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DOI: 10.1177/1532673X073016542007; 35; 790American Politics Research

Jason A. MacDonald and William W. Franko, JRCongress Tie Policy Authority to Performance?

Bureaucratic Capacity and Bureaucratic Discretion: Does

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supported in part by the University Research Council of Kent State University.

Bureaucratic Capacity andBureaucratic Discretion

Does Congress Tie Policy Authority

to Performance?

Jason A. MacDonaldWilliam W. Franko Jr.

Kent State University

This article assesses whether the managerial capacity of agencies influences

the volume of policy authority that lawmakers delegate. Examining a sample

of agencies whose managerial capacities were assessed along the same criteria,

and allowing for the comparison of performance across agencies, we observe

that poorly performing agencies are more likely to lose policy authority. Our

findings suggest that lawmakers promote effective policymaking by giving

agencies the incentive to perform well and that models of discretion that do notaccount for performance underestimate the effect of another factorpolicy

conflict between the legislative and executive brancheson how much discretion

agencies receive.

Keywords: bureaucracy; Congress; public policy; bureaucratic discretion;

agency capacity; delegation; policy authority

Modern democracies confront complex problems, often employingpolicies that combine scientific knowledge across disciplines fromthe natural sciences and engineering to economics and policy analysis.Given this complexity, it is unlikely that modern lawmakerswith their

backgrounds in law, business, and public servicewill ever be the most

well-equipped individuals in government to design policy mechanisms to

pursue favorable outcomes for these problems. Yet electoral status confers

on lawmakers both the legitimacy to make policy decisions and the incen-

tive to balance competing societal values and interests successfully. In part,

lawmakers manage this responsibilitycapacity mismatch by delegating


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MacDonald, Franko / Bureaucratic Capacity and Bureaucratic Discretion 791

authority to make policy decisions to the bureaucracy, as has been documented

in diverse research traditions from the political economy of institutional

design (e.g., McCubbins, Noll, & Weingast, 1987; Moe, 1989) to Americanpolitical development (e.g., Carpenter, 2001; James, 2000).

A major area of research on delegation involves assessing why lawmak-

ers vary the level of discretion provided to agencies, where discretion is

defined as bureaucratic freedom to make policy decisions free from con-

straints, such as rulemaking-requirements, and other tools used by law-

makers to influence the substance of bureaucratic decisions (Epstein &

OHalloran, 1999, chapter 5). A central finding of this research is that as

policy disagreement between lawmakers and agencies increases lawmakersreduce the volume of discretion that agencies receive (Epstein & OHalloran,

1999; Huber & Shipan, 2002; Huber, Shipan, & Pfahler, 2001; Lewis,

2003; Potoski, 1999; Wood & Bohte, 2004). The theoretical basis for this

finding is that policy disagreement prevents lawmakers from trusting agen-

cies to render policy decisions consistent with lawmakers priorities. In the

language of the transaction cost approach taken by these studies, such dis-

agreement increases the costs of delegation to lawmakers to the point at

which it becomes less costly for them to make policy themselves by writingdetailed laws (see especially Epstein & OHalloran, 1999 and Huber &

Shipan, 2002).

These studies constitute significant theoretical and empirical progress in

understanding why agencies receive discretion to make policy. However,

this literature does not address the question of whether lawmakers vary dis-

cretion based on the capacity of agencies to make policies that are effective

in meeting policy goals. This issue is central to understanding whether law-

makers decisions in delegating policy authority contribute to the capacity

of democratic governments to solve problems. Some agencies perform the

tasks delegated to them effectively, solving problems that the architects of

legislation place in their hands, whereas other agencies flounder (Ingraham,

Joyce, & Donahue, 2003). To be sure, theories of delegation stress that law-

makers provide greater discretion to agencies as policy complexity increases

(Bawn, 1995; Epstein & OHalloran, 1999; Huber & Shipan, 2002). For

any number of equally complex policy areas, though, the capacity of

agencies to make effective policy can vary. Do agencies with greater/

lesser capacities receive higher/lower levels of discretion? If the answeris in the affirmative, then there is reason to believe that democratic govern-

ments can create effective solutions to important problems; however, if

lawmakers do not tie bureaucratic authority, at least in part, to bureaucratic

capacity, students of government should be more sanguine about the ability

of democracies to solve many problems.




