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\chapter{Going for Goals: Presidential Appointments and Agency Goal Change}\label{CH2_RelatedWork}
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\section[Introduction]{Introduction\footnote{I would like to particularly thank Dave Lewis and Sharece Thrower for ongoing feedback and advice on this project. I also thank Nick Bednar, George Krause, Scott Limbocker, Meredith McLain, David Miller, Yu Ouyang, Hal Rainey, Jen Selin, Brad Smith, Alan Wiseman, and the participants of the “Discretion and Implementation in Different Contexts” panel at the 2020 Annual Meeting of the Southern Political Science Association for helpful comments. This chapter has been published in \textit{Presidential Studies Quarterly}.}}

When President Trump assumed office, agencies like the Environmental Protection Agency (EPA) had among their most important priorities to “reduce greenhouse gas emissions” and “strengthen environmental protections,” even though Trump had said on the campaign trail that he would “get rid of [the EPA] in almost every form” and promised to “end intrusive EPA regulations” \citep*{epa_fy17-congressional-justificationpdf_2016,trump_president_2017,post_trumps_2017}. Attached to each priority goal was a high-level goal leader, the Deputy Assistant Administrator of the Office of Air and Radiation (OAR) and the Deputy Chief Financial Officer, whose job it was to ensure the achievement of the goal and report to agency leadership and outside stakeholders on the progress toward completion. The EPA mentioned these goals specifically in their FY 2016 and 2017 budget justifications and outlined how funds would be used to reach these goals \citep*{epa_epa_fy_2016_congressional_justificationpdf_2015,epa_fy17-congressional-justificationpdf_2016}.\footnote{Furthermore, in a 2017 Government Accountability Office (GAO) survey, nearly two-thirds of managers within the EPA reported that performance information related to these goals is paid attention to by those they report to and that upper management demonstrates a strong commitment to achieving results toward the completion of their goals \citep{gao_government-wide_2018}. Additionally, in a 2016 Office of Personnel Management (OPM) survey, nearly 85\% of EPA employees agreed or strongly agreed that they knew how their work relates to their agency's goals \citep{opm_2016_2016}.}For example, in FY 2017, the EPA requested over \$200 million to support “regulatory activities and partnership programs to reduce [greenhouse gas] emissions domestically and internationally” \cite[p.20]{epa_fy17-congressional-justificationpdf_2016}.

For a president skeptical of the effect of greenhouse gases on climate change and who campaigned against the EPA, getting these goals changed presented an immediate challenge to the incoming Trump administration. Given the numerous mandates provided to the EPA and their many directives to do things like improving air quality, regulating the release of radioactive material, ensuring the quality of drinking water and safety of chemicals, preserving and restoring land and natural resources, and protecting the health of ecosystems, the agency has considerable discretion in what they will prioritize and how they will expend their limited time and resources. To get the EPA to direct their attention toward tasks more agreeable to the Trump administration and, more importantly, away from tasks the president was in direct opposition to, the agency's goals needed to be changed. The agency was set to begin determining its new short- and long-term goals in the spring of 2017, as part of the FY 2019 budget process. The president moved quickly to nominate Scott Pruitt, Susan Bodine, and Andrew Wheeler to help him change the direction of the agency \citep{post_donald_2020}.

This example from the Trump administration raises the more general question of the president's influence over agency goals. Through the administration of policy programs, federal agencies are central in policymaking. With the authority delegated and direction provided by Congress, the president, and the courts, agencies prosecute violations of law, review claims for benefits, adjudicate disputes between individuals and the government, and promulgate binding regulations. However, with dozens of mandates, agencies have considerable discretion in how they focus their time and resources. Agencies must make decisions about what to prioritize. They do so by explicitly setting goals. Goals are a “guide to action” \citep{mohr_concept_1973}, serving as the basis by which agencies make critical decisions about how they will allocate spending post-appropriations, what they will put on their limited rulemaking docket, and where to focus personnel. If agency policy change is to mean anything, it must include a change in the stated policy goals and priorities of agencies like the EPA. Furthermore, these goals ensure that both internal and external stakeholders have a common understanding of the mission and direction of the agency. By publicly committing to explicitly stated goals, external stakeholders can hold agencies accountable for reaching these goals. Therefore, presidents need to get control over goals first, in order to influence policymaking.

Unfortunately, we know very little systematically about how presidents influence agency goals and the factors that determine their success in doing so. Existing scholarship has illuminated the importance of goals for agency performance and the administration of federal policy programs \citep{jung_extending_2014,jung_performance_2018,jung_goals_2013,meyers_achieving_2001,wilson_bureaucracy_1989}. Presidency scholars have shown that presidents strategically select and place appointed personnel \citep{hollibaugh_jr_presidents_2014,hollibaugh_jr_who_2018,kinane_control_2019,kinane_control_2021,lewis_politics_2008}, but their efforts to connect strategy to outcomes has focused on agency performance of specific tasks \citep[e.g.,][]{wood_dynamics_1991,bolton_organizational_2015} or program assessment ratings (PART) \citep{gilmour_does_2006,lewis_testing_2007,lewis_politics_2008} rather than the overall aims of agencies themselves. This is important because agency priority setting occurs prior to policy outcomes. A change in the priorities of an agency will have ramifications for how policy is pursued by the agency in the future. We are further limited in the policy outcomes we can empirically observe and compare across agencies. The stated priorities, or goals, of agencies provide a more direct and comparable measure of policy change within an agency. Additionally, one of the key functions of executive power is to drive the direction of the executive branch, given the many possible foci of any given agency. Fundamental to our understanding of democratic politics, the influence of elections on agency policymaking, and executive power is understanding executive influence over agency goals.

In this article, I explain how presidents influence agency goals, focusing on how appointee teams are essential to prioritizing and resetting agency goals. I use original data on federal agency goals from the end of the Obama administration to the beginning of the Trump administration. My analysis of strategic and priority goals\footnote{Strategic goals, set quadrennially, are “to identify ‘general goals and objectives’ for the major functions and operations of the agency.” Strategic goals are further elaborated into objectives and objectives into performance goals. Objectives and performance goals provide more specific, quantifiable targets for agencies to report on the success of their more general strategic goals. A selection of agencies determines a small subset (typically 3–5) of their many performance goals to be identified as priority goals every two years. For further explanation of strategic goals, see the “Strategic Goals” subsection in the “Data, Variables, and Methods” section. For further explanation of priority goals, see the “Robustness Check Using Short- Term Goal Change” subsection in the “Results and Discussion” section.} across 56 agencies reveals that agencies experience a high degree of goal change across administrations. Additionally, I find that agencies in which the president has a higher percentage of key presidentially appointed and Senate-confirmed (PAS) positions filled experienced higher levels of long-term goal change. These findings suggest that agencies may struggle accomplishing long-term objectives, as they will experience frequent changes to their core directives. Further, they highlight the president's immediate need to get their appointee teams in place to alter the course of agency policymaking for their whole first term.

