Management controls on agent’s risk-taking incentive:
The organizational architecture of Public-Private-Partnerships
Demi Chung*
demi.chung@unsw.edu.au
School of Accounting
The Australian School of Business
University of New South Wales
Australia 2052
David A. Hensher
david.hensher@sydney.edu.au
The Institute of Transport and Logistics Studies
The Business School
University of Sydney
Australia 2006
John M. Rose
john.rose@sydney.edu.au
The Institute of Transport and Logistics Studies
The Business School
University of Sydney
Australia 2006
January 2013
[Please do not quote.]
*
Corresponding author
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Management controls on agent’s risk-taking incentive:
The organizational architecture of Public-Private-Partnerships
Abstract
This paper investigates the extent to which the three pillars of management controls, namely
allocation of decision rights, performance measures, and rewards and punishments, affect an
agent’s risk-taking incentive. This is identified by analysing the organizational architecture of
public-private-partnerships (PPPs) in a framework that draws on ideas embodied in
transaction cost economics, agency theory and social contract theory. Using a sample of
partners in PPP roads and a computer-assisted-personal-instrument survey that includes a
stated choice experiment and a record of prior PPP experience, we test the direct effects of
management controls on the agent’s risk preferences. The paper provides new evidence that
the incentive effect of right to pricing decision must be supported by social expectations.
Performance evaluation based on noisy measures and the fixed-price reward weaken the
agent’s risk-taking incentive. The double moral hazard has resulted in the strengthening effect
of financial penalty. Surplus-sharing to curtail the management control problem of decision
right misuse weakens the risk-taking incentive, whilst long contract duration to mitigate effort
aversion strengthens the effect.
Key words: management controls, incentive contract, risk preference, transaction cost
economics, agency theory, social contract, public-private-partnerships, decision right, fixedprice compensation, performance measures, penalties, contract duration, surplus-sharing,
media coverage
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1.
Introduction
This paper examines the extent of management controls on an agent’s risk-taking incentive.
We define management controls as allocation of decision rights, performance measurement,
and rewards and punishments (Jensen and Meckling, 1992). We follow the sentiment of
March and Shapira (1987) in which a risk-taking incentive is dictated by the perceived ability
to control risks to measure risk-taking incentives using varying degrees of risk aversion.
Accounting, management and economics literatures have long recognized the importance of
the three pillars of management controls in organizational architecture design. Many of these
studies assume managerial moral hazard, only reporting the design and implementation of
management controls from the principal’s perspective (Bajari and Tadelis, 2001; Nagar, 2002;
Anderson and Dekker, 2005; Widener et al., 2008; Campbell et al., 2009). Other studies that
take into account the agent’s perspective consider each control in isolation (Joskow, 1985;
Polinsky, 1987; Luft, 1994; Fehr and Schmidt, 2007; Christ et al., 2012), none of these
studies have examined all three issues jointly. Most recently, a growing body of empirical
research integrates agency theory and transaction cost economics (TCE) to examine the
association between management controls and transaction characteristics (Anderson and
Dekker, 2005; Sedatole et al., 2012). This literature is redefining a new era in the study of
organizational architecture design.
Building on the prior literature, we develop a framework integrating three strands of theories:
the TCE, agency theory, and social contract theory, to investigate the three pillars of
management controls on the agent’s risk-taking incentive. TCE makes provision for
management controls through the impact they have on the adaptive capacity to transaction
characteristics (Tadelis and williamson, 2012). Agency theory explicitly recognises the riskbearing implication in control strategies (Eisenhardt, 1985). We extend these two streams of
literature to examine whether management controls designed in a transaction costs
economising way motivates the agent to bear risks. Social contract takes into consideration
the social context in which the transaction occurs. Under this notion, a firm is expected to
perform certain socially desirable functions and its pursuit of private benefits should not
compromise social benefits, or sanctions will be imposed by the society (Ramanathan, 1976).
While the power of social legitimacy influencing corporate disclosure has been afforded much
attention in the accounting literature (O'Dwyer, 2002; Aerts and Cormier, 2009), their role in
strengthening (or weakening) the power of management controls has not received the same
attention.
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The empirical examination of this integrated framework is studied in the context of publicprivate-partnership (PPP) transactions of road infrastructure. The multiple dimensions of
PPPs make them a suitable setting to test our integrated framework of control involving TCE
transaction characteristics, agency-theoretical personal attributes and risk-bearing provisions,
and the social contract implicit in the transacting environment. The main thrust of PPPs is to
allocate risks to the party who is best able to manage those risks; therefore risk-sharing plays
an important role in the choice of management controls. The management control outcomes
are concerned with the service delivery (road availability) generated from an asset-specific
investment (road infrastructure), although the PPP scheme typically bundles the finance and
construction of the asset with operations and maintenance of the asset.
Our results show that decision right to pricing control matters to the agent’s risk aversion only
when approval by social contract is taken into account, highlighting the social contract
dimension in strengthening the effects of formal control. Consistent with prior empirical
results, we find that noisy performance measures (Krishnan et al., 2011) and fixed-price
reward (Evans III et al., 2006) impose risks on agents, and hence weaken their risk-taking
incentive. However, contrary to previous results reporting the lower agent effort under
financial penalty punishment (Christ et al., 2012), we demonstrate that the propensity to rely
on legal recourse of court ordering under double moral hazard explains the opposite effect of
penalty on the agent’s incentive. We show how the double moral hazard makes the case for
long contract duration an incentive. We offer evidence to show that the mechanism to the
control problem of misuse of a decision right over which the principal can expropriate agent’s
surplus above predetermined earning threshold, is positively associated with agent’s risk
aversion. Social approval through media coverage explains the effects of these management
controls on agent’s risk-taking incentive over and above TCE and agency theory.
This study contributes to the literature in several ways. First, we supplement the literature
concerning risk aversion as the determinant of the design of management controls, to show
that risk aversion is also the outcome of these designs. The extant literature generally focuses
on the role that risk aversion plays in the design of incentive contracts (Lafontaine and
Bhattacharyya, 1995; Evans III et al., 2006), and therefore is mainly concerned with testing
the implication of risk aversion from the principal’s perspective. Our research is one of the
first studies to investigate from the agent’s perspective, how management controls in
incentive contracts jointly affect their risk aversion.
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Second, it adds to the emerging literature that jointly tests TCE and agency theory. This
literature provides analytical as well as empirical evidence to show that transaction
characteristics and transactors’ attributes jointly determine management control structures.
We further show the extent to which management controls in response to transaction hazards
and agency problems affect agent’s risk-taking incentives. Unlike prior research, which
examines how agency theory can strengthen TCE’s explanatory power in determining the
association between transaction characteristics and investment choice (Sedatole et al., 2012),
we examine the effectiveness of management controls resulting from the joint consideration
of TCE and agency theory on agent’s incentive to take risk. The empirical models in this
discipline are generally lacking in that they typically rely on indirect proxies for
measurements of controls and risk preferences. We are able to obtain direct measures using a
computer-assisted-personal-instrument (CAPI) survey to capture these variables. Our results
demonstrate that the choices of management controls affect risk preferences, providing
empirical support for the (de)motivating effects associated with management controls.
Third, we contribute to the growing accounting literature in interorganizational relationships,
where the problem of double moral hazard is paramount. This problem has long been
acknowledged in franchising (Luft, 1994; Lafontaine and Bhattacharyya, 1995)1. Our
investigation, from the agent’s perspective, will enhance the understanding of the extent to
which management controls induce risk-taking behaviour. Following Sedatole et al. (2012),
we measure risk-taking behaviour using the likelihood of an agent investing in risky projects.
