The Politics of Biased Information
John W. Patty∗
Department of Government
Harvard University
October 7, 2008
Abstract
The effects of any important political decision are always to some degree uncertain. This
uncertainty may be ameliorated by the collection of policy-relevant information. Predictably, if
such information is biased, then political decisions based on that information will be biased as
well. This paper explores the converse of this statement: if the policymaker is biased, will the
information provided to him or her also be biased? It is shown in this paper that, in equilibrium,
information provided to a sufficiently biased policymaker will inherit the policymaker’s bias.
Accordingly, the provision of biased policy-relevant information is not evidence of an attempt
to produce biased policy decisions. The implications of the theory are examined within the
context of modern administrative policymaking within the United States Federal Government.
∗
I thank Steve Callander, Randy Calvert, Sean Gailmard, John Geer, Maggie Penn, Matthew Stephenson, and three
anonymous reviewers for many helpful comments and suggestions on earlier drafts of this paper, as well as audience
members at MIT, the 2007 Midwest Political Science Association, and the 2007 Public Economic Theory meetings.
As usual, I am responsible for all remaining errors.
1
Information provision is a central, practical feature of political and economic governance. The
reality of modern policymaking is that more decisions than not depend upon, and are justified
by, information made available to the decision-maker by one or more of his or her agents. This
paper examines a model of information provision by a strategic expert who is aware of the ultimate
decision-maker’s preferences. I show that, even if both the decision-maker and the expert prefer
unbiased information, information collection in equilibrium will be slanted in favor of the decisionmaker’s preferences over what actions to take based on that information.
The theory’s findings are based on a straightforward logic: policy-relevant information should
be collected in proportion to the likelihood that it will actually be used for policymaking. Accordingly, a principal’s biases in terms of the types of policies that he or she wishes to pursue should
be reflected in the relative focus of information gathered for his or her use – even if this information is gathered by an agent who does not share the principal’s biases. This has far-reaching
implications for the study of political information specifically and political institutions generally.
For example, scholars who have examined bureaucratic and administrative procedures have argued
that seemingly innocuous informational requirements may have significant policy effects and, accordingly, may themselves be the result of policy-seeking behavior by elected legislators and/or
executives.1 The results presented in this paper extend this literature by providing a strategic theory of information collection. One of the main contributions in this regard is a demonstration of
why informational deck-stacking (e.g., Bendor et al. (1985, 1987), Balla and Wright (2001)) can
affect policy outcomes in a credible fashion. However, the results also indicate both a difficulty
regarding the empirical detection of informational deck-stacking – the “informational deck” may
be “stacked” by the presence of a politically biased principal – as well as a normative concern with
this form of political control of policymaking. In short, the direct control of policy-relevant information in pursuit of manipulating final policy outcomes may be inefficient when this control is
1
Consider, for example, McCubbins et al. (1987, 1989), Epstein and O’Halloran (1994, 1995, 1996, 1999), Bawn
(1995), de Figueiredo, Jr et al. (1999), Gordon and Hafer (2007). Recently, scholars have paid more attention to the
empirical nature of bureaucratically-generated and utilized information (Engstrom and Kernell (1999), Krause et al.
(2006), Krause and Corder (2007)
2
exercised by an agent other than the individual responsible for choosing what policy to implement.
If the deck is stacked in such a way as to frustrate the policy aims of an otherwise independent
decision-maker, much of the information provided to that decision-maker may have little or no
impact on the final policy choice. Insofar as information is costly to obtain and/or analyze, informational deck-stacking of this type will be suboptimal in instrumental terms.
As a means of explicating the basics of the theory, consider the following simple example.
An elected official is faced with deciding between two public projects, R and S: “Roads” and
“Schools.” The elected official has two motivations in mind when choosing which project to pursue: economic growth and reelection. The politician believes that S will probably generate higher
economic growth in the long-run, but also knows that, all other things equal, R represents an immediate short-run economic stimulus and will accordingly have a stronger positive impact on his
or her probability of being reelected. To be slightly more precise, suppose that the official strictly
prefers building roads to building schools in the sense that he prefers to build schools only if the
expected economic gain from building schools exceeds the expected economic gain from building
roads by some positive amount, b > 0.
Before choosing which project to pursue, the official asks his economic adviser to collect information about how best to implement each project. Once collected, this information will also
generate estimates of the impacts of both projects on economic growth. Given the different natures
of the two projects, the adviser must then decide how much effort to spend on estimating the economic impact of, and optimal approach to, each project. Finally, the adviser, as an unelected civil
servant and therefore insulated against electoral cycles, is simply interested in economic growth.
In other words, the adviser is presumed to be unbiased in the sense that he or she prefers that the
official pursue the project that, when implemented optimally, offers the greatest economic growth.
In this paper, it is shown that, if the official is sufficiently biased, then the unbiased adviser
should collect more information about the action that the bias favors. In other words, if the elected
official’s bias in favor of building roads, b, is large enough, the agent should gather more information about how to best build roads, R, than about how to best build schools, S. The theory’s
3
principal conclusion is that, even in the absence of explicit controls such as contractual obligations
to, or potential reprisals by, the elected official, the adviser should bias his or her information collection in a manner that confirms the political principal’s ex ante bias. Furthermore, and somewhat
surprisingly, this is exactly what a third party with the adviser’s preferences, such as a legislature
or a voter, would have the adviser do as well. The key to this result is the fact that, at the end of
the day, the “buck stops with” the elected official: he or she makes the final decision about which
project to pursue. The logic behind why the adviser voluntarily biases information gathering is
that the pursuit of unbiased information for its own sake by the adviser is counterproductive for the
adviser in the sense that it frequently does not sway the official’s choice of which action to take but
does, due to opportunity costs, reduce the quality with which the official implements his ex ante
preferred choice.
This theory has several interesting and important implications: first, the provision of biased
information is not evidence of de facto or de jure manipulation of information gathering. Second,
the results indicate that elimination of biased information gathering is difficult and, more controversially, normatively suspect in terms of aggregate welfare. Finally, since the seminal work of
Calvert (1985), it has been recognized that political principals may have an incentive to seek out
the opinions of “biased” agents (see also Prendergast (1993)). The theory offered here presents the
“supply side” calculus of biased information provision: whereas Calvert’s model presumes that a
political principal with bias will seek out advisers with similar biases because the advice offered
by similarly biased agents will be more likely to be effective in affecting the principal’s final decision, the results presented below confirm that an instrumentally rational adviser – even one who is
unbiased – may have an incentive to provide biased information to the principal.
1
Information Collection with Multiple Tasks
I examine a situation in which a political principal, P , will choose one of two tasks, either x or y,
to undertake after receiving policy-relevant and verifiable information from an agent, A, about a
4
two-dimensioanl state of nature, ω ∈ R2 , which will determine both how the principal should best
perform each task and, by implication, which task the principal should choose to pursue. The task
chosen by the principal is denoted by τ ∈ {x, y}. After choosing τ , the principal must choose a
policy to pursue within that task. This policy choice is represented as a real number, zτ ∈ R. After
the policy is chosen, it and the state of nature are revealed to both players and the game concludes.
In summary, decision-making proceeds within the model as follows:
1. The agent collects verifable information and presents it to the principal.
2. The principal observes information and chooses a task to pursue, τ ∈ {x, y}.
3. The principal sets policy on the chosen task, zτ ∈ R.
4. The state of nature, ω, is revealed and all players receive payoffs based on ω and zτ .
Information Collection and Provision. Prior to the principal’s choices of τ and zτ , the agent, A,
collects information about ωx and ωy as follows. For simplicity of comparison and presentation,
I assume that both components of the state of nature, ωx and ωy , are independently distributed
according to the Normal distribution, each with mean zero and variance σ 2 > 0. The agent chooses
two fixed sample sizes, nx ≥ 0 and ny ≥ 0, the pair of which is denoted by n ≡ (nx , ny ). For lack
of a better term, a pair of non-negative integers n is referred to as the agent’s information collection
strategy. The nx observations about ωx (respectively, the ny observations about ωy ) are each drawn
from an unbiased Normal distribution with mean ωx (respectively, ωy ) and variance υ 2 > 0. Each
observation about either ωx or ωy costs the agent a fixed and constant cost k > 0.2 Thus, the total
cost to the agent A is k · (nx + ny ).
2
It is straightforward to incorporate different cost structure, but such an extension would not alter the results
presented below in any substantively interesting way. Additionally, note that assuming that the variances of ωx and
ωy are equal and that the variances of the sampling distributions are equal represents a useful baseline: it implies that
there exists no ex ante informational difference between the two actions. As discussed briefly later in the paper (p.
15), weakening this assumption is straightforward. Furthermore, though descriptive interesting in some ways, such an
extension of the model leads to no additional insights for the purposes of this paper. In particular, from the standpoint
of considering the motivations for gathering relatively more information about one task than the other, alterations of
the relative informativeness of the two information-collection processes enter the agent’s in a straightforward fashion.
5
Political Preferences & Bias.
As mentioned earlier, the focus of the paper is on the effect of the
principal’s preferences on the agent’s choice of how to collect information about ω = (ωx , ωy ).
The principal P ’s preferences are assumed to be represented by the following utility function:


 −(z − (ωx + b))2 − ω 2
y
uP (τ, z) =

 −(ω − b)2 − (z − ω )2
x
y
if p = x
,
(1)
if p = y
where b ≥ 0 is a commonly known parameter. Notice that when P chooses τ = x, he or she is
implicitly setting policy zy = 0, and vice-versa if P chooses τ = y. Thus, on each dimension,
zero represents the public policy which follows from the principal “doing nothing” in the policy
area in question (i.e., the status quo). In order to capture the notion of an unbiased agent, I assume
that the agent A’s most-preferred policy after task choice τ is equal to ωτ . These preferences are
represented by the following utility function:


 −(z − ωx )2 − ω 2 − k(nx + ny )
y
uA (τ, z, n) =

 −(ωx )2 − (z − ωy )2 − k(nx + ny )
if p = x
.
(2)
if p = y
Comparing the players’ preferences, the parameter b can interpreted as the principal’s “bias” toward task τ = x in the following way. Throughout, I assume that both ωx and ωy are Normally
distributed with mean zero and that this is common knowledge among P and A. Accordingly, in
the absence of additional information, the principal would prefer to choose τ = x and set z = b.
Put another way, the principal is indifferent between (optimal completion of) the two tasks if and
only if b = 0. Note that the principal’s choices of both the task, τ , and his or her subsequent policy
choice, zτ , are based on both the information gathered and the principal’s political preferences,
as represented by b. In order to clarify the presentation of the results, the information-gathering
agent is presumed to be politically neutral in the sense that he or she has no ex ante preference for
either x or y. In addition to clarifying the results, it is straight-forward to verify that the parameter
b can be simply reinterpreted as the political principal’s bias relative to the bias of the agent. As
6
an important aside, note that when the framework is viewed in this way, the paper’s main result
indicates that any fixed level of bias on the part of the information-gathering agent can be negated
and reversed by a sufficiently large bias of the principal in the determination of the agent’s optimal
information gathering strategy.
After the agent chooses n = (nx , ny ), the principal P observes the sample means, ω̄ ≡
(ω̄x , ω̄y ). It is straightforward to show that the principal’s optimal policy choice, conditional on
choosing task τ ∈ {x, y}, the principal’s bias b, the observed sample mean ω̄τ , the sample size nτ ,
and the variances σ 2 and v, is3
zτ∗ (b, ω̄τ , nτ , σ 2 , v) =