\section{Agency Goals and Presidential Personnel}
Important work has examined the key role that goals play in agency operations and management and presidential efforts to direct agency activities, but little work has examined how presidents change goals. While organizational goals are “indispensable to organization theory” \citep[p.1]{simon_concept_1964}, there is considerable debate regarding how to define or identify an organization's (often unstated) goals \citep{cyert_behavioral_1963,mohr_concept_1973,simon_concept_1964}. As two scholars put it: “the concept of organizational goals is among the most slippery and treacherous of all those employed by organizational analysts” \citep[p.183]{scott_organizations_2015}. Helping to provide clarity, \citet{kotlar_organizational_2018} offer a broad definition of organizational goals that accounts for this scholarly disagreement: “desired organizational outcomes that [are] used to guide action and appraise organizational performance \citep[e.g.,][]{mohr_concept_1973}, but [are] distinct from measurable targets \citep{march_organizations_1958}” (p.84). Fitting with this definition, organizational goals have similarly been referred to as nonoperational goals \citep{vancouver_exploratory_1991,wilson_bureaucracy_1989,witt_enhancing_1998}. A nonoperational goal, for example, would be to “reduce greenhouse gas emissions,” while an operational goal would be to “decrease carbon dioxide emissions by 10\%.”

Despite differences in how the concept is defined, scholars generally agree that organizational goals are important for the operation and performance of an organization. In fact, the identification and communication of these goals is one of the essential roles of an organization's leadership \citep{colbert_role_2009,riccucci_unsung_1995,stogdill_leadership_1950}. Given the large number of possible intentions or foci of an organization, organizational goals impose order by creating a “collective intent” that is a “guide to action” for the organization and its members \citep{mohr_concept_1973}. Among the many positive effects associated with organizational goals are an increase in individual and organizational performance \citep{colbert_role_2009,weldon_group_1993,witt_enhancing_1998}, clarification of employee role requirements and task focus \citep{colbert_role_2009,mohr_concept_1973,rainey_galloping_1999,simon_concept_1964,vancouver_exploratory_1991,weldon_group_1993}, improvement in employee morale \citep{kristof-brown_goal_nodate,meyers_achieving_2001,vancouver_exploratory_1991,witt_organizational_1992}, the identification of the mission of the organization for both internal and external stakeholders \citep{campbell_sense_1992}, and an increase in the profitability of the organization \citep{terpstra_relationship_1994}.

At the federal level, scholars have confirmed the importance of goals for agency operations and management. Agency goals, or the explicitly stated priorities of the agency, are essential in the defining of the tasks, culture, and mission of an agency for personnel within the agency \citep{wilson_bureaucracy_1989}. \citet{chun_goal_2005} pioneered research in the area of federal agency goals.\footnote{\citet{chun_goal_2005} created a measure of goal ambiguity made up of four components---mission comprehension, directive goal ambiguity, evaluative goal ambiguity, and priority goal ambiguity. The ambiguity of these goals has been used as a dependent variable, finding that goal ambiguity is heightened due to factors such as institutional age, complexity and political salience of the policy, routineness of the related tasks, and the regulatory status of the policy area \citep{chun_goal_2005,lee_politics_2009,lee_goal_2010}.} The content of agency goals has been shown to affect agency performance and the administration of federal programs \citep{gao_enhanced_2014,jung_extending_2014,jung_performance_2018,jung_goals_2013,service_presidential-transition-guide-2018pdf_2018}. This work demonstrates the importance of goal setting for multiple internal and external stakeholders and for measuring and improving agency performance.

Given their importance for agency direction, agency goals should be a target for new administrations both to shift policy and engage the agency's workforce. As \citeauthor{krause_organizational_2009} explains, “a president's capacity to effectively coordinate the executive branch around policy goals is crucial for gaining effective influence over policy administration” (2009, p.76). Indeed, executives employ two primary strategies to control administrative policymaking—politicization and centralization \citep{moe_politicized_1985}. Presidents politicize by selecting loyal appointees to head agencies, direct policy implementation, select priorities, make budgetary decisions, monitor civil servants, reflect presidential ideology and policy priorities, and convey the president's vision to the agency \citep{kinane_control_2019,kinane_control_2021,lewis_politics_2008,moe_politicized_1985,resh_appointee_2021}. Presidents centralize by pulling key policy decisions (e.g., regulatory review) into the White House and inserting White House personnel in agency decision making \citep[e.g.,][]{bolton_organizational_2015,rudalevige_managing_2002}. These tools provide the president with the access and the ability to influence the bureaucracy.

While existing literature has done a great job explaining the importance of agencies for policymaking and strategies for presidential control, it has failed to connect this work to goals, a central element of control. It also rarely recognizes key differences in agencies and has yet to catch up with new and increasing difficulties associated with the appointments process.

To begin with, scholars have yet to address presidential efforts to influence agency goals. Existing work has shown that appointees have influence over agency outputs \citep[e.g.,][]{bolton_organizational_2015,wood_dynamics_1991} and the performance of programs \citep{gilmour_does_2006,lewis_testing_2007,lewis_politics_2008}. However, variation in correlations between appointee placement and outputs \citep[e.g.,][]{wood_dynamics_1991} has not been fully explained. One mechanism that would explain variation in success in changing outputs could be the details of goal change within agencies and differential success in the appointments process. Further, we know less about how the tool of politicization impacts agency priorities and the course of agency policymaking \citep{oconnell_vacant_2009}. The goal-setting process is the starting point at which agencies decide how they will focus their attention and limited resources. Therefore, meaningful policy change must start with the changing of agency goals. By only examining limited agency outputs, scholars have missed the bigger picture in which agency policymaking takes place.

Additionally, the literature neglects a key difference among agencies—that some are targets of ideological fights and others are not. We know that presidents target their appointments to agencies ideologically opposed to them \citep{hollibaugh_jr_presidents_2014,hollibaugh_jr_when_2017,hollibaugh_jr_who_2018,lewis_politics_2008} and appointees target and marginalize ideologically opposed civil servants and those who otherwise may resist presidential priorities \citep{bolton_organizational_2015,doherty_executive_2019,mendelson_agency_2003,richardson_politicization_2019}. However, less attention is given to the president's strategy toward ideological allies. While it is true that presidents will find a more welcome and compliant civil service in these agencies, these arguments neglect the remaining need of an appointee to direct the policymaking of the agency to be in line with the president's preferences. Allies of the incoming administration are commonly the ideological opponents of the previous administration. During the previous administration, these agencies were likely constrained from implementing their preferred policies. If the president hopes to institute new initiatives or reenergize an agency, especially after a previous administration of the opposing party, they must take control and have a unified vision channeled through an appointee.

In contrast, some agencies will not be a priority or a source of conflict across administrations. If an agency's activities are agreed upon by both parties or are never salient, presidents have little incentive to exert limited resources on gaining control over these agencies. Instead, the president will be content with leaving the agency on “autopilot.”