Our additional analyses on the varying degrees of risk aversion on contract choice involving
investment decisions in PPP infrastructure, shows that management controls through their
effects on agent’s risk aversion can affect agent’s risk-taking behaviour.
Finally, the most significant contribution lies with the contribution to the theory of
management controls. Our inclusion of elements of TCE, agency theory and social contract
offers an explanation for why the management controls formalised by contract must be
supplemented by informal controls endogenized in the social context in order to achieve
management control outcomes. The empirical analysis is the first study to test the potential
role of social legitimacy in impacting the effectiveness of management controls.
1
A franchise is a two-way contract where the franchisor and the franchisee share risk by giving each party
property rights in those aspects of the transaction that they can efficiently control (Rubin, 1978).
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The paper is organised as follows. In the next section we develop a framework integrating
three strands of theories: the TCE, agency theory and social contract, to show how
management controls are established in response to transaction characteristics, transaction
parties’ attributes and the characteristics of the transaction environment. We formulate our
hypotheses based on analyses of management controls deployed in PPPs and their intended
outcomes. Section 3 describes our approach to measure risk aversion. Section 4 outlines our
research method and presents descriptive data. Section 5 reports the empirical results.
Additional analyses between risk aversion and investment choice are included to demonstrate
the extent to which management control choices, through their affects on decision maker’s
risk-taking incentives, as determinants of investment decisions. The last section offers a
number of conclusions and a summary of the major contributions of the paper.
2.
Theory and hypothesis development
In this section, we develop an integrated framework involving TCE, agency theory and social
contract of control, in order to study the organization architecture of PPPs. We then formulate
hypotheses to test the management controls deployed in PPPs on agent’s risk aversion within
the outcome bounds of the PPP scheme.
2.1 Triangulation of management controls: Transaction cost economics, agency theory
and social contract
Management controls are central to theories of contract although their functions differ within
TCE, agency theory and social contract theory, as illustrated in Figure 1.
[Insert Figure 1 here]
Management controls in TCE are in response to transaction characteristics, and adaptations
are taken into account as the central purpose of management controls. Organizing transactions
with different characteristics becomes the structure of control mechanisms that create the
governance dimensionalities with parallel adaptive ability. The critical dimensions of
alternative modes of TCE governance are incentive intensity (strong under fixed-price reward
schemes and weak under cost-plus reward schemes), administrative command and control
(strong under ownership of decision rights), and a contract laws regime (strong under legal
recourse by court ordering and weak under private ordering) (Williamson, 2010). TCE
maintains the transactors’ behavioural assumption of risk neutrality to bring out the
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imperative of an efficient alignment between governance structures and transaction
characteristics (Williamson, 1985, pp. 388-390).
Agency theory assumes that economic actors have different preferences toward risk (Arrow,
1971) and display the propensity of shirking opportunism (Alchian and Demsetz, 1972). The
theory embraces the risk-bearing perspective to explain the effects of management controls in
inducing agent’s incentive and in mitigating moral hazard (Jensen and Meckling, 1976;
Eisenhardt, 1989).
The force of implicit and explicit societal expectations is applied through an unwritten social
contract underscoring society’s approval of organizational behaviour. A social contract relies
on the notion that there is a contract between an organization and the society in which it
operates. It is assumed that society allows the organization to continue operations as long as it
generally meets society expectations. If an organization cannot justify its continued operation,
the community may revoke the organization’s ‘contract’ to continue its operations. This might
occur through consumers reducing or eliminating the demand for the products of the business,
factor suppliers eliminating the supply of labour and financial capital to the business, or
constituents lobbying government for increased taxes, fines or laws to prohibit the actions that
do not conform to acceptable social norms (Deegan and Rankin, 1996). When faced with
threats of sanction by the society, organizations will adapt their methods of operation to
conform to prevailing norms of the transaction environment (Dowling and Pfeffer, 1975).
Studies of managerial behaviour therefore must look beyond formal controls by a contract
mechanism as a way to understand behaviour ‘regulated’ by the social contract.
2.2 The organization architecture of PPPs
In PPP transactions, government sells a private consortium an ownership concession. This is a
right to generate income from ownership (Buitelaar et al., 2007) through designing, building,
financing and operating an infrastructure asset as well as profit from the sale of ancillary
services like road availability generated from the asset. Using the insights from van der MeerKooistra and Vosselman (2000), the characteristics of PPPs can be separated into three
elements (see Table 1): the transaction, the transaction environment, and the transaction
parties.
[Insert Table 1 here]
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The transaction characteristics feature in a bundling product in which the contracted assetbased service (road availability) is derived from the road infrastructure built and financed by
the agent. The primary rationale of the contract to the principal is the purchase of the service
and not the asset itself. The payment of service is linked to requirements set out in the output
specification (Akbiyikli et al., 2006) and is payable only when the service meets required
performance measures (Debande, 2002). The characteristics of site specificity of the road
infrastructure (Williamson, 1991), and the intensity of capital investment2, mean that the
period it takes for the agent to recoup the cost of capital and earn a required rate of return on
equity can be lengthy.
The above characteristics of the transaction are associated with a number of uncertainties in
the transaction environment such as future contingencies and market demand in the long-run.
These uncertainties highlight the relevance of the institutional environment, its specified legal
rules, systems and social norms in minimising the impact of adverse uncertain events, and
opportunistic behaviour of transactors.
Transaction parties display, as assumed in agency theory, heterogeneity in objectives and risk
preferences (Jensen and Meckling, 1976). de Palma et al. (2012) argue that transaction parties
in PPPs are characterised by two-way information asymmetry and double moral hazard. Twoway information asymmetry exists because there is a very real possibility that the principal
could hold more information than the agent, and thus the agent may equally face risks
associated with the behaviour of the principal. When both the principal and the agent make
non-contractible actions to the transaction, the relationship is characterised by double moral
hazard (Lutz, 1995). Double moral hazard is substantiated by the experience that private
sector operators involved in PPPs have been subject to abusive behaviour by the state (for
example, nationalization or the sudden cancellation of a contract) with little recourse if any
(de Palma et al., 2012), which underlines the relevance of the characteristics of the
institutional environment. The institutional environment extends protection to the agent
through public regulatory bodies including the courts and the justice system. Although such
bodies are part of the public sector, they usually enjoy a level of independence that, in good
circumstances, makes them able to discipline the government (i.e., principal) if it behaves
improperly. This, however, only applies in countries where regulatory bodies are strong, and
the rule of law is binding. Finally, the social nature of contracts looks beyond the bilateral
2
The capital cost of road infrastructure in Australia is in the gamut of A$246 million to A$2.3 billion (Chung,
2009, Appendix B).
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relationship of the principal and the agent to recognize any organizational architecture as a
nexus of obligations within the social context (Cuevas-Rodríguez et al., 2012). Transaction
parties to PPPs often face risks originating from perception biases from the transaction
environment (Chung et al., 2010) which underscore the social embededness in explaining
their risk behaviour.
In light of the elements in Table 1, the organizational architecture of PPPs consists of two
main components: incentives to align the agent’s behaviour to management control outcomes;
and devices to mitigate management control problems. The incentive components are made
up of the delegation of decision rights that offers flexibility in managing performance risk,
and compensation schemes designed for inducing performance effort. Incentives are subject
to various inefficiencies including distorted performance effort (Lyons, 1996; Fehr and
Schmidt, 2007). The mitigating elements involve the use of contract duration to guard against
poor quality in ex ante investment and ex post shirking, performance measures to evaluate
service outcome, financial penalties to punish shirking behaviour, and the sharing of surplus
to prevent service quality being compromised by the profit maximisation motive.
