- Email: [email protected]
Contracting contractors
Journal of Business Research 64 (2011) 338–343
Contents lists available at ScienceDirect
Journal of Business Research
Contracting contractors Marcos Singer ⁎, Patricio Donoso 1 Pontificia Universidad Católica de Chile, Chile Escuela de Administración, Vicuña Mackenna 4860, Macul, Santiago de Chile, Chile
a r t i c l e
i n f o
Article history: Received 1 March 2009 Received in revised form 1 September 2009 Accepted 1 November 2009 Available online 15 March 2010 Keywords: Contractors Agency relationship Incentives
a b s t r a c t Many companies relay on contractors to execute different tasks of the value chain. This paper develops an agency model subject to moral hazard to study the general structure of the contract offered by a firm (the principal) to several contractors (agents) that perform the same task. A Generalized Least Squares regression tests the model with a panel data of 58 carriers that work for a shipper in Santiago, over 93 weeks. The regression verifies that the principal rewards some performance dimensions, but neglects others. The regression also confirms that contracting prices are sensitive to the alternatives available for the company and the contractors. © 2009 Elsevier Inc. All rights reserved.
1. Introduction Outsourcing is the delegation of a business function from a firm to a contractor, under the terms and conditions of a contract. Such an arrangement has become a widespread industrial organization strategy. According to a survey of 747 firms in US and Europe, the main reasons for firms to outsource are to improve cost discipline and control, to achieve best practices, and to improve service quality (Kakabadse and Kakabadse, 2002). To reach those objectives, they have externalized basic services such as catering and cleaning (67% in US, 59% in Europe), information technologies (52% and 56%), human resources (48% and 44%), telecommunications (40% and 37%), e-commerce (20% and 16%), logistics (19% and 11%) and many other functions. Despite the expected benefits, several surveys report high levels of dissatisfaction with outsourcing (Lacity et al., 1995; Quinn, 1999; Kakabadse and Kakabadse, 2002). Unfortunately, the comprehension of the firm–contractor relationship is still limited. According to Regan and Garrido (2002), “Research […] is typically heavy on qualitative analysis […] Most findings are based on relatively limited surveys of industry segments. Relatively simple hypotheses are tested against these data.” In order to introduce quantitative criteria, this article will understand the outsourcing relationship within the context of agency theory. According to Harris and Raviv (1978), in an agency relationship a number of agents (the contractors) act as representatives of the principal (the firm). As in the majority of agency
⁎ Corresponding author. Tel.: + 56 2 354 7214; fax: + 56 2 553 1672. E-mail addresses: [email protected] (M. Singer), [email protected] (P. Donoso). 1 Tel.: + 56 2 354 7214; fax: + 56 2 553 1672. 0148-2963/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2009.11.022
relationships, the contractors' performance depends not only on his effort, but also on fortuitous events that the firm cannot easily monitor. Such information asymmetry can expose the relationship to a moral hazard problem: in order to optimize his own business, a contractor may harm the firm. In the case of outsourcing the transportation function, the carrier (the contractor) could deny service to a complicated client, and then deceive the shipper (the firm) by saying “the roads were inaccessible.” Agency theory is a useful framework to undertake the moral hazard problem. For instance, Baker and Hubbard (2004) show that on-board computers (OBC) improve the ability of carriers (the principal) to verify how drivers (the agents) drive. Fernández et al. (2000) describe another solution for the problem: vertical quasi-integration, whereby shippers and carriers act as a coordinated unit, but without assuming the rigidity of common ownership. This investigation concentrates on a third strategy to restrain moral hazard; to provide incentives to the agents so to align them to the principal's interest (Eisenhardt, 1989). Since the firm prefers to attend as many clients as possible, the firm might reward the contractor for each client appropriately served. However, quality of service is a multidimensional and often subjective performance measure (Lambert and Burduroglu, 2000). Also, contracts cannot reward every single attitude desired by the firms. In transportation, rates usually consider only one dimension ($/tons, $/trip, $/month). They consider two dimensions ($/tons/distance) only sometimes. Accordingly, Lafontaine and Masten (2002) say, “Driver compensation arrangements in trucking, for example, appear to bear little or no relation to the incentive functions traditionally ascribed to compensation schemes in the agency literature”. The objective of this research is to verify in practice whether the firm–contractor relationship follows the principal-agent logic. This
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
article considers as a case study a shipper in Santiago that contracts many carriers. The hypothesis is that, even though the rates structure may seem simple, the shipper finds a way to reward the carriers' performance. To do so, the shipper covers each carrier's operational cost, and in addition, transfers a margin = ((income − cost) / cost). If the agency relationship exists, such margin may serve to motivate the carrier to behave in a way that satisfies the shipper. The following section proposes an agency model that derives inequalities that describe the relationship between firms and contractors. From those inequalities, Section 3 obtains propositions about the determinants of the contractor's margins related to the contractor, to the firm, or to the market. Section 4, describes the data: the performance of 58 carriers that work for a shipper over 93 weeks. Section 5 tests the propositions with Generalized Least Squares regression models. Finally, Section 6 revises which propositions the data supports and also discusses the conclusions and suggestions of new research questions. 2. An agency model for outsourcing 2.1. Parameters and variables This article models the information asymmetry as a “moral hazard game with hidden action”, adapted from Rasmusen (1989). Fig. 1 defines the utility per resource-unit for the firm and for the contractor. The resource-unit is one truck in the case of transportation, one person in the case of human resources, one workstation in the case of a call center, and so on. In the first stage, the firm decides whether he offers the contract to the contractor or not. Then (second stage) the contractor decides whether to accept or not. In a third stage, the contractor decides to exert a high or a low effort. Finally, a random event either benefits or harms the contractor. The information's asymmetry occurs because the firm is unable to distinguish between two scenarios: one, when the contractor exerts a high effort but fate does not favor him, and two, when the contractor exerts a low effort and fate benefits him. 2.2. Define the following (exogenous) parameters of the utility functions of both parties
a b
ci
Profit the firm obtains for operating each resource-unit. Additional profit for the firm if the contractor exerts a high effort and luck favors him. Assume that the profit is equal to the loss for the firm if the contractor exerts a low effort and fate harms him. Operational cost of the resource-unit in scenarios i {1, 2, 3, 4} in Fig. 1, with ci N 0. The cost includes the fixed and the variable part. The firm assumes it, so ci only appears in the contractor's utility function for calculating its margin. This definition does not assume any inequality relationship between ci, (i.e. ci ≥cj or vice versa).
Magnitude that determines the cost ci, with ∂ci(d) / ∂d N 0. Assume that ∂ci(d) / ∂d = 1, selecting an adequate scale for d. For instance, d is the cost of diesel in the case transportation. x Savings in operational costs of the resource-unit due to the contractor's expertise. In the case of transportation, Fernández et al. (2000) explain that an experienced carrier is more efficient because he knows the routes, coordinates distribution centers and clients, and handles conflict effectively. This efficiency is specific to the distribution system, so the carrier becomes less efficient if he works for another shipper. Assume that x is independent of ci and that x b ci. e Difference of personal or subjective cost for the contractor between exerting a high or a low effort. Assume that e N 0. v Firm's sales. u − α · v Profit per resource-unit that the firm obtains when the contractor does not operate with the firm, so the firms must operate with a new, and possibly inexperienced, carrier. The value u is exogenous and constant α is positive, since losing an experienced carrier harms the shipper more if sales are high. n Number of resource-units contracted by the firm to the contractor. In transportation it is the size of the carrier's fleet contracted by the shipper. β∙n Replacement cost of one resource-units. Assume that if the parties do not reach an arrangement, the firm loses the n resource-units owned by the contractor. Therefore, the constant β is positive, as losing one resource-unit is more problematic for the firm when the loss occurs simultaneously with many other resource-units. t Transactions index related to the resources outsourced. In transportation, the higher t, the higher the freight volume transported in the country. w + γ · t Opportunity cost per resource-unit, (i.e., margin that the contractor would obtain if he worked for another firm). The value of w is exogenous and γ is a positive constant, since if the country's transportation activity rises, the market rates increase. p4 and p5 Probability that fate favors the contractor at node 4 and 5. A higher p4 and a lower p5 imply a higher supervision capability for the firm. To simplify calculations, define p4 =p5 = 50%. d
Define the following (endogenous) variables of the utility function, which will result from the equilibrium of the game. f
2m
m
Fig. 1. Agency model for the firm and the contractor.
