bureaucratic perceptions of discretion in the u.s ... · brehm and gates 1997; gailmard and patty...

Bureaucratic Perceptions of Discretion in the U.S. Separation of Powers: Evidence from Cabinet Departments, 1998-2004

Anthony M. Bertelli University of Southern California

September 5, 2012

Abstract: Theories of delegation posit that Congress has the incentive to decrease discretion when ideological conflict between an administrative agency and Congress increases. Yet agencies can use their expertise to appropriate informational benefits from delegation helping to increase bureaucratic autonomy. Such theories only indirectly address the impact of ideological conflict on bureaucrats’ perceptions about the extent of the discretion they are afforded on the job. Does the perception of discretion by bureaucrats depend on ideological conflicts between the legislative and executive branches? Statistical results from dynamic panel models provide some evidence that that closer ideological alignment with Congress than the president increases perceived discretion; that a negative relationship emerges between confirmation times and perceived discretion, but a small and opposite relationship exists among supervisory levels; that variance in the ideological portfolio of cabinet secretaries decreases perceived discretion overall, but has no effect on supervisory cadres; and that divergence between the goals and legal context of an agency and the president’s policy orientation are associated with lower perceived discretion.

I thank George Krause, David Lewis and Peter Robertson for helpful comments as well as Dyana Mason, Jennifer Connolly, and David Gastwirth for extensive help in data collection for the perceived discretion measure. The Bedrosian Center on Governance and the Public Enterprise provided financial support of this project. This is preliminary work; comments are most welcome. Mistakes remain my own. Email: [email protected]

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Scholars of legislative, executive, and administrative politics have a profound interest in

discretion afforded to and exercised by agents of the state. The delegation of discretionary

authority to administrative agents is an important strategic policy decision for legislators. Yet

the extent of discretion can also be “an unavoidable consequence of the political and

institutional contexts” of particular public policies (Huber and Shipan 2002, 9). In the U.S.

federal setting, actions clearly in excess of discretion can be checked by Congress through

corrective legislation or reined in by the courts via judicial review. Federal bureaucrats must

nonetheless develop a subjective sense of the discretion afforded them in relation to a

particular governance task. That is to say that perceived discretion is not actual discretion. The

former captures bureaucrats’ cognitive and emotive responses to what Redford (1969, 193)

called “directive activity,” which sets “the preconditions of administration on the basis of

consensus on what will be expected of it.” The nature and function, as well as the legal and

political environments of delegated policymaking vary over time, and perceptions of discretion

among bureaucrats fluctuate with them. How do institutional factors in separation-of-powers

policymaking impact levels of perceived discretion in the federal government? I seek to

address this question using data for cabinet-level agencies between 1998 and 2004.

A variety of theoretical contributions suggest that perceptions bureaucrats have about

their policymaking discretion play an essential role in shaping policy implementation (cf.

Brehm and Gates 1997; Gailmard and Patty 2007). For instance, Brehm and Gates (1997, 98)

claim that the extent to which a bureaucrat “feels in control of his or her surroundings” impacts

her effort levels and, consequently, policy implementation. Perceptions of greater discretion

can also provide non-pecuniary incentives for policy-motivated bureaucrats to choose public

service rather than more lucrative careers outside government (Gailmard and Patty 2007) or to

engage in “diagonal” careers that move them in and out of government (Teodoro 2011). Actual

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levels of discretion are directly related to the policy conflict inherent in the separation of

powers in many of these theories. Bureaucrats perceive shifts in discretion brought about by

political means, and while formal discretion in statutory enactments has been captured in prior

work (cf. Huber, Shipan and Pfahler 2001; Huber and Shipan 2002; Clinton, Bertelli, Grose,

Lewis and Nixon 2012), the important relationship between such perceptions and those forces

remains largely unstudied.

Recent measurement work allows scholars to understand ideological conflict at the

agency-level between politicians and bureaucratic actors (e.g., Bertelli and Grose 2011; Clinton,

et al. 2012; Chen and Johnson 2011; Bonica, Chen and Johnson 2012). Using two sets of

bureaucratic ideal point estimates, I test several hypotheses regarding the cross-pressures of

ideological conflict from multiple political principals—Congress and the president—in U.S.

Cabinet agencies during portions of the Clinton and G.W. Bush administrations (1998-2004).

My results provide some evidence to suggest, first, that closer ideological alignment with the

Senate filibuster pivot (Krehbiel 1998) than the president increases perceived discretion in

Cabinet departments, but has an opposite impact among supervisory-level staff in those

agencies. Second, a negative relationship emerges between confirmation times and perceived

discretion, but a small and opposite relationship exists among supervisors. Third, variance in

the ideological portfolio of cabinet secretaries (Bertelli and Grose 2011) decreases perceived

discretion overall, but has no effect on that trait among supervisory cadres. Finally, divergence

between the goals and legal context of an agency (Clinton and Lewis 2008) and the president’s

policy orientation are associated with lower perceived discretion among all agency personnel.

In the next section, I briefly review the theoretical literature on separation-of-powers

policymaking as it relates to the nature and extent of administrative discretion. This is

followed by a discussion of the nature and measurement of perceptions of discretion by

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administrative actors. My hypotheses are then stated formally and the data and methods used

to assess them are described. Results are subsequently presented. The paper concludes with

some brief remarks on the implications my findings have for the literatures engaged in this

study.

Delegation and Perceived Discretion

As conflict in the policy preferences between Congress and an administrative agency

diminishes in the canonical model of delegation, the principal gives the agent more discretion

(see, e.g., Epstein and O’Halloran 1999). Huber and Shipan (2002, 147) sum up the general idea

of the impact of policy conflict on statutory discretion quite well: “if the preferences of the

agency diverge from those of legislators, then the immediate benefits of specifying details in

legislation should be larger than if these preferences converge” (see also Gailmard and Patty

2007). Multiple political principals have weaker collective control over agencies than does a

unitary principal (e.g., Gailmard 2009). In the multiple principals framework of Volden (2002a,

127), it is problematic under certain circumstances for Congress to rescind discretion given the

existence of executive vetoes. When agency and presidential preferences are aligned, he

argues, we should observe more discretion. Volden (2002a, 127) goes on to suggest that when

agencies are more independent from the president, proximity between Congress and the

president tends to decrease discretion. This kind of argument has been tested with divided

versus unified government as a proxy for ideological disagreement (e.g., Epstein and O’Halloran

1999; Volden 2002b). I test the following hypothesis

Hypothesis 1: As policy conflict between Congress and the president increases, perceived discretion decreases. While incentives exist for the president to appoint an ideological clone to a cabinet

secretary post as opposed to an independent agency (Volden 2002), there are a variety of

reasons why a divergent agent might be preferred even in a cabinet department (cf. Bertelli

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and Feldmann 2007; Prendergast 2007) and empirical evidence of divergence is observed

among cabinet secretaries and their appointing presidents (Bertelli and Grose 2011). Even in

cabinet agencies, policy conflict can exist between president and Congress as well as between

the agency and either principal. Indeed, McCarty (2004) argues that the president can appoint

a cabinet secretary that has less policy conflict with Congress in exchange for the department

receiving more budgetary resources or policymaking autonomy. Bertelli and Grose (2011) find

support for that claim in data capturing discretionary budget authority in cabinet departments.