In the following subsections, we will develop a number of hypotheses to test the effectiveness
of management controls implemented in PPPs. The relationships between these controls and
their intended outcome to motivate agent’s risk-taking incentives (measure by agent’s risk
aversion) are summarised in Figure 2.
[Insert Figure 2 here]
Decision rights to pricing control
TCE’s adaptations argue that delegating an agent a decision right to control an asset’s use
under uncontracted contingencies gives them a high power of incentive which enhances
adaptability (Hart and Moore, 1990). Agency theory adds an additional view that decision
rights should be delegated to the agents who are familiar with the circumstances in which
changes occur and who possess the specific knowledge relevant to the decision (Jensen and
Meckling, 1992). In an outcome contract concerning road availability, the agent who has the
first-hand real-time knowledge about the traffic conditions on the road facility under their
management should be delegated the decision to control pricing, varying the price levels to
adjust traffic demand has the effect of easing congestion which is in line with the principal’s
preference for ensuring road availability to motorists. From the agent’s perspective, this
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control mechanism can facilitate their fulfilment of contractual obligations to deliver road
availability, and comes with the motivating incentive for them to bear the risk of managing
traffic congestion. We predict that:
H1a: The decision right to pricing control is negatively associated with the
agent’s risk aversion.
Fixed-price compensation
TCE maintains that the high power of coordinated adaptation must be complemented with
weak cost-control incentives in order to enhance adaptive ability. Empirical studies of agency
theory are in support of TCE. For example, the incentive structures prevailing in the managed
care services in the US demonstrate how the decision autonomy involving the exercise of
specialised knowledge and the associated weak cost-control compensation incentives rolled
out in practice (Evans III et al., 2006). Similarly, evidence from franchising supports the
positive association between authority delegation to agents with superior local knowledge and
the use of high-powered incentive pay with the agent retaining residual profits from their units
(Campbell et al., 2009). Both theories suggest that characteristics of PPP transactions and
transactors will need the support of a reward scheme with weak cost-control incentives.
However, compensation schemes in PPPs were designed to help in realising the principal’s
project objectives (Aziz, 2007) rather than aligning transaction characteristics. Prima facie,
the purpose of the fixed-price contract3 in PPPs is to remove revenue uncertainty for the agent
so that they will exert greater effort in managing performance risks. Polinsky (1987) has
shown analytically that when the agent is risk averse, a fixed-price contract will insure the
agent against demand uncertainty. An implicit but overpowering purpose of the fixed-price
scheme is its budget certainty to the principal. From an incentive viewpoint, the fixed-price
scheme has the agent bearing all the cost of operations and maintenance (Bajari and Tadelis,
2001) leaving all of the risk of cost uncertainty to the agent. As argued by Joskow (1985), in
circumstances where the agent’s actual costs rise significantly above the fixed price, a fixed
price contract creates breach incentives such as reduced quantities provided, cessation of
3
There are a variety of compensation schemes used in PPPs: toll payments directly by motorists, shadow
payments from the principal based on the number of vehicles travelling on the road, availability payments that
are equivalent to a fixed-price contract involving a series of periodic payments of fixed sum agreed in the
contract, government subsidies, or a combination of these methods (Chung, 2009). Here we focus on the effect
of the fixed-price contract. The effect of surplus-sharing which we will discuss is related to the compensation
involving toll payments collected directly from motorists at the point of travel.
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investments in order to maintain production capacity, and the running down of the value of
equipment. We hypothesise that:
H1b: A fixed-price contract is positively associated with the agent’s risk aversion.
Performance measures
TCE takes the efficient alignment between transactions with different characteristics, and
organizational forms with different cost and competences, as the solution to adaptive
problems (Williamson, 2010). There is thus little mention of the roles of performance
measures and the resultant rewards and punishments required to control moral hazards arising
from delegation. Agency theory argues that the inevitable agency costs resulting from the
delegation of decision rights, necessitates the principal to devise a set of performance
measures primarily because the agent’s behaviour is often unobservable
(Jensen and
Meckling, 1992). Reliable performance measures can reduce the risks imposed by delegation
and compensation schemes on the agents (Widener et al., 2008). However, in a risky
operating environment with high task uncertainty and demand uncertainty, performance
measures become difficult to specify with precision ex ante, therefore have limited or little
use for contracting (Krishnan et al., 2011). Performance of road service providers is measured
against the extent that the road is available to motorists; therefore any vehicle breakdowns,
accidents, or overflow of traffic with the consequence of failing to clear road congestion in
time are indications of poor performance, even in situations where the causes of accidents
(e.g., poor weather condition) are well beyond the agent’s control, resulting in high task
uncertainty. Demand uncertainty is exacerbated in contracts of long duration – it makes traffic
demand forecast problematic; hence difficulties in budgeting for repair and maintenance.
Measures that suffer from task and demand uncertainties become counter-effective in
reducing risks that have been imposed on the agent by delegation and fixed-price
compensation. On this basis, we expect that the agent’s risk aversion increases with the use of
such noisy measures.
H1c: The performance measures specified ex ante in PPP contracts are positively
associated with the agent’s risk aversion.
Penalties
A fixed-price compensation with penalty clauses linked to performance measures can be
optimal for the principal to incentivise the agent to perform tasks in an uncertain environment
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(Hannan et al., 2005; Zhao, 2008). Many experimental studies on contract framing have,
however, challenged this proposition and concluded that control-averse behaviour actually
increased with the use of penalties. Luft (1994) noted that the use of a “penalty label”
generated persistent aversion in the parties being evaluated, arguing that contracts framed as
financial sanctions magnified the loss effect. Further evidence showed that when penalty and
fines were interpreted as an explicit threat of sanction, they could be seen as a hostile act
(Fehr and Schmidt, 2007). Contract incompleteness was found to exacerbate this adverse
effect. Incomplete contracts present greater opportunities for the principal’s discretionary
behaviour, representing higher risk of opportunism on the part of the principal. The presence
of a double moral hazard has the agent perceived penalties as a sense of distrust from the
principal leading to higher risk and effort aversions (Christ et al., 2012). Conclusions drawn
from these studies of agency theory are contextualised in governance by hierarchy, with the
implicit assumption of a weak contract regime. Recourse to the court-orderings to delimit
threat positions has little role to play in governance by hierarchy (Williamson, 2010), so when
double moral hazard presents, the hierarchy is its own court of ultimate appeal for agent’s
disputes over principal’s discretionary compensation. In contrast, TCE’s concept of contract
laws asserts that court-ordering plays a significant role in a hybrid mode of contracting
(Williamson, 1991). The study of hybrid governance based on a large sample of survey data
has led Anderson and Dekker (2005) to conclude that bilateral dependence between parties to
contracts involving asset specificity has made the reliance on legal recourse more pronounced.
Other empirical work has confirmed that explicit penalty clauses make the legal process less
costly and more certain, and are used to protect specific investments against opportunistic
breach (Lyons, 1996, p. 44). On the basis of this literature and the double moral hazard
prevailing in PPPs, we predict that the penalties specified in the contract will reduce the
agent’s risk aversion.
H1d: Penalty clauses are negatively associated with the agent’s risk aversion.