339
Fixed margin per resource-unit that the firm transfers to the contractor for simply accepting the contract, independently of his effort and random events. If negative, as in Section 3.1, then f is a fixed payment by the contractor to the firm, for instance as a certification fee. Incentive per resource-unit paid to the contractor if he exerts a high effort and fate favors him. As any incentive, m ≥ 0. Incentive per resource-unit paid to the contractor when he exerts a high effort but fate harms him, or when the contractor exerts a low effort and fate favors him. Therefore, the firm's utility in both scenarios is the same, which is necessary for the information asymmetry condition, whereby the firm cannot distinguish between them.
To fix ideas, assume that the contractor has accepted the contract provided that the firm compensates its cost ci of operation. The contractor can exert a high effort to give the best possible service to the firm. In transportation, to visit as many clients as possible, the carrier must hasten each delivery in order to increase the number of trips. This effort imposes on him a subjective cost e. Alternatively, the carrier could
340
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
get a lot of freight to deliver just by good luck, without having to assume any personal cost.
Solving for m: m≤2½a + 1=2b–1=2ð1 + f Þðc1 + c2 –2xÞ–ðu–α · v–β · nÞ
2.3. Valid inequalities at equilibrium
ð7Þ
+ ð1 + w + γ · tÞc4 = ð2c1 + c2 –3xÞ:
The subgame perfect equilibrium arises from backward induction. As usual, the calculation assumes that the principal is risk neutral (Hart, 1995), since he is contracting several unconnected contractors and therefore is diversified in its source of income. This article also assume for the contractor to be risk neutral, since the transaction in Fig. 1 occurs many times, say once a week, and therefore does not compromise the contractor's wealth. Hence, both parties consider their payments according to their expected value. The contractor will exert a high effort only if the Incentive Compatibility constraint holds. In this case, the contractor must prefer node 4 rather than node 5: Expected utility of high effort ≥ Expected utility of low effort =2ðf + 2mÞðc1 –xÞ + 1=2ðf + mÞðc2 –xÞ–e≥1=2ðf + mÞðc2 –xÞ + 1=2f ðc3 –xÞ:
1
Finally, assume that the firm always wants the contractor to exert a high effort, so:Expected utility of a high contractor's effort ≥ Expected utility of a low contractor's effort =2ða + b–ð1 + f + 2mÞðc1 –xÞÞ + 1=2ða–ð1 + f + mÞðc2 –xÞÞ≥
1
ð8Þ
=2ða–ð1 + f + mÞðc2 –xÞÞ + =2ða–ð1 + f Þðc3 –xÞÞ:
1
1
3. The determinants of the incentives Besides m ≥ 0, this article has made no assumption about f and m, or about their relationship with the parameters of the agency model. The following section deduces how m depends on the contractor (Section 3.1), on the firm (Section 3.2) and on market conditions (Section 3.3).
ð1Þ 3.1. Determinants related to the contractor The expected operational cost of a high effort is (c1 + c2 − 2x) / 2, and of a low effort is (c2 +c3 − 2x) / 2. Define Δc as the additional operational cost when the contractor exerts a high effort, so: Δc = (c1 + c2 − 2x) / 2 − (c2 +c3 − 2x) / 2 = (c1 −c3) / 2. Notice that not necessary Δc N 0. If the personal cost e is high enough, the contractor might exert a low effort even though Δc ≤ 0. Solving for m: m≥ðe–f ΔcÞ = ðc1 –xÞ:
ð2Þ
If the firm does not reward the contractor, he will exert a low effort. Therefore, if m = 0 then Eq. (1) does not hold. Since ci N 0 and c1 −x N 0: e–f Δc≥0:
ð3Þ
The contractor will accept the contract only if the Participation constraint holds, which means that the agent has to have incentives to participate in the business relationship. From the Incentive Compatibility constraint (1), to exert a high effort is more convenient, so: Expected utility of high effort ≥ Expected utility of rejecting the contract =2ðf + 2mÞðc1 –xÞ + 1=2ðf + mÞðc2 –xÞ–e ≥ ðw + γ · tÞc4 :
1
ð4Þ
The model assumes that if m = 0 the contractor prefers to reject the contract, or at least, is indifferent between rejecting the contract and working for the firm. If so, the firm will never offer a fixed margin f so high that the contractor will accept the contract but will exert a low effort. Therefore, if m = 0, inequality (4) gets reversed, so:
P1. A higher effort cost e for the contractor maintains or increases the margin m that the firm must pay. Demonstration: at request to the corresponding author. P2. If the fixed margin f is non-negative, or if f is negative but not too low, then a higher expertise x of the contractor maintains or increases the margin m paid by the firm. Demonstration: at request to the corresponding author. Recall that the expertise x is a measurement of efficiency, that is, how much a particular contractor can lower the cost ci. In transportation, x can be any type of competitive advantage, such as an optimal configuration of a truck (size, motor power, number of axles, etc.) in relation to the firm's distribution network. According to Nickerson and Silverman (2003), an adequate configuration may decrease in half the operational cost. Proposition P2 shows that the firm will transfer its efficiency to the contractor. P3. If the firm occasionally exchanges contractors, then a larger number n of resource-units will induce a higher incentive m for the contractor. Demonstration: at request to the corresponding author. In transportation, when n grows, the scenario for the shipper becomes more difficult if he does not reach an agreement. As in any negotiation model, when the shipper's best alternative to a negotiated agreement (BATNA) becomes worse, the final arrangement for the counterpart gets better. 3.2. Determinants related to the firm
ð5Þ
Recall that, by definition, b measures the additional utility that the firm obtains when the contractor exerts a high effort and the fate favors him, and also measures the loss for the firm if the contractor exerts a low effort and fate harms him.
The firm will offer the contract to the contractor only if his expected utility in node 4 is higher than or equal to the utility of dealing with an alternative contractor, so:
P4. Assuming that b is independent of the cost, a higher profit b for the firm due to the contractor's effort will induce a higher incentive m. Demonstration: at request to the corresponding author. To investigate how m behaves, the firms have to make explicit what type of contractor's effort requires. For transportation, assume that b is a function of:
Expected utility of high effort m = 0 ≤ Expected utility of rejecting the contract with m = 0 =2f ðc1 –xÞ + 1=2f ðc2 –xÞ–e = f ðc1 + c2 –2xÞ = 2–e≤ðw + γ · tÞc4 :
1
=2ða + b–ð1 + f + 2mÞðc1 –xÞÞ + 1=2ða–ð1 + f + mÞðc2 –xÞÞ≥
1
ðu–α · v–β · nÞ–ð1 + w + γ · tÞc4 :
ð6Þ
• The load amount, since this effort means higher volume of sales for the shipper;
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
• The amplitude of the zone of coverage, since this effort allows accessing a larger potential market; • The number of clients, since with a more atomized client base the shipper gains bargaining power, which allows him to charge higher prices; and • The quality of service in logistics, as defined by Lambert and Burduroglu (2000), since is one of the attributes that the clients value the most. For transportation, the following corollaries come from Proposition P4. C1. assuming that the shipper wants to dispatch as much cargo as possible, the more cargo mobilized by the carrier, the higher the incentive m. Trucks with higher load capacity are more expensive, harder to maintain, and more difficult to load. Given a certain capacity, to transport more cargo prolongs the time of loading and unloading, increases fuel consumption, and deteriorates the truck faster. Corollary C1 states that, unless the shipper pays according to the cargo mobilized, the carriers will invest in smaller trucks, and they will avoid the assignment of large loads. C2. assuming that the shipper wants to extend his coverage zone, longer distance traveled increases the incentive m for the carrier. Carriers naturally prefer to deliver within nearby zones, so that they spend less time, fuel and other resources. According to Corollary C2, unless the shipper rewards the distance traveled, the carriers will avoid or delay deliveries far away. In such case, and due to the information asymmetry, the shipper will never know whether the undelivered cargo was due to bad luck, or because the carrier never intended to deliver it. C3. assuming that the shipper is interested in serving as many clients as possible, carriers that visit more clients should obtain a higher incentive m. The carrier prefers to visit the least number of clients, since each one demands time and work. According to Corollary C3, unless the shipper motivates the carrier to visit several clients, the carrier will avoid trips with many deliveries. Here again, the information asymmetry will impede the shipper to judge if a certain carrier serves to few clients due to fortuitous reasons, or because he manages to have the personnel in charge of assigning cargo give him convenient routes. C4. assuming the shipper is interested in his carriers performing the best possible service, a better-evaluated carrier will receive a higher incentive m. Providing a good service generates an extra cost for the carrier. Following strict time schedules limits the carrier's flexibility. Meeting the clients' needs satisfactorily demands time and effort. According to Corollary C4, unless the shipper rewards the quality of service, the carriers will not bother giving it. P5. The higher the sales level v of the firm, the higher the incentive m for the contractor. Demonstration: at request to the corresponding author. Proposition P5 states that the margin m is dependent on the cost α·v for the shipper of not reaching an agreement. As in P3, a deterioration of the BATNA for the shipper gives negotiation power to the carrier, and hence increases the earned margin. 3.3. Market-related determinants P6. If the fixed margin f is high enough, then a higher cost d (of the diesel, for instance), which implies a higher operational cost c, will decrease the incentive m. If f is not high enough, then m does not depend on d. Demonstration: at request to the corresponding author.