It is possible to construct a spatial measure that can assess the relative impact of policy

conflict between an agency and its political principals, namely, the difference in absolute

distances |Agency – President| – |Agency – Congress|. When this difference is positive, the

agency is ideologically closer to Congress than it is to the president. That is, in ideological

conflicts between Congress and the President, the agency’s preferred policies are more in line

with those of Congress. When the aforementioned difference is negative, the agency has less

policy conflict with the president than with Congress. Since Congress is the principal having the

power to delegate, influential literature reviewed above suggests that it should grant more

discretion as policy conflict is reduced. Yet this is not specifically a theory of how bureaucrats

experience such changes in discretion as restraints on their autonomy on the job. Indeed, the

exchange of policy preferences for autonomy in the McCarty (2004) thesis suggests that on a

delegation-by-delegation basis in large departments, a balance is struck. The aggregate effect

of these considerations is likely to be reflected in the aggregate data employed in this study.

Moreover, the relative distance measure allows to capture more nuance than have the divided

government tests.

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I test the following alternative hypotheses. The first is motivated by the constitutional

position of Congress for purposes of delegation (e.g., Epstein and O’Halloran 1999; Huber and

Shipan 2002).

Hypothesis 2a (Congressional Dominance): Perceived discretion increases when policy conflict between the agency and key congressional actors is less than that between the agency and president.

The second is inspired by the coordination, appropriation, information, and agenda

setting facets of the president’s executive powers (Moe and Howell 1999, 137-38). These

presidential characteristics are likely to be much stronger in the case of cabinet departments

than independent agencies.

Hypothesis 2b (Presidential Dominance): Perceived discretion increases when policy conflict between the agency and president is less than that between the agency and key congressional actors. Various scholars have suggested that presidents can restructure administrative

agencies and their management to help achieve their policy objectives (cf. Howell 2003; Lewis

2003; 2008; Waterman 2009). However, the structural design of agencies has long been

understood as a mechanism of congressional control of the bureaucracy (cf. McCubbins and

Schwartz 1984; Weingast and Moran 1983; McCubbins, Noll, and Weingast 1987) and can even

be used to work against presidential control (Moe 1987). It is, however, widely recognized

that cabinet departments are less insulated from presidential influence than independent

agencies (e.g., Lewis 2003, 44-45). Scholars of the administrative presidency observe a variety

of influence strategies in regard to agencies with divergent policymaking characteristics

(Rudalevige 2002). When a cabinet department has a structural climate that is more conducive

to the policy preferences of the president, it allows for a greater level of autonomy in practice.

Such a climate is created by the laws that delegate authority to the agency, budgetary histories,

and so forth (Clinton and Lewis 2008). I test the following hypothesis.

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Hypothesis 3: Perceived discretion increases when the structural climate of an administrative agency is increasingly aligned with the president’s policy goals. Presidential or congressional influence over bureaucratic discretion does not

necessarily depend on policy conflict with a single department. Krause (2009, 77) draws

attention to vertical coordination dilemmas between the president and cabinet agencies, noting

“the institutional presidency encompasses a host of individuals whose views on administration

policy often differ from those of the chief executive.” Conflicting ideologies—or greater

diversity in the ideological portfolio of the executive branch (Bertelli and Grose 2011)—can

make it difficult for presidential administrations to commit to policies (Krause 2009, 81). The

weakened sense of policy commitment from an ideologically diverse executive branch may give

the president to an incentive to tighten discretion. Such diversity can increase uncertainty on

the part of both political principals about the relationship between policy outcomes and their

preferences. Department personnel may perceive less discretion in such scenarios as the

political principals make efforts to increase their policy benefit at the expense of the

informational benefit of their expertise. I test the following hypothesis.

Hypothesis 4: Perceived discretion in an agency decreases when the ideological portfolio of cabinet increases.

These hypotheses are tested using measures of discretion based on the survey

responses, first, by all employees and, second, by self-reported supervisory employees. In this

preliminary analysis, I do not construct hypotheses that differentiate these cadres of federal

employees. This is due in part to limitations in the data underlying these measures to which I

now turn.

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Data and Methods

The dependent variable, Perceived discretion, is a survey-based measure of the

discretion climate in a cabinet level agency (cf. Glick 1985; James and Jones 1974; Herrigel and

Slocum 1974). In offering a perceptual basis for discretion, Carpenter and Golden (1997, 190)

claim that an employee’s “locus of control” — or her generalized perceptions of the extent to

which she may control, or is controlled by, her environment (Rotter 1966) — is related to

extent to which she perceives her general discretion level. Psychologists have studied the

concept of locus of control at a variety of levels, the most pertinent of which to the principal

study is the “work locus of control,” which “represents the extent to which people attribute

rewards at work to their own behavior” (Wang, Boling, and Eschleman 2010, 762). In the

federal service, such rewards come in both policy and pecuniary form (see e.g., Gailmard and

Patty 2007). I selected questions related to work locus of control for use in measuring

perceived discretion. Thus the dependent variable does not measure policy discretion but

more general functional discretion or autonomy (Brehm and Gates 1997). Unfortunately, no

items on the surveys employed were amenable to creating a measure of perceived policy

discretion. I argue that the measure captures an organizational trait that applies to the ability

to exercise policy discretion.