Contract duration
A number of empirical studies give strong support to TCE finding that long contract durations
were structured to promote durable asset-specific investments in heavily regulated industries
with high publicity like gas (Masten and Crocker, 1985) and coal (Joskow, 1987). The
incentive effect of long duration becomes more pronounced in contracts involving bundling
tasks. Bundling is optimal if it can create “countervailing incentives”. That is, if the costs of
accomplishing the two tasks are negatively correlated, allocating the second task to the same
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agent may make eliciting the first task’s cost realization less costly, with the benefit of
reducing an agent’s total information rent (Baiman and Rajan, 2002, p. 216). Bundling,
however, can distort optimal level of investments. For instance, if an agent is given a difficultto-measure task (such as the design quality of the asset) and an easy-to-measure task (such as
the maintenance of the asset), they will have a tendency to overinvest in the maintenance task
and to under-invest in the quality of the asset (Holmstrom and Milgrom, 1991). Long contract
duration serves to monitor these two aspects of the bundling product: the longer the contract
duration, the more difficult it is for the agent to cover up any design defects and maintenance
problems arising from shirking. From the agent’s view, a lengthy duration is a protection
against moral hazard of the principal. Infrastructure assets often require substantial capital
investment upfront, and many contracts have a duration ranging between 30 and 99 years
(Chung, 2009) outliving governments. A long contract duration protects the agent against
opportunistic hold-up by generations of governments. This safeguarding property of contract
duration under a double moral hazard acts to minimise the agent’s risk aversion.
H1e: The contract duration is negatively associated with the agent’s risk aversion.
Control device to mitigate authority misuse
Agency theory recognises that the delegation of decision rights like pricing control is subject
to the moral hazard problem of authority misuse (Nagar, 2002). This hazard is particularly
concerning to a principal who is also a regulatory body, especially when the problem can
potentially generate social welfare loss. Surplus-sharing arrangements are often applied to
regulated industries, under which the regulated firm shares any earnings above a specified
level with the regulatory authority (Sappington and Weisman, 1996). PPP road contracts
generally impose surplus-sharing conditions to curtail the agent’s private objective of profit
maximisation at the cost of road congestion to motorists. This ability of the principal to
expropriate some of the ex post surplus created by the agent’s investment weakens the agent’s
incentive (Baiman and Rajan, 2002, p. 220). We argue that surplus-sharing contributes to the
agent’s increasing degree of risk aversion.
H1f: Provision of surplus-sharing is positively associated with the agent’s risk
aversion.
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Control by social contract
The implicit expectation that organizational behaviour should be commensurate with the
norms embodied within the social contract, is a powerful influence on an organization’s
continuous operations (Mathews, 1997; Deegan, 2002). Transactions involving public utilities
in particular tend to attract high public visibility and are prone to threats voiced through
negative media coverage (van der Meer-Kooistra and Vosselman, 2000). Consistent with
prior research showing that the media can be particularly effective in shaping community
perceptions concerning an organisation’s legitimate behaviour (Brown and Deegan, 1998),
our in-depth interview study has confirmed that media risk is a undermanaged social variable
in PPPs (Chung et al., 2010). The power of media in influencing organization behaviour has
been documented in a number of accounting studies (Deegan et al., 2000; Deegan et al.,
2002; Islam and Deegan, 2010; Mäkelä and Näsi, 2010), and organization behaviour was
reportedly highly sensitive to the intensity and negativity of media attention (Brown and
Deegan, 1998; Aerts and Cormier, 2009). We argue, in the absence of negative media
attention, i.e., if media attention toward these PPP projects is neutral, the effects of formal
control mechanisms on the agent’s risk-taking incentives can be strengthened.
H2:
Neutral media attention within the transaction environment
strengthens the effects of formal management controls on the
agent’s risk aversion.
3.
Measure of risk aversion
We draw on the Hensher Service Quality Index (HSQI) empirical framework (Hensher and
Prioni, 2002; Hensher et al., 2003) as a way to establish a set of risk indices relevant to PPP
roads as measures of the agent’s risk aversion. The HSQI represents a set of quantitative
performance indicators to measure service delivery quality and effectiveness in the context of
bus services. Under this framework, the overall level of satisfaction is measured by how an
individual evaluates the total package of services offered. The evaluation process involves the
search for appropriate weights attached to each service dimension to identify the strength of
positive and negative source of overall satisfaction. To fulfil this objective, stated choice (SC)
methods were used in the original study (Hensher and Prioni, 2002), whereby a sample of
passengers were asked to choose their most preferred package from a number of alternative
packages of service levels based on these attributes. Logit models were estimated to establish
the relative weights attached to the statistically significant attributes, representing the
contribution of each service attribute to the calculation of an overall service quality index. In
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addition, a set of attribute reference levels were obtained in order to apply the weights. For
this purpose, revealed preference (RP) data of the perceptions of passengers relative to the
levels of each attribute as experienced in a current trip were obtained; they were then
multiplied by the relevant weight; summing these calculations across all attributes to produce
the service quality index for each sampled passenger.
To implement the HSQI framework, we need to define a set of quantitative risk attributes
pertinent to PPP roads. Based on prior literature (Monteiro, 2010, p. 263), risk is defined as:
An event that may or may not occur and can lead to failure to satisfy
project requirements…and is being considered as having an upside and a
downside: a party facing risk suffers from negative events, but may also
benefit from positive events. In this way, the party will have higher
incentives for putting effort into preventing negative outcomes.
Risk comprises the expected value of a trade-off outcome associated with downside risk (the
likelihood of an outcome reaching a disaster level), upside gain (the likelihood of an outcome
reaching an optimistic level), and risk neutrality (the likelihood of an outcome reaching the
expected level) (Lafontaine and Bhattacharyya, 1995). In the seminal paper by March and
Shapira (1987), risk preference is subject to a decision maker’s ability to control the odds, is
conditional upon their experience in the underlying investment, their knowledge and skills in
pooling resources to mitigate downside outcomes and in trading off one risk with another, and
their informational advantage. The considerations of trade-off are framed by attention factors
that considerably affect action. Risk-averse individuals tend to pay greater attention to the
dangers of downside risk, hence displaying a propensity for risk-avoiding; risk-seeking
individuals have a predilection for opportunities for upside gain and thus exhibit risk prone
behaviour; and risk-neutral individuals favour certainty over variability, with a strong reaction
to risk neutrality.
Based on the sentiment of HSQI, in our CAPI survey (discussed in research method section)
we included a series of SC experiments designed to obtain SC data on respondents’
perception of risk associated with alternative packages of attributes that define the dimensions
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of PPP risk4 (see Table 2). The SC experiments contain five hypothetical scenarios that are
well-defined within the transaction context of PPP road contracts; which overcomes real-life
constraints confronted by the decision maker while maintaining the realism in the choice
scenarios (Hensher et al., 2005).
[Insert Table 2 here]
Construction of the empirical risk index entails using parameter estimates obtained from a
choice model, using data gathered by way of a SC experiment to condition the role of
reference levels representing the attribute risk levels perceived by stakeholder experience in
real PPP settings. We used the latent class (LCM) model to obtain estimates of the
parameters. The LCM model is preferred over the standard multinomial logit model because
of the increased behavioural richness of the model in accommodating, through discrete
distributions of the estimates of each attributes parameters, heterogeneity of stakeholder
preferences for specific levels of risk (given the likely outcome associated with the full
attribute package). LCM models also avoid the controversial implications of arbitrarily
selecting specific continuous distribution for each parameter that is required in mixed (or
random parameter) logit models, and are also starting to accumulate evidence of improved
goodness-of-fit over all alternative discrete choice model forms (Greene and Hensher, 2003).
The risk index of interest is given in equation (1).
· (1)
(RI=risk index; n=decision maker, j=investment alternative, k=attribute weight)
Given the reported reference levels from respondents’ prior experience in terms of risk borne
in PPP road projects, i.e., in Equation (1), we multiply the by the betas (parameter
estimates) and sum them across all risk attributes to produce the risk index as the measure of
each respondent’s risk preference. A respondent is risk-averse if the outcome of Equation (1)
is negative; a respondent is risk-seeking if the outcome is positive; and a respondent is riskneutral if the outcome is zero.