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792 American Politics Research

Agency Capacity and Bureaucratic Discretion

Research on discretion, bureaucratic autonomy, and agency terminationprovides a basis for the hypothesis that agency capacity affects the latitude

agencies receive to make policy decisions, though no empirical research

employs a systematic indicator of capacity that varies across agencies to

assess the relationship. With respect to discretion, formal models predict

that lawmakers provide greater discretion to agencies as policy complexity

increaseseven when lawmakers expect that agencies will make policy

decisions that stray from lawmakers priorities (Bawn, 1995; Epstein &

OHalloran, 1999; Huber & Shipan, 2002). The basis for this result is thatlawmakers need for policy solutions trumps the loss of utility they experi-

ence from bureaucratic shirking. The implication of this research for the

relationship between capacity and discretion is clear. If lawmakers did not

care about effective policy solutions, there would be no reason to delegate

when agencies are likely to make decisions that stray from lawmakers

priorities. Of course, if agencies reputations for effective policymaking are

poor, then lawmakers have reason to doubt that agencies will produce effec-

tive solutions, undercutting the rationale for ceding discretion. This literature,therefore, suggests that agency capacity to perform the tasks delegated

effectively is positively related to discretion.

Research on bureaucratic autonomy suggests the same relationship,

though the causal mechanism differs. Examining the histories of three agen-

cies during the late 19th and early 20th centuries, Carpenter (2001) argues

that elected institutions cede policy authority after agencies evidence the

capacity to solve policy problems. Briefly, this capacity was created by

middle-level managers, whose career longevities and institutional positions

fostered policy learning and the ability to build support for policies they

favored among diverse sets of interest groups. After securing interest group

support for the policies they wanted to pursuein part because of sound

reputations for the capacity to solve problems effectivelythese managers

were able to place electoral pressure on members of Congress to give man-

agers authority to create policies that they favored. Bureaucratic autonomy,

then, was largely the result of sound policy performance engineered by man-

agerial leadership.

Research on agency termination also provides a basis for the hypothesisthat discretion is because of the capacity to make policies effectively.

Carpenter (2000) and Carpenter and Lewis (2004) argue that failure by an

agency, accompanied by media coverage of the negative consequences of

its bungling of the tasks to which it was assigned, imposes political fallout

on the legislators that delegated authority. Such costs might include the loss




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of electoral support from key constituencies, the provision of an election

issue to an opponent who could trace the lawmaker to the failure (Arnold,

1990) by credibly arguing that the lawmaker presided over the fiasco, andhaving to allocate scarce time on the legislative calendar in the future to

revisit the policy. These costs increase the likelihood that legislators will

exercise the ultimate act of political control (Carpenter & Lewis, 2004,

p. 202), eliminating the agencyan act that, to understate the point,

reduces discretion.

In summary, various research traditions on the authority that agencies

receive either suggest, or explicitly state, that discretion increases with

agency capacity. The basis for this relationship is that lawmakers observethe capacity of agencies to perform the policymaking tasks delegated to

them through a variety of means, including media reports (Carpenter &

Lewis, 2004) and information from interest groups and constituents dissat-

isfied with agencies (McCubbins & Schwartz, 1984) and react by manipu-

lating discretion in the future. Yet empirical support for this hypothesis is

wanting. Neither research on discretion nor agency termination incorpo-

rates variables into empirical models of these phenomena to assess the

influence of capacity on bureaucratic policy authority. Although Carpenter(2001) shows that the authority of the three agencies was due in large part

to their reputation for effective policymaking, this finding has not been

extended to contemporary politics.

Data and Methods

One reason why the relationship between capacity and discretion is

not well understood empirically is because of the difficulty of calibrating

capacity/performance in a valid manner and comparing it systematically

across agencies. We take advantage of the availability of such a measure

for a sample of 27 federal agencies. This sample was created by a joint

effort of the Federal Performance Project (FPP), teamed by scholars in the

Department of Public Administration at The George Washington University

(GW) and correspondents for Government Executive, a biweekly periodical

that provides specialized coverage of federal agencies. The project evaluated

how well all agencies managed the tasks to which they were assigned, gradingagencies in 1999, 2000, and 2001. To be clear, the FPP did not assess how