Twenty-six questions over nine survey years from the Federal Human Capital (FHCS),

Merit Principles, and Reinventing Government surveys were used to measure perceived

discretion. The FHCS survey used the questions “I feel encouraged to come up with new and

better ways of doing things.” (2004 Q4; 2006 Q4; 2008 Q4; 2010 Q3) “Employees have a

feeling of personal empowerment with respect to work processes.” (2004 Q26; 2006 Q24; 2008

Q24; 2010 Q30), “Creativity and innovation are rewarded.” (2004 Q29; 2006 Q26; 2008 Q26;

2010 Q32), and “How satisfied are you with your involvement in decisions that affect your

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work?” (2004 Q59; 2006 Q54; 2008 Q55; 2010 Q63). The Merit Principles survey used “I have

been given more flexibility in how I accomplish my work” (2000 Q6) and “Creativity and

innovation are important” (2005 Q2h). Reinventing Government asked “Creativity and

innovation are rewarded” (1998 Q11; 1999 Q11; 2000 Q11) and “How satisfied are you with

your involvement in decisions that affect your work?” (1998 Q30, 1999 Q29; 2000 Q29) and “In

the past 2 years, I have been given more flexibility in how I accomplish my work” (1999 Q18;

2000 Q18). The agency-level measures were constructed via a dynamic item response model

(Martin and Quinn 2002). One measure is constructed using all respondents and a second uses

only those who self-report as supervisors,1 which is a response positively correlated with being

in higher GS pay grades.2 Unfortunately, the supervisory designation restricts the sample by

two years (existing only for 2000-2004) and decreases the analytic power of my statistical

models. Nonetheless, hypotheses 1, 2a, 2b and 3 are tested using both measures. A detailed

description of the measurement strategy and results of the measurement model are presented

in the appendix.

1 Six surveys provided opportunities to split the sample into worker or supervisor categories. The four FHCS datasets, along with the two MPS datasets asked the respondents a question about supervisory status. The Reinventing Government surveys did not ask a question distinguishing job category and are not included in this analysis. The Merit Principles Survey asked “Are you a supervisor?” While the FHCS surveys asked their supervisory status, providing options from non-supervisor, team leader, supervisor, manager and executive. These questions allowed us to distinguish between those in non-supervisory positions from those in supervisory positions. Because not all surveys asked the question in the same way, I treat this variable as a dichotomous indicator for supervisor. If “Team Leader” was an option, it was included in the non-supervisor category, while supervisors, managers and executives were included in the supervisor category. Samples were then split between supervisors and non-supervisors. Those that did not answer the question on supervisor status were dropped from the sample. An average of 5.3 percent of cases is dropped for this reason. In one survey, MPS 2000, there were a large number of nonrespondents, and the survey team manually coded respondents as supervisors if additional answered questions suggested that they were a supervisor. These observations were included in the supervisory category. 2 Logistic regression models indicate the relationship between those who responded and pay category are positive and significant for all but one survey employed. In other words, the higher the rank an individual had, the more likely they were to respond to the supervisor question. Since the FHCS survey pay categories were nominal, multinomial logistic regression models estimate the relationship of pay category to response, finding the same trend. Those in the higher categories were more likely to respond to the supervisor question than the base case (a lower ranking category). An exception to this trend were FHCS 2004, where statistical models failed to indicate a significant relationship, most likely due to the high number of nonresponses to both the supervisor and pay category questions. In addition, the MPS only asked a salary amount question rather than a pay category question, which was positively related to response.

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To assess Hypothesis 1, I seek a measure of interbranch conflict between the president

and Congress correlates with policy conflict but not the distance variables so that Hypothesis 1

and the two variants of Hypothesis 2 can be assessed in a unified model. In the case of cabinet

agencies, confirmation battles represent such conflict between the president and Congress that

is relevant to the mandate of the agency. Days to confirm the current head is a count of days (a)

from inauguration to confirmation for a new president or term of office or (b) from nomination

to confirmation in the case of a sitting president. Hypothesis 1 would be supported by a

negative relationship between confirmation time and perceived discretion.

To construct the relative distance measures used to test Hypothesis 2, I employ two sets

of ideal point estimates, the Bertelli and Grose (2011) estimates based on cabinet secretary

testimony before Congress and the Chen and Johnson (2011) estimates for agencies based on

the campaign contributions of agency staff. These arguably represent different levels of an

agency. The Bertelli-Grose measures capture the revealed policy preferences of cabinet

secretaries, the top of the hierarchy. Chen and Johnson (2011, 20) “assume that contributions

from upper-level bureaucrats, who wield more influence on agency policy, are typically larger

than contributions from rank-and-file agency employees,” thereby capturing more influential

executives in these departments.3 The correlation between these variables is 0.37, but a

regression of the Chen-Johnson agency estimates on the Bertelli-Grose secretary estimates with

fixed effects for cabinet departments yields a coefficient of 0.92 (t = 2.33, N=91). This

suggests that the measures are strongly related. In the analyses that follow, I treat the

hypotheses similarly in each set of measures, i.e., as robustness checks, yet interpret potential

differences when discussing the results.

3 Chen and Johnson (2011) do not provide ideal point estimates for the Department of Defense, which appears in the Bertelli and Grose (2011) estimates. This creates a difference in the number of observations for the statistical models.

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To capture relative conflict between an administrative agency and its constitutional

principals, Congress and the president, I develop two measures. The Senate measure

takes positive values when the absolute difference of the agency and

president’s position exceeds the absolute difference between the agency and the Senate

filibuster pivot and vice versa (Krehbiel 1998). The model employing this measure is

referenced the Senate Model in the discussion that follows. Similarly,

examines relative conflict using the ideal point estimate for the median member of the House of

Representatives. I use this measure in the House Model discussed below. Hypothesis 2a

receives support if the relationship between these variables and perceived discretion is

negative, that is, as the secretary or department moves relatively closer to Congress in revealed

policy preferences. The alternative (Hypothesis 2b) is supported by a positive coefficient.

Hypothesis 3 is assessed using a measure of Agency Climate Divergence from the

president drawn from the Clinton and Lewis (2008) measures of the ideological climate of

administrative agencies. The original measure developed in that study takes positive values as

the agency climate becomes more conservative. I reverse the scale of this measure during

Republican presidential administrations so that it takes positive values as the agency climate

moves away from the president’s policy preferences. The extremity of the president’s policy

preferences relative to other institutional actors has been evidenced in a variety of contexts,

which offers empirical justification for my approach (e.g., Poole and Rosenthal 1997; Clinton,

Jackman, and Rivers 2004; Treier 2010; Bertelli and Grose 2011; Clinton, Bertelli, Grose, Lewis,

and Nixon 2012). A negative coefficient in the statistical models I estimate provides support

for Hypothesis 3.