4
We adopted the nine key risk attributes pertaining to PPP roads identified in Chung et al. (2010); these are:
traffic risk, financial risk, network risk, force majeure, sovereign risk, risk of unclear project objectives, political
and reputation risk, media risk and risk of public perceptions.
16 of 43
4.
Research method and data description
Following the empirical testing structure developed in Section 2, we designed a CAPI which
contains a SC experiment and a series of non-stated-choice questions that seek out
information on the respondent’s experience with PPPs as well as their subjective views on the
key factors influencing their choice of contract. There are several distinct parts to the survey:
(1) general questions capturing the socio-demographic attributes of respondents and other
contextual effects; (2) stated-choice menus corresponding to a PPP-road-concession setting;
(3) RP questions surveying respondents’ “prior experience” to determine the reference levels
for the derivation of the risk index; (4) questions intended to assess the extent to which
management controls and other factors impact on respondents’ evaluations of the riskiness of
PPPs; and (5) attitudinal questions designed to obtain respondents’ preferences for PPPs.
Before the commencement of each survey, a semi-structured interview was conducted with
each respondent in order to understand their current or past role in the field. All interviews
were transcribed. These qualitative data serve as the reference point to make sense of each
participant’s risk preference and their willingness to take on risks. Data collection was
completed in 2010.
Demographic attributes of respondents
We solicited participants from the mailing list of the Institute of Transport and Logistics
Studies at the University of Sydney based on the criteria that they must have had direct input
in the decision-making process of entering into a PPP road contract. Additional subjects were
recruited through referrals by respondents.
One hundred and one people participated in the survey of which 41 represent the public sector
principal (PRINCIPAL) and 60 represent the private sector agent (AGENT). All have had
direct input into the decision-making associated with entering into a PPP road contract. The
respondents’ experience in PPP years (contracts) runs the gamut of one to 46 years (one to
120 contracts). They bring to this study their contract experience in 32 countries covering six
regions. The distributions in Table 3 show that the AGENT5 cohort represents a good spread
in roles and organisations, suggesting that our results are not biased toward any category.
[Insert Table 3 here]
5
Since this study focuses on the agent’s perspective, we only report data description concerning the AGENT
cohort mainly.
17 of 43
Respondents were asked to list the three most recent projects that they have been involved in.
As presented in Table 4, the locations of projects are diverse, showing that PPPs are an
important and popular procurement method of road infrastructure across the world.
[Insert Table 4 here]
Stated-choice data of incentive contracts as a source of attribute weights
During the interview, each respondent was briefed that they were about to make an
investment choice related to PPP road out of two alternatives. Each alternative has a unique
risk profile and the respondent was specifically instructed to exercise their judgement based
upon their prior experience and their ability to manage the risks associated with the
alternatives. Stated differently, the project risks are presented as the level of downside, upside
and neutrality in the experiment; whether they are acceptable to the decision maker is
dependent on the attitude the decision maker formed from their prior experience with respect
to their ability to manage and (or) trade-off these project risks, taking into account
cooperative efforts of all contracting. The AGENT’s choice in the “1st row” in Figure 3 and
the PRINCIPAL’s choice in the “2nd row” in Figure 3 are used to obtain parameter estimates
under the LCM model form for each attribute for the AGENT and PRINCIPAL respectively.
Five choice scenarios were shown to each respondent, allocated from D-efficient design that
ensures that each choice scenario is equally represented (i.e., balanced) throughout the entire
sample. Full details of how the stated choice experiment was designed are given in Chung
(2012).
[Insert Figure 3 here]
Revealed preference data of “prior experience” as a source of reference levels
After completing the SC experiments, respondents were presented with the opportunity
describe their real experience in terms of risk borne (Figure 4). These RP data determine the
reference levels referred to in a previous section as input into the risk indices.
[Insert Figure 4 here]
Table 5 contrasts the mean value for each risk attribute associated with the PRINCIPAL and
the AGENT6. The last column shows AGENT’s share of each downside risk (upside gain)
6
The three risk attributes of each risk category, i.e., downside, upside and neutrality (omitted from the table)
sum to 100 percent.
18 of 43
relative to PRINCIPAL, while the third (second) column shows AGENT’s (PRINCIPAL’s)
upside-downside trade-off in each risk category. The contrast shows that the AGENT has
suffered mainly from market risks. The last column in Table 5 suggests that the downside risk
associated with the traffic volume (financial return) has been borne by the AGENT is almost
four (three) times more than by the PRINCIPAL, while the AGENT’s shares of the related
upside gains (1.5 times more relative to the PRINCIPAL in both risk attributes) are far less
than the losses they have suffered.
For the remaining contractible risk categories, i.e., network risk, force majeure, and sovereign
risk; in comparison with the PRINCIPAL, the mean shares for the AGENT in upside gain
differ notably from the downside risks they have borne, with the gains relative to losses being
much greater for network risk and much smaller for force majeure, and sovereign risk.
Compared with the PRINCIPAL, the AGENT’s shares in the upside gains related to noncontractible risks (risk of unclear project objectives and political and reputational risk) trade
off the downside risk fairly well (third column of Table 5). But the downside loss associated
with political and reputational risk to the PRINCIPAL (39.20) far outweighs the upside gain
(13.41), with the ratio close to three, suggesting that the PRINCIPAL has suffered badly from
the loss of reputation. In terms of media risk, both the AGENT and the PRINCIPAL have
suffered greatly from negative media coverage, but the PRINCIPAL is the greatest loser,
experiencing three times more in downside risk (41.17 percent) compared with upside gain
(13.10 percent). This evidence highlights the importance of media attention in impacting
contracting parties’ perceptions regarding the social contract of control. Finally, similar to
media risk, both the AGENT and the PRINCIPAL have suffered greatly from risk of public
perception while the magnitude associated with the PRINCIPAL is more severe, being almost
four times more in downside risk (45.37 percent) compared with upside gain (12.68 percent).
This experience illustrates not only how the design of management controls needs to consider
the incentive effects on the agent but also must take into account the resultant agent’s
behaviour, given the legitimacy of the principal in the eyes of the public community. In
testing H2, we will use “media-risk neutral” (omitted from the table) to measure neutral
media attention.
[Insert Table 5 here]
19 of 43
Measures of management controls
A number of survey questions were included to understand respondents’ views on the
importance of the management controls specified in a contract and the factors prevailing in
the external environment that impact on their assessment of the riskiness of PPP road
infrastructure investments (Figure 5). These variables are measured using a one to seven likert
scale, where “one” indicates very unimportant and “seven” indicates very important. Table 6
lists the explanatory variables to be investigated (as described in H1) as potential sources of
systematic influence on the risk index obtained for each AGENT.
[Insert Figure 5 here]
[Insert Table 6 here]
Preferred contract choice as a measure of risk-taking behaviour
Respondents’ decisions to invest in PPPs are captured in the survey through their preferred
choice of contract, which are presented in an ordered outcome scale of seven levels (see
Figure 6). Table 7 shows that there is a much higher proportion of AGENT that prefer PPPs
(71.67 percent in scales 1 and 2 combined) than their PRINCIPAL counterparts (24.39
percent in scales 1 and 2 combined). The scale is related to any PPP project and has not
incorporated risk-bearing. We show below that AGENT’s contract preference will shift
dramatically once actual experience of risk-bearing enters into decision making.
[Insert Figure 6 here]
[Insert Table 7 here]
5.