Finally, scholars have rarely addressed the consequences of the changing appointments system for control. The Senate confirmation process, while long being a hurdle for incoming presidents, has become more challenging for and has brought increased scrutiny on presidents and their nominees. The process is increasingly slow-moving, and the long norm of Senate deference to presidential nominees has eroded \cite{oconnell_staffing_2017}. The inability of presidents to get confirmed appointees into key PAS positions, resulting in vacancies, is believed to negatively affect agency capacity and cause instability and inaction \citep{dull_continuity_2009,mendelson_uncertain_2014,oconnell_vacant_2009}. Furthermore, \citeauthor{oconnell_vacant_2009} argues that “vacancies undercut agency accountability” to the president and Congress (2009, p.921). Unconfirmed officials serving in an acting capacity or delegated the duties of the office are believed to be less accountable, as they likely have not gone through the Senate confirmation process, were not selected by the president, and have civil service protection from removal. Therefore, we should expect that agencies in which the president cannot get “his people” confirmed will be both less able and less willing to respond to new directions from the White House.

\section{How Presidents Go for Goals}
Upon inauguration, every new president is faced with the managerial task of taking control of the administrative state. The executive branch of the federal government is comprised of more than 2.7 million civilian employees, hundreds of organizations and subunits, and a budget in excess of \$4 trillion \citep{lewis_deconstructing_2019,selin_sourcebook_nodate}. Federal agencies are responsible for translating the authority delegated and direction provided by Congress, the president, and the courts into public policy \citep{lewis_presidents_2003}. With dozens of mandates to focus their attention on \citep[e.g.,][]{acs_which_2016,clouser_mccann_life_2020}, agencies have considerable leeway. Agencies make decisions regarding where they will focus their attention and limited resources \citep[e.g.,][]{potter_bending_2019,wilson_bureaucracy_1989}, how they pursue policy goals and enforce the law \citep[e.g.,][]{herd_administrative_2019,potter_bending_2019,wilson_bureaucracy_1989}, and how they interact with and consider the views of external stakeholders \citep[e.g.,][]{wilson_bureaucracy_1989,yackee_bias_2006,yackee_politics_2012,yackee_invisible_2015,yackee_sweet-talking_2006}. This presents a problem for an incoming president wanting to make administrative policymaking align with their political agenda, as the aims and policy views of the agency may differ from those of the new administration.

New presidents, especially those of competing partisanship of the outgoing president, will work to change agency goals, represented by the agency's stated priorities, to be in alignment with their preferences. Presidents are invested in creating “responsive competence” within the administrative state \citep{moe_politicized_1985}. New administrations will use the tools at their disposal to attempt to change the habits and course of action instilled by the previous administration. Changing the goals of the agency will serve as the first step by which new presidents can redirect the activities of the agency to be in alignment with the new administration, as goals inform and direct personnel within the agency on how they should make the types of discretionary decisions discussed above \citep{wilson_bureaucracy_1989}.

\noindent \textbf{Hypothesis 1}: Agencies will experience more goal change across presidential administrations than within administrations.

To drive these changes, presidents need people to direct this work for them. Control requires that the agency focuses its goals around the president's agenda. Appointees in leadership positions are able to highlight and communicate presidential priorities to the agency. If left alone, careerists in the agency may be unsure of how to meet the demands of the new administration and arbitrate between various views of the administration, as the White House may be internally divided on policy goals \citep{bressman_inside_2006}. However, appointees can communicate a unified, narrow focus to center their goals around \citep{resh_value_2017}. These high-level appointees have direct say and involvement in the process of revising and altering goals.

Additionally, agencies filled with vacancies will be prone to inaction. The instability caused by a lack of stable, confirmed leadership will make careerists less likely to implement goal change, as a new appointee may come into the agency and change the direction of their policymaking \cite{oconnell_vacant_2009}. Confirmation also confers both the backing of the White House and of the Senate for the individual serving in the position. This provides the appointee with a certain level of prestige, allowing them to better bargain and engage with agency personnel.

While Office of Management and Budget (OMB) review of goals\footnote{Agencies go through three rounds of OMB review of goals in the year prior to goals being published (once in June, September, and December) \citep{omb_circular_2020}. This is highlighted in Figure 2.1.} can assist in gaining control over agency goals, their review alone is not enough. Despite having regular contact with external contacts during the goal making process, agencies have a high level of discretion over the goals they select \citep{kroll_tools_2020}. With numerous potential goals the agency could select that are at least acceptable to the administration, OMB cannot micromanage an agency's goal selection process. OMB can serve more as a “backstop,” preventing goals clearly contrary to the president's agenda from getting through, rather than a “pitcher,” directing the selection and formulation of goals as a high-level appointee can.\footnote{\citeauthor{kroll_tools_2020} further note that, under GPRAMA, OMB “[was] not granted the power that [the Program Assessment Rating Tool (PART)] provided to closely question the goals that agencies pursued, the management processes employed, or the level of performance achieved” (2020, p.4).}

Therefore, presidents will be more successful at changing goals in agencies in which the president has been able to gain more control from inside the agency. Politicization provides the president with a direct connection within the agency and a role at the top levels of the organization \citep{lewis_politics_2008}. Having more confirmed appointees in place will allow presidents to be more successful in changing agency goals.

\noindent \textbf{Hypothesis 2}: After a change in administration, agencies in which the president has more control through confirmed appointees will experience more goal change.

Of course, across the departments and agencies of government there are a large number of goals. Some goals are more or less consistent with the views of the president, and some are more or less important to their agenda. While presidents have interest in changing the goals of all agencies, they will focus their activities on particular agencies of interest.

Presidents will prioritize changing goals in ideological, or politically contentious, agencies. Certain agencies, such as the EPA and the Department of Education, are policy battlegrounds across administrations and foster debate between both parties. Further, personnel within these agencies have views of their own on how policy should be executed. These views often lead civil servants to self-select into agencies that align with their policy preferences, helping to create an ideological reputation for an agency \citep{clinton_separated_2012}. When an agency handles politically contentious policy and is comprised of a workforce that is ideologically opposed to the president, we can expect that the presidents will target goal change to rein them in. These agencies, like the EPA for the Trump administration, will need to have their policy goals adjusted and moderated to be more in-line with the president's policy views. For example, a president who does not believe in climate change would not want the EPA to focus on greenhouse gas emissions. However, we should also expect that new presidents will be invested in changing goals for agencies that are home to ideological allies of the president. These agencies are likely to have seen their priorities and long-term planning conducted in opposition to their beliefs during the course of the previous administration. Therefore, presidents must also reinforce natural allies with more ambitious and targeted goal change after a change in administration. For example, the Biden administration will be focused on shifting the Department of Education's goals away from the subject of school choice.

Moderate or apolitical agencies are less likely to see their policy area politicized or become salient and will be less likely to change from administration to administration. In these cases, Republicans and Democrats more or less agree about the activities of the agency, and what disagreements do exist are generally muted. For example, the General Services Administration (GSA) and National Aeronautics and Space Administration (NASA) should not be as likely to see their goals change across administrations due to the nonpartisan activities of their agencies. One of NASA's strategic goals that remained substantively similar across the Obama and Trump administrations was to “expand the frontiers of knowledge” and to “expand human knowledge” \citep{nasa_nasa_2014,nasa_nasa_2018}. The pursuit of knowledge by the scientists at NASA is not subject to the same type of political debate as seen in agencies like the EPA.

