out of regions are roughly equal to one another – large volumes of freight into a particular

region in a short time frame will increase network costs. If unexpectedly large volumes occur,

the carrier faces the prospect of having too many trucks in a region, resulting in follow-on loads

with decreasing profitability. Similar to above, carriers that plan around average volumes from a

shipper will experience higher network costs on loads that are offered above the average volume.

Likewise, predictability is important for a carrier; the more predictable the outbound

freight, the more incentive a carrier has to establish a network that complements the shipper’s

freight. On average, the relocation cost for predictable freight should be lower than the

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relocation cost for less predictable freight. Higher relocation costs, as shown in (1), means that

freight will be less profitable for a carrier. Hence we expect that load offers that occur in a less

predictable setting will be rejected more frequently than load offers in a more predictable setting.

H1a: Load offers that are above the average demand on a lane are rejected more

frequently than load offers that are below the average demand

H1b: Load offers that are increasingly above the average demand on a lane are rejected

with an increasing frequency

H1c: Load offers that occur in a less predictable setting are rejected more frequently

than load offers in a more predictable setting

2.3.2 Economic Drivers

Economic drivers that influence a contract carrier’s decision to accept or reject a load capture the

highest-valued alternative use of the asset. Industry publications have suggested that carriers

balk at contract rates when spot rates are high. For instance, one of the largest freight brokers in

the industry, C.H. Robinson, claim that “when equipment becomes scarce, carriers may shift

their equipment to transactional customers who will pay higher spot market rates” (C.H.

Robinson 2013, p. 7).

When the market is out of balance in a carrier’s favor, the carrier must decide between

competing requests for the same truck. To the extent that shippers value getting the product to

their customers more than the transportation cost to get it there, shippers will be willing to pay a

premium to get spot capacity. With spot rates averaging more than double contracted rates in

some markets at some times, the carrier’s opportunity cost can be quite high if it accepts a

contract load. During periods of high spot prices, it behooves the carrier to allocate more

capacity to the spot market than in normal and slow periods.

H2: Load offers that occur when spot market prices are high are rejected more frequently

than when spot market prices are low

2.3.3 Relational Deterrents

Relationship deterrents that influence a contract carrier’s decision to accept or reject a load

measure the ongoing relationship between a shipper and a carrier that is important for both

14

parties. Shippers rely on carriers for consistent and high quality service at predictable prices

during times of scarce capacity and volatile transportation costs. Carriers invest in networks of

shippers with complementary freight to achieve economies of scope; losing a single shipper’s

business removes a link in the network, necessitating either costly search to find a replacement

shipper or the reorganization of their interrelated network of freight.

Yet, interestingly, industry practices present conflicting evidence concerning the value of

shipper and carrier relationships. The prevalence of annual auctions that require carriers to

compete anew suggests that shippers consider carriers to be good substitutes for one another, at

least over time horizons as long as a year. On the other hand, business is not always awarded

simply to the carrier with the lowest price (Caplice 2007). Both Thompson (2013) and C.H.

Robinson (2013) stress the importance of long-term relationships with carriers. For instance, one

supply chain manager works with “asset-based providers to build long-term relationships” to,

“most importantly […] guarantee capacity” (Thompson 2013, p. 1). While we recognize (and

respect) the former arguments, our conjectures are consistent with the latter (i.e., that

relationships affect freight rejection).

Thus, consistent with the findings of Zsidisin et al. (2007), we predict that a stronger

relationship, stemming from dependence, results in better performance (i.e., fewer rejections)

from the carrier. To capture this, we adopt the value of business transacted between the shipper

and carrier prior to a load offer as a measure of relational influence. As the value of business

increases, the loss of a shipper’s business is more acute and we predict that a carrier will provide

better service.

Long-term commitments often have aspects that leave one or both transacting parties

vulnerable to opportunism (Sako and Helper 1998). We adopt the length of the relationship

between the shipper and carrier as a measure of relationship strength. This measure has been

used elsewhere in the operations management literature (Krause et al. 2007), and is consistent

with the finding that trust and communications are important to shippers and carriers (Zsidisin et

al. 2007).

15

H3a: Load offers to carriers with whom more business has been transacted in the recent

past are rejected less frequently

H3b: Load offers to carriers with whom the shipper has a longer relationship are

rejected less frequently

2.4. Data and Specification

To analyze why carriers reject freight, we adopt an empirical approach using a detailed industry

dataset. Given the lack of studies addressing our research question, using observations from a

large industrial dataset provides a good basis for understanding carrier behavior. We test the

hypotheses outlined in Section 2.2 with data from a national shipper, which we will call Acme.

In this section, we describe our approach at a high level and provide context on the source of our

data. We then define our variables, describe our data cleaning process, and specify our

regression model.

2.4.1 Approach and Data Source

The outcome in our study is whether a contracted carrier hauled a load or not. Because this is a

binary outcome, we adopt a probit specification for our analysis. Furthermore, the decision to

accept a load and the spot market price is potentially endogenous – a carrier could conceivably

reject a load at the contracted price and then offer their services at a higher price, perhaps

through a broker to disguise their behavior. Indeed, this is precisely the behavior described by

C.H. Robinson in Section 2.2.2. To correct for endogeneity, we adopt an instrumental variables

approach (Angrist and Pischke 2009), which we describe in more detail in Sections 2.3.2 and

2.3.4.

Transportation is one of Acme’s largest expenditures, and they rely solely on for-hire

truckload carriers. Their product is relatively low value to weight and essentially non-perishable.

They have production facilities in every region of the United States, giving us national coverage

of truckload markets. Truckloads usually flow directly from their plants to a customer’s

distribution centers.

16

The dataset contains considerable transaction-level details for every truckload shipment

between January 1, 2012 and December 31, 2013. For each load we observe the origin,

destination, date and time of pickup, the carrier that moved the load, the rate paid (broken down

into categories such as line haul, fuel, accessorials, etc.), and whether the load was moved at a

contracted rate or a spot rate. We also have access to less detailed data for 2011 that allows us to

better estimate the length of the relationships. Because the 2011 data do not indicate whether the

load was moved under a contract or spot rate, we cannot use it to test our hypotheses.

Also included in the dataset is a “fair contract price” index distributed by a firm that

collects transportation cost data from a large number of shippers. The index provides the

average contract price paid from origins to destinations, aggregated at a five-digit zip code level,

for each year. This is useful because it allows us to compare observed spot prices on different

lanes; the index controls for heterogeneity caused by origins and destinations.

2.4.2 Unit of Analysis and Variables

Unit of Analysis

The unit of analysis for our model is at the lane-day-“rank” level. Lanes are pairs of origins and

destinations. An origin can contain multiple physical locations, but all locations in the same

origin are in the same geographic area. Destinations are typically all locations within the same

5-digit zip code, but in some cases individual ship-to locations are grouped separately (e.g., for a

large customer). We define the rank of a load as the order in which it was picked up for a given

lane on a given day. For example, the first load picked up on a day for a lane is assigned the rank

of one and the fifth load picked up on the same day for the same lane is assigned the rank of five.

Dependent Variable

Spot Load. Our primary interest is understanding and explaining why a contract carrier might

reject a load offered by a shipper. For each load, we observe whether it was moved at a contract

rate or at the spot market rate. We code a load as 0 if it moved under contract and 1 if the spot

market was used. We note a contract rate is used when any contract carrier accepts the freight.

On some lanes there is one contract carrier, on others there are multiple. Unfortunately, we do

17

not observe the actual offer and subsequent rejection; we infer load rejection from the necessity

to use the spot market. This assumption was confirmed by Acme.

Independent Variables

There are five broad categories of independent variables: operational, economic, relational,

instrumental, and controls. The independent variables should be calculated at the point when the

carrier evaluated the load offer and not when the load was moved. While the exact timing of the

offer is unknown, Acme confirmed that most freight is offered three days in advance and the

time window for rejection is 90 minutes. We therefore calculate the variables for a load three

days before the load is moved. For example, the Spot Premium for a load hauled on day t is

calculated with respect to day t - 3.

Operational

As the number of loads offered on a lane increases in a short time frame, two things happen that

can increase load rejections. First, the set of possible trucks that could service the load is reduced

resulting in restricted capacity. Second, the relocation, network, and opportunity costs increase,

reducing the profitability of accepting the load.

Low Volume, Medium Volume, High Volume, Very High Volume. To determine whether

increases in load volume results in more load rejections, we calculate the average daily volume

(number of loads on a lane on a given day) for the 30 days prior to each load. We then use the

rank of the load and compare it to the average load volume on each lane. If the rank is above the

average but below the average plus one standard deviation, we classify it as Medium Volume

and include it as an indicator variable. The assumption of 30 days was made to capture recent

behavior on a lane; we test this for robustness in Section 2.4.3, assuming 15 days and 45 days. If

the rank of the load is above the average plus one standard deviation but less than the average

plus two standard deviations, then we classify it as High Volume and include it as an indicator

variable. If the rank of the load is above the average plus two standard deviations, then we

classify it as Very High Volume and include it as an indicator variable. The excluded category is

18

below average load volume. Consistent with H1a and H1b, we predict that rejections will be

positively affected by these variables at an increasing rate.

Log of Standard Deviation of Load Volume. One measure of uncertainty on a lane is the

variability in daily load volume. To capture this, we calculate the standard deviation for daily

load volume for the 30 days prior to the load offer consideration. We use the log to reduce the

positive skewness of the standard deviation.

Days Since Previous Load. Another measure of uncertainty is the number of days since the

previous contract load was moved. For each load, we find the date of the previous contract

shipment on the lane and calculate the number of days since the previous contract load.

Economic

Spot Premium. Truckload carriers usually serve a wide variety of customers; there is little

specialization, particularly in the dry van segment that Acme utilizes. Trucks also have

exclusivity and a lack of scalability: when one is used to haul a load, it cannot be used to haul

anything else for the duration of the move. This means that a carrier’s opportunity cost of

accepting a contract load depends on what other shippers are willing to pay at that point in time.

Due to the many decentralized geographic markets and the proprietary nature of industry data,

there is no public, timely, and accurate measure of truckload spot prices. Our data present a

unique opportunity to construct an index of spot market prices specific to shipping regions and

times. Specifically, we are able to capture the tightness of truckload markets and hence the

opportunity cost of accepting a contract load. To measure the status of the market, we use the

shipper’s own spot prices, which are generated by a competitive bidding process involving

numerous carriers and brokers. Given the competitiveness of truckload markets, with many

shippers and carriers, we feel that our data are reasonably representative of prevailing market

conditions.

To construct a spot market index, we first define an Origin Area as a single location or

combination of locations that are geographically close to each other (i.e., always within the same

city). We group locations together that are part of the same general geographic market. This

19

approach provides more consistent and continuous spot prices, as not all locations have spot

loads every day.

For each Origin Area, we compare observed spot prices and fair contract prices

(described in Section 2.3.1) to estimate market status. Specifically, for every load in the dataset,

we sum the price paid for every spot load from the same Origin Area for seven days prior to each

load offer. We also sum the fair contract price for those same spot loads. The ratio of the two

sums gives us a “Spot Premium.” For example, if an Origin Area had 50 spot loads in a seven

day period with a total spot cost of $25,000 and a fair contract price of $17,000, then the Spot

Premium would be 1.471.

Industry experts suggest that this is a valid method for measuring the status of the local

spot market. For instance, Kirkeby (2013) says: “S&P believes pricing trends in the spot market

provide insight into the general availability of capacity and demand for that capacity.” Figure 2-

1 illustrates the average daily national spot price divided by the fair contract price over the

sample period, alongside the freight rejection rate, illustrating the extent to which the two are

related.

Figure 2-1. Rejection percentage and average spot price premium by month. Rejections

and spot prices appear to be highly correlated and seasonal.

0%

60%

130%

Month

ly S

pot

Pri

ce P

rem

ium

0%

5%

10%

15%

20%

Month

ly R

eje

ction P

erc

enta

ge

January 2012 July 2012 January 2013 July 2013 December 2013

Monthly Rejection % Monthly Spot Price Premium

20

To test the validity of our measure of market conditions, we compare it to a “truckload

supply and demand sentiment index” calculated by Morgan Stanley, a large US-based

investment firm, and found at the website of a third-party logistics provider (Transplace 2015).

The Morgan Stanley index surveys a broad array of shippers, carriers, and brokers to assess

overall supply and demand sentiment. We expect that our measure of spot prices, if spot prices

reflect supply and demand in the market, positively correlate with the Morgan Stanley index. To

compare the two, we first converted the Morgan Stanley index into daily digital points. We

calculated the Spot Premium measure nationally (i.e., across all regions as opposed to region-

specific). Finally, we compared the national Spot Premium variable with the Morgan Stanley

index.

As seen in Figure 2-2, the two measures largely align: the correlation between the two

time series is 77.3%. Thus, we conclude that our measure of market conditions is valid. Our

measure is advantageous to the Morgan Stanley index for two reasons: 1) it measures actual

price swings, instead of an arbitrary “sentiment”; and 2) it can be calculated regionally, capturing

important variation (e.g., the Florida harvest season is well known to cause truckload demand

and supply imbalances).

Relationship

To capture the complexity of relationships, we use three measures: two revenue-based measures

and one time-based measure. We note that we don’t observe the actual rejections in the data.

Instead, we have to look at active carriers on a lane, which we define as those that hauled a

contract load within the past 30 days. We calculate each measure at the carrier level and use a

weighted average for the lane-day.

Log of Carrier Revenue. The more business transacted between the two parties, the higher the

value of the business is to the carrier. At the firm level, we capture the rolling 30-day revenue

between the shipper and carrier on all lanes. We use the log of revenue to reduce rightward

skewness.

21

Figure 2-2. Morgan Stanley sentiment index versus the spot premium index.

Log of Lane Revenue. A lane with higher revenue is more valuable to a carrier. To measure this,

we calculate the rolling 30-day revenue on a lane prior to each load. We use the log of revenue

to reduce rightward skewness.

Length of Relationship. We measure the length of the relationship between the two parties in

days, and classify them into buckets for every one hundred days (e.g., 0-100 days is 1, 101-200

days is 2). We include these as dummy variables.

Instrumental Variables

Using instrumental variables to correct for endogeneity is a common approach used in

econometrics (Angrist and Pischke 2009). There are two requirements for an instrumental

variable: the inclusion restriction and exclusion restriction. The inclusion restriction requires

that an instrumental variable is related to the endogenous independent variable – in our case,

Spot Premium. The exclusion restriction requires that an instrumental variable is not directly

22

related to the dependent variable – that is, any relationship with the dependent variable must be

through the endogenous variable. Below we describe our instrumental variable and how it

satisfies these conditions.

Other Region Spot Premium. Short-term spot prices in regions that are geographically far away

from the origin and destination of a particular load should not affect a carrier’s decision because

the truck on the contracted load cannot be used to service these spot loads. However, because

the US economy has a strong seasonal component to it, we expect spot prices across regions to

be positively correlated with one another. Hence, using a spot price index in regions other than

the origin and destination of a load meets the requirements for an instrumental variable.

Furthermore, the method of using prices in other geographical regions as an instrument for prices

in a specific region has been used in other studies (Hausman, Leonard, and Zona 1994; Pinkse,

Slade, and Brett 2003).

To calculate the Other Region Spot Premium instrument, we first classify the origin and

destination region of every load, using the region classification system of the US Census Bureau

(US Census Bureau 2015). The US Census Bureau classifies the United States into four regions

– Northeast, South, Midwest, and West. Given the origin and destination region for a given load,

we observe the regions at which the load does not originate or end in. Then, for every load we

calculate a variable in a similar manner to the Spot Premium variable, but instead of using the

origin location, we use the other geographic regions. We include this variable as an instrument

for the Spot Premium variable.

Controls

Lane fixed effects. We control for time-invariant heterogeneity across the 1,129 lanes in our data

using lane fixed effects. These control for differences in the lanes that are constant through time

such as the distance between the origin and destination, population densities at the two locations,

and road quality (as long as there are no major changes in road quality during our sample).

Contract Carriers. For various reasons, the shipper has a different number of carriers under

contract on different lanes, even at different times within lanes. To control for this, we include

23

the number of unique contract carriers that are active on the lane. We calculate this as the

number of carriers that have hauled a load at a contracted rate within the previous 30 days.

Month-year fixed effects. We include temporal fixed effects to control for economy-wide factors

that may vary over time, such as changes in the broad U.S. economy, different weather

conditions, and changes to regulations governing the industry (e.g., new Hours of Service rules

were implemented on July 1, 2013, restricting work-hours for drivers between required breaks).

We include fixed effects for the month-year-region as a robustness check in Section 2.4.3.

Day-of-week fixed effects. We include day-of-week fixed effects to control for the different

behavior by the day of week. Specifically, it is conceivable that carriers behave differently early

in the week versus late in the week or on weekends.

Hour-of-day fixed effects. We include hour-of-day fixed effects to control for the possibility that

carriers behave differently during different times of the day. For instance, carriers may prefer

loads to be picked up early in the day versus late in the day.

2.4.3 Data

Our primary dataset contains 452,846 observations on lanes that have both spot and contract

loads on them. For various reasons we are not able to use the entire dataset. First, some variables

are calculated as 30-day running averages. These variables cannot be calculated for the first 30

days of the dataset. Excluding these observations removes 12,116 loads (2.67%). Second, several

variables can only be calculated when there was a load on the same lane within the previous 30

days. For example, the standard deviation of volume on a lane in the previous 30 days cannot be

calculated if there are no loads carried on the lane in the previous 30 days. This removes 21,449

loads (4.74%). Third, the spot premium can only be calculated when there have been spot loads

from an origin in a given 7-day period, which is not always the case. Excluding observations for

which a reliable spot premium cannot be calculated removes 4,303 loads (0.95%). Fourth, to

minimize bias (which is discussed in more detail in Section 2.3.4), we include only lanes that

have at least 20 loads on them, which removes 6,565 loads (1.45%). Finally, after applying the

above filters there are some lanes that no longer have variation in them (i.e., some lanes have all

24

spot loads or all contract loads). Excluding these observations removes 7,454 loads (1.65%),

leaving 400,959 records in our dataset, which is 88.5% of the original. Summary statistics of the

final dataset and a description of the variables are presented in Table 2-1.

2.4.4 Model Specification

In our model, carrier profitability is a latent variable, and we observe the actual accept or reject

decision. The dependent variable in our model, , is coded 0 if the shipment is moved by any

contracted carrier and 1 if the spot market is used (i.e., all of the contracted carriers rejected the

freight). Our observed dependent variable is related to the latent variable as such:

(2)

where is determined by (1).

Table 2-1. Variable descriptions and summary statistics. NR means Not Reported due to

confidentiality agreements with Acme. N = 400,959.