341
P7. The higher the market's transaction index t, the higher the incentive m. Demonstration: at request to the corresponding author. Proposition P7 might suggest that the firm will reward contractors according to their luck, since t has nothing to do with their performance. Oyer (2004) observes something similar in the salary structure of many executives, who are paid according to the firm's profit, or the firms grant them stock options. Since one employee has little influence on the firm's profit or on its stock price, to use such type of incentives seems contradictory. The author explains that such compensation practice ensures the Participation constraint if the salary of the executive's job alternatives highly correlates with the performance of the firm. In transportation, the index is a weighted average of all the shippers in the country, and as the index grows, the rates that the carriers can obtain also increase.
4. Source of the data This section evaluates empirically the propositions of Section 3 by considering the fleet that operates “Compañía Molinera San Cristóbal (CMSC)”, the largest flour milling company in Chile, with 12% of market share. Besides flour and its by-products, CMSC also delivers other types of food such as rice, cereals and noodles, from a warehouse in Santiago and from two other warehouses at the outskirts of the city. Recall that margin = ((income − cost) / cost), not the income, is a measure of how much the shipper rewards the carrier's performance. To estimate costs, several CMSC employees and carriers revised the structure and parameters of the transportation cost model presented by Singer et al. (2002). Then the IT department implemented a software for calculating the payments for the carriers according to the cost model (plus a given margin), and tested the system for one month. During such period, different key players made various corrections in order to fine-tune it. According to Kleindorfer et al. (1998), the validation done by the users has the advantage of combining different points of view, each of one interested in avoiding arbitrary definitions. For example, if the model overestimated the diesel cost, then the CMSC accountants would warn about it; if the model underestimated it, the carriers would complain. As most transportation contracts in Chile, those offered by CMSC to his carriers are rather simple. As many companies do, CMSC also operates with a spot fleet for high demand situations. Some carriers change their status: in some periods they work exclusively with CMSC, and in others they attend other shippers. The econometric study below considers different definitions of when the carrier is working in an exclusive manner. The database consists of 58 trucks contracted by CMSC for 93 weeks, from January 2004 to October 2005. The capacity of the trucks ranges from 1 to 34 tons. Small and large trucks serve indistinctively most clients, so for the econometric model the 58 trucks belong to the same pool. As mentioned in Section 1, the margin obtained by the carrier is on a weekly basis. Although the round trips may last half a day, an entire day or even more, CMSC pays the carriers once a week. To measure the weekly income, the database considers actual payments, which on average exceed by 15% the official published rates. The difference occurs due to last minute negotiations and due to additional compensations registered by the accounting system. The carrier assumes most of the fixed costs (payment to drivers and loaders) week by week. The weekly variable costs (diesel, tires) aggregate the variable costs caused by all the round trips during each week. The round trips do not overlap from one week to the other, because the carriers do not work on Sundays. Further details about the data are in Singer et al. (2007).
342
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
5. Econometric model One way to verify empirically the propositions and corollaries is to filter the random noise of weekly margin with an econometric model. Define the following independent variables related to each truck:
MARGIN (corrected with + 1) is a lineal function of the natural logarithms of the independent variables. Defining i as the truck index, t as the time index and ε as a random variable that distributes normally with mean zero, Model I is: lnðMARGINit + 1Þ = β0 + βYEARS · lnðYEARSi Þ + βLUXURY · lnðLUXURYi Þ
YEARS
Number of years that the carrier has worked for CMSC. The average in the sample is 16 years, with a standard deviation of 8.8 years. LUXURY Truck quality, measured as a ratio between the truck's market value and its load capacity. The average is 637, with a standard deviation 368. FLEET Number of trucks that the carrier owns. The average is 3.4, with a standard deviation of 2.4. GRADE Evaluation from 1 (worst) to 7 (best) of the service provided by the truck. The evaluation was done by the head of logistics and, two weeks later, by the transport manager of CMSC. Both evaluations took place at different time spans in order to avoid the mutual influence between both executives. The average grade of the sample is 6.3, with a standard deviation of 0.57. Define the following independent variables that relate to the truck and the week: KILOS
KM CLIENT
Total load transported by a given truck during a certain week. The average of the sample is 57,877 kg, with a standard deviation of 32,031 kg. Distance traveled by a given truck during a certain week. The average is 748 km, with a standard deviation of 780 km. Number of clients served by a given truck during a certain week. The average is 18 clients, with a standard deviation of 8.7 clients.
Define the following independent variables that relate to each week: SALES
Total cargo volume delivered by CMSC during the week, measured in tons. The average of the sample is 2999, with standard deviation of 376. EMPLOY National employment index given by the transport and telecommunication sector. The government publishes this index every two months. The average is 195 and the standard deviation is 9.35. DIESEL Cost of fuel adjusted by inflation. The average of the sample is 0.02 UF/liter (UF or Unidad de Fomento is the monetary unit indexed to inflation, close to US$45), and the standard deviation is 0.0026 UF/liter.
+ βFLEET · lnðFLEETi Þ + βGRADE · lnðGRADEi Þ + βKILOS · lnðKILOSit Þ + βKM · lnðKMit Þ + βCLIENT · lnðCLIENTit Þ + βSALES · lnðSALESt Þ + βEMPLOY · lnðEMPLOYt Þ + βDIESEL · lnðDIESELt Þ + εit : Corollary C1, which predicts a higher margin for those carriers that transport the most tonnage, is not invalid if βKILOS N 0. However, CMSC pays $/ton, so such correlation is tautological. In what follows the models disregard KILOS, in order to capture the influence of the other independent variables. The Hausman test rejects fixed effects hypothesis related to the trucks, so is not necessary to define one dummy variable for each truck. In other words, some trucks earn better margins due to their specific value for the independent variables; not because they are somehow privileged. Model I in Table 1 shows the results of a Generalized Least Squares (GLS) regression, which controls for autocorrelation within panels and cross-sectional correlation and heteroskedasticity across panels. The variables depend on the truck (rows 1–4), on the truck-week (rows 5–6) or on the week alone (rows 7–9). The database contains 3726 valid observations between January 2004 and October 2005 where the MARGIN was equal to or above −50%. As the coefficients represent elasticities, βYEARS = 0.037 means that a truck that doubles the seniority of another, perceives a margin 3.7% higher. According to the econometric analysis, Proposition P2 is not invalid since βYEARS N 0. Proposition P3 is not invalid since βFLEET N 0. Proposition P5 is not invalid if βSALES N 0. Proposition P6 predicts βDIESEL N 0 only if the fixed part of the margin is high enough. Since CMSC guarantees a low margin, the data shows no correlation. Proposition P7 is not invalid since βEMPLOY N 0. P1 refers to the subjective effort of the carrier, hence the proposition is not empirically verifiable. Proposition P4 is indirectly verifiable through Corollaries C1, C2, C3 and C4. The regression can Table 1 Model's outcome.
YEARS LUXURY FLEET
The dependent variable is: GRADE
MARGIN Margin that each truck obtains a certain week. The average in the sample is 19%, with a standard deviation of 40%.
KM CLIENT
As expected, the correlation coefficients between LUXURY and YEARS (ρ = −0.39) and between LUXURY and GRADE (ρ = 0.49) are high in absolute value. The correlations between CLIENT and YEARS (ρ = −0.26) and between CLIENT and LUXURY (ρ = 0.37) are also high in absolute value, as the firm often assigns newer and better trucks to clients. The correlation between CLIENT and KM (ρ = 0.4) is high because the more clients assigned, the longer the traveling distance. The other correlations are less significant. The profit should not depend on the KILOS transported summed with the KM traveled (βKILOS · KILOS + βKM · KM), but on KILOS and KM (β0KILOSβKILOS · KMβKM). Therefore, a regression model test that
SALES EMPLOY DIESEL Constant Sample size Prb. F-statistic ⁎ Significant at 95%. ⁎⁎ Significant at 99%.