Diversity in the policy preferences of cabinet departments is captured by the Variance

of the ideological portfolio. This is simply the variance of the ideal points of cabinet secretaries

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or departments and is used to assess Hypotheses 4 and 5. Hypothesis 4 is supported by a

negative coefficient in the statistical models of perceived discretion.

To control for unobserved influences within presidential administrations (Clinton and

George W. Bush in my sample), I include an indicator variable for measures realized in the G.W.

Bush Administration. Summary statistics for all variables are presented in Table 1.

My dataset comprises a panel of 14 agencies over seven years. Wooldridge (2002) tests

fail to reject the null hypothesis of no first-order autocorrelation in all models. Due to the

presence of autocorrelation and the small number of time periods, I employ the one-step

dynamic fixed-effects estimator proposed by Arellano and Bond (1991).4 Because the Clinton

and Lewis (2008) measures of agency ideological climate are based on a survey that asks

respondents for their views over a long time span and remain constant over the period, this

measure and the indicator for the Bush administration are treated as exogenous. Confirmation

times are also considered exogenous as they represent conflict occurring before a secretary

takes office. The remaining variables are potentially endogenous and are treated as such in the

statistical models.

Results

Results for are presented in Tables 2-5. Because instrument proliferation is a problem

with this type of dynamic model (Roodman 2009), each model is estimated with three types of

restrictions on the instrument set. Models indicated (1) have the all available instruments, (2)

impose single-lag limitations on instruments for the endogenous variables, and (3) collapse the

instrument set for the endogenous variables (see Roodman 2009, 148-49). Each table indicates

the instrument count and p-values for Arellano-Bond tests against the null hypothesis of

4 I assessed the potential for efficiency gains through the Arellano and Bover (1998) approach, but tests for over-identifying restrictions suggest that such an approach is not appropriate in all models.

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autocorrelation in first-differenced errors5 and Hansen and Sargan tests against the null

hypothesis that over-identifying restrictions imposed are valid. Test statistics are presented in

Table 2. Models of perceived discretion using the all employee measure are presented in

tables 2 and 4 while those for supervisors only in Senate models appear in Table 3. House

models for supervisory levels were estimated, but all specifications raise concerns regarding

instrument validity and proliferation due to failure of the aforementioned tests . In Table 2, the

use of all available instruments is problematic for the Chen-Johnson scores as evidenced by the

Hansen statistic of 1.0 for model C-J (1). In table 3, model B-G (1) displays a weakly satisfied

Sargan test.

Tables 2 and 4 show robust support for Hypothesis 1. As first-differenced policy

conflict between the president and Congress as measured by confirmation times increases,

first-differenced perceived discretion decreases. In the supervisory tests of Table 3, the Chen-

Johnson models show a very small but significant positive effect, which is not robust in the

Bertelli-Grose models. The spatial measures of ideological conflict in the Senate models of

Table 2 provide robust support for Hypothesis 2a. Higher levels of perceived discretion occur

when the agency head’s ideal point lies closer to the Senate filibuster pivot than the president.

With the exception of model B-G (3), similar effects are not seen in the House models of Table

4; more perceived discretion is not observed where the agency is relatively closer the median

member of the House than to the president. Among supervisory cadres of departments, the

Chen-Johnson Senate models in Table 3 show robust support for Hypothesis 2b. This suggests

the possibility that among supervisors, the president’s ideological gravity increases discretion

rather than that emanating from Congress. The differences across models using different ideal

point estimates as measures offer a rationale for considering more carefully the level of

5 It is expected that the Arellano-Bond test of first-order autocorrelation will not be rejected for these models.

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departmental ideology they measure. Because Chen-Johnson estimates capture more

executives with policymaking authority, the impact of presidential influence in cabinet

departments may be more acutely felt by supervisors on the basis of policy conflict at levels

below the cabinet secretary.

Hypothesis 3 can be tested only in the full agency models as the removal of Clinton

administration observations because late introduction of supervisory questions on the surveys

employed in my measurement strategy reduces the variation of the mission divergence

measure to near collinearity. In tables 2 and 4, Hypothesis 3 receives robust support. As the

agency’s ideological climate diverges from the president, perceived discretion is observed at

lower levels. It is likely that the president’s influence is robust in this context as the measure

captures structural characteristics which are manifest throughout the agency.

Hypothesis 4 states that perceived discretion decreases when the ideological portfolio

of cabinet becomes more diverse. It receives robust support in Senate models of Table 2 as

well as the House models of Table 4. Suggestions of an opposite effect in the supervisory levels

are not robust in Table 3. There is thus some evidence that the president’s coordination

problems rooted in policy disagreements present an opportunity for supervisory staff closer to

the action of policymaking.

Conclusion

The preliminary evidence in this paper finds evidence for several important

implications of spatial theories of separation-of-powers policymaking. Policy conflict plays an

important role in shaping perceived discretion in administrative agencies. Closer ideological

alignment with the Senate filibuster pivot than the president increases perceived discretion in

Cabinet departments. Contrary evidence is seen among supervisory-level staff in those

agencies, where an opposite effect is observed, but is not robust in the cabinet secretary

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ideology models using the Bertelli and Grose (2011) measures. A negative relationship

emerges between confirmation times and perceived discretion, but a small and opposite

relationship exists among supervisors in models using the Chen and Johnson (2011) measures.

Diversity in the ideological portfolio of cabinet secretaries decreases perceived discretion

overall, but has no effect on supervisory cadres in these departments. Goal conflict between

the policy and legal context of an agency (Clinton and Lewis 2008) and the president’s policy

orientation relates to lower perceived discretion. In future iterations, I intend to consider the

veto pivot in House analyses. The preliminary results show evidence of relationships but

substantive impact profiles will also reveal a fuller story.

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System.” American Journal of Political Science 46:111-133. Volden, Craig. 2002b. “Delegating Power to Bureaucracies: Evidence from the States.” Journal of

Law, Economics, and Organization 18(1): 187-220. Wang, Qiang, Nathan A. Bowling, and Kevin J. Eschleman. 2010. “A Meta-Analytic Examination

of Work and General Locus of Control.” Journal of Applied Psychology 95(4): 761–768. Waterman, Richard W. 2009. “The Administrative Presidency, Unilateral Power and the Unitary

Executive Theory.” Presidential Studies Quarterly 39: 5-9. Wooldridge, Jeffrey. 2002. Econometric Analysis of Cross-Section and Panel Data. Cambridge,

MA: MIT Press.