Because we have a binary outcome, we adopt a probit model with instrumental variables

for our analysis. The unobserved lane-specific characteristics necessitate the use of fixed effects

in our model. It is well known that nonlinear models with fixed effects are inconsistent due to

the “incidental parameters problem” (Neyman and Scott 1948). However, when T (in our case,

*

*

0 if 0,

1 if 0,

*

Variable names Definitions Mean St Dev Min Max

Spot Load 0 if the load moved at a contract rate; 1 if it moved via the spot market 0.109 0.311 0 1

Spot Premium 7-day rolling average of the spot prices divided by the fair contract prices

at the origin region 1.489 0.412 0.572 4.004

Log of Standard Deviation of Load Volume Natural log of 30-day rolling standard deviation of the number of loads on

the lane 0.697 0.963 -1.698 3.105

Medium Volume 1 if the rank of the load is above the 30-day rolling average number of

daily loads but less than 1 standard deviation above the average, 0

otherwise 0.287 0.452 0 1

High Volume 1 if the rank of the load is between 1 and 2 standard deviations above the

average number of loads, 0 otherwise 0.189 0.392 0 1

Very High Volume 1 if the rank of the load is more than 2 standard deviations above the

average number of loads, 0 otherwise 0.120 0.324 0 1

Log of Lane Revenue Natural log of 30-day weighted revenue for carriers on the lane; revenue

measure at the lane level NR NR NR NR

Log of Carrier Revenue Natural log of 30-day weighted revenue for carriers on the lane; revenue

measure at the firm level NR NR NR NR

Length of Relationship ('00s) 30-day weighted length of relationship for carriers on the lane 7.423 2.103 1 11

Days Since Previous Load Days since the previous load, capped at 30 2.375 3.370 1 30

Contract Carriers The number of contracted carriers seen on the lane within 30 days of

shipment 2.636 1.697 1 14

25

the number of loads on a lane throughout time) is reasonably large, bias in the coefficients is

small (Greene 2004). Katz (2001) and Coupé (2005) show that bias is negligible when T is

greater than or equal to 20; hence, we restrict our analysis to lanes with 20 or more loads over

two years. Our model is:

ldr l ldr my w h ldrx (3)

where ldrx are the independent variables as described in Table 2-1 for lane l on day d with rank

r, denotes lane fixed effects, denotes month-year fixed effects, w denotes the day-of-

week fixed effects, h denotes the hour-of-day fixed effects, and is the idiosyncratic error

term. We instrument Spot Premium with Other Region Spot Premium and use the maximum-

likelihood estimator.

We use clustered standard errors to allow for heteroskedasticity and correlation of errors

in the variance-covariance matrix. One potential option is to cluster errors at the lane level but

this does not allow correlated errors for two lanes that are geographically similar, i.e., two lanes

may originate and terminate in the same general area but errors clustered at the lane level would

not allow for any correlation in errors corresponding to these two lanes. To allow for these types

of correlations we instead cluster errors at the origin-destination state so that errors on any lanes

with the same origin and destination states can be correlated. This level of error clustering

results in 174 of cluster groups which is larger than the threshold of 50 suggested by Wooldridge

(2003). Alternative error clustering (such as clustering errors at the origin level) would be less

restrictive than clustering errors at the origin-destination state, but would result in too few cluster

groups.

2.5 Results

In this section, we first discuss the results of the main model, which are presented Table 2-2. We

then run separate models for primary and secondary carriers to investigate whether accept/reject

decisions are affected differently by the operational and economic drivers and relationship

exp( )Pr( 1 ) ,

1 exp( )

ldrldr ldr

ldr

x

l my

ldr

26

deterrents. These results are presented in Table 2-3. We then analyze the margins from our

models and discuss the cost of rejections for shippers, with the results shown in tables 2-5 and 2-

6. Following that we perform a number of robustness checks, presented in Table 2-6, to ensure

the conclusions are not the result of a choice we made.

Table 2-2. Main results.

2.5.1 Main Effect

Operational Drivers

We find considerable support for the hypothesis that shippers’ operations significantly impact

load rejection. As load volume increases, so do rejections as evidenced by the positive

coefficients for the Medium Volume, High Volume, and Very High Volume variables. We find

evidence that the likelihood of rejection increases as load volumes increase. High Volume loads

Variables

Spot Premium 1.864***

(0.221)

Log of Standard Dev. of Load Volume 0.428***

(0.058)

Medium Volume 0.0904***

(0.017)

High Volume 0.174***

(0.024)

Very High Volume 0.246***

(0.028)

Log of Lane Revenue -0.277***

(0.039)

Log of Carrier Revenue -0.0555**

(0.022)

Days Since Previous Load 0.0128***

(0.003)

Constant -4.830***

(0.33)

Observations 400,959

Number of Lanes 1,129

Lane FE Yes

Month-Year FE Yes

Day-of-Week FE Yes

Hour-of-Day FE Yes

Standard errors clustered at the origin-destination state

in parentheses. ***p<0.01, **p<0.05, *p<0.10.

27

are rejected more frequently than Medium Volume loads (p-value<0.01), and Very High Volume

loads are rejected more frequently than High Volume loads (p-value<0.01). Hence, Hypothesis

1a and Hypothesis 1b are supported. This result means that increases in load volumes in a short

period of time requires increased utilization of the spot market, which in turn results in non-

linear transportation costs.

Hypothesis H1c is also supported. The log of the standard deviation and the number of

days since the previous load both increase the likelihood of rejection. This indicates that a

carrier is less willing to service loads that are less predictable, perhaps because finding follow-on

loads for less predictable loads is more difficult. This, in essence, negatively affects their

economies of scope.

Economic Drivers

Economic factors are clearly important in the carrier’s decision-making process. The coefficient

for Spot Premium is large and significant, supporting Hypothesis 2. This supports the assertion

that carriers trade off contracted loads with spot market loads. When the market turns in a

carrier’s favor, they are less willing to live up to previously contracted prices

Relationship Deterrents

Both national and lane-based measures of the strength of a relationship are deterrents to

rejections. The local relationship (Log of Lane Revenue) appears

to have a larger deterring effect than the national relationship (Log of Carrier Revenue). This is

likely due to the fact that the lane is more valuable to a carrier, which in theory will be

interconnected with other shippers’ freight. Hence, Hypothesis 3a is supported.

Interestingly, the length of relationship does not show a meaningful trend in the indicator

variables. Maintaining a long relationship with a carrier does not appear to result in better (or

worse) performance. This is consistent with the industry practice of annual freight auctions; if

longer relationships mattered, then annual auctions would be less likely to be the standard.

Hypothesis 3b is not supported.

28

2.5.2 Differences in Commitment between Primary and Secondary Carriers

In the main analysis, we did not attempt to differentiate among contract carriers; we have

assumed that primary carriers behave the same as secondary carriers. There is potential

heterogeneity in the relationship, however, to the extent that the primary carrier feels a higher

level of interorganizational commitment to the shipper than secondary carriers. For instance, a

primary carrier is given the first right of refusal on a lane but is also expected to maintain some

load acceptance ratio, whereas secondary carriers are only expected to provide service at their

convenience (Caplice, 2007).

Unfortunately, we do not observe the order in which load offers are rejected; we only

observe if a load is rejected by all contract carriers (i.e., it goes to the spot market). We therefore

do not observe which carrier was designated primary when a load was moved. Moreover, the

primary designation can change throughout the year for various reasons, such as poor carrier

performance or willing opt-outs by either party.

Upon discussion with Acme, we found that it is possible to estimate the primary carrier

on a lane in most cases. Primary carriers move a majority of the freight, about 72% based on our

calculations. If they do not, then they are replaced by a carrier that will. To determine the

primary carrier, we looked at the number of contract loads hauled on each lane for each month-

year. If a carrier hauled most of the contract loads, then we designated them as the primary

carrier for that month-year and all of the other carriers as secondary. For 90% of the loads, the

designation is clear because a carrier hauled more than half of the total; in the rest, the primary

carrier was designated as the carrier that hauled the most loads. While less than perfect, we are

confident that we are able to identify the primary carrier for a vast majority of the loads.

After classifying primary and secondary carriers, we create two sub-samples. The first

includes all observations in which the accept-or-reject decision is made by the primary carrier.

The second includes only accept-or-reject decisions made by any contract carrier after the

primary carrier has rejected the load. For the two sub-samples, we repeat the analysis of Section

2.4.1.

29

Table 2-3 shows the results of the analysis. We find that while rejection ratios for the two

sub-samples are similar (primary carriers reject 28% of loads offered to them and secondary

carriers reject 31%), their decisions are driven by very different factors. Interestingly, primary

carriers are more heavily influenced by operational and relationship factors and relatively less by

economic factors (Column 1). Secondary carriers on the other hand are strongly influenced by

economic factors but much less so by operational factors and not at all by relationship factors

(Column 2). This result indicates that primary carriers behave less opportunistically to external

market. It is consistent with primary carriers expecting and integrating a certain volume of

freight from this shipper into their overall pickup/delivery framework and considering the

longer-term profitability of the relationship rather than the very short-run profits from diverting a

single truck. Conversely, secondary carriers make decisions based on whether it is the most

profitable load for them given the status of the market. Since the shipper has made no

commitment to turn to them first with a load, there apparently is not a feeling of commitment in

return if a more profitable load is available. These results make it clear that the extent of

interorganizational commitment moderates opportunistic behavior by carriers.

2.5.3 Primary Carrier and Secondary Carrier Margins Analysis

Having estimated the key drivers and deterrents of truckload rejections, we now perform a

margins analysis to illustrate how primary carriers and secondary carriers respond to different

conditions. For the following analysis, we use the model estimates for primary and secondary

carriers, shown in Table 2-3. To calculate margins, we need to use specific inputs – lanes,

months, market conditions, and volume conditions. We define the “Average Lane” as follows.

First, we sort the lanes in ascending order based on their associated fixed effects. We then

choose the lane for which half of the loads traveled on a lane with a smaller fixed effect and half

of the loads traveled on a lane with a larger fixed effect. We define a “Well-performing Lane”

and “Poorly-performing Lane” in a similar manner, where we use the 25th

percentile of fixed

effects for the former and the 75th percentile of fixed effects for the latter.

30

We also define the “Average Market” condition as that at which the Spot Premium

variable is at its average value. When the Spot Premium variable is one standard deviation above

the average, we define these as “Above Average” market conditions. We define “Below

Average” market conditions in a similar manner, except as one standard deviation below the

average. The volume conditions that we use are defined in Section 2.3.2. Month 8 in our 24-

month period was used because it was the closest to the “average month,” as determined by the

fixed effects for months. Unless otherwise noted, covariates were assigned to their means.

Table 2-3. The effect of interorganizational commitment.

Tables 2-4 and 2-5 show the differential behavior of primary and secondary carriers

under a variety of conditions. Table 4 shows the impact that volume has on carrier behavior as it

Variables (1) (2)

Spot Premium 1.213*** 1.956***

(0.263) (0.284)

Log of Standard Dev. of Load Volume 0.362*** 0.283**

(0.038) (0.144)

Medium Volume 0.0961*** 0.0614***

(0.023) (0.018)

High Volume 0.176*** 0.0845***

(0.03) (0.028)

Very High Volume 0.236*** 0.187***

(0.034) (0.039)

Log of Lane Revenue -0.236*** -0.124

(0.032) (0.11)

Log of Carrier Revenue -0.0511** 0.00825

(0.021) (0.027)

Days Since Previous Load 0.008*** -0.0007*

(0.003) (0.001)

Constant -3.323*** -2.786**

(0.253) (1.12)

Observations 385,362 95,238

Number of Lanes 1,094 483

Lane FE Yes Yes

Month-Year FE Yes Yes

Day-of-Week FE Yes Yes

Hour-of-Day FE Yes Yes

R-squared N/A N/A

Standard errors clustered at the origin-destination state in

parentheses. ***p<0.01, **p<0.05, *p<0.10. Primary carriers are

shown in column 1 with a probit specification. Secondary carriers

are shown in column 2 with a probit specification.

31

varies from Low Volume to Very High Volume. For example, on the Average Lane, primary

carriers are more likely to reject loads offered at Very High Volume conditions 8% more

frequently than loads offered at Low Volume, in average market conditions. For the same

calculation, secondary carriers are 6.4% more likely to reject a load. Overall, primary carriers

are about 2.3% more responsive to volume conditions than secondary carriers.

On the other hand, secondary carriers are much more responsive to market conditions, as

shown in Table 2-5. On the average lane and at Low Volume conditions, secondary carriers are

47% more likely to reject loads offered during above average market versus below average

markets. This is more than two and half times the primary carrier response rate differential of

18.4%. Overall, secondary carriers are about 2.3 times more responsive to market conditions

than primary carriers.

When performing the margins analysis, we used the month with the average fixed effect

– month 8. Our month fixed effects control for factors not captured in our other variables – e.g.,

weather disruptions. However, the month fixed effects necessarily consume some of the

variation that can be explained by changing market conditions over time. To estimate the impact

of different months in our time period, we ran a similar analysis to the above while using the 25th

(“good month”) and 75th

percentiles (“bad month”) of the month fixed effects. Secondary

carriers, as expected, are impacted more by the month fixed effects. For example, secondary

carriers are 13.9% more likely to reject loads in the bad month than in the good month, whereas

primary carriers are only 4.7% more likely to reject loads in the bad month than in the good

month.

32

Table 2-4. Differential carrier response rates by volume conditions.

Table 2-5. Differential carrier response rates by market conditions.

Market Status Volume Differential Primary Secondary Primary Secondary Primary Secondary

Below Average Very High Volume - Low Volume 6.2% 3.0% 3.7% 1.0% 8.5% 4.9%

Average Very High Volume - Low Volume 8.0% 6.4% 5.4% 3.5% 9.5% 7.4%

Above Average Very High Volume - Low Volume 9.2% 7.4% 7.2% 6.8% 9.7% 6.1%

Note: This table shows the differential response rates for primary and secondary carriers. The percentages reported represent a carrier's mean response under Very High

Volume conditions minus a carrier's mean response under Below Average Volume conditions. Three different market conditions are shown: below average market

conditions, average market conditions, and above average market conditions. Three lanes are reported: an average lane, a well-performing lane, and a poorly-performing lane.

Average Lane Poorly-performing LaneWell-performing Lane

Volume Status Market Differential Primary Secondary Primary Secondary Primary Secondary

Low Volume Above Average - Below Average 18.4% 47.0% 11.9% 27.2% 23.2% 55.4%

Medium Volume Above Average - Below Average 19.6% 48.6% 13.3% 29.1% 23.8% 56.0%

High Volume Above Average - Below Average 20.7% 49.1% 14.4% 29.8% 24.2% 56.2%

Very High Volume Above Average - Below Average 21.4% 51.4% 15.4% 33.0% 24.4% 56.6%

Note: This table shows the differential response rates for primary and secondary carriers. The percentages reported represent a carrier's mean response during above average

market conditions minus a carrier's mean response during below average market conditions. The four different volume conditions are shown. Three lanes are reported: an

average lane, a well-performing lane, and a poorly-performing lane.

Average Lane Poorly-performing LaneWell-performing Lane

33

Finally, we issue a note of caution with these results. Trucking and shipper operations

are extremely dynamic, and while our dataset has significant detail, we are not able to observe

everything perfectly. As mentioned earlier in the paper, we do not observe the actual accept and

reject decisions – we observe the contract or spot outcome. So it is possible that some of the spot

loads were not offered to contracted carriers (e.g., perhaps due to lead time constraints). In that

scenario, our estimates of carrier responses to market conditions will be overstated. With regards

to volume conditions, we have assumed that the order of pickup is consistent with the order of

offering and accepting. When this is not the case, our estimates of carrier response to volume

conditions will be understated. A conservative estimate to bound both of these responses is to

divide the response to market conditions by two, and to multiply the response to volume

conditions by two. Despite these imperfections, our estimates provide a rough estimate of carrier

marginal responses under various settings.

2.5.4 Robustness

We performed a number of specification checks to test the robustness of our results.

Specifically, we 1) include only lanes with at least 20 loads, 2) measure recent volumes over the

previous 30 days, 3) measure local market conditions by spot premiums in the past 7 days, and 4)

assumed a probit specification. Table 2-6 shows that the results are consistent when we alter

these decisions. In column 1, we show the main results reported in Table 2-2. In column 2, we

report the main model without correcting for endogeneity. While this model is mis-specified, the

results stay directionally the same. Our results are unaltered when we change the number of

loads on a lane to be included in the analysis from 20 to 10 (Column 3) and 30 (Column 4), use a

linear probability model specification (Column 5), use the Morgan Stanley sentiment index

instead of our measure of market conditions (Column 6), calculate the Spot Premium using local

market conditions in the previous 4 days (Column 7) and 10 days (Column 8) as opposed to 7

days, and use a 15-day (Column 9) and 45-day (Column 10) window rather than a 30-day

window for calculating some variables. The robustness of the results to alternative variable

34

definitions and specifications makes us confident that the conclusions are not due to any

decisions we made.

2.6 Discussion

2.6.1 Contributions

Shippers who contract with for-hire truckload carriers often have their freight rejected at

previously established prices and thus have to engage the spot market, which can drive up costs

considerably. Despite its importance, the problem of freight rejection by contracted carriers is

largely unstudied in the literature and thus the magnitude of its effect is unclear. By examining a

two-year transactional dataset, we contribute to the understanding of this problem in several

important ways.

First, while it is expected that truckload spot prices are higher than contract prices, few studies

analyze actual truckload spot prices. We observe that spot prices are surprisingly high relative to

contract prices, with an average premium of 62%; more than 25% had a cost at least 100%

higher than the corresponding average contract price. Given that the for-hire truckload

transportation space is about $299 billion and that spot moves comprise an estimated 5% to 10%

of the total market (Caplice 2007; Kirkeby 2013), we estimate that shippers spend between $15

to $29.9 billion on spot transportation at an incremental expense between $5.7 and $11.4 billion

a year industry-wide. To put that in perspective, the estimated cost of the options considered for

the contentious changes to the truck driver Hours of Service rules that went into effect in 2013

was substantially lower, between $470 million and $2.3 billion (Department of Transportation

2011). On the face of it, this suggests that managers have a strong incentive to adopt strategies

to avoid the spot market in times of tight capacity. As we discuss below, our findings show that

there are indeed factors that drive and deter the need to engage the spot market.

35

Table 2-6. Robustness checks.

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Spot Premium 1.864*** 0.428*** 1.862*** 1.869*** 0.297*** 0.921*** 1.459*** 2.378*** 1.793*** 1.886***

(0.221) (0.055) (0.218) (0.223) (0.01) (0.088) (0.158) (0.301) (0.225) (0.215)

Log of Standard Dev. of Load Volume 0.428*** 0.536*** 0.422*** 0.427*** 0.127*** 0.532*** 0.471*** 0.368*** 0.353*** 0.442***

(0.058) (0.057) (0.058) (0.058) (0.002) (0.06) (0.056) (0.064) (0.047) (0.063)

Medium Volume 0.0904*** 0.0970*** 0.0899*** 0.0900*** 0.00853*** 0.0928*** 0.0905*** 0.0888*** 0.0661*** 0.0805***

(0.017) (0.016) (0.017) (0.017) (0.001) (0.015) (0.016) (0.017) (0.016) (0.02)

High Volume 0.174*** 0.188*** 0.174*** 0.173*** 0.0204*** 0.186*** 0.176*** 0.169*** 0.155*** 0.168***

(0.024) (0.022) (0.024) (0.024) (0.002) (0.022) (0.022) (0.025) (0.023) (0.026)

Very High Volume 0.246*** 0.270*** 0.246*** 0.247*** 0.0264*** 0.265*** 0.251*** 0.238*** 0.237*** 0.254***

(0.028) (0.027) (0.027) (0.028) (0.002) (0.029) (0.026) (0.028) (0.029) (0.036)

Log of Lane Revenue -0.277*** -0.334*** -0.275*** -0.277*** -0.0854*** -0.331*** -0.304*** -0.246*** -0.235*** -0.280***

(0.039) (0.034) (0.039) (0.039) (0.001) (0.034) (0.036) (0.044) (0.033) (0.039)

Log of Carrier Revenue -0.0555** -0.0660*** -0.0541** -0.0555** -0.0109*** -0.0650*** -0.0579*** -0.0529** -0.0543*** -0.0541**

(0.022) (0.022) (0.022) (0.022) (0.001) (0.023) (0.022) (0.022) (0.02) (0.022)

Days Since Previous Load 0.0128*** 0.0156*** 0.0127*** 0.0131*** 0.00325*** 0.0149*** 0.0134*** 0.0115*** 0.00634 0.0113***

(0.003) (0.003) (0.003) (0.003) (0) (0.003) (0.003) (0.003) (0.005) (0.002)

Constant -4.830*** -3.626*** -4.855*** -4.825*** -0.308*** -4.443*** -4.502*** -5.151*** -4.660*** -3.988***

(0.33) (0.273) (0.33) (0.332) (0.055) (0.286) (0.314) (0.333) (0.325) (0.355)

Observations 400,959 400,959 403,040 399,049 403,979 400,959 397,559 400,959 388,333 403,984

Number of Lanes 1,129 1,129 1,315 1,033 1,186 1,129 1,126 1,129 1,050 1,093

Lane FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Month-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Day-of-Week FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Hour-of-Day FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

R-squared N/A 0.285 N/A N/A 0.191 N/A N/A N/A N/A N/A

Standard errors clustered at the origin-destination state in parentheses. ***p<0.01, **p<0.05, *p<0.10. Column 1 shows the main result. Column 2 shows the model estimation without

correcting for endogeneity. Columns 3 and 4 vary the minimum number of loads on a lane to 10 and 30, respectively. Column 5 uses the main model with a linear probability model

specification instead of probit. Column 6 uses the Morgan Stanley Index instead of the Spot Premium variable. Columns 7 and 8 vary the measures of Spot Premium from 7 days to 4 days

and 10 days, respectively. Columns 9 and 10 vary the time window for the calculation of several variables from 30 days to 15 days and 45 days, respectively.