nτ
v
1
σ2
+
nτ −1
v
ω̄τ + b
if p = x


nτ
v
1
σ2
+
nτ −1
v
ω̄τ
if p = y
.
(3)
The optimal task choice for the principal, given his or her bias b, the pair of sample sizes n, the
observed samples means ω̄, and the variances σ 2 and v, is4


 x
∗
2
τ (ω̄, n, σ , v) =
 y

if ω̄x2 + b2 − ω̄y2 +
1
σ2
+
ny −1
v
−
1
σ2
+
nx −1
v
≥0
if ω̄x2 + b2 − ω̄y2 +
1
σ2
+
ny −1
v
−
1
σ2
+
nx −1
v
<0
.
(4)
(Because I will not be varying σ 2 or v in the remainder of the analysis and to condense the notation,
I drop these parameters from the notation where possible in the remainder of the paper.) For any
pair of sample sizes n and bias b, let α(x; n, b) denote the ex ante probability that the principal will
choose task τ = x after observing two samples of sizes n = (nx , ny ). Formally, this function is
defined by
"
α(x; n, b) = Pr ω̄y2 − ω̄x2 ≤ b2 +
3
1
ny
+
2
σ
v
−1
−
1
nx
+
2
σ
v
−1 #
,
(5)
The derivations of Equations (3) and (4) are omitted. The derivation of the principal’s optimal policy decision
in this environment (i.e., Equation (3)) is standard (e.g., Theorem 1, DeGroot (2004), p.167), and the application of
sequential rationality to the principal’s optimal choice of task is similarly straightforward and yields Equation (4).
4
To avoid some uninteresting tedium, I presume that the principal chooses τ = x when otherwise indifferent
between the two tasks.
7
and characterized in the following proposition.5
Proposition 1 For any bias b ≥ 0 and any information collection n = (nx , ny ) such that nx ≤ ny ,
α(x; n, b) is an increasing function of nx and
∂α(x;n,b)
∂nx
is a decreasing function of b > 0.
Proof : Proofs of all numbered results are presented in the appendix.
While Proposition 1 is technically useful (the proof of the paper’s main result relies upon it), the
proposition has a key substantive implication as well. In particular, the result formally justifies the
claim that larger information collection about τ = x (for example) “justifies” or “reinforces” the
principal’s predisposition to choose that task. In other words, the collection of more information
about τ = x by the agent is not motivated by a desire to “prove to” the principal that he or she
should choose τ = y.
1.1
Optimal Information Collection
My main focus in this paper is the structure of optimal information gathering by an unbiased
agent when confronted by a potentially biased political decision-maker. Accordingly, I define
optimality in terms of what is instrumentally rational for the agent. Since the agent is presumed
to be pursuing informed policymaking (Equation (2)), this definition of optimality also represents
a clear normative baseline. Formally speaking, as represented by Equation (2) above, informed
policymaking with respect to either task τ is equivalent to minimizing the variance of the difference
between the principal’s policy choice, zτ , and the true state of nature, ωτ . As discussed below,
when τ is known ex ante by the agent, this problem is well-understood in our setting (Equation (8)).
However, as soon as the choice of τ ∈ {x, y} is itself dependent on the to-be-collected information,
the agent should incorporate his or her understanding of how the information collection itself will
affect not only policy choices, but the choice of task itself (i.e., the effect of n on α). To be
5
For reasons of space, some other facts about α that are unnecessary for the purposes of this analysis (including
the fact that α(x; n, b) is a concave function of nx for nx < ny ) are omitted from the proposition.
8
precise about optimal information collection in this setting, define the following functions for any
information collection strategy n = (nx , ny ) and either τ ∈ {x, y}:
ρ(τ ; n) =
1
nτ
+
σ2
v
−1
,
In words, ρ(τ ; n) represents the “risk” of rational policymaking with respect to task τ .6 Then, for
any information collection strategy n = (nx , ny ), principal’s bias b ≥ 0, and cost of information
collection k, the agent’s (interim) expected payoff, conditional on the principal acting rationally
following the choice of task τ , is