Empirical results
Risk-transfer and risk aversion
We estimated a LCM by pooling both segments of data, i.e., PRINCIPAL and AGENT.7
After the weights are identified, we multiply each attribute level associated with the RP data
in “prior experience” by the relevant weight and sum these calculations across all attributes
for each of the 101 respondents to produce the sector-specific risk index as specified in
Equation (1). All but one PRINCIPAL, who displays risk neutrality, are risk averse. The
values of risk indices associated with the PRINCIPAL are in the range of -18.53 percent and
zero percent with a mean value of -7.26 percent; the range of risk indices associated with the
7
We specified two latent classes, but changes in classes did not improve model fit nor did it increase the number
of significant parameters. Results are available on request from the first author.
20 of 43
AGENT lies between -56.98 percent and -3.47 percent with a mean value of -23.15 percent;
these risk indices (RIA) measure the agents’ risk aversion. For easy interpretation, we convert
all indices into the positive range by normalising the index of the RIA with the highest relative
value to a base of zero (see Figure 7).8
[Insert Figure 7 here]
Management control mechanisms and agent’s risk aversion
We estimated the following model specifications to test H1 and H2.
H1:
RIA = f ( PRIXCNL, FXPRIX, PERIDC, PENALTY, DURATION, SURSHARE)
-
H2:
+
+
-
-
+
RIA = f ((PRIXCNL, FXPRIX, PERIDC, PENALTY, DURATION, SURSHARE),MEDIANEU)
-/-
+/+
+/+
-/-
-/-
+/+
PRIXCNL = decision rights to pricing control, FXPRIX = fixed-price compensation scheme,
PERIDC = performance measures specified in contract ex ante, PENALTY = financial
penalties to punish not meeting specified performance measures, DURATION = contract
duration, SURSHARE = surplus-sharing between the principal and the agent, and
MEDIANEU denotes neutrality of media attention within the transaction environment as
measured by a ratio data of “prior experience” provided by respondents in the media risk
(risk-neutrality) category. A descriptive summary and the correlation matrix of these
independent variables are presented in Table 8. -/- (+/+) = strengthening effects by the
inclusion of MEDIANEU.
[Insert Table 8 here]
The results of a multivariate regression reported in Table 9 show the association between RIA
and the identified management controls by contract. All parameter estimates are significant at
the one percent level except PRIXCNL. The significant parameter estimates are all of
expected sign.
[Insert Table 9 here]
8
From this point onward, all analysis will be based on normalised indices, i.e., risk aversion indices are
presented in positive values; higher value means greater risk aversion.
21 of 43
These results shed important light on the organizational architecture of PPPs. Fixed-price
compensation schemes will not motivate agent’s incentive to take risk. The message to the
principal is that the trade-off between loss of incentive intensity and budget certainty needs to
be weighed ethically because PPP roads are not just projects of the procuring authority; they
have long-lasting implications for generations of the public. Performance measures specified
ex ante in a risky environment characterised with high task and demand uncertainties worsen
agent’s risk-taking incentive. The expected negative sign of PENALTY confirms that in the
presence of double moral hazard, recourse to court-ordering can reduce an agent’s risk
aversion. The negative PENALTY also highlights the substitute nature of control and trust
(Vosselman and van der Meer-Kooistra, 2009); the potential lack of trust between the agent
and the principal has led to the agent’s greater reliance on formal control through the legal
system. The expected negative sign of DURATION suggests that long contract duration
protects the agent from opportunistic breach by the principal and hence enhances agent’s risktaking incentive. The positive sign of SURSHARE suggests that conditions on the agent’s
earning cap by way of surplus expropriation weaken incentive intensity.
We analysed the correlation matrix (Table 8) and interview transcripts to investigate the
possible cause for the insignificant PRIXCNL. The correlation between PRIXCNL and RIA is
trivial compared with PRIXCNL’s correlations with other variables, while its correlation with
MEDIANEU is the strongest. Reference to interview data confirms our predication that strong
public adversity to organisation behaviour can make a decision right to pricing control
counter-effective. In our case, agents were reluctant to exercise this right as they did not wish
to be seen as using their right for private gain at the expense of motorists, which may have
been seen as violating the social contract. Both observations underscore our proposition that if
media attention can be neutralised, the agent would be more willing to exercise their decision
right.
Based upon the correlations in Table 8, we use the interaction term PRIXCNL*MEDIANEU
in the following empirical model to test H2.
RIA = · · · · · ! · " # · $$%
The results reported in the second column of Table 9 support H2. After interacting PRIXCNL
with MEDIANEU, the effect of PRIXCNL becomes highly significant at the one percent
22 of 43
level. The negative sign indicates that as long as media attention is neutralised, delegating the
agent the decision right to pricing control has a strong effect on reducing their risk aversion.
Furthermore, the adjusted R2 in the second column of Table 9 (0.387) is much higher than the
adjusted R2 (0.243) in the first column of Table 9, suggesting that incorporating MEDIANEU
in the model strengthens the explanatory power of the independent variables in explaining
RIA.
The evidence that social approval through media attention explains the effects of these
management controls on agent’s risk-taking incentive over and above TCE and agency
theory, emphasises the complementary power of informal management controls. The
organizational architecture of PPPs not only needs to consider the choice of control
mechanisms based upon an understanding of each party’s risk preferences, but all parties need
be wise to the opportunity to use the media as a way of fulfilling obligations under the
implicit social contract within the transaction environment.
Additional analysis: management controls and investment decisions
We undertook additional analyses to investigate the extent to which management control
choices, through their effects on decision maker’s risk-taking incentives, are determinants of
risk-taking behaviour. Following Sedatole et al. (2012), we measure risk-taking behaviour
using the likelihood of agent’s decisions to invest in risky projects. We apply the following
ordered logit form using the derived risk index to test:
Ci_AGENT = &' &( · )*+
where Ci_AGENT denotes the agent’s contract preference and data are as reported in Table 7;
with model results presented in Table 10.
Testing a model that has an ordered scale as the dependent variable requires an ordered
response model that recognises the nonlinearity of a ranking scale and defines points on the
observed rating scale as thresholds (Jones and Hensher, 2004). The ordered logit model has a
constant term, one, as the first right hand side variable, therefore one of the threshold
parameters (µs) is not identified, so µ0 is normalised to zero. A direct interpretation of the
parameter estimates in Table 10 is not possible given the logit transformation of the outcome
dependent variable required for model estimation. We therefore provide the marginal effects
of the two end scales, i.e., Y=1 (PPP is the most preferred choice) and Y=7 (PPP is the least
preferred choice), defined as the derivatives of the probabilities, to explain the influence a one
23 of 43
unit change in an independent variable, i.e., risk aversion, has on the probability of selecting a
particular outcome, i.e., choice of investment, ceteris paribus.
[Insert Table 10 here]
The results suggest that RIA has a strong statistical impact, i.e., both parameter estimates are
significant at the one percent level, on the probability of choosing PPP as the most preferred
investment. The negative parameter estimate for Prob(Y=1) indicates that a one unit change in
the mean of PRVRI leads to a -0.56 change in the probability of Y=1, i.e., one unit increase in
AGENT’s risk aversion reduces the probability of PPP being favoured by them with a
magnitude of 56 percent, ceteris paribus; the positive parameter estimate for Prob(Y=7)
suggests the otherwise although at a much less scale, i.e., increase in one unit of the risk
aversion increase the odds of non-PPPs being chosen by four percent, ceteris paribus.
Overall, the results suggest that the greater the risk aversion of the AGENT the less they
prefer PPP investments. Stated differently, degree of the AGENT’s risk aversion is negatively
associated with their decision to invest in PPPs.