36

Second, a shipper’s operations significantly affect carrier rejections. Demand volatility

causes rejections and therefore higher transportation costs. While shippers may have limited

options, there are two potential strategies that could be invoked. One strategy is a flexible

pricing mechanism that responds to upswings in demand; this could reduce rejections by primary

carriers, thereby reducing the need for the spot market. Another strategy that a shipper can adopt

is to smooth their demand for truckload services by utilizing pricing incentives for customers,

multi-sourcing, or pre-positioning inventory in a hub and spoke network.

Third, economics drives decision-making for truckload carriers. When spot market

prices are high, carriers are less willing to accept freight at contracted prices. Similar to above, a

more flexible pricing mechanism that responds to market conditions might mitigate some of this

behavior. Counterintuitively, this indicates that the price that a shipper is paying might be too

little, meaning that they should willingly pay more, in exchange for more capacity. Of course,

this would likely require a change in mindset for many shippers and would therefore have to

overcome inertia.

Fourth, we confirm that, as suggested by previous literature (Zsidisin et al. 2007),

relationships matter. Carriers with whom the shipper does more business accept loads more

frequently than carriers with whom the shipper does less. The relationship is significant at both

the local and national level, but more so at the local level. Hence, this study confirms the value

of core carrier programs and the need to keep the number of carriers to a manageable size.

Fifth, carrier behavior is influenced by the interorganizational commitment between the

carrier and shipper. Primary carriers respond to relatively more to operational and relationship

factors, while secondary carriers respond mostly to economic factors. Because primary carriers

are less sensitive to short-run economic conditions than secondary carriers, strategies to elicit

more capacity during times when spot prices are high could be beneficial for shippers.

Finally, our results can enhance operations management and transportation models. For

example, it is common in the operations management and transportation literatures to model

transportation costs at a constant rate in volume, either per truck or per unit. While this is useful,

37

in a for-hire truckload environment, shippers often face transportation costs that are non-linear in

volume due to the possibility of contracted carrier rejections and the need to use the spot market.

2.6.2 A Flexible Pricing Mechanism?

Our results suggest that flexible pricing could be beneficial for shippers in the following

scenario. Suppose that spot market prices are high, the shipper needs to ship more loads on a

lane than a primary carrier is willing to haul at the contracted price, and that some of these loads

will end up on the spot market. If there is additional capacity that the primary carrier is willing

to provide at a price higher than the contracted price but less than spot market price, then the

shipper could reduce their transportation costs by eliciting more capacity out of their primary

carrier.

Demonstrating a willingness to renegotiate prices, however, could have negative

consequences on carrier behavior. As a large broker advises: “shippers do need to be cautious

about how often they renegotiate rates – too often, and they’re just playing the market” (C.H.

Robinson 2013, p. 6). Hence, a shipper would need to design a pricing mechanism that

incentivizes additional capacity from primary carriers in expensive markets without encouraging

balking or frequent renegotiations.

Thus, the design of a flexible pricing contract in for-hire trucking is worthy of future

research. Accordingly, to provide a starting point, we propose an initial direction that may be

enlightening. Suppose, for example, that a shipper knows: (1) how many loads a carrier is

willing to haul on a lane next week; (2) how many total loads they are going to ship on a lane

next week; (3) how many of these loads will require spot capacity; and (4) the expected cost of

spot capacity. To incentivize extra capacity, the shipper could offer quantity-conditional price

incentives to the carrier, calculated based on expected spot prices. There are several positive

aspects of such an incentive: it would require the primary carrier to meet their volume targets to

receive the additional revenue; provide a higher price on loads that are more expensive or more

undesirable for a carrier to haul; and could vary based on spot prices. For example, if spot prices

are low, the shipper could offer small or no incentives, and when spot prices are high they could

38

offer larger incentives.

2.6.3 Future Research and Limitations

Findings from this study suggest future research possibilities. First, considering a shipper’s cost

function and a carrier’s profit function, a game theoretic model could be used to analyze different

contractual forms between shippers and carriers. This approach could consider different pricing

and contracting schemes and analyze how different policies affect shipper-carrier welfare.

Another modeling approach could address the price-service tradeoff for carriers. That is,

what is the optimal price that a shipper should establish on a lane with a carrier, assuming that

price affects the level of service (i.e., number of loads accepted) from the carrier? Insurance

models from economics could be helpful when analyzing this question (e.g., Crocker and Moran

2003). Finally, despite our best efforts, there are limitations to our study. Our findings and

consequent contributions need to be considered in light of these limitations. The primary

limitation is that our data comes from a single firm over a two-year period. While we do not

believe that there is anything particularly unique about the operations of our industry partner or

the years that we have used – particularly given the significant anecdotal evidence that other

shippers face this problem – it would be useful to validate our findings across other companies

and years. This would, of course, be a difficult task as transactional data sources for researchers

have remained elusive. Also, due to the empirical nature of our study, it is impossible to rule out

all alternative explanations for some of the behavior that we observe. Despite these caveats, we

believe our research makes a significant step forward in this important, yet understudied area.

39

CHAPTER 3: THE VALUE OF INFORMATION SHARING FOR

TRUCKLOAD SHIPPERS

This study explores the potential value to shippers of sharing load offers with carriers and

obtaining carriers’ responses in advance of the scheduled pickup date. Using a private

transactional dataset from a large national shipper, we find that truckload spot prices increase

considerably as the lead time before pickup decreases. As an extension of this empirical

analysis, we develop a method to estimate near-real-time market prices, which does not currently

exist in the truckload industry. A key insight is that market prices persist through time, meaning

that current prices are good predictors of future prices.

3.1 Introduction

Why should truckload shippers share load information in advance with contracted carriers?

Previous studies have shown the value of advance load information for truckload carriers

because it allows them to plan more efficiently (Tjokroamidjojo, Kutanoglu, and Taylor 2006;

Zolfagharinia and Haughton 2012). The incentive for shippers to provide such information is not

so clear. Modeling studies past and present (e.g., Hirsch and Dantzig, 1968; Roberti, Bartolini,

and Mingozzi 2014, among many others) frequently assume that costs for shippers are simply

linear in volume. Contract rates for carriers are typically negotiated in advance with a duration

of a year or more (Caplice 2007), so it would seem that a shipper only benefits from sharing load

information if it reduces the likelihood that the load is rejected.

Despite the significant breadth of research on information sharing for supply chain

partners (e.g., Gavirneni, Kapuscinski, and Tayur 1999; Lee, So, and Tang 2000; Sahin and

Robinson 2002; Angulo, Nachtmann, and Waller, 2004), including in a truckload transportation

40

context (Powell 1996; Tjokroamidjojo, Kutanoglu, and Taylor 2006; Zolfagharinia and

Haughton 2012), we are unaware of any studies that address information sharing from a

shipper’s perspective in a shipper-carrier relationship. This is surprising given the size ($300

billion in 2013; Corridore 2014) and importance (approximately 67% of freight by weight in the

United States moves by truck; Costello 2014) of the trucking industry to the economy of the

United States.

In this study, we empirically examine a year of spot transactions to explore whether and

to what extent shippers benefit from the advance sharing of load information. We find that spot

market truckload prices increase considerably as the time between load offer and pickup

decreases. This finding, combined with the right-of-refusal in industry-standard shipper-carrier

contracts in for-hire trucking (Scott, Parker, and Craighead 2015), means that shippers are

naturally incentivized to offer loads in advance and receive timely responses from their carriers

to minimize spot prices.

We estimate the value of lead time for truckload shippers. Because the estimates result

from an analysis of tens of thousands of bids in a competitive spot-bidding process from

numerous brokers and carriers spread throughout the country, these results are likely

generalizable to other shippers in the United States. Knowledge of the value of lead time is

important for shippers because they regularly decide how far in advance to offer freight to

contract carriers, with some probability of rejection, or offer it on the spot market.

Further, we provide other insights into the truckload spot market, a market that is

virtually unstudied despite its economic significance.1 While there is not much publicly-

available empirical information about the spot market, there is anecdotal evidence that it is

1 In a study of truckload futures contracting, Tsai, Saphores, and Regan (2011) acknowledged that finding “reliable

truckload spot price data can be challenging” and that no published papers were found “that analyzed industry data.”

This is consistent with our review of the literature.

41

important for carriers. For instance, in a 2012 earnings release, J.B. Hunt Transport stated that

“operating income increased 27% compared to 2011” due to “favorable changes in freight mix,

strong seasonal spot pricing [emphasis added]…and improvements in fuel efficiency” (J.B. Hunt

2012). Our analysis of the data supports J.B. Hunt’s emphasis on spot pricing, because the spot

prices we observe have significantly higher revenue than loads moving at previously-contracted

rates.

Finally, we propose a method to estimate market prices for for-hire trucking in the United

States. Due to the highly private nature of and lack of a centralized market in for-hire trucking

(Tsai, Regan, and Saphores 2009), there is no near-real-time index that adequately captures

market prices (Bignell 2013). A novel insight from our analysis is that market prices have

significant serial correlation. Knowing that prices persist from week to week impacts decision-

making for both shippers and carriers. For example, a carrier who observes high spot prices

might be better off accepting fewer contract loads in the near future and allocating more capacity

to spot customers. Likewise, a shipper might benefit from providing price incentives above

contract rates to encourage more capacity from contract carriers in expensive markets.

The rest of the paper proceeds as follows. Section 3.2 reviews the relevant literature. We

propose our hypotheses and discuss factors that affect carrier pricing in Section 3.3. Section 3.4

discusses our methodology and data. Results and robustness checks are discussed in Section 3.5.

Section 3.6 discusses the major findings of the study and implications for truckload shippers.

3.2 Literature review

3.2.1 Information sharing

Information sharing has become central to supply chain research over the past couple of decades

(e.g., Cooper, Lambert, and Pagh 1997; Gavirneni, Kapuscinski, and Tayur 1999), and is still

active (e.g., Özer, Zheng, and Ren 2014). Chen (2003) and Sahin and Robinson (2002) provide

42

thorough reviews of the significant problems considered in information sharing. Most of this

research has focused on the interplay of suppliers, manufacturers, and retailers. A fundamental

insight is that information sharing between partners can reduce inventory costs and improve

forecast accuracy.

With regards to the transportation industry, the value of information sharing has been

studied from a truckload carrier’s perspective by Powell (1996), Tjokroamidjojo et al. (2006),

and Zolfagharinia and Haughton (2012). The insight from these studies is that carriers are able

to plan more efficiently with knowledge of their future loads.

Lindsey et al. (2015) provided the only study that we are aware of that included a

measure of the impact of lead time on truckload prices. These authors analyzed truckload spot

prices observed at a broker. They included a dummy variable to capture lead time as “greater

than 8 days” (p. 11). For the large national shipper used in our study, spot market lead times are

much shorter than 8 days and the impact of lead time appears to drop off quickly after the second

day.

The lack of research attention is due to the fact that finding information about truckload

spot prices is challenging (Tsai et al. 2011) because these transactions occur between private

parties. We have overcome this problem by receiving access to a full year’s worth of spot

transactions from a large national shipper (henceforth, “Acme”).

3.2.2 Dynamic pickup and delivery problem

The dynamic pickup and delivery problem is central to transportation research, and has been for

decades (e.g., Powell 1987). Berbeglia, Cordeau, and Laporte (2010) provide an excellent

review. For the current study, the essence of the problem is as follows. A shipper contracts with

for-hire truckload carriers who operate fleets of trucks and service demand (loads) from many

43

customers. From a carrier’s perspective, load offers are received at random geographic locations

at random times; hence planning how to most efficiently operate a fleet is a very challenging

problem. Quite often, carriers will reject freight at previously contracted rates2. If all contracted

carriers reject a load offer from a shipper, the shipper must utilize the spot market.

Within the dynamic pickup and delivery problem, future knowledge of potential loads is

valuable for a carrier because it allows them to plan more efficiently. Interestingly,

Zolfagharinia and Haughton (2012) claimed that the value of knowledge decreases considerably

after the second day. For example, they estimated that the second day of advance information

makes a carrier 22% more profitable on average, but the third additional day makes the carrier

only 6% more profitable.

3.2.3 Truckload contracts

Truckload contracts between shippers and for-hire carriers in the United States differ from other

types of contracts because they specify a price but not a legally-enforceable service obligation

(Caplice 2007; Scott et al. 2015). When a shipper needs a load hauled from an origin to a

destination (“lane”), the load is offered to a contracted carrier at the previously agreed-upon

price. The carrier has the right to refuse a load at the time of execution; in fact, the carrier can

even accept a load but “push it back” (i.e., reject it) at some later time.

The carrier’s right-of-refusal has evolved due to the relatively low value of the

transactions involved. Truckload moves rarely cost more than a couple of thousand dollars, and

the rejection of a few loads does not justify the cost of legal enforcement. This contractual

flexibility introduces an interesting dilemma for carriers when market prices change. After a

price has been agreed upon and if the market turns in their favor, carriers must balance the

conflicting objectives of maintaining a good relationship with their customers by hauling loads

2 Scott et al. (2015) provide an example where spot market usage can, at times, exceed 20% of freight for a shipper.

44

at the contracted price and moving spot loads with significantly higher revenue. C.H. Robinson,

a large transportation broker, acknowledged a carrier’s conflict: “when equipment becomes

scarce, carriers may shift their equipment to transactional customers who will pay higher spot

market rates” (C.H. Robinson 2014, p. 6). To counteract opportunism, shippers do not sue but

threaten to remove future business.3

Masten (2009) examined a class of contracts between drivers and carriers; these exist one

level below the shipper-carrier relationship. In these contracts, carriers and drivers agree on

terms of pay via a rate-per-mile or revenue-sharing agreement for the duration of the

relationship. Because loads are heterogeneous in the space-time continuum, Masten argued that

drivers and carriers pre-specify prices to save on haggling costs which would occur every time a

new load arises.

While similarities exist between shipper-carrier and carrier-driver contracts, there are also

significant differences. First, shipper-carrier contracts pre-specify prices only on particular

lanes; in this sense, they are less general. Second, they often have general volume commitments4

associated with them. Despite these differences, Masten’s explanation that pre-specified prices

reduce negotiation costs seems reasonable in the context of shipper-carrier contracts.

3.2.4 The truckload spot market

The truckload spot market, much like shipper-carrier contracts, is unusual. There is no

centralized exchange (Tsai et al. 2009). Instead, when a shipper needs short-term capacity, there

are at least three mechanisms that can be used: 1) negotiate directly with a carrier in their

3 In a related context, Masten (2009, p. 83) stated in a study examining contracting practices between drivers and

carriers, “Given…the expense of invoking legal sanctions, parties are likely to prefer self-help (such as termination) to court ordering in dealing with transgressions.” This argument is valid with regards to contracts between shippers

and carriers as well as drivers and carriers. 4 Through this research, it is clear that volume commitments are at best rough guidelines for both parties. Shipper’s

demand often falls well below or above the communicated commitment, and carrier’s capacity for loads also often

falls well below or above the communicated commitment.

45

network for capacity; 2) contact a broker and pay it to find capacity (Lindsey et al. 2015); or 3)

utilize an online mechanism to allow carriers and brokers to bid competitively on a load.

Despite the estimation that the spot market is between 5% and 10% of the $300 billion

for-hire trucking market (Caplice 2007; Corridore 2014), there has been little academic analysis

of it. Lindsey et al. (2015) fill some of this void by analyzing the factors that affect the price per

mile that a broker observes. They found that distance, number of stops, lead time, and type of

equipment used are important drivers of spot transportation cost. Scott et al. (2015) also provide

some insight into the spot market. In their study, they estimate that spot prices are on average

62% higher than corresponding contract prices. However, the focus of their study was not on the

spot market, but the causes of contract freight rejection.

Our study adds to this emerging research stream in several ways. First, we empirically

estimate the value of lead time, which has been theoretically analyzed in the literature and

estimated using simulation but not examined using actual industry data. Second, we evaluate

and estimate the factors that affect spot prices in a short-term competitive bidding process,

including day-of-week effects, the impact of weather, and the effect of regional expertise. Third,

we propose a simple-to-implement method to estimate market prices, which shows that price

conditions persist in the short-term.

3.3 Hypotheses

For-hire trucking has been described as a perfectly competitive industry (Belzer 2000) due to the

large number of buyers and sellers, the low barriers to entry, and the fact that a truck and trailer

can be used to haul a wide variety goods. With plenty of lead time and a large number of

carriers, shippers can expect to pay rates roughly equal to the marginal cost of hauling a load.5

5 In this case, “large numbers” bargaining conditions exist, and price will be driven to marginal cost (Williamson

1975, p. 27).

46

Not surprisingly, carrier profit margins are typically less than 10% (Clancy et al. 2008) and in

unfavorable markets, carrier mortality rates are high (Silverman, Nickerson, and Freeman 1997).

Furthermore, the marginal revenue to a shipper of a fulfilled order is generally much

higher than the marginal cost of transportation. Hence, if a shipper faced a monopoly supplier of

transport services, that carrier could extract significantly more revenue than the marginal cost to

haul a load. In time-sensitive spot market settings, we expect prices to exceed contract prices.

We do not formulate this as a formal hypothesis as this is obviously true directly from our

dataset.

We adopt a cost-based pricing lens for our hypotheses, as this is the most prevalent

pricing strategy in industry (Diamantopoulos 1991; Noble and Gruca 1999). In the cost-based

pricing paradigm, the “primary consideration is the internal costs of the firm” (Noble and Gruca

1999, pp. 438-439). Previous transportation research has found that less lead time increases the

operational costs of a carrier (Tjokroamidjojo et al. 2006; Zolfagharinia and Haughton 2012).

This leads to the following hypotheses:

H1A: Carriers bid higher as the time between the auction and load pickup decreases

H1B: Carriers bid higher, at an increasing rate, as the time between the auction and load pickup

decreases

Furthermore, our empirical analysis will allow us to estimate the time profile of spot rates as lead

time diminishes.