18

Table 1: Summary Statistics

Variable N Mean Std. Dev. Min. Max.

Perceived Discretion 70 -0.443 1.353 -3.371 2.471

42 -0.299 0.942 -1.925 1.196

C-J Agency Ideal Point 65 0.084 0.233 -0.397 0.361

39 0.179 0.144 -0.058 0.361

B-G Secretary Ideal Point 70 0.266 0.208 -0.112 0.855

42 0.303 0.208 -0.011 0.855

|A – P| – |A – SFP| using B-G 70 0.058 0.402 -0.943 0.727

42 -0.056 0.372 -0.943 0.592

|A – P| – |A – SFP| using C-J 65 0.026 0.343 -0.608 0.613

39 0.136 0.288 -0.235 0.613

|A – P| – |A – HM| using B-G 70 0.325 0.291 -0.561 0.561

42 0.278 0.315 -0.561 0.561 |A – P| – |A – HM| using C-J 65 0.286 0.307 -0.439 0.600

39 0.419 0.149 0.124 0.600

Variance of the Ideological Portfolio using B-G 70 0.041 0.010 0.022 0.048

42 0.046 0.001 0.045 0.048 Variance of the Ideological Portfolio using C-J 70 0.042 0.018 0.009 0.062

42 0.052 0.007 0.047 0.062

Agency Climate Divergence 70 -0.026 1.052 -2.210 2.210

42 -0.044 1.056 -2.210 1.430

Days to Confirm Current Head 70 9.129 20.190 0.000 113.000 42 5.452 13.712 0.000 63.000

GW Bush (=1) 70 0.800 0.403 0 1 42 1 0 1 1

First row for each variable indicates summary statistics for models using all respondents to surveys for computation of agency perceived discretion indicators. Second row indicates summary statistics for models including only self-reported supervisory-level respondents. B-G indicates ideal point estimates in Bertelli and Grose (2011) and C-J indicates ideal point estimates in Chen and Johnson (2012).

1

Table 2: The Impact of Ideological Conflict on Perceived Discretion, U.S. Cabinet Agencies, Senate Difference GMM Models

1998-2004

DV: Perceived Discretion B-G (1) B-G (2) B-G (3) C-J (1) C-J (2) C-J (3)

Perceived Discretion (t-1) 0.946*** 0.928*** 0.879*** 0.924*** 0.929*** 0.936***

(0.038) (0.052) (0.089) (0.110) (0.112) (0.125) |A – P| – |A – SFP| 0.176† 0.280** 0.430** 2.927** 2.866* 2.682

(0.129) (0.138) (0.190) (1.490) (1.514) (2.536)

Variance of Ideological Portfolio -15.607* -22.044* -26.390* 0.073 0.068 0.063

(8.549) (12.036) (15.251) (0.434) (0.403) (0.358)

Agency Climate Divergence -0.086*** -0.106*** -0.114*** 0.018 0.001 0.004 (0.029) (0.023) (0.028) (0.133) (0.129) (0.119)

Days to Confirm Current Head -0.005** -0.006*** -0.005*** -0.009*** -0.010*** -0.009†

(0.002) (0.002) (0.002) (0.004) (0.004) (0.007)

GW Bush (=1) 0.489† 0.664* 0.869* -1.655** -1.627** -1.509 (0.318) (0.390 (0.511) (0.822) (0.823) (1.574)

Number of Observations 70 70 70 65 65 65

Number of Instruments 33 25 17 29 23 17

Arellano-Bond AR(1) p-value 0.070 0.056 0.139 0.050 0.044 0.108 Arellano-Bond AR(2) p-value 0.104 0.189 0.203 0.493 0.492 0.545

Sargan p-value 0.460 0.288 0.940 0.999 0.998 0.987

Hansen p-value 0.997 0.915 0.942 1.000 0.997 0.961

Significance *** .01, ** .05, * .10, † .10 (one-tailed test). Year indicator variables included in each regression. One-step robust standard errors appear below coefficients. Models indicated (1) have full instrument sets, (2) lag limitations, and (3) collapsed instrument sets.

Wald 2 tests of reject the null of no joint significance of the independent variables at p<.01.

2

Table 3: The Impact of Ideological Conflict on Perceived Discretion, Supervisory levels of U.S. Cabinet Agencies, Senate

Difference GMM Models, 1998-2004



(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) |A – P| – |A – SFP| -0.003 -0.027† -0.014 -0.118† -0.113† -0.118†

(0.011) (0.019) (0.017) (0.082) (0.082) (0.082)

Variance of Ideological Portfolio 0.523 1.144** 0.830† -0.020 -0.020 -0.020

(0.496) (0.542) (0.590) (0.052) (0.052) (0.052)

Days to Confirm Current Head 0.000 -0.000 -0.000 0.000*** 0.000*** 0.000*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)


Number of Instruments 18 13 15 15 13 15 Arellano-Bond AR(1) p-value 0.077 0.077 0.067 0.085 0.090 0.085

Arellano-Bond AR(2) p-value 0.812 0.843 0.810 0.876 0.877 0.876

Sargan p-value 0.119 0.202 0.579 0.249 0.193 0.249

Hansen p-value 0.627 0.215 0.367 0.451 0.352 0.450



3

Table 4: The Impact of Ideological Conflict on Perceived Discretion, U.S. Cabinet Agencies, House Difference GMM Models,

1998-2004



(0.030) (0.039) (0.035) (0.047) (0.047) (0.064) |A – P| – |A – HM| 0.099 0.147 0.679** -0.324 0.214 -0.528

(0.202) (0.218) (0.343) (0.613) (0.619) (0.773)

Variance of Ideological Portfolio -9.596* -13.287† -17.897** 0.033 0.014 0.030

(5.954) (8.716) (8.430) (0.179) (0.114) (0.173)

Agency Climate Divergence -0.070* -0.086** -0.119** -0.077* -0.092** -0.109** (0.044) (0.039) (0.055) (0.045) (0.044) (0.049)

Days to Confirm Current Head -0.004* -0.006** -0.004** -0.004† -0.007** -0.004

(0.003) (0.003) (0.002) (0.003) (0.003) (0.003)

GW Bush (=1) 0.289 0.361 0.605** 0.286 -0.114 0.438 (0.237) (0.302) (0.276) (0.465) (0.509) (0.604)