\noindent \textbf{Hypothesis 3}: After a change in administration, ideological agencies (i.e., conservative/liberal agencies) will experience more goal change.

In total, presidents need to try to change agency goals to alter the course of agency policymaking. This is harder with the breakdown in the appointments system. Presidents naturally will focus on some agencies more than others, focusing particularly on ideological, or politically contentious, agencies that are both aligned with and distant from the president.

\section{Data, Variables, and Methods}
In order to test the expectations above, we need data on agency goals across a long enough period to observe changes across administrations. Federal agency goals present an ideal case. I have collected new data on all federal agency goals under the current goal process. Using these goals, I examine goal change across the Obama and Trump administrations, using the administration change as an intervention. I then compare treated agencies (i.e., those where appointees were quickly confirmed) to control agencies (i.e., those where appointed positions were left vacant).

\subsection{Strategic Goals}
In federal agencies, the Government Performance and Results Modernization Act of 2010 (GPRAMA) and its predecessor (the Government Performance and Results Act [GPRA] of 1993)\footnote{GPRAMA is one of a series of performance management reforms over the last few decades. However, GPRAMA has stood out from its predecessors, having been shown to be more effective at getting agencies to use performance data in their decision making and having been implemented in a nonpartisan way \citep{kroll_tools_2020}.} statutorily require agencies to plan, evaluate, and report on the central activities of the agency. As part of the president's first budget, agencies produce a strategic plan.\footnote{The process begins with agencies developing draft goals in the spring of the year prior to goals being reported \citep{omb_circular_2020}. Agencies then continue the process of revising these goals for more than eight months \citep{omb_circular_2020}. This process runs parallel with the formation of agency budgetary requests. Most agencies include long-term goals and outline specific allocation requests for each long-term goal in their budget requests. Agencies are also required to report on their performance of achieving their previous goals in accordance with their annual budget request. Evidence of good performance is used to convince political actors that they will see a return on their investment in the agency.} In these plans, the agency is “to identify ‘general goals and objectives’ for the major functions and operations of the agency,” defined as agency strategic goals, that they will focus on for the subsequent four fiscal years. These strategic goals remain in place until a new strategic plan is produced at the beginning of the next presidential term. For each subsequent year covered by the strategic plan, agencies are required to publish an annual performance report that details their progress toward accomplishing their strategic goals. The timeline for the development and review of strategic plans at the beginning of the Trump administration is outlined in Figure 3.1. This highlights the length of time that agencies spend on the development of strategic goals and the reporting of progress toward those goals, the regularity of OMB review, and the close connection of performance reporting with the budget process.

\begin{figure}[htp]
    \centering
    \caption{Agency Strategic Goal Timeline}
    \includegraphics[width=15cm]{Figures/chapter2_figure1.jpg}
    \label{fig:goaltimeline}
\end{figure}

Strategic goals cover the very core of the activities, priorities, and direction of the agency. Emphasizing this point, Sonny Perdue, President Trump's Secretary of Agriculture, stated that their strategic plan “serves as a roadmap for everything the [Department of Agriculture (USDA)] family and I will do” and “will guide the work of all of us at USDA in the coming years” \citep{usda_usda_2018}. Agencies set 2–10, typically 3–5, of these goals for a period of four years. Strategic goals are further elaborated into objectives and objectives into performance goals.\footnote{Strategic plans contain four types of agency goals, each a subcategory of the previous type of goal: (a) strategic goals, (b) objectives, (c) performance goals, and (d) priority goals. Objectives and performance goals provide more specific, quantifiable targets for agencies to report on the success of their more general strategic goals.} This elaboration takes the “what” of the agency's overarching goals and breaks it down into the “how” for tasks and benchmarks the agency will use to guide specific action and allow for the evaluation of performance \citep{gagne_strategy_2018}.

For the purposes of illustration, Figure 3.2 highlights an example from the Department of Education's 2018–2022 Strategic Plan. In this plan, the Department of Education identified four strategic goals. Figure 3.2 includes information on the first strategic goal and a subset of its objectives and performance goals. This first strategic goal had a total of four objectives and 22 performance goals, with each objective having connected performance goals.

\begin{figure}[htp]
    \centering
    \caption{Department of Education 2018–2022 Goal Levels}
    \includegraphics[width=15cm]{Figures/chapter2_figure2.jpg}
    \label{fig:edgoals}
\end{figure}

This example highlights several features of the goal-making process—the broad nature of strategic goals, the narrowing of focus from strategic goals down to performance goals, the quantifiable nature of performance goals, and the large number of metrics the agency can focus their attention on. This example further accentuates the difficulty in changing strategic goals, as it is unlikely the Department of Education's commitment to P–12 education could be eliminated.

I focus my analysis on strategic goals because they speak to the core of the agency's mission and activities. Therefore, a change in a strategic goal will have a larger and more long-term effect on an agency than a change in a lower-level goal. Additionally, the broad nature of these goals makes them a hard test case. For example, it is unlikely for the Department of Homeland Security to not have a goal focused on preventing terrorism.

One natural question we may have about strategic goals is the degree to which they matter or represent tangible policy intentions. To answer this question, we may first look to managers within federal agencies that are responsible for carrying out these goals. In a 2017 GAO survey, approximately 70\% of career Senior Executive Service (SES) managers reported that managers at their level are held accountable to a great or very great extent for accomplishing agency strategic goals and that they have the decision-making authority needed to help the agency accomplish those goals \citep{gao_further_2017}. We may also look to those who have overseen the goal process. Robert Shea, former associate director of OMB during the George W. Bush administration, stated that the administration attempted to make agency goal setting “represent[] the priorities the administration [was] trying to accomplish” \citep{beitsch_white_2020}. Shea further argued that performance measures provide the White House with “a lot of leverage” to hold agencies accountable \citep{beitsch_white_2020}. Finally, we can look to agency budgets to assess whether goal change is associated with changes in agency focus and budget allocations. When the EPA removed “Addressing Climate Change” from their strategic goals in their 2018–2022 strategic plan, they removed the roughly \$200 million that had been allocated to achieving this goal annually over the last several years \citep{epa_fy_2018}. Similarly, the agency's number of mentions of “climate change” in their budget justification decreased from 344 to 3 (one of which was to highlight the cutting of \$16 million in funding for climate change research). Additionally, when the Department of Justice (DOJ) added the strategic goal of “Secure the Borders and Enhance Immigration Enforcement and Adjudication” in their 2018–2022 strategic plan, they highlighted an increase of \$65.9 million in funding for “immigration-related program enhancements” \citep{doj_us_2018-1}. This included funding to hire 450 new immigration judges and support staff at the Executive Office for Immigration Review, 70 new immigration prosecutors at the Offices of the United States Attorneys, and 20 new paralegal support staff at the Office of Immigration Litigation \citep{doj_us_2018}. While this evidence is only suggestive, it all points to agency goals having a meaningful impact on the work of agency employees and goal change symbolizing significant policy and resource realignment.