Economies of scope (Keeler 1989; Caplice and Sheffi 2003) impact carrier costs. Before

picking up a load, a carrier must relocate a truck to a shipper’s location; likewise, after delivering

a load, the carrier must relocate the truck to another pickup location. Ceteris paribus, a carrier

with a denser network of interconnected freight is able to operate more efficiently than a carrier

47

with a less dense network. We define a “region of expertise” as a geographic region in which

carriers have more domiciled drivers, equipment, and shippers that they service; that is, a denser

network. We predict that a carrier is able to service loads at lower cost in their region of

expertise. This gives the following hypothesis:

H2: Carriers bid lower on loads that are in their region of expertise

3.4 Setting and model specification

3.4.1 Setting

We use a detailed transactional dataset from a large shipper (Acme) in the United States, which

includes all 133,271 bids from its 2014 spot market auctions. Acme operates plants in every

region of the United States. Its loads occupy full trucks in the dry van segment (as opposed to

refrigerated or flatbed trucks). Due to the density of the product, trailers always hit their weight

limits (i.e., they “weigh out” instead of “cube out”). Their product is essentially homogenous

and low value with no practical shelf-life constraints; hence, all loads are basically the same and

well within typical carrier insurance limits, facts well known to all potential carriers.

When an order arrives from a customer, which is usually 5 to 7 days in advance, Acme

plans a load to meet the customer’s requested delivery time window. The load is offered

(“tendered”) to the primary carrier on the lane, with the pickup and delivery time communicated

at the time of offering. The primary carrier has roughly 90 minutes to respond; if they do not

accept the load, it is offered to the next contracted carrier on the lane (i.e., “backup carriers”). If

the load is not accepted by any carrier with 3 days of lead time remaining, it is offered on the

spot market. These spot market transactions are the focus of this paper.

For Acme, spot loads are entered into a software tool that emails carriers in their network

that a load is up for bid. Carriers are given 90 minutes to respond with a bid price, and the load

48

is awarded to the lowest bidder. Hence, the auction process is a first-price sealed-bid auction

(Milgrom and Weber 1982). Sometimes (about 11% in the dataset) loads are not awarded to any

bidder because no prices were acceptable. When this happens, Acme either individually

negotiates capacity with a carrier or plans the load for another time.

This dataset is ideal for estimating the value of lead time because many difficult-to-

observe factors are either absent or controlled for. First, long-term commitments between

shippers and carriers are difficult to observe or measure; in the spot auction, there is no long-

term commitment between the two parties. Second, communication between the parties is

homogenous: they all receive the same emails and see the same auction screen. Finally, there is

no relationship consideration: carriers are not held to a load-acceptance standard and are not

evaluated with respect to their spot behavior.

3.4.2 Variables

In the current study, the unit of analysis is the load-level. Every load that is auctioned in the spot

market has a set of characteristics uniquely associated with it, such as the lane, day of the week,

and hour of day. Variable names and definitions are shown in Table 3-1, along with descriptive

statistics.

3.4.2.1 Dependent Variable

Price premium. The variable of interest, Price premium, is defined as the premium of a spot load

relative to its corresponding contract price on the associated lane. Price premium is calculated as

the observed spot price for a load divided by the 90-day rolling average contract rate on the lane.

For example, if Acme observes a bid of $1,000 on a particular load but the 90-day rolling

average contract rate on the lane was $500, then the Price premium for the load is 2. We test the

90-day rolling average assumption for robustness in Section 3.5.

49

We measure the dependent variable in this manner for two reasons. First, shippers plan

their transportation budgets around contracted rates. Knowing the spot market premiums relative

to contract rates helps shippers understand transportation costs. Second, measuring the premium

as a percentage of contract rates allows comparison across lanes with different characteristics.

Table 3-1. Summary statistics.

3.4.2.2 Independent Variables

Lead time. The difference between the time when a load is made available on the spot market

and the time it is supposed to be picked up is included as an independent variable. It is

calculated as follows. Every load has three timestamps associated with it. The timestamp

indicates the time at which a load is entered into the auction tool (INSERT_DATE). Because the

auction is open for only 90 minutes, this timestamp accurately captures the time at which the

load carriers and brokers consider the load. The second timestamp shows the estimated pickup

Variable Definition N Mean St. Dev

All Bids

Price premium Bid price divided by the average contract rate on a load 117,158 2.372 1.155

Lead time Amount of time, in days, from bid until estimated pickup 117,158 1.501 0.676

Lead time squared The square of Lead time 117,158 2.711 2.203

Same region 1 if the carrier's primary region is in the same region as

the load origin; 0 otherwise

117,158 0.296 0.456

Only Wins

Price premium (above) 24,372 1.969 0.965

Lead time (above) 24,372 1.474 0.703

Lead time squared (above) 24,372 2.666 2.256

Same region (above) 24,372 0.319 0.466

Controls (all bids reported)

Number of invitees Number of carriers invited to auction 117,158 45.566 19.454

Number of bids Number of bids placed during auction 117,158 5.868 2.714

Average temperature Dummy variable for the average temperature at time of

pickup, separated into 10-degree buckets

117,158 59.242 16.986

Lane Lane upon which load travels

Day of week Day of week that load is to be picked up

Hour of day Hour of day that load is to be picked up

Bid day of week Day of week that load is bid upon

Bid hour of day Hour of day that load is bid upon

Carrier Carrier that placed the bid

Calendar week Week during which load is to be picked up

Notes: The table presents the summary statistics for the data in the study.

50

date displayed to the participants of the auctions (PICKUP_DATE). The second timestamp

minus the first timestamp gives the expected lead time for the load. A third timestamp indicates

when the load actually leaves Acme’s dock (START_DATE); we use this as a robustness check

because it serves as a proxy for the estimated pickup date, which is not always entered correctly.

For example, if PICKUP_DATE – INSERT_DATE is 1 hour and 15 minutes, then lead time

takes on a value of 1.25. Thus, lead time is a continuous variable.

Lead time squared. To allow the relation between the spot price premium and lead time to be

nonlinear, the square of lead time is included in the model. A positive coefficient indicates that

the value of lead time decreases in time.

Same region. A dummy variable is included to capture the effect of regional expertise of

carriers. We classify the origin pickup of each load into the general region of the United States

(e.g., Northeast, Midwest). We also have the self-reported primary region of expertise from

most of the carriers. For the few carriers for whom we do not have this information, we used the

region of their headquarters, gleaned from their website. If the load originated in the same

region of the carrier’s primary region, we coded a dummy variable as 1. Because a vast majority

(~80%) of the loads in this study are less than 500 miles, the origin region captures the entire

operating region of a load.

3.4.2.3 Control Variables

Several other factors could affect the cost of a load and hence the spot market price premium.

These are (1) the lane upon which the load travels; (2) the day of week and hour of day that a

load is to be picked up; (3) day of week and hour of day that a load is bid upon; (4) the carrier or

broker placing the bid; (5) the number of bidders invited to the auction and the number of

51

carriers who placed bids; (6) local weather conditions; and (7) the calendar week of the year

during which the load is to be picked up.

Lane. Lane fixed effects (i.e., dummy variables) are included to control for time-invariant

heterogeneity across lanes in the data. These control for observable and unobservable differences

(to the econometrician) that are constant through time such as the distance between the origin

and destination, population densities at the two locations, origin and destination effects, local and

regional competitive factors (e.g., carriers may compete more heavily on some lanes than

others), and average traffic patterns and road quality.

Day of week. The day of the week when a load is to be picked up is important because, for

instance, weekends are likely to be more costly to service because fewer drivers may be

available. Day-of-week fixed effects control for this.

Hour of day. Loads shipped during rush hour may be more costly to serve than loads picked up

in off-peak periods. Hour-of-day fixed effects control for this.

Bid day of week. The day of week when a load is bid upon is likely to impact which firm bids.

Some carriers might have busier days than others and be more or less likely to bid. To control

for this, we include day-of-week fixed effects for the bidding day.

Bid hour of day. The hour of day when a load is bid upon is also likely to impact who

participates in the auction. Fixed effects for the hour of day when each load is bid upon are

included to control for this.

Number of invitees. The number of carriers invited to each auction is not constant across

auctions. We include categorical dummy variables for the number of invitees to control for this

and to allow the estimated relationship to be nonlinear.

52

Number of bids. The number of invited carriers who place bids is also not constant across

auctions. To control for the amount of participation in each auction and allow for nonlinearity,

we include categorical dummy variables for the total number of bids per auction.

Average temperature. Above and beyond the season of the year, outside temperature affects the

operating cost of a carrier. For example, extremely hot or cold temperatures require trucks to

“idle” more when stopped to use air conditioning or heating to keep the driver in a comfortable

environment. Werner, a large truckload carrier, stated in a press release that “severe winter

weather in the first two months of 2014…caused significant freight disruption and weather-

related costs” (Werner 2015, p.1). To control for this, we collected daily high and low

temperatures for 2014 for every origin location in our dataset. These were retrieved from the

United States National Oceanic and Atmospheric Administration National Climatic Data Center

website. We calculated the average of the observed daily high and low temperatures.

To capture non-linear effects of temperature, we include dummy variables for

temperature ranges. The temperature ranges are between 30 and 40 degrees Fahrenheit (°F) (-

1.1 to 4.4 degrees Celsius (°C)), between 40 and 50 °F (4.4 to 10.0 °C), between 50 and 60 °F

(10.0 to 15.5 °C), between 60 and 70 °F (15.5 and 21.1 °C), between 70 and 80 °F (21.1 and 26.7

°C), between 80 and 90 °F (26.7 and 32.2 °C), and above 90 °F (32.2 °C). The excluded variable

is below 30 °F (-1.1 °C).

Carrier. Carrier fixed effects control for differences across carriers, such as size or profit targets.

3.4.2.4 Estimating market prices

Calendar week. The balance between supply and demand is not known with any great precision

or timeliness in truckload transportation (see Bignell 2013 for a discussion of the lack of an

effective truckload index). Hence, the current study provides a textbook example of an implicit

53

market (Rosen 1974), because market prices are not centralized or public. There are many

buyers and sellers of truckload transportation services, so it is a thick market. Prices adjust

quickly to balance supply and demand. As a result, the prices paid by any individual shipper will

generally be a good indicator of local supply and demand conditions. Hence, we propose that

observing carrier and broker bids over time and controlling for all relevant operational factors

provides a good representation of supply and demand conditions. This method resembles the

operation of the only transportation exchange operated, the BIFFEX, where a group of eight

brokers submitted prices daily on a set of 13 ocean routes (Denning, Riley, and Delooze 1994).

Weekly dummy variables included in the regression model capture the relative magnitude

of national market prices. This approach is commonly used to estimate prices in implicit markets

(Diewert, Heravi, and Silver 2007). The proposed method allows for the granular estimation

(e.g., at the regional level) of market prices in near-real-time. Such analysis currently does not

exist publicly, so our results may be useful for shippers, carriers, and financial investors.

4.3 Data

Access was granted from Acme to a dataset that included all bids for spot loads in calendar year

2014. In all, there were 133,271 bids and 31,218 offered loads, for an average of 4.27 bids per

offered load. 27,802 of the offered loads were awarded to a bidder. As with any large industry

dataset, some records were clearly inaccurate and needed to be removed. Per discussions with

Acme, all spot loads that go through the normal spot process are offered between 0 and 3 days.

Removing loads that showed a lead time less than 0 days and greater than 3 days resulted in

117,924 bids and 24,506 awarded loads. The removed records are likely the result of incorrectly

entered pickup appointments; for example, a pickup appointment before the load offer is clearly

inaccurate. Second, some bid prices were entered erroneously or disingenuously by the bidder.

54

For example, some Price premiums are quite large, more than 8 times the corresponding contract

price. We removed all Price premiums that were less than 0.5 or greater than 8. This resulted in

117,158 bids and 24,372 awarded loads, or 87.9% and 87.6% of the original dataset,

respectively. The decisions to remove these observations are tested for robustness in Section 5.

3.4.4 Model specification

The level of analysis is at the load level – all variables are calculated for every load. The

controls included in the model capture the relevant operational and market factors that are likely

to affect the bid price for a load. The model is specified as follows:

0 1 2 3

Price Premium Lead time Lead time squared Same region Lane

Day of week Hour of day Bid day of week Bid hour of day

Number of invitees N

umber of bids Average temperature

Carrier Calendar week

(2)

Each i is the coefficient for the variables defined in Section 3.4.2, is the idiosyncratic error

term for each load, and each categorical variable and fixed effect is represented by its name in

(1).

We use clustered standard errors to allow for heteroskedasticity and correlation of errors

in the variance-covariance matrix. Clustering at the carrier level allows for correlation within a

carrier’s spot bids; this seems likely as the same person at a carrier may place the bids. This

results in 57 clusters, which is above the threshold of 50 suggested by Wooldridge (2003).

3.5 Results

3.5.1 Main results

Table 3-2 shows the results of the main analysis. Hypotheses 1a and 1b are both supported, as is

hypothesis 2. The control variables generate reasonable results, such as higher observed prices

55

during extreme low and high temperatures and higher prices on weekends. Truckload market

prices, as measured by the weekly dummy variables, exhibit high serial correlation.

Lead time has a significant economic impact on truckload spot prices. The impact of lead

time is decreasing in time, consistent with previous findings from the carrier’s point-of-view.

Therefore, hypothesis 1A and 1B are supported. It is important to be clear on the interpretation

of the coefficients. Given that the coefficient in column 1 for Lead time is -0.202 and Lead time

squared is 0.030, the estimated value of one day of lead time versus 0 days of lead time is -0.202

* 1 + 0.030 * 12 = 0.172, or 17.2% of the corresponding contract price. For example, if the

contract price on a lane is $500, then the estimated effect of one day of Lead time and Lead time

squared is -0.172 * $500 = -$86 for a load on that lane.

For the following we focus on winning bids, as shown in column “Wins only” of Table 3-

2. Results are similar for winning bids and all bids, but because the shipper only pays winning

bids, they are more relevant for the following analysis. The mean Price premium for winning

bids is 1.969, meaning that the average winning bid is 96.9% higher than the corresponding

contract price. When the lead time increases from zero days to one day, the price premium

declines by 17.2 percentage points; or, on average, from 1.969 to 1.797. When lead time

increases from one to two days, the price premium further declines by 11.2 percentage points, or

on average from 1.797 to 1.685. A third day of lead time causes a further 5.2 percentage point

decline. Hence, a load with three days of lead time has a Price premium about 33.6 percentage

points lower than a load with no lead time; a load with three days of lead has a Price premium

that is 16.4 percentage points lower than a load with one day of lead time. Even so, with three

days of lead time, a spot load still has a Price premium of 1.633, or 63.3% higher than the

56

associated contract price. Figure 3-1 shows the reduction of Price premium as the days of lead

time increase.

Table 3-2. Effect of lead time on spot market bid premium.

For a shipper that spends $100 M on transportation at contract rates and with a spot percentage

of 10% (these numbers are for illustration and not necessarily representative of Acme), the

difference in cost given zero days of lead time versus three days of lead time is significant. In

the first case, spot transportation would cost $19.69 M, while with three days of lead time the

cost is $16.85 M, a difference of $2.84 M. In this scenario, the value of the first day of lead time

is $1.72 M, the second day is $1.12 M, and the third day is $520,000. Depending on the cost of

Dependent Variable:

Wins only All bids

Lead time -0.202*** -0.235***

Lead time squared 0.030** 0.046***

Same region -0.063** -0.160***

Intercept 2.588*** 3.460***

Lane Yes Yes

Day of week Yes Yes

Hour of day Yes Yes

Bid day of week Yes Yes

Bid hour of day Yes Yes

Number of invitees Yes Yes

Number of bids Yes Yes

Average temperature Yes Yes

Carrier Yes Yes

Calendar week Yes Yes

Observations 24,372 117,158

R-squared 0.732 0.642

Notes: The table displays the effect on lead time, measured in days

(continuous), on Price premium. Column "Wins only" includes only

winning bids. Column "All bids" includes all bids. Errors are clustered

by carrier.

Price premium

* denotes a 10% significance level, ** denotes a 5% significance level,

*** denotes a 1% significance level

Included?

57

advance notification, there are tangible and significant benefits for a shipper to share load

information in a timely manner, and to access the spot market as soon as a load rejection occurs.

Figure 3-1. Price premium decrease versus days of lead time, for winning bids. The curve

is nearly flat at 3 days of lead time.

3.5.2 Regional expertise

Regional expertise significantly affects carrier bidding behavior. On average, carriers bid 16%

less on loads that are in their region of expertise versus loads that are not in their region of

expertise. This decreases Acme’s observed prices by 6.3%, as seen in column 1 of Table 3-2.

Hence, economies of scope clearly matter and hypothesis 2 is supported. For shippers, it

behooves them to identify appropriate regional carriers and encourage their participation in the

spot market.

3.5.3 Day-of-week and bid day-of-week effects

1.969

1.797

1.685 1.633

1

1.25

1.5

1.75

2

2.25

0 1 2 3

Pri

ce P

rem

ium

Days of Lead Time

58

The day of week that a load is picked up and the day of week that a load is bid upon are

significant determinants of carrier bid price. Loads that are picked up on the weekends are

generally more expensive than loads picked up during the week; weekday loads are 15-20% less

expensive than weekend loads. This is not surprising, as it is likely that carriers have fewer

drivers available on weekends than on weekdays.

What is surprising is the effect of the bid day-of-week on prices. Carrier bids are much

higher when the auction occurs on weekends instead of weekdays. As seen in Table 3-3, the

magnitude of the price increase averages about 3 times the effect of the pickup day-of-week. It

is not clear what drives this premium. The number of bids and amount of competition do not

explain it, as those variables are controlled for. Perhaps carriers sense urgency from the shipper

or expect less competition from other carriers and hence bid higher.

This is further supported by model-free evidence. Using loads that are picked up on

Tuesday, the average price premium for bids placed on Sunday is 2.74 while the average

premium for bids placed on Monday is 2.37. In this case, the bid day-of-week effect more than

cancels out the extra day of lead time, by about 40%. A clear managerial implication is that spot

loads should be procured if at all possible on weekdays, even if shorter lead times are given to

carriers.

Table 3-3. Day of week and bid day-of-week fixed effects.

Day of week Bid day of week Day of week Bid day of week

Sunday omitted omitted omitted omitted

Monday -4% -58% -5% -45%

Tuesday -21% -61% -22% -49%

Wednesday -21% -64% -22% -53%

Thursday -14% -62% -16% -50%

Friday -11% -50% -14% -37%

Saturday -7% -10% -9% -4%

All bidsWins only

59

3.5.4 The impact of temperature

Cold weather clearly impacts carrier bids. Considering all bids, moderate temperatures (60 to 80

degrees Fahrenheit) result in 34% lower prices than temperatures below 30 degree Fahrenheit, as

seen in Figure 3-2. Extremely high temperatures also seem to affect bid prices, as temperatures

greater than 80 degrees Fahrenheit show an increase in price relative to moderate temperatures,

but still less than very cold temperatures. These results are consistent with the fact that carriers

use more fuel to maintain a comfortable environment for drivers when temperatures are extreme.

Figure 3-2. The effect of temperature on spot prices. Temperatures of less than 30 degrees

Fahrenheit is the omitted category.

3.5.5 Persistence of market prices

The weekly time dummies, which we will call a “weekly price index,” capture general supply

and demand conditions. Because the dataset is generated from thousands of bids each week on

many lanes by numerous carriers and brokers (each of whom have connections with hundreds or

thousands of individual carriers) in a highly competitive setting, and because Acme has plants

and warehouses in every geographic region of the United States, it is our contention that the

-40.0%

-35.0%

-30.0%

-25.0%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

30 to 40 40 to 50 50 to 60 60 to 70 70 to 80 80 to 90 Greater than 90

Effe

ct o

n p

rice

pre

miu

m

Temperature category (degrees F)

Wins only

All bids

60

price index captures market prices nationally. At the very least, it captures market prices within

Acme’s network.

Figure 3-3. Weekly price index for 2014. A higher value indicates relatively higher spot

prices during that week. Week 1 (January 1st through 4

th) is the basis (0%).