Number of Instruments 33 23 27 29 22 17

Arellano-Bond AR(1) p-value 0.071 0.065 0.066 0.088 0.086 0.119 Arellano-Bond AR(2) p-value 0.167 0.191 0.670 0.203 0.257 0.233

Sargan p-value 0.780 0.583 0.682 0.229 0.223 0.547

Hansen p-value 0.994 0.756 0.948 0.976 0.720 0.498



1

Appendix 1: Construction of the Perceived Discretion Measure

Three surveys of federal government employees, spanning nearly fifteen years, are utilized in

this study. The first — the Merit Principles Survey — is administered by the MPSB. In these

surveys, participation is voluntary and each individual’s responses are kept confidential. The

Merit Principles Survey is distributed to a random sample of full-time, permanent employees,

supervisors, and mangers representing a wide spectrum of the federal government. The first

survey was completed in 1983 and the most recent in 2010. MSPB staff provided the 2000 and

2005 data.6

OPM administers the Federal Human Capital Survey (FHCS), now named the Federal

Employee Viewpoint Survey. The FHCS is distributed using a stratified random sample of full-

time federal employees; approximately 550,000 employees will be invited to participate in the

survey in 2011. Conducting the survey biennially from 2002-2010, OPM is currently collecting

2011 responses for the now annual Federal Employee Viewpoint Survey that will allow future

extensions of our measures. The 2004, 2006, 2008, and 2010 data were obtained from the

OPM website.7

The National Partnership for Reinventing Government was a task force created during

the Clinton administration to identify ways that the federal government could modernize and

streamline operations. One tool designed to advance this goal was a survey distributed to a

random sample of federal employees representing 48 different agencies and organizations. The

survey was created in conjunction with MPSB, OPM, and the Federal Aviation Administration.

OPM was the primary agency tasked with overseeing the distribution of the surveys. Surveys

6 Data from iterations prior to 2000 could not be included in the study due to data conversion issues. Archival formats proved incompatible with various methods of data importation. 7 The 2002 data is not available through public sources and could not be provided by OPM.

2

were taken in 1998, 1999, and 2000. The data was obtained from the Inter-University

Consortium for Political and Social Research database (Study Nos. 3419, 3420, and 3421).

While evaluating the data, it became clear that different agencies and sub-agencies were

included in each survey and occasionally in each survey year. To produce comparable

measures across agencies and years, and to ensure that employees were not inadvertently

double-counted among different agencies, the research team instituted three data rules.

1. If a bureau (or sub-agency) was missing for more than two survey years in the data set,

it was combined into its parent agency. For example, Marine Corps respondents

identified in one survey for one year (one survey year) were combined with the

Department of the Navy. The only exception occurred where the gap was spread over

time (i.e., included in 1998 and 2005). In such cases, we “smoothed” the measure over

the missing years as described below.

2. If a sub-agency was not included for at least two survey years, then it was either

combined or dropped altogether, i.e., if it is an independent agency. This primarily

affected smaller independent agencies surveyed only once or twice with the FHCS.

3. A sub-agency must be included in all survey-years to stand alone, otherwise it was

combined with its parent agency.8

Measuring Behavioral Attributes at the Agency Level

Our measurement strategy begins by constructing a comparable set of data across the

agencies in our sample. This requires aggregation of a wide array of individual responses in a

8 The only interesting, and outstanding item was the creation of the Department of Homeland Security (DHS) following the terrorist attacks of 2001. The Immigration and Naturalization Service was renamed Immigration and Customs Enforcement and moved from the Department of Justice (DOJ) to DHS. Although this makes measuring the impact of the individual agency more difficult, the data were combined using the rules above. For example, INS/ICE was combined with the DOJ prior to 2002, and DHS after. We implicitly assume that bureaus were already subsumed in their parent agencies in those surveys that had a narrower scope. For instance, we assume that Federal Aviation Administration (FAA) employee responses were indicated as coming from the Department of Transportation (DOT) in those surveys where the DOT was included but not FAA.

3

consistent and meaningful way and begins with creating a consistent directionality for the

underlying survey items. FHCS and Reinventing Government surveys used one of two response

sets.

5 “Strongly Agree”, 4 “Agree”, 3 “Neither Agree nor Disagree”, 2 “Disagree”, 1 “Strongly

Disagree”

5 “Very Satisfied”, 4 “Satisfied”, 3 “Neither Satisfied nor Dissatisfied”, 2 “Dissatisfied”, 1

“Very Dissatisfied”9

Merit Principles surveys used the following response sets:

5 “Strongly Disagree”, 4 “Disagree”, 3 “Neither Agree not Disagree”, 2 “Agree”, 1

“Strongly Agree”

5 “Very Dissatisfied”, 4 “Dissatisfied”, 3 “Neither Satisfied nor Dissatisfied”, 2 “Satisfied”,

1 “Very Satisfied”

Merit Principles Survey response sets were reversed to be consistent with those employed in

the FHCS and Reinventing Government questionnaires. Consequently, in all cases, when an

individual working in agency j responds to item i in year t with a higher valued element of the

response set, he or she expresses sentiment that is positively related to the trait we are

measuring — for instance, higher valued responses are associated with higher levels of job

satisfaction. Because they are not used consistently in the surveys and are somewhat rare, we

exclude “don’t know” responses. An accompanying appendix (not for publication) provides full

summary statistics for all questions used in our measurement models. Those questions that

9 Three questions relating to the job satisfaction construct employ this satisfaction scale: “Considering everything, how satisfied are you with your job?”; “In General, I am satisfied with my job.”; “Considering everything, how satisfied are you with your pay?” One item relating to the discretion construct uses this satisfaction scale: “How satisfied are you with decisions that affect your work?” All other items employ the agreement scale.

4

used “don’t know” options in the response set or had missing data (possibly due to coding or

clerical errors in the original surveys) are described by summary statistics.

Agency-level Responses

We do not intend to make interpersonal, but, rather, interagency comparisons. We

therefore must devise a method to aggregate individual responses to the agency level. We

create an agency-level response variable in the following three steps and do so separately for

each construct. We further use the supervisor identifier discussed in the text to construct a

separate set of estimates, summing only supervisory responses in step 1.

1. We sum all of the responses by employees of agency j to produce a raw score for each

agency in a given year.10

2. We calculate an inter-agency median raw score from the sums, which is simply the

median of the raw scores for all agencies in the sample.

3. We create two groups – low and high satisfaction – by placing agencies above the

median in the “high” group and those at or below the median in the “low” group.