\subsection{Dependent Variables}
In order to observe change across agencies in their strategic goals, I have gathered every agency strategic plan since the full implementation of GPRAMA. For each agency, I looked for every strategic plan in the Obama administration and their first strategic plan in the Trump administration. In total, I gathered 56 strategic plans from 56 agencies, including 219 goals from the end of the Obama administration (2014–2018) with subsequent strategic goals in the Trump administration (2018–2022).

By comparing goals in the Obama administration to the goals in the Trump administration, I coded an ordinal measure of goal change.\footnote{I also coded and ran models with a bivariate dependent variable of goal elimination. These models provide substantively similar results and are reported in Appendix Table 5.12. They were excluded from the body of the article, as strategic goals are broad in nature and are unlikely to be eliminated.} Specifically, I examine whether the policy area addressed by the goal continues in the next period and, if the subject is addressed, whether the substance of the goal changes. For each goal, I coded the policy area using the Comparative Agendas Project (CAP) subtopic codes. The CAP categorizes all areas of public policy into 21 broad areas (e.g., Agriculture, Health, Labor) and 220 subtopics that fall within those broad areas (e.g., Subsidies to Farmers, Health Care Reform, Worker Safety). The level of change variable is coded 2 if the topic or subject of the goal does not continue in the next period. This is coded by determining whether the CAP subtopic code of the goal does not remain in one of the sequential period's strategic goals. This level of change is indicative of a major modification of agency priorities, as described above in the case of the EPA's elimination of a strategic goal.\footnote{Because strategic goals are broad in nature, are often closely tied to a core component of an agency's directives, and have numerous connected performance metrics, it is unlikely that this level of change is indicative of the agency “accomplishing” the prior goal.} The variable is coded 0 if the goal remains exactly the same. This is coded by determining whether the text of the goal is an exact match or within a few words, while retaining the same meaning and substance, with a strategic goal in the subsequent period. Finally, the level of change is coded 1 when the topic or subject of the goal remains the same, while the meaning or substance of the goal changes. Therefore, in these instances the CAP subtopic code remains the same, while the text of the goal changes in a significant manner. Figure 3.3 highlights examples of each type of goal change.

\begin{figure}[htp]
    \centering
    \caption{Examples of Types of Goal Change}
    \includegraphics[width=15cm]{Figures/chapter2_figure3.jpg}
    \label{fig:typesofchange}
\end{figure}

Across the Obama and Trump administrations, there was considerable goal change. Approximately 15.5\% of strategic goals were eliminated in the subsequent period, with an additional 45.7\% experiencing some level of change between periods. The remaining 38.8\% of strategic goals remained exactly or substantively the same across periods. These descriptive statistics provide support for Hypothesis 1, suggesting that agencies experience frequent changes to even their most core directives from one administration to the next.

\subsection{Key Independent Variables}
To assess the impact of presidential appointment control on goal change, I measure the degree to which key appointed positions were confirmed prior to when goals were reported. Specifically, for executive agencies, I measure the proportion of key presidentially appointed and Senate-confirmed (PAS) positions filled at the end of 2017. For boards and commissions, I measure the proportion of seats held by Trump appointees by the end of 2017. This variable measures the amount of control the president has over the agency through politicization. The Partnership for Public Service and Washington Post's “appointee tracker”\footnote{\textit{The Washington Post} and the Partnership for Public Service, a nonpartisan nonprofit, created an “appointee tracker” to follow the status of roughly 700 PAS positions since the beginning of the Trump administration. The tracker has information on whether and when the president made a nomination, the appointee was referred to and reported out of a committee, the nominee was confirmed by the Senate or returned to the president, and the official left the position.} of President Trump's appointments was used to classify “key” PAS positions. This tracker was also used to observe the level of key appointee positions filled in executive agencies at the end of President Trump's first year in office. The Agency Spotlight project\footnote{The Demand Progress Education Fund, a nonprofit, and the Revolving Door Project, a project of the Center for Economic and Policy Research think tank, created the Agency Spotlight project to provide information on the leadership of independent agencies and their voting records. The project is home to information on the status of board and commission seats, the term length of positions and the expiration dates of current leadership, whether the agency requires partisan balancing, when the president made a nomination and when the individual in the position was confirmed, among a variety of other useful descriptive information on each of the 39 agencies they track.} from the Demand Progress Education Fund and the Revolving Door Project was used to track board and commission positions. Across the 56 agencies in my sample, 21.9\% of an agency's key positions were filled on average at the end of Trump's first year in office.

To assess whether presidents focus goal change to ideological or politically contentious agencies, I use a measure of agency ideological reputation---liberal, moderate, or conservative. I use the \citet{richardson_elite_2018} survey on perceptions of agency ideology from federal executives across the administrative state.\footnote{I used the \citet{clinton_expert_2008} ideology measure to fill in ideological ratings for the Defense Nuclear Facilities Safety Board, Federal Mine Safety and Health Review Commission, Inter--American Foundation, and Railroad Retirement Board.} Agency officials were asked: “In your opinion, do the policy views of the following agencies tend to slant liberal, slant conservative, or neither consistently in both Democratic and Republican administrations?” \citep[p.304]{richardson_elite_2018}. Responses were then aggregated and estimates of ideological rating, from --1 (most liberal) to 1 (most conservative), were produced after accounting for each respondent's conception of the general liberal--conservative ideological dimension and the respondent's experience with the agency. Although their measure was based on a single survey at one point in time, they have shown that agency ideology is stable, through comparison with earlier measures \citep{bonica_senate_2015,clinton_separated_2012}. They also included phrasing of “across Democratic and Republican administrations” to prompt respondents to think more generally about an agency's ideology over time. Ideological group is coded as moderate if the estimate of the agency's ideology is indistinguishable from 0, meaning that the 95\% confidence interval contains 0. It is coded as liberal or conservative if the estimate of the agency's ideology is distinguishable from 0 in either a liberal (negative) or conservative (positive) direction. Moderate agencies are used as the reference group to assess whether conservative and liberal agencies experienced more goal change.

\subsection{Control Variables}
Of course, other factors are correlated with both the dependent variable and potentially the independent variables. Therefore, I include two goal-level controls and three agency-level controls. The first control variable is a measure of priority to the president. The Trump priority variable is coded with a 1 if the topic of the goal is contained within President Trump's \textit{Contract with the American Voter}\footnote{This comes from \url{https://assets.donaldjtrump.com/_landings/contract/O-TRU-102316- Contractv02.pdf}. Individual goals from the EPA, Office of Government Ethics, International Trade Commission, and the Departments of Agriculture, Commerce, Education, Health and Human Services, Homeland Security, Interior, Justice, State, Transportation, Treasury, and Veterans Affairs were coded as priorities. The complete coding of priority topic areas with the agencies and bureaus connected to that policy area is reported in supporting information Appendix Table 5.13.} and 0 otherwise. It is expected that these goals were of heightened importance to the incoming administration and might experience a higher level of goal change. Additionally, it is possible that the president more quickly made nominations to the agencies connected to these goals. Therefore, by not controlling for priority, my measure of appointment fill could be picking up on the president's overall focus on the agency rather than his control over the agency through politicization.