well agencies achieved the policy goals assigned to them by laws enacted

in the past. Rather, the project assessed how well agency managers managed

for results based on the criteria employed by the researchers.1 Managing





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for Results refers to administrating agencies, so that they can realize the out-

comes they are charged with achieving by law. The researchers employed

surveys of agency managers and employees, external reports on agencyeffectiveness, and interviews with experts on the agencies inside and outside

government to gauge performance.2 Researchers at George Washington

University and correspondents for Government Executive then evaluated

this information across agencies and assigned grades based on the performance

of agencies in relation to one another. Hence, according to the collective

judgment of the researchers, agencies receiving As performed better than

agencies receiving Bs on these criteria and so on. As such, the grades

constitute assessments of the managerial capacity of agencies. A presence/lack of capacity indicates the success/failure of managers to facilitate the

realization of policy results desired by political principals.

Although the sample of agencies is small, it represents the best data on

agency capacity that are comparable across multiple agencies.3 Hence, in

evaluating the connection between capacity and discretion, it makes sense

to use this data as a starting point. Twenty-seven agencies were selected by

FPP researchers because of their close interaction with the public. Therefore,

the agencies do not represent a random sample of all federal agencies andour findings cannot be so generalized. Appendix A itemizes these agencies.

The unit of analysis is the agency, that is, each agency has one observation

and includes information on the grade received in the year that it was

graded, as well as the other independent variables and the dependent

variable described below.

In evaluating the agencies management of their tasks, the FPP employed

a grade range from F to A. However, no agency received F to D

grades, limiting the range of the variables created to measure performance.

Below, we assess the relationship between how much Congress limits the

agencies discretion in the year after the grades were assigned and these

grades. We do so by measuring capacity in three ways. First, we employ the

plus/minus grades on a 1-13 scale with a grade of F coded as 1 and an

A coded as 13 (in practice, the scale ranged between 4 and 13). Second,

we employ a collapsed, traditional grade version of this variable ranging

from F (1) to A (5) to guard against the possibility that there is little dif-

ference between, for example, a B and B (in practice, this scale

ranged between 2 and 5). Finally, to account for the possibility that perfor-mance affects discretion in a nonlinear manner, we create dummy variables

for whether the agencies received grades of D, C, or B (1 if the agen-

cies received these grades; 0 otherwise), with A as the reference category

(because no agency received an F, no dummy variable for this grade was





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created). The basis for this variable is that Congress may not view the dif-

ference between an A and B performance as it does a C and D per-

formance. For example, Congress may take no action to limit the discretionof an agency when it receives a B instead of an A; although the

agencys performance is not excellent, its mere good performance probably

will not cause a political fallout. However, once an agencys performance

becomes lackluster, for example, if the agencys performance is worthy of

a D, political principals may limit discretion. Calibrating performance in

this way allows us to observe such nonlinear effects. To be clear, we offer

no theory that specifies precisely how poorly an agency must perform to

lose discretion; rather, we note that it makes sense theoretically thatCongress will limit discretion after performance has become sufficiently

poor. How low capacity must be to reach this tipping point will remain

an empirical question.4

To measure discretion, we create a variable that is the count of the

number of limitation riders (LRs) attached to agencies appropriations in

the year after their managerial capacities were assessed by the FPP. LRs

forbid agencies from spending money for specific purposes. Importantly,

agencies possess the authority to use funds for these purposes from pastlaws. However, when an LR is included in an appropriations bill that

becomes law, agencies are prohibited from exercising that authority during

the next fiscal year, limiting their discretion. For example, the fiscal year

2001 Labor, Health and Human Services, and Education appropriations bill

mandated that none of the funds . . . may be used by the Occupational

Safety and Health Administration to promulgate, issue, implement, admin-

ister, or enforce any proposed, temporary, or final standard on ergonomic

protection. Congress includes hundreds of such LRs in appropriations bills

annually (MacDonald, 2007), giving it an annual opportunity to reign in

agency authority. As such, LRs constitute a tool that Congress employs to

constrain agency authority regularly, making these tools of political control

a valid indicator of the limitation of discretion.5

To measure how much Congress impinged on discretion, we counted the

number of LRs in Congresss annual appropriation acts that applied to each

of the 27 agencies in the year after their performances were evaluated by the

FPP.6 We examined LRs in the subsequent year because that was Congresss

first opportunity to limit discretion after performance was assessed duringthe period for which grades were assigned.7 Therefore, for agencies whose

performances were assessed in 1999/2000/2001, we counted LRs imposed

on these agencies in the appropriations bills passed in 2000/2001/2002.