 −ρ(x; n) − b2 − k(nx + ny )
U (τ ; n, b, k) =

 −ρ(y; n) − k(n + n )
x
y
if p = x
(6)
if p = y
The agent’s ex ante expected payoff from information collection strategy n (i.e., prior to the choice
of task by the principal), given the principal’s bias b, is then expressed as
Ū (n; b, k) = α(x; n, b)U (x; n, b, k) + (1 − α(x; n, b))U (y; n, b, k).
(7)
Finally, for any b ≥ 0, an information collection strategy n∗ (b) ∈ Z2+ is referred to as optimal (for
the agent) if it maximizes Equation (7).7
1.2
Information Gathering When the Task is Chosen Exogenously
Before presenting the analysis of the full model, it is useful to consider a slightly altered and
simpler version of the model in which the agent believes that the task τ will be chosen independent
6
By “rational policymaking,” I mean that the principal chooses zτ according to Equation (3) with b = 0 and, by
“risk,” I mean the variance of the absolute difference between zτ∗ and ωτ . For reasons that will become clear, the
agent’s disutility flowing from the principal’s bias b biased policymaking following the choice of τ = x is treated
separately from the sampling risk that is represented by ρ(τ ; n). See Equation (6), below.
7
The notation Z2+ represents the set of pairs of non-negative integers. As with σ 2 and v, the notation for k is
dropped from the expression n∗ (b) for reasons of space and because I will not be verying this parameter in the ensuing
analysis.
9
of his or her information collection strategy and the information collected. Suppose instead for
the moment that the task τ will be chosen probabilistically and independent of n: τ = x with
probability φ ∈ [0, 1] and τ = y with probability 1 − φ. After the information is gathered and τ is
chosen, the principal sets zτ according to the information gathered as described in Equation (3).8
While this environment may seem strange in some ways, it is at least suggestive of administration
within some areas of great importance, such as contingency planning for natural disasters. In
addition, events such as Presidential successions and other less noteworthy forms of administrative
turnover may intervene between the design and completion of bureaucratic information gathering.9
Let n∗ (φ, b) ≡ (n∗x (φ, b), n∗y (φ, b)) denote the agent’s optimal information collection strategy
in this setting and first consider the situation faced by the agent when he or she knows that the
principal will choose task τ = x (i.e., φ = 1). In this situation, standard arguments imply that the
optimal information gathering is10
∗
r
n (1, b) =
v
v
− ,0 .
k σ2
(8)
Equation (8) merely formalizes a relatively straightforward fact: collecting costly information
about how to perform a task that will not be performed is never beneficial. It should be noted
that the optimal information collection strategy does not depend on the size of the principal’s bias,
8
In spite of the fact that τ will be chosen independently of the information gathered, the agent clearly may have an
incentive to gather costly information, since zτ depends upon the information gathered.
9
As I return to later in the paper (Section 2), the existence and functions of agencies such as the Office of Management and Budget in the U.S. Federal Government clearly provides opportunities for political intervention after the
commencement, but prior to the conclusion, of administrative information gathering (Kerwin and Furlong (1992)).
The development and ultimate rejection of OSHA’s ergonomics standard in 2001 following the election of President
George W. Bush is an interesting, high profile, and recent example of turnover arguably having had this effect (Shapiro
(2007)).
10
For example, see DeGroot (2004), p. 260. Note that Equation (8) generally does not return an integer value for
its first component. As mentioned in the appendix (Proof of Proposition 2), the analysis essentially treats information
collection as a continuous variable. This approach (which allows one to use standard calculus techniques) is justified
by the smoothness of ρ(τ, n) with respect to nτ (e.g., DeGroot (2004), Chapter 11). The actual calculation of the
optimal integer values involves a simple comparison of at most four pairs of “neighbors” that bound the optimal
values found using the continuous approximation. For the sake of presentation, I ignore this disconnect – the focus of
the paper is on comparative statics, which are unaffected by the generic need to approximate the truly optimal strategy.
This is because the information gatherer “goes first,” so there are no strategic issues one needs to confront in terms of
how the agent’s need to approximate his or her continuous best response.
10
b. The general solution for optimal information gathering in this environment is provided in the
following proposition.
Proposition 2 If the task is chosen independent of n such that, for any information collection
strategy n, the probability that τ = x is φ ∈ [0, 1], then
n∗x (φ, b)
√
vφ
v
= √ − 2
σ
k
p
and
n∗y (φ, b)
=
v(1 − φ)
v
√
− 2.
σ
k
Accordingly,
1. n∗x (φ, b) is independent of b,
2. n∗x (φ, b) is weakly increasing (and n∗y (φ, b) is weakly decreasing) in φ
3. n∗x
1
,b
2
= n∗y
1
,b
2
, and
4. n∗x (0, b) = n∗y (1, b) = 0.
Proposition 2 states that the agent will bias his or her information collection only if the choice of
action is biased (i.e., only if one action is ex ante more likely to be chosen than the other), any such
bias is dependent only on the probability of task selection (i.e. φ), but not on the political decisionmaker’s policy bias as measured by b, and, most importantly, any bias in information collection
will favor the action that is more likely to be selected.
The fact that the optimal information collection strategy, n∗ (φ, b), is independent of b is important to note. Specifically, this fact elucidates the impact of b on information gathering when the
principal chooses the task after observing the information collected by the agent. Put another way,
the political principal’s bias is relevant to information gatherer only insofar as the agent can affect
it. The effect of the bias on the policy implemented when x is chosen (i.e., the effect of b on zx∗ ,
as defined in Equation (3)) is unaffected by the information provided by the agent. As described
in Proposition 1, though, this is not true when the principal is allowed to choose the task after
11
observing the information collection strategy and information collected. It is the analysis of this
fuller model to which I now turn.
1.3
Information Gathering When the Principal Chooses the Task
Returning now to the full model in which the principal, P , chooses the task τ ∈ {x, y} rationally
after observing the sample sizes n and the sample means ω̄, note that Equation (7), in conjunction
with the definition of α (Equation (5)), clearly indicates that the information collection strategy
(i.e., the agent’s choice of sample sizes) will affect the principal’s choice of task. As expressed
in Equation (4), the principal should take into account the sample size independent of the sample
mean. Furthermore, according to Proposition 1, above, the likelihood of a task being pursued by
the principal is increasing in the amount of information gathered about that task by the agent, ceteris paribus. Accordingly, the agent’s optimal information collection strategy involves balancing
the gains from information about the favored task (i.e., τ = x) with the degree to which additional information about ωx increases the likelihood that the principal will in fact choose to pursue