The following dichotomous indicator records the median-split scores from step three and

serves as our agency-level response variable for item i in agency j at time t in the measurement

model described below.

10 This calculation assumes that each respondent in an agency contributes equally to his or her employer’s raw score. We make this assumption for two reasons. First, the administrative surveys we employ provide little and inconsistent information about a respondent’s position in the agency available to the secondary analyst . The Reinventing Government 1998, 1999, and 2000 surveys do not include any information about the position of respondents in the agency hierarchy. The Merit Principles survey in 2005 provides only an indicator of supervisory status, while the earlier 2000 version of that survey provided both the supervisory status indicator and the GS-level of the respondent. The FHCS 2004, 2006, 2008, & 2010 provided both supervisory status AND GS-level. Beyond this, no information about the hierarchy is offered. Second, extant survey research at the agency level has shown that weighting schemes do not make much difference in the aggregate latent measures generated through item response theory. Clinton, et al. (Forthcoming 2012), for example, produce influence weights for latent measures of ideology on the basis of agency aggregate responses to a survey question about the influence of appointees and appointee status. Their resulting weighted ideology estimates correlated very highly (0.7) with unweighted estimates. While we would like to provide such correlations for our measures, it is impossible to create weights without ignoring large numbers of items and responses over time.

5

{

Statistical Measurement Model

Armed with this response variable, our statistical problem is to model agency j’s

probability of being in the “high” group on item i in year t, , conditional on the

unobserved value of agency j’s latent behavioral attribute in year t, , for instance, job

satisfaction in the Department of Energy in 1998. To do so, we use what is known as a two-

parameter item response model from the psychometric literature. Formally, we model the

response variable as a linear function of the latent trait ( .

(1)

The intercept in equation (1) is known as the difficulty parameter and is the

discrimination parameter for item i and the error term is i.i.d. standard normal (Martin and

Quinn 2002, 138-39).

We estimate this model in a Bayesian framework due to Martin and Quinn (2002).11

Bayesian inferences are drawn from a summary of the following posterior density.

(2)

In relationship (2), are “stacked” vector representations of the item parameters,

are the latent behavioral attributes, and represents the observed responses across agencies

and time.12

Priors

11 Specifically, we use the MCMCdynamicIRT1d routine for MCMCpack in the R statistical computing environment. 12 The sampling density is ∏ ∏ ∏ ( )

where

represents the total number of items for which there are responses at time t and is the total number of agencies for which a response has been observed at time t.

6

We take a Bayesian approach; the term in (2) represents our prior beliefs

about the parameters which are specified in the following way. The item parameters are given

a standard normal prior distribution [

] . The behavioral attributes have the prior

distribution where the mean is the attribute in the last period and

is

an “evolution variance parameter” that determines “how much borrowing of strength (or

smoothing) takes place from one time period to the next” (Martin and Quinn 2002, 140). When

, the behavioral attribute is fixed across time; by contrast, values of the evolution

variance parameter that approach infinity would construct attributes that are independent in

each year of our study. Neither of these extremes is realistic. It is natural to believe that some

temporal dependence is present given the persistence of personnel and even survey

respondents from one year to the next. Moreover, years in which surveys are not taken

provide no response data and a prior indicating dependence is required to smooth the

estimates for missing years.13 Nonetheless, we do not want to build excessive dependence into

our prior specification and choose .14 The Martin and Quinn (2002, 140) estimator

also requires us to anchor each time series of latent behavioral attributes to an initial value at

the unobserved period . That is, we make the assumption that

where is

the total number of individual responses to all survey items related to the behavioral attribute

13 To retain years in which no questions were included, an indicator for a non-survey year was included with the values for all survey items in that year indicated as missing. These indicators represent the equivalent of a question asked in that year to which no agency provided responses. As expected, these items have discrimination and difficulty parameters equal to zero and do not bias the scale construction in the years for which responses are present. 14 We experimented with a variety of evolution variance parametric choices and did not choose a value below 0.5 for reasons of numerical stability.

7

for agency j across time.15 This allows us to incorporate uncertainty into the measurement

model given variation in responses collected across agencies for each latent attribute. While

the response rate to these administrative surveys is high, sometimes exceeding 70 percent, the

variation across agencies in the number of responses used to create is substantial.16 In our

specification, as the number of responses from an agency increases, the prior variance

decreases to reflect the fact that we are basing our estimates on more information from

employees of the agency. Conversely, when an agency has a small number of respondents, we

impose a wider variance on the behavioral attribute due to limitations in our information from

that agency’s employees. The included appendix (not for publication) provides detailed

information on responses to each item. To identify the direction of the latent scales, we used

the highest and lowest raw agency item sums ( to inform the estimation by holding constant

the behavioral attribute for agency with the highest mean at an arbitrarily high value in the

first time period and for the agency with the lowest mean an arbitrarily low value of

for in the first time period.17

15 We intend for this prior restriction to relate to our confidence in the measured behavioral attributes from a statistical perspective, introducing information about cross-agency response frequencies into the model. While full information on each question is available in the appendix (not for publication), an example is warranted here. From the 2004 FHCS, response data for Q4 is used in our measurement model for perceived discretion. That question received 147,899 total responses; responses from the Office of Management and Budget totaled 249 while 10,402 responses were logged from the Department of Agriculture. Such differential patterns exist in all of the item responses we use in this study. While it is possible to bring more information into the model, for example, by considering total responses in proportion to agency size or by considering responses relative to respondents’ position hierarchy, it is difficult to reliably collect such data. Thus we focus on a proxy for the richness of information within an agency. We simply want to provide more conservative uncertainty estimates when we base the attributes on fewer responses (less information) and provide a prior that can be overwhelmed by the data rather than a constraint that cannot. 16 Response rates for the Reinventing Government surveys in 1998 (40 percent), 1999 (56 percent) and 2000 (42 percent). The Merit Principles surveys had rates as high as 74 percent in 1989, falling to 64 percent in 1992, 53 percent in 1996, 43 percent in 2000 and 50 percent in 2005. Exemplary Federal Human Capital Survey response rates for 2004 (54 percent), 2006 (57 percent), 2008 (51 percent) and 2010 (52 percent) were also quite high. 17 Low and high values are indicated in parentheses as used in identifying the perceived discretion (Department of Labor, Department of State), job satisfaction (Consumer Product Safety Commission, National Aeronautic and Space Administration) and intrinsic motivation (Department of the Housing and Urban Development, Social Security Administration) scales. Where agencies tied for high or low values, agencies with the greatest coverage across

8

Estimation

For each agency-level attribute, our estimates are based on 1.5 million draws from the

posterior distribution in (2), with the first 150,000 discarded as “burn-in” with every fiftieth

observation used for inference (a total of 27,000 draws). Strong evidence of convergence was

gleaned from an examination of trace and autocorrelation plots. We observed failure of the

Geweke (1992) diagnostic in less than 5 percent of the parameters for all four attributes. Such

results would be expected by chance at conventional levels of statistical significance.