Model I
Model II
0.04 (5.41)⁎⁎ − 0.18 (− 14.2)⁎⁎ 0.05 (7.8)⁎⁎ 1.16 (20.4)⁎⁎ − 0.01 (− 1.3) 0.21 (20.7)⁎⁎ 0.37 (10.2)⁎⁎ 0.26 (2.4)⁎ − 0.07 (− 1.7) − 8.74 (− 10.9)⁎⁎ 3726 0.00
0.06 (6.18)⁎⁎ − 0.23 (− 12.6)⁎⁎ 0.06 (5.8)⁎⁎ 1.17 (20.4)⁎⁎ 0.00 (0.1) 0.28 (29.2)⁎⁎ 0.35 (12.3)⁎⁎ 0.04 (0.4) − 0.08 (− 1.5) − 4.91 (− 7.0)⁎⁎ 3726 0.00
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
neither prove nor disprove Corollary C2, since βKM is not different from zero. Corollary C3 is not invalid since βCLIENT N 0. Corollary C4, which predicts a higher MARGIN for those carriers that deliver a better service, is not invalid if βLUXURY N 0, or if βGRADE N 0. The regression obtains βGRADE N 0 but βLUXURY b 0, which implies that the shipper does not reward the quality of trucks. Corollary C2 predicts a positive correlation between MARGIN and KM, but the data shows no evidence that βKM N 0. The model tests other pools of information, for instance considering 3872 valid observations whose MARGIN is equal to or higher than −70%. Another pool considers 3885 valid observations by filtering those that have the ratio income/(fixed costs) higher than or equal to 30%. The outcomes do not differ much, indicating that the results are robust on the definition of whether the truck works in a spot bases. Although the agency model is static, margins for the same truck could exhibit autocorrelation. The data reveals a lag of one period, so margins in week t depend on margins in week t − 1. Model II in Table 1 shows the results of a GLS regression, allowing the presence of AR(1) autocorrelation within panels and cross-sectional correlation and heteroskedasticity across panels. The results are similar to the results of Model I, with the exception of EMPLOY, which is not significant anymore. 6. Conclusions The empirical evidence allows to conclude that the rate does play a role in aligning the shipper and the carrier's interests. The regression finds evidence that the firm rewards with higher margins carrier's experience (P2), a higher number of clients served (C3) and a better evaluation (Corollary C4). The available information cannot evaluate whether the incentives overcome the subjective cost of the carrier's effort, hence rates may or may not modify the carrier's behavior. The data shows no evidence that the shipper compensates carriers that travel longer distances (C2), or carriers that have better trucks (Corollary C4). The regression model verifies that the rates quickly react to the alternatives the parties have to a deal. Since losing a carrier affects the shipper more intensively during high sales periods, the higher the sales, the higher the rates (P5). Losing a carrier with numerous trucks affects the shipper significantly; hence carriers with larger fleets earn higher margins per truck (P3). When the transportation market falls, the exit options for the carrier worsen, hence he obtains a lower margin in Model I (P7), but not in Model II. Variables that change periodically, such as the number of visited clients and the sales, have an almost immediate effect on the carrier's margin. Such result suggests that parties update their agreement frequently. The theoretical analysis asserts that if the fixed margin is low, changes in the operation cost of the trucks do not translate into changes in the margin (P6). Consistently, in the econometric model the margin correlates with the diesel value. A possible conjecture is that a change in the costs does not modify the bargaining power of the parties, and therefore does not influence how much margin the shipper transfers to the carrier. With empirical evidence that rates do function as incentives for the carriers, at least in some dimensions, this article refutes the comment of Lafontaine and Masten (2002) expressed at the beginning of this article: “Driver compensation […] appear to bear little or no relation to the incentive functions […]”. Probably, the discrepancy lies in their understanding of compensation. While they define compensation as the carrier's gross income, margin depends on the costs as well. The shipper that would like to reward a specific carrier for his effort can pay the carrier a higher rate or assign him the less expensive deliveries. Another reason to disregard the agency role of the rates is that they show an apparent chaotic behavior, consequence of the reciprocal
343
favors between shipper and carrier. The econometric analysis filters the random noise and allows observing that, on average, the rates are consistent with the agency theory. Recall that the data allows accepting the random effect hypothesis, so trucks do not show inexplicable high and low margins. In summary, this article is contributing to the understanding of firm– contractor relationship by proposing, and empirically testing, a principal-agent model that calculates the rewards necessary to maintain the Incentive Compatibility and the Participation constraints. Furthermore, this article provides additional evidence of the usefulness of incentives to mitigate moral hazard, arguably the main difficulty of contracting contractors. Nevertheless, incentives may generate unexpected side effects. Osterloh and Frey (2000) and Gneezy and Rustichini (2000) report than when agents are subject to strong pecuniary rewards and penalties, sometimes they deviate from the principal's interests because the task looses its significance. This phenomenon named crowding-out consists in people running away from the personal gratification of doing the work right, towards maximizing profit only. So far the literature has not reported this effect for organizations, which opens new questions: does crowding-out occur in the context of interfirm relationship? Is the effect significant? From which point pecuniary incentives deteriorate the relationship? In many situations incentives may seem unsuitable, reason why agency theory provides alternative tools such as an expensive supervision, vertical integration or quasi-integration. However, scholars and practitioners should take a second look¸ as apparently unstructured agency relationship sometimes hide the opportunities to use incentives. Acknowledgment FONDECYT partially financed this investigation, project number 108/0292. References Baker GP, Hubbard TN. Contractibility and asset ownership: on-board computers and governance in U.S. trucking. Q J Econ 2004;119:1443–79. Eisenhardt KM. Agency theory: an assessment and review. Acad Manag Rev 1989;14:57–74. Fernández A, Arruñada B, González-Díez M. Quasi-integration in less-than-truckload trucking. In: Ménard C, editor. Institutions, contracts and organizations. Cheltenham and Northampton: Edward Elgar; 2000. p. 293–312. Gneezy U, Rustichini A. A fine is a price. J Legal Stud 2000;XXIX:1-18. Harris, Raviv. Some results on incentive contracts with applications to education and employment, health insurance, and law enforcement. Am Econ Rev 1978;68:20–30. Hart O. Firms contracts and financial structure. Oxford: Oxford University Press; 1995. Kakabadse A, Kakabadse N. Trends in outsourcing: contrasting USA and Europe. Eur Manag J 2002;20:189–98. Kleindorfer GB, O'Neill L, Ganeshan R. Validation in simulation: various positions in the philosophy of science. Manag Sci 1998;44:1087–99. Lacity MC, Willcocks LP, Feeny DF. IT outsourcing maximizes flexibility and control. Harvard Bus Rev 1995;73:84–93. Lafontaine, F., Masten, S.E. (2002) “Contracting in the absence of specific investments and moral hazard: Understanding carrier-driver relations in U.S. trucking” NBER Working Paper No. W8859. Available at SSRN: http://ssrn.com/abstract=305603. Lambert DM, Burduroglu R. Measuring and selling the value of logistics. Int J Logist Manag 2000;11:1-17. Nickerson JA, Silverman BS. Why aren’t all truck drivers owner-operators? Asset ownership and the employment relation in interstate for-hire trucking. Journal of Economics and Management Strategy 2003;12:91-118. Osterloh M, Frey BS. Motivation knowledge transfer, and organizational forms. Organ Sci 2000;11:538–50. Oyer P. Why do firms use incentive that have no incentive effects? J Finance 2004;59:1619–49. Quinn JB. Strategic outsourcing: leveraging knowledge capabilities. Sloan Manage Rev 1999;40:9-22. Rasmusen E. Games and information. Oxford, UK: Basil Blackwell Ltd.; 1989. Regan A, Garrido RA. Modeling freight demand and shipper behavior: state of the art, future directions. In: Hensher DA, editor. The leading edge of travel behaviour research. Amsterdam: Pergamon Press; 2002. Singer M, Donoso P, Jara S. Fleet configuration subject to stochastic demand: an application in the distribution of liquefied petroleum gas. J Oper Res Soc 2002;53:961–71. Singer M, Donoso P, Widdel S. ¿Premian las tarifas el desempeño del transportista? Abante 2007;10:21–55.