τ = x.11 The main result of this paper is that, for a sufficiently biased principal, the positive effect
of additional information about how best to perform τ = x outweighs the negative effect of “justifying” the principal’s choice of the biased task. This result is presented formally as Proposition
3.
Proposition 3 If the principal chooses τ = x with probability α(x; n, b) as defined in Equation
(5) then there exists b̃0 ≥ b̃(σ 2 , v) > 0 such that b > b̃(σ 2 , v) implies that n∗x (b) > n∗y (b), n∗x (b) is
weakly increasing in b, and b > b̃0 implies that n∗y (b) = 0.
In words, Proposition 3 can be restated as follows. For a sufficiently biased principal,
1. an unbiased information collecting agent should collect more information about the principal’s favored task than about the other task,
To see this balancing motivation formally, note that the role of b2 > 0 in Equation (6), along with the agent’s
desire to maximize Equation (7), implies that the agent prefers that the principal not choose τ = x, ceteris paribus.
11
12
2. the amount of information that should be collected about the principal’s favorite task is
increasing in the principal’s bias toward that task, and
3. furthermore, when the principal’s bias is extreme enough, no information should be collected
about the non-favored task.
Proposition 3 has two important and broader implications. First, the provision of biased policyrelevant information is not sufficient reason to conclude that the information provider has biased
preferences. Second, a third party whose political preferences differ from those of the principal
may have little incentive to impose a different information collecting strategy on the agent. Finally,
to understand why these implications are broader than the framework presented here, note that,
while it is assumed that the agent knows b, Proposition 2 verifies the supposition that neither of
these conclusions require the presumption that the agent knows the principal’s bias.12
1.4
Extensions of the Theory
Space constraints preclude a full exposition of the various directions in which the theory can be
extended, but several avenues are both interesting and relatively straightforward to present. These
include increasing the number of potential tasks both available for P to choose from and the number of those that may be chosen by P , allowing the agent to alter (i.e., lie about) the collected
information and/or the information collection strategy, and considering richer informational environments. I briefly discuss these in turn.
Different Numbers of Tasks.
Expanding the set of tasks that may be chosen from is straightfor-
ward in many respects. One direct fashion of doing so with T ≥ 2 tasks would involve defining the
state of nature as ω = (ω1 , . . . , ωT ) and maintaining the assumption that the different components
of ω as well as the observations collected by the agent are independently distributed. It is clear
12
To be precise, one could in a straightforward fashion extend the model so that the agent holds some beliefs about
the true value of β, which would then be integrated into his or her beliefs about the principal’s behavior (i.e., φ in
Proposition 2 or α in Proposition 3).
13
that a direct extension of Proposition 2 would hold. The proper analogue of Proposition 3 is not
immediately clear. For example, the agent’s information collection strategy will clearly be large
(consisting of T numbers), and the principal’s bias is now clearly multidimensional. (For example,
one could model the bias as a vector of real numbers, β = (b1 , . . . , bT −1 ).) Regardless of how one
models a multidimensional notion bias, though, the question of how one defines a notion of “more
biased than” requires careful thought about what comparisons one is interested in.13 Accordingly,
while such an extension is clearly feasible – it would require discussion of issues that fall outside
the scope of this paper.
Expanding the number of tasks that the principal may choose is a straightforward exercise. It
follows immediately that, with the informational assumptions imposed here, allowing the principal
to perform all tasks will result in the principal performing all tasks, and the agent’s information
provision will be completely unbiased. In some ways, of course, this is the point of the model –
if one believes that the decision-maker is constrained to select a limited number of possible tasks,
then one should incorporate this reality into the derivation of optimal information gathering and
provision to this decision-maker. Extending the model in this direction, then, should consider
the factors limiting the number of tasks that can be pursued, and move toward a more explicit
characterization of “bureaucratic capacity,” as has recently been examined by Ting (2005).
Signaling by the Agent.
The theory presented above presumes that the agent’s information col-
lection is transparent and verifiable in the sense that both n and ω̄ are perfectly observed by the
principal. Allowing the agent to misrepresent his or her strategy and possibly the results of the
information collection to the principal is, at first blush, a promising direction for extension. A full
treatment of signaling between the agent and principal is of course beyond the scope of the present
discussion. The following two points are relevant and easily summarized, though. First, in one
sense, the principal need not actually observe nx or ny so long as the parameters of the problem
are common knowledge to the two players. The reason for this is simple – the supposition that the
13
For example, the agent’s information collection strategy with respect to task τ may depend upon the relative sizes
of the principal’s biases with respect to a set of other tasks, say, τ 0 and τ̂ .
14
agent and principal are both rational is the basis of the optimality of n∗ as derived above – if the
principal presumes that (ω̄x , ω̄y ) are the sample means of (n∗x , n∗y ) observations, then it is easily
verified that the agent has no incentive to follow a different strategy. Second, if the agent is allowed to lie about the sample means ω̄, the setting should be analyzed as a signaling game (Banks
(1991)). The difficulty in this type of setting, then, is that lying in equilibrium by the agent necessarily involves the introduction of noise (through mixing by the agent) into the implementation
choice of the principal.14
Richer Informational Environments. The theory utilizes some canonical (and strong) assumptions about the distributions of ωx and ωy , as well as the distribution of signals about their realizations. While the Normality assumptions are strong, the general workings of the agent’s incentives
rely only on the reality that more information is never worse than less in this type of setting (i.e.,
ρ(x; n, b) is decreasing in nx ). Accordingly, the independence assumption will ensure that the general tenor of the results here hold for more general distributions. Clearly, the details of some of
the calculations reported in the appendix will change, but these changes are not due to any feature
of this framework, per se. Relaxing independence offers interesting possibilities, but is not to be
undertaken by the faint of heart.15
2
The Politics of Administration in the U.S.
In this section, I discuss some of the implications of the theory for the oversight, design, and
operation of modern administrative policymaking in the U.S. Federal Government. The purpose
of this discussion is to provide a linkage between the somewhat sterile theoretical environment
14
It should be noted that any proper notion of equilibrium in this setting would require specifying beliefs for all the