Figure 3-3 shows the price index by week for 2014. It is clear from Figure 3-3 that the

index has significant serial correlation. Table 3-4 shows that a one-week lag of the price index

has high predictive ability (column (1)). Column (2) shows that a lagged second week is not a

significant predictor; all information is captured in the previous week’s price index. An

interesting further study could analyze potential year-to-year seasonality (data do not currently

exist within Acme to test seasonality).

The high level of correlation across time is a significant finding. As a counterexample,

consider if the opposite were true. This would mean that this week’s prices are not related to

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

Prec

ent P

rem

ium

of C

ontr

act R

ates

Week of Year

Wins only All bids

JAN

FEB

MA

R

AP

R

MA

Y

JUN

JUL

AU

G

SEP

OC

T

NO

V

DEC

61

next week’s prices, and there would not be much that a shipper or carrier could do with

information on current prices. However, given that weekly prices are related, it may be in a

shipper or carrier’s best interest to alter their behavior. For example, a shipper may take

proactive managerial steps to ensure adequate capacity from their contracted carriers at

reasonable prices. This could take the form of personal intervention or a pricing incentive to

ensure that a carrier provides capacity.

Because freight at contract prices is often offered up to a week in advance, a carrier must

decide how much capacity to allocate to contracted customers and how much to the spot market.

A carrier must also decide where to position trucks and when to send trucks empty from one

location to another (Powell 1987). The predicted magnitude of spot prices certainly affects that

decision.

Table 3-4. Serial correlation of the weekly price index.

3.5.6 Robustness checks

Several robustness checks were conducted for winning bids and for all bids. Results for winning

bids only are reported but the analysis is robust for both.

Dependent Variable:

(1) (2)

One week lag 0.833*** 0.673***

Two week lag 0.179

Intercept 0.017 0.009

R-squared 0.605 0.621

Weekly Price Index

Notes: The table shows the serial correlation of the weekly time dummies

from the main analysis. Column (1) includes a one-week lag and column

(2) includes both a one-week and two-week lag. Robust standard errors

are used. N = 51 for column (1) and N = 50 for column (2).

* denotes a 10% significance level, ** denotes a 5% significance level,

*** denotes a 1% significance level

62

One check is the use of ninety days as the basis for rolling average contract rates. We

used 45 days and 135 days instead of 90 days, shown in columns (3) and (4), respectively, in

Table 3-5. The results do not change significantly, as can be seen by comparing to columns (1)

and (2), which repeat the initial results from Table 3-2.

Bids with a Price premium below 0.5 and above 8 were excluded in the main analysis.

Column (5) shows that the findings do not change if they are included.

In Section 3.4, we discuss that the PICKUP_DATE timestamp is not always entered

accurately. Every load has another timestamp, START_TIME, associated with it. This indicates

when the load left the shipper’s actual dock, and is mostly consistent with PICKUP_DATE. To

test robustness around the PICKUP_DATE timestamp, we calculate lead time based on

START_TIME instead of PICKUP_DATE. The results, shown in column (6), are mostly robust

to the usage of either timestamp. In this case, the square of lead time is not statistically

significant. We do not believe this affects our main findings.

Weekly time dummies were included to capture market prices. The implicit assumption

is that conditions are national. In addition to national market prices, there is likely also a

regional component. To ensure the lead time results remain consistent, we replaced the weekly

time dummies with regional-weekly time dummies, where loads were assigned to a region based

on their origin (e.g., Northeast, Southeast, West). Including regional-weekly time dummies does

not change the results, seen in column (7).

3.6 Discussion

We find that (1) industry-standard truckload contracts naturally incentivize information sharing

for shippers without the need for specific clauses; (2) the amount of lead time when procuring

truckload spot capacity affects the observed price, but the effect drops off after the second day;

63

(3) spot prices are significantly affected by factors such as the bid day-of-week, regional

expertise of a carrier, and weather conditions; and (4) truckload market prices are serially

correlated, meaning that prices observed today are good predictors of prices tomorrow.

These findings have implications for future research. Volume and price commitments

between shippers and carriers are quite loose, meaning that service commitments are not binding

and prices are open to negotiation should the market change. Contract pricing indexed by

various input costs and market factors exists in other industries, such as coal (Joskow 1985) and

natural gas (Crocker and Masten 1985). Why does a national market index for truckload services

not exist? Would shippers and carriers benefit from a market-indexed contract? We believe that

both a practical examination and a theoretical explanation of truckload contracts is a fruitful area

for future research.

Findings of this research also inform the “load offering” problem faced by shippers.

Shippers across the United States decide millions of times a year to whom to offer a load. This

decision involves a tradeoff that has not been previously recognized in the literature: (1) offer a

load to a contract carrier with some probability of rejection, at which point lead time is lost; or

(2) offer the load on the spot market, paying a random price that is typically more than the

contract price but without lost lead time. We believe that further analysis of this overlooked

problem would be beneficial for shippers.

Finally, a robust index of market prices in for-hire trucking in the United States does not

exist (Bignell 2013), which is surprising given the magnitude and importance of trucking to the

national economy. We propose an easy-to-implement method of estimating market prices and

find that market prices are highly autocorrelated with a lag of 1 week. Knowing this, it may be

in a shipper’s best interest to adopt mitigation strategies, such as managerial intervention or price

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incentives for carriers. Furthermore, an insight from this proposed method is that a centralized

exchange, such as the BIFFEX for ocean transportation (Denning et al. 1994), is not required to

gain an understanding of truckload market prices. Brokers place thousands of bids daily on spot

loads on various lanes throughout the country with various shippers. If a significant portion of

these bids was collected and analyzed using our method to control for all of the operational

factors of a load, a robust, near-real-time regional index could be created. This could be useful

for at least three parties: shippers, carriers, and financial investors.

3.6.1 Limitations

Finally, our study has limitations. Because it analyzes one shipper’s data for one year, there may

be idiosyncrasies that cannot be controlled for. While we do not believe there is anything

particularly unique about the shipping operations of Acme, we cannot completely rule it out. It

would be useful to replicate this study with more data from other shippers and years.

Furthermore, 2014 experienced extreme weather conditions for the first quarter, popularly

termed the “polar vortex.” While we’ve controlled for temperature, there are possible adverse

effects on carrier capacity that cannot be captured by temperature alone. Further studies of this

under-examined transportation market would be beneficial. We believe that empirical

transportation research is an area ripe for future studies.

65

Table 3-5. Robustness checks.

Dependent Variable:

(1) (2) (3) (4) (5) (6) (7)

Lead time -0.202*** -0.235*** -0.210*** -0.211*** -0.221*** -0.144** -0.187**

Lead time squared 0.030** 0.046*** 0.033*** 0.032*** 0.034** 0.0001 0.024**

Same region -0.063** -0.160*** -0.074** -0.064** -0.065* -0.063* -0.057*

Intercept 2.588*** 3.460*** 2.274*** 2.694*** 2.713*** 2.441*** 2.227***

Lane Fixed Effects (FE) Yes Yes Yes Yes Yes Yes Yes

Day of week FE Yes Yes Yes Yes Yes Yes Yes

Hour of day FE Yes Yes Yes Yes Yes Yes Yes

Bid day of week FE Yes Yes Yes Yes Yes Yes Yes

Bid hour of day FE Yes Yes Yes Yes Yes Yes Yes

Number of invitees FE Yes Yes Yes Yes Yes Yes Yes

Number of bids FE Yes Yes Yes Yes Yes Yes Yes

Average temperature FE Yes Yes Yes Yes Yes Yes Yes

Carrier FE Yes Yes Yes Yes Yes Yes Yes

Calendar week dummies Yes Yes Yes Yes Yes Yes No

Calendar week-region dummies No No No No No No Yes

Observations 24,372 117,158 24,372 24,372 24,506 24,372 24,372

R-squared 0.732 0.642 0.698 0.739 0.737 0.729 0.757

Notes: The table displays the effect on lead time, measured in days (continuous), on Price premium. Column (1) includes only winning bids. Column

(2) includes all bids. Columns (3) and (4) assume 45 days and 135 days as the basis for contract rates, respectively, instead of 90 days. Column (5)

includes all Price premium s. Column (6) uses START_TIME instead of PICKUP_DATE as the estimated time of pickup for the carriers. Column

(7) includes week-region controls instead of only week controls. In all cases , errors are clustered by carrier.

* denotes a 10% significance level, ** denotes a 5% significance level, *** denotes a 1% significance level

Price premium

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CHAPTER 4: OUR AGREEMENT ONLY GOES SO FAR: CONTRACT

EXPLICITNESS AND THE PAYOFF TO OPPORTUNISM

Buyers utilize governance mechanisms to ensure good performance and restrain opportunism

from their suppliers. Despite a large literature, the performance of these mechanisms as a

supplier’s payoff to act opportunistically changes has largely been overlooked. Observing the

governance mechanisms enacted by a buyer of a common service on their suppliers and the

subsequent transactional fulfillment decisions made by the suppliers ex post agreement, we find

that more explicit contracts are effective when market conditions are unfavorable for suppliers.

As market conditions change in the supplier’s favor, the effectiveness of more explicit contracts

weakens relative to other governance mechanisms (e.g., commitment and output monitoring).

This study highlights the importance of selecting complementary governance mechanisms and

shows their limitations during shifts in the broader market.

4.1 Introduction

When the demand for a supplier’s goods or services exceeds capacity, the supplier must decide

which customers to serve (Cachon and Lariviere 1999). Given a mix of long-term buyers with

relatively static prices and spot buyers willing to pay high prices, suppliers need to balance the

incentive of short-term profits with the potentially long-lasting ramifications of providing poor

service to long-term buyers (Adelman and Mersereau 2013). To ensure consistent supply at

previously-negotiated prices, buyers utilize a variety of governance mechanisms, including

contracts, output monitoring, incentives, and the promise of future business (Jap and Anderson

2003).

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The effectiveness of governance mechanisms to improve supplier performance and

restrain opportunism – where opportunism is defined as “self-interest seeking with guile”

(Williamson 1975, p. 26) – has interested business scholars for decades (e.g., Williamson 1983,

John 1984). More recent research has focused on how multiple governance mechanisms interact

with one another to improve performance and restrain opportunism (Heide, Wathne, and Rokkan

2007, Kashyap, Antia, and Frazier 2012) and how the form of opportunistic behavior affects

exchange outcomes (Wathne and Heide 2000, Seggie, Griffith, and Jap 2013, Lumineau and

Quelin 2011). Importantly, Zhou and Poppo (2010) and Zhou and Xu (2012) suggest that the

legal enforceability of the business environment moderates the relative effectiveness of

governance mechanisms.

In addition to these factors, we propose that the payoff to a supplier’s opportunistic

behavior impacts the effectiveness of governance mechanisms. Some governance mechanisms

impose high costs on suppliers – e.g., the removal of future business – while others impose lower

costs – e.g., social pressure to perform better. We predict that mechanisms with a lower-imposed

cost will be effective in ensuring performance and restraining opportunism when the payoff to a

supplier is low but become relatively less effective as the payoff increases. When the payoff to a

supplier to renege on static prices is very high, we predict that supplier performance will

converge based on the governance mechanism with the highest imposed cost.

We study how suppliers with three distinct combinations of governance mechanisms –

(1) no effective governance, (2) commitment and output monitoring, and (3) commitment, output

monitoring, and more explicit contracts – respond to requests for service at static prices. The

industry in which we study these concepts – truckload transportation in the United States – is a

common service with significant supply and demand fluctuation throughout the year. We are

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able to estimate when the market is favorable or unfavorable for suppliers (“carriers”) using a

large database of private spot prices. Concurrent with the spot prices, we observe more than

160,000 requests for service from a large buyer of services (“shipper” 6) and estimate the causal

effect of different governance mechanisms on carrier performance and opportunism. An

important aspect of the business environment is that contracts and prices are established at one

point in time, but the actual provision of service is not legally enforceable – i.e., shippers and

carriers operate in an extralegal environment. Interestingly, carriers refuse to provide service a

significant percentage of the time.

We contribute to the literature in several ways. Our analysis of contract explicitness

alongside other governance mechanisms responds to Kashyap et al.’s (2012, p. 274) call for

future research “related to combinations of contractual completeness…with other relevant ex

ante governance mechanisms.” Few studies use actual industry contracts to analyze the effect of

contract explicitness on exchange-partner performance (Kashyap et al. 2012, p. 261); we use

actual contracts and then analyze observed transactional behavior ex post agreement. We are

unaware of any other studies that do so; thus, we contribute to empirical research on contracts,

which is considerable (Eigen 2012). Our consideration of a specific type of opportunism – that

of suppliers reneging on static prices because the market has swung in their favor – responds to

Seggie et al.’s (2013, p. 85) call for “future studies of opportunism” that “consider the distinction

between active and passive opportunism and try to shed light on the fundamental differences in

responses to these types in addition to their effects on exchange behavior and stability.” Zhou

and Xu (2012, p. 690) hope that “future research will continue to explore and document how

economic and social mechanisms interact with institutional factors to affect opportunism and

6 We use “buyer” and “supplier” when discussing general relationships and previous research. We use “shipper”

and “carrier” when discussing the trucking industry and our particular results.

69

exchange performance in different market contexts” and call for a focus on causality. Using

detailed longitudinal datasets and econometric techniques suggested by Vella (1998) and Tucker

(2012), our analysis establishes causal inference on the effectiveness of specific governance

mechanisms to improve supplier performance and restrain opportunism. Finally, we are unaware

of studies that consider the differential performance of governance mechanisms as the payoff for

opportunism changes. Thus, we contribute by demonstrating the importance of the interaction

between the payoff for opportunism and the type of governance mechanism.

4.2 Theoretical Foundation and Hypotheses

Transaction cost economics (TCE) asserts that exchange partners select and utilize governance

mechanisms that economize exchange with one another (Williamson 1985). These mechanisms

take a variety of forms, including contracts, incentives (Jap and Anderson 2003), monitoring

(Heide, Wathne, and Rokkan 2007), and the promise of future business (Heide and Miner 1992).

Instead of being used in exclusivity, multiple mechanisms are often employed to take advantage

of the differential effects each has on exchange-partner behavior (Anderson and Dekker 2005).

Contracts serve as a tool of control and coordination (Lumineau 2014, Reuer and Arino

2007). From a control perspective, contracts outline acceptable exchange-partner behavior and

impose sanctions should a partner act contrary to the specifications. For example, contracts can

determine prices (Crocker and Masten 1991), goods and services to be exchanged (Argyres,

Bercovitz, and Mayer 2007), timelines (Mayer and Argyres 2004), and dispute resolution

processes (Barthelemy and Quelin 2006). On the other hand, contracts coordinate by aligning

expectations between partners. For example, contract terms can clarify each partner’s role and

responsibility (Carson, Madhok, and Wu 2006), define mutual objectives (Mooi and Ghosh

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2010), and outline communications processes, contingency plans, and how systems interact with

one another (Mayer and Argyres 2004).

Contracts are used to restrain exchange-partner opportunism. Defined by Williamson

(1975, p. 26) as “self-interest seeking with guile,” opportunism is fundamental to TCE. As

Williamson (1985, p. 31) says, “in the absence of opportunism,” contract “reduces to a world of

promise.” Opportunism takes many forms, such as shirking on obligations, dishonestly

negotiating, or violating specific agreements (Wathne and Heide 2000). One strategy to decrease

partner opportunism is to write a more explicit7 contract (Wuyts and Geyskens 2005, Zhou and

Poppo 2010). In so doing, firms tradeoff the cost of writing a more explicit contract versus the

expected value that the additional detail brings (Crocker and Reynolds 1993).

The effectiveness of more explicit contracts, in and of themselves, in reducing partner

opportunism is unclear from the extant research. Achrol and Gundlach (1999) did not find

support for their hypothesis that contracts reduce opportunism. Wuyts and Geyskens (2005) did

not find general support for the hypothesis that more detailed contracts reduce partner

opportunism; however, they did conclude that more detailed contracts are effective in reducing

opportunism when non-close partners are involved in the exchange. On the other hand,

Dahlstrom and Nygaard (1999) found that more formal procedures and role responsibilities

reduced opportunism in exchange relationships. In a study of buyers and suppliers in China, Liu,

Luo, and Liu (2009) found that transactional mechanisms, such as contractual clauses, reduced

opportunism.

7 More explicit contracts have been described alternatively in the literature as more (in)complete contracts

(Anderson and Dekker 2005, Ghosh and John 2005, Kashyap, Antia, and Frazier 2012), more specific contracts

(Mooi and Ghosh 2010), more detailed contracts (Wuyts and Geyskens 2005, Vanneste and Puranam 2010), and

more formal contracts (Carson, Madhok, and Wu 2006, Poppo and Zenger 2002).

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There are several reasons for the conflicting evidence. First, recent research suggests that

an important moderator of the effectiveness of contractual detail is the legal enforceability of the

environment (Zhou and Poppo 2010). When exchange partners understand that the violation of a

contract will not be enforced in a court of law (i.e., an extralegal environment), more explicit

contracts will strengthen the coordinating aspect of contracts due to the additional time spent

drafting and the additional specifications in the documents. The controlling aspect of contracts

in an extralegal environment is unclear. Zhou and Xu (2012, p. 688) suggest that, in the absence

of other governance mechanisms, more “detailed contracts could lead to a higher propensity for

opportunism” because the longer negotiation process could hurt exchange partner relations and

signify distrust (Jap and Ganesan 2000). Given the longstanding view in TCE that contracts are

used to restrain opportunism, Zhou and Xu’s is a provocative proposition.

A second reason for the conflicting evidence is that the effectiveness of more explicit

contracts depends on what other governance mechanisms are in place (Heide, Wathne, and

Rokkan 2007). In a study of franchisors and franchisees, Kashyap et al. (2012) found that

franchisors who have more “complete contracts” with their franchisees are less likely to monitor

the franchisees’ behavior. They then found that behavior monitoring is positively associated

with opportunism. In a survey of foreign buyers and local suppliers in China, Zhou and Xu

(2012) found that more explicit contracts are negatively associated with supplier opportunism

when used in concert with relational governance mechanisms (e.g., social relations and shared

norms), but positively associated with supplier opportunism when not. Clearly, contract

explicitness and other governance mechanisms interact with one another to affect exchange-

partner opportunism (Wang et al. 2013).

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A prominent governance mechanism other than contract is the monitoring of a partner’s

output and/or behavior (Heide et al. 2007). Given an agreement to do business together, the

presence of monitoring mechanisms allow each partner to measure whether one another is living

up to the agreement. Monitoring mechanisms are more effective when paired with more explicit

contracts because the additional contractual detail itself enhances the ability to monitor. If a

failure to live up to the contract is detected, the explicit contract increases the social pressure one

partner can exert on another (Liu, Luo, and Liu 2009). Indeed, when used in conjunction with a

“microlevel social contract,” Heide et al. (2007) found that monitoring reduces opportunism in

exchange partners. Based on the coordinating aspects of more explicit contracts and the positive

interaction between contract explicitness and monitoring, we propose the following hypothesis:

H1: Suppliers with more explicit contracts provide better service on average than suppliers

without more explicit contracts.

Often overlooked in research on opportunism is the form (Seggie et al. 2013). Wathne

and Heide (2000) argue that opportunistic behaviors take on two forms: active or passive

opportunism. Because the form of opportunism can affect exchange relationships differently,

governance strategies should be designed accordingly. The key differentiator between active and

passive opportunism is the payoff to the opportunistic party. In existing exchange relationships,

which is the context of this study, active opportunism results in a revenue increase for the

opportunistic party. Passive opportunism, on the other hand, results in a cost decrease for the

opportunistic party.