The second control variable is a measure of the complexity of the goal. More specifically, I measure the Simple Measure of Gobbledygook (SMOG) grade of the text of each goal \citep{mclaughlin_clearing_1969,michalke_korpus_2018}. These grades estimate the number of years of education required to understand a text based on the number of polysyllables (words with three or more syllables) and the number of sentences. SMOG grades are a common measure of the readability of a text. This variable accounts for variation in how complex or generic a goal is. For example, the Department of Transportation's goal of “Quality of Life in Communities” was on the low end of SMOG grades, whereas the Equal Employment Opportunity Commission's goal of “Combat Employment Discrimination Through Strategic Law Enforcement” was on the high end of complexity. More general and simple goals may be less likely to change, as they are broader and more difficult to achieve in any one goal period. In contrast, goals that are more complex may be more specific and easier to accomplish.

The third and fourth control variables measure the structure of agencies. I code the “department” variable with a 1 if the goals are produced by a cabinet department and 0 otherwise. Cabinet departments are likely more important to the president, as they are the highest-profile and largest agencies. Additionally, I code the “board or commission” variable with a 1 if the goals are produced by a multimember board or commission and 0 otherwise. It is possible that these agencies are more independent from the president and will be less likely to experience goal change.

The fifth and final control variable measures the employment level of agencies. Specifically, I use logged employment, as reported by the Office of Personnel Management (OPM) in September 2017 on FedScope. It is possible the president was more focused on changing the goals of the largest agencies. Further, these agencies may be more resistant to change.

\subsection{Methods}
I estimate models to observe the determinants of goal change with individual agency goals as the unit of observation. Since the dependent variable, goal change, is ordinal, I estimate ordered logit models.\footnote{I also estimate multinomial logit models and find substantively similar results. These models are reported in Appendix Table 5.11.} I estimate models with only the key independent variables of interest and then models with controls. The first model is on the entire sample of goals, and the remaining models remove any goals from agencies for which an ideological rating does not exist. I discuss substantive results in terms of the average marginal effect\footnote{This is done due to the difficulty in interpreting coefficients from logistic regression models. The marginal effect “communicates the rate at which y changes at a given point in covariate space, with respect to one covariate dimension and holding all covariate values constant” \citep[p.7]{leeper_interpreting_2018}. Average marginal effects “calculate marginal effects at every observed value of X and average across the resulting effect estimates” \citep[p.8]{leeper_interpreting_2018}. Average marginal effects are viewed as providing the most informative summary of the effect of an independent variable across its whole distribution \citep{bartus_estimation_2005,leeper_interpreting_2018,norton_log_2018}.} of each independent variable on the probability of goal change (reported in Table 2).

We may be concerned that some of the same factors that influence goal change influence appointment fill. These models attempt to control for the most prominent potential omitted factors, namely, priority to the president and agency structure. Despite these efforts, some caution should be given in interpretation of the results, as it is hard to establish causation with the available data.

\section{Results and Discussion}
Several patterns are illuminated by the results of my models. To begin with, agencies experience a high level of goal change between administrations. Rather than see the aims of their agency remain consistent, agency officials are constantly faced with new directions to focus their work. Additionally, President Trump's success at altering agency goals was concentrated in particular agencies. Agencies in which the president had heightened control through politicization saw their goals change at significantly higher rates.

I start by examining the degree to which strategic goals were eliminated or changed across the Obama and Trump administrations: specifically, President Trump's success at eliminating or changing the Obama administration's strategic goals. As showcased in Figure 3.4, I find there is a substantial goal change that occurs between the transition period and that there is a high degree of variation across agencies. Furthermore, the distribution of change is bifurcated to the two poles of the distribution. Agencies either see most of their goals change or little to no change at all. This suggests that some agencies may be more stable than others. To examine this variation, I turn to my model results.

\begin{figure}[htp]
    \centering
    \caption{Histogram of Proportion of Goals Eliminated or Changed across Administrations}
    \includegraphics[width=15cm]{Figures/chapter2_figure4.jpg}
    \label{fig:histgoalchange}
\end{figure}

I next consider the effects of the president having significant PAS positions filled on the level of goal change accomplished. President Trump was considerably more successful at either changing or eliminating goals when he had individuals confirmed in the top positions of the agency. As shown in Figure 3.5, an increase in control over PAS positions has a strong, consistent effect on the probability of each type of goal change. Once accounting for potential confounders, moving from having 25\% of key positions filled to 75\% of positions filled decreases the likelihood of a goal remaining the same by more than 10\%, while increasing the probability of change and elimination by approximately 5\% and 8\%, respectively. Tables 3.1 and 3.2 further highlight this effect. On average, an agency moving from having no positions filled to all positions filled is 23\% less likely to have their goals remain the same, 9\% more likely to have their goals change, and 14\% more likely to have their goals eliminated. These effects are significant at the 0.05 p-value threshold across model specifications. While the coefficient size decreases with the addition of controls, this is what we should expect. Without accounting for potential confounders, the effect of appointment fill will be inflated because of its correlation with other important factors (e.g., agency ideology, priority to the president, agency structure).

\begin{figure}[htp]
    \centering
    \caption{Predicted Probability of Level of Goal Change by PAS Fill (Model 4 of Table 1)}
    \includegraphics[width=15cm]{Figures/chapter2_figure5.jpg}
    \label{fig:predictedprobgoalchange}
\end{figure}

\begin{table}
\caption{Models of Strategic Goal Change}
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{4}{c}{Goal Level of Change} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
\% Confirmed End Y1 & 2.483$^{***}$ & 1.989$^{***}$ & 1.939$^{***}$ & 1.175$^{**}$ \\ 
  & (0.529) & (0.564) & (0.567) & (0.596) \\ 
  & & & & \\ 
 Conservative &  & 0.721$^{*}$ & 0.656 & 0.363 \\ 
  &  & (0.404) & (0.409) & (0.449) \\ 
  & & & & \\ 
 Liberal &  & 0.380 & 0.316 & $-$0.022 \\ 
  &  & (0.303) & (0.307) & (0.321) \\ 
  & & & & \\ 
 Priority Goal &  &  & 0.189 & $-$0.440 \\ 
  &  &  & (0.436) & (0.477) \\ 
  & & & & \\ 
 SMOG &  &  & $-$0.018 & $-$0.008 \\ 
  &  &  & (0.013) & (0.013) \\ 
  & & & & \\ 
 Department &  &  &  & 0.712 \\ 
  &  &  &  & (0.454) \\ 
  & & & & \\ 
 Board or Commission &  &  &  & $-$0.442 \\ 
  &  &  &  & (0.376) \\ 
  & & & & \\ 
 Log Employment &  &  &  & 0.121 \\ 
  &  &  &  & (0.086) \\ 
  & & & & \\ 
\hline \\[-1.8ex] 
Observations & 219 & 203 & 203 & 203 \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