If discretion is reduced because of low capacity, the relationship between this





8/19

dependent variable and the independent variables measuring capacity using

the plus/minus and traditional grade scales should be negative and sig-

nificant, whereas its relationship with the dummy variables indicating thepresence of low capacity (e.g., the dummy variable for agencies who received

the grade of D) should be positive and significant.

Because research demonstrates that Congress limits discretion when

faced with a president of the opposite party (Epstein & OHalloran, 1999),

a finding that holds for state governments (Huber & Shipan, 2002; Huber

et al., 2001), we control for partisan conflict between the legislative and

executive branches. To do so, we employ a dummy variable assuming the

value of 1 when there is pure divided government, meaning that the pres-idency was controlled by one party and both chambers of Congress were

controlled by its rival. Therefore, observations for agencies graded in 1999,

for which we counted LRs applied in 2000 (under Democratic control of the

presidency and Republican control of the House and Senate), were coded 1.

Observations for agencies graded in 2000 and 2001, for which we counted

LRs applied in 2001 and 2002, respectively (under Republican control of the

presidency and House and Democratic control of the Senate), were coded as

0. Although the Democratic Senate had the opportunity to influence the sub-stance of appropriations bills during these years, its capacity to do so was

limited to a greater degree than was the case for the Republicans who con-

trolled both chambers in 2000. We expect this variable to be positively and

significantly related to the number of LRs imposed on agencies.

We also account for congressional and presidential policy disagreement

with agencies missions. Carpenter and Lewis (2004) show that agencies are

more likely to be eliminated when the same party controls the U.S. House and

the presidency but held minority status in the House and did not control the

presidency when the agency was created. Agencies in this situation are in a

precarious position because the current majority party is likely to object to the

policy priorities embodied in the agencies missions. This conditiona uni-

fied governments hostility toward agenciesmissionsmakes it more likely

for such agencies to lose discretion. Therefore, following Carpenter and

Lewis (2004), we create a variable assuming the value of 1 when this hos-

tility condition holds, 0 otherwise, and expect this variable to be positively

and significantly associated with the imposition of LRs.

Additionally, because research shows that the public salience of policiesincreases the likelihood that members of Congress make, rather than dele-

gate, policy (Epstein & OHalloran, 1999, chap. 8; Gormley, 1986, 1989) and

that Congress tries to influence bureaucratic policy decisions to a greater

extent in salient policy areas (Ringquist, Worsham, & Eisner, 2003), we control





9/19

for the salience of the policy area over which agencies have jurisdiction. This

variable is a count of the number of stories appearing in theNew York Times

in which the agency was mentioned during the year before the agency wasgraded.8 In general, the Timess coverage is followed by other media outlets

(Page, 1996). As such, this count approximates the degree to which the agen-

cies missions are made salient to the public. We expect that it will be posi-

tively and significantly related to the imposition of LRs.9

Finally, we control for the size of the appropriation bill in which the agen-

cies were funded, because bigger bills may contain a higher volume of LRs.

Appendix B provides summary statistics for all variables employed in the

analysis.10

Because the dependent variable is a count, we employ the Poissonmaximum likelihood estimator to assess the effects of the independent vari-

ables on the volume of LRs using the traditional grade-scale specification

and the dummy variable grade specification. In using the plus/minus grade

scale specification, likelihood ratio tests indicated that the variance of the

dependent variable exceeded its mean; therefore, we employed the negative

binomial maximum likelihood estimator for this model.

Findings

Models 1, 2, and 3 of Table 1 present estimates for the influence of the

independent variables on the volume of LRs, employing the traditional

grade scale variable (Model 1), the plus/minus grade scale variable

(Model 2), and dummy variables for agency grades (Model 3). The hypoth-

esis that Congress limits discretion as capacity declines is supported in all

three models. Measuring capacity using the traditional and plus/minus

grades assigned to the agencies yields a negative and significant association

between the number of LRs imposed on agencies and the coefficient for

agency grades. Additionally, in Model 3, the coefficients of the dummy vari-

ables indicating that the agency received Cs and Ds are positively and

significantly associated with the number of LRs with which agencies were

burdened. This specification supports the interpretation that once agency per-

formance drops below some adequate level at which Congress is willing to

leave agencies alone, Congress will reduce discretion. Empirically, Model 3

identifies this threshold level as the performance that merits a C based onthe FPPs criteria.