Perhaps just as important as the form is the payoff to opportunism. Consider a buyer-

supplier relationship in which the supplier’s output is easily monitored. If the payoff to act

opportunistically is small, the monitoring mechanism will effectively restrain opportunism if the

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social pressure that can be exerted on the supplier exceeds the payoff to opportunism. As the

payoff increases, the cost threshold of the social pressure is surpassed and the supplier will act

opportunistically. When this happens, the buyer must utilize some other governance mechanism

to restrain opportunism, such as threatening to remove future business.

Cachon and Lariviere (1999) describe the tradeoffs that a supplier must make when the

demand they face exceeds supply. With a mix of long-term buyers with fixed prices and short-

term “spot” buyers willing to pay higher prices, suppliers must balance short-run and long-run

profits considering the negative effects of poor service to long-term buyers (Adelman and

Mersereau 2013). The refusal to provide a good or service at a pre-specified price because the

market has turned in a supplier’s favor is a type of active opportunism, which we call “market

opportunism.”

Consider how a more explicit contract restrains market opportunism as the payoff

changes. Assume that suppliers are monitored for their output, their output is easily observed,

and if a supplier provides “bad” service, they face the loss of future business. When the payoff

for market opportunism is low, suppliers with more explicit contracts should provide better

service because explicit contracts better coordinate and increase social pressure. As the payoff

for market opportunism increases – i.e., as the market turns in favor of the suppliers – the relative

cost of the increased social pressure and positive aspects of better coordination become smaller.

The behavior of suppliers with and without the more explicit contract should converge – the

value of future business exceeds the short-term payoffs of market opportunism, hence monitored

suppliers provide “good enough” service to maintain the relationship, with or without the more

explicit contract. This suggests the following hypothesis:

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H2: The difference in service levels between suppliers with more explicit contracts and suppliers

with less explicit contracts is larger when the payoff to market opportunism is low than when the

payoff to market opportunism is high.

4.3 Industry Setting

We use the U.S. trucking industry to test our hypotheses for several reasons. First, it is an

important industry with many buyers and sellers of a common service, hundreds of billions of

dollars in revenue is exchanged, and the industry-wide balance of supply and demand fluctuates

significantly within a year. Second, there are well-specified relational governance mechanisms

between firms – some sellers are designated “primary” and others “backup,” each type with

differing commitments (Caplice 2007). Third, contracts are exchanged and relatively

standardized but are not enforced via legal remedies (Scott, Parker, and Craighead 2016).

Hence, a contract does not guarantee that either firm will live up to its promise. Finally, due to

the widespread adoption of Electronic Data Interchange (EDI), it is easy to observe

communications between buyers and sellers (Hart and Saunders 1997).

Industry characteristics

With annual revenues of roughly $682 billion in 2012 and moving about 68% of all tonnage

shipped in the United States (Corridore 2014), trucking has been described as the “lifeblood of

the US economy” (Costello 2014). In trucking, shippers that want to move goods through their

supply chain use either their own assets or hire a supplier of transportation services to transport it

– that is, they either serve as or hire an external carrier.

An important subsector of trucking is the for-hire truckload (FHTL) sector, which is the

setting of this study. Truckload indicates that a full trailer (i.e., a “load”) is occupied and “for-

hire” indicates that a carrier can be hired by a variety of shippers as opposed to a “private fleet”

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that is owned and operated by an individual shipper. FHTL is ubiquitous across supply chains:

in the U.S., there are thousands of shippers, hundreds of thousands of carriers, annual revenues in

2012 were roughly $298 billion, it moved approximately 33.4% of all tonnage (Corridore 2014),

and almost every major shipper hires FHTL carriers, including the largest shippers that own

private fleets, such as Wal-Mart and Sysco (Caplice 2007).

Shippers contract with carriers at static prices to haul loads as demand arises over time.

When a shipper needs a load to be hauled, they offer the load to the contracted carrier via EDI

and the carrier accepts or rejects the load offer, also via EDI. To generate competitive prices on

origin-destination pairs (“lanes”), shippers typically conduct annual procurement auctions

(Caplice 2007). In these auctions, numerous carriers are invited and presented with lanes upon

which to bid in a first-price sealed-bid format. The winner of a lane becomes the “primary”

carrier on the lane; the primary designation means that the carrier has the right-of-first-refusal at

the contracted price for load offers as the shipper’s demand materializes throughout the year.

Carriers who bid on a lane but lose are often used as “backup” carriers – when a primary carrier

refuses a load offer, backup carriers are automatically offered the load at a previously-agreed-

upon price.

Contractual governance

Contracts between shippers and carriers are typically exchanged. While some aspects of the

contracts are court-enforceable (e.g., price, insurance), the requirement to actually haul the loads

is not because the residual right of control of the assets rest with the owner – i.e., the carrier

(Baker and Hubbard 2003). Caplice (2007, p. 427) says that “the outcome of a strategic TL

[truckload] auction is not completely binding…the carrier will not always have equipment

available when the shipper requests it.” In the words of a vice president at a carrier, “the ‘rate

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[price] agreements’ and ‘load commitments’ for the most part have no contractual obligation or

penalties on either party” (Taylor 2011, p. 2). For the time period of this study, there were no

lawsuits, threats of lawsuits, or attempted financial penalties between our focal shipper

(henceforth: “Acme”) and any of its carriers. Hence, load offer requests in the FHTL industry

can be described as extralegal.

There are two important contractual considerations with regards to our study. First,

contracts are standardized between shippers and carriers8. Acme uses a standard template for

contracts with carriers, meaning that significant variation across contracts is controlled for. A

key difference between contracts with primary carriers and backup carriers is that primary-carrier

contracts typically specify a load-acceptance percentage (e.g., 95%), whereas backup-carrier

contracts do not. The load-acceptance percentage is not legally-binding and often violated, but it

serves to codify intentions between the parties (Mayer and Argyres 2004).

Second, in the time frame of our study, Acme specified an additional contractual term

with some primary carriers on some lanes – a “maximum accept-up-to” daily volume limit. With

this additional clause, the carrier promises to accept up to a specific number of loads each day on

a lane, which can vary on a day-of-week basis. In return for this additional “guarantee,” the

shipper promises not to offer more than the specified number of loads on a day. This protects the

carrier from having their load acceptance percentage hurt by large, unexpected spikes in demand

that may be hard for the carrier to service. The extra clause is not legally-enforceable and we

observe a significant number of load offer refusals, even when they are offered within the limits

of the accept-up-to promise. Interestingly, the extra protection that the additional contractual

8E.g., the American Trucking Assocations and the National Industrial Transportation League have model contracts

available at

http://www.trucking.org/ATA%20Docs/What%20We%20Do/Law%20and%20Litigation/General%20Documents/M

odel%20Agreement%20-%20Shipper.pdf

77

clause actually provides is minimal. Based on a report from Acme, the “maximum accept-up-to”

limit was hit less than 3% of the lane-days, affecting an even smaller percentage of load offers.

Nonetheless, we adopt strategies to control for unobservable carrier preferences to select into this

additional clause.

Output monitoring

Primary carriers are expected to accept most loads and their performance is scrutinized by output

monitoring (Heide et al. 2007); if they do not perform adequately, they are replaced by other

carriers. Backup carriers, on the other hand, are not held to load-acceptance performance

standards (Caplice 2007) and are not monitored. In essence, they only accept loads that are in

their own objective interest to haul without considering long-term ramifications. Acme’s

systems automatically monitor carrier load-acceptance performance of primary carriers and they

have regular reviews with carriers.

Market opportunism in FHTL

Carriers reject load offers a significant percentage of the time. Caplice (2007) analyzed a large

dataset and found that load offers were rejected 26% of the time. There are two main reasons

why they do so (Scott et al. 2016): (1) if the market shifts in the favor of the carrier, at which

point previously negotiated prices are no longer attractive, or (2) if the shipper has a spike in

demand, at which point the carrier either does not have assets to service the demand or it is

prohibitively expensive to reposition assets to do so.

C.H. Robinson, a large buyer and seller of truckload services, describes market

opportunism in FHTL: “when equipment becomes scarce, carriers may shift their equipment to

transactional customers who will pay higher spot market rates” (C. H. Robinson 2014, p. 6).

This causes major problems for shippers, including higher costs and poor service levels to their

78

customers (Zsidisin, Voss, and Schlosser 2007). The tension caused by load offer rejections due

to static contract prices and high spot prices is palpable in the words of a vice president at a

carrier: “generally, there are no volume guarantees, nor financial penalties, so essentially when

load acceptance rates fall, a lot of yelling and hollering is what happens” (Taylor 2011, p. 3).

4.4 Empirical Strategy and Data Source

The data to test our hypotheses come from Acme, a large shipper with plants throughout the

United States. We focus only on Acme’s FHTL carriers, who haul > 95% of all of their goods.

Because Acme and their carriers endogenously select into the form of governance, we use a

method to account for selection bias suggested by Vella (1998) and Tucker (2010) and used by

other business researchers (e.g., Main and Reilly 1993, Holloway and Parmigiani 2014). Below

we discuss our treatment and control groups, how we measure FHTL market conditions (and

hence the payoff for market opportunism), the data used in the analysis, the variables and model

specification, our identification strategy, and our clustering strategy.

Treatment and control groups

Our control group is the set of backup carriers. They are not governed by contractual clauses,

monitoring, or commitments – hence, they are an excellent control group because they provide a

glimpse into how a carrier would behave in the absence of the governance mechanisms. One

alternative treatment group is the set of “implicit carriers”; their treatment is output monitoring

and the commitment of a primary carrier. The other alternative treatment group is the set of

“explicit carriers”; their treatment is output monitoring, the commitment of a primary carrier, and

the additional “accept-up-to” clause in the contract. To be clear, the actual business entity that is

a carrier can be a backup carrier on some lanes, an implicit carrier on others, and an explicit

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carrier on other lanes. Table 4-1 shows the treatment and control groups and their corresponding

forms of governance.

Table 4-1. Control and treatment groups.

Detecting FHTL market conditions

A widely-used centralized marketplace in FHTL does not exist (Tsai, Regan, and Saphores 2009)

– hence, detecting market conditions in near-real-time is challenging. One solution is to identify

the relevant operational variables describing a load, include a time-period dummy variable to

capture latent market conditions (Diewert, Heravi, and Silver 2007), and regress these variables

on time-varying prices. The included dummy variables capture time-period effects in the

varying prices, thus generating a price index indicative of market conditions.

Two elements of data are necessary for this strategy – time-varying prices and the

operational aspects of the loads associated with the prices. We use carrier bids in online spot

auctions conducted by Acme for our purposes. In the spot auctions, a large number of carriers

(typically 50 or so) are invited to bid on one-off loads in a first-price sealed-bid format. They are

displayed the relevant load information, such as the origin and destination locations, number of

miles, expected weight of the load, and pickup time. Auctions are open for 90 minutes.

To estimate market conditions, we adopt a specification suggested by transportation

researchers (Scott 2015). We provide a brief discussion of the approach here; for a detailed

Group Type No governance Mutual

commitment

Output

monitoring

Extra contractual

clause

Backup carriers Control x

Implicit carriersAlternative

treatmentx x

Explicit carriersAlternative

treatmentx x x

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justification, see Scott (2015). First, given contract prices9 on a lane and a bid price on a spot

load on the same lane, the ratio of the bid and contract price provide a measure of the “premium”

associated with a particular bid. For example, if the contract price on a lane is $500 and a spot

bid is $1,000, then the premium would be 2. All else equal, a higher premium indicates that

supply and demand conditions are more favorable to carriers, whereas low premiums indicate the

opposite10

. The advantage of converting raw price data to a ratio is the ability to compare across

loads, most of which have different characteristics.

The spot premium for a load can be broken down into cost components. For example, the

cost of a load depends on the distance, lead time, the day of week of pickup, origin and

destination attributes, and even the temperature of the external environment (e.g., it is costlier to

operate a truck in extremely cold weather). The model specification is

0 1 2 3

Price Premium Lead time Lead time squared Same region Lane

Day of week Hour of day Bid day of week Bid hour of day

Number of invitees N

umber of bids Average temperature

Carrier Number of origin loads Calendar week

(1)

which is the same specification as Scott (2015) except that we have included fixed effects for the

number of loads at each origin for each day to account for any capacity depletion effects. Table

4-2 describes the variables included and associated summary statistics, which are described in

detail in chapter 3.

9 Contracted prices are close to the marginal cost to operate a load due to the highly competitive nature of the

industry and the “large numbers” bargaining conditions that exist at the time of price negotiations (Williamson 1975,

p. 27). 10 In a transportation industry report by Standard & Poor’s, Kirkeby (2013, p. 7) says: “S&P believes pricing trends

in the [trucking] spot market provide insight into the general availability of capacity and demand for that capacity.”

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Table 4-2. Data description and summary statistics for price index analysis.

Our dataset contains 133,271 bids in the calendar year 2014. We remove bids that are

less than 50% or greater than 800% of the corresponding contract prices on a lane, because some

bids were clearly data entry errors (e.g., $9,999; $0). This removes 878 observations, less than

0.6% of our sample.

To capture regional variation, we assigned the origin and destination region for each load

using the regional classifications from the U.S. Census Bureau (Northeast, Midwest, South,

West). For example, if a load begins in Arizona and ends in Indiana, it would be classified as

originating in the West and ending in the Midwest. We then ran regression model (1) for each

region combination, where all loads that either begin or end in a region (or both) are included:

within Northeast, within Midwest, within South, within West, Northeast-Midwest, Northeast-

South, Northeast-West, Midwest-South, Midwest-West, and South-West. For example, a bid on

a load that begins in the West region and ends in the Midwest region would be included in the

Midwest-West price index; a load that begins in the Midwest and ends in the Midwest would be

included in the within Midwest, Northeast-Midwest, Midwest-South, and Midwest-West price

index. The 10 resultant indexes are shown in Figure 4-1.

Variable Definition Mean St. Dev

Price premium Bid price divided by the average contract rate on a load 2.382 1.165

Lead time Amount of time, in days, from bid until estimated pickup 1.764 1.026

Lead time squared The square of Lead time 4.165 7.142

Same region 1 if the carrier's primary region is in the same region as the

load origin; 0 otherwise

0.296 0.456

Number of invitees Number of carriers invited to auction 45.852 19.387

Number of bids Number of bids placed during auction 5.834 2.695

Average temperature Dummy variable for the average temperature at time of

pickup, separated into 10-degree buckets

5.953 1.696

Lane Lane upon which load travels N/A N/A

Day of week Day of week that load is to be picked up N/A N/A

Hour of day Hour of day that load is to be picked up N/A N/A

Bid day of week Day of week that load is bid upon N/A N/A

Bid hour of day Hour of day that load is bid upon N/A N/A

Carrier Carrier that placed the bid N/A N/A

Number of origin loads Number of loads bid upon on that day from that location 12.385 9.49

Calendar week Week during which load is to be picked up N/A N/A

82

Figure 4-1. The ten indexes over 2014 (Northeast = NE, Midwest = MW, South = S, West =

W).

There are a couple of interesting observations about the price indexes. First, there is

significant variation in the spot price of FHTL services over the year. Given the mean spot

premium of a bid of 238% (shown in Table 4-2), the average spot premium varies from the

minimum to maximum by about 150 percentage points. This means that spot prices can, at

times, approximately equal contract prices, while at other times average more than 2.5 times the

contract price. Second, the price indexes are highly correlated. The average correlation is about

84% and the least correlated series, “within Midwest” and “within West”, have a positive

correlation of 65.8%. This is expected because of seasonal nation-wide economic output and if

imbalances in a particular region were to persist over time, a truck could be relatively easily

repositioned to the region with high prices.

Despite the fact that our spot price dataset consists of more than a hundred thousand spot

prices from a large number of carriers, including bids from large brokers who themselves have

-150%

-100%

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50%

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150%

NE-NE NE-MW NE-S NE-W MW-MW

MW-S MW-W S-S S-W W-W

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50%

100%

150%

NE-NE NE-MW NE-S NE-W MW-MW

MW-S MW-W S-S S-W W-W

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

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83

connections with a large number of carriers, it is possible that these spot prices do not represent

conditions in the overall market. For validation, we compared a nationalized version of our spot

price index (i.e., model (1) not subdivided into regions) with a biweekly “supply and demand

sentiment” index, published by Morgan Stanley, a large U.S.-based investment bank, which can

be found at the website of Transplace, a third-party logistics provider (Transplace 2016). We

converted the “sentiment” index into weekly data points and compared it to our national price

index. The correlation between the time series is 68%, which further validates the representation

of our spot price index of the broader market. The time series are shown in Figure 4-2.

Figure 4-2. The national spot price index versus the Morgan Stanley sentiment index.

Market opportunism

We use our regional indexes of market conditions to classify when the market is favorable or

unfavorable for a carrier. For our main analysis, we classify the market into thirds: unfavorable

market, moderate market, and favorable market market conditions. To test this classification for

robustness, we also run our main analysis including the market segmented into halves and with a

linear and quadratic term instead of discrete classifications.

0

1

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Spot Price Index Morgan Stanley Sentiment IndexJAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

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Spot Price Index Morgan Stanley Sentiment Index

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84

For each time series, we rank each weekly observation by magnitude and classify each

week into one of the three categories. For example, in week 45 of the “within Midwest” time

series, the index value is -129%, which is the lowest value in the series; week 50 has an index

value of -90%, in the middle third of series values; and week 13 has an index value of 42%,

which is the highest in the series. Hence, week 45 is classified as an unfavorable market for a

carrier, week 50 is a moderate market, and week 13 is a favorable market. We use these

classifications as dummy variables in our analysis. If a load offer occurs in the “within

Midwest” region in, say, week 50, then this load offer will have occurred during a moderate

market – i.e., the moderate market dummy variable will be 1 and the unfavorable market and

favorable market dummy variables will be 0. Table 4-3 shows an example of our classification

system, where we also show classification of the market into halves.

Data

Acme’s loads typically travel from a plant-warehouse to a customer’s distribution center. Due to

the density of the product, trailers always hit their weight limits. For practical purposes, their

product is homogenous, low value, and with no shelf-life constraints; hence, all loads are

basically the same and well within typical carrier insurance limits, facts well known to carriers.

Because negotiations with carriers took place early in 2014, our analysis is based on load

offers from March 1st to December 31

st, 2014. We use several datasets for our analysis, listed in

Table 4-4. The first dataset contains all EDIs between Acme and their carriers. From this

dataset, we observe whether the carrier accepted or rejected the load offer and a timestamp of

each transmission. A second dataset contains general load information for all loads hauled in

2014 – origin, destination, carrier, price paid, time of pickup, whether it was at a contracted or

85

spot price, and other data not used in our analysis. A third dataset indicates whether the carrier at

the

86

Table 4-3. Example of market status classification, using the “within Midwest” series.