uncountably infinite possible realizations of ω̄, none of which will possess positive probability in a truthful equilibrium.
This technical issue is surmountable, but it illustrates in a clear fashion why this extension is beyond the scope of the
current paper.
15
In particular, the problem becomes potentially highly multidimensional (in terms of the model’s parametrization)
and derivation of the optimal strategy for the principal, upon observation of the collected information, may become
much more complicated than the expressions in Equations (3) and (4).
15
examined above and the substance and structure of real-world policymaking. As alluded to in the
introduction, the paper’s findings are relevant to public policymaking in several ways. Indeed, the
empirical reality of political involvement in administrative policymaking (e.g., Meier and O’Toole
(2006)) is itself an indication of the applicability of the paper’s results. For example, simply
confining attention to administrative policymaking at the highest levels of the United States Federal
Government, it is unquestionable that many final “administrative” policy decisions are subject to
direct and centralized control by political appointees (Kerwin (2003)).
Executive Involvement in Administration. The explicit role of the President of the United
States (through his appointees) in the final determination of when and how to make administrative policy has grown signficantly over the past 35 years. In particular, beginning with President
Nixon’s efforts in the early 1970s and continuing through both terms of George W. Bush’s presidency, the role of the Office of Management and Budget (OMB) in “preclearing” regulations of
various forms and various sources has steadily grown. For example, since President Reagan issued
Executive Order (E.O.) 12291 in 1981, the Office of Information and Regulatory Policy (OIRA),
a subunit of the OMB, has reviewed all major regulations issued by executive agencies.16 Some
observers have questioned whether such review, though in principle motivated by matters of procedure and efficiency, is in fact a last chance for political manipulation of bureaucratic policymaking
(e.g., Heclo (1975), Cooper and West (1988), Kerwin (2003)).
This concern is certainly reasonable at least at first glance. For example, President George W.
Bush’s most recent update of E.O. 1286617 explicitly requires that the regulations and “guidance
documents” of each executive or independent regulatory agency be approved by its Regulatory
Policy Officer (RPO), a political appointee. Furthermore, each agency’s regulatory plan is subject
to the approval of the RPO,18 and the RPO “shall be involved at each stage of the regulatory
16
Similar reviews are also required by various statutes, including the Regulatory Flexibility Act of 1980 and Paperwork Reduction Acts of 1980 and 1995.
17
Throughout, references to E.O. 12866 are as amended by President Bush on January 18, 2007.
18
Note §4(c) of E.O. 12866, which states in part, “[u]nless specifically authorized by the head of the agency, no
rulemaking shall commence nor be included on the Plan without the approval of the agency’s Regulatory Policy
Officer. . .”
16
process.”19 Finally and perhaps most importantly, the agency is charged with providing a regulatory
plan ensuring “that new or revised regulations promote the Presidents priorities.”20
Supposing that the President (possibly through his appointees) possesses the final say on administrative policy decisions, this paper’s theory highlights an informational rationale for involving
the President’s appointees in the administrative policy development process. Even setting aside interesting and undoubtedly relevant arguments about “representativeness” and “responsiveness” of
administrative decisions and processes (e.g., Meier (1993)), the theory presented here implies that
policy-relevant information should be collected with an eye to the predilections of the decider. Put
another way, the theory presented here provides a justification for political intervention in administrative policy development under the supposition that “politics and administration” are inextricably
linked at the end of the process. This point straddles a broader swath of the political landscape than
the proper reach and involvement of the political principal, P . As alluded to in the introduction,
this point has important implications for third-party (e.g., Congressional) involvement in the informational operations of administrative policymaking.
Congressional Regulation of Administration.
Suppose that a third party, such as a legislature,
can impose controls on the information gathering process. Unsurprisingly, such controls do exist
in the real world. For example, the procedures of OIRA under E.O. 12866 have been reviewed
several times in recent years by Congress (acting through the Government Accountability Office
(GAO)). More specifically, under the Congressional Review Act of 1996, the GAO reviews each
rule promulgated by Federal agencies to ensure that the process leading to its promulgation was in
accord with the various relevant statutes and executive orders, including the Regulatory Flexibility
Act of 1980, the Unfunded Mandates Reform Act of 1995, the Administrative Procedure Act of
19
§6(a)(2) of E.O. 12866.
§4 of E.O. 12866. Note that executive intervention in administration is bipartisan: President Clinton originally
promulgated E.O. 12866, which replaced President Reagan’s E.O. 12291. Furthermore, President Clinton’s version of
the Order made explicit that review by OIRA should ensure that regulations are “consistent with . . . the President’s
priorities” (Blumstein (2001), p. 853).
20
17
1946, the Paperwork Reduction Acts of 1980 and 1995, and Executive Orders 12866 and 13132.21
Such efforts provide clear evidence of Congressional interest in the collection and analysis of
information within the executive branch. The vast majority of this information is collected and
analyzed by Federal employees possessing statutorily defined civil service protection. Should
Congress use its power over these employees to direct information gathering within the executive
branch? In some ways, while Congress clearly cares about information and policy within the
executive branch, it has essentially (in both explicit and implicit ways) delegated the review of
regulations and the deliberative processes underlying policymaking to other appointed bodies such
as OIRA and the courts.22
The results presented above provide one justification for this deference – in particular, the
theory indicates that Congress should not attempt to directly ameliorate perceived biases in the
collection and analysis of information within the executive branch. Indeed, it is straightforward
to see that, given the presumption of a politically neutral information gathering agent, the theory
indicates that information provision biased in accordance with the bias of the decision-maker is
welfare-improving in an unambiguous sense. This is not to say that Congress should necessarily
accept the bias, per se, of course. Rather, the theory suggests that the appropriate way to combat
biased information gathering is to remove the bias in policymaking. The ability to do this differs
widely across and within institutions, of course. In the U.S. Federal Government, the constitutional
and practical reality within most issue areas is that both de jure and de facto authority for policy implementation resides in the executive branch.23 The results here suggest that, lacking direct control