Table 2 provides information on the magnitude of the influence of per-

formance, as measured by the traditional grade scale, on LRs use, as

estimated by Model 1. Setting the values of the independent variables to





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the modal or mean values, the model predicts that about 20 LRs will beimposed on agencies receiving a B.11 However, when agencies receive an

A, this prediction drops to approximately 17 LRs. Conversely, when

agencies managerial capacities are graded at the C and D levels, the

model predicts that agencies will be saddled with about 25 and 30 LRs,

Table 1

Poisson Regression Models of the Imposition of Limitation

Riders on the FPP Sample of Agencies in the Year AfterAgency Performance Was Graded

Independent Variables Model 1 Model 2 Model 3

Traditional grade scale .191***

(.056)

Plus/minus grade scale .060***

(.024)

B .127

(.145)C .277*

(.141)

D .784***

(.230)

Divided government .188 .107 .193

(.144) (.132) (.121)

Unified and hostile government .298* .321* .358**

(.141) (.179) (.152)

No. ofNew York Times stories .0010* .0010 .0010*

(.0005) (.0007) (.0005)No. of pages in bill .005* .006* .004*

(.002) (.003) (.002)

Constant 3.254 3.120 2.636

(.270) (.320) (.188)

Log likelihood 86.181 85.57 85.02

Chi-square 31.30*** 14.49*** 33.63***

N 27 27 27

Note: Coefficients are unstandardized. Standard errors are in parentheses. The estimates for

Models 1 and 3 are Poisson maximum likelihood estimates, because likelihood ratio tests didnot reject the null hypothesis that the mean and variance of LRs were equal. The estimates for

Model 2 are negative binomial maximum likelihood estimates, because the null hypothesis

that the mean and variance of LRs were equal could be rejected (p < .05) and the alpha statis-

tic was positive. Models 1 and 3 were also analyzed using the negative binomial estimator; the

only change in the significance of the coefficients was that, for both models, the no. ofNew

York Times stories variable was significant at the .1, rather than at the .05, level.p < .10. *p < .05. **p < .01. ***p < .001 (one-tailed tests).




11/19


respectively. Hence, a decline in performance from the maximum to mini-

mum grades assigned by the FPP leads to almost a 2 standard deviation

increase in the number of LRs that Congress is expected to impose on agen-

cies. Simulationsnot presentedbased on the coefficients from Model 2

present a similar story. Simulations using the coefficients from Model 3

paint a more nuanced picture of the relationship between managerial per-

formance and discretion. Setting the independent variables to their mean

and modal values with the dummy variable for a B grade equal to 1,

Model 3 predicts that Congress will impose about 21 LRs on agencies, a

prediction that decreases to 18 if agencies receive As, and increases to 24

if agencies receive Cs. However, Model 3 predicts that Congress will

attach about 41 LRs to appropriations language funding agenciesprograms

during the next fiscal year if a D is assigned to their managerial perfor-mance. This finding suggests that there is a nonlinear relationship between

performance and discretion. It is also consistent with a bounded ratio-

nality explanation of political institutions (Jones & Baumgartner, 2005).

Specifically, the finding suggests that Congress underreacts to information

Table 2

The Predicted Number of Limitation Riders by Agency

Characteristics (Model 1 Estimates)

Predicted Values for Model 1:

Agency Characteristics Traditional Grade Scale

Performance

A 16.62

B (baseline) 20.30

C 24.65

D 29.98

Policy disagreement with agenciesAgency has support in at least one branch (baseline) 20.30

Unified and hostile government 27.93

No. ofNew York Times stories

Mean (baseline) 20.30

+ 1 Standard deviation 21.61

Note: Predicted values were calculated using Clarify (Tomz et al., 2003). The predicted values

are compared with a hypothetical, or baseline model, where the variables are set to the

modal or mean categories. The baseline was calculated using an agency operating under

divided government that received a B, was created during a period when the presidency andthe House were not controlled by a unified and hostile party, and was covered by theNew York

Times at the mean level for all agencies (58.63 stories). The bill in which the baseline agency

was funded spanned 65.85 pages.




