12-11-2012, 04:59 PM
Agency Theory
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Agency Theory
This note considers the simplest possible organization: one boss (or “Principal”)
and one worker (or “Agent”). One of the earliest applications of this Principal-Agent
model was to sharecropping, where the landowner was the Principal and the tenant
farmer the Agent, but in this course we will typically talk about more familiar
organization structures. For example, we might consider a firm’s shareholders to be the
Principal and the CEO to be the Agent. One can also enrich the model to analyze a chain
of command (i.e., a Principal, a Supervisor, and an Agent), or one Principal and many
Agents, or other steps towards a full-fledged organization tree.
The central idea behind the Principal-Agent model is that the Principal is too busy
to do a given job and so hires the Agent, but being too busy also means that the Principal
cannot monitor the Agent perfectly. There are a number of ways that the Principal might
then try to motivate the Agent: this note analyzes incentive contracts (similar to profit
sharing or sharecropping); later notes discuss richer and more realistic models.
Taken literally and alone, the basic Principal-Agent model may seem too abstract
to be useful. But we begin with this model because it is an essential building block for
many discussions throughout the course—concerning not only managing the incentives
of individuals but also managing the incentives of organizational units (such as teams or
divisions) and of firms themselves (such as suppliers or partners). Furthermore, this
abstract model allows us to consider the nature and use of economic models more
generally, as follows.
An Introduction to Economic Modeling
We will use several economic models in this course, so it may be helpful to begin
by describing what an economic model is and what it can do. We will defer discussion of
whether such models are useful until after we have a few under our belts!
An economic model is a simplified description of reality, in which all assumptions
are explicit and all assertions are derived. Such a model can produce qualitative and/or
quantitative predictions. A qualitative prediction is that “x goes up when y falls.” A
quantitative prediction is that x = 1/y. A model’s (qualitative or quantitative) predictions
are useful when they are robust within the environment(s) of interest.
Quantitative predictions often hinge on specific assumptions from the model. If
the model will be applied in one particular environment (such as a queuing model
describing the lines at the Refresher Course, or the Black-Scholes model for option
pricing) then the specific assumptions need to match the environment fairly closely,
otherwise the quantitative predictions will not be useful in that environment. One might
call this “engineering modeling” rather than “economic modeling.”
Qualitative predictions are often more robust, in two senses. First, qualitative
predictions may continue to hold if one makes small changes in the model’s specific
assumptions. For example, a model’s quantitative predictions might depend on whether a
particular probability distribution is normal, exponential, or uniform, but the model’s
qualitative predictions might hold for any single-peaked (i.e., hill-shaped) distribution,
including the three mentioned above as well as others.
Qualitative predictions can also be robust in a second (and, for our purposes, more
important) sense: a simple model’s qualitative predictions may be preserved even if one
adds much more richness to the model. The major points we will derive from the
economic models in this course are robust predictions in this latter sense. That is, adding
greater richness and realism to these models will certainly change the models’
quantitative conclusions, but the major points we derive from the simple models will still
be part of the package of qualitative conclusions from the richer models.
Pay for Performance: The Basic Principal-Agent Model
During this course we will frequently use the term “incentives.” In some settings
we will mean a cash payment for a measured outcome, but in other settings our use of
this term will be much broader. Lest anyone be misled or disaffected by the narrowness
of the former meaning, we will start our discussion of the basic Principal-Agent model by
attempting some broader definitions: let “rewards” mean outcomes that people care about
(not just dollars), let “effort” mean actions that people won’t take without rewards (not
just hours worked), and let “incentives” mean links between rewards and effort (not just
compensation contracts). We will refine these definitions throughout the course. For now
we simply note that, according to these definitions, there are clearly lots of incentives out
there, even if there are many fewer dollar-denominated incentive contracts.
To be more precise about rewards, effort, and incentives, we turn now to the
elements of the basic Principal-Agent model: (A) the technology of production, (B) the
set of feasible contracts, © the payoffs to the parties, and (D) the timing of events.
A. The Technology of Production
In this simple model, the production process is summarized by just three
variables: (1) the Agent’s total contribution to firm value (or, for now, the Agent’s
“output”), denoted by y; (2) the action the Agent takes to produce output, denoted by a;
and (3) events in the production process that are beyond the Agent’s control (i.e.,
“noise”), denoted by ε.
(1) The Agent’s contribution to firm value, y: In the sharecropping context, the
Agent’s contribution is simply the harvest. In the CEO context, one definition of the
Agent’s contribution is the change in the wealth of the shareholders through appreciation
in the firm’s stock price. For workers buried inside an organization, it is sometimes very
difficult to define and measure a contribution to firm value. Later in this note we will
discuss alternative objective performance measures (which sometimes raise “get what
you pay for” issues); in later notes we will discuss subjective performance measures.
(2) The action the Agent takes to produce output, a: The most straightforward
interpretation is that the Agent’s action is effort. This interpretation may be reasonably
accurate in the sharecropping context and for low-level workers in large organizations.
For a CEO, however, one should think of “effort” not in terms of hours worked but rather
in terms of paying attention to stakeholders’ interests—for example, does the CEO take
actions that increase shareholder value (versus taking actions that indulge pet projects)?
Later in this note we will consider “multi-task” situations, in which the Agent can take
some actions that help the Principal but also others that hurt. For example, the Agent
might increase current earnings in two ways: by working hard to increase sales and cut
production costs, but also by cutting R&D and marketing expenses, thereby hurting
future earnings.