of policymaking, Congress has an incentive to “keep its hands off” the bureaucratic informationgathering and deliberative processes.24 That this point has not been lost on Congress is indicated
21
Executive Order 13132 requires additional information and justification regarding rules with implications for
federalism issues.
22
Recent works examining delegation within very similar environments include Bueno de Mesquita and Stephenson
(2006), Stephenson (2006), and Wiseman (2007).
23
For a recent analysis of this issue within a signaling environment that reaches a similar conclusion, see Gailmard
and Patty (2006).
24
The only exception to this conclusion is that a Congress might benefit from reductions in the marginal cost of
information collection (i.e., k), and it is arguable that many Congressional attempts to expand and clarify administrative
agencies’ rights to investigate, collect information, and render adjudicatory decisions (e.g., the Hepburn Act of 1906,
18
both by specific exemptions for “deliberative information” in transparency legislation and repeated
and explicit recognition of this point in Congressional reviews of the regulatory process.25
3
Conclusion
This paper’s theory examines the interaction of politics and administration when policymaking occurs in an uncertain world. Part of the theory’s conclusion – that political bias “filters down” into
the administrative process, even when bureaucrats do not share the politicians’ bias – is not particularly surprising. However, the conclusion that the bias in the administrative process reinforces
the bias of a sufficiently biased politician, is surprising. Additionally, it should be noted that this
reinforcement is the result of the bureaucrats’ desire to achieve informed policy outcomes.
These conclusions are particularly relevant to debates about the role of political information in
administrative policymaking. In particular, it has been well-argued in a large literature that pluralistic inclusion of political information within the policymaking process increases the legitimacy of
both the decision-making process and any decision that is ultimately reached (e.g., Meier (1993)).
This theory highlights a difficulty with this line of argument. Not only is the provision of information above and beyond that predicted here more than likely irrelevant for the final decision: it will
be seen to have been as such after the final decision is made. Assuming that much policy-relevant
information must be procured voluntarily from citizens, firms, and interest groups (e.g., Boehmke
et al. (2006)), the elicitation of excessive information into the policymaking process is clearly not
“weakly dominant” with respect to social welfare.
Finally, the theory presented here, while stylized, provides another illustration of complexities
of “the politics of information.” In particular, the translation of political preferences into public
policy is complicated when policymaking authority and information-gathering responsibilities are
held by distinct actors. Since information is useful only insofar as it potentially affects policy
the Securities Exchange Act of 1934, the Administrative Procedure Act) have been in pursuit of exactly such cost
reductions.
25
For example, consider Exemption 5 of the Freedom of Information Act and Government Accountability Office
Report 03-929 (United States General Accounting Office (2003)).
19
decisions, the separation of authority for policy decisions and control/design of policy analysis
will not (and perhaps should not) be a clean one.26 Accordingly, the results have implications
beyond the operation of policymaking within any one agency – they highlight the difficulty of
discussing notions such as “representativeness” or “bias” when considering the use of information
by any delegate (or delegates) faced with policy uncertainty.27
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21
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23
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24
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A
Proofs
Proof of Proposition 1. In line with the statement of the proposition, presume that ρx < ρy and
b > 0. Recalling that ω̄ = (ω̄x , ω̄y ), define the random variable Ū (ω̄) as follows:
2
2
U (x; ω̄, n, σ , υ ) =
ω̄x2
−
ω̄y2
+
1
ny
+
σy2 υy2
−1
−
1
nx
+
σx2 υx2
−1
.
Rational task selection involves choosing τ = x if Ū (ω̄) > 0, and the conditional probability of
this occurring is
ω̄y2 − ρy (ny ) − b2
ω̄x2
≤
α(x; n, b) = Pr[ω̄ : Ū (ω̄) > 0; n, b] = Pr ω̄ : 1 +
ρx (nx )
ρx (nx )
Note that
ω̄x2
ρx
is a χ21 random variable. Letting f denote the probability density function (pdf) of
the χ21 distribution, g(·; ρx ) denote the pdf of the random variable defined by t =
defining µ(s; ρ, b) ≡
b2 +ρy +ρx (s−1)
,
2ρy
one obtains
s
e− 2
for s > 0, and
f (s) = √
2sπ
√
e−µ(t;ρ,b) ρx
ρy + b 2
p
g(t; ρx ) =
for t > 1 −
.
ρx
2ρy πµ(t; ρ, b)
25
ω̄y2 −ρy −b
ρx
+ 1, and
Then, letting Erf[r] =
√2
π
Rr
0
2
ρy e−t dt denote the error function, it follows that
Z
∞
f (s) Erf[
α(x; n, b) =
p
µ(s; ρ, b)] ds.
0
Recognizing that ρx , ρy , b, and v are all positive, the following expressions and inequalities can be
obtained:
Z
∂α(x; n, b)
∂ρx ∂α(x; n, b)
ρ2x ∞
e−µ(s;ρ,b) (1 − s)
p
ds > 0,
=
=
f (s) √
∂nx
∂nx
∂ρx
v 0
2 πρy µ(s; ρ, b)
Z ∞
1
∂ 2 α(x; n, b)
e−µ(s;ρ,b) (1 − s)
p
1+
f (s) √
= 2b
ds > 0,
∂ρx ∂b
(2µ(s; ρ, b))
4 πρ2y µ(s; ρ, b)
0
∂ 2 α(x; n, b)
ρ2 ∂ 2 α(x; n, b)
= − x
< 0,
∂nx ∂b
v
∂ρx ∂b
establishing the conclusions of the proposition. Proof of Proposition 2.
Fixing φ ∈ [0, 1] and b ≥ 0, the agent’s optimal information collection
strategy, n∗ (φ, b), is equivalent to a solution of the following minimization problem:
min2 L0 (n; b) = (ρ(x; n) + b2 )φ + ρ(y; n)(1 − φ) + knx + kny .
(9)
n∈Z+
Omitting the full derivation for reasons of space, the reader is referred to the proof of Proposition
3, below. After substituting the fact that
∂φ
∂nx
=
∂φ
∂ny
= 0, Equations (13) and (15) yield (treating n∗τ
as a continuous approximation for the optimal integer solution):
√
−1 √
1
n∗x (φ, b)
vk
vφ
v
∗
+
ρ(x; n (φ, b), b) =
= √
⇒ nx (φ, b) = √ − 2 and
2
σ
v
σ
φ
k
p
√
−1
n∗y (φ, b)
v(1 − φ)
1
vk
v
√
ρ(y; n∗ (φ, b), b) =
+
=p
⇒ n∗y (φ, b) =
− 2,
2
σ
v
σ
k
(1 − φ)
∗
as was to be shown. 26
Proof of Proposition 3.
The optimal information collection strategy is equivalent to the solution
of the following minimization problem:
min2 L(n; b) = (ρ(x; n) + b2 )α(x; n, b) + ρ(y; n)(1 − α(x; n, b)) + knx + kny .
(10)
n∈Z+
The first order conditions for a critical point of Equation (10) reduce to the following:
δx (n∗ ; b) ρ(x; n∗ , b) + b2 − ρ(y; n∗ , b) + ρ0 (x; n∗ , b)α(x; n∗ , b) + k = 0,
δy (n∗ ; b) ρ(y; n∗ , b) − ρ(x; n∗ , b) − b2 + ρ0 (y; n∗ , b)α(y; n∗ , b) + k = 0,
where δτ (n; b) ≡
∂α(τ ;n,b)
.
∂nτ
(11)
(12)
According to Proposition 1, δτ (n; b) ≥ 0 for both τ ∈ {x, y} and all
2
n whenever the principal chooses the task to perform optimally. Noting that ρ0 (τ ; n) = − ρ(τv;n) ,
the solution of (10) is most easily carried out in terms of ρ∗x ≡ ρ(x; n∗ , b) and ρ∗y ≡ ρ(y; n∗ , b), as
these are continuous variables. The calculation of which of the appropriate integer solutions for n∗x
and n∗y is straightforward and irrelevant for the purposes of examining comparative statics. Using
this approach, the first order conditions are easily expressed as a quadratic function of ρ∗x , yielding
a solution and the comparative static of ρ∗x with respect to αx as follows:
δx (ρ∗x + b2 − ρ∗y ) + v −1 (ρ∗x )2 αx + k = 0
q
δx ± δx2 + 4v −1 αx δx (b2 − ρ∗y ) + k
⇒ ρ∗x =
2v −1 αx