12/19

stressing that the performance of agencies is merely average (C grades),

limiting discretion slightly. However, when Congress learns that agency

performance is poor (D grades), it decreases discretion greatly.The hypothesis that divided government reduces discretion receives

some support for the models presented in Table 1. The coefficient for the

divided government variable is positively and significantly related to the

number of LRs imposed on agencies in Models 1 and 3, albeit at the .1

level. Turning to the hypothesis that a unified and hostile government leads

to less discretion, the coefficient for a hostile and unified government is

positive and significant in all three models, providing support for the

hypothesis. Table 2 provides information on the magnitude of this relation-ship, showing that Model 1 predicts about eight additional LRs (approxi-

mately 1 standard deviation of the dependent variable) for an agency

supervised by a U.S. House and a president controlled by the opposite party

that created the agency.

Additionally, the number of stories in theNew York Times mentioning

agencies is positively and significantly related to the number of LRs

imposed on agencies in all three models presented in Table 1. Table 2 shows

that Model 1 predicts two additional LRs for agencies mentioned by theTimes at a standard deviation above the mean of that variable. This finding

supports the hypothesis derived from the work of Gormley (1986, 1989) by

Ringquist et al. (2003) that Congress will try to influence the bureaucracys

policy decisions to a greater degree in policy areas that the public views as

salient. Ringquist et al. (2003) find that Congress both introduces and

passes a greater number of new bills to reverse agency decisions when

agencies preside over policy areas of salience to the public. The positive

and significant coefficients for this variable, though not the main focus of

the research presented in this article, reinforce this finding.

One objection to the analysis presented above involves the ability to

draw conclusions based on this small sample of agencies. What if the few

agencies receiving very low grades happened to be burdened with relatively

high numbers of LRs for idiosyncratic reasons, and these reasons led to the

significant relationships between performance and discretion observed in

the models? If this is the case, a sample with more agencies, or an analysis

including the entire population of federal agencies, would make it less

likely that we would make a Type I error and reject the null hypothesis thatperformance does not influence discretion when it is in fact true. To

respond to this very real concern, we reestimated Models 1 and 2 without

the one observation for which an agency received a D. In Model 1, the

coefficient for the Traditional Grade Scale variable remained positive and





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significant (p < .05). This finding held for Model 2, though the significance

slipped to the .1 level.12 In other words, it is not simply one observation dri-

ving the finding that Congress imposes a greater number of LRs on agenciesas the managerial performance of agencies decreases.13

Conclusions

The Federal Emergency Management Agencys (FEMA) bungling of the

federal response to the devastation wrought by Hurricane Katrina and the

scurrying by members of Congress and President Bush to avoid the politi-cal fallout (e.g., VandeHei, 2005; Weisman & Goldstein, 2005) demonstrate

that poor performance on the part of agencies managers can create substantial

trouble for elected officials.14 Additionally, formal theories of delegation

stress that legislatures are willing to trade control over policy for the technical

expertise agencies offer (Bawn, 1995; Epstein & OHalloran, 1999). It should

be no surprise, then, that when agencies perform poorly elected officials

respond by stripping agency authority.

Yet to our knowledge, no prior research has examined the relationshipbetween agency capacity and discretion across a large sample of agencies.

To be sure, research on agency termination emphasizes agency failure as a

factor that increases the risk of termination (Carpenter, 2000; Carpenter &

Lewis, 2004); however, this research does not show a relationship between

these phenomena because of the lack of a measure of performance across

the sample of agencies it examines. Using a unique data set, we observe that

lower levels of performance are indeed associated with the loss of discre-

tion, controlling for the political environment, the salience of the agency,

and the size of appropriations legislation through which lawmakers scale

back discretion.

These findings suggest the need to account for performance for a more

complete understanding of why lawmakers grant discretion. Importantly,

this emphasis on performance implies that lawmakers give agencies the

incentive to make effective policies. If agencies want freedom to design the

programs under their direction, as research on bureaucratic policymaking

emphasizes (Carpenter, 2001), then convincing lawmakers of their effec-

tiveness is one way to obtain this freedom. Although prior studies of discre-tion improved the understanding of interbranch policymaking substantially,

they provided no evidence as to whether lawmakers rewarded effective

agencies by increasing their authority to make policy decisions (or punished

ineffective agencies by reducing such authority). Our analysis, however,





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implies that lawmakers promote effective policymaking, a hopeful finding

in an era in which many problems are complex and require bureaucratic

expertise. As such, it is consistent with recent research stressing thatCongress sometimes fosters effective policymaking by employing policy

research (Esterling, 2004).