\begin{table}
\caption{Average Marginal Effects on Goal Change (From Model 4 of Table 1)}
\begin{tabular}{@{\extracolsep{4pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
\\[-1.8ex] & \multicolumn{3}{c}{Average Marginal Effects on Pr(Outcome=Exact Same)} \\ 
\\[-1.8ex] Variable & AME & Standard Error & P-Value \\ 
\hline \\[-1.8ex] 
\% Confirmed End Y1 & -0.2337  & 0.1144 & 0.0410 \\
Conservative  & -0.0712  & 0.0862 & 0.4086 \\
Liberal & 0.0045 & 0.0637 & 0.9443 \\
Priority Goal & 0.0886  & 0.0960 & 0.3560 \\ 
SMOG & 0.0015 & 0.0027 & 0.5719 \\
Department & -0.1429 & 0.0898  & 0.1117  \\
Board or Commission & 0.0907 & 0.0790  & 0.2511  \\
Log Employment & -0.0241 & 0.0169  & 0.1534  \\
\hline \\[-1.8ex] 
\\[-1.8ex] & \multicolumn{3}{c}{Average Marginal Effects on Pr(Outcome=Changed)} \\ 
\\[-1.8ex] Variable & AME & Standard Error & P-Value \\ 
\hline \\[-1.8ex] 
\% Confirmed End Y1 & 0.0945  & 0.0471 & 0.0448 \\
Conservative  & 0.0254  & 0.0269 & 0.3450 \\
Liberal & -0.0018 & 0.0258 & 0.9443 \\
Priority Goal & -0.0412  & 0.0500  & 0.4095  \\ 
SMOG & -0.0006 & 0.0011 & 0.5708 \\
Department & 0.0527 & 0.0324  & 0.1036  \\
Board or Commission & -0.0399 & 0.0382  & 0.2960  \\
Log Employment & 0.0098 & 0.0070  & 0.1621  \\
\hline \\[-1.8ex] 
\\[-1.8ex] & \multicolumn{3}{c}{Average Marginal Effects on Pr(Outcome=Eliminated)} \\ 
\\[-1.8ex] Variable & AME & Standard Error & P-Value \\ 
\hline \\[-1.8ex] 
\% Confirmed End Y1 & 0.1392 & 0.0722 & 0.0537 \\
Conservative  & 0.0458  & 0.0601 & 0.4461 \\
Liberal & -0.0027 & 0.0380 & 0.9443 \\
Priority Goal & -0.0474  & 0.0467  & 0.3100  \\ 
SMOG & -0.0009 & 0.0016 & 0.5748 \\
Department & 0.0902 & 0.0612  & 0.1404  \\
Board or Commission & -0.0508 & 0.0419  & 0.2255  \\
Log Employment & 0.0144 & 0.0103  & 0.1622  \\
\hline \\[-1.8ex] 
\hline \\[-1.8ex] 
\end{tabular}
\end{table}

Matching with my second hypothesis, these results suggest that the president has a heightened need to have appointees in place to alter the long-term course of the agency. Appointees play a key role in channeling the views of the new president into the agency's goals. By ensuring the placement of these appointees, the administration will have a greater chance of altering the direction of the agency away from the course set by the previous administration and toward the incoming administration's vision for the agency.

Finally, I examine whether strategic goal change is focused to ideological agencies. Figure 3.6 showcases the level of goal change across administrations for three ideological groups—liberal, moderate, and conservative. Both liberal and conservative agencies are shown to experience higher levels of goal change than moderate agencies. The difference is much starker in the case of conservative agencies, with the median proportion of goal change being nearly 75\% higher for conservative agencies than moderate agencies. Liberal agencies, by contrast, have a median that is roughly 30\% larger.

\begin{figure}[htp]
    \centering
    \caption{Proportion of Strategic Goals Changed in Next Period by Ideological Group}
    \includegraphics[width=15cm]{Figures/chapter2_figure6.jpg}
    \label{fig:histgoalchange}
     \fnote{\textbf{Note:} Each box plot displays the distribution of agency-level proportion of goals changed for each ideological group.}
\end{figure}

The results in Tables 3.1 and 3.2, however, provide limited support for my third hypothesis. While ideology is not a significant predictor of goal elimination or change, conservative agencies are predicted to have a higher probability of both types of change. However, these results are not significant across models and the effect sizes are relatively small. Conservative agencies, on average, were 7\% less likely to see their goals remain the same, were 2.5\% more likely to see their goals change, and 4.6\% more likely to see their goals eliminated compared to moderate agencies. In contrast, liberal agencies, on average, were less likely to experience either type of change, compared to moderate agencies. Therefore, President Trump seemed to have slightly more success in altering agency goals to be in alignment with his preferences in conservative agencies. These agencies may have been more receptive to aligning their goals with the incoming Trump administration.

\subsection{Robustness Check Using Short-Term Goal Change}
A more fine-grained case in the goal-making process is priority goals. GPRAMA requires that the most high-profile agencies\footnote{Twenty--two of the most prominent agencies are required to produce priority goals. These are comprised of 22 of the 24 Chief Financial Officer Act (CFO Act) agencies. These include all cabinet-level departments, the Environmental Protection Agency (EPA), the General Services Administration (GSA), the National Aeronautics and Space Administration (NASA), the National Science Foundation (NSF), the Office of Personnel Management (OPM), the Small Business Administration (SBA), and the Social Security Administration (SSA).} determine, in consultation with OMB,\footnote{While both strategic and priority goals must go through multiple rounds of OMB review, priority goals have additional requirements that give OMB even greater influence. To begin with, OMB determines the number of priority goals provided to the agency. Additionally, agencies are explicitly instructed to select goals that advance priorities for agency leadership and the administration \citep{omb_circular_2020}. They are further advised to take objectives set in the president's State of the Union Address and in presidential executive orders when deciding their priority goals \citep{omb_circular_2020}. Finally, they oversee the performance of the agency in achieving their goals, with mandated quarterly reports. When agencies do not meet their goals, they are required to report to OMB on why they did not achieve their goal and how they will make improvements to reach the goal in the future.} a small subset (typically 3–5) of their many performance goals to be identified as priority goals every two years. Recall that performance goals are two levels below strategic goals (as shown in Figure 3.2) and that any one strategic goal can have more than 20 performance goals connected to it.\footnote{Performance goals are subcomponents of objectives, which are subcomponents of strategic goals.}

Priority goals are to “reflect the highest priorities of the agency” as decided by the head of the agency and informed by OMB's government-wide priority goals. In addition to identifying these goals on a biannual basis, agencies are required to select a high-level agency official, often a political appointee, to be a goal leader responsible for the oversight of the goal's completion and are required to report quarterly on their progress toward the completion of these goals.

Examining these goals has two key advantages. First, these goals allow for the observation of policy change at a lower level. Given the narrow, short-term focus of priority goals, a president focused on making a quick change in the direction of agencies' activities may look to target these goals. Second, these goals allow us to explicitly test whether goals change more across administrations than within, as these goals were set twice during the Obama administration (2014–2015 and 2016–2017) and twice during the Trump administration (2018–2019 and 2020–2021).