Contents lists available at ScienceDirect
Journal of Business Research
Contracting contractors Marcos Singer ⁎, Patricio Donoso 1 Pontificia Universidad Católica de Chile, Chile Escuela de Administración, Vicuña Mackenna 4860, Macul, Santiago de Chile, Chile
a r t i c l e
i n f o
Article history: Received 1 March 2009 Received in revised form 1 September 2009 Accepted 1 November 2009 Available online 15 March 2010 Keywords: Contractors Agency relationship Incentives
a b s t r a c t Many companies relay on contractors to execute different tasks of the value chain. This paper develops an agency model subject to moral hazard to study the general structure of the contract offered by a firm (the principal) to several contractors (agents) that perform the same task. A Generalized Least Squares regression tests the model with a panel data of 58 carriers that work for a shipper in Santiago, over 93 weeks. The regression verifies that the principal rewards some performance dimensions, but neglects others. The regression also confirms that contracting prices are sensitive to the alternatives available for the company and the contractors. © 2009 Elsevier Inc. All rights reserved.
1. Introduction Outsourcing is the delegation of a business function from a firm to a contractor, under the terms and conditions of a contract. Such an arrangement has become a widespread industrial organization strategy. According to a survey of 747 firms in US and Europe, the main reasons for firms to outsource are to improve cost discipline and control, to achieve best practices, and to improve service quality (Kakabadse and Kakabadse, 2002). To reach those objectives, they have externalized basic services such as catering and cleaning (67% in US, 59% in Europe), information technologies (52% and 56%), human resources (48% and 44%), telecommunications (40% and 37%), e-commerce (20% and 16%), logistics (19% and 11%) and many other functions. Despite the expected benefits, several surveys report high levels of dissatisfaction with outsourcing (Lacity et al., 1995; Quinn, 1999; Kakabadse and Kakabadse, 2002). Unfortunately, the comprehension of the firm–contractor relationship is still limited. According to Regan and Garrido (2002), “Research […] is typically heavy on qualitative analysis […] Most findings are based on relatively limited surveys of industry segments. Relatively simple hypotheses are tested against these data.” In order to introduce quantitative criteria, this article will understand the outsourcing relationship within the context of agency theory. According to Harris and Raviv (1978), in an agency relationship a number of agents (the contractors) act as representatives of the principal (the firm). As in the majority of agency
⁎ Corresponding author. Tel.: + 56 2 354 7214; fax: + 56 2 553 1672. E-mail addresses: [email protected] (M. Singer), [email protected] (P. Donoso). 1 Tel.: + 56 2 354 7214; fax: + 56 2 553 1672. 0148-2963/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2009.11.022
relationships, the contractors' performance depends not only on his effort, but also on fortuitous events that the firm cannot easily monitor. Such information asymmetry can expose the relationship to a moral hazard problem: in order to optimize his own business, a contractor may harm the firm. In the case of outsourcing the transportation function, the carrier (the contractor) could deny service to a complicated client, and then deceive the shipper (the firm) by saying “the roads were inaccessible.” Agency theory is a useful framework to undertake the moral hazard problem. For instance, Baker and Hubbard (2004) show that on-board computers (OBC) improve the ability of carriers (the principal) to verify how drivers (the agents) drive. Fernández et al. (2000) describe another solution for the problem: vertical quasi-integration, whereby shippers and carriers act as a coordinated unit, but without assuming the rigidity of common ownership. This investigation concentrates on a third strategy to restrain moral hazard; to provide incentives to the agents so to align them to the principal's interest (Eisenhardt, 1989). Since the firm prefers to attend as many clients as possible, the firm might reward the contractor for each client appropriately served. However, quality of service is a multidimensional and often subjective performance measure (Lambert and Burduroglu, 2000). Also, contracts cannot reward every single attitude desired by the firms. In transportation, rates usually consider only one dimension ($/tons, $/trip, $/month). They consider two dimensions ($/tons/distance) only sometimes. Accordingly, Lafontaine and Masten (2002) say, “Driver compensation arrangements in trucking, for example, appear to bear little or no relation to the incentive functions traditionally ascribed to compensation schemes in the agency literature”. The objective of this research is to verify in practice whether the firm–contractor relationship follows the principal-agent logic. This
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
article considers as a case study a shipper in Santiago that contracts many carriers. The hypothesis is that, even though the rates structure may seem simple, the shipper finds a way to reward the carriers' performance. To do so, the shipper covers each carrier's operational cost, and in addition, transfers a margin = ((income − cost) / cost). If the agency relationship exists, such margin may serve to motivate the carrier to behave in a way that satisfies the shipper. The following section proposes an agency model that derives inequalities that describe the relationship between firms and contractors. From those inequalities, Section 3 obtains propositions about the determinants of the contractor's margins related to the contractor, to the firm, or to the market. Section 4, describes the data: the performance of 58 carriers that work for a shipper over 93 weeks. Section 5 tests the propositions with Generalized Least Squares regression models. Finally, Section 6 revises which propositions the data supports and also discusses the conclusions and suggestions of new research questions. 2. An agency model for outsourcing 2.1. Parameters and variables This article models the information asymmetry as a “moral hazard game with hidden action”, adapted from Rasmusen (1989). Fig. 1 defines the utility per resource-unit for the firm and for the contractor. The resource-unit is one truck in the case of transportation, one person in the case of human resources, one workstation in the case of a call center, and so on. In the first stage, the firm decides whether he offers the contract to the contractor or not. Then (second stage) the contractor decides whether to accept or not. In a third stage, the contractor decides to exert a high or a low effort. Finally, a random event either benefits or harms the contractor. The information's asymmetry occurs because the firm is unable to distinguish between two scenarios: one, when the contractor exerts a high effort but fate does not favor him, and two, when the contractor exerts a low effort and fate benefits him. 2.2. Define the following (exogenous) parameters of the utility functions of both parties
a b
ci
Profit the firm obtains for operating each resource-unit. Additional profit for the firm if the contractor exerts a high effort and luck favors him. Assume that the profit is equal to the loss for the firm if the contractor exerts a low effort and fate harms him. Operational cost of the resource-unit in scenarios i {1, 2, 3, 4} in Fig. 1, with ci N 0. The cost includes the fixed and the variable part. The firm assumes it, so ci only appears in the contractor's utility function for calculating its margin. This definition does not assume any inequality relationship between ci, (i.e. ci ≥cj or vice versa).
Magnitude that determines the cost ci, with ∂ci(d) / ∂d N 0. Assume that ∂ci(d) / ∂d = 1, selecting an adequate scale for d. For instance, d is the cost of diesel in the case transportation. x Savings in operational costs of the resource-unit due to the contractor's expertise. In the case of transportation, Fernández et al. (2000) explain that an experienced carrier is more efficient because he knows the routes, coordinates distribution centers and clients, and handles conflict effectively. This efficiency is specific to the distribution system, so the carrier becomes less efficient if he works for another shipper. Assume that x is independent of ci and that x b ci. e Difference of personal or subjective cost for the contractor between exerting a high or a low effort. Assume that e N 0. v Firm's sales. u − α · v Profit per resource-unit that the firm obtains when the contractor does not operate with the firm, so the firms must operate with a new, and possibly inexperienced, carrier. The value u is exogenous and constant α is positive, since losing an experienced carrier harms the shipper more if sales are high. n Number of resource-units contracted by the firm to the contractor. In transportation it is the size of the carrier's fleet contracted by the shipper. β∙n Replacement cost of one resource-units. Assume that if the parties do not reach an arrangement, the firm loses the n resource-units owned by the contractor. Therefore, the constant β is positive, as losing one resource-unit is more problematic for the firm when the loss occurs simultaneously with many other resource-units. t Transactions index related to the resources outsourced. In transportation, the higher t, the higher the freight volume transported in the country. w + γ · t Opportunity cost per resource-unit, (i.e., margin that the contractor would obtain if he worked for another firm). The value of w is exogenous and γ is a positive constant, since if the country's transportation activity rises, the market rates increase. p4 and p5 Probability that fate favors the contractor at node 4 and 5. A higher p4 and a lower p5 imply a higher supervision capability for the firm. To simplify calculations, define p4 =p5 = 50%. d
Define the following (endogenous) variables of the utility function, which will result from the equilibrium of the game. f
2m
m
Fig. 1. Agency model for the firm and the contractor.