q
δx + δx2 + 4v −1 αx δx (b2 − ρ∗y ) + k
ρ∗x > 0 & δx ≥ 0


∗
 ⇒ ρx =

2v −1 αx
2
∗
& δx (b − ρy ) + k > 0
vδx2 + 2αx δx (b2 − ρ∗y ) + k
vδx
∗
q
∂αx ρx = − 2 −
2αx 2α2 δ 2 + 4v −1 α δ (b2 − ρ∗ ) + k x
27
x
x
x
y
(13)
(14)
Note that, for any b ≥ ρ∗y , the supposition that δx (b2 − ρ∗y ) + k > 0 is true and, furthermore,
(14) is negative.28 Accordingly, there exists some b̃ such that b > b̃ implies that ρ∗x is decreasing
in αx and, therefore, n∗x is increasing in αx .29 Omitting the analogous calculations, the following
conditions for ρv can be obtained:



ρ∗y

> 0 & δy ≥ 0
2
& (k − δy (b +
ρ∗x ))
>0

∗
 ⇒ ρy =
∂αy ρ∗y = −
(Notice that (16) involves δy =
∂ρ(y;n)
,
∂ny
δy +
q
δy2 + 4v −1 αy (k − δy (b2 + ρ∗x ))
2v −1 αy
vδy2 + 2αy (k − δy (b2 + ρ∗x ))
vδy
q
.
−
2αy2 2α2 δ 2 + 4v −1 α (k − δ (b2 + ρ∗ ))
y
y
y
y
x
(15)
(16)
and note that α(y; n, b) = 1 − α(x; n, b).) The supposition
k−δy (b2 +ρ∗x ) > 0 is holds for sufficiently large b since it can be verified that limb→∞ 2b2 δy (n; b) =
0 uniformly (i.e., for all n ∈ Z2+ ).
To complete the proof, note that n∗τ (b) is a decreasing function of ρ∗τ for both τ ∈ {x, y},
ρ∗τ > σ 2 implies that n∗τ = 0, and limατ →0 ρτ = ∞. 28
29
This is stronger than necessary, but the exact value of b̃ is unimportant for the purposes of this result.
Note that, since this holds for any critical point, it necessary holds for any solution to (10).
28