Region Week Price Index Unfavorable Moderate Favorable Bottom Half Top Half

within Midwest 45 -129% 1 0 0 1 0

within Midwest 46 -122% 1 0 0 1 0

within Midwest 47 -121% 1 0 0 1 0

within Midwest 37 -121% 1 0 0 1 0

within Midwest 44 -121% 1 0 0 1 0

within Midwest 39 -120% 1 0 0 1 0

within Midwest 33 -117% 1 0 0 1 0

within Midwest 40 -112% 1 0 0 1 0

within Midwest 38 -111% 1 0 0 1 0

within Midwest 31 -111% 1 0 0 1 0

within Midwest 34 -110% 1 0 0 1 0

within Midwest 32 -110% 1 0 0 1 0

within Midwest 51 -110% 1 0 0 1 0

within Midwest 41 -109% 1 0 0 1 0

within Midwest 43 -105% 1 0 0 1 0

within Midwest 42 -103% 0 1 0 1 0

within Midwest 35 -102% 0 1 0 1 0

within Midwest 49 -99% 0 1 0 1 0

within Midwest 30 -96% 0 1 0 1 0

within Midwest 50 -90% 0 1 0 1 0

within Midwest 36 -78% 0 1 0 1 0

within Midwest 29 -71% 0 1 0 1 0

within Midwest 48 -69% 0 1 0 0 1

within Midwest 23 -53% 0 1 0 0 1

within Midwest 18 -46% 0 1 0 0 1

within Midwest 22 -43% 0 1 0 0 1

within Midwest 21 -40% 0 1 0 0 1

within Midwest 19 -38% 0 1 0 0 1

within Midwest 20 -38% 0 1 0 0 1

within Midwest 16 -27% 0 1 0 0 1

within Midwest 28 -26% 0 0 1 0 1

within Midwest 26 -24% 0 0 1 0 1

within Midwest 17 -19% 0 0 1 0 1

within Midwest 25 -8% 0 0 1 0 1

within Midwest 27 -1% 0 0 1 0 1

within Midwest 15 -1% 0 0 1 0 1

within Midwest 52 0% 0 0 1 0 1

within Midwest 24 4% 0 0 1 0 1

within Midwest 14 15% 0 0 1 0 1

within Midwest 12 28% 0 0 1 0 1

within Midwest 10 30% 0 0 1 0 1

within Midwest 9 33% 0 0 1 0 1

within Midwest 11 38% 0 0 1 0 1

within Midwest 13 42% 0 0 1 0 1

87

time of load offer was a primary carrier or a backup carrier. A fourth dataset contains

contractual “accept-up-to” limits (i.e., explicit commitments). A fifth dataset contains bids in

spot auctions from 2014, which we use to estimate market conditions. A sixth table contains

load information in 2013.

Table 4-4. Data tables.

Our dataset has 178,643 load offers, representing well over $100 million in offered

business. We took several steps to ensure the records were accurate. First, a small number of

records had extraordinarily small prices associated with them. Removing load offers showing

prices less than 50% of other contract prices on a lane removes 741 (0.4%) of the records. Logit

and probit estimation requires variation on lanes and carriers – i.e., some carriers rejected no load

offers, and on some lanes there were no rejections. Removing these takes out 8,617 (4.8%) of

the records. We removed all lanes that had less than 20 load offers on them to minimize bias

when estimating the logit and probit models with fixed effects (Katz 2001). This removed 44 (<

0.1%) of the records. Finally, we have to include data from 2013 for reasons discussed shortly.

Removing lanes that did not have loads moved on them in 2013 removed 4,127 (2.3%) of the

records. This leaves a total of 165,114 (92.4%) of the original 178,643 records.

To check for potential bias due to the removal of these records, we compared the

rejection rate of the load offers. In the dropped records, 20.5% of load offers were rejected; in

Table number Name Description Useful fields

1 EDIsLoad offers and carrier responses in 2014, including

timestamps of each transmissionShipment ID, carrier ID, carrier response, timestamps

2 Load information - 2014 Information on every load shipped in 2014Shipment ID, origin, destination, carrier ID, price paid, time of

pickup, whether it was at a contracted or spot price

3 Primary or backup Primary and backup carriers on each lane in 2014 Shipment ID, origin, destination, carrier ID, primary or backup

4 Explicit commitmentsCarriers and their contractual daily "accept-up-to"

commmitments in 2014Origin, destination, carrier ID, day of week, maximum up-to limit

5 Spot bids Bids in spot auctions in 2014Shipment ID, carrier ID, origin, destination, bid price, winner or

not flag, bid timestamp

6 Load information - 2013 Same as (2), but for 2013Shipment ID, origin, destination, carrier ID, price paid, time of

pickup, whether it was at a contracted or spot price

88

the kept records, 26.4% of the load offers were rejected. We compared the percentage of load

offers that went to explicit, implicit, and backup carriers. In the dropped records, these

percentages are 50.0%, 30.0%, and 20.0%, respectively. In the kept records, the percentages are

58.2%, 22.1%, and 19.7%, respectively. Because less than 10% of our records were dropped and

there are no glaring disparities in any of these numbers, we are unconcerned with bias from their

removal.

Self-selection

Acme and its carriers endogenously select into the backup, implicit, and explicit carrier

agreements. Without correcting for endogeneity, estimates will be biased and inconsistent

(Greene 2008). In our case, there are two endogenous carrier preferences: preferences for the

lanes that are up for bid in the auction and preferences to adopt each contract type.

We include two variables to control for lane preferences. First, we include the carrier-

lane prices11

associated with each load offer. Because carrier-lane prices are generated via a

competitive, first-price sealed-bid annual procurement auction in which many carriers

participate, the bid prices closely reflect the carrier’s private valuation of a load on a lane12

.

Second, we include lane fixed effects to control for average lane preferences across carriers. For

example, if one lane is particularly unattractive for all contracted carriers, the lane fixed effect

will capture this fact.

To control for the endogenous selection into backup, implicit, and explicit contracts, we

adopt a strategy discussed by Vella (1998) and Tucker (2010) that is used when selection

11 We transform these into “price premiums” in our model for reasons discussed shortly. 12 Technically, a first-price sealed-bid auction is not a demand revealing auction. However, the optimal strategy is

to bid between the bidder’s own valuation and the expected valuation of the other bidders. As the number of bidders

increases, the optimal bid strategy gets arbitrarily close to the bidder’s private value (Kagel and Levin 1993).

Because there are close to a hundred carriers invited to the annual auctions, the bidders bid very close to their own

private values.

89

involves more than two groups. The type of contract is ordered in the number of governance

mechanisms – backup carriers have fewer than implicit carriers who have fewer than explicit

carriers. Hence, we create a contract type variable with backup carriers as 1, implicit carriers as

2, and explicit carriers as 3. The selection into the contract type depends on the projected lane

volume by Acme, which is common in FHTL procurement auctions (Caplice 2007), and

individual carrier-region preferences, where we define region the same as the U.S. Census

Bureau. After discussions with Acme, we included the lane volumes from 2013 as the

projections for 2014, and we included carrier-region preferences as dummy variables. We then

estimated an ordered probit model where the dependent variable is contract type and the

independent variables are the lane volume projections and carrier-region dummies. From the

selection model, we can then retrieve selection correction factors (selection correction) to

include in the main model. The selection model is identified by the inclusion of lane volumes

from 2013; these are exogenous to the decision to accept or reject a load offer, except through

the selection into the governance mode. The procedure and results from the selection model are

discussed in detail below. As expected, the higher the projected load volume, the more likely

Acme and carriers are to select into more governance – the explicit contract.

Selection correction procedure

As discussed in Section 3.4, we have a variable, contract type, that takes on three values and is

ordered in the number of governance mechanisms. Acme suggested that they and the carriers

generally select into more governance based on projected load volumes. Hence, we model the

selection process of Acme and the carriers using projections of load volumes for every lane and

unobserved carrier-region preferences. We include dummy variables for carrier-region

preferences, allowing the selection model the maximum amount of flexibility. If we had more

90

than one shipper, we would include dummy variables to allow for their unobserved preferences;

since we only have one, a dummy variable would be redundant. To estimate projected load

volumes, we use volumes from 2013 (2013 load volumes) on the same lanes and check this for

robustness using actual load volumes from 2014. Our results are consistent using either.

The procedure is to regress contract type on 2013 load volumes and the carrier-region

dummy variables using an ordered probit model. Two thresholds are calculated from this model,

and we calculate the selection correction using the equation discussed in Vella (1998, p. 147,

Section VI A). Main and Reilly (1993) and Holloway and Parmigiani (2014) discuss this

approach in more detail. Our Stata code is below.

*contract_type is contract type, lane_loads_2013 is 2013 load volumes

oprobit contract_type i.carrier_region lane_loads_2013

predict probitxb_2013, xb

gen selection_correction = normalden( _b[/cut1] - probitxb_2013)*-1/normal( _b[/cut1] -

probitxb_2013) if contract_type ==1

replace selection_correction = (normalden( _b[/cut1] - probitxb_2013) - normalden(

_b[/cut2] - probitxb_2013))/(normal( _b[/cut2] - probitxb_2013) - normal( _b[/cut1] -

probitxb_2013)) if contract_type ==2

replace selection_correction = normalden( _b[/cut2] - probitxb_2013)/(1 - normal( _b[/cut2] -

probitxb_2013)) if contract_type ==3

Outcome variable

When the carrier responds (via EDI) with an acceptance, we code this as 0. When they reject the

offer, we code this as 1. Hence, accept or reject is a binary outcome variable. Table 4-5

describes our variables along with summary statistics.

91

Table 4-5. Summary statistics and correlations.

Treatment variables

We create dummy variables to indicate whether a load offer is sent to a backup carrier (backup

carriers), an implicit carrier (implicit carriers), or an explicit carrier (explicit carriers).

Interactions

The differences between the interactions of the backup carriers, implicit carriers, and explicit

carriers dummy variables and the unfavorable market, moderate market, and favorable market

dummy variables are used to test hypothesis 3. We define nine interaction terms: backup

carriers * unfavorable market, backup carriers * moderate market, backup carriers * favorable

market, implicit carriers * unfavorable market, implicit carriers * moderate market, implicit

carriers * favorable market, explicit carriers * unfavorable market, explicit carriers * moderate

market, and explicit carriers * favorable market.

Controls

We include a variety of controls to account for factors that may affect the accept or reject

decision. Carrier fixed effects (carrier) are included to account for time-invariant characteristics

such as service strategy, regional expertise, and whether they are asset-based or not-asset-based.

Lane fixed effects (lane) control for factors such as the average load volume on a lane, average

traffic density, road quality, origin and destination characteristics, and distance. Hour-of-day and

day-of-week fixed effects (hour-of-day, day-of-week) control for factors such as rush hours and

Variable Mean St. dev. Min Max (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(1) accept or reject 0.26 0.44 0.00 1.00 1.00

(2) backup carriers 0.11 0.31 0.00 1.00 0.18 1.00

(3) implicit carriers 0.25 0.43 0.00 1.00 0.01 (0.20) 1.00

(4) explicit carriers 0.65 0.48 0.00 1.00 (0.13) (0.47) (0.77) 1.00

(5) unfavorable market 0.34 0.48 0.00 1.00 (0.09) (0.02) (0.01) 0.02 1.00

(6) moderate market 0.32 0.47 0.00 1.00 (0.03) 0.01 0.02 (0.02) (0.49) 1.00

(7) favorable market 0.34 0.47 0.00 1.00 0.12 0.01 (0.01) 0.00 (0.52) (0.49) 1.00

(8) above average volume 0.63 0.48 0.00 1.00 0.16 0.16 0.07 (0.16) (0.02) (0.00) 0.03 1.00

(9) price premium 0.03 0.17 -0.50 3.38 (0.02) 0.32 0.03 (0.23) (0.01) 0.00 0.01 0.10 1.00

(10) selection correction 0.77 0.78 -1.78 3.04 (0.15) (0.66) (0.38) 0.77 0.02 (0.02) 0.00 (0.10) (0.25) 1.00

N = 165,114

92

weekends. Month dummy variables (month) control for economy-wide shocks or shifting

weather patterns. To control for possible regional shocks or weather events, we include region-

month fixed effects as a robustness check. The lead time (in days) between the time of load offer

and the time the load was picked up by a carrier is included as a fixed effect (lead time). We also

include a dummy variable to account for unexpected spikes in demand causing carrier rejections.

For this, we calculate the 30-day moving average of loads hauled on the lane by the carrier to

whom the load is offered, and include a dummy variable (above average volume) that is 1 if the

load is higher than the 30-day moving average and 0 if not.

Finally, we include the price premium minus one (price premium) associated with each

load offer to control for the possibility that accept or reject behavior is merely a function of

relative prices. For example, if the average primary contract price on a lane is $500, and the load

offer is $600 (e.g., to a backup carrier), then 1.2 – 1 = 0.2 is the price premium. If the load offer

is $500 on the same lane, then 0 (1 – 1) is the price premium. We convert to a premium instead

of using the raw prices in order to account for differences across lanes (e.g., distance).

Outcome model specification

The unit of analysis for our model is the load offer and the response to the offer is our dependent

variable. Because responses are binary decisions, we could model this in several ways. The

three most common modeling strategies are a probit, logit, and linear probability model (LPM)

specification, each with their own set of advantages and disadvantages. Due to the incidental

parameters problem in the logit and probit models with fixed effects (Greene 2004) and because

we are interested in consistent estimates of the treatment effect, we adopt the LPM as our main

model and estimate it via OLS. To ensure robustness to this decision, we also run probit and

logit models and show that our results are consistent throughout. Our model is:

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0 1 2 3

4 5 6

accept or reject implicit carrier explicit carrier moderate market

favorable market above average volume price premium

cont

rols selection correction

(2)

Identification

Our assumptions to identify the causal effect of the forms of governance are threefold. First, we

assume that the selection correction factor corrects for unobserved preferences for the form of

governance and the impact of such unobserved preferences. Second, we assume that our control

variables account for all potential confounders outside of the endogenous selection into contract

type. Third, we assume that market conditions are exogenous to an individual shipper and

carrier. Given that there are thousands of shippers, hundreds of thousands of individual carriers,

and that Acme’s spend represents well less than 1% of market spend, we believe this is a safe

assumption. Again turning to C.H. Robinson, they assert that FHTL is “a perfect market – that

is, no single party controls the entire market enough to affect rates” (C.H. Robinson 2014, p. 6).

Nonetheless, we test our measure of spot price premium for endogeneity and fail to reject the

null hypothesis of exogeneity by a large margin. We discuss this after the robustness section.

Clustering

We cluster standard errors to account for possible within-group correlation. Without clustering,

default standard errors can be greatly overstated (Cameron and Miller 2015). There is no simple

rule to choose precisely which groups to cluster over. One principle is to reason which

regressors and errors are likely correlated. A second principle is to avoid too few clusters, where

there is no clear rule for “few.” We begin by reasoning which accept or reject decisions are

likely to be correlated. It is reasonable to assume that each carrier makes decisions for

themselves – they do not collude on which loads to haul. It is also reasonable to assume that

decisions in one geographic decision by a carrier do not affect decisions in another geographic

94

region. Finally, it is also reasonable to assume that decisions made temporally far apart from one

another are independent – a decision to accept a load in May does not affect the decision to

accept a load in September. Thus, we cluster at the carrier-census origin region-week level.

This gives 2,842 clusters. To test this assumption for robustness, we also cluster at the carrier-

census origin region-month level and carrier-week level and show that most of our results remain

significant despite more conservative clustering strategies.

4.5 Results

Model-free evidence

Of the 165,114 offers, 17,692 (10.7%) were to backup carriers, 40,569 (24.6%) were to implicit

carriers, and 106,853 (64.7%) were to explicit carriers. 56,779 (34.4%) offers occurred in

unfavorable market conditions, 52,256 (31.6%) occurred in moderate market conditions, and

56,059 (34.0%) occurred in favorable market conditions. Offers are evenly spread out across

market conditions, which is not surprising because we defined the market in three equal-sized

buckets. More importantly, the offers are evenly spread across market conditions for each

governance mode, shown in Table 4-6.

Table 4-6. Number and percentage of load offers by governance mode and market

condition.

Overall, load offers were rejected 26.4% of the time, with significant differences across

the mode of governance. Backup carriers rejected slightly less than half of all load offers

(49.5%) and implicit carriers rejected 27.5% of offers, about 5 percentage points higher than

explicit carriers (22.2%), shown in Table 4-7. Backup carriers are more responsive to market

Governance mode Unfavorable market Moderate market Favorable market Total

backup carriers 5,681 (32.1%) 5,845 (33.0%) 6,166 (34.9%) 17,692 (10.7%)

implicit carriers 13,588 (33.5%) 13,423 (33.1%) 13,558 (33.4%) 40,569 (24.6%)

explicit carriers 37,530 (35.1%) 32,988 (30.9%) 36,335 (34.0%) 106,853 (64.7%)

Total 56,799 (34.4%) 52,256 (31.6%) 56,059 (34.0%) 165,114 (100%)

95

conditions – when the market is in their favor, they reject almost two-thirds of all load offers.

This is further validation of our measure of market conditions – if market conditions have truly

changed in a carrier’s favor, a priori one would expect a carrier who is not influenced by

commitment, monitoring, or contracts to be less likely to honor static prices.

Table 4-7. Average responses by governance form and market condition.

Main results

Table 4-8 reports the regression results of the model specified in equation 2 and variants thereof.

Columns 1 and 2 show the main model with and without the selection correction factor. Column

3 includes dummy variables for every market conditions/governance mode interaction. Columns

4 and 5 report a probit and logit specification, respectively. Column 6 shows the same model as

column 3 but with standard errors clustered at the carrier-census region-month level instead of

the carrier-census region-week level.

Our controls perform as expected. As price premium increases, the likelihood of

rejection decreases. When the load is offered above the 30-day average (i.e., above average

volume is 1), the likelihood of rejection increases. Also, the measures of market conditions in

the main model take the expected sign. Moderate market conditions have a positive and

significant effect on the likelihood of rejection, and favorable market conditions an even larger

one (p-value < 0.01).

Committed and monitored carriers provide more reliable service than carriers that are not.

Implicit carriers reject loads 4.4% less frequently (p-value = 0.093) on average than backup

Governance mode Unfavorable market Moderate market Favorable market Average response

backup carriers 38.2% 46.2% 62.9% 49.5%

implicit carriers 22.9% 26.8% 32.8% 27.5%

explicit carriers 17.6% 19.9% 29.1% 22.2%

Total 20.9% 24.6% 33.7% 26.4%

96

Table 4-8. Main results.

Dependent variable:

(1) (2) (3) (4) (5) (6)

implicit carriers -0.133*** -0.044*

(0.0193) (0.0265)

explicit carriers -0.301*** -0.123***

(0.0156) (0.0373)

moderate market 0.043*** 0.043***

(0.0094) (0.0094)

favorable market 0.096*** 0.096***

(0.0117) (0.0117)

backup carriers * unfavorable market 0.237*** 0.308** 0.499** 0.237**

(0.0665) (0.1314) (0.2265) (0.0917)

backup carriers * moderate market 0.308*** 0.555*** 0.922*** 0.308***

(0.0647) (0.1264) (0.2163) (0.0928)

backup carriers * favorable market 0.417*** 0.883*** 1.485*** 0.417***

(0.0636) (0.1284) (0.2197) (0.0926)

implicit carriers * unfavorable market 0.245*** 0.272*** 0.449*** 0.245***

(0.0622) (0.0785) (0.1364) (0.0867)

implicit carriers * moderate market 0.287*** 0.461*** 0.756*** 0.287***

(0.0617) (0.0861) (0.1498) (0.0871)

implicit carriers * favorable market 0.308*** 0.527*** 0.879*** 0.308***

(0.0622) (0.0903) (0.1569) (0.0886)

explicit carriers * unfavorable market 0.156** 0.156*

(0.0617) (0.0842)

explicit carriers * moderate market 0.196*** 0.189*** 0.317*** 0.196**

(0.0613) (0.0455) (0.0793) (0.0839)

explicit carriers * favorable market 0.253*** 0.437*** 0.724*** 0.253***

(0.0607) (0.0526) (0.0915) (0.0837)

above average volume 0.074*** 0.073*** 0.073*** 0.303*** 0.519*** 0.073***

(0.0056) (0.0057) (0.0057) (0.0221) (0.0388) (0.0088)

price premium -0.341*** -0.346*** -0.346*** -1.171*** -2.103*** -0.346***

(0.0231) (0.0231) (0.0232) (0.078) (0.1457) (0.0343)

selection correction -0.076*** -0.075*** -0.217*** -0.381*** -0.075***

(0.0145) (0.0447) (0.0763) (0.0232)

constant 0.375*** 0.287*** -1.37*** -2.385***

(0.0619) (0.0641) (0.1921) (0.3327)

R2

0.286 0.287 0.476 0.283 0.284 0.476

N 165,114 165,114 165,114 165,017 165,017 165,114

accept or reject

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are clustered at the carrier-census region-week level and are reported in parentheses. Column

(1) reports the linear probability model (LPM) with the main effects but without the selection correction factor, (2) reports the LPM with the main effects

and the selection correction factor, (3) reports the LPM and the interactions between governance form and market conditions, (4) reports a probit

specification, (5) reports a logit specification, and (6) reports the LPM but with standard errors clustered by carrier-census region-month instead of

carrier-census region-week. Lane, carrier, hour-of-day, day-of-week, month, and days of lead time fixed effects are included in all models. For the

probit and logit models, the constant term is included and explicit carriers * unfavorable market is the omitted category. Pseudo R-squared values

are reported for the probit and logit models.