An additional implication of this study is that models of discretion that do

not control for performance risk underestimating the influence of legislative

executive policy conflict on discretion. Consider one hypothetical case dur-

ing unified government when legislativeexecutive conflict is low, and, all

else equal, lawmakers are prone to grant discretion (Epstein & OHalloran,

1999; Huber & Shipan, 2002). Nevertheless, the agency that would imple-ment a law under consideration has a poor record of performance, leading

lawmakers to scale back the agencys discretion. In contrast, a second case

occurs during divided government when lawmakers, all else equal, lean

toward slashing discretion. In this case, however, the agency that would

receive authority has a record of good performance, enticing lawmakers to

take advantage of its technical capacity (Bawn, 1995) by providing more

discretion than they would have if the agency had a mediocre reputation.

Ignoring considerations about performance, theory would predict a rela-tively high level of discretion for the former observation and a relatively low

level of discretion for the latter. However, considerations about performance

attenuate this relationship. Any model using these observations to probe the

relationship between legislativeexecutive conflict and discretion that did

not control for performance, then, would underestimate the magnitude of the

relationship between interbranch policy conflict and discretion. Therefore,

the findings of this research suggest that prior studies of discretion that do

not control for performance observe a weaker connection than exists.

Of course, our findings are based on a relatively small number of agen-

cies whose inclusion in the sample was based on their close interaction with

the public. Do lawmakers limit discretion based on performance generally?

Or is this relationship conditional on the nature of agencies interaction

with the public? Future research on the link between capacity and discre-

tion should focus on developing measures of performance across more, and

different types of, agencies to answer these questions. This is especially the

case given there is good reason to believe that many of the factors influ-

encing the volume of discretion agencies receive are conditional. Forexample, Volden (2002) theorizes that interbranch policy disagreement

influences how much discretion agencies receive conditional on the existence

of an executive veto. Similarly, Huber and Shipan (2002) theorize that the

professional capacity of legislatures to craft effective policies conditions





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whether interbranch disagreement affects how much discretion agencies

receive, and they provide empirical support for this perspective.

Additionally, although our findings support the hypothesis that (a lackof) capacity (reduces) increases the policymaking authority that agencies

receive, there are several causal mechanisms that can account for this rela-

tionship. A goal of future research should be to explore the underlying

cause. Do lawmakers cede (reduce) authority based on assessments of how

likely the agencys actions are to lead to negative political fallout? Are law-

makers concerned intrinsically about the quality of policies created by

agencies when providing authority?

Appendix A

Federal Agencies Graded by the Federal Performance Project

Agency Year of Grade Grade

Coast Guard 2000 A

National Weather Service 2001 A

Social Security Administration 1999 A

Postal service 2001 A

Administration for Children and Families 2001 B

Army Corps of Engineers 2000 B

Federal Emergency Management Agency 1999 B

Food and Drug Administration 1999 B

Food and Nutrition Service 1999 B

Food Safety and Inspection Service 1999 B

NASA 2001 B

Veterans Health Administration 1999 B

Environmental Protection Agency 1999 BFederal Housing Administration 1999 B

Occupational Safety and Health Administration 1999 B

Patent and Trademark Office 1999 B

Veterans Benefits Administration 2000 B

Bureau of Consular Affairs 2001 C

Customs service 1999 C

Federal Aviation Administration 1999 C

Forest service 2001 C

Health Care Financing Administration 1999 C

Internal Revenue Service 1999 CNational Park Service 2000 C

Office of Student Financial Assistance 2000 C

Immigration and Naturalization Service 1999 C

Bureau of Indian Affairs 2001 D




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Appendix B

Descriptive Statistics

Variable Minimum Maximum Mean Standard Deviation

Limitation riders 6 36 21.63 7.62

Grade dummies

B 0 1 0.48 0.51

C 0 1 0.33 0.48

D 0 1 0.04 0.19

Traditional grade scale 2 5 3.74 0.76

Plus/minus grade scale 4 13 8.96 2.24

Divided government 0 1 0.56 0.51Unified and hostile government 0 1 0.11 0.32

No. ofNew York Times stories 0 304 58.63 75.65

No. of pages in bill 28 98 65.85 21.33

Notes

1. Briefly, the criteria included how well managers defined and measured success, man-

aged agency resources, took measures to ensure that managers were held accountable for deci-

sions and performance, communicated effectively and honestly with stakeholders and politicalprincipals, ensured that employees possessed information necessary to perform tasks to

achieve results, staffed individuals with appropriate skills for the tasks they performed, pos-

sessed the physical infrastructure necessary to achieve the results, and provided sound fiscal

management.