These findings are consistent with Sitkin and Weingart (1995) which provided clear support
for the importance of risk perception as a crucial influence on individual risk-taking
behaviour. We have shown choices of management controls can affect agent’s risk aversion
measured by SC data on the perceived riskiness of two contracts on offer. If the objective of
the principal is to encourage agent’s risk-taking behaviour (investment choice), then the
design of management controls should consider the way in which control choices can modify
agent’s risk-taking incentives (risk aversion).
6.
Conclusions
This paper has empirically tested a framework of management controls integrating TCE,
agency theory and social contract in order to incentivise agent’s risk-taking incentives within
a choice experiment setting. Within the context of the PPP procurement contract, we examine
the effects of agency theory on management controls formalised in the contract on enhancing
the agent’s incentive to take risks. We have provided evidence on how fixed-price
compensation, performance measures, financial penalties, contract duration, and surplussharing can affect agent’s risk-taking incentive. Our results confirm that social approval
through neutral media attention strengthens the effects of these management controls on
24 of 43
agent’s risk-taking incentive over and above TCE and agency theory, and decision rights to
pricing control only matters when social approval is taken into account.
From a practical standpoint, consideration of double moral hazard raises an important
question for both sides of the contractual relationship to rethink how the organizational
architecture of PPPs can achieve the intended risk-sharing outcomes. A number of strong
messages emerge: i) organizational design must go beyond formal management controls to
consider the effects of informal controls on non-contractible effort; ii) there is lack of trust
between the agent and the principal as reflected in agent’s greater reliance on penalty clauses
for court-ordering for conflict resolution; if the trust issue remains unresolved it is unlikely
the long-term cooperative effort can be sustainable; and iii) risk behaviour can be modified by
the design of management controls. Supported by the coverage and diversity of the experience
and knowledge of the respondents who took part in this study, the findings are of significant
international relevance.
The study, however, is not without limitations. First, we acknowledge that trust plays a
significant role in sustaining long-term cooperative effort in the PPP environment, but we
have not undertaken an empirical examination of its effects. Second, empirical studies on the
role of management controls on ex post outcomes are scarce; Anderson and Dekker (2005)
and Susarla (2012) are two exceptions. Although our test of this relationship from the agent’s
perspective has made a small contribution to the void, the respondents who participated are
mainly involved in the decision-making phase of contract entering, with the exceptions of 24
primary decision makers and six tollroad operators who also have been actively engaging in
operating PPP projects. The limitation is that some respondents may not have experienced the
incentive effects associated with the management controls over the operational phase. A
further limitation is associated with the inherent nature of PPPs. Their long contract duration
means that there is little opportunity to collect data on ex post outcomes before these contracts
have run their course. These limitations justify our reliance on behavioural data reflecting
respondents’ perceptions of accumulated experiences to date. In spite of these drawbacks, the
results are consistent with Sitkin and Weingart (1995) who find that risk perceptions are
important determinants of risk-taking behaviour. These findings support the validity of our
approach, relying on respondents’ perceptions to draw conclusions on the extent to which
management controls motivate their risk-taking incentives and hence their risk-taking
behaviour.
25 of 43
In closing, two emerging opportunities – the recently concluded M4 motorway in Australia
and the Dartford Bridge in the UK, can provide critically needed evidence on ex post
outcomes in order to attest the predicted role of management controls in risk and effort
aversions. We leave the investigation of trust and the ex post outcomes of management
controls for future research.
26 of 43
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Table 1: Characteristics of PPPs in the road sector
Characteristics of the
transaction
Asset-based service
Outcome-based performance
and payment
Site specific road asset built
and financed by the agent
Intense capital investment
Lengthy period to recoup
capital investment
Characteristics of the
transaction environment
Uncertainty about future
contingencies
Degree of market risks cannot
be estimated reliably
Institutional environment (rules,
systems and social norms)
Characteristics of the
transaction parties
Heterogeneity in objectives
Heterogeneity in risk preference
Two-way information
asymmetry
Double moral hazard
31 of 43
Table 2: Definitions of Risk Attributes
Traffic risk
Financial risk
Network risk
force majeure
Downside risk of X%
indicates that there is a X% probability that
the actual traffic volume will be below forecast
changes in economic conditions will adversely
affect the financial returns the tollroad is expected
to earn
future transport network developments by
government may reduce traffic flows to the
tollroad
the occurrence of uninsured events may worsen
the tollroad’s performance
Sovereign risk
future changes in government policies may
worsen policy fragmentation across different
levels of government
Risk of unclear
project objectives
project objectives are unspecified or are unclear
to contracting parties and the community
Political and
reputation risk
contracting parties will not deliver the project in
the public interest, the public sector is seen as
offloading public accountability, thus causing
public resentment to the PPP scheme and the
project
the media is critical of the PPP scheme/project,
thus exposing the tollroad to poor publicity
Media risk
Risk of public
perception
public acceptance of private ownership of
tollroad, public expectations of benefits derived
from the tollroad, and of both sectors’
commitment to the community are poor
Risk neutral of Y%
indicates that there is a Y% probability that
the actual traffic volume will be meeting the
forecast
changes in economic conditions will make no
difference to the financial returns the tollroad is
expected to earn
future transport network developments by
government may have no major impact on traffic
flows to the tollroad
in the event that uninsured events occur, the other
party will agree to a transparent approach to
redress the aggrieved party
future changes in government policies may not
have an effect on the existing overall PPP policy
framework
project objectives are clearly specified and there
are clear communications amongst contracting
parties and the community
political and reputational risk is not of significant
concern
the media is neutral to the PPP scheme/project,
thus resulting in low publicity for the tollroad
public perceptions of private ownership of
tollroad, public expectations of benefits derived
from the tollroad, and of both sectors’
commitment to the community are of insignificant
concern
Upside gain of Z%
indicates that there is a Z% probability that
the traffic volume will be above the forecast
changes in economic conditions will increase
the financial returns the tollroad is expected to
earn
future transport network developments by
government may increase traffic flows to the
tollroad
all events are well insured, or if not both parties
are willing to negotiate in good faith to redress
the aggrieved party
future changes in government policies may
result in a more consistent and coherent PPP
policy framework across all political
jurisdictions
project objectives are made clear to the market
and project deliveries will adhere to stated
objectives throughout all project phases
all parties understand this risk and are willing to
internalise this risk within its own sector as well
as to collaborate with the other party to resolve
public resentment
the media is supportive to the PPP
scheme/project, it conveys to the community the
public benefits of the tollroad, resulting in
welcome publicity
the public welcomes private ownership of
tollroad and public expectations of benefits
derived from the tollroad, and of both sectors’
commitment to the community are high
32 of 43
Table 3: Demographic attributes of AGENT#
Role
Primary decision maker
Tollroad operator
Debt financier
Equity investor
Consultant#
TOTAL
No
24
6
5
12
13
60
Experience in years
(no. of projects)
15 (25)
12 (10)
14 (21)
14 (10)
12 (27)
Organization
No
Tollroad company
Construction company
Commercial bank
Investment bank
Consultancy
#
15
17
9
11
8
60
Multiple roles exist in an organization. For instance, a primary decision maker may come from a construction company
while the company may also contribute significant equity finance to the project and hire consultants for advice.