FIGURES 2-4 ABOUT HERE

Table A1 presents the estimated item parameters in for measurement models using all

employees while Table A2 presents them for models using supervisory employees only.

Positive discrimination parameters indicate that higher values of the latent variable are

associated with a higher probability of being in the “high” group of agencies on the

item indicated and negative values indicate the inverse relationship.18 Table A1 shows that all

items used to measure the perceived discretion attribute have a positive valued discrimination

parameter and Table A2 shows all but one does so.

A portrait of our perceived discretion estimates for the all-employee measure (mean = -

0.01, SD = 1.77, number of agencies = 71, number of agency-year observations = 573) for

selected agencies appears in figure A2. The Department of Education (EDUC) score remains

above the mean until 2001 when the No Child Left Behind legislation was signed into law, then

steadily declines over the next decade to a low of -2.81 in 2010. The discretion measure for the

Department of Energy (DOE) hovers around 0.4 until 2005, and then the score begins a steady

years were selected for identification restrictions (only the Department of State in the perceived discretion measure). 18 While we have coded the data such that higher values of the question are expected to be associated with higher values of the latent variable, but that relationship is estimated in the measurement models as the discrimination parameter.

9

decline to -2.27 in 2010. The Environmental Protection Agency (EPA), on the other hand,

begins the study period in 1998 at 1.79, peaks at 2.12 in 2000, the final year of the Clinton

Administration, and slowly decreases each year thereafter to a low of 0.30 in 2010. Both the

Army and Navy have scores near the mean in 1998, but their scores steadily increase over the

next decade, approaching a value of three by 2010. OPM sees one of the most pronounced

changes in perceived discretion over the study period. That agency had a score of 1.15 in 1998

at the peak of the National Partnership for Reinventing Government initiative; its perceived

discretion measure turned negative by 2002 when PART began to be applied, sharply declining

to a low of -2.83 in 2010. Department of State (STATE) estimates nearly tripled over the 12-

year period from 1.22 to 3.27, and Department of Justice (DOJ) estimates steadily increased

from 2000 to 2008 from well below to just above the mean. The 2001 terrorist attacks and

anti-terrorism efforts are said to have profoundly changed important activities of both

agencies. The Equal Employment Opportunity Commission (EEOC) sees a dramatic decline in

perceived discretion beginning with George W. Bush’s inauguration in 2001, but levels off by

the beginning of the Obama administration, albeit far below the mean at -3.03.

10

Table A1: Measurement Model Item Parameters, All Employee Measure

Item FHCS Q4 2004 -0.341 1.760* FHCS Q26 2004 -0.281 1.501* FHCS Q29 2004 -0.341 1.765* FHCS Q59 2004 -0.258 1.269* FHCS Q4 2006 0.055 1.815* FHCS Q24 2006 0.044 1.597* FHCS Q26 2006 0.056 1.850* FHCS Q54 2006 0.042 1.047* FHCS Q4 2008 -0.030 1.466* FHCS Q24 2008 -0.027 1.171* FHCS Q26 2008 -0.034 1.375* FHCS Q55 2008 -0.016 1.808* FHCS Q3 2010 0.093 1.566* FHCS Q30 2010 0.053 1.350* FHCS Q32 2010 0.079 1.495* FHCS Q63 2010 0.084 1.355* MP Q6 2000 -0.118 0.701* MP Q2h 2005 0.060 1.712* RG Q11 1998 -0.012 1.328* RG Q30 1998 -0.025 1.590* RG Q11 1999 -0.051 1.523* RG Q18 1999 -0.042 1.594* RG Q29 1999 -0.043 1.622* RG Q11 2000 0.079 1.703* RG Q18 2000 0.077 0.955* RG Q29 2000 0.080 1.004* Note: “RG” indicates the Reinventing Government surveys, “MPS” the Merit Principles Survey,

and “FHCS” the Federal Human Capital Survey. Each survey abbreviation is followed by the

question number (i.e. Q4 = Question 4 on the survey) and the year it was administered. *

indicates 95 percent HPD region does not include zero.

11

Table A2: Measurement Model Item Parameters, Supervisory Measure

Item MPS 2000 Q6 -0.009 0.474 FHCS 2004 Q4 -0.162 1.723* FHCS 2004 Q26 -0.112 1.196* FHCS 2004 Q29 -0.177 1.855* FHCS 2004 Q59 -0.088 1.140* HMPS 2005 Q2h 0.049 1.106* FHCS 2006 Q4 -0.012 1.943* FHCS 2006 Q24 0.012 1.285* FHCS 2006 Q26 -0.019 1.345* FHCS 2006 Q54 0.011 1.147* FHCS 2008 Q4 0.038 2.093* FHCS 2008 Q24 0.035 1.055* FHCS 2008 Q26 0.041 1.179* FHCS 2008 Q55 0.020 1.119* FHCS 2010 Q3 -0.052 1.521* FHCS 2010 Q30 -0.014 1.313* FHCS 2010 Q32 -0.051 1.781* FHCS 2010 Q63 -0.004 1.052*

Note: “RG” indicates the Reinventing Government surveys, “MPS” the Merit Principles Survey,

and “FHCS” the Federal Human Capital Survey. Each survey abbreviation is followed by the

question number (i.e. Q4 = Question 4 on the survey) and the year it was administered.

12

Figure A2: Time Trends in Perceived Discretion, 1998-2010

Year

Perc

eiv

ed D

iscre

tion

−5

0

5

1998 2002 2006 2010

afc agr

1998 2002 2006 2010

army com

1998 2002 2006 2010

dod doi

doj dol dot edu eeoc

−5

0

5

engy

−5

0

5

epa gsa hhs hud nasa navy

opm

1998 2002 2006 2010

sba ssa

1998 2002 2006 2010

state treas

1998 2002 2006 2010

−5

0

5

va

bureaucratic perceptions of discretion in the u.s ... · brehm and gates 1997; gailmard and patty...

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