2. The FPP interviews included groups such as congressional oversight and appropria-

tions committees, the GAO, the OMB, think tanks, the press, client and advocacy groups, aca-

demic institutions, and government commissions (Laurent, 1999). More detailed information

about the FPP can be found on the projects Web site: http://www.gwu.edu/~fpp/. Also see

Ingraham et al. (2003).

3. Another source on agency performance is the Performance Assessment Rating Tool

(PART) through which the Office of Management and Budget (OMB) evaluates the performance

of federal programs. Using PART data would increase the size of the sample of agencies.

However, given that the OMB works directly for the president within the executive office of

the president (EOP), and given that presidents politicize the work of the EOP (Lewis, 2005;

Moe, 1985), we believe that PART ratings are likely to be endogenous to the political priori-

ties of the president, making them an invalid measurement of agency performance.

4. The grades assigned to agencies by the FPP can be found in Laurent (1999, 2000, 2001).

5. Measuring the volume of authority stripped from agencies in this way has an advan-

tage. It is possible that agencies that possess high levels of discretion develop greater capaci-

ties than agencies that possess low levels of discretion. If this were the case, employing a

dependent variable that measures the volume of discretion that agencies possess would pre-

clude unbiased estimates of the influence of capacity on discretion because higher levels of

discretion also cause high levels of capacity. However, our indicator measures the volume of

authority taken away from an agency in year t+ 1. Authority taken away in the future cannot





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affect agency capacity in the present. Additionally, existing research on the link between

agency capacity and authority emphasizes that capacity leads to authority (Carpenter, 2001)

rather than the other way around, suggesting that, at any rate, managerial capacity is notendogenous to the volume of discretion agencies possess.

6. All the agencies graded by the FPP receive funding from these bills. We examined the

versions of bills that became public lawrather than House/Senate committee passed or

House/Senate passed versions.

7. To count LRs, we first identified the bill in which the agency was funded. Next, we

counted the number of instances stating no funds or none of the funds could be spent for

specific purposes that applied to the agency. LRs applied to the agency if they were located in

the specific section funding the agency, or a general provisions section, or title that applied to

the agency. Using counts of the number of LRs in the year during which, and the year before,

performance was graded did not change the findings reported below.8. This count was obtained through a Lexis/Nexis search of agency names, as they

appear in Appendix A, in the titles and lead paragraphs ofNew York Times stories in the year

before the agencys managerial performance was graded.

9. Ringquist et al. (2003) also examine the relationship between complexity and con-

gressional efforts to direct the bureaucracy, finding no direct relationship between complexity

and such efforts even though complexity does condition the relationship between salience and

such efforts. We opt not to control for complexity in our analysis because of the following:

The primary focus of our analysis does not involve the relationship between policy type

and discretion.

These authors found no direct relationship between complexity and such efforts.There is no readily available measure of complexity across agencies.

The degrees of freedom in our analysis is small to begin with.

10. Huber and Shipan (2002) identify factors, such as legislative capacity that vary across

legislatures influencing the level of discretion agencies receive. In the analysis below, we

focus on cases in which a single legislature delegates authority. Therefore, these factors are

constant and cannot explain variance in our dependent variable.

11. The note in Table 2 provides information on these baseline values. Predictions were

calculated using Clarify 2.1 (Tomz, Wittenberg, & King, 2003).

12. These results are available from the authors on request.

13. The results of this estimation are available from the authors on request.14. The title of VandeHeis article in The Washington Post illustrates the alacrity with

which members of Congress and the President sought to avoid the political costs of Federal

Emergency Management Agencys (FEMA) failure: Officials Deal with Political Fallout by

Pointing Fingers.

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Bureaucratic Capacity

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