339
Fixed margin per resource-unit that the firm transfers to the contractor for simply accepting the contract, independently of his effort and random events. If negative, as in Section 3.1, then f is a fixed payment by the contractor to the firm, for instance as a certification fee. Incentive per resource-unit paid to the contractor if he exerts a high effort and fate favors him. As any incentive, m ≥ 0. Incentive per resource-unit paid to the contractor when he exerts a high effort but fate harms him, or when the contractor exerts a low effort and fate favors him. Therefore, the firm's utility in both scenarios is the same, which is necessary for the information asymmetry condition, whereby the firm cannot distinguish between them.
To fix ideas, assume that the contractor has accepted the contract provided that the firm compensates its cost ci of operation. The contractor can exert a high effort to give the best possible service to the firm. In transportation, to visit as many clients as possible, the carrier must hasten each delivery in order to increase the number of trips. This effort imposes on him a subjective cost e. Alternatively, the carrier could
340
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
get a lot of freight to deliver just by good luck, without having to assume any personal cost.
Solving for m: m≤2½a + 1=2b–1=2ð1 + f Þðc1 + c2 –2xÞ–ðu–α · v–β · nÞ
2.3. Valid inequalities at equilibrium
ð7Þ
+ ð1 + w + γ · tÞc4 = ð2c1 + c2 –3xÞ:
The subgame perfect equilibrium arises from backward induction. As usual, the calculation assumes that the principal is risk neutral (Hart, 1995), since he is contracting several unconnected contractors and therefore is diversified in its source of income. This article also assume for the contractor to be risk neutral, since the transaction in Fig. 1 occurs many times, say once a week, and therefore does not compromise the contractor's wealth. Hence, both parties consider their payments according to their expected value. The contractor will exert a high effort only if the Incentive Compatibility constraint holds. In this case, the contractor must prefer node 4 rather than node 5: Expected utility of high effort ≥ Expected utility of low effort =2ðf + 2mÞðc1 –xÞ + 1=2ðf + mÞðc2 –xÞ–e≥1=2ðf + mÞðc2 –xÞ + 1=2f ðc3 –xÞ:
1
Finally, assume that the firm always wants the contractor to exert a high effort, so:Expected utility of a high contractor's effort ≥ Expected utility of a low contractor's effort =2ða + b–ð1 + f + 2mÞðc1 –xÞÞ + 1=2ða–ð1 + f + mÞðc2 –xÞÞ≥
1
ð8Þ
=2ða–ð1 + f + mÞðc2 –xÞÞ + =2ða–ð1 + f Þðc3 –xÞÞ:
1
1
3. The determinants of the incentives Besides m ≥ 0, this article has made no assumption about f and m, or about their relationship with the parameters of the agency model. The following section deduces how m depends on the contractor (Section 3.1), on the firm (Section 3.2) and on market conditions (Section 3.3).
ð1Þ 3.1. Determinants related to the contractor The expected operational cost of a high effort is (c1 + c2 − 2x) / 2, and of a low effort is (c2 +c3 − 2x) / 2. Define Δc as the additional operational cost when the contractor exerts a high effort, so: Δc = (c1 + c2 − 2x) / 2 − (c2 +c3 − 2x) / 2 = (c1 −c3) / 2. Notice that not necessary Δc N 0. If the personal cost e is high enough, the contractor might exert a low effort even though Δc ≤ 0. Solving for m: m≥ðe–f ΔcÞ = ðc1 –xÞ:
ð2Þ
If the firm does not reward the contractor, he will exert a low effort. Therefore, if m = 0 then Eq. (1) does not hold. Since ci N 0 and c1 −x N 0: e–f Δc≥0:
ð3Þ
The contractor will accept the contract only if the Participation constraint holds, which means that the agent has to have incentives to participate in the business relationship. From the Incentive Compatibility constraint (1), to exert a high effort is more convenient, so: Expected utility of high effort ≥ Expected utility of rejecting the contract =2ðf + 2mÞðc1 –xÞ + 1=2ðf + mÞðc2 –xÞ–e ≥ ðw + γ · tÞc4 :
1
ð4Þ
The model assumes that if m = 0 the contractor prefers to reject the contract, or at least, is indifferent between rejecting the contract and working for the firm. If so, the firm will never offer a fixed margin f so high that the contractor will accept the contract but will exert a low effort. Therefore, if m = 0, inequality (4) gets reversed, so:
P1. A higher effort cost e for the contractor maintains or increases the margin m that the firm must pay. Demonstration: at request to the corresponding author. P2. If the fixed margin f is non-negative, or if f is negative but not too low, then a higher expertise x of the contractor maintains or increases the margin m paid by the firm. Demonstration: at request to the corresponding author. Recall that the expertise x is a measurement of efficiency, that is, how much a particular contractor can lower the cost ci. In transportation, x can be any type of competitive advantage, such as an optimal configuration of a truck (size, motor power, number of axles, etc.) in relation to the firm's distribution network. According to Nickerson and Silverman (2003), an adequate configuration may decrease in half the operational cost. Proposition P2 shows that the firm will transfer its efficiency to the contractor. P3. If the firm occasionally exchanges contractors, then a larger number n of resource-units will induce a higher incentive m for the contractor. Demonstration: at request to the corresponding author. In transportation, when n grows, the scenario for the shipper becomes more difficult if he does not reach an agreement. As in any negotiation model, when the shipper's best alternative to a negotiated agreement (BATNA) becomes worse, the final arrangement for the counterpart gets better. 3.2. Determinants related to the firm
ð5Þ
Recall that, by definition, b measures the additional utility that the firm obtains when the contractor exerts a high effort and the fate favors him, and also measures the loss for the firm if the contractor exerts a low effort and fate harms him.
The firm will offer the contract to the contractor only if his expected utility in node 4 is higher than or equal to the utility of dealing with an alternative contractor, so:
P4. Assuming that b is independent of the cost, a higher profit b for the firm due to the contractor's effort will induce a higher incentive m. Demonstration: at request to the corresponding author. To investigate how m behaves, the firms have to make explicit what type of contractor's effort requires. For transportation, assume that b is a function of:
Expected utility of high effort m = 0 ≤ Expected utility of rejecting the contract with m = 0 =2f ðc1 –xÞ + 1=2f ðc2 –xÞ–e = f ðc1 + c2 –2xÞ = 2–e≤ðw + γ · tÞc4 :
1
=2ða + b–ð1 + f + 2mÞðc1 –xÞÞ + 1=2ða–ð1 + f + mÞðc2 –xÞÞ≥
1
ðu–α · v–β · nÞ–ð1 + w + γ · tÞc4 :
ð6Þ
• The load amount, since this effort means higher volume of sales for the shipper;
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
• The amplitude of the zone of coverage, since this effort allows accessing a larger potential market; • The number of clients, since with a more atomized client base the shipper gains bargaining power, which allows him to charge higher prices; and • The quality of service in logistics, as defined by Lambert and Burduroglu (2000), since is one of the attributes that the clients value the most. For transportation, the following corollaries come from Proposition P4. C1. assuming that the shipper wants to dispatch as much cargo as possible, the more cargo mobilized by the carrier, the higher the incentive m. Trucks with higher load capacity are more expensive, harder to maintain, and more difficult to load. Given a certain capacity, to transport more cargo prolongs the time of loading and unloading, increases fuel consumption, and deteriorates the truck faster. Corollary C1 states that, unless the shipper pays according to the cargo mobilized, the carriers will invest in smaller trucks, and they will avoid the assignment of large loads. C2. assuming that the shipper wants to extend his coverage zone, longer distance traveled increases the incentive m for the carrier. Carriers naturally prefer to deliver within nearby zones, so that they spend less time, fuel and other resources. According to Corollary C2, unless the shipper rewards the distance traveled, the carriers will avoid or delay deliveries far away. In such case, and due to the information asymmetry, the shipper will never know whether the undelivered cargo was due to bad luck, or because the carrier never intended to deliver it. C3. assuming that the shipper is interested in serving as many clients as possible, carriers that visit more clients should obtain a higher incentive m. The carrier prefers to visit the least number of clients, since each one demands time and work. According to Corollary C3, unless the shipper motivates the carrier to visit several clients, the carrier will avoid trips with many deliveries. Here again, the information asymmetry will impede the shipper to judge if a certain carrier serves to few clients due to fortuitous reasons, or because he manages to have the personnel in charge of assigning cargo give him convenient routes. C4. assuming the shipper is interested in his carriers performing the best possible service, a better-evaluated carrier will receive a higher incentive m. Providing a good service generates an extra cost for the carrier. Following strict time schedules limits the carrier's flexibility. Meeting the clients' needs satisfactorily demands time and effort. According to Corollary C4, unless the shipper rewards the quality of service, the carriers will not bother giving it. P5. The higher the sales level v of the firm, the higher the incentive m for the contractor. Demonstration: at request to the corresponding author. Proposition P5 states that the margin m is dependent on the cost α·v for the shipper of not reaching an agreement. As in P3, a deterioration of the BATNA for the shipper gives negotiation power to the carrier, and hence increases the earned margin. 3.3. Market-related determinants P6. If the fixed margin f is high enough, then a higher cost d (of the diesel, for instance), which implies a higher operational cost c, will decrease the incentive m. If f is not high enough, then m does not depend on d. Demonstration: at request to the corresponding author.