97

carriers, and explicit carriers reject loads 12.3% less frequently (p-value < 0.01) than backup

carriers. The difference in behavior is more apparent when market conditions are included, as

shown in Figure 4-3. In unfavorable market conditions, a Wald test that implicit carriers behave

the same as backup carriers (i.e., H0: implicit carriers*unfavorable market = backup

carriers*unfavorable market) is not rejected, but in favorable markets implicit carriers reject

loads 11.6% (p-value < 0.01) less frequently than backup carriers . Explicit carriers reject loads

8.1% (p-value = 0.057) less frequently than backup carriers in unfavorable markets, but 16.4%

(p-value < 0.01) less frequently in favorable markets. Commitment and monitoring protect

against short-run opportunistic behavior.

Carriers with explicit contracts provide better service on average than carriers with

implicit contracts, supporting the increased coordination and enhanced monitoring that more

explicit contracts entail. On average, explicit carriers reject loads 7.9% (p-value < 0.01) less

frequently than implicit carriers, and better performance persists across market conditions.

Hence, H1 is supported.

Our second hypothesis postulates that the effectiveness of a more explicit contract

weakens relative to commitment and monitoring as the payoff to market opportunism increases.

To analyze this, we look at the rejection rate differential for each type of carrier across market

conditions. Figure 4-3 and column 3 of Table 4-8 show marginal rejection rates by market

condition and mode of governance.

98

Figure 4-3. Marginal response rates by governance mode and market conditions.

First, all three carrier types increase in their likelihood to reject load offers as the market

becomes more favorable for them. The marginal rejection rate for backup carriers is 23.7% in

unfavorable markets but 41.7% in favorable markets; the difference between the two, 18.0%, is

significant (p-value < 0.01). Likewise, implicit carriers are 5.6% (p-value < 0.01) more likely

and explicit carriers are 9.7% (p-value < 0.01) more likely to reject load offers when market

conditions change in their favor. None of the governance mechanisms completely restrain

market opportunism.

We are interested in testing the response differential of the carrier types. A Wald test on

the hypothesis that implicit carriers respond the same as backup carriers to changes in market

conditions from unfavorable to favorable (i.e., H0: implicit carriers*favorable market conditions

– implicit carriers*unfavorable market conditions = backup carriers*favorable market

conditions – backup carriers*unfavorable market conditions) rejects the null hypothesis with

23.7%

30.8%

41.7%

24.5% 28.7%

30.1%

15.6%

19.6%

25.3%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Unfavorable Moderate Favorable

backup carriers implicit carriers explicit carriers

Load

Re

ject

ion

Pe

rce

nta

ge

Market Conditions

99

high confidence (p-value < 0.01). Similarly, explicit carriers are less responsive to market

conditions than backup carriers (p-value < 0.01). Table 4-9 reports the results of all of the Wald

tests.

The key test for the second hypothesis is whether explicit carriers respond the same as

implicit carriers to changes in market conditions. Testing whether explicit carriers*favorable

market conditions – explicit carriers*unfavorable market conditions = implicit

carriers*favorable market conditions – implicit carriers*unfavorable market conditions, the null

hypothesis of equality is rejected (p-value = 0.046). Because the mean difference for implicit

carriers (5.6%) is smaller than the mean difference for explicit carriers (9.7%), the result

indicates explicit carriers are more responsive to market conditions than implicit carriers,

supporting hypothesis 2. We conclude that more explicit contracts lose their effectiveness as the

payoff for market opportunism becomes large.

Table 4-9. Wald tests of rejection rates by market condition and governance mode.

Robustness checks

In Table 4-8, we show that our results are robust to the specification of the outcome model.

Although we use the LPM for most of our tests, our results are consistent using a probit and logit

Test P-value Conclusion

explicit carriers*moderate market conditions - explicit carriers*unfavorable market conditions =

implicit carriers*moderate market conditions - implicit carriers*unfavorable market conditions0.896 no statistical difference

explicit carriers*favorable market conditions - explicit carriers*unfavorable market conditions =

implicit carriers*favorable market conditions - implicit carriers*unfavorable market conditions0.046

H2 supported; implicit carriers are less responsive

to market conditions than explicit carriers

explicit carriers*favorable market conditions - explicit carriers*moderate market conditions =

implicit carriers*favorable market conditions - implicit carriers*moderate market conditions0.056

H2 supported; implicit carriers are less responsive

to market conditions than explicit carriers

implicit carriers*moderate market conditions - implicit carriers*unfavorable market conditions =

backup carriers*moderate market conditions - backup carriers*unfavorable market conditions0.313 no statistical difference

implicit carriers*favorable market conditions - implicit carriers*unfavorable market conditions =

backup carriers*favorable market conditions - backup carriers*unfavorable market conditions< 0.01

implicit carriers are less responsive to market

conditions than backup carriers

implicit carriers*favorable market conditions - implicit carriers*moderate market conditions =

backup carriers*favorable market conditions - backup carriers*moderate market conditions< 0.01

implicit carriers are less responsive to market

conditions than backup carriers

explicit carriers*moderate market conditions - explicit carriers*unfavorable market conditions =

backup carriers*moderate market conditions - backup carriers*unfavorable market conditions0.233 no statistical difference

explicit carriers*favorable market conditions - explicit carriers*unfavorable market conditions =

backup carriers*favorable market conditions - backup carriers*unfavorable market conditions< 0.01

explicit carriers are less responsive to market

conditions than backup carriers

explicit carriers*favorable market conditions - explicit carriers*moderate market conditions =

backup carriers*favorable market conditions - backup carriers*moderate market conditions0.051

explicit carriers are less responsive to market

conditions than backup carriers

100

specification. Table 4-10 show that our results are consistent for several robustness checks. In

column 1, we replace month fixed effects with region-month fixed effects to allow different

regions to behave differently throughout the year. In column 2, we allow carriers to have

different regional strategies by including carrier-region fixed effects instead of carrier fixed

effects. In column 3 we cluster standard errors at the carrier-region level instead of the carrier-

region-week level, and column 4 shows standard errors clustered at the carrier-week level.

Column 5 reports the main model but excluding loads with lead times less than 3 days. In the

selection model, we used projections based on 2013 data; in column 6, we use 2014 actual data

for the selection model. In this case, we assume a perfect forecast in the auction process. In

column 7, we segment the market into halves instead of thirds (i.e., unfavorable and favorable

instead of unfavorable, moderate, and favorable). In column 8, we include linear and quadratic

terms instead of the market-status dummies. Our results are consistent throughout. After this

section, we show how we tested market conditions for endogeneity and find strong support for

exogeneity. With all of these tests, we are confident that our findings are not the result of an

assumption we have made.

4.6 Spot market exogeneity

Based on the structure of the industry and the observations of experts, we assumed that

prevailing spot prices are exogenous to a carrier’s accept or reject decision. Here, we propose

and calculate an instrument for the “same region spot price index” and test whether the same

region spot price index is endogenous or exogenous. A valid instrument must be correlated with

the spot price index but not correlated to the accept or reject decision.

101

Table 4-10. Robustness checks.

Spot prices in regions that a load does not leave or enter (“other region spot prices”), at

the same timeframe as the accept or reject decision, fits our purposes. These prices are

correlated with the “same region spot prices,” as shown in Section 4.4, as shocks to the overall

economy affect all spot prices. The decision to accept or reject a load offer in a particular region

of the country, however, is not affected by spot prices other than through the spot prices in the

same region. In addition to the face validity of the instrument, similar instruments have been

used by previous researchers (Hausman, Leonard, and Zona 1994, Pinkse, Slade, and Brett

2003).

Dependent variable:

(1) (2) (3) (4) (5) (6) (7) (8)

backup carriers * unfavorable market 0.237*** 0.175** 0.237** 0.237*** 0.311*** 0.237** 0.243*** 0.191***

(0.0665) (0.0837) (0.106) (0.0642) (0.0673) (0.0665) (0.0663) (0.0235)

backup carriers * moderate market 0.308*** 0.267*** 0.308*** 0.308*** 0.374*** 0.307*** 0.051*

(0.0647) (0.0835) (0.102) (0.0640) (0.0655) (0.0646) (0.0309)

backup carriers * favorable market 0.417*** 0.369*** 0.417*** 0.417*** 0.488*** 0.416*** 0.397***

(0.0636) (0.0837) (0.107) (0.0623) (0.0640) (0.0636) (0.0636)

implicit carriers * unfavorable market 0.245*** 0.322*** 0.245** 0.245*** 0.310*** 0.244*** 0.255*** 0.068***

(0.0622) (0.0808) (0.0978) (0.0600) (0.0623) (0.0621) (0.0627) (0.0142)

implicit carriers * moderate market 0.287*** 0.364*** 0.287*** 0.287*** 0.351*** 0.286*** 0.015

(0.0617) (0.0802) (0.0973) (0.0593) (0.0618) (0.0617) (0.0183)

implicit carriers * favorable market 0.308*** 0.383*** 0.308*** 0.308*** 0.373*** 0.307*** 0.307***

(0.0622) (0.0813) (0.105) (0.0597) (0.0622) (0.0622) (0.0623)

explicit carriers * unfavorable market 0.156** 0.295*** 0.156 0.156*** 0.221*** 0.157** 0.168*** 0.114***

(0.0617) (0.0794) (0.0948) (0.0603) (0.0616) (0.0617) (0.0625) (0.0121)

explicit carriers * moderate market 0.196*** 0.330*** 0.196** 0.196*** 0.263*** 0.197*** 0.038***

(0.0613) (0.0794) (0.0957) (0.0597) (0.0611) (0.0613) (0.0121)

explicit carriers * favorable market 0.253*** 0.383*** 0.253** 0.253*** 0.326*** 0.253*** 0.241***

(0.0607) (0.0792) (0.0977) (0.0589) (0.0605) (0.0607) (0.0614)

above average volume 0.0732*** 0.0564*** 0.0732*** 0.0732*** 0.0786*** 0.073*** 0.074*** 0.075***

(0.00567) (0.00539) (0.0189) (0.00557) (0.00594) (0.0057) (0.0057) (0.0057)

price premium -0.346*** -0.301*** -0.346*** -0.346*** -0.344*** -0.346*** -0.345*** -0.340***

(0.0232) (0.0227) (0.0555) (0.0224) (0.0251) (0.0232) (0.0232) (0.0232)

selection correction -0.0752*** -0.139*** -0.0752* -0.0752*** -0.0786*** -0.076*** -0.078*** -0.119

(0.0145) (0.0152) (0.0390) (0.0147) (0.0151) (0.0147) (0.0148) (0.0071)

R2

0.476 0.479 0.493 0.476 0.476 0.487 0.476 0.477

N 165,114 165,114 165,114 165,114 165,114 144,949 165,114 165,114

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are clustered at the carrier-census region-week level unless otherwise noted. Lane, carrier, hour-of-day, day-of-week, month, and

days of lead time fixed effects are included in all models unless otherwise noted. All models use the LPM specification. Column (1) reports the main model except with month-region fixed

effects instead of month fixed effects, (2) uses carrier-region fixed effects instead of carrier fixed effects, (3) clusters standard errors at the carrier-region level instead of the carrier-region-

week level, (4) clusters standard errors at the carrier-week level instead of the carrier-region-week level, (5) omits load offers with a lead time of less than 3 days, (6) uses 2014 as the lane

selection criteria instead of 2013, (7) segments the market into halves instead of thirds, and (8) uses a linear- and quadratic-term instead of dummy variables, where the linear term is in the

"unfavorable market" rows and the quadratic term is in the "moderate market" rows.

accept or reject

102

For every same region spot price index, we calculated an other region spot price index

using the same model specified in Section 4.4. We then run a two-stage least squares (2SLS)

estimation procedure including the instrument and run the Wu-Hausman test post-estimation.

The null hypothesis is that the same region spot price index is exogenous. The test fails to reject

the null hypothesis by a large margin (p-value = 0.6569). Further, estimates from the OLS

model with the same region spot price index as exogenous and estimates from the 2SLS

procedure are nearly identical, shown in Table 4-11. We conclude that spot prices are exogenous

to a particular carrier.

Table 4-11. Regression using OLS and 2SLS.

4.7 Discussion

The constellation of governance mechanisms enacted between a buyer and supplier significantly

affects exchange outcomes. Some governance mechanisms are fundamentally complements of

Dependent variable:

(1) (2)

same region price index 0.109*** 0.096***

(0.0111) (0.0287)

implicit carriers -0.043 -0.044

(0.0267) (0.0267)

explicit carriers -0.121*** -0.122***

(0.0375) (0.0374)

above average volume 0.073*** 0.073***

(0.0057) (0.0057)

price premium -0.347*** -0.346***

(0.0232) (0.0232)

selection correction -0.077*** -0.077***

(0.0146) (0.0145)

constant 0.311*** 0.318***

(0.0640) (0.0658)

R2

0.288 0.288

N 165,114 165,114

accept or reject

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are clustered at the carrier-census

region-week level. Lane, carrier, hour-of-day, day-of-week, month, and days of lead time

fixed effects are included for both models. Column 1 includes the same region price index as an

exogenous variable. Column 2 uses 2SLS with the other region price index as an instrument.

103

one another – e.g., contracts that are more explicit about a supplier’s output enhances the

monitoring of such output (Heide et al. 2007). Others are substitutes – e.g., strong-arming a

supplier to provide service at static prices is naturally at odds with providing dynamic price

incentives for performance. Buyers should consider these interactions when deciding which

governance mechanisms to negotiate and adopt with their suppliers.

To complicate matters, the legal enforceability of the environment affects the

performance of governance mechanisms individually and in relation to one another (Zhou and

Poppo 2010). One can imagine a spectrum of legal enforceability, with “fully and clearly legally

enforceable” on one extreme and “completely unenforceable” on the other. Where a particular

business environment falls on this spectrum affects the behavior of suppliers.

In this study, we consider an environment closer to “completely unenforceable” than

“fully and clearly legally enforceable.” Contracts are signed and exchanged, but the threat of

legal enforcement is not credible. We find that monitoring a carrier’s output, combined with the

promise of future business, improves performance and restrains opportunism. More explicit

contracts improve coordination between shipper and carrier and enhance output monitoring; we

observe this via better average performance by carriers who are subject to more explicit

contracts.

Previous researchers have made the important observation that the form of opportunism

affects the performance of governance mechanisms (Wathne and Heide 2000, Seggie et al.

2013). We argue that not only does the form matter, but that the payoff to a supplier to act

opportunistically does as well. This is rarely addressed or discussed by researchers. We find

that, when the payoff to opportunism is large, all three of the governance mechanisms considered

in this study – commitment, monitoring, and more explicit contracts – “break down” to some

104

degree. All carriers are more likely to reject load offers when the market is highly in their favor

than when it is not. However, commitment and monitoring are relatively more effective than

explicit contracts when the payoff to opportunism is high. We argue that the high cost associated

with the removal of future business keeps carriers from reneging completely on their

commitments even when short-term payoffs are high; the relatively lower cost of the shipper

“yelling and hollering” at the carrier to fully live up fully to their explicit contract loses its

effectiveness when the payoff to opportunism becomes too large. Thus, when market conditions

become highly favorable for suppliers, buyers might be better off utilizing other mechanisms,

such as price incentives.

Limitations and future research

Our study has several limitations. First, we use data from one company in one particular

industry for ten months for our analysis. While we do not believe that Acme’s operations are

particularly unique and many other truckload shippers operate and contract with carriers in a

similar manner (Caplice 2007), we cannot completely rule out idiosyncrasies. Our time frame,

2014, was a relatively strong year for carriers in terms of market supply and demand (Transplace

2016). While this makes carrier decision making more interesting as they balance short-term and

long-term profitability, if this analysis were performed in a less favorable time frame for carriers,

we might observe different behavior. Moreover, FHTL is a large, well-established industry, with

its own norms and practices. Thus, the generalizability of our findings to other industries is

unclear.

From a study-design perspective, the effectiveness of the primary carrier commitments

and output monitoring cannot be distinguished from one another because they go hand-in-hand –

there are no primary carriers who are not monitored and there are no monitored carriers who are

105

not primary. Also, the coordinating advantages of more explicit contracts along with the

enhancement of output monitoring that more contract explicitness brings cannot be separated

from one another. We believe studies that try to tease out these individual effects are worthwhile

pursuits. Despite these drawbacks, we believe our high level of institutional detail, usage of

longitudinal transactional datasets, observation of actual contracts, and ability to focus on

causality makes a significant contribution to the literature.

Opportunities for future research are ample. A significant number of studies have

focused on the effectiveness of governance mechanisms; however, we are unaware of a

comprehensive review of all of the different governance mechanisms observed in practice, their

characteristics and interactions with one another, and how they affect supplier performance and

opportunism. There are few studies that consider the legal enforceability of the business

environment, and fewer still that consider how mechanisms perform as the supplier’s payoff to

act opportunistically changes. Finally, few studies focus on causality by using longitudinal data.

Research that addresses these issues would be valuable to our understanding of buyer-supplier

interactions.

106

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VITA

Alex Scott

EDUCATION

2012 – Present Ph.D. in Business Administration (Concentration: Supply Chain

Management)

Smeal College of Business, Department of Supply Chain & Information

Systems, Pennsylvania State University, University Park, PA

Dissertation Title: Service Refusals, Information Sharing, and Commitments:

Empirical Essays in For-Hire Trucking

2002 - 2003 Georgia Institute of Technology, M.S., Operations Research, Atlanta, GA

Degree in the Department of Industrial and Systems Engineering (ISyE) with

courses in supply chain management, optimization, and statistics.

1998 - 2002 Purdue University, B.S., Industrial Engineering, West Lafayette, IN

HONORS & AWARDS

Recipient of Frank and Jean Smeal Annual Scholarship of $15,000 (2012 – 2016)

Nominated for the Ossian R. MacKenzie Teaching Award (2015)

RESEARCH INTERESTS

Supply chain management, transportation contracts and pricing, applied econometrics, additive

manufacturing

REFEREED PUBLICATIONS

Scott, A., Parker, C., & Craighead, C.W., “Service Refusals in Supply Chains: Drivers and

Deterrents of Freight Rejection,” forthcoming at Transportation Science.

Scott, A., 2015, “The value of information sharing for truckload shippers,” Transportation

Research Part E, vol. 81, September, pp. 203-214.

Scott, A. & Harrison, T.P., 2015, “Additive Manufacturing in an End-to-End Supply Chain

Setting,” 3D Printing and Additive Manufacturing, vol. 2, no. 2, pp. 65-77.

INVITED SEMINARS

Rutgers University & Northeastern University, November 2015

MIT Center for Transportation & Logistics & West Virginia University, October 2015

TEACHING ACTIVITIES

SCM 421 - Supply Chain Analytics, Fall 2014

Teaching Evaluations: Overall quality of the instructor: 6.67 / 7