To observe the determinants of changes in priority goals, I collected every agency priority goal from the Obama administration and the first period of the Trump administration. In total, I collected 266 agency priority goals that span across three periods—2014–2015, 2016–2017, and 2018–2019.\footnote{Of the 22 agencies responsible for producing priority goals, 21 have reported three sets of priority goals. This provides a point of comparison for each agency, minus the Department of Energy. The Department of Energy has not reported priority goals for the 2018--2019 period. They are the only one of the other 22 statutorily required agencies that have not done so. I made numerous attempts to contact the agency to see whether these goals were in existence but had not been publicly reported. Overall, 183 agency priority goals have a subsequent comparison to observe how these goals change within and across administrations.} I then used the same measures and ran comparable models for priority goals as I did for strategic goals.\footnote{I also ran binomial models of priority goal elimination, as I did for strategic goals. The results of these models are reported in supporting information Appendix Table 5.10.}

I find that priority goals, as expected from my first hypothesis, are more likely to change across administrations than within. Matching with Hypothesis 3, this is especially the case in ideological agencies. For example, goals in liberal agencies were more than 40\% more likely to be eliminated between the Obama and Trump administrations compared to moderate agencies. Conservative agencies, on the other hand, were approximately 20\% more likely to see their goals eliminated. However, these results are only significant for liberal agencies, as shown in Table 3.3. The president was particularly successful at changing goals in these ideologically opposed agencies, as opposed to ideological allies in the strategic goal case.

\begin{table}
  \caption{Models of Priority Goal Change} 
\begin{tabular}{@{\extracolsep{5pt}}lcccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{4}{c}{\textit{Dependent variable:}} \\ 
\cline{2-5} 
\\[-1.8ex] & \multicolumn{4}{c}{Priority Goal Level of Change} \\ 
\\[-1.8ex] & (1) & (2) & (3) & (4)\\ 
\hline \\[-1.8ex] 
 Administration Change & 1.492$^{***}$ & 1.261 & $-$0.672 & $-$0.508 \\ 
  & (0.297) & (0.852) & (1.393) & (1.411) \\ 
  & & & & \\ 
 \% Confirmed End Y1 &  & $-$0.095 & $-$0.814 & $-$0.600 \\ 
  &  & (1.045) & (1.375) & (1.429) \\ 
  & & & & \\ 
 Conservative &  &  & 0.472 & 0.821 \\ 
  &  &  & (0.656) & (0.755) \\ 
  & & & & \\ 
 Liberal &  &  & $-$0.392 & $-$0.347 \\ 
  &  &  & (0.608) & (0.619) \\ 
  & & & & \\ 
 SMOG &  &  &  & 0.008 \\ 
  &  &  &  & (0.020) \\ 
  & & & & \\ 
 Department &  &  &  & 0.175 \\ 
  &  &  &  & (0.393) \\ 
  & & & & \\ 
 Log Employment &  &  &  & $-$0.187 \\ 
  &  &  &  & (0.158) \\ 
  & & & & \\ 
 Administration Change:\% Confirmed End Y1 &  & 0.536 & 1.460 & 1.395 \\ 
  &  & (1.425) & (1.713) & (1.725) \\ 
  & & & & \\ 
 Administration Change:Conservative &  &  & 1.071 & 0.955 \\ 
  &  &  & (0.949) & (0.955) \\ 
  & & & & \\ 
 Administration Change:Liberal &  &  & 1.901$^{**}$ & 1.822$^{**}$ \\ 
  &  &  & (0.835) & (0.840) \\ 
  & & & & \\ 
\hline \\[-1.8ex] 
Observations & 183 & 183 & 183 & 183 \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{4}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

I also find that appointment fill had a positive effective on the likelihood of priority goal change and elimination across administrations. However, the effect does not approach statistical significance in any of the models. It is possible that the increased involvement of OMB and the emphasis on addressing presidential priorities diminish the need for getting confirmed appointees in place. Therefore, for priority goals, it may not be as necessary to have confirmed leadership in place, as OMB will take on a larger role of imparting the voice of the White House. This may provide evidence that processes of centralization are sufficient substitutes to the need for control through politicization rather than a complement \citep{rudalevige_parsing_2005}.

Comparing results across the models of strategic and priority goal change, there seem to be clearly distinguishable patterns of goal change. In particular, agencies that receive more direct presidential attention are more likely to experience goal change. This was shown in the strategic goal case, with the fill of significant PAS positions, and suggested in the priority goal case, due to the positive impact of PAS fill and the amount of OMB involvement in these types of goals. These results speak to the high level of influence presidents have in shifting the short- and long-term goals and activities of agencies following a change in administration. Furthermore, they provide strong support for the benefits presidents gain through the tools of administrative management.

\section{Conclusion}
As showcased in the case of the EPA, President Trump needed to alter the agency's goals that had been set under the Obama administration in order to change the policy direction of the agency to be in alignment with his agenda. Trump acted quickly by making nominations to all top leadership positions and to a number of key assistant administrator positions within the EPA \citep{post_donald_2020}. By the end of his first year in office, 75\% of the top leadership positions and nearly 40\% of all key PAS positions in the agency were filled \citep{post_donald_2020}. These appointees assisted the president in altering the goals of the agency to focus on things like “accelerat[ing] permitting-related decisions” and “accelerat[ing] the pace of cleanups” \citep{epa_fy_2018}. Rather than taking on ambitious goals to combat climate change, the EPA was left with a restricted and narrow focus on issues of procedure and process. This type of goal change is representative of a larger pattern. Across administrative agencies, the president was particularly successful in altering the direction of agencies in which he had increased control through personnel. These results display President Trump's success in reaching policymaking success through the route of administrative control of the internal agency prioritization process.

Using agency goals, I have been able to make the connection between presidential appointment strategy and outcomes related to the overall aims of agencies. While agencies have immense authority and discretion, they must prioritize their activities. My analysis suggests that presidents are able to systematically change the direction of an agency's policymaking focus. Across administrations, agencies not only experience turnover in personnel \citep{richardson_politicization_2019}, but also experience changes to their core directives. This poses a problem for the performance of agencies, as they will not be able to commit to the same long-term goals over time. Further, it suggests that any analysis of federal agency goals must account for the influence of the changing political environment on the content and stability of goals. Additionally, I find the tools of personnel moderate how successful the president is at changing agency goals. Matching with existing descriptions of the consequences of vacancies, agencies without confirmed leadership will be less responsive to a new administration. Therefore, any new administration must get their appointees in place immediately if they hope to achieve their policy goals and bring the long-term policy priorities of agencies in alignment with their agenda. If presidents do not act quickly, they will find themselves working against the priorities and goals of the agency and unable to change them for two to four years.

This article takes the first step in incorporating agency goals into the analysis of presidential policymaking. However, there is room for future research using agency goals. To begin with, further analysis can be conducted to examine finer changes and types of changes in goals. Additionally, future research should further observe the outcomes related to goal change. Spanning from civil service personnel management to dynamics of agency rulemaking, change in agency goals can be informative to a wide span of issues. This research provides the opportunity to gain a better understanding of the internal processes of agencies and how they are driven by and affect politics.