341
P7. The higher the market's transaction index t, the higher the incentive m. Demonstration: at request to the corresponding author. Proposition P7 might suggest that the firm will reward contractors according to their luck, since t has nothing to do with their performance. Oyer (2004) observes something similar in the salary structure of many executives, who are paid according to the firm's profit, or the firms grant them stock options. Since one employee has little influence on the firm's profit or on its stock price, to use such type of incentives seems contradictory. The author explains that such compensation practice ensures the Participation constraint if the salary of the executive's job alternatives highly correlates with the performance of the firm. In transportation, the index is a weighted average of all the shippers in the country, and as the index grows, the rates that the carriers can obtain also increase.
4. Source of the data This section evaluates empirically the propositions of Section 3 by considering the fleet that operates “Compañía Molinera San Cristóbal (CMSC)”, the largest flour milling company in Chile, with 12% of market share. Besides flour and its by-products, CMSC also delivers other types of food such as rice, cereals and noodles, from a warehouse in Santiago and from two other warehouses at the outskirts of the city. Recall that margin = ((income − cost) / cost), not the income, is a measure of how much the shipper rewards the carrier's performance. To estimate costs, several CMSC employees and carriers revised the structure and parameters of the transportation cost model presented by Singer et al. (2002). Then the IT department implemented a software for calculating the payments for the carriers according to the cost model (plus a given margin), and tested the system for one month. During such period, different key players made various corrections in order to fine-tune it. According to Kleindorfer et al. (1998), the validation done by the users has the advantage of combining different points of view, each of one interested in avoiding arbitrary definitions. For example, if the model overestimated the diesel cost, then the CMSC accountants would warn about it; if the model underestimated it, the carriers would complain. As most transportation contracts in Chile, those offered by CMSC to his carriers are rather simple. As many companies do, CMSC also operates with a spot fleet for high demand situations. Some carriers change their status: in some periods they work exclusively with CMSC, and in others they attend other shippers. The econometric study below considers different definitions of when the carrier is working in an exclusive manner. The database consists of 58 trucks contracted by CMSC for 93 weeks, from January 2004 to October 2005. The capacity of the trucks ranges from 1 to 34 tons. Small and large trucks serve indistinctively most clients, so for the econometric model the 58 trucks belong to the same pool. As mentioned in Section 1, the margin obtained by the carrier is on a weekly basis. Although the round trips may last half a day, an entire day or even more, CMSC pays the carriers once a week. To measure the weekly income, the database considers actual payments, which on average exceed by 15% the official published rates. The difference occurs due to last minute negotiations and due to additional compensations registered by the accounting system. The carrier assumes most of the fixed costs (payment to drivers and loaders) week by week. The weekly variable costs (diesel, tires) aggregate the variable costs caused by all the round trips during each week. The round trips do not overlap from one week to the other, because the carriers do not work on Sundays. Further details about the data are in Singer et al. (2007).
342
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
5. Econometric model One way to verify empirically the propositions and corollaries is to filter the random noise of weekly margin with an econometric model. Define the following independent variables related to each truck:
MARGIN (corrected with + 1) is a lineal function of the natural logarithms of the independent variables. Defining i as the truck index, t as the time index and ε as a random variable that distributes normally with mean zero, Model I is: lnðMARGINit + 1Þ = β0 + βYEARS · lnðYEARSi Þ + βLUXURY · lnðLUXURYi Þ
YEARS
Number of years that the carrier has worked for CMSC. The average in the sample is 16 years, with a standard deviation of 8.8 years. LUXURY Truck quality, measured as a ratio between the truck's market value and its load capacity. The average is 637, with a standard deviation 368. FLEET Number of trucks that the carrier owns. The average is 3.4, with a standard deviation of 2.4. GRADE Evaluation from 1 (worst) to 7 (best) of the service provided by the truck. The evaluation was done by the head of logistics and, two weeks later, by the transport manager of CMSC. Both evaluations took place at different time spans in order to avoid the mutual influence between both executives. The average grade of the sample is 6.3, with a standard deviation of 0.57. Define the following independent variables that relate to the truck and the week: KILOS
KM CLIENT
Total load transported by a given truck during a certain week. The average of the sample is 57,877 kg, with a standard deviation of 32,031 kg. Distance traveled by a given truck during a certain week. The average is 748 km, with a standard deviation of 780 km. Number of clients served by a given truck during a certain week. The average is 18 clients, with a standard deviation of 8.7 clients.
Define the following independent variables that relate to each week: SALES
Total cargo volume delivered by CMSC during the week, measured in tons. The average of the sample is 2999, with standard deviation of 376. EMPLOY National employment index given by the transport and telecommunication sector. The government publishes this index every two months. The average is 195 and the standard deviation is 9.35. DIESEL Cost of fuel adjusted by inflation. The average of the sample is 0.02 UF/liter (UF or Unidad de Fomento is the monetary unit indexed to inflation, close to US$45), and the standard deviation is 0.0026 UF/liter.
+ βFLEET · lnðFLEETi Þ + βGRADE · lnðGRADEi Þ + βKILOS · lnðKILOSit Þ + βKM · lnðKMit Þ + βCLIENT · lnðCLIENTit Þ + βSALES · lnðSALESt Þ + βEMPLOY · lnðEMPLOYt Þ + βDIESEL · lnðDIESELt Þ + εit : Corollary C1, which predicts a higher margin for those carriers that transport the most tonnage, is not invalid if βKILOS N 0. However, CMSC pays $/ton, so such correlation is tautological. In what follows the models disregard KILOS, in order to capture the influence of the other independent variables. The Hausman test rejects fixed effects hypothesis related to the trucks, so is not necessary to define one dummy variable for each truck. In other words, some trucks earn better margins due to their specific value for the independent variables; not because they are somehow privileged. Model I in Table 1 shows the results of a Generalized Least Squares (GLS) regression, which controls for autocorrelation within panels and cross-sectional correlation and heteroskedasticity across panels. The variables depend on the truck (rows 1–4), on the truck-week (rows 5–6) or on the week alone (rows 7–9). The database contains 3726 valid observations between January 2004 and October 2005 where the MARGIN was equal to or above −50%. As the coefficients represent elasticities, βYEARS = 0.037 means that a truck that doubles the seniority of another, perceives a margin 3.7% higher. According to the econometric analysis, Proposition P2 is not invalid since βYEARS N 0. Proposition P3 is not invalid since βFLEET N 0. Proposition P5 is not invalid if βSALES N 0. Proposition P6 predicts βDIESEL N 0 only if the fixed part of the margin is high enough. Since CMSC guarantees a low margin, the data shows no correlation. Proposition P7 is not invalid since βEMPLOY N 0. P1 refers to the subjective effort of the carrier, hence the proposition is not empirically verifiable. Proposition P4 is indirectly verifiable through Corollaries C1, C2, C3 and C4. The regression can Table 1 Model's outcome.
YEARS LUXURY FLEET
The dependent variable is: GRADE
MARGIN Margin that each truck obtains a certain week. The average in the sample is 19%, with a standard deviation of 40%.
KM CLIENT
As expected, the correlation coefficients between LUXURY and YEARS (ρ = −0.39) and between LUXURY and GRADE (ρ = 0.49) are high in absolute value. The correlations between CLIENT and YEARS (ρ = −0.26) and between CLIENT and LUXURY (ρ = 0.37) are also high in absolute value, as the firm often assigns newer and better trucks to clients. The correlation between CLIENT and KM (ρ = 0.4) is high because the more clients assigned, the longer the traveling distance. The other correlations are less significant. The profit should not depend on the KILOS transported summed with the KM traveled (βKILOS · KILOS + βKM · KM), but on KILOS and KM (β0KILOSβKILOS · KMβKM). Therefore, a regression model test that
SALES EMPLOY DIESEL Constant Sample size Prb. F-statistic ⁎ Significant at 95%. ⁎⁎ Significant at 99%.