Table 4: Project Experience in PPP roads (Regions and Countries)
REGION
Africa (2
countries)
Asia-Pacific (9
countries)
Caribbean (2
countries)
Europe (13
countries)
COUNTRY
South Africa
Mozambique
Australia
Bangladesh
India
Indonesia
Korea
New Zealand
Russia
Thailand
Vietnam
Jamaica
Puerto Rico
Austria
Belgium
Croatia
REGION
Europe
(continued)
North
America (3
countries)
South
America (3
countries)
Total
COUNTRY
France
Greece
Hungry
Ireland
Italy
Netherlands
Poland
Portugal
Spain
UK
Canada
Mexico
USA
Chile
Brazil
Colombia
32
33 of 43
Table 5: Prior Experience of Risk Borne (Contrast of Mean)
Traffic_downside risk (TRAD)
Traffic_upside gain (TRAU)
Financial_downside risk (FIND)
Financial_upside gain (FINU)
Network_downside risk (NETD)
Network_upside gain (NETU)
Force majeure_downside risk (FORD)
Force majeure_upside gain (FORU)
Sovereign_downside risk (SOVD)
Sovereign_upside gain (SOVU)
Unclear project objectives_downside risk (UNCD)
Unclear project objectives_upside gain (UNCU)
Political and reputational_downside risk (POLD)
Political and reputational_upside gain (POLU)
Media_downside risk (MEDD)
Media_upside gain (MEDU)
Public perception_downside risk (PUBD)
Public perception_upside gain (PUBU)
PRINCIPAL
Mean (%)
14.15
11.37
13.41
15.20
19.32
21.15
21.88
5.98
23.90
7.93
33.24
12.20
39.20
13.41
41.17
13.10
45.37
12.68
AGENT
Mean (%)
54.07
17.38
45.47
22.30
22.78
31.50
14.57
8.12
17.40
9.63
18.60
16.43
21.87
21.03
25.13
18.05
27.63
20.57
Ratio
AGENT/PRINCIPAL
3.82
1.53
3.39
1.47
1.18
1.49
0.67
1.36
0.73
1.21
0.56
1.35
0.56
1.57
0.61
1.38
0.61
1.62
Table 6: Survey Questions and Measures of Management Controls
Question
Measure (1= very unimportant; 7=very important)
PRIXCNL
c
7 indicates that the ability to exercise this decision right is a very
important consideration in evaluating the riskiness of PPPs.
FIXPRIX
k
7 indicates that the fixed-price compensation scheme is a very important
consideration in evaluating the riskiness of PPPs.
PERIDC
e
7 indicates that the performance measures prescribed in contract are a
very important consideration in evaluating the riskiness of PPPs.
PENALTY
f
7 indicates that the penalty clauses are a very important consideration in
evaluating the riskiness of PPPs.
DURATION
d
7 indicates that the long contract duration is a very important
consideration in evaluating the riskiness of PPPs.
SURSHARE
j
7 indicates that the revenue-sharing between the principal and the agent
is a very important consideration in evaluating the riskiness of PPPs.
34 of 43
Table 7: Preferred Investment Choice – PRINCIPAL versus AGENT
Scale#
Freq.
1
2
3
4
5
6
7
Mean
#
3
7
7
18
3
3
0
5.86
PRINCIPAL
Accumulative Accumulative
Freq.
%
3
7
10
24
17
41
35
85
38
93
41
100
41
100
Freq.
22
21
4
6
1
5
1
8.57
AGENT
Accumulative Accumulative
Freq.
%
22
36.67
43
71.67
47
78.33
53
88.33
54
90.00
59
98.33
60
100.00
1=PPP is the most preferred; 7=PPP is the least preferred choice
35 of 43
Table 8: Correlations between risk aversion and management controls by incentive and social contracts (N=550)
1
2
3
4
5
6
7
8
9
RIA
PRIXCNL
FIXPRIX
PERIDC
PENALTY
DURATION
SURSHARE
MEDIANEU
PRIXCNL*MEDIANEU
Mean
Mode
Min
Max
1
2
3
4
5
6
7
8
9
0.238
0.855
0.873
0.291
0.182
0.982
0.091
56.89%
50.82%
-4
7
7
4
5
4
60%
240.00%
0.035
1
1
1
1
1
1
0%
0%
0.570
7
7
7
7
7
7
100%
100%
1
0.007
0.165
0.133
-0.142
-0.235
0.197
-0.507
-0.405
1
0.152
-0.190
-0.207
-0.056
-0.228
0.231
0.645
1
0.124
0.039
0.356
-0.069
-0.002
0.068
1
0.632
0.087
0.354
-0.141
-0.182
1
0.179
0.039
0.028
-0.077
1
0.043
0.035
0.003
1
-0.104
-0.164
1
0.850
1
36 of 43
Table 9: The relationship between risk aversion and management controls by incentive and social contracts
H1
Expected
sign
Constant
Parameter t-value
0.456 (12.81) ***
H2
Parameter
0.517
t-value
(17.03) ***
PRIXCNL
-
-0.011
FIXPRIX
+
0.103
(6.98) ***
0.113
(8.62) ***
PERIDC
+
0.071
(5.27) ***
0.052
(4.20) ***
PENALTY
-
-0.106
(-7.06) ***
-0.097
(-7.23) ***
DURATION
-
-0.313
(-8.71) ***
-0.314
(-9.74) ***
SURSHARE
+
0.079
(4.68) ***
0.066
(4.33) ***
PRIXCNL*MEDIANEU
-
Adjusted R2
(-0.80)
-0.001 (-11.31) ***
0.243
0.387#
#
Adjusted R2 is higher than that in that in H1 (0.24305), supports H2.
N = 550 (50 pilot observations to which FIXPRIX was not surveyed are excluded).
***, **, * indicate a p value of =< 1%, 5%, 10%.
37 of 43
Table 10: Risk Aversion and Choice of Incentive Contract
Dependent
Variable
Ci_AGENT
Independent
Variable
Constant
RIA
Threshold parameters
MU (1)
µ (1 to 2)
MU (2)
µ (2 to 3)
MU (3)
µ (3 to 4)
MU (4)
µ (4 to 5)
MU (5)
µ (5 to 6)
MU (6)
µ (6 to 7)
Marginal effects
Independent variable
RIA (at mean)
AIC
LL function
N
Parameter
t-value
Hypothesis
Support
Model
-0.001
2.419
(-0.01)
(3.77)
1b
NO
Ordered
logit
0
1.505
1.870
2.614
2.787
4.675
(18.22)
(20.15)
(21.14)
(20.98)
(14.69)
Prob (Y=1)
-0.560
t-value
(-3.75)
Prob (Y=7)
0.038
t-value
(3.30)
1787.61
-886.80
600 (N= 60PRVP×5experiments×2contracts)
38 of 43
TCE: management
controls to enhance
adaptations
Management controls
Agency theory: management
controls embedded with riskbearing implications to foster
incentive alignment
Social contract: informal control
implicit in social contracts to ensure
behaviour is consistent with social
norms
Figure 1: Framework of Triangulation on Management Controls
39 of 43
Agent’s risk aversion
H1a: (-)
Delegation of
decision right to
pricing control
H1b: (+)
H1c: (+)
Fixed-price
compensation
H1d: (-)
Performance
measures
Penalties
H1e: (-)
Contract
duration
H1f: (+)
Surplus-sharing
Mitigate authority misuse
H2: strengthens effects of management controls on agent’s risk aversion
Neutral media attention from transaction environment
Figure 2: Effects of management controls on agent’s risk aversion
40 of 43
1st row
2nd row
Figure 3: The Stated Choice Experiment – Contract Choice
Figure 4: The Reference Levels – Prior Experience
41 of 43
Figure 5: Other Factors Influencing the Assessment of Riskiness of PPPs
Figure 6: Prefer Contract Choice as the Measure of Risk-taking Behaviour
42 of 43
Figure 7: Risk Indices: PRINCIPAL vs. AGENT
43 of 43