Model I
Model II
0.04 (5.41)⁎⁎ − 0.18 (− 14.2)⁎⁎ 0.05 (7.8)⁎⁎ 1.16 (20.4)⁎⁎ − 0.01 (− 1.3) 0.21 (20.7)⁎⁎ 0.37 (10.2)⁎⁎ 0.26 (2.4)⁎ − 0.07 (− 1.7) − 8.74 (− 10.9)⁎⁎ 3726 0.00
0.06 (6.18)⁎⁎ − 0.23 (− 12.6)⁎⁎ 0.06 (5.8)⁎⁎ 1.17 (20.4)⁎⁎ 0.00 (0.1) 0.28 (29.2)⁎⁎ 0.35 (12.3)⁎⁎ 0.04 (0.4) − 0.08 (− 1.5) − 4.91 (− 7.0)⁎⁎ 3726 0.00
M. Singer, P. Donoso / Journal of Business Research 64 (2011) 338–343
neither prove nor disprove Corollary C2, since βKM is not different from zero. Corollary C3 is not invalid since βCLIENT N 0. Corollary C4, which predicts a higher MARGIN for those carriers that deliver a better service, is not invalid if βLUXURY N 0, or if βGRADE N 0. The regression obtains βGRADE N 0 but βLUXURY b 0, which implies that the shipper does not reward the quality of trucks. Corollary C2 predicts a positive correlation between MARGIN and KM, but the data shows no evidence that βKM N 0. The model tests other pools of information, for instance considering 3872 valid observations whose MARGIN is equal to or higher than −70%. Another pool considers 3885 valid observations by filtering those that have the ratio income/(fixed costs) higher than or equal to 30%. The outcomes do not differ much, indicating that the results are robust on the definition of whether the truck works in a spot bases. Although the agency model is static, margins for the same truck could exhibit autocorrelation. The data reveals a lag of one period, so margins in week t depend on margins in week t − 1. Model II in Table 1 shows the results of a GLS regression, allowing the presence of AR(1) autocorrelation within panels and cross-sectional correlation and heteroskedasticity across panels. The results are similar to the results of Model I, with the exception of EMPLOY, which is not significant anymore. 6. Conclusions The empirical evidence allows to conclude that the rate does play a role in aligning the shipper and the carrier's interests. The regression finds evidence that the firm rewards with higher margins carrier's experience (P2), a higher number of clients served (C3) and a better evaluation (Corollary C4). The available information cannot evaluate whether the incentives overcome the subjective cost of the carrier's effort, hence rates may or may not modify the carrier's behavior. The data shows no evidence that the shipper compensates carriers that travel longer distances (C2), or carriers that have better trucks (Corollary C4). The regression model verifies that the rates quickly react to the alternatives the parties have to a deal. Since losing a carrier affects the shipper more intensively during high sales periods, the higher the sales, the higher the rates (P5). Losing a carrier with numerous trucks affects the shipper significantly; hence carriers with larger fleets earn higher margins per truck (P3). When the transportation market falls, the exit options for the carrier worsen, hence he obtains a lower margin in Model I (P7), but not in Model II. Variables that change periodically, such as the number of visited clients and the sales, have an almost immediate effect on the carrier's margin. Such result suggests that parties update their agreement frequently. The theoretical analysis asserts that if the fixed margin is low, changes in the operation cost of the trucks do not translate into changes in the margin (P6). Consistently, in the econometric model the margin correlates with the diesel value. A possible conjecture is that a change in the costs does not modify the bargaining power of the parties, and therefore does not influence how much margin the shipper transfers to the carrier. With empirical evidence that rates do function as incentives for the carriers, at least in some dimensions, this article refutes the comment of Lafontaine and Masten (2002) expressed at the beginning of this article: “Driver compensation […] appear to bear little or no relation to the incentive functions […]”. Probably, the discrepancy lies in their understanding of compensation. While they define compensation as the carrier's gross income, margin depends on the costs as well. The shipper that would like to reward a specific carrier for his effort can pay the carrier a higher rate or assign him the less expensive deliveries. Another reason to disregard the agency role of the rates is that they show an apparent chaotic behavior, consequence of the reciprocal
343
favors between shipper and carrier. The econometric analysis filters the random noise and allows observing that, on average, the rates are consistent with the agency theory. Recall that the data allows accepting the random effect hypothesis, so trucks do not show inexplicable high and low margins. In summary, this article is contributing to the understanding of firm– contractor relationship by proposing, and empirically testing, a principal-agent model that calculates the rewards necessary to maintain the Incentive Compatibility and the Participation constraints. Furthermore, this article provides additional evidence of the usefulness of incentives to mitigate moral hazard, arguably the main difficulty of contracting contractors. Nevertheless, incentives may generate unexpected side effects. Osterloh and Frey (2000) and Gneezy and Rustichini (2000) report than when agents are subject to strong pecuniary rewards and penalties, sometimes they deviate from the principal's interests because the task looses its significance. This phenomenon named crowding-out consists in people running away from the personal gratification of doing the work right, towards maximizing profit only. So far the literature has not reported this effect for organizations, which opens new questions: does crowding-out occur in the context of interfirm relationship? Is the effect significant? From which point pecuniary incentives deteriorate the relationship? In many situations incentives may seem unsuitable, reason why agency theory provides alternative tools such as an expensive supervision, vertical integration or quasi-integration. However, scholars and practitioners should take a second look¸ as apparently unstructured agency relationship sometimes hide the opportunities to use incentives. Acknowledgment FONDECYT partially financed this investigation, project number 108/0292. References Baker GP, Hubbard TN. Contractibility and asset ownership: on-board computers and governance in U.S. trucking. Q J Econ 2004;119:1443–79. Eisenhardt KM. Agency theory: an assessment and review. Acad Manag Rev 1989;14:57–74. Fernández A, Arruñada B, González-Díez M. Quasi-integration in less-than-truckload trucking. In: Ménard C, editor. Institutions, contracts and organizations. Cheltenham and Northampton: Edward Elgar; 2000. p. 293–312. Gneezy U, Rustichini A. A fine is a price. J Legal Stud 2000;XXIX:1-18. Harris, Raviv. Some results on incentive contracts with applications to education and employment, health insurance, and law enforcement. Am Econ Rev 1978;68:20–30. Hart O. Firms contracts and financial structure. Oxford: Oxford University Press; 1995. Kakabadse A, Kakabadse N. Trends in outsourcing: contrasting USA and Europe. Eur Manag J 2002;20:189–98. Kleindorfer GB, O'Neill L, Ganeshan R. Validation in simulation: various positions in the philosophy of science. Manag Sci 1998;44:1087–99. Lacity MC, Willcocks LP, Feeny DF. IT outsourcing maximizes flexibility and control. Harvard Bus Rev 1995;73:84–93. Lafontaine, F., Masten, S.E. (2002) “Contracting in the absence of specific investments and moral hazard: Understanding carrier-driver relations in U.S. trucking” NBER Working Paper No. W8859. Available at SSRN: http://ssrn.com/abstract=305603. Lambert DM, Burduroglu R. Measuring and selling the value of logistics. Int J Logist Manag 2000;11:1-17. Nickerson JA, Silverman BS. Why aren’t all truck drivers owner-operators? Asset ownership and the employment relation in interstate for-hire trucking. Journal of Economics and Management Strategy 2003;12:91-118. Osterloh M, Frey BS. Motivation knowledge transfer, and organizational forms. Organ Sci 2000;11:538–50. Oyer P. Why do firms use incentive that have no incentive effects? J Finance 2004;59:1619–49. Quinn JB. Strategic outsourcing: leveraging knowledge capabilities. Sloan Manage Rev 1999;40:9-22. Rasmusen E. Games and information. Oxford, UK: Basil Blackwell Ltd.; 1989. Regan A, Garrido RA. Modeling freight demand and shipper behavior: state of the art, future directions. In: Hensher DA, editor. The leading edge of travel behaviour research. Amsterdam: Pergamon Press; 2002. Singer M, Donoso P, Jara S. Fleet configuration subject to stochastic demand: an application in the distribution of liquefied petroleum gas. J Oper Res Soc 2002;53:961–71. Singer M, Donoso P, Widdel S. ¿Premian las tarifas el desempeño del transportista? Abante 2007;